SignificanceIn recent decades, light-sheet fluorescence microscopy (LSFM) has emerged as a groundbreaking technique in the field of fluorescence microscopy, offering unparalleled tomographic capabilities. Using thin light sheets for imaging, LSFM dramatically reduces phototoxicity and photo-bleaching, enabling researchers to capture high-quality and real-time images of living specimens over extended periods without compromising the integrity of the sample. This noninvasive approach has revolutionized the visualization and quantification of biological processes, providing insight into developmental biology, cellular dynamics, and disease mechanisms. As research in the life sciences continues to expand, the demand for more sophisticated imaging technologies capable of capturing complex biological phenomena across spatial and temporal scales has increased. Despite the advantages of LSFM, several challenges remain in overcoming traditional imaging trade-offs, such as spatial resolution, temporal resolution, field-of-view, and the health of the sample. To address these issues, the integration of artificial intelligence (AI) and deep learning algorithms has become a focal point for enhancing the capabilities of LSFM and paving the way for the next generation of intelligent imaging systems.ProgressThis review summarizes the recent advances in LSFM, particularly in the integration of AI-driven intelligent imaging systems and image restoration technologies. Recent developments in smart adaptive imaging schemes (Fig. 3), powered by deep learning techniques, represent a transformative role in overcoming the inherent limitations of traditional microscopies. These methods enable real-time adjustments to imaging conditions, ensuring that images are captured at optimal resolution and contrast, while minimizing phototoxicity and preserving sample integrity. In particular, AI algorithms enable the automatic selection of imaging parameters based on sample characteristics, thus enhancing the efficiency and accuracy of data acquisition.Significant progress has also been made in developing image restoration techniques that enhance the quality of LSFM images, even under challenging conditions, such as low signal-to-noise ratios or large-scale imaging (Fig. 4). By applying deep learning models to noise reduction, resolution enhancement, and artifact removal, researchers can now achieve high-fidelity reconstructions of biological structures, even when confronted with imperfect or incomplete data. These advancements in image processing are important for unlocking the full potential of LSFM in complex biological studies.This review also highlights the role of AI in optimizing data processing workflows (Fig. 5). The enormous volume of data generated by LSFM, particularly in multidimensional imaging, necessitates efficient algorithms for data management, analysis, and visualization. Machine learning algorithms, particularly convolutional neural networks and other deep learning frameworks, have been instrumental in automating the interpretation of large-scale datasets, which has facilitated the extraction of meaningful biological insights with minimal manual intervention.The integration of AI and deep learning into LSFM has already demonstrated marked improvements in a wide range of applications, including live-cell imaging, developmental biology, neuroimaging, and cancer research. These innovations not only extend the capabilities of LSFM, but provide life scientists with powerful tools to explore biological processes in unprecedented detail and over longer time frames.Conclusions and ProspectsIn the future, the development of LSFM is poised to be shaped by continued advancements in AI and machine learning. The next phase of LSFM innovation will focus on further enhancing spatial and temporal resolution, increasing imaging throughput, and improving multiscale imaging capabilities. AI will likely continue to play a major role in refining image restoration techniques and streamlining data processing workflows, thus enabling the capture of more accurate and informative biological images.In particular, the integration of LSFM with complementary imaging technologies, such as super-resolution microscopy and multi-photon microscopy, holds promise for achieving more comprehensive, high-resolution imaging at the molecular and cellular level. As these technologies advance, they will offer unprecedented insight into the molecular mechanisms underlying various diseases, including cancer and neurodegenerative disorders, and open up new possibilities for drug discovery and personalized medicine.As LSFM systems become automated, the need for standardized protocols and best practices is also growing to ensure consistency and reproducibility across diverse research environments. Efforts to establish universal standards for AI-driven LSFM systems will be necessary for facilitating the widespread adoption of these technologies in research and clinical settings. Ultimately, the convergence of AI, deep learning, and LSFM will unlock new frontiers in biomedical research, enabling the next generation of discoveries in the life sciences.

SignificanceThe kidney, a vital organ in the human body, is responsible for filtering blood, removing waste and excess water, and precisely regulating electrolyte balance and acid?base levels. Additionally, it plays a crucial role in controlling blood pressure, stimulating red blood cell production, and activating vitamin D. However, acute or chronic kidney injuries can lead to physiological changes that impair these normal functions. Existing clinical assessment methods have limitations in detecting kidney health status, including the inability to provide real-time monitoring, invasiveness, high cost, and low image resolution. Optical coherence tomography (OCT), with its advantages of high resolution, rapid imaging, and non-invasiveness, holds considerable promise for assessing kidney health and diagnosing related diseases.ProgressWe review the latest advancements in OCT for kidney detection, focusing on the technological development of OCT in kidney research, its application in imaging kidney microstructure and microcirculation, its clinical applications, its role in providing comprehensive optical characterization of the kidney, and the use of large-field OCT for kidney assessment. First, we introduce the application of OCT imaging in assessing kidney microstructure and microcirculation. Since 2007, OCT has been widely used to study the microstructure of murine kidneys, yielding results consistent with histopathological findings (Fig. 1). Renal imaging with OCT enables real-time monitoring of morphological changes in transplanted kidneys and provides quantitative information through image analysis, aiding in the assessment of renal function and acute tubular injury. Vascular imaging techniques such as Doppler OCT, OCT angiography (OCTA), and optical microangiography (OCMA) enhance the ability to monitor kidney microcirculation and accurately quantify blood flow (Fig. 2). Studies have demonstrated the effectiveness of OCMA in monitoring the effects of drug delivery systems on renal blood circulation. Previous research has shown that OCT can effectively penetrate the connective tissue surrounding the human kidney, enabling non-destructive imaging of the renal cortex and identification of histopathological changes in kidney microstructure (Fig. 3). Machine learning algorithms have been employed to automatically identify and segment kidney microstructures in OCT images, achieving accuracy comparable to manual segmentation while eliminating subjective bias. Additionally, OCT can distinguish between renal tumors and normal renal parenchyma using attenuation coefficient measurements, enabling accurate tumor characterization (Fig. 4). These findings suggest that OCT is a safe and reliable technique for real-time observation of donor renal structures during transplantation, providing valuable quantitative data for predicting post-transplant renal function recovery. Advances in automated scanning technology and artificial intelligence (AI)-based image analysis have further simplified OCT usage, rendering it more accessible and precise.Next, we explore the expanding role of OCT in comprehensive optical characterization of the kidney. Polarization-sensitive OCT (PS-OCT) has demonstrated a strong ability to accurately identify tumor regions at various depths and locations, as well as to reconstruct tumor structures in 3D (Fig. 5). The combination of OCT with optical coherence elastography (OCE) has proven valuable for identifying and classifying nephritis. Morphological differences in renal tubule functional activity can be effectively visualized using dynamic OCT (Fig. 6). These studies highlight the potential clinical applications of OCT in diagnosing and evaluating multiple kidney conditions in humans. Advancements in imaging technology have further enhanced OCT’s clinical utility. Handheld imaging probe positioning systems and robotic arm-assisted OCT represent significant developments in the field. The handheld imaging probe positioning system can precisely locate and reconstruct images based on the OCT probe’s position, as determined by visual odometry (VO) (Fig. 7). Robotic arm-assisted automated scanning streamlines medical workflows, accelerating the acquisition of objective quantitative analysis results (Fig. 8). Although robotic arms enable automatic, high-precision, and large-field scanning, further testing, adjustments, and optimization in clinical settings are necessary to improve adaptability to medical applications. Researchers have also investigated the feasibility of needle-based OCT for renal tumor assessment, concluding that it offers real-time data acquisition, improved tumor localization, and enhanced diagnostic accuracy (Fig. 9). Additionally, the integration of OCT endoscopy with deep learning has yielded remarkable results in the automatic classification of OCT renal images, significantly improving diagnostic efficiency.Finally, we introduce studies exploring the correlation between fundus OCT blood vessel images and kidney injury. Given that renal microvascular changes play a key role in chronic kidney disease (CKD) and that renal biopsy has limitations in assessing these changes longitudinally and in real time, the visualization of the inner retina as a surrogate marker for kidney disease progression presents a novel avenue for noninvasive diagnosis. Retinal and choroidal changes observed using OCT, as potential indicators of renal pathology, have garnered significant interest. The potential ability of OCT-derived parameters from retinal and choroidal imaging to detect and monitor intraocular vascular injury, as well as their feasibility as alternative biomarkers for renal vascular injury, offers a promising approach for simplifying and enhancing the noninvasive diagnosis of kidney diseases. This novel strategy provides a safer and more effective means of identifying renal abnormalities without inconveniencing the patients.Conclusions and ProspectsOCT has been widely utilized in nephrology due to its noninvasiveness, high resolution, and real-time imaging. Although minimally invasive imaging techniques and fundus-based correlation studies are still in their early stages, they offer a progressive diagnostic pathway—from noninvasive retinal and choroidal examinations to minimally invasive imaging, and ultimately, if necessary, to surgical intervention. This hierarchical diagnostic strategy is expected to improve the efficiency and accuracy of kidney disease diagnosis and support the advancement of precision medicine in clinical nephrology.

