Abstract:Ischemia is a significant factor affecting the repair of peripheral nerve injuries， while exosomes have been shown to promote angiogenesis. To further investigate the detailed processes and efficacy of exosome therapy for ischemic peripheral nerve injuries， this study utilized glucose-modified near-infrared-II （NIR-II） quantum dots （QDs） to label adipose-derived stem cell exosomes （QDs-ADSC-Exos）， enabling long-term in vivo NIR-II imaging of exosome treatment for ischemic peripheral nerve damage. Experimental results confirmed that QDs can be used for non-invasive in vitro labeling of exosomes， with QDs-ADSC-Exos exhibiting strong fluorescence signals in the NIR-II window and demonstrating favorable NIR-II imaging characteristics in vivo. Notably， QDs-ADSC-Exos showed accumulation at the site of nerve injury in cases of ischemic peripheral nerve damage. Functional neurological assessments indicated that QDs-ADSC-Exos effectively promoted neural regeneration. This study highlights the potential of exosomes in treating ischemic peripheral nerve injuries and elucidates the spatiotemporal characteristics of exosome therapy， providing objective evidence for the further optimization of exosome-based treatment protocols.

Abstract:Coherent Raman scattering microscopy is widely regarded as a powerful tool for solving biomedical problems due to its chemical specificity， label-free imaging capability， high spectral resolution and high sensitivity. However， the clinical application of coherent Raman scattering imaging technology has long been hindered by environmental sensitivity and large volume solid-state lasers. Ultrafast fiber lasers， with their compactness and stability， can effectively overcome these shortcomings. In this paper， different realization methods and research progress of fiber-based laser sources in coherent Raman scattering imaging are reviewed， including supercontinuum fiber source， soliton self-frequency shift fiber source， fiber optical parametric oscillator and synchronized fiber source， and the future development is prospected.

Abstract:NIR-II fluorescence imaging demonstrates significant advantages in biological imaging with its high signal-to-background ratio （SBR） and deep tissue penetration， showing broad application prospects in biomedical fields. The classification of NIR-II imaging windowsfacilitates the optimization of imaging processes. Among these， the 1400-1500 nm imaging window benefits from its unique water absorption characteristics， enabling effective suppression of scattering background and achieving high-contrast imaging. This study systematically evaluates the imaging potential of the 1400-1500 nm window through simulation studies and in vivo experiments. To advance the clinical translation of fluorescence imaging in the 1400-1500 nm window， indocyanine green （ICG）， an organic small-molecule dye approved by the U.S. Food and Drug Administration （FDA）， was employed as the fluorescent probe. Utilizing its extended fluorescence emission tail in the NIR-II region， high-contrast and high-resolution imaging of mouse vasculature and intestinal structures was achieved in the 1400-1500 nm window. Furthermore， in combination with methylene blue （MB）， another FDA-approved agent， high-quality dual-channel NIR-II imaging was successfully implemented enabling precise localization of blood vessels and lymph nodes in mice. This research further explores the unique advantages of the 1400-1500 nm imaging window in biological imaging and its clinical application potential. It also provides valuable references for the clinical translation of NIR-II fluorescence imaging.

Abstract:Fluorescence imaging in the second near-infrared window （NIR-II， 900-1880 nm） offers high signal-to-background ratio （SBR）， enhanced definition， and superior tissue penetration， making it ideal for real-time surgical navigation. However， with single-channel imaging， surgeons must frequently switch between the surgical field and the NIR-II images on the monitor. To address this， a coaxial dual-channel imaging system that combines visible light and 1100 nm longpass （1100LP） fluorescence was developed. The system features a customized coaxial dual-channel lens with optimized distortion， achieving precise alignment with an error of less than ±0.15 mm. Additionally， the shared focusing mechanism simplifies operation. Using FDA-approved indocyanine green （ICG）， the system was successfully applied in dual-channel guided rat lymph node excision， and blood supply assessment of reconstructed human flap. This approach enhances surgical precision， improves operational efficiency， and provides a valuable reference for further clinical translation of NIR-II fluorescence imaging.

