Acquired brain injury (ABI) is an injury that affects the brain structure and function. Traditional ABI treatment strategies, including medications and rehabilitation therapy, exhibit their ability to improve its impairments in cognition, emotion, and physical activity. Recently, near-infrared (NIR) photobiomodulation (PBM) has emerged as a promising physical intervention method for ABI, demonstrating that low-level light therapy can modulate cellular metabolic processes, reduce the inflammation and reactive oxygen species of ABI microenvironments, and promote neural repair and regeneration. Preclinical studies using ABI models have been carried out, revealing the potential of PBM in promoting brain injury recovery although its clinical application is still in its early stages. In this review, we first inspected the possible physical and biological mechanisms of NIR-PBM, and then reported the pathophysiology and physiology of ABI underlying NIR-PBM intervention. Therefore, the potential of NIR-PBM as a therapeutic intervention in ABI was demonstrated and it is also expected that further work can facilitate its clinical applications.

Blood cells are the most integral part of the body, which are made up of erythrocytes, platelets and white blood cells. The examination of subcellular structures and proteins within blood cells at the nanoscale can provide valuable insights into the health status of an individual, accurate diagnosis, and efficient treatment strategies for diseases. Super-resolution microscopy (SRM) has recently emerged as a cutting-edge tool for the study of blood cells, providing numerous advantages over traditional methods for examining subcellular structures and proteins. In this paper, we focus on outlining the fundamental principles of various SRM techniques and their applications in both normal and diseased states of blood cells. Furthermore, future prospects of SRM techniques in the analysis of blood cells are also discussed.

Laser speckle contrast imaging (LSCI) is a noninvasive, label-free technique that allows real-time investigation of the microcirculation situation of biological tissue. High-quality microvascular segmentation is critical for analyzing and evaluating vascular morphology and blood flow dynamics. However, achieving high-quality vessel segmentation has always been a challenge due to the cost and complexity of label data acquisition and the irregular vascular morphology. In addition, supervised learning methods heavily rely on high-quality labels for accurate segmentation results, which often necessitate extensive labeling efforts. Here, we propose a novel approach LSWDP for high-performance real-time vessel segmentation that utilizes low-quality pseudo-labels for nonmatched training without relying on a substantial number of intricate labels and image pairing. Furthermore, we demonstrate that our method is more robust and effective in mitigating performance degradation than traditional segmentation approaches on diverse style data sets, even when confronted with unfamiliar data. Importantly, the dice similarity coefficient exceeded 85% in a rat experiment. Our study has the potential to efficiently segment and evaluate blood vessels in both normal and disease situations. This would greatly benefit future research in life and medicine.

Neuronal soma segmentation plays a crucial role in neuroscience applications. However, the fine structure, such as boundaries, small-volume neuronal somata and fibers, are commonly present in cell images, which pose a challenge for accurate segmentation. In this paper, we propose a 3D semantic segmentation network for neuronal soma segmentation to address this issue. Using an encoding-decoding structure, we introduce a Multi-Scale feature extraction and Adaptive Weighting fusion module (MSAW) after each encoding block. The MSAW module can not only emphasize the fine structures via an upsampling strategy, but also provide pixel-wise weights to measure the importance of the multi-scale features. Additionally, a dynamic convolution instead of normal convolution is employed to better adapt the network to input data with different distributions. The proposed MSAW-based semantic segmentation network (MSAW-Net) was evaluated on three neuronal soma images from mouse brain and one neuronal soma image from macaque brain, demonstrating the efficiency of the proposed method. It achieved an F1 score of 91.8% on Fezf2-2A-CreER dataset, 97.1% on LSL-H2B-GFP dataset, 82.8% on Thy1-EGFP-Mline dataset, and 86.9% on macaque dataset, achieving improvements over the 3D U-Net model by 3.1%, 3.3%, 3.9%, and 2.3%, respectively.