SignificanceThe macular fovea in the retina has the highest density of photoreceptor cells making it the region with the sharpest visual acuity. It contains the only deep capillary network in the human body that can be directly observed using optical imaging methods. Obtaining high-resolution imagery of the deep capillary network and functional assessment of blood flow are of paramount importance for early diagnosis of retinal diseases. With the integration of adaptive optics technology and various retinal imaging techniques, such as flood illumination ophthalmoscopy, confocal scanning laser ophthalmoscopy, and optical coherence tomography, photoreceptors and retinal capillaries can be clearly distinguished, allowing precise measurement of the blood flow velocity in the capillaries. This advancement offers new perspectives and insights into the diagnosis and research of retinal diseases. This study explores the applications and recent advancements of adaptive optics single-cell-resolution retinal imaging technology, specifically in retinal capillary imaging and blood flow measurement. Further, it analyzes existing difficulties and technical challenges and summarizes strategies to overcome these challenges and future development prospects.ObjectiveOptical imaging is the most prevalent, well-established, and reliable method for observing retinal blood vessels. The most commonly used ophthalmic optical imaging devices in clinical practice currently, such as fundus cameras and confocal laser scanning ophthalmoscopes, can directly collect retinal reflected light to generate retinal reflection images. Moreover, they can produce high-contrast images of the retinal vasculature when combined with fluorescein angiography. Optical coherence tomography (OCT) is based on the principle of low-coherence light interference and enables three-dimensional imaging of retinal tissue structures. When combined with Doppler imaging techniques, OCT can provide additional real-time data on the blood flow velocity within the retinal vessels. However, owing to the influence of ocular aberrations, these methods can only resolve retinal vessels with a diameter greater than 30 μm. By the time these blood vessels begin to exhibit pathological features, the retinal tissue structures and functions may have already undergone irreversible damage. Consequently, treatment at this stage is mostly poor compared to interventions initiated during the early stages of the disease.Adaptive optics technology has been integrated into retinal imaging techniques to improve spatial resolution. By correcting the dynamic aberrations produced by the living human eye, which vary in both time and space, it is possible to achieve imaging resolutions close to the diffraction limit of the human eye, thus enabling retinal imaging at the single-cell-resolution level. With advancements in technology, adaptive optics has been successfully integrated with imaging techniques, such as flood illumination ophthalmoscopy, confocal laser scanning ophthalmoscopy, and optical coherence tomography, to obtain high-resolution images of various cells and tissue structures in the living human retina. Combined with the previously mentioned methods for detecting blood flow in retinal capillaries at single-cell-resolution, these techniques enable the observation and investigation of hemodynamic changes associated with diseases at an early stage.ProgressThe first adaptive optics flood illumination ophthalmoscope/fundus camera (AOFIO/AOFC) was built and commissioned in the United States in 1997. This was the result of the pioneering effort of the Center for Visual Science at the University of Rochester in the United States, towards the integration of the adaptive optics technology with flood illumination ophthalmoscopy. Consequently, detection and correction of higher-order aberrations in the human eye and obtaining high-resolution images of living human retinal cells became a reality for the first time in the world. Subsequently, adaptive optics technology was integrated with various retinal imaging techniques, including confocal scanning laser ophthalmoscopy, optical coherence tomography, and line scanning ophthalmoscopy. This led to the development of several adaptive optics ophthalmic imaging systems, such as the adaptive optics scanning laser ophthalmoscope (AOSLO), adaptive optics optical coherence tomography (AOOCT), and adaptive optics line scanning ophthalmoscope (AOLSO). These advancements have enabled single-cell-resolution imaging of retinal structures, including photoreceptor cells, the retinal pigment epithelium, retinal capillaries, and retinal nerve fibers. Consequently, it is possible to perform retinal hemodynamic assessments at the cellular level. In the early detection of diseases such as diabetic retinopathy, age-related macular degeneration, Alzheimer’s disease, and hypertension, adaptive optics ophthalmic imaging systems have successfully observed structural and physiological changes in retinal capillaries caused by these diseases, thereby demonstrating their potential for clinical applications.Conclusions and ProspectsThe introduction of adaptive optics technology has enabled in vivo optical imaging of the human retina at single-cell-resolution, leading to significant advancements in retinal research for both healthy and diseased eyes. To advance clinical application of single-cell-resolution retinal imaging technology and single-cell-resolution retinal capillary blood flow detection, future research and development will focus on the key areas listed hereunder.1) Substantial expansion of the field-of-view for single-cell-resolution imaging, which is bound to be a remarkable advancement in the development of high-resolution imaging. Technologies such as multi-conjugate adaptive optics, multi-pupil imaging, and rapid image montaging are dedicated to achieving large field-of-view in single-cell-resolution retinal imaging.2) Expanding the integration of single-cell-resolution imaging systems through system size reduction, simplification of operational procedures, development of more straightforward methods for wavefront aberration measurement and closed-loop correction, and reduction of hardware costs and operational complexity.3) Developing more accurate and faster image processing and analysis softwares to clarify the efficacy, reproducibility, and application scope of single-cell-resolution imaging in clinical diagnostics. Ultimately, this will result in the establishment of standardized clinical detection methods and criteria. With continuous technological advancements, single-cell retinal imaging is expected to find wider applications in both clinical practice and scientific research, thereby establishing a new standard for high-resolution single-cell diagnosis and treatment. This development will facilitate a deeper understanding of the pathogenesis of various ophthalmic diseases, clarify the relationship between visual perception and retinal structure, enhance the accuracy and effectiveness of patient treatment, and enable the early diagnosis and treatment of ocular diseases.

SignificanceCells are fundamental structural and functional units of all organisms. Visual tracking and analysis of intracellular processes are essential for understanding biological mechanisms, preventing disease, and developing therapeutic interventions. However, traditional optical microscopy is inherently limited by a diffraction barrier, which restricts its resolution to approximately 200 nm. This limitation poses significant challenges in resolving subcellular structures and observing dynamic biological processes at the nanoscale. To overcome these limitations, super-resolution microscopy techniques, such as stimulated emission depletion (STED), structured illumination microscopy (SIM), and photoactivated localization microscopy (PALM), have been developed. These technologies have successfully pushed the boundaries of optical resolution, unlocking new possibilities for nanoscale visualization. However, their practical applications are often constrained by challenges, such as high phototoxicity, complex system configurations, limited imaging depth, and time-consuming image processing. These issues have hindered their adoption in live-cell imaging and thick-sample studies.Recently, the introduction of lanthanide-doped upconversion nanoparticles (UCNPs) has provided innovative solutions for several of these challenges. UCNPs possess unique optical properties that enable them to convert near-infrared (NIR) light into higher energy emissions such as visible and ultraviolet light. This upconversion process offers several advantages: (1) NIR light enhances tissue penetration depth because of its low scattering and absorption in biological tissues; (2) the reduced energy of NIR excitation minimizes photodamage and phototoxicity, making UCNPs suitable for long-term live-cell imaging; (3) their high photostability mitigates photobleaching, enabling extended imaging sessions; and (4) their ability to produce multicolor emissions through precise doping strategies facilitates multiplexed imaging. These properties make UCNPs exceptional tools for advancing super-resolution microscopy, particularly in applications requiring deep tissue imaging and dynamic biological studies.ProgressThis review comprehensively discusses the integration of UCNPs into super-resolution microscopy techniques, highlighting their impact in overcoming the existing challenges and expanding their application potential. Key advances include(1) Stimulated emission depletion super-resolution microscopy based on UCNPs. The combination of UCNPs with STED microscopy leverages their nonlinear optical properties and photon avalanche effects to achieve resolutions below 30 nm. UCNPs-enhanced STED systems require a lower depletion laser power, reducing photodamage and enabling long-term imaging. These systems have been successfully applied to dual-color imaging and temperature-sensitive measurements, offering unprecedented precision in nanoscale visualization (Fig. 3).(2) Stimulated emission depletion-like super-resolution microscopy based on UCNPs. UCNPs provide a broader prospect for the development and enrichment of STED-like super-resolution imaging by significantly simplifying the complexity of imaging systems, significantly reducing the excitation power, and improving the imaging resolution. This lays the theoretical foundation for the proposal and progress of Fourier-domain heterochromatic fusion and near-infrared emission saturation microscopy. Simultaneously, UCNPs can be integrated with current microscopy systems, and the current hardware and algorithms can be continuously optimized, which is expected to achieve instant subtraction. This in turn enables real-time, low-power super-resolution microscopy and promotes major breakthroughs in fields such as biomedical research and materials science (Figs. 5 and 6).(3) Structured illumination microscopy based on UCNPs. UCNPs provide more nonlinear information for SIM super-resolution imaging. This in turn effectively improves the resolution of SIM. Specifically, the dual near-infrared light characteristics of near-infrared excitation and fluorescence emission enhance SIM suitability for biological tissue imaging by increasing biological penetration. Combined with the fluorescence lifetime characteristics of UCNPs, SIM highlights its multi-channel imaging capabilities, achieving higher decoding accuracy and high-throughput photomultiplexing imaging efficiency. In the future, by further enhancing the emission intensity of UCNPs and optimizing imaging equipment, the performance of U-SIM is expected to improve further, promoting its application in biomedical imaging (Fig. 8).(4) Super-linear and photon avalanche mechanisms. Photon avalanche effects in UCNPs amplify nonlinear responses, enabling ultrahigh-resolution imaging with minimal system modifications. Super-linear excitation-emission (SEE) microscopy takes advantage of these high-order nonlinear effects, achieving a resolution of 62 nm. These approaches simplify the integration of UCNPs into existing imaging systems while maintaining high precision (Fig. 9).(5) Other super-resolution techniques. UCNPs have been integrated into cutting-edge imaging methods, including single-molecule localization microscopy (SMLM) and nanoscale optical writing. Their reversible photophysical properties facilitate dynamic imaging and nanoscale patterning. Furthermore, UCNPs show promise in emerging applications such as temperature-sensitive imaging, multiplexed fluorescence detection, and functionalized imaging for specific biomolecular interactions (Fig. 10).Conclusions and ProspectsUCNPs represent a transformative leap in super-resolution microscopy, addressing key challenges such as phototoxicity, limited imaging depth, and system complexity. By enabling deeper tissue imaging, reducing photodamage, and achieving higher resolution, UCNPs are poised to revolutionize biomedical imaging and material sciences. Future research should focus on optimizing the UCNPs design, enhancing the emission intensity, and developing advanced computational algorithms to fully unlock their potential. Improving the scalability and integration of UCNPs with existing imaging systems is critical for their broad adoption in real-time, low-power, and high-resolution applications. These advancements promise the establishment of UCNPs as indispensable tools for next-generation imaging technologies and drive innovations in biomedical research, diagnostics, and materials science.