Abstract:High spatiotemporal resolution multi-region brain synchronization imaging is a critical requirement in neural circuit research. However， traditional multiphoton microscopy is limited by its single field-of-view （FOV） imaging mode， making it difficult to achieve large-scale observation of neural activity across multiple brain regions. The multi-FOV multi-photon imaging technology employs a FOV segmentation strategy in both the front and rear optical paths of the objective lens and combines multi-dimensional signal analysis methods （such as wavelength encoding， spatial demultiplexing， and time gating） to effectively overcome the spatiotemporal resolution limitations of traditional techniques. This technology enables millisecond-level temporal resolution and micron-level spatial resolution for synchronous imaging across brain regions， providing a novel research paradigm for revealing cortical functional coupling， cortical-subcortical neural circuit coordination mechanisms， and whole-brain neural signal propagation dynamics. In the future， through in-depth integration with techniques such as endoscopic imaging， adaptive optical aberration correction， optical stimulation and deep learning-based image analysis， multi-FOV multi-photon imaging will further advance the precise decoding of neural circuit functional architecture and demonstrate significant value in clinical translation fields such as neurodegenerative disease diagnosis and brain-machine interface development.

Abstract:Optical Coherence Tomography （OCT） provides high-resolution images of skin tissue structure and pathological features. Automated image analysis methods （such as segmentation and classification） are important for assisting skin disease diagnosis and treatment evaluation. These methods provide quantitative support for medical decisions. Compared with traditional methods and early machine learning （ML） techniques， deep learning （DL） improved analysis efficiency and reproducibility. It also reduced manual processing time significantly. This paper systematically reviewed the application progress of DL in skin OCT image analysis. It focused on technical approaches for image denoising， skin layer segmentation， and skin cancer diagnosis. The study identified key challenges including model generalization and data heterogeneity. The findings provide theoretical references and technical guidance for future research directions.

Abstract:Coherent Raman spectroscopy and imaging technology， as a new type of label-free detection technology， has been widely used in biomedicine， material science， and other fields owing to its high specificity and noninvasive advantages. In recent years， the combination of time stretching and coherent Raman spectroscopy has effectively overcome the limitations of traditional spectrometers in terms of sampling rate and spectral range and provides a new idea for high-speed and broadband Raman spectroscopy and imaging. This paper first describes the basic principle of time stretching and its theory， summarizes the results of the application of this technology in other fields， and then systematically combs through the research progress of coherent Raman spectroscopy based on time stretching. Finally， it looks forward to the future development of coherent Raman spectroscopy based on time-stretching.

Abstract:Traumatic brain injury is one of the most serious diseases that endanger human health. Sensitive and rapid detection method is a kind of powerful guarantee for the accurate and effective treatment of traumatic brain injury. In recent years， terahertz （THz） wave and Raman spectroscopy have broad application prospects in biomedical diagnosis and other fields due to their complementarity in technology. In this article， the researches of terahertz wave and Raman spectroscopy technology in traumatic brain injury detection were summarized in response to the needs and difficulties of traumatic brain injury diagnosis. Firstly， the development status of THz imaging and THz spectroscopy technology was introduced， and the applications of the two technologies in traumatic brain injury detection were also introduced， respectively. In addition， the principle and classification of Raman spectroscopy were summarized， and the research of Raman spectroscopy in the detection of traumatic brain injury tissues， body fluids， and biomarkers were discussed. Finally， the development trend of THz wave and Raman spectroscopy in the detection of traumatic brain injury was analyzed， which provides a new research idea for the application of THz wave and Raman spectroscopy in the rapid and accurate diagnosis of traumatic brain injury.

Abstract:Enamel demineralization often occurs in the early stage of dental caries. Studying the microscopic mechanism of enamel demineralization is essential to prevent and treat dental caries. Terahertz （THz） technology， especially continuous wave （CW） THz near-field scanning microscopy （THz-SNOM） with its nanoscale resolution， can be promising in biomedical imaging. In addition， compared with traditional THz time-domain spectroscopy （TDS）， portable solid-state source as the emission has higher power and SNR， lower cost， and can obtain more precise imaging. In this study， we employ CW THz-SNOM to further break the resolution limitations of conventional THz imaging techniques and successfully achieve the near-field imaging of demineralized enamel at the nanoscale. We keenly observe that the near-field signal of the enamel significantly lowers as demineralization deepens， mainly due to the decrease in permittivity. This new approach offers valuable insights into the microscopic processes of enamel demineralization， laying the foundation for further research and treatment.

