Journal of biophotonics最新文献_第2页

Exploring Immediate Photon Effects From 635 nm Light on Mitochondrial Bioenergetics. 探索635 nm光对线粒体生物能量学的即时光子效应。

Journal of biophotonics Pub Date : 2025-07-10 DOI: 10.1002/jbio.202500162

Natasha F Mezzacappo, Natalia M Inada, Edilene S Siqueira-Santos, José Dirceu Vollet-Filho, Roger F Castilho, Michael L Denton, Vanderlei S Bagnato

{"title":"Exploring Immediate Photon Effects From 635 nm Light on Mitochondrial Bioenergetics.","authors":"Natasha F Mezzacappo, Natalia M Inada, Edilene S Siqueira-Santos, José Dirceu Vollet-Filho, Roger F Castilho, Michael L Denton, Vanderlei S Bagnato","doi":"10.1002/jbio.202500162","DOIUrl":"https://doi.org/10.1002/jbio.202500162","url":null,"abstract":"Visible light primarily targets mitochondria at the cellular level, but photon interaction mechanisms are still not fully understood. This study examined the in vitro impacts of 635 nm laser irradiation using mitochondria isolated from mouse liver. Mitochondria samples were irradiated for 330 s inside the respirometer chamber, with delivered powers ranging from 100 to 800 mW, corresponding to power densities ranging from 31.6 to 211.7 mW/cm2 inside the chamber. Analysis of real-time oxygen consumption showed an elevated proton leak during ATP synthase inhibition at 800 mW (211.7 mW/cm2, 69.9 J/cm2), suggesting enhanced permeability of the mitochondrial inner membrane. Under different experimental conditions, post-irradiation analysis revealed increased basal respiration with 400 mW (129.1 mW/cm2, 42.6 J/cm2) and 800 mW, along with increased susceptibility to Ca2+-triggered mitochondrial swelling. The investigation of mitochondrial bioenergetics demonstrated that red light induces transient and localized effects, highlighting the complexities of cellular and mitochondrial photostimulation mechanisms.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500162"},"PeriodicalIF":0.0,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602643","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

In Vivo Bacterial Tracking Technology Based on Membrane Dye Labeling. 基于膜染料标记的体内细菌跟踪技术。

Journal of biophotonics Pub Date : 2025-07-09 DOI: 10.1002/jbio.202500172

Liang Zhou, Jiahe Li, Xian He, Mingxiao Liu

{"title":"In Vivo Bacterial Tracking Technology Based on Membrane Dye Labeling.","authors":"Liang Zhou, Jiahe Li, Xian He, Mingxiao Liu","doi":"10.1002/jbio.202500172","DOIUrl":"https://doi.org/10.1002/jbio.202500172","url":null,"abstract":"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.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500172"},"PeriodicalIF":0.0,"publicationDate":"2025-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144602644","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Quantitative Hemodynamic Evaluation Method of Laser Therapy for Scars Based on Diffuse Correlation Spectroscopy. 一种基于漫射相关光谱的疤痕激光治疗定量血流动力学评价方法。

Journal of biophotonics Pub Date : 2025-07-09 DOI: 10.1002/jbio.202500178

Zhe Li, Yongjian Liu, Peng Tian, Chong Wang, Feng Tu, Chao Gao, Jiangtao Bai, Ruixin Fu, Jinchao Feng, Pengyu Liu, Kebin Jia

引用次数: 0

Cellular Identification of Single-Base Mutations in KRAS Gene Fragments Based on Nonhomologous Spectroscopic Data Fusion Modeling. 基于非同源光谱数据融合模型的KRAS基因片段单碱基突变的细胞鉴定。

Journal of biophotonics Pub Date : 2025-07-08 DOI: 10.1002/jbio.202500239

Chenchen Wang, Alimire Abudureyimu, Qin Zhang, Weiquan You, Dandan Li, Xiaofan Jia, Yating Zhang, Chengjie Chen, Rong Hu, Mengyao Wang, Shangyuan Feng, Pengfei Guo, Yang Chen

{"title":"Cellular Identification of Single-Base Mutations in KRAS Gene Fragments Based on Nonhomologous Spectroscopic Data Fusion Modeling.","authors":"Chenchen Wang, Alimire Abudureyimu, Qin Zhang, Weiquan You, Dandan Li, Xiaofan Jia, Yating Zhang, Chengjie Chen, Rong Hu, Mengyao Wang, Shangyuan Feng, Pengfei Guo, Yang Chen","doi":"10.1002/jbio.202500239","DOIUrl":"https://doi.org/10.1002/jbio.202500239","url":null,"abstract":"The sensitivity of KRAS gene mutation detection in colorectal cancer (CRC) can affect prognosis. This study established a nonhomologous spectroscopic data fusion method based on nuclear magnetic resonance (NMR) and laser tweezers Raman spectroscopy (LTRS), in order to analyze the metabolic characteristics of wild-type cells DKS-8 and HEK-3, and their respective mutant cells DLD-1 and HCT-116. Through multivariate statistical analysis, it was found that there were significant differences between mutant and wild-type cells. Four metabolites including taurine, glucose, phosphorylcholine, and tyrosine were screened as characteristic metabolites. Single-base KRAS mutations commonly alter metabolic pathways like d-glutamine and d-glutamate metabolisms, alanine, aspartate, and glutamate metabolism, and arginine biosynthesis. It is concluded that the combination of nonhomologous spectral data fusion would enhance reliability of the single source-derived characteristic markers. The proposed strategy will benefit congeneric researches in the biomedical field.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500239"},"PeriodicalIF":0.0,"publicationDate":"2025-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144593246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rapid and Noninvasive Detection of Brucellosis in Human Based on Serum Fluorescence Spectrum Combined With Machine Learning Algorithms. 基于血清荧光光谱结合机器学习算法的人类布鲁氏菌病快速无创检测。