Foundation models (FMs) have rapidly evolved and have achieved significant accomplishments in computer vision tasks. Specifically, the prompt mechanism conveniently allows users to integrate image prior information into the model, making it possible to apply models without any training. Therefore, we proposed a workflow based on foundation models and zero training to solve the tasks of photoacoustic (PA) image processing. We employed the Segment Anything Model (SAM) by setting simple prompts and integrating the model’s outputs with prior knowledge of the imaged objects to accomplish various tasks, including: (1) removing the skin signal in three-dimensional PA image rendering; (2) dual speed-of-sound reconstruction, and (3) segmentation of finger blood vessels. Through these demonstrations, we have concluded that FMs can be directly applied in PA imaging without the requirement for network design and training. This potentially allows for a hands-on, convenient approach to achieving efficient and accurate segmentation of PA images. This paper serves as a comprehensive tutorial, facilitating the mastery of the technique through the provision of code and sample datasets.

Time-resolved flow cytometry (TRFC) was used to measure metabolic differences in estrogen receptor-positive breast cancer cells. This specialty cytometry technique measures fluorescence lifetimes as a single-cell parameter thereby providing a unique approach for high-throughput cell counting and screening. Differences in fluorescence lifetime were detected and this was associated with sensitivity to the commonly prescribed therapeutic tamoxifen. Differences in fluorescence lifetime are attributed to the binding states of the autofluorescent metabolite NAD(P)H. The function of NAD(P)H is well described and in general involves cycling from a reduced to oxidized state to facilitate electron transport for the conversion of pyruvate to lactate. NAD(P)H fluorescence lifetimes depend on the bound or unbound state of the metabolite, which also relates to metabolic transitions between oxidative phosphorylation and glycolysis. To determine if fundamental metabolic profiles differ for cells that are sensitive to tamoxifen compared to those that are resistant, large populations of MCF-7 breast cancer cells were screened and fluorescence lifetimes were quantified. Additionally, metabolic differences associated with tamoxifen sensitivity were measured with a Seahorse HS mini metabolic analyzer (Agilent Technologies Inc. Santa Clara, CA) and confocal imaging. Results show that tamoxifen-resistant breast cancer cells have increased utilization of glycolysis for energy production compared to tamoxifen-sensitive breast cancer cells. This work is impacting because it establishes an early step toward developing a reliable screening technology in which large cell censuses can be differentiated for drug sensitivity in a label-free fashion.

This paper aims to develop a nonrigid registration method of preoperative and intraoperative thoracoabdominal CT images in computer-assisted interventional surgeries for accurate tumor localization and tissue visualization enhancement. However, fine structure registration of complex thoracoabdominal organs and large deformation registration caused by respiratory motion is challenging. To deal with this problem, we propose a 3D multi-scale attention VoxelMorph (MA-VoxelMorph) registration network. To alleviate the large deformation problem, a multi-scale axial attention mechanism is utilized by using a residual dilated pyramid pooling for multi-scale feature extraction, and position-aware axial attention for long-distance dependencies between pixels capture. To further improve the large deformation and fine structure registration results, a multi-scale context channel attention mechanism is employed utilizing content information via adjacent encoding layers. Our method was evaluated on four public lung datasets (DIR-Lab dataset, Creatis dataset, Learn2Reg dataset, OASIS dataset) and a local dataset. Results proved that the proposed method achieved better registration performance than current state-of-the-art methods, especially in handling the registration of large deformations and fine structures. It also proved to be fast in 3D image registration, using about 1.5 s, and faster than most methods. Qualitative and quantitative assessments proved that the proposed MA-VoxelMorph has the potential to realize precise and fast tumor localization in clinical interventional surgeries.

Purpose: The major limitation of tumor microwave ablation (MWA) operation is the lack of predictability of the ablation zone before surgery. Operators rely on their individual experience to select a treatment plan, which is prone to either inadequate or excessive ablation. This paper aims to establish an ablation prediction model that guides MWA tumor surgical planning. Methods: An MWA process was first simulated by incorporating electromagnetic radiation equations, thermal equations, and optimized biological tissue parameters (dynamic dielectric and thermophysical parameters). The temperature distributions (the short/long diameters, and the total volume of the ablation zone) were then generated and verified by 60 cases ex vivo porcine liver experiments. Subsequently, a series of data were obtained from the simulated temperature distributions and to further fit the novel ablation coagulated area prediction model (ACAPM), thus rendering the ablation-dose table for the guiding surgical plan. The MWA clinical patient data and clinical devices suggested data were used to validate the accuracy and practicability of the established predicted model. Results: The 60 cases ex vivo porcine liver experiments demonstrated the accuracy of the simulated temperature distributions. Compared to traditional simulation methods, our approach reduces the long-diameter error of the ablation zone from 1.1cm to 0.29cm, achieving a 74% reduction in error. Further, the clinical data including the patients’ operation results and devices provided values were consistent well with our predicated data, indicating the great potential of ACAPM to assist preoperative planning.

