Traditional diagnostic techniques including visual examination, ultrasound (US), and magnetic resonance imaging (MRI) have limitations of in-depth information for the detection of nail disorders, resolution, and practicality. This pilot study, for the first time, evaluates a dual-modality imaging system that combines photoacoustic tomography (PAT) with the US for the multiparametric quantitative assessment of human nail. The study involved a small cohort of five healthy volunteers who underwent PAT/US imaging for acquiring the nail unit data. The PAT/US dual-modality imaging successfully revealed the fine anatomical structures and microvascular distribution within the nail and nail bed. Moreover, this system utilized multispectral PAT to analyze functional tissue parameters, including oxygenated hemoglobin, deoxyhemoglobin, oxygen saturation, and collagen under tourniquet and cold stimulus tests to evaluate changes in the microcirculation of the nail bed. The quantitative analysis of multispectral PAT reconstructed images demonstrated heightened sensitivity in detecting alterations in blood oxygenation levels and collagen content within the nail bed, under simulated different physiological conditions. This pilot study highlights the potential of PAT/US dual-modality imaging as a real-time, noninvasive diagnostic modality for evaluating human nail health and for early detection of nail bed pathologies.

Bladder urothelial carcinoma is the most common malignant tumor disease in urinary system, and its incidence rate ranks ninth in the world. In recent years, the continuous development of hyperspectral imaging technology has provided a new tool for the auxiliary diagnosis of bladder cancer. In this study, based on microscopic hyperspectral data, an automatic detection algorithm of bladder tumor cells combining color features and shape features is proposed. Support vector machine (SVM) is used to build classification models and compare the classification performance of spectral feature, spectral and shape fusion feature, and the fusion feature proposed in this paper on the same classifier. The results show that the sensitivity, specificity, and accuracy of our classification algorithm based on shape and color fusion features are 0.952, 0.897, and 0.920, respectively, which are better than the classification algorithm only using spectral features. Therefore, this study can effectively extract the cell features of bladder urothelial carcinoma smear, thus achieving automatic, real-time, and noninvasive detection of bladder tumor cells, and then helping doctors improve the efficiency of pathological diagnosis of bladder urothelial cancer, and providing a reliable basis for doctors to choose treatment plans and judge the prognosis of the disease.

The cerebral vasculature plays a significant role in the development of Alzheimer’s disease (AD), however, the specific association between them remains unclear. In this paper, based on the benefits of photoacoustic imaging (PAI), including label-free, high-resolution, in vivo imaging of vessels, we investigated the structural changes of cerebral vascular in wild-type (WT) mice and AD mice at different ages, analyzed the characteristics of the vascular in different brain regions, and correlated vascular characteristics with cognitive behaviors. The results showed that vascular density and vascular branching index in the cortical and frontal regions of both WT and AD mice decreased with age. Meanwhile, vascular lacunarity increased with age, and the changes in vascular structure were more pronounced in AD mice. The trend of vascular dysfunction aligns with the worsening cognitive dysfunction as the disease progresses. Here, we utilized in vivo PAI to analyze the changes in vascular structure during the progression of AD, elucidating the spatial and temporal correlation with cognitive impairment, which will provide more intuitive data for the study of the correlation between cerebrovascular and the development of AD.

The parafoveal area, with its high concentration of photoreceptors and fine retinal capillaries, is crucial for central vision and often exhibits early signs of pathological changes. The current adaptive optics scanning laser ophthalmoscope (AOSLO) provides an excellent tool to acquire accurate and detailed information about the parafoveal area with cellular resolution. However, limited by the scanning speed of two-dimensional scanning, the field of view (FOV) in the AOSLO system was usually less than or equal to 2°, and the stitching for the parafoveal area required dozens of images, which was time-consuming and laborious. Unfortunately, almost half of patients are unable to obtain stitched images because of their poor fixation. To solve this problem, we integrate AO technology with the line-scan imaging method to build an adaptive optics line scanning ophthalmoscope (AOLSO) system with a larger FOV. In the AOLSO, afocal spherical mirrors in pairs are nonplanar arranged and the distance and angle between optical elements are optimized to minimize the aberrations, two cylinder lenses are orthogonally placed before the imaging sensor to stretch the point spread function (PSF) for sufficiently digitizing light energy. Captured human retinal images show the whole parafoveal area with 5×5° FOV, 60Hz frame rate and cellular resolutions. Take advantage of the 5° FOV of the AOLSO, only 9 frames of the retina are captured with several minutes to stitch a montage image with an FOV of 9×9°, in which photoreceptor counting is performed within approximately 5° eccentricity. The AOLSO system not only provides cellular resolution but also has the capability to capture the parafoveal region in a single frame, which offers great potential for noninvasive studying of the parafoveal area.