Journal of biophotonics Pub Date : 2025-07-03 DOI: 10.1002/jbio.202500100

Ziyi Fang, Quan Wang, Yiwei Gong, Xiangxiang Zheng, Wubulitalifu Dawuti, Shengke Xu, Hui Zhao, Guodong Lü

{"title":"Rapid and Noninvasive Detection of Brucellosis in Human Based on Serum Fluorescence Spectrum Combined With Machine Learning Algorithms.","authors":"Ziyi Fang, Quan Wang, Yiwei Gong, Xiangxiang Zheng, Wubulitalifu Dawuti, Shengke Xu, Hui Zhao, Guodong Lü","doi":"10.1002/jbio.202500100","DOIUrl":"https://doi.org/10.1002/jbio.202500100","url":null,"abstract":"Brucellosis is a notable zoonotic disease caused by Brucella that is often overlooked. Diagnosis involves both clinical symptoms and serological examinations, which are accurate but time-consuming. Therefore, a simple and accurate method is needed. This study aims to assess the potential for diagnosing human brucellosis using serum fluorescence spectra in conjunction with principal component analysis-linear discriminant analysis (PCA-LDA), linear support vector machine (linear SVM), medium radial basis function support vector machine (RBF SVM), K-nearest neighbors (KNN), and decision tree (DT). The study of serum fluorescence spectra in brucellosis-infected compared to healthy revealed that patients with brucellosis had reduced peaks at 452, 624, and 688 nm and elevated peaks at 495 and 643 nm. SVM (linear/RBF) provides more accurate classification results than other algorithms. The method achieved an overall diagnostic accuracy of 89.0%. In conclusion, the serum fluorescence spectrum paired with the SVM (linear/RBF) algorithm is highly promising for human brucellosis detection.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500100"},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144562433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Differential Diagnosis of Papillary Thyroid Carcinoma and Nodular Goiter With Papillary Hyperplasia Using Hyperspectral Imaging Technology. 应用高光谱成像技术鉴别甲状腺乳头状癌和结节性甲状腺肿伴乳头状增生。

Journal of biophotonics Pub Date : 2025-07-02 DOI: 10.1002/jbio.202500200

Baohua Zhang, Chunlei Wang, Xiaoqing Yang, Tiefeng Sun, Mengqiu Zhang, Hao Chen, Lingquan Meng

{"title":"Differential Diagnosis of Papillary Thyroid Carcinoma and Nodular Goiter With Papillary Hyperplasia Using Hyperspectral Imaging Technology.","authors":"Baohua Zhang, Chunlei Wang, Xiaoqing Yang, Tiefeng Sun, Mengqiu Zhang, Hao Chen, Lingquan Meng","doi":"10.1002/jbio.202500200","DOIUrl":"https://doi.org/10.1002/jbio.202500200","url":null,"abstract":"Papillary thyroid carcinoma (PTC) and nodular goiter with papillary hyperplasia (NGPH) share similar histological features, complicating both preoperative and intraoperative diagnoses. We assessed hyperspectral imaging (HSI) combined with deep learning to differentiate PTC from NGPH. Forty-three paraffin-embedded PTC and 39 NGPH samples were imaged across 400-1000 nm, with reflectance calibration and Savitzky-Golay smoothing applied. Extracted spectral features were input into a one-dimensional convolutional neural network with a self-attention mechanism. HSI demonstrated sensitivity above 90% in the 500-600 nm and near-infrared regions for distinguishing PTC and NGPH. The model achieved an area under the ROC curve of 0.8635 and pixel-level classification accuracy of 87.07%, with both sensitivity and specificity at 87%. Spectral feature depth correlated significantly with histopathological parameters. These findings indicate that HSI combined with deep learning can accurately capture spectral differences between PTC and NGPH, supporting its potential for rapid intraoperative guidance and noninvasive preoperative screening.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500200"},"PeriodicalIF":0.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144556262","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Tartrazine for Optical Clearing of Tissues: Stability and Diffusion Issues. 酒黄石用于组织的光学清除：稳定性和扩散问题。

Journal of biophotonics Pub Date : 2025-07-01 DOI: 10.1002/jbio.202500160

Ana R Guerra