Super-resolution structured illumination microscopy (SR-SIM) relies heavily on post-processing reconstruction to obtain high-quality SR images from raw data. Although many SIM reconstruction algorithms have been developed to recover fine cellular structures with high fidelity even from the noisy data, whether the pixel intensities of reconstructed SR images are still proportional to the original fluorescence intensity has been less explored. The linearity between the intensity before and after reconstruction is defined as the intensity fidelity. Here, we proposed a method to evaluate the reconstructed SR image intensity fidelity at different spatial frequencies. With the proposed metric, we systematically investigated the impact of the key factors on the intensity fidelity in the standard Wiener-SIM reconstructions with simulated data, then evaluated the intensity fidelity of the SR images reconstructed by representative open-source packages. Our work provides a reference for SR-SIM image intensity fidelity improvement.

Deep learning (DL)-based image reconstruction methods have garnered increasing interest in the last few years. Numerous studies demonstrate that DL-based reconstruction methods function admirably in optical tomographic imaging techniques, such as bioluminescence tomography (BLT). Nevertheless, nearly every existing DL-based method utilizes an explicit neural representation for the reconstruction problem, which either consumes much memory space or requires various complicated computations. In this paper, we present a neural field (NF)-based image reconstruction scheme for BLT that uses an implicit neural representation. The proposed NF-based method establishes a transformation between the coordinate of an arbitrary spatial point and the source value of the point with a relatively light-weight multilayer perceptron, which has remarkable computational efficiency. Another simple neural network composed of two fully connected layers and a 1D convolutional layer is used to generate the neural features. Results of simulations and experiments show that the proposed NF-based method has similar performance to the photon density complement network and the two-stage network, while consuming fewer floating point operations with fewer model parameters.

Male infertility affects 10–15% of couples globally, with azoospermia — complete absence of sperm — accounting for 15% of cases. Traditional diagnostic methods for azoospermia are subjective and variable. This study presents a novel, noninvasive, and accurate diagnostic method using surface-enhanced Raman spectroscopy (SERS) combined with machine learning to analyze seminal plasma exosomes. Semen samples from healthy controls (n=32) and azoospermic patients (n=22) were collected, and their exosomal SERS spectra were obtained. Machine learning algorithms were employed to distinguish between the SERS profiles of healthy and azoospermic samples, achieving an impressive sensitivity of 99.61% and a specificity of 99.58%, thereby highlighting significant spectral differences. This integrated SERS and machine learning approach offers a sensitive, label-free, and objective diagnostic tool for early detection and monitoring of azoospermia, potentially enhancing clinical outcomes and patient management.

Photoacoustic imaging (PAI) employs short laser pulses to excite absorbing materials, producing ultrasonic waves spanning a broad spectrum of frequencies. These ultrasonic waves are captured surrounding the sample and utilized to reconstruct the initial pressure distribution tomographically. Despite the wide spectral range of the laser-generated photoacoustic signal, an individual transducer can only capture a limited segment of the signal due to its constrained bandwidth. Herein, we have developed a multi-bandwidth ring array photoacoustic computed tomography (PACT) system, incorporating a probe with two semi-ring arrays: one for high frequency and the other for low frequency. Utilizing the two semi-ring array PAIs, we have devised a specialized deep learning model, comprising two serially connected U-net architectures, to autonomously generate multi-bandwidth full-view PAIs. Preliminary results from simulations and in vivo experiments illustrate the system’s robust multi-bandwidth imaging capabilities, achieving an excellent PSNR of 34.78 dB and a structural similarity index measure (SSIM) of 0.94 in the high-frequency reconstruction of complex mouse abdominal structures. This innovative PACT system is notable for its capability to seamlessly acquire multi-bandwidth full-view PAIs, thereby advancing the application of PAI technology in the biomedical domain.