SignificanceThe optical attenuation coefficient (AC), a fundamental tissue parameter, quantifies the rate at which light diminishes as it propagates through a medium. This parameter is crucial for the quantitative analysis of tissue properties using optical coherence tomography (OCT) signals. Pathological changes in biological tissues induce considerable alterations in their complex morphological structures and optical properties, which are influenced by factors such as tissue composition, architecture, and physiological conditions. OCT technology, which measures the intensity and temporal delay of faint coherent reflections and backscattered light, enables rapid, noncontact, and high-resolution in vivo imaging of tissues. Quantitative OCT (qOCT) combines OCT with advanced algorithms to extract tissue optical properties. This technique provides detailed morphological insights and quantitatively evaluates AC, providing highly precise information on tissue morphology, composition, and lesion detection.This review explores the principles of tissue AC extraction using qOCT (Fig. 2). The review also provides a comprehensive summary of the algorithms used to extract tissue AC values via qOCT, and examines the advantages, limitations, and applicable scenarios of the selected commonly used algorithms. Finally, the clinical applications of qOCT for extracting tissue AC values are discussed, along with the associated challenges and potential future directions for development.ProgressThe theoretical foundation of the algorithms used in qOCT primarily encompasses single scattering (SS) and multiple scattering (MS) models (Fig. 3). The SS model, which is suitable for weakly scattering samples or thin layers of densely scattering tissues, assumes a single backscattering event. Prominent SS-based algorithms include curve-fitting, fast frequency-domain, and depth-resolved methods, each with distinct strengths and limitations. In contrast, the MS model accounts for multiple scattering events, requiring a detailed analysis of the photon propagation pathways and probability distributions within tissues. Although these models are based on complex physical frameworks and computational methods that offer higher precision, they typically come with a tradeoff of slower processing speeds. Common MS-based algorithms include the Monte Carlo method, the extended Huygens?Fresnel model, and the Maxwell’s equations?based method. In addition, our research group has introduced an innovative approach called the multi-reference phantom-driven network. This approach employs multi-reference phantoms and deep learning techniques to implicitly model factors influencing OCT signal propagation, thereby enabling automated and accurate regression of the AC.The accuracy of AC extraction is influenced by various factors, including the detection systems, signal acquisition protocols, and processing methodologies (Fig. 4). Hardware parameters of the OCT detection system, such as the light source specifications, probe design, and system type, are critical for ensuring reliable calculation of the AC values. Preprocessing steps, including noise reduction, contrast enhancement, artifact removal, and motion correction, are essential for achieving accurate AC computations. Moreover, tissue heterogeneity, multilayer structures, and pathological changes, such as cell aggregation, neovascularization, or fibrosis, can complicate light?tissue interactions and reduce the accuracy of AC calculations.qOCT offers high-resolution, quantitative insights into the optical properties of tissue and has demonstrated initial clinical applications in detecting ophthalmic, luminal, cancerous, superficial, and other diseases (Fig. 6). In ophthalmology, qOCT has become a vital tool for detecting and monitoring conditions such as glaucoma, macular degeneration, and diabetic retinopathy, enabling early intervention and improving patient outcomes (Fig. 7). In cancer diagnostics, qOCT can identify discernible changes in tissue morphology that result in notable alterations in AC and serve as a valuable biomarker for cancer monitoring and staging (Fig. 8). Moreover, qOCT has been increasingly recognized for its application in cardiovascular assessments, particularly in the detection and analysis of atherosclerotic plaques (Fig. 9). The ability of qOCT to precisely extract tissue AC values enables the quantitative assessment of lesions, offering robust support for the diagnosis and management of superficial conditions, such as skin lesions (Fig. 10). Additionally, qOCT holds great promise in the examination of lymphatic tissues, providing high-resolution images that reveal lymph nodes, vessels, blood vessels, and other microscopic structures, offering valuable insights into the structure and function of lymphatic tissues (Fig. 11).Conclusions and ProspectsAs an innovative quantitative analysis technique, qOCT enables the nondestructive, real-time acquisition of structural information and tissue AC in vivo, allowing for precise quantification of tissue structure and composition. The notable changes in tissue AC associated with pathological alterations provide robust clinical diagnostics using qOCT. Ongoing research focuses on developing depth-resolved, high-sensitivity, and high-resolution qOCT technologies for the real-time in vivo quantification of human tissues. These advancements aim to address existing challenges and broaden the clinical applications of this promising technology.

ObjectiveMedical image fusion integrates lesion features and complementary information from different modalities, offering a comprehensive and accurate description of medical images for clinical diagnosis. Traditional methods often result in reduced contrast and spectral degradation due to differences between multimodal images. Frequency-domain techniques mitigate these issues but rely on manually designed feature extraction and fusion rules, lacking robustness and adaptability. While deep learning-based fusion methods, such as convolutional neural networks and Transformer, have shown promising results in feature extraction and reconstruction, they often overlook complementary characteristics between modalities, leading to insufficient global information capture. Although frequency-domain methods preserve high-frequency information, they fail to adequately correlate global and local features, neglecting unique aspects of each modality and resulting in excessive smoothing and blurring. This study proposes an adaptive medical image fusion method based on cross-modality perception and spatial-frequency interaction.MethodsAn adaptive medical image fusion network combining cross-modality perception with spatial-frequency interaction was developed. First, a cross-modality perceptual module utilizing channel and coordinate attention mechanisms extracts multiscale deep features and local abnormality information, reducing information loss between modalities. Second, a spatial-frequency cross-fusion module based on frequency information exchange and spatial domain adaptive cross-fusion was proposed. This module alleviates information imbalance between modalities by exchanging phase information in the frequency domain and dynamically learning global interaction features in the spatial domain. This process highlights prominent targets while preserving critical pathological information and texture details. Finally, a loss function comprising content, structure, and spectral terms was designed to further improve the quality of the fused image.Results and DiscussionsFusion experiment for mild Alzheimer's disease demonstrates that the proposed method better preserves positron emission tomography functional information and edge details of magnetic resonance imaging (MRI) soft tissue, improving image contrast and detail presentation compared to other methods. The fusion experiment for metastatic bronchogenic carcinoma shows that other methods suffer from low resolution, blurred textures, and noise interference, hindering the observation and diagnosis of the lesion area. In contrast, the proposed method effectively retains single-photon emission computed tomography metabolic information and MRI soft tissue edge details, enabling doctors to comprehensively evaluate lesion status. The sarcoma fusion experiment further validates the algorithm’s effectiveness in preserving tissue edges, grayscale information, and density structure integrity. From tables 1?3, the AG (average gradient), MI (mutual information), SF (spatial frequency), QAB/F (fusion quality), CC (correlation coefficient), and VIF (visual information fidelity) indicators demonstrate strong performance. Specifically, the high MI value indicates that the fusion image contains rich features and edge information. The high SF value shows that the fusion image retains additional global information from the source images, with clear details and texture features. The high VIF value reflects consistency with the human eye’s visual characteristics, while the high QAB/F value indicates that the fusion image maintains spatial details consistent with the source images. Compared with other algorithms, the proposed method emphasizes the perception and interaction of structural image texture contours and functional image metabolic brightness during feature extraction and fusion, addressing issues such as structural edge loss and lesion detail blurring in existing fusion methods.ConclusionsTo enhance the quality of multimodal medical image fusion, this study proposes a method combining cross-modality perception with spatial-frequency interaction. During feature extraction, a multiscale cross-modality perception network facilitates the interaction of structural and functional information, fully extracting source image data and enhancing local lesion features. In the fusion stage, functional and anatomical key information is preserved through frequency-domain exchange, followed by cross-attention for adaptive fusion, ensuring that detailed texture and overall edge profile information are fully fused. Additionally, content, structural, and spectral losses were designed to retain complementary and chromatic information. Experimental results demonstrate that the proposed method improves AG, MI, SF, QAB/F, CC, and VIF by 4.4%, 13.2%, 2.7%, 3.4%, 11%, and 3%, respectively, showing that the method effectively retains unique information from each modality, resulting in fusion images with clear edges, rich lesion details, and high visual fidelity. In the task of fusing multimodal medical images of the abdomen with green fluorescent protein and phase contrast images, the proposed method demonstrates strong generalization, supporting its potential for application in other biomedical diagnostic tasks and enhancing clinicians’ diagnostic efficiency.

ObjectiveIn ophthalmic medical imaging, the stitching of ultra-wide-angle fundus images is essential for comprehensively observing and assessing patients' retinal health. This technology provides a broader visual field, allowing doctors to gain a more intuitive understanding of the entire fundus region, thereby offering crucial support for early screening, diagnosis, and treatment planning of diseases. However, stitching ultra-wide-angle fundus images faces multiple challenges, particularly in multi-scene and multi-angle shooting scenarios in practical applications. Variations in perspective and exposure differences can lead to significant issues in image matching and fusion, such as the formation of stitching seams. This phenomenon not only affects image quality but may also obscure the identification of lesion areas by doctors. To address these challenges, we aim to develop an efficient algorithm that ensures high-quality, seamless stitching under varying exposure conditions and angles. By minimizing stitching seams and enhancing image smoothness, we seek to provide more precise and reliable technical support for the diagnosis and monitoring of fundus diseases.MethodsWe present a novel image-stitching approach based on computer vision to address critical challenges in stitching ultra-wide-angle fundus images. The speeded-up robust features (SURF) algorithm was first employed to extract key feature points that accurately depict prominent regions, such as bifurcation points of retinal vessels and structural boundaries, from fundus images. Potential correspondences between feature points in different images were identified through initial matching. However, the initial matching outcomes may include a considerable number of mismatched points, affecting stitching accuracy. To refine the results, the random sample consensus (RANSAC) algorithm was applied after initial matching. Through an iterative approach, the RANSAC algorithm eliminates mismatched points and preserves true feature matches, ultimately deriving an accurate transformation matrix for geometric image registration. To address stitching seam issues caused by varying viewpoints and exposure differences, this study introduces an innovative bidirectional linear weight fusion method. This method followed a structured process. Firstly, the center point of the overlapping area was extracted, and an image rotation alignment technique was used to ensure correct geometric alignment of the images. Then, weights were assigned to overlapping areas, forming a bidirectional linear weight mask, enabling the pixel values in the transition area to be smoothly fused. Then, a mask with linearly decreasing weights was generated, ensuring a smooth transition between images and effectively eliminating the stitching seams caused by exposure variations.Results and DiscussionsThrough an experimental verification, the proposed stitching algorithm—combining SURF and bidirectional linear weight fusion—demonstrates significant performance advantages under various exposure conditions and viewing angles. Compared with the traditional algorithms, such as maximum fusion algorithm and gradual in and gradual out fusion algorithm, this algorithm achieves superior visual effects and smoother stitching. Experimental results show that this algorithm significantly reduces the stitching saliency when processing images with substantial exposure differences. This improvement is reflected as a 50.43% reduction in the average gradient and an 11.91% decrease in standard deviation in the stitching area compared to traditional algorithms, indicating a significant enhancement in seamlessness. Additionally, this algorithm effectively preserves image integrity. Information entropy, a key metric for measuring image information content, is only 3.13% lower than that of traditional algorithms. This finding suggests that while weight fusion eliminates stitching seams, the overall richness of image information remains nearly intact. As a result, the stitched images are not only more visually coherent but also retain critical medical details, providing reliable support for fundus disease diagnosis.ConclusionsBased on experimental results and theoretical analysis, we propose a fundus image stitching method that integrates SURF and bidirectional linear weight fusion, demonstrating excellent performance in addressing multi-scene and multi-angle stitching challenges. SURF extracts precise feature points, while the RANSAC algorithm ensures geometric registration, thereby enhancing stitching accuracy. To resolve exposure differences in overlapping areas, a bidirectional linear weight mask is designed for effectively eliminating stitching seams and significantly improving image smoothness and visual coherence. Experimental results further confirm that this algorithm outperforms traditional approaches in terms of mean gradient and standard deviation in stitched areas while also preserving information integrity. The slight 3.13% reduction in information entropy indicates that the method effectively balances seamless stitching with the retention of medical image details. This advancement is particularly valuable for medical diagnostic applications requiring high-precision panoramic fundus images, such as the early detection and monitoring of diabetic retinopathy and glaucoma. In conclusion, the proposed algorithm not only achieves high-precision, seamless stitching, but also introduces innovative tools and techniques in the field of ophthalmic medical imaging. These findings highlight its broad clinical applicability and potential for extension to more complex medical image analysis scenarios, fostering advancements in medical imaging technology.

