Journal of biophotonics最新文献

The aim of this study is to validate an approach to monitor the spatial and temporal hemodynamics of intraperitoneal organs using a commercially available laparoscopic system. The approach to create a spatial map of tissue oxygen saturation (StO₂) and total hemoglobin concentration (C_HbT) is based on a multiple regression model using Monte Carlo simulation of light transport in tissues to specify relationships between RGB values, oxygenated hemoglobin concentration, and deoxygenated hemoglobin concentration. Experiments with an optical phantom are performed to confirm the ability of the approach to detect changes in StO₂ and C_HbT under different working distances of the endoscope that may occur during actual surgery. In vivo experiments in rats confirm that the proposed approach can quantitatively monitor changes in StO₂ and C_HbT induced in the small intestine, liver, and cecum. The proposed approach has the potential as a tool for monitoring intraperitoneal organs in real time during laparoscopy.

Colorectal cancer (CRC) is one of the most prevalent gastrointestinal malignancies, necessitating the study of cellular and molecular changes within the tumor microenvironment. While pathological image analysis remains the gold standard, its labor-intensive nature limits its broad application. This study proposes a label-free CRC typing approach using intelligent optical time-stretch (OTS) imaging flow cytometry combined with multi-instance learning. Specifically, we construct a high-throughput cell image acquisition system by integrating OTS imaging with microfluidic cell focusing, capturing 363 931 cell images from 10 clinical samples. To address cell diversity and heterogeneity, we employ a multi-instance learning framework, which incorporates a multi-level attention mechanism to explore feature interactions at both channel and instance levels. Finally, we apply a majority voting mechanism to enable efficient label-free CRC typing. Our method achieves an accuracy of 85.78% in distinguishing normal and cancerous cells, while encouraging CRC typing performance across all 10 clinical samples.

The conventional method of screening for anemia requires pathologists to manually examine slides via microscope, a tedious process during health emergencies. This study presents an automated high-throughput optical digital microscope system capable of sequentially scanning and analyzing 10 blood smear slides per batch in under 15 min using a Laplacian-based autofocusing algorithm at 40x magnification. The acquired images are segmented via the YOLOv5 algorithm, and morphological features of red blood cells (RBCs) are classified using a multilayer perceptron (MLP) model. The system achieved 90.6% accuracy, 95% precision, 91% sensitivity, and 94% specificity in classifying anemia subtypes (macrocytic, microcytic, normocytic) and healthy samples. The trained model is integrated into an Android application for real-time geographic mapping of anemic clusters, enabling healthcare workers to prioritize interventions efficiently. This high-throughput approach eliminates the need for immersion oil and manual slide handling, demonstrating significant potential for rapid, scalable anemia screening in resource-limited settings.

Hyperspectral imaging is a promising tool for identifying ischemic necrotic small intestine. To analyze the causes of small bowel necrosis, studying characteristic bands is crucial. However, differences in samples and spectral acquisition devices limit the availability of all bands for analysis, posing challenges in selecting bands adapted to individual variations. This study proposed a method based on the least trimmed squares algorithm, enhanced with regularization, to identify characteristic bands. The method successfully differentiated normal and necrotic tissue and analyzed necrosis causes, which originated from the same rabbit, different rabbits, and different necrosis durations. It identified 763 nm as the characteristic band, corresponding to the deoxyhemoglobin absorption peak. This approach offers accurate, automated band identification while addressing sample and device discrepancies, enabling the selection of more suitable characteristic bands.

Muscle spasticity, common in conditions such as cerebral palsy, spinal cord injury, and multiple sclerosis, is traditionally assessed using the Modified Ashworth Scale, which lacks consistency. This study evaluates near-infrared spectroscopy (NIRS) as a non-invasive tool for measuring muscle contraction intensity. Thirty-seven healthy adults performed isometric contractions at varying intensities (15%, 30%, 45%, and 60% of maximal voluntary contraction), with NIRS sensors monitoring changes in the Tissue Oxygenation Index (TOI) and electromyography (EMG) measuring muscle activity. Results demonstrated a significant negative correlation between contraction intensity and ΔTOI, indicating that higher contraction levels resulted in greater reductions in muscle oxygenation. Additionally, a multinomial logistic regression model confirmed that TOI could reliably predict contraction intensity (p < 0.001). This technique could provide real-time, objective data for spasticity assessment, potentially improving treatment plans.