Deep learning is capable of greatly promoting the progress of super-resolution imaging technology in terms of imaging and reconstruction speed, imaging resolution, and imaging flux. This paper proposes a deep neural network based on a generative adversarial network (GAN). The generator employs a U-Net-based network, which integrates DenseNet for the downsampling component. The proposed method has excellent properties, for example, the network model is trained with several different datasets of biological structures; the trained model can improve the imaging resolution of different microscopy imaging modalities such as confocal imaging and wide-field imaging; and the model demonstrates a generalized ability to improve the resolution of different biological structures even out of the datasets. In addition, experimental results showed that the method improved the resolution of caveolin-coated pits (CCPs) structures from 264nm to 138nm, a 1.91-fold increase, and nearly doubled the resolution of DNA molecules imaged while being transported through microfluidic channels.

The low detection rate of Mycobacterium tuberculosis in clinical practice leads to a high rate of missed diagnosis for pulmonary tuberculosis (PTB). As a noninvasive, high-resolution, real-time imaging technology, polarization-sensitive optical coherence tomography (PS-OCT) may be feasible for the rapid identification of pathological feature. This study aimed to explore the feasibility of using PS-OCT to identify pathological features of PTB. In the experiments, PTB samples containing some surrounding lung tissues were imaged using PS-OCT. It is demonstrated that PS-OCT images showed good consistency with the corresponding pathological images and were able to identify PTB-related characteristic pathological regions. We think PS-OCT can serve as an effective supplement for the diagnosis of PTB, enabling rapid and accurate diagnosis, and aiding in the understanding of the pathological characteristics and pathophysiological processes of PTB.

Optical coherence tomography (OCT) imaging technology has significant advantages in in situ and noninvasive monitoring of biological tissues. However, it still faces the following challenges: including data processing speed, image quality, and improvements in three-dimensional (3D) visualization effects. OCT technology, especially functional imaging techniques like optical coherence tomography angiography (OCTA), requires a long acquisition time and a large data size. Despite the substantial increase in the acquisition speed of swept source optical coherence tomography (SS-OCT), it still poses significant challenges for data processing. Additionally, during in situ acquisition, image artifacts resulting from interface reflections or strong reflections from biological tissues and culturing containers present obstacles to data visualization and further analysis. Firstly, a customized frequency domain filter with anti-banding suppression parameters was designed to suppress artifact noises. Then, this study proposed a graphics processing unit (GPU)-based real-time data processing pipeline for SS-OCT, achieving a measured line-process rate of 800kHz for 3D fast and high-quality data visualization. Furthermore, a GPU-based real-time data processing for CC-OCTA was integrated to acquire dynamic information. Moreover, a vascular-like network chip was prepared using extrusion-based 3D printing and sacrificial materials, with sacrificial material being printed at the desired vascular network locations and then removed to form the vascular-like network. OCTA imaging technology was used to monitor the progression of sacrificial material removal and vascular-like network formation. Therefore, GPU-based OCT enables real-time processing and visualization with artifact suppression, making it particularly suitable for in situ noninvasive longitudinal monitoring of 3D bioprinting tissue and vascular-like networks in microfluidic chips.