SignificanceThe rapid advancement of precision medicine has established liquid biopsy as a transformative tool for disease screening, diagnosis, and monitoring. By analyzing circulating biomarkers in bodily fluids, such as extracellular vesicles (EVs), cell-free nucleic acids, proteins, metabolites, and circulating tumor cells, liquid biopsy offers a non-invasive method for disease detection. It enables real-time monitoring of disease progression, transforming clinical diagnostics and patient management. Among the various liquid biopsy methodologies, surface plasmon resonance (SPR) stands out due to its rapid response, high sensitivity, real-time monitoring, multimodal detection capabilities, and tunable micro-nanostructures. These features demonstrate its potential to significantly enhance liquid biopsy applications and revolutionize diagnostic strategies.ProgressThis review focuses on recent advancements in SPR technology for liquid biopsy, focusing on the major biomarkers used.We first discuss the fundamental principles of four commonly used SPR technologies. SPR uses resonance phenomena occurring at the interface of metal films and dielectric mediums, enabling highly sensitive detection of biomolecular interactions. Its ability to perform label-free detection and real-time analysis makes it essential for detecting biomarkers at very low concentrations. This foundational understanding provides insights into addressing several challenges faced by liquid biopsy, such as the limited number of disease-specific markers, the complexity of biological samples, limited detection throughput, high cost, and issues related to clinical relevance. SPR addresses these challenges by integrating micro-nanostructure design, molecular probes, microfluidic chips, and sophisticated data analysis. These enhancements improve sensitivity, specificity, and detection speed while enabling multiple biomarker analyses. Such advancements promise more robust and reliable SPR-based platforms for clinical applications.A particularly promising area is the application of SPR technologies to EVs. These vesicles serve as novel circulating biomarkers, offering advantages such as stability, spatial-temporal heterogeneity, diverse molecular content, and high abundance in body fluids. We describe recent advances in designing innovative SPR platforms for detecting and analyzing EVs. These advancements provide crucial insight into the molecular characteristics of EVs, creating opportunities for improved disease diagnostics and monitoring. However, challenges such as standardizing sample processing, correlating EV markers with clinical outcomes, and addressing variability in detection signals across techniques must be overcome to define the full clinical potential of EVs. SPR’s adaptability, combined with complementary technologies, provides a promising method to overcome these challenges, maximizing the clinical usefulness of EVs.Inherent disease complexity and heterogeneity often require a comprehensive analysis of multiple biomarkers. Single-marker diagnostics may fall short of addressing these complexities. SPR multiplexing allows simultaneous detection and analysis of proteins, nucleic acids, metabolites, and tumor cells, offering a comprehensive view of disease biology. We highlight these advancements, which significantly enhance diagnostic accuracy and provide a deeper understanding of disease mechanisms.Conclusions and ProspectsLiquid biopsy is a pivotal technology for early disease diagnosis and ongoing monitoring because of its non-invasive nature and real-time monitoring capabilities. SPR, with its unique micro-nano photonic properties, offers transformative potential in enhancing liquid biopsy applications. However, several challenges must be addressed to facilitate the widespread adoption of SPR-based liquid biopsy.A primary obstacle is the low concentration of circulating biomarkers, which requires additional sample purification and enrichment for effective detection. These steps increase experimental complexity and time, impeding rapid clinical translation. Furthermore, specialized SPR platforms and detection systems involve significant costs, limiting their accessibility and use in routine medical diagnostics. Limited clinical sample sizes and a lack of standardized sample collection methods hinder the reproducibility and validation of findings. Addressing these issues will be critical to ensure the reliability and scalability of SPR technologies. Innovation in SPR materials, device configurations, and detection methods is essential to overcome these challenges. Advancements in areas such as molecular probes, rare-earth nanomaterials, metamaterials, microfluidics, and artificial intelligence can expand the capabilities of SPR systems. These innovations will enhance sensitivity, automate processes, and lower costs, making SPR more accessible for clinical and personal health applications.The growing demand for personalized medicine and home-based testing confirms the need for compact, intelligent, cost-effective SPR devices. Miniaturization and portability are essential to meet the needs of dynamic health monitoring and expand the reach of liquid biopsy technologies. Standardizing clinical protocols, ensuring the clinical relevance of biomarkers, and improving data analysis frameworks will enhance the reproducibility and translational potential of SPR-based systems. SPR-based liquid biopsy technologies can revolutionize diagnostics by solving these challenges, enabling robust, accessible, and cost-effective solutions. Integrating SPR’s optical properties with cutting-edge micro-nanotechnology and data-driven insights has set the stage for a new era in precision medicine, transforming how diseases are detected, understood, and managed.

ObjectivePhotoacoustic tweezers (PATs) are a promising noninvasive technology for precise microparticle manipulation that leverage the photoacoustic effect to exert controlled forces on particles within a liquid medium. Conventional methods, such as optical and acoustic tweezers, present limitations such as potential damage to particles, inadequate spatial control, and restricted manipulation in three-dimensional (3D) environments. Hence, this study focuses on the theoretical development of a PAT model that integrates photo-thermo-acoustic-mechanical coupling effects. Using a ring-shaped laser beam, this study aims to investigate particle motion and force characteristics under various laser conditions. Additionally, this study seeks to establish a robust theoretical framework for PATs, thus filling the gaps in 3D particle manipulation and providing guidance for the optimization of experimental parameters.MethodsA multiphysics coupling model for PATs was constructed to describe the interactions among photo-thermal, acoustic, and mechanical effects in a liquid medium. The model accounts for the transformation of laser energy density into heat and the subsequent thermal expansion, and the generation of acoustic waves that induces acoustic radiation forces and acoustic streaming. A ring-shaped laser beam was employed to create spatially confined acoustic fields that can trap and manipulate particles. Using the finite-element method (FEM) in Comsol software, the model was used to simulate high-frequency pulsed laser effects, including energy transfer, thermal expansion, acoustic wave propagation, and the resultant particle dynamics. The governing equations for the coupled fields were derived by incorporating the spatial and temporal distributions of the laser, the thermal and acoustic properties of the medium, and force interactions on the particles. The acoustic radiation forces were modeled based on Gor’kov potential theory, whereas the acoustic streaming fields were calculated using nonlinear fluid-dynamics equations. Simulations were performed to analyze the effects of laser energy densities, pulse widths, and frequencies on the behavior of the system, which revealed the distributions of forces and streaming flows in 3D space. The particle motion under combined acoustic radiation and streaming forces was analyzed to elucidate the dynamics of particle trapping and manipulation.Results and DiscussionsThe simulation results provide key insights into the behavior of PATs in 3D particle manipulation, which highlight the dependence of acoustic radiation forces, Gor’kov potential, and acoustic streaming on the laser parameters. The acoustic radiation forces show significant dependence on the laser energy density, pulse width, and frequency, as illustrated in Fig. 3, where the spatial distribution around the ring-shaped laser beam is symmetrical, with the maximum forces occurring near the ring boundary. Increasing the laser energy density increases these forces, as shown in Fig. 5(a), where higher energy densities (e.g., 50 mJ·cm-2) generate forces up to 65 pN. Similarly, reducing the pulse width amplifies the radiation forces, as shown in Fig. 5(c). The Gor’kov potential wells, as depicted in Figs. 5(b) and (d), deepen with higher densities or shorter pulse widths, thereby improving particle-trapping stability. Additionally, the frequency-dependent behavior, as shown in Figs. 5(e) and (f), reveals that a frequency of 10 MHz yields the maximum forces and well-defined potential wells, thus enabling efficient particle trapping. However, as the frequency increases further, the wave attenuation significantly reduces the force and potential depth, as shown in Fig. 6.In addition to acoustic radiation forces, acoustic streaming is crucial in particle manipulation and serves as an auxiliary force. This secondary force, which is generated via nonlinear fluid interactions, is particularly effective at 10 MHz, where the streaming velocity peaks at 0.05 μm/s, as shown in Fig. 7. The streaming-flow patterns effectively stabilize particles within the trapping zones. However, as the frequency increases beyond 10 MHz, the energy density dissipation causes the streaming effect to diminish significantly, thus decreasing the velocities, as shown in Figs. 7(b)?(d). This relationship between radiation forces and streaming provides a robust mechanism for particle trapping, which is further supported by the trajectories shown in Fig. 9. The particles propagate toward the trapping zones with increasing velocity, with the maximum number recorded near the traps before a stable number is indicated within the Gor’kov potential wells. The color-coded trajectories in Fig. 9 highlight the deceleration of the particles as they approach the traps, thereby illustrating the dynamic balance between the radiation forces and streaming-induced drag.Finally, the capability of the system to manipulate particles along the Z-axis was examined, as shown in Fig. 4. Although the acoustic radiation forces in the Z-direction are weaker than those in the radial direction, the combined effect of forces and streaming provides sufficient control for effective 3D manipulation. This capability underscores the effectiveness of PATs in achieving stable particle trapping and precise manipulation. In general, these findings demonstrate that PATs are highly effective for 3D particle manipulation, particularly at moderate laser frequencies (approximately 10 MHz) and energies, where the combined effect of radiation forces and streaming ensures stability and precision. These results establish a solid foundation for the practical application of PATs in biomedicine and materials science.ConclusionsIn this study, a comprehensive theoretical framework for PATs was established via a multiphysics coupling model that integrates photo-thermo-acoustic-mechanical interactions. The findings underscore the importance of laser parameters in optimizing particle manipulation. Specifically, this study identified 10 MHz as an ideal frequency for achieving maximum acoustic radiation forces and stable Gor’kov potential wells. High laser energy densities and short pulse widths are critical for enhancing the trapping efficiency, although practical considerations, such as avoiding thermal damage, must guide parameter selection. The integration of acoustic radiation forces and streaming flows highlights the versatility of this system for 3D manipulation. Although radiation forces dominate the trapping mechanism, streaming flows provide auxiliary control, particularly for stabilizing particle trajectories. The insights obtained from this study contribute to a broader understanding of PAT mechanisms and offer practical guidelines for experimental implementation. The potential applications of PATs include targeted drug delivery, cell manipulation, and the assembly of nanostructured materials, thus highlighting their transformative potential in biomedicine and nanotechnology.