Abstract:Fibroblasts support a broad range of essential organ functions via microarchitectural， biomechanical， and biochemical cues. Despite great advances in fluorescence， photoacoustic conversion， and Raman scattering over the past decades， their invasiveness and limited spatial resolution hinder the characterization of fibroblasts in a single cell. Here， taking mouse embryonic fibroblasts （MEFs） as an example， we propose a novel noninvasive approach to investigate the compositional distribution of MEFs at the single-cell scale via terahertz （THz） nanoscopy. Compared to the topological morphology， THz nano-imaging enables the component-based visualization of MEFs， such as the membrane， cytoplasm， nucleus， and extracellular vesicles （EVs）. Notably， we demonstrate the real-space observation of the influence of rapamycin treatment on the increase of EVs in MEFs. Moreover， the line-cut and area-statistical analysis establishes the relationship between the topological morphology and the THz near-field amplitudes for different cellular components of MEFs. This work provides a new pathway to characterize the effects of pharmaceutical treatments， with potential applications in disease diagnosis and drug development.

Abstract:As a valuable Chinese herbal medicine， Panax notoginseng exhibits therapeutic efficacy and quality closely associated with its saponin content， which demonstrates significant geographical variations. To accurately authenticate the geographical origin and ensure medicinal quality， a novel method integrating terahertz precision spectroscopy with a convolutional neural network （CNN） algorithm was proposed. 40 Panax notoginseng samples from 4 regions in Yunnan Province， China—Honghe Autonomous Prefecture， Kunming， Qujing， and Wenshan Autonomous Prefecture—were analyzed using terahertz spectroscopy and high-performance liquid chromatography （HPLC）. A CNN model was constructed and trained based on the acquired spectral and chromatographic data to classify the geographical origins. Experimental results revealed that the terahertz spectroscopy combined with the CNN model achieved a classification accuracy of 92.5%， significantly outperforming the 82.5% accuracy attained by the HPLC-CNN model. This finding highlights the potential of terahertz spectroscopy in component analysis and geographical traceability of herbal medicines， providing a novel scientific approach for rapid， non-destructive， and precise identification of Chinese medicinal materials.

Abstract:This study used a terahertz metamaterial sensor for the rapid and accurate detection of the antithrombotic drug Plavix， addressing the increasing demands for efficiency and sensitivity in drug content monitoring. Utilizing the terahertz vibration characteristics of Plavix， characteristic absorption peaks within the 1-3 THz band were identified. Based on these findings， a dual-polarization resonance metamaterial sensor was designed to simultaneously enhance the sensing signals of these characteristic absorption peaks. Experimental results indicate that the sensor attains a high level of fit （R²>0.97） for quantitative analysis in the quantitative detection of Plavix through the established two-indicator decision model. Consequently， the terahertz metamaterial sensing technology presented in this study exhibits superior performance in monitoring Plavix content and offers a new tool for clinical drug monitoring and broader biochemical sample analysis.

Abstract:Central precocious puberty （CPP） is mainly caused by the premature activation of the hypothalamic-pituitary-gonadal axis， which leads to abnormal hormone levels and triggers structural and functional changes in the brain， making the neurovascular coupling mechanisms of children with CPP different from those of normal children in the task state. Addressing current limitations of clinical diagnosis， such as false negatives， interference from obesity， and physiological discomfort， this study utilized functional near-infrared spectroscopy （fNIRS） to analyze task-related brain activation characteristics in 167 children from Tianjin Hospital， including 85 normal children and 82 children with CPP. An auxiliary diagnostic model for CPP was established based on these analyses. It was found that the prefrontal activation areas during mental arithmetic （MA） were more in the normal group than in the CPP group， and the activation areas were more in females than in males. By selecting mean， variance， kurtosis， and skewness from the two channels with the highest frequency of correlation and the highest magnitude of negative correlation as input features， the constructed classification model achieved an accuracy rate of 79.1%. This study provides a new and important reference for the rapid screening and pathogenesis study of CPP.