Puncture biopsy is an important clinical technique to obtain diseased tissue for pathological diagnosis, where imaging guidance is critical. In this paper, we describe a metal reflector-enhanced microwave-induced thermoacoustic imaging (TAI) approach capable of guiding puncture biopsy for detection of breast cancer and joint diseases. Numerical experimentations simulating puncture guidance in breast cancer and knee gout models were first conducted using (CST STUDIO SUITE) (CST) software, and then ex-vivo experiments were performed followed by qualitative observations and semi-quantitative analysis. The results of both the simulations and ex-vivo experiments showed that our reflector-enhanced TAI could image the puncture needle in high resolution with a large depth of >12cm.

Pathological myopia (PM) is a severe ocular disease leading to blindness. As a traditional noninvasive diagnostic method, fundus color photography (FCP) is widely used in detecting PM due to its high fidelity and precision. However, manual examination of fundus photographs for PM is time-consuming and prone to high error rates. Existing automated detection technologies have yet to study the detailed classification in diagnosing different stages of PM lesions. In this paper, we proposed an intelligent system which utilized Resnet101 technology to multi-categorically diagnose PM by classifying FCPs with different stages of lesions. The system subdivided different stages of PM into eight subcategories, aiming to enhance the precision and efficiency of the diagnostic process. It achieved an average accuracy rate of 98.86% in detection of PM, with an area under the curve (AUC) of 98.96%. For the eight subcategories of PM, the detection accuracy reached 99.63%, with an AUC of 99.98%. Compared with other widely used multi-class models such as VGG16, Vision Transformer (VIT), EfficientNet, this system demonstrates higher accuracy and AUC. This artificial intelligence system is designed to be easily integrated into existing clinical diagnostic tools, providing an efficient solution for large-scale PM screening.

Tumor vaccine therapy offers significant advantages over conventional treatments, including reduced toxic side effects. However, it currently functions primarily as an adjuvant treatment modality in clinical oncology due to limitations in tumor antigen selection and delivery methods. Tumor vaccines often fail to elicit a sufficiently robust immune response against progressive tumors, thereby limiting their clinical efficacy. In this study, we developed a nanoparticle-based tumor vaccine, OVA@HA-PEI, utilizing ovalbumin (OVA) as the presenting antigen and hyaluronic acid (HA) and polyethyleneimine (PEI) as adjuvants and carriers. This formulation significantly enhanced the proliferation of immune cells and cytokines, such as CD3, CD8, interferon-γ, and tumor necrosis factor-α, in vivo, effectively activating an immune response against B16–F10 tumors. In vivo fluorescence flow cytometry (IVFC) has already become an effective method for monitoring circulating tumor cells (CTCs) due to its direct, noninvasive, and long-term detection capabilities. Our study utilized a laboratory-constructed IVFC system to monitor the immune processes induced by the OVA@HA-PEI tumor vaccine and an anti-programmed death-1 (PD-1) antibody. The results demonstrated that the combined treatment of OVA@HA-PEI and anti-PD-1 antibody significantly improved the survival time of mice compared to anti-PD-1 antibody treatment alone. Additionally, this combination therapy substantially reduced the number of CTCs in vivo, increased the clearance rate of CTCs by the immune system, and slowed tumor progression. These findings greatly enhance the clinical application prospects of IVFC and tumor vaccines.

Retinal diseases pose significant challenges to global healthcare systems, necessitating accurate and efficient diagnostic methods. Optical Coherence Tomography (OCT) has emerged as a valuable tool for diagnosing and monitoring retinal conditions due to its noncontact and noninvasive nature. This paper presents a novel retinal layering method based on OCT images, aimed at enhancing the accuracy of retinal lesion diagnosis. The method utilizes gradient analysis to effectively identify and segment retinal layers. By selecting a column of pixels as a segmentation line and utilizing gradient information from adjacent pixels, the method initiates and proceeds with the layering process. This approach addresses potential issues arising from partial layer overlapping, minimizing deviations in layer segmentation. Experimental results demonstrate the efficacy of the proposed method in accurately segmenting eight retinal boundaries, with an average absolute position deviation of 1.75 pixels. By providing accurate segmentation of retinal layers, this approach contributes to the early detection and management of ocular conditions, ultimately improving patient outcomes and reducing the global burden of vision-related ailments.