ObjectiveBidirectional-output fiber lasers have broad application prospects in industrial, medical, communication and other fields due to their unique features of achieving two laser outputs through a resonant cavity. However, the reported high-power bidirectional-output fiber lasers are mainly concentrated in the conventional wavelength band of 1080 nm. Compared with fiber lasers with conventional wavelengths, short-wavelength fiber lasers with wavelengths less than 1060 nm have a wide demand in fields such as biological imaging, nonlinear frequency conversion, and spectral beam combination. However, they are susceptible to nonlinear effects such as amplified spontaneous emission (ASE) and transverse mode instability (TMI), due to the wider absorption cross-section of short wavelength Yb-ions.MethodsIn this study, a novel short-wavelength composite cavity bidirectional-output all-fiber laser is constructed and shown in Fig. 1. The laser resonant cavity consists of two output-coupler fiber Bragg gratings (OCFBG-A and OCFBG-B) with reflectivity of approximately 10%, one fiber Bragg grating (FBG) with a reflectivity of approximately 50%, and two pieces of ytterbium doped fibers (YDF#1 and YDF#2) with a core/cladding diameter of 20/400 μm. FBG is placed inside the resonant cavity and fused with YDF#1 and YDF#2 at both ends. The central wavelengths of the OCFBG-A, OCFBG-B and FBG are approximately 1050 nm. Wavelength-locked fiber-pigtailed 976 nm laser diodes (LDs) are utilized as the pump sources, which are injected into the resonant through two (6+1)×1 pump/signal combiners. The signal laser is output through fiber endcaps (QBH-A and QBH-B) with a core diameter of 25 μm.Results and DiscussionsWhen the maximum pump power of 6407 W is injected, the total output power reaches 5489 W, with 2278 W of end A and 3211 W of end B, and the corresponding optical-to-optical conversion efficiency of 85.7%, as shown in Fig. 2(a). Figure 2(b) shows the output spectra at the maximum output powers,indicating the center wavelength is 1050 nm, and the Raman suppression ratios at both ends are ~21.9 dB and ~20.1 dB, respectively. Figure 2(c) shows the temporal signals and the corresponding Fourier transform spectra recorded from A and B ends, which confirms that there is no sign of TMI during the power scaling. The measured beam quality factors (M2) of both ends at the maximum power are 1.52 and 1.34, respectively [Fig. 2(d)].ConclusionsWe propose and demonstrate a novel short-wavelength composite cavity bidirectional-output all-fiber laser, and the near single-mode bidirectional output with a central wavelength of 1050 nm and a total output power of 5.5 kW is achieved. The research results are of great significance for suppressing nonlinear effects and transverse mode instability in short-wavelength fiber lasers and bidirectional-output fiber lasers.

SignificanceFluorescence microscopy technology is one of the most direct and effective method for studying the structure and function of complex biological systems. Neurobiology involves the study of the structure, function, development, genetics, and pathology of the nervous system, which is essential for understanding how the brain controls behavior, processes information, and changes under disease states. Conventional fluorescence microscopy has become a commonly used tool in neurobiology and is widely applied to the study of neuronal connections and information transmission processes. However, traditional optical microscopes are limited by diffraction, which restricts scientists from observing nanoscale structures. The 2014 Nobel Prize in Chemistry was awarded for super-resolution fluorescence microscopy technology, which has developed into a highly effective research tool for subcellular fluorescence imaging and resolving organelle structures. We introduce the main single-molecule localization super-resolution microscopy techniques, elaborates on the basic principles of three-dimensional single-molecular localization microscopy (3D-SMLM), and describes point spread function (PSF) fitting, aberration analysis, and multi-color imaging techniques. We also summarize the research results of single-molecule localization super-resolution microscopy in the structural analysis of neurons at the nanoscale, including the morphological changes of pathological signals in Alzheimer disease (AD), such as β-amyloid and tau-protein aggregates, and their mechanisms of interaction with neuronal proteins. Finally, we explore the future development prospects of single-molecule localization microscopy (SMLM) technology.ProgressFirst, the basic imaging principles of single-molecule localization technologies, including photoactivated localization microscopy (PALM), stochastic optical reconstruction microscopy (STORM), and DNA-based point accumulation for imaging in nanoscale topography (DNA-PAINT), are summarized (Fig. 1). The growth of cells occurs in a three-dimensional space, surrounded by other cells, extracellular matrix, and tissues. The refinement of their structure and function requires higher axial resolution in super-resolution microscopy technology, leading to the development of 3D-SMLM. The key to 3D-SMLM is to characterize the axial information of a single molecule using the planar shape of the PSF. Major principles based on astigmatism method, double-Helix PSF (DH-PSF), biplane-PSF, 4Pi-PSF and supercritical angle localization microscopy (SALM) are compared (Fig. 2). Given that aberrations caused by instruments and biological samples are often introduced during the imaging process, adaptive optics technology is used to compensate for aberrations through optical equipment to improve imaging quality. Alternatively, an in situ PSF model is directly constructed from the obtained single-molecule dataset to capture the true aberration morphology of biological data and improve positioning accuracy (Fig. 3). The study of biomolecule interactions typically requires imaging multiple target molecules in the same experiment. Multi-color SMLM technology not only provides high-resolution structural information, but also analyzes the spatial structure and interactions of different target molecules (Fig.4). Moreover, the excellent spatial resolution and potential for specific target recognition of single-molecule localization super-resolution microscopy technology have been demonstrated in neurons and the neurodegenerative disease AD. Key discoveries include the periodic membrane skeleton in neurons (Fig. 5), dynamic changes in the nanostructured domains of synaptic proteins (Fig. 6), and applications in amyloid fibril aggregation and neurofibrillary tangles (Fig. 8). Furthermore, in situ 3D-SMLM enables high-throughput quantitative analysis of high-density neuronal dendritic spine structures in brain tissue slices (Fig. 7). The arrangement of fibrils in the center of β-amyloid plaques is captured and resolved in the axial direction, and local details of individual fibrils are displayed, allowing for the tracing of their growth morphology (Fig. 9). Finally, suggestions for research on single-molecule localization super-resolution microscopy technology in neurobiology are presented.Conclusions and ProspectsSMLM has overcome the spatial resolution limitations caused by optical diffraction, achieving super-resolution imaging by accurately locating the position of a single fluorescent molecule. This breakthrough has significantly advanced modern life sciences and biomedical research. Its ability to analyze fine structures at the nanoscale, dynamically map changes in response to pathological and physiological activities, and quantitatively assess high-throughput, high-density structures in three-dimensional in situ environments highlights the broad potential applications of SMLM. In the future, SMLM can be further developed for in situ super-resolution analysis of complex biological samples and exploited as a quantitative biological analysis tool in neurobiology research.

