Journal of Biophotonics最新文献

Surgical incision closure and trauma repair are critical in surgery. Dual-beam laser tissue welding achieves full-layer skin tissue connection with minimal thermal damage via selective absorption of different laser wavelengths. This study investigates the impacts of laser scanning speed, spot spacing, and deflection angle on mechanical properties and thermal damage of skin tissues to optimize parameters. Through incision morphology observation, tensile strength tests, thermal denaturation analysis, and microtissue quantitative characterization, the optimal process parameters were obtained. Results show that tensile strength and thermal denaturation degree increase with scanning speed, exhibit a cyclic decreasing pattern with spot spacing, and significantly improve with deflection angle. When V = 200 mm/s, D = 0.5 mm, and θ = 60°, the incision achieves the highest connection strength, best thermal damage control, and overall performance. These findings provide technical references and data support for refined parameter models in vivo tests.

Melanin deposition and erythema mainly constitute physiological responses of the skin to environmental changes and represent important factors evaluating and diagnosing the skin conditions. This study investigates the critical roles of melanin and hemoglobin in skin-light interaction and combines spectral reflectance with single-point pigment values (collected by Mexameter MX18) to achieve the objective imaging skin color assessment. Feature wavelengths selected by the competitive adaptive reweighted sampling algorithm aligned well with narrow wavelength band designed by MX18, effectively removing redundant data while maintaining the model accuracy. Furthermore, seven machine learning methods were compared and evaluated, among which the stacked generalization model demonstrated the best performance (RMSEV = 14.23, $��R_v^2�=�0.8634�$, RPD_v = 2.706 for melanin index; RMSEV = 31.74, $��R_v^2�=�0.7505�$, RPD_v = 2.002 for erythema index). Finally, hyperspectral imaging technology enabled the visualization of skin pigment distribution, providing a rapid and non-invasive analytical tool for dermatological diagnosis and aesthetic evaluation.

We introduce a quantitative phase imaging-based flow cytometer that integrates dual-view transport of intensity phase imaging with microfluidics into a commercial microscope, enabling label-free cell analysis and classification. By capturing under-focus and over-focus images simultaneously, the phase distributions of flowing cells are reconstructed to extract morphological parameters for subsequent classification. This system achieves high-accuracy phase imaging, as demonstrated by tests on a standard phase plate sample, and successfully recognizes and classifies cells, validated using mixtures of RAW264.7 cells and MC3T3-E1 cells in varying proportions. Given its simple configuration, precise phase retrieval, and robust classification capabilities, we believe this quantitative phase imaging-based flow cytometer holds great promise as an efficient tool for cell analysis in microfluidics, with potential applications in both fundamental research and clinical studies.

Glomerular diseases, characterized by primary glomerular injury, impose a significant global health burden. While renal biopsy remains the diagnostic gold standard, this study explores hyperspectral imaging (HSI) as a novel non-invasive methodology combining spectral and spatial analysis. Urine samples from patients with four glomerular disease subtypes (Minimal Change Disease, Diabetic Nephropathy, Membranous Nephropathy, IgA Nephropathy; 40 samples/subtype) underwent HSI acquisition. Using dimensionality-reduced HSI spectral data, we developed a ResNet-50 classification model. The model achieved high performance with 96.8% average five-fold cross-validation accuracy and a 0.982 AUC, confirming accurate multiclass differentiation feasibility from limited samples. Comparative analysis validated the superior efficacy of the integrated ResNet-50 and HSI approach for this classification task.

COVID-19 has increased the likelihood of cognitive impairment in patients with post-acute sequelae of COVID-19 (PASC). There is a lack of direct evidence regarding the working memory performance of mild patients during the recovery period. This study employed functional near-infrared spectroscopy (fNIRS) to construct a mixed effects model for PASC patients performing the N-back task, assessing brain activation levels and brain connectivity. PASC patients exhibited abnormally low activation in the parietal lobe (β = −0.21) and abnormally high activation in the occipital lobe (β = 0.40). There was a significant reduction in brain connectivity within the frontal–parietal and frontal–occipital networks. These findings suggest that PASC patients experience impaired fronto-parietal network connectivity, rely more on the visual cortex to compensate for executive function deficits, and use this as a compensatory mechanism to reduce overall cerebral blood oxygenation. This study provides evidence of altered brain activation patterns in PASC patients during the recovery period due to cognitive impairment.

