Journal of Biophotonics最新文献

Accurate assessment of skin elasticity is critical for understanding its physiological and pathological conditions. Conventional models often neglect anisotropy, leading to inconsistent measurements. We investigated skin anisotropic using Surface acoustic wave (SAW) based Optical Coherence Elastography (OCE), analyzing the angular ($�\; ��\; �\theta ��$) dependence of SAW velocity ($�\; ��\; �C_R��$) relative to fiber orientation. Validation experiments were conducted on chicken thighs and human forearms. In chicken thighs, $�\; ��\; �C_R��$ showed significant differences across propagation directions ranging from 90° to 0° ($�\; ��\; �p��$ = 0.008 < 0.05). In the dermis layer of forearms, the $�\; ��\; �C_R��$ demonstrated significant angular dependence ($�\; ��\; �p��$ = 0.031), with a percentage change of 31% while Young's modulus ($�\; ��\; �E��$) increased by 21.7 ± 11.5 kPa (60.32%) from 90° to 0°. No significant dependence was found in the hypodermis layer. These results demonstrate that incorporating anisotropy improves elasticity estimation and provides a practical foundation for skin assessment.

This study proposes an improved method for subsurface detection of neurovascular structures and their diameter and depth prediction as crucial feedback to neurosurgeons to prevent critical damage. The method relies on frequency-domain near infrared spectroscopy and machine learning algorithms based on numerical modeling data. The tasks solved include: analyzing the impact of the technical implementation of the spectrometer, forming effective feature vectors for classification and regression, selecting algorithms, developing training methods, and experimentally testing the results. Variational autoencoder-based algorithms demonstrate superior performance in classification and strong results in regression. A key advantage of these algorithms is their ability to train on unlabeled data while preserving the physical meaning of the latent space due to the applied custom constraint. It is essential that the light detectors of the spectrometers have a high internal gain. Experimental tests confirm the feasibility of partial training on simulated data.

Diabetic neuropathy (DN) is a prevalent chronic complication of diabetes. Sweat glands are directly controlled by the sympathetic nervous system, whose neuropathy affects the thermal regulation of the skin and results in morphological changes in sweat ducts. This study aims to investigate the correlation between the characteristics of fingerprint-guided sweat ducts assessed by optical coherence tomography and DN based on a predictive model using a back propagation neural network (BPNN) and principal component analysis (PCA). The results demonstrate that the number, volume, and spacing of sweat ducts are correlated with the severity of DN. The Voronoi diagram of the sweat duct distribution demonstrates irregularities in the spatial distribution among patients with DN. Furthermore, the PCA-based BPNN model has good predictive accuracy between patients with non-neuropathic, neuropathic, and severe neuropathic diabetes. These findings suggest that OCT-assessed sweat duct features may serve as non-invasive biomarkers for DN in patients with diabetes.

Fluorescence polarization imaging provides critical insights into molecular orientation, yet existing methods face limitations in parameter extraction efficiency and implementation complexity. This study proposes Wide-Field Multiparametric Fluorescence Imaging (WMPFI) using a Liquid Crystal Polarization Rotator (LCPR) for rapid polarization state modulation that generates pixel-level intensity modulations that encode fluorophore orientation. By analyzing fluorescence intensity variations under different polarization excitations, WMPFI reconstructs sample structural information through parametric imaging without requiring optical lock-in detection or computational reconstruction algorithms. Comparative experiments with Conventional Microscopy (CM) demonstrate WMPFI's enhanced sensitivity to anisotropic fluorescent dipole orientations, achieving superior contrast and resolution in imaging neural stem cells and skin tissues. The method's capacity for multi-parameter acquisition through polarization modulation offers a simplified approach for probing subcellular material exchange dynamics, with potential extensions to super-resolution imaging modalities.

Collagen plays a key role in maintaining skin structure and function. Energy based devices such as radiofrequency and ultrasound stimulate collagen synthesis through thermal stimulation, but lack precise temperature regulation. This study evaluated collagen synthesis induced by controlled thermal stimulation using a 980 nm laser. An ex vivo test identified conditions to achieve 50°C–60°C. Based on these results, 2.5 W laser irradiation for 35 s was applied to in vivo rat skin. Skin samples were collected on days 0, 14, and 28. Histology showed a three-fold increase in dermal thickness and a 15% increase in collagen density at day 28. RT-qPCR confirmed upregulation of FGF2, AKT, and COL3A1, with no significant changes in IL-1β or IL-6, and decreased NF-κB expression, indicating minimal inflammation. These findings demonstrate that controlled 980 nm laser stimulation enhances collagen synthesis without damaging skin tissue. Future studies will assess thermal distribution using fiber Bragg grating sensors.

This study proposed an intelligent intraoperative diagnostic framework that combines hyperspectral imaging (HSI) with deep reinforcement learning to accurately differentiate hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (ICC), the two main subtypes of primary liver cancer. To address the limitations of conventional imaging techniques and serum biomarkers, the authors constructed the first clinical HSI dataset of liver tumors (n = 131, spectral range 400–1000 nm). The proposed method integrates a 3D residual neural network (3D-ResNet) with a Proximal Policy Optimization (PPO)-based reinforcement learning algorithm, framing spectral band selection as a Markov decision process. An intraclass constrained cross-entropy loss further enhances class separability and compactness. Experimental results demonstrate a classification accuracy of 95%, outperforming traditional band selection approaches. This framework enables rapid, real-time tumor subtyping during surgery, addressing the critical clinical need for timely and accurate liver cancer diagnosis, and offers a promising tool for advancing precision oncology and improving intraoperative decision making.

Alterations in the undulation pattern of the basement membrane zone (BMZ) reflect early pathological changes in oral squamous cell carcinoma (OSCC). Optical coherence tomography (OCT) provides real-time, high-resolution, in vivo three-dimensional (3D) imaging of the oral mucosal microstructures, including BMZ. In this study, we quantified the undulation index of BMZ at four oral sites: the floor of the mouth, lower lip, buccal mucosa, and hard palate, and visualized their 3D morphological structures. Among regular participants, the mean undulation index varied across these sites: 14.64% ± 9.07% for floor, 8.74% ± 4.65% for lower lip, 9.45% ± 3.64% for buccal mucosa, and 14.84% ± 7.71% for hard palate. The corresponding epithelial thicknesses were 209.48 ± 87.51, 311.31 ± 106.85, 596.10 ± 138.40, and 444.83 ± 61.83 μm. It highlights the significance of BMZ morphology and epithelium thickness as potential diagnostic markers, offering a new approach for the early detection of OSCC.

Present methodologies for assessing antimicrobial effectiveness in living systems are heavily dependent on terminal detection approaches, including colony-forming unit enumeration and histological examination after animal euthanasia, for evaluating antimicrobial characteristics. Such conventional assessment techniques fail to monitor real-time alterations in infectious conditions throughout therapeutic interventions. This investigation introduces an innovative approach employing lipophilic near-infrared fluorophores for bacterial fluorescent tagging, integrated with IVIS (in vivo imaging system) technology, to accomplish continuous surveillance of bacterial infections in targeted infection models. Subsequently to localized administration of fluorescently marked bacteria, IVIS imaging demonstrated temporal variations in fluorescent signals within infection sites, which were subsequently employed to assess the in vivo performance of antimicrobial biomaterials. This methodology has been successfully verified using a rat tibial bone defect infection model. Experimental findings indicate that this technique provides immediate visualization of antimicrobial treatment effects and enables accurate quantitative evaluation, offering a methodological foundation for in vivo antimicrobial efficacy assessment.