SignificanceThe clinical treatment of neuropsychiatric disorders remains challenging owing to the lack of adequate and safe therapies, with pharmacological interventions often accompanied by significant side effects. As crucial tools for revealing mechanisms and achieving effective disease control, near-infrared-based monitoring and intervention technologies provide a non-invasive, real-time approach to diagnosing and treating neuropsychiatric disorders.As a near-infrared-based technique for monitoring, functional near-infrared spectroscopy (fNIRS) can identify changes in oxyhemoglobin and deoxyhemoglobin caused by neuronal activity. Furthermore, fNIRS has the advantages of good motion tolerance and resistance to electromagnetic interference, support for long-term non-invasive monitoring, and low medical costs. These advantages have led to its widespread application in, for example, brain diseases diagnosis , motor rehabilitation, and hyperscanning.As a near-infrared-based technique for intervention, transcranial photobiomodulation (tPBM) therapy has emerged in recent years as an innovative treatment in neurological and psychological research. By enhancing metabolic capacity and providing neuroprotection, tPBM therapy is increasingly recognized as a safe, effective, and feasible clinical treatment option.This paper provides an overview of near-infrared-based technologies for monitoring and intervention in preclinical and clinical neuroscience research, encompassing studies on healthy populations as well as various brain disorders such as stroke, depression, and dementia. Additionally, it offers insights into future development trends in this field.ProgressThis paper first outlines the fundamentals of tPBM and its applications in neuroscience research. Then, it reviews the critical role of functional near-infrared spectroscopy in revealing the functional regulation of the brain by tPBM therapy and evaluating its therapeutic effects.In terms of tPBM fundamentals, this paper points out the exponential attenuation of light energy delivered via transcranial application methods and emphasizes that a forward-looking modeling analysis of cumulative light energy in brain tissue can enhance the reliability and comparability of studies in this field.Regarding neuroscience research applications, the ability of tPBM therapy to enhance performance on various cognitive tasks in healthy populations is first reviewed. Subsequently, a general overview of the NEST series of studies on stroke, depression, and dementia is introduced. Finally, results for different phototherapy parameters are summarized.In terms of fNIRS-based tPBM therapy research, this paper reviews the site-specific neuro mechanisms underlying changes in cerebral blood flow and functional connectivity across different cognitive tasks. Finally, this paper points out the limitations of current fNIRS-based research, and we suggest enhancing the comparability of current research findings across different studies and multimodal research through standardizing data processing procedures and reporting channel localization.Conclusions and ProspectsIn clinical practice, interventions for brain dysfunction induced by neuropsychiatric disorders often require multiple cycles, encompassing processes such as assessment-treatment, rehabilitation-reassessment-continued treatment, and rehabilitation-follow-up of therapeutic efficacy. Currently, interventions and imaging procedures related to tPBM therapy in research are typically conducted asynchronously. However, a real-time synchronized imaging-intervention protocol facilitates comprehensive disease assessment and timely adjustment of intervention strategies to enhance therapeutic responsiveness. Given that both near-infrared spectroscopy (NIRS)-based brain functional imaging and tPBM technology are rooted in the application of near-infrared light, their organic integration can significantly reduce the complexity of clinical management. At the system level, secondary development can be carried out based on open-source brain-computer interface (BCI) platforms such as OpenBCI or MetaBCI to achieve low-cost and high-precision synchronous control of imaging and intervention. Formulating and dynamically optimizing personalized treatment strategies can further enhance clinical outcomes. This process initially involves comprehensive assessment and monitoring of clinical manifestations and brain functional activities across multiple cycles in patient populations. Subsequently, dose-effect relationship studies based on tPBM therapy with various phototherapy parameters are conducted to identify group-level intervention targets associated with improvements in brain function, such as restoration of abnormal cerebral blood oxygenation activation, and alterations in functional connectivity in specific brain regions. Finally, these group-level intervention targets are aligned with individual-level brain functional networks to achieve personalized and precise targeted interventions. Concurrently, advanced algorithms such as those based on deep learning are employed to identify the onset and progression states of individual diseases, thereby enabling timely optimization and adjustment of intervention protocols. Based on this approach, the integrated fNIRS-tPBM imaging-intervention scheme holds promise as a novel and more convenient method for disease assessment, diagnosis, and treatment in both clinical management and home care for neuropsychiatric disorders, as illustrated in Fig. 3.To further advance near-infrared spectroscopy-based brain functional imaging and neuromodulation technologies, efforts should also focus on promoting technical standardization. Given the significant heterogeneity in intervention protocols across studies, one approach is to use photon transport simulation algorithms such as Monte Carlo. Through numerical simulations, factors such as age, gender, and skin color of the subjects can be comprehensively considered to optimize the cumulative distribution of cortical light energy. Additionally, integrating neuronavigation technology can further reduce intervention errors in non-target brain areas during tPBM therapy, enhancing the reliability and comparability among different studies. Furthermore, the advantages of near-infrared optical imaging and optical intervention technologies should be fully leveraged to conduct multicenter, high-quality clinical randomized controlled trials. These trials will elucidate the specific effects of various parameters, including wavelength, power density, intervention duration, and intervention targets, on brain metabolism and cognitive functions in humans. This will lay the foundation for establishing standardized intervention protocols and improving the reproducibility and comparability of research studies.

SignificanceThe ongoing advancement of information technology and the advent of the post-Moore’s Law era highlight the limitations inherent in the conventional von Neumann architecture, which is increasingly becoming a bottleneck in enhancing computational capabilities. Neuromorphic computing, which emulates the functionalities of biological neurons and synapses, has emerged as a promising solution, facilitating the development of novel computing paradigms. These new paradigms are capable of processing complex information with enhanced efficiency and minimal energy consumption. Artificial synapses are crucial components of neuromorphic computing and play a significant role in determining the computational power and efficiency of these systems. This review underscores the recent advancements in optoelectronic synapses, magnetic synapses, and the burgeoning field of optomagnetic neural networks, which have the potential to significantly transform artificial intelligence and data processing (Fig. 1).ProgressThis review provides a comprehensive analysis of the latest developments in both optoelectronic and magnetic synapses, as well as the cutting-edge field of optomagnetic neural networks. Artificial optoelectronic synapses convert light signals into electrical signals, thereby mimicking the release of neurotransmitters at biological synapses. These artificial synapses exhibit plasticity by altering the electrical properties of photoelectric materials, enabling the simulation of long-term potentiation (LTP) and long-term depression (LTD). Modulation methods for optoelectronic synapses are categorized into three primary types: photoelectric synergistic modulation, all-optical modulation, and other modulation strategies. Among these, the photoelectric synergistic modulation is more compatible with integration into existing systems (Fig. 2). However, all-optical modulated synapses can reduce energy consumption and prevent thermal damage to device structures. Significant progress has been made in developing all-oxide-based synaptic devices for visible neural network computing, photosynaptic devices based on chlorophyll heterojunctions for neuromorphic edge detection, hemispherical plasmonic optoelectronic memristor arrays for neuromorphic stereo vision, and all-optically controlled artificial synapses for logic gate implementations (Figs. 3?6). Other types of synaptic modulation represent novel forms of synergistic control, utilizing light and additional physical quantities (beyond electricity). These approaches provide significant advantages by incorporating other characteristics, particularly those of sensors, which satisfy application scenarios and enable the integration of sensing, storage, and computation. For example, modulation utilizing light, distinct gas environments, and humidity stimuli has demonstrated the potential to simulate cross-modal sensory perception, bridging vision and olfaction (Fig. 7).Magnetic synapses leverage the nonvolatility, programmability, and low power consumption of magnetic materials to emulate synaptic functions. Materials such as magnetic tunnel junctions (MTJs), nanocluster magnetic Josephson junctions (nMJJs), and magnetic Skyrmions have been investigated owing to their potential to serve as artificial magnetic synapses. These materials have demonstrated significant advancements in their weight modulation and storage capabilities. MTJs operate on the principle of tunneling magnetoresistance effect, wherein resistance is modulated by altering the relative orientation of magnetization in the ferromagnetic layer, thereby simulating synaptic plasticity. Several methods for controlling the magnetization state of MTJs have been developed, including magnetic field control, spin-transfer torque (STT), and voltage control, each of which facilitates the simulation of synaptic functions such as LTP and LTD. nMJJs combine the natural pulse behavior of Josephson junctions with the plasticity of magnetic nanoclusters, exhibiting synaptic-like characteristics. The critical current of the nMJJs, which are modulated by altering the magnetic ordering of the device, enables the simulation of synaptic weight. Magnetic Skyrmions, a novel class of magnetic artificial synapses, are nanoscale topological spin textures that can be controlled via electric currents. These synapses provide low-energy operation, indicating significant potential for high-density data storage and information processing (Figs. 8?11).Optomagnetic neural networks integrate the strengths of optical and magnetic materials, offering significant potential in storage-integrated neuromorphic computing. These networks leverage the magneto-optical effect, wherein the polarization state of light changes owing to the magnetic state of the material, thereby enabling the construction of neural networks capable of complex information processing. A study conducted at Radboud University demonstrates that an optomagnetic neural network is capable of learning and classifying digitized 3×3 characters. This network employs Co/Pt multilayer structures to generate optomagnetic synapses that can be controlled and detected by picosecond laser pulses, thereby integrating the high-speed and low dissipation of optical networks with the low-energy adaptability and nonvolatility of magnetic materials. Additionally, researchers from the Department of Materials Science and Bioengineering at Nagaoka University of Technology, led by Hotaka Sakaguchi, utilized Nd0.5Bi2.5Fe4GaO12 (Bi2.5Ga∶NIG) films as magneto-optical materials to design a magneto-optical diffractive neural network for recognizing handwritten datasets (Fig. 13).Conclusions and ProspectsDespite distinct physical implementation, both biological and artificial synapses share similarities in their information processing mechanisms, learning rules, and dynamic behaviors. These similarities offer valuable insights into understanding and simulating biological neural networks, providing a foundation for the development of new artificial neural network architectures and algorithms. Optoelectronic synapses provide distinct advantages in terms of speed, latency, and bandwidth, whereas magnetic synapses hold significant potential for building stable and efficient neuromorphic computing systems owing to their nonvolatility and low power consumption. However, challenges remain regarding integrating these synapses with existing microelectronic systems, reducing manufacturing costs, improving reusability, ensuring compatibility with existing electronic devices, and addressing consistency and performance fluctuations during mass production. The integration of optical and magnetic effects into optomagnetic neural networks has expanded the scope of neuromorphic computing, introducing novel ideas for developing more efficient and intelligent computing systems. With ongoing research, the integration of computing and storage is projected to significantly accelerate the development of neuromorphic systems.

