Journal of Biophotonics最新文献

Accurate differentiation between adrenocortical tumors (ACTs) and pheochromocytomas (PHEOs) remains challenging in intraoperative pathology. This study proposes a hyperspectral–deep-learning fusion framework integrating spectral and spatial features for rapid, label-free diagnosis. Using Savitzky–Golay-filtered hyperspectral data (400–1000 nm) and a lightweight dual-branch convolutional network, the model achieved an AUC of 0.96 and an accuracy of 92.8%. Mutual information-based band selection and mid-level feature fusion enhanced efficiency, while Grad-CAM and spectral sensitivity analyses revealed interpretable wavelength–feature associations corresponding to lipid and hemoglobin absorption. The proposed system demonstrates strong potential for real-time, interpretable intraoperative discrimination between cortical and medullary adrenal tumors, supporting precision endocrine surgery.

This study investigates the potential of hyperspectral imaging (HSI) as a label-free, quantitative tool for differentiating chronic obstructive pulmonary disease (COPD) from normal mouse lung tissues based on their intrinsic optical signatures. High-resolution HSI data (400–1000 nm) were acquired from paraffin-embedded sections and analyzed using principal component analysis (PCA) for dimensionality reduction, followed by partial least squares–discriminant analysis (PLS-DA) and support vector machine (SVM) classification. Distinct spectral disparities, particularly in hemoglobin and water-associated absorption bands, enabled accurate discrimination with classification performance exceeding 93%. These findings establish HSI as a non-destructive and objective modality capable of capturing subtle pathological alterations in pulmonary tissue, offering a robust experimental and theoretical foundation for its translation into pathological diagnostics and quantitative respiratory disease assessment.

Second harmonic generation (SHG) imaging technique can specifically image components such as collagen fibers, which provides diagnostic information for prostate cancer (PCa). Gleason grading serves as the primary criterion for assessing PCa. However, obtaining high signal-to-noise ratio (SNR) SHG images requires prolonged acquisition, limiting clinical feasibility. In this study, rapid SHG imaging was combined with a deep-learning-based denoising network, Selective Residual M-Net (SRMNet), to reconstruct high-fidelity images from low-SNR inputs. The denoising procedure markedly enhanced image quality while preserving collagen alignment features essential for quantitative assessment. The enhanced image fidelity enabled more robust extraction of collagen orientation metrics under low-SNR conditions, facilitating accurate assessment of stromal organization with reduced acquisition time. These results highlight that deep-learning-assisted SHG imaging provides an effective strategy for reliable collagen orientation analysis in PCa, supporting the development of rapid and objective optical histopathology.

Conventional analytical methods for fluorescence lifetime imaging (FLI) require the instrument response function (IRF) alongside temporal fluorescence decay to accurately estimate lifetime parameters. While the IRF remains spatially and temporally invariant in microscopy, it exhibits significant pixel-wise spatial variation and temporal offsets at mesoscopic and macroscopic scales due to sample size and surface topography. Existing deep learning approaches, trained on limited synthetic or simplified experimental datasets, often fail to generalize to complex biological or in vivo imaging. Here, we introduce MFliNet, a deep learning framework working in accordance with the physics of photon time-of-flight deconvolution for accurate multi-exponential decay parameter estimation. Utilizing a Differential Transformer encoder–decoder architecture, MFliNet jointly processes temporal fluorescence decay and corresponding IRF inputs to correct topographical distortions in photon arrival distributions. Validation with tissue-mimicking phantoms and preclinical tumor models demonstrates exceptional robustness and precision, enabling reliable, real-time macroscopic FLI for complex biological and intraoperative applications.

The polarization-sensitive THz solid immersion (SI) microscope is applied to quantify the heterogeneity and birefringence of freshly-excised rat tissues: the abdominal and tongue muscles, tendons of the flexor digitorum and extensor digitorum of the forelimb, Achilles tendon, abdominal aorta, and Corpus callosum of the brain. THz refractive indices of tissues are retrieved for the two orthogonal linear polarizations of the incident beam, which are collinear and transverse toward the fibrous structures. The observed THz birefringence, as high as a few percent, is attributed to tissue morphology relying on the hematoxylin and eosin (H&E)-stained histology. Expectedly, the THz refractive index is higher for the incident field collinear to the fibrous structures. The muscle tissues and Corpus callosum manifest the most pronounced birefringence, while that of tendon is sufficiently lower. Our findings uncover notable heterogeneity and birefringence of tissues; those should be accounted for when developing novel methods of THz biophotonics.

This study evaluates whether a deep-tissue microcirculation measurement can serve as an indicator of laser acupuncture treatment efficacy in an osteoarthritis (OA) animal model. Four groups of rats (normal, OA, OA10, and OA20, n = 6 each) were prepared where OA10 and OA20 mean treated groups with different light doses. Blood flow index (BFI) was measured at four time points (T0–T3) near acupoints GB34 and GB39 using a custom-made diffuse speckle contrast analysis (DSCA) system. BFI measurement showed an increase after OA induction and it remained elevated, whereas treatment groups exhibited a decrease in BFI. Spectral analysis revealed meaningful changes in the myogenic wave band, and a support vector machine classification demonstrated that incorporating BFI at T1 along with anatomical features significantly improved group separation accuracy, indicating a strong correlation between acute inflammatory response and anatomical indices.

Blood systemic photobiomodulation (BSP) is an integrative approach using low-intensity lasers or LEDs. This is a therapeutic strategy to modulate inflammatory, metabolic, and hemostatic biomarkers, promoting potentially beneficial systemic effects. Fifty-six female volunteers participated in this study that investigated the effects of different transcutaneous photobiomodulation protocols applied over the radial artery, using red and infrared light under different dosimetry. Volunteers were allocated into experimental groups, and biomarkers were evaluated. Results show that the IR-V group exhibited a significant increase in HDL levels after treatment. Regarding insulin, a significant reduction was observed in the IR-LD group after treatment; ferritin levels were significantly reduced in the LED group. No significant differences were found for the other biomarkers analyzed. These findings reinforce the importance of personalizing photobiomodulation protocols and suggest that specific wavelength- and dose-dependent parameters may lead to distinct systemic biochemical responses.