Abstract:Near-infrared spectroscopy is a type of molecular vibration spectroscopy. Temperature variations cause changes in molecular vibrations such as O-H and inter molecular forces such as hydrogen bonding， which lead to absorption spectral intensity and peaks changes， affecting the prediction accuracy of minor components such as blood glucose. To address the impact of temperature perturbation on spectral detection and modeling analysis， a method of temperature perturbation discrimination based on aquaphotomics and two-trace two-dimensional correlation spectroscopy （2T2D-COS） was proposed. The 2T2D-COS analysis was applied to diffuse reflectance spectra of simulated solutions under temperature perturbation and varying glucose concentrations. Spectral features induced by changes in temperature and glucose concentration were successfully extracted， revealing distinct water spectral patterns under different perturbations. Quantitative analysis shows that temperature changes of 0.1 °C is equivalent to glucose concentration changes of 45 mg/dL in terms of intensity. A temperature perturbation outliers discrimination model was further established based on raw spectra， water spectral features， and 2T2D-COS asynchronous spectra. The accuracy rates of the model based on 2T2D-COS asynchronous spectra is 95.83%. After removing outliers， the root mean square error of glucose concentration prediction is reduced by 51.89%. This work provides a foundation for improving the accuracy of in vivo blood glucose detection using near-infrared spectroscopy.

Abstract:The concentration of exhaled CO as a biomarker for certain diseases has attracted significant attention. However， existing CO concentration detectors suffer from low sensitivity and slow response times. To address this， we developed a high-sensitivity， rapid-response exhaled CO measurement system based on absorption spectroscopy， utilizing a quantum cascade laser with a central wavelength of 4.59 μm and a 3.8 m multi-pass cell. The CO concentration was analyzed using both direct absorption spectroscopy （DAS） and wavelength modulation spectroscopy （WMS）. The DAS method demonstrated a linearity of 0.998 with a detection limit of 3.68 × 10??. For WMS， the linearity remained 0.998 at CO concentrations below 6.00 × 10??， achieving a detection limit of 3.00 × 10??. Through Allan variance analysis， optimal integration times of 170 s for DAS and 250 s for WMS were determined， corresponding to improved detection limits of 2.00 × 10?? and 3.00 × 10?1?， respectively. Finally， exhaled CO concentrations from 14 volunteers were measured， demonstrating the system''s capability to distinguish between smokers and non-smokers. This provides a scientifically validated tool for assessing smoking status in clinical smoking cessation programs.

Abstract:An overview is provided of the research progress in the application of hyperspectral detection technology for non-destructive testing of key parameters in tobacco leaf quality. Methods and equipment for the rapid detection of chemical components such as total sugar， reducing sugar， total nitrogen， nicotine， starch， chloride， and potassium in tobacco leaves using this technology are explored. The impact of different tobacco sample forms on spectral data is pointed out. The advantages and challenges of hyperspectral technology in applications such as field management， harvest optimization， and online grading in tobacco production are analyzed. The promising prospects of combining hyperspectral technology with artificial intelligence to build predictive models for tobacco leaf chemical composition are proposed. This combination provides scientific evidence and references for improving detection efficiency and quality in the tobacco industry.

Abstract:This paper presents a high-speed and robust dual-band infrared thermal camera based on an ARM CPU. The system consists of a low-resolution long-wavelength infrared detector， a digital temperature and humidity sensor， and a CMOS sensor. In view of the significant contrast between face and background in thermal infrared images， this paper explores a suitable accuracy-latency tradeoff for thermal face detection and proposes a tiny， lightweight detector named YOLO-Fastest-IR. Four YOLO-Fastest-IR models （IR0 to IR3） with different scales are designed based on YOLO-Fastest. To train and evaluate these lightweight models， a multi-user low-resolution thermal face database （RGBT-MLTF） was collected， and the four networks were trained. Experiments demonstrate that the lightweight convolutional neural network performs well in thermal infrared face detection tasks. The proposed algorithm outperforms existing face detection methods in both positioning accuracy and speed， making it more suitable for deployment on mobile platforms or embedded devices. After obtaining the region of interest （ROI） in the infrared （IR） image， the RGB camera is guided by the thermal infrared face detection results to achieve fine positioning of the RGB face. Experimental results show that YOLO-Fastest-IR achieves a frame rate of 92.9 FPS on a Raspberry Pi 4B and successfully detects 97.4% of faces in the RGBT-MLTF test set. Ultimately， an infrared temperature measurement system with low cost， strong robustness， and high real-time performance was realized， achieving a temperature measurement accuracy of 0.3°C.