At present, the research to predict the efficacy of tacrolimus (TAC) mainly focuses on serological indexes and urine analysis. Because these indicators are affected by many factors, they cannot accurately predict the therapeutic effect of primary membranous nephropathy (PMN) patients. In this study, a novel classification model (RCN) based on hyperspectral imaging combined with one-dimensional convolutional neural networks (1D CNN) and relevance vector machine (RVM) was proposed for predicting patients' response to TAC. Based on the treatment outcomes of corticosteroids combined with TAC, the patients were divided into a remission group and a nonremission group. Through the analysis of hyperspectral data of pathological slices of patients in both the remission group and the nonremission group, the research results show that the model can effectively extract key features from the spectral data and achieve high classification performance, and it can predict the therapeutic effect of TAC in PMN patients.

Objective: To investigate the impact of aging in different skin layers on the formation of wrinkles and sagging.

Methods: In a single-center clinical study, the skin micromorphology, wrinkles, and sagging of 34 participants aged 42-60 years who use an antiaging product for 56 days are discussed retrospectively and observationally. The variation trend of each parameter and the correlation between micromorphology parameters and wrinkles and sagging are analyzed.

Results: Parameters related to epidermal aging show a strong correlation with wrinkles and sagging, whereas parameters related to dermal aging show no significant correlation with wrinkles and sagging; in addition, parameters related to the dermal-epidermal junction (DEJ) show a moderate correlation with wrinkles and sagging.

Conclusion: For individuals in the rapid and stable aging phases, aging of the epidermis has a greater impact on wrinkles and sagging than aging of the dermis and DEJ.

Background and aim: This study evaluated mitochondrial and osteogenic activity in MG-63 pre-osteoblastic cells after photobiomodulation (PBM) using multiple near-infrared LED sources (Nuralyte) emitting wavelengths from 700 to 1100 nm.

Materials and methods: MG-63 cells were irradiated daily for 3, 5, or 7 days with energy densities of 5.3 J/cm² (30 s, optimal dose) and 10.6 J/cm² (60 s, high dose). Mitochondrial function was assessed using the XF Seahorse analyzer, and gene expression of osteogenic markers was analyzed.

Results: Maximal mitochondrial oxygen consumption rate (OCR) significantly decreased at the optimal dose but increased at the high dose (p < 0.001) in 5-day irradiated cultures. Upregulation of osteogenic markers (OCN, OPN, BMP-2, COL-1, RUNX2) occurred after 3-5 consecutive days of irradiation, with greater activation at the optimal dose.

Conclusion: MG-63 cells respond to PBM using MNI-LEDs (700, 850, 980 nm) by modulating mitochondrial respiration and boosting bone-related gene expression in a dose- and time-dependent manner.

Rapid detection of infectious diseases like COVID-19, flu, and dengue is crucial for healthcare professionals preparing for contagious outbreaks. Given the constant mutations in viruses and the recurring emergence of threats like Nipah and Zika, there is an urgent demand for a technology capable of distinguishing between infections that share similar symptoms. In this paper, we utilize laser-based Raman scattered signals from a drop of dried blood plasma, combined with generative artificial intelligence, to provide a rapid and precise diagnosis. Our optimized model exhibits exceptional performance, yielding high predictive scores of 96%, 98%, and 100% for flu, COVID-19, and dengue, respectively. The proposed Raman spectroscopic analysis, with a rapid turnaround time, can ensure a near-accurate diagnosis and proper quarantining of highly infectious cases. Furthermore, the potential extension of our method to include other viral diseases offers an alternative to the challenge of developing different diagnostic kits for each disease.

While SERS-based detection can bring some advantages, it is far away from being established as a routine method in clinical diagnostics. In this study, a SERS-labeled immunochromatographic test paper was prepared. The rapid detection of SARS-CoV-2 was realized by the machine learning algorithm of the Raman probe; the whole testing process takes less than 25 min, and the rapid detection of SARS-CoV-2 can be realized. After experimental evaluation, the sensitivity of the test strip for SARS-CoV-2 N protein detection can be 1 pg/mL, which is 3 orders of magnitude higher than that of the colloidal gold antigen detection strip on the market. In the detection of clinical samples, nucleic acid detection was used as the gold standard, and the accuracy was 84.21%.