SignificanceBiomedical detection holds irreplaceable importance in the medical field, providing robust support for disease diagnosis and treatment, driving progress in medical research, and enhancing public health standards. Compared to conventional blood tests, which can reveal signs of infection, inflammation, or metabolic abnormalities, laser spectroscopies based on Raman, direct absorption, and photoacoustic effects have garnered widespread attention for biomedical detection owing to their high sensitivity, specificity, and speed, and their label-free nature. In particular, photoacoustic spectroscopy relies on the detection of optically excited acoustic waves, a hybrid integration of light and sound. The signal strength depends on the amount of light energy absorbed, making it less influenced by reflected or scattered light. Consequently, background signal interference is precluded, which is suitable for detecting samples in solid, liquid, and gaseous forms. This characteristic, combined with the low scattering of acoustic waves compared to light, enables the detection of biochemical molecules with weak absorption or biological tissues with strong scattering given that the signal-to-noise ratio of photoacoustic spectroscopy can be easily enhanced by increasing the pump light power. From a technical perspective, photoacoustic spectroscopy typically uses microphones to detect acoustic signals rather than directly measuring changes in light intensity. This enables the use of low-cost, easy-to-maintain microphones to detect signals excited by light across the visible to mid-infrared range, eliminating the need for expensive mid-infrared photodetectors that require cooling devices. From an application perspective, photoacoustic signals are generated by the non-radiative relaxation process of excited molecules transitioning to lower energy states. This complements traditional stimulated emission and photochemical processes, making it useful for studying fluorescence, photochemical phenomena, and quantum efficiency of relaxation and radiation processes. These advantages make photoacoustic spectroscopy attractive to biomedical detection for biological research and clinical diagnosis.Although photoacoustic spectroscopy technology has been developed since the 1970s with commercial instruments and equipment already available on the market, its primary applications have been in environmental detection, industrial process control, energy development, and other fields. In recent years, research in the biomedical detection areas such as blood glucose and oxygen detection, disease biomarker detection, and respiratory gas analysis has grown rapidly. Therefore, summarizing existing research on photoacoustic spectroscopy techniques relevant to biomedical detection is both important and necessary for guiding future developments in this field toward practical clinical applications.ProgressFirst, the basic working principle of photoacoustic spectroscopy is introduced, and the photoacoustic equations for biomedical detection in both aqueous and gaseous environments are derived. These equations serve as a guide for designing photoacoustic spectroscopy systems for various targets. Models describing the photoacoustic signals in both non-resonant and resonant gas cells are also provided. Next, the core components of the photoacoustic system, including the pump light source and acoustic detector, are reviewed. In particular, the spectrum from ultraviolet to mid-infrared covered by different types of lasers is summarized, along with the absorption fingerprints of various biomedical molecules, offering intuitive guidance for system construction. This is followed by an introduction to acoustic detectors, including electrical and optical types, which may vary in sensitivity, operating frequency, and bandwidth. Promising techniques for achieving high sensitivity are also discussed, including pump power enhancement through a multi-pass gas cell or a high-quality-factor resonant cavity, and acoustic wave amplification using rationally designed resonant photoacoustic cells. Subsequently, the applications of photoacoustic spectroscopy for detecting biomolecules and trace gases are reviewed, with emphasis on blood glucose detection and breath gas analysis. Other biomolecules such as hemoglobin, uric acid, DNA, and percutaneously released carbon dioxide/oxygen gases are also included. The current status and challenges faced by photoacoustic spectroscopy in detecting blood glucose and oxygen, biological disease markers, and breathing/releasing gases are also discussed. Finally, recent research efforts aimed at improving signal stability, increasing response speed, and reducing device footprint are introduced. The prospects and suggested future directions for advancing photoacoustic spectroscopy in biomedical applications are also outlined.Conclusions and ProspectsPhotoacoustic spectroscopy, with its advantages of high sensitivity, large penetration depth, low background noise, and compact structure, is suitable for detecting samples in solid, liquid, and gas forms. It holds significant potential for the detection of biochemical components, including blood glucose and oxygen, lipids, biological disease markers, pathogens and human exhaled/released gas. With the continuous progress in key performance parameters such as sensitivity, response speed, size, and cost, along with rapid developments in laser technology, acoustic detection technology, and artificial intelligence algorithms, photoacoustic spectroscopy is expected to be a promising solution in the biomedical field. Potential applications include home health monitoring, bedside point-of-care diagnostics, and early disease screening.

ObjectiveTargeting the microenvironment of a tumor represents a cutting-edge approach in cancer therapy, offering a novel frontier in understanding and treating cancer at the cellular level. The objective of this study is to leverage label-free multiphoton microscope imaging technology to investigate the microenvironment of a meningioma, which is a common type of brain tumor. This research is driven by the need for advanced visualization techniques that can reveal the intricate details of the microenvironment of a tumor, which is crucial for developing targeted therapies. By employing multiphoton microscopy, we aim to provide a detailed characterization of the meningioma microenvironment, including the visualization of collagen fibers, blood vessels, and other structural components, which is pivotal in understanding progression of a tumor and its response to treatment. The significance of this study lies in its potential to enhance our understanding of meningioma biology and contribute to the development of more effective treatment strategies.MethodsWe employed a combination of second harmonic generation (SHG) technology, two-photon excited fluorescence (TPEF) technology, and advanced image analysis techniques for a label-free investigation of the meningioma microenvironment. The multiphoton microscopy system utilized in this research integrated a laser scanning confocal microscope, detector, and mode-locked titanium sapphire laser, which together enabled the simultaneous acquisition of SHG and TPEF signals from endogenous fluorophores within the tissue samples. Two independent channels were configured: one channel (430?716 nm) for TPEF imaging and the other channel (389?419 nm) for SHG imaging. The excitation wavelength was set at 810 nm, with a pulse width of 110 fs and pulse frequency of 76 MHz, ensuring minimal phototoxicity and photobleaching. The integration of these advanced imaging technologies with image analysis techniques allowed for the automatic detection and quantification of collagen fibers and blood vessels, providing a comprehensive quantitative analysis of the meningioma microenvironment.Results and DiscussionsThe results demonstrate that in normal dura mater (Fig. 2), the microenvironment is characterized by a dense and parallel arrangement of collagen fibers. These collagen fibers produce both TPEF and SHG signals. The normal dura mater microenvironment, with its wavy collagen fiber bundles, is distinctly different from that of a meningioma. In the meningioma microenvironment (Fig. 4), distinct features are observed across the five main subtypes. Fibrous meningiomas are rich in collagen fibers, which produce strong SHG signals and are arranged in parallel or interwoven patterns. Angiomatous meningiomas display a rich vasculature with varying caliber and wall thickness, many of which exhibit hyaline degeneration. Psammomatous meningiomas are characterized by psammoma bodies that produce intense TPEF signals and are clearly distinguishable in multiphoton microscopy images, typically appearing round. Transitional meningiomas combine features of meningothelial and fibrous meningiomas, with collagen fibers forming spiral structures and areas containing collagen corpuscles that produce strong SHG signals. Multiphoton microscopy, combined with image analysis techniques, not only enables the visualization of the tumor microenvironment but also allows for the automatic detection of blood vessels and collagen fibers, along with an accurate assessment of their density and distribution. The quantification of the collagen-fiber and vascular densities reveals significant differences among the various meningioma subtypes (P<0.001). The detailed quantitative data obtained from image analysis, as summarized in Table 2, demonstrate the potential of multiphoton microscopy in providing accurate and reliable measurements of collagen and vascular densities in meningioma microenvironments. These findings underscore the importance of multiphoton microscopy in delineating the structural and compositional heterogeneity of meningiomas, which is crucial for understanding tumor biology and the development of targeted therapies.ConclusionsThis study underscores the utility of multiphoton microscopy in elucidating the intricate details of meningioma microenvironments. By combining multiphoton microscopy with sophisticated image analysis techniques, we successfully demonstrate the ability of this technology to not only visualize but also quantitatively assess the meningioma microenvironment with unprecedented precision. The results of this study reveal significant insights into the structural and compositional heterogeneity of meningiomas, highlighting the differential collagen fiber density and vascular distribution across various subtypes. Our findings indicate that multiphoton microscopy offers superior contrast and resolution compared to traditional histological methods such as H&E staining. The ability to automatically detect and quantify collagen fibers and blood vessels without the need for exogenous markers represents a significant advancement in the field of neuropathology. This approach provides a more accurate and comprehensive assessment of tumor microenvironmental features, which is critical for understanding tumor biology and developing targeted therapies. The implications of our study extend beyond mere diagnostic capabilities. As fiber optic and multiphoton endoscopy technologies continue to evolve, we envision multiphoton microscopy becoming a cornerstone in the arsenal of tools for the real-time, intraoperative assessment of brain tumors. This technology has the potential to revolutionize surgical decision-making by providing surgeons with immediate, high-resolution images of tumor margins and microenvironmental features, thereby facilitating more precise tumor resection and potentially improving patient outcomes. Furthermore, the quantitative data obtained from our multiphoton microscopy analysis can serve as a valuable benchmark for evaluating the efficacy of novel therapeutic strategies aimed at modulating the tumor microenvironment. As research in immunotherapy and targeted therapies progresses, the ability to accurately assess changes in the collagen density and vascular architecture will be crucial for determining treatment response and refining therapeutic approaches. In conclusion, this study marks a significant step forward in the application of multiphoton microscopy to meningioma research and clinical management. The potential of this technology to transform our understanding of brain tumor microenvironments and contribute to the development of more effective, targeted therapies is immense. We anticipate that multiphoton microscopy will play a pivotal role in future studies and clinical applications, ultimately leading to improved treatment strategies for patients with meningiomas.

ObjectiveThe Monte Carlo (MC) method has become a cornerstone for simulation of light propagation in biological tissues, particularly for applications in laser therapy and medical imaging. However, traditional voxel-based Monte Carlo (VMC) methods often exhibit significant errors when dealing with complex curved surfaces due to the inherent limitations of structured grid discretization. By contrast, tetrahedron-based Monte Carlo (TMC) methods, based on unstructured meshes, offer better adaptability to complex geometries but incur higher computational cost. This study aims to address the trade-off between computational efficiency and accuracy in light propagation simulations by proposing a novel correction method to improve the performance of VMC in handling curved interfaces with large refractive index differences. The objective is to enhance the accuracy of VMC while maintaining its computational efficiency, making it more viable for practical biomedical applications, such as laser treatment and optical diagnosis.MethodsThis study compares the performance of VMC and TMC in light propagation simulations under different refractive index conditions. We propose a coordinate transformation-based correction method to reduce the errors in VMC caused by the inaccurate handling of reflection and refraction at curved interfaces. The simulations were carried out on a multi-layer skin tissue model with varying refractive index conditions; the laser light was directed at blood vessels embedded within the dermis. The correction method involves transforming the photon direction vector in a way that accounts for the curvature of the interface, thereby improving the accuracy of light photon interactions at the tissue boundaries. Simulations were conducted using MATLAB, and the results were validated through experimental cases, such as diabetic retinopathy laser treatment. The computational setup included an Intel i7-9700K processor and an NVIDIA GTX 3080 graphics card (GPU).Results and DiscussionsThe comparative analysis of VMC and TMC under different refractive index contrasts reveals that VMC performs well when the refractive index difference is small, yielding results that are very close to those of TMC. However, when large refractive index differences are present, such as when laser light transitions between tissue and air, VMC suffers from significant errors (Fig. 5). The proposed coordinate transformation method significantly reduces these errors, improving the accuracy of VMC simulations. For instance, in the case of a large refractive index mismatch (the refractive index of the blood is 1.33 and that of the surrounding tissue is 1.0), the maximum error in VMC is reduced from 51.3% to 3.9%, and the average error is reduced from 16% to 2.9%, making the results comparable to those of TMC (Figs. 6 and 8). The corrected VMC method was also validated in a practical scenario involving laser treatment for diabetic retinopathy. The results demonstrate that the corrected VMC significantly reduces the artifacts commonly observed in traditional VMC methods while providing a more accurate photon energy deposition profile, especially at the gas-tissue interface (Figs. 11 and 12). The computational efficiency of the corrected VMC method is superior to that of TMC, with a time cost approximately 30% of that required by TMC, while still maintaining an acceptable level of accuracy (Fig. 13).ConclusionsThe proposed coordinate transformation-based correction method effectively improves the accuracy of VMC in simulating light propagation in biological tissues, especially at complex optical interfaces with large refractive index mismatches. This method allows VMC to achieve results similar to those of TMC with significantly reduced errors while maintaining its computational efficiency. The corrected VMC method is shown to be highly effective in practical applications such as laser treatment for diabetic retinopathy, where it reduces artifacts and improves photon energy deposition accuracy. The method offers a promising solution for applications requiring high-precision light propagation simulations, including laser therapies and optical diagnostics, where both accuracy and computational efficiency are critical. This study not only provides a deeper understanding of VMC and TMC performance but also paves the way for optimizing light propagation simulations in complex biological systems.

ObjectivePort-wine stains (PWS) are congenital vascular malformations caused by the abnormal dilation of capillaries and venules in the skin. Affecting 0.3%?0.5% of newborns, PWS typically manifest as pinkish patches on the face and neck that darken and thicken over time, significantly impacting patients’ physical and mental well-being. Pulsed dye laser (PDL) therapy, based on selective photothermolysis, is the gold standard for PWS treatment. By targeting hemoglobin in abnormal blood vessels with specific wavelengths, PDL induces thermal damage to the vasculature while sparing surrounding tissues. However, due to variability in skin types, lesion depths, and laser parameters, clinical outcomes often depend on subjective evaluations and physician experience. These limitations underscore the urgent need for objective, quantitative, and noninvasive methods to assess skin structure and monitor treatment efficacy. This study integrates reflectance spectroscopy with an inverse Monte Carlo radiation method to quantify changes in key skin parameters before and after laser treatment. By measuring and reconstructing parameters such as epidermal thickness, melanin volume fraction, blood volume fraction, and blood oxygen saturation, this study establishes a robust framework for evaluating treatment outcomes and optimizing laser parameters. Additionally, it investigates differences in treatment responses between pediatric and adult patients, providing critical insights for personalized therapeutic strategies.MethodsEleven patients with PWS underwent pulsed dye laser (PDL) therapy at a wavelength of 595 nm, with an energy density of 8 J/cm2 and a fixed spot diameter of 7 mm. Reflectance spectra of the skin were measured before and after treatment using an HR400 CG-UV-NIR spectrometer (Fig. 1). To enhance efficacy, a double-irradiation strategy was employed, in which the second laser exposure followed the first after five minutes. For spectral analysis, an inverse Monte Carlo radiation method was developed to reconstruct key skin structural parameters. The multilayered skin model (Fig. 2) incorporated the epidermis, papillary dermis, reticular dermis, and subcutaneous tissue. The inverse Monte Carlo radiation method iteratively optimized nine variables—epidermal thickness (depi), dermal thickness (dde), melanin volume fraction (m), epidermal blood volume fraction (bepi), dermal blood volume fractions (bpap and bret), dermal oxygen saturations (Spap and Sret), and the subcutaneous scattering coefficient (μs,sub)—to minimize the discrepancy between measured and simulated reflectance spectra (Fig. 3). The simulated spectra showed high concordance with clinical measurements [error <7%, Fig. 4(b)], validating the accuracy of the algorithm.Results and DiscussionsThis study demonstrates that integrating reflectance spectroscopy with the inverse Monte Carlo method provides a robust framework for quantifying skin structural changes and evaluating the efficacy of laser treatment in patients with PWS. Analysis of the reconstructed parameters reveals significant reductions in blood volume fraction and blood oxygen saturation among patients exhibiting vascular blanching, which correlates with superior clinical outcomes. Specifically, the average reduction in blood volume fraction in this group is 11.6%, while blood oxygen saturation decreases by 15.6%, indicating successful obliteration of abnormal vasculature. In contrast, patients with vascular constriction experience more modest changes, with a 7.1% reduction in blood volume fraction and a 4.3% decrease in oxygen saturation, suggesting partial vessel narrowing without complete closure. Cases with no significant effects exhibit minimal changes in reflectance spectra and structural parameters. Pediatric patients demonstrate enhanced therapeutic outcomes compared with adults, attributed to a thinner epidermis and lower melanin content, allowing deeper laser penetration and higher treatment efficiency. For instance, in clinical case Ⅰ (a child), blood volume fraction decreases by 18.9% at three months post-treatment, compared to 7.9% in clinical case Ⅱ (an adult male). These findings underscore the advantages of early intervention and the potential for improved outcomes in children with favorable skin characteristics. Moreover, melanin volume fractions remain stable throughout treatment, with variations below 3%, validating the protective role of cryogen spray cooling in preventing thermal damage to the epidermis. According to reflectance spectra analysis, we categorize treatment responses into three groups: vascular blanching, vascular constriction, and no significant effects. This classification provides insights into the physiological mechanisms underlying therapeutic outcomes, highlighting vascular blanching as the most effective clinical endpoint. These results emphasize the importance of tailoring laser parameters to individual skin characteristics, such as lesion depth, epidermal thickness, and melanin content, to achieve optimal outcomes. This approach demonstrates the potential of quantitative, non-invasive methods to enhance the precision and personalization of laser therapies for PWS and other vascular skin conditions. The proposed method bridges the gap between advanced computational techniques and practical clinical applications by providing objective metrics and real-time monitoring.ConclusionsThis study demonstrates the efficacy of combining reflectance spectroscopy with an inverse Monte Carlo algorithm for the quantitative, noninvasive assessment of PWS laser treatment. By providing objective metrics for evaluating treatment responses, such as vascular blanching and constriction, this method enhances clinical precision and informs optimal laser parameter selection. It also facilitates real-time monitoring and personalized therapy planning, particularly for pediatric patients, who exhibit superior therapeutic outcomes due to their favorable skin characteristics. Future studies should expand the sample size to validate the generalizability of this method and explore its applicability to other vascular skin lesions, including hemangiomas and spider veins. These findings highlight the potential of integrating advanced optical measurement techniques with computational algorithms to transform dermatological laser therapy.

ObjectiveReal-time fluorescent quantitative polymerase chain reaction (PCR) is the gold standard for nucleic acid detection of infectious diseases and plays an irreplaceable role in many fields, including disease prevention and control, clinical diagnosis, food safety, and inspection and quarantine. However, PCR detection encounters significant problems. Changes in the specifications of consumables affect the collection of fluorescent signals. Traditional manual focusing has many drawbacks, including cumbersome operation, frequent calibration, low efficiency, increased labor intensity, and poor image acquisition accuracy. When the consumables change slightly, it is difficult to adapt to multiple consumable specifications. Although there have been research achievements in autofocus technology both at home and abroad, the research conducted on autofocus imaging of fluorescent PCR with multiple specifications of consumables is still limited.MethodsIn this study, an optical detection system was constructed for the acquisition of 96-flux fluorescent PCR images. Firstly, the fluorescence imaging situations were observed under different cycle numbers. The performances of evaluation methods, such as the Tenengrad gradient, Laplacian gradient, image variance, Laplacian inhomogeneity, and Sobel operator, were compared during the process of coarse focusing of fluorescent PCR images to determine the appropriate definition evaluation index. Based on the autofocus optical system and the selected definition evaluation function, the image data in the process of the autofocus algorithm were collected, and coarse, fine, and horizontal calibration were performed. After the focusing was completed, a series of image processing operations were performed on the original image in turn, including binarization processing, detection of circular targets by the Hough circle transform, constructing a mask, and calculating the fluorescence intensity by extracting the gray values of the pixels in the fluorescent region. Finally, FAM fluorophore solutions and DNA samples with different gradient concentrations were prepared, and fluorescent quantitative PCR detection experiments were conducted using consumables of different specifications (0.2 mL and 0.1 mL polypropylene PCR8 strip tubes). The Ct values of the two types of consumables were compared.Results and DiscussionsThe Sobel operator algorithm demonstrates optimal performance during the coarse calibration process. This algorithm accurately identifies the position near the ideal working distance, and its calculation results yield obvious level and gradient changes in the entire focal length range for the sharpness interpretation, which is identified as the most suitable sharpness evaluation index. After coarse calibration, fine calibration, horizontal calibration, and image processing, the image meets the requirements for automatic extraction of fluorescent signals; accordingly, the accuracy of image processing and fluorescent detection was improved considerably. When reading the same fluorescein with 0.1 mL and 0.2 mL PCR tubes, it is found that there is no significant difference in the average fluorescence intensity. The quantification of HBV DNA shows that there are minor Ct value differences regarding the tested nucleic acid concentrations, ranging from 0 to 0.32. It is shown that the autofocus fluorescence detection device and algorithm developed can satisfy the needs of fluorescence signal detection at different consumable specifications.ConclusionsThe autofocus algorithm proposed in this study can replace manual focusing, meets the imaging requirements of multispecification consumables, greatly improves the focusing accuracy, and enhances the image’s resolution/contrast. This performance guarantees the accurate acquisition of fluorescence signals, reduces the detection error, and improves the reliability of the results. Accordingly, this provides an efficient and reliable solution for fluorescence PCR imaging, which will be conducive to the future development of fluorescence PCR technology.

ObjectiveThis study examines the ocular damage effects induced by prolonged exposure to near-infrared lasers in the transition zone (1.25?1.40 μm). In addition, it evaluates whether the maximum permissible exposure (MPE) specified in the laser safety standards can ensure eye safety.MethodsA continuous-wave 1.319 μm laser with a spot diameter of 20 mm and a corneal irradiance of 1 W/cm2 (equal to the MPE) was employed for ocular irradiation. Experiments were conducted on chinchilla gray rabbits, which were divided into one control and four experimental groups (A, B, C, and D). The experimental groups were subjected to 1.319 μm laser irradiation for 50, 100, 200, and 400 s, respectively. During irradiation, an infrared thermal imager was used to measure the central temperature of the corneal surface. Ocular examination involving slit-lamp microscopy, optical coherence tomography, fundus photography, and histopathological techniques was performed pre-exposure and at 1 and 24 h post-exposure to assess the potential damage to the cornea, iris, ciliary body, lens, and retina.Results and DiscussionsThe results indicate that laser irradiation for 50 s does not induce any observable eye damage. However, exposure durations of 100?400 s damage the cornea, iris, ciliary body, and lens (Figs. 3 and 4), but no retinal damage is detected. With increasing exposure duration, the ocular damage worsens, and the corneal temperature also increases (Fig. 5).ConclusionsProlonged exposure to a 1.319 μm laser at the MPE level can induce ocular damage. Thus, to ensure eye safety, the exposure duration should not exceed 50 s.