Journal of Biophotonics最新文献

A 3D phase scanning method was applied to study blood plasma facies, generating layered polarization maps of the object field. The most sensitive parameters to changes in birefringence distribution were identified. Multifractal analysis using wavelet transforms and fractal dimension spectra provided specific insights into the scale self-similarity of the polarization maps. The multifractal spectra of ellipticity distributions were algorithmically derived, revealing that the third- and fourth-order statistical moments were most sensitive to changes in the supramolecular networks of the facies. These findings were successfully applied to differentiate post-COVID-19 effects with high accuracy.

Zebrafish serves as a valuable model for studying tissue regeneration due to their comprehensive regenerative abilities, particularly in bone tissue. In this study, a Mueller matrix optical coherence tomography (OCT) system was applied to monitor the regenerative processes of zebrafish caudal fins in vivo. The analysis focused on evaluating the thickness of the caudal fin tip and the distribution of internal bone tissue during the regenerative process. Subsequently, the effect of ectoine solution on the regeneration process was observed and discussed. Our findings revealed that the caudal fin blastema did not exhibit phase-induced polarization characteristics in the Mueller matrix OCT images. Statistical analyses indicated that the caudal fins did not fully regenerate to their original state within 21 days. Furthermore, the results suggested that ectoine solution could enhance tissue regeneration. This approach provides a method for quantifying zebrafish caudal fin regeneration and advances observation techniques for biomedical and clinical applications.

Fluorescence imaging (FI) employing near-infrared (NIR) light within the range of ~750–1350 nm enables biomedical imaging several millimeters beneath the tissue surface. More recent investigations into the short-wave IR (SWIR) transparency windows between ~1550–1870 and 2100–2300 nm highlight their superior capabilities. This research presents a comparison of IR-FI of PbS quantum dots, emitting at 990, 1310, and 1580 nm, through the mouse scalp skin, skull, and brain. The SWIR fluorescence is the most effectively transmitted signal, showing particularly significant enhancement when passing through the skull, which causes high light scattering. For the analysis of the imaging results and light propagation through the organs, their spectra of attenuation, absorption, and scattering coefficients are measured. In view of biomedical imaging, attenuation due to light scattering is a more destructive factor. Hence, the spatial resolution and imaging contrast can be improved by operating in SWIR due to decreased light scattering.

The detection of skin's structure lays the foundation for personalized laser surgery of vascular skin disease, which can be noninvasively achieved by diffuse reflectance spectroscopy (DRS). A two-step inverse Monte Carlo radiation method based on DRS under two source-detector separations was proposed to quantify the skin structure, including chromophore concentration (melanin f _m and hemoglobin f _b), epidermal thickness t _epi, average vessel diameter D _ves, depth d _pws and thickness t _pws of the vascular layer for diseased skin. The method fitted the simulated DRS to the measured DRS iteratively, differences between which were described by a specific objective function to amplify blood absorption at 500–600 nm, and D _ves, d _pws, and t _pws were estimated based on f _m, f _b, and t _pws fitted in the first step. The results showed that the two-step method dramatically improve the inversion accuracy with mean errors of f _m, f _b, t _pws, and d _pws less than 5%.

Bacteria are the primary cause of infectious diseases, making rapid and accurate identification crucial for timely pathogen diagnosis and disease control. However, traditional identification techniques such as polymerase chain reaction and loop-mediated isothermal amplification are complex, time-consuming, and pose infection risks. This study explores remote (~3 m) bacterial identification using laser-induced breakdown spectroscopy (LIBS) with a Cassegrain reflective telescope. Principal component analysis (PCA) was employed to reduce the dimensionality of the LIBS spectral data, and the accuracy of support vector machine (SVM) and Random Forest (RF) algorithms was compared. Multiple repeated experiments showed that the RF model achieved a classification accuracy, recall, precision, and F1-score of 99.81%, 99.80%, 99.79%, and 0.9979, respectively, outperforming the SVM model and providing more accurate remote bacterial identification. The method based on laser-induced plasma spectroscopy and machine learning has broad application prospects, supporting noncontact disease diagnosis, improving public health, and advancing medical research and technological development.

Gleason grading system is dependable for quantifying prostate cancer. This paper introduces a fast multiphoton microscopic imaging method via deep learning for automatic Gleason grading. Due to the contradiction between multiphoton microscopy (MPM) imaging speed and quality, a deep learning architecture (SwinIR) is used for image super-resolution to address this issue. The quality of low-resolution image is improved, which increased the acquisition speed from 7.55 s per frame to 0.24 s per frame. A classification network (Swin Transformer) was introduced for automated Gleason grading. The classification accuracy and Macro-F1 achieved by training on high-resolution images are respectively 90.9% and 90.9%. For training on super-resolution images, the classification accuracy and Macro-F1 are respectively 89.9% and 89.9%. It shows that super-resolution image can provide a comparable performance to high-resolution image. Our results suggested that MPM joint image super-resolution and automatic classification methods hold the potential to be a real-time clinical diagnostic tool for prostate cancer diagnosis.