Spectral fluctuations in fluorescence spectroscopy, often ignored as noise, contain significant information about the fluorophore microenvironments. We present a discrete wavelet transform (DWT)-based technique to extract spectral fluctuations from the intrinsic fluorescence signals and utilize them to classify normal and precancerous patients. The fluctuations are extracted by applying the inverse DWT after zeroing the approximation and noisy detail coefficients. Multifractal detrended fluctuation analysis revealed stronger multifractality for precancer signals manifested in the singularity spectrum. The Hurst exponent ($��H�$) and the Hausdorff dimension $��\left(�\Delta �\alpha �\right)�$ clearly distinguish two groups. Random Forest classification of generalized Hurst and Holder exponents achieves 96% sensitivity, specificity, and accuracy with an AUC of 0.98. This indicates that the spectral fluctuations derived from the intrinsic fluorescence data capture the subtle, distinctive features, resulting in better classification between the two grades. Further, a comparison among various mother wavelet functions reveals the best performance for the “bior2.4” wavelet.

Non-invasive glucose monitoring using Raman spectroscopy with 830 nm excitation presents a promising alternative to traditional fingerstick methods for diabetes management research. An integrated in vivo Raman system enables transcutaneous glucose detection and has demonstrated robust performance in oral glucose tolerance tests (OGTT), validating its reliability. Inter-subject correlation between spectral features and glucose concentration was addressed by the intensity of the fingerprint peak (I₁₁₂₅), peak intensity ratio (I₁₁₂₅/I₁₄₄₅), and the spectral area ratio (S₁₁₂₅/S₁₄₄₅), whose correlation coefficient (R) was 0.9266, 0.8946, and 0.9061, respectively. A partial least squares regression (PLSR) model was also adopted for quantitatively bridging the measured Raman spectral information and the actual glucose concentration, showing reliable predictive performance within a wide glucose concentration range of 82.8 to 180 mg/dL. This work demonstrates promising feasibility for in vivo transcutaneous Raman-based glucose monitoring, laying a foundation for subsequent technique transformation in the field of diabetes management and personalized health monitoring.

Ovarian cancer (OvCa) remains the leading cause of gynecological cancer mortality, with most patients developing chemoresistance. Drug repurposing offers promising alternatives, with mebendazole (MBZ) showing anticancer activity. This study evaluates MBZ efficacy using Spectral Domain Optical Coherence Tomography (SD-OCT). We conducted longitudinal imaging of 40 wild-type (WT) and cisplatin-resistant (CPR) OVCAR8 multicellular tumor spheroids over 11 days. Four analyses were performed: volume analysis, optical attenuation analysis, uniformity analysis, and texture feature analysis. Volume analysis showed MBZ reduced spheroid growth in both groups, with greater effects in CPR-MCTs. Optical attenuation analysis revealed increased necrotic tissue ratios in treated spheroids. Uniformity analysis demonstrated MBZ targets heterogeneous tissues effectively. Texture analysis identified significant structural changes, with 866 altered features in CPR spheroids versus 124 in WT spheroids. Cell viability assays confirmed MBZ's effectiveness against standard and chemo-resistant OVCAR8 tumors. This study demonstrates SD-OCT's utility for noninvasive therapy monitoring in 3D cancer models.

Macrophages (MΦs) are integral cellular components responsible for immune response and tissue homeostasis. Evaluation of their pro-inflammatory (M1) and anti-inflammatory (M2) polarization states, along with their metabolic profiles, typically conducted via flow cytometry, is crucial for assessing the immune status of an organism. Traditional flow cytometry relies on extrinsic fluorescent labels, which may interfere with cellular function. Here, using multispectral flow cytometry, we demonstrate how the autofluorescence profiles of human monocyte-derived macrophages change under M1/M2 polarization, hypoxia and starvation stress factors, and interaction with low-density lipoproteins as an atherosclerosis model. Extending these findings to clinical samples, we demonstrated that leukocyte AF profiles could distinguish atherosclerosis patients from healthy controls with a ROC-AUC of 0.84 ± 0.09, advanced predictive models. These findings highlight AF as a sensitive, non-invasive tool for assessing macrophage activation and metabolic states, with potential applications in atherosclerosis diagnostics and immune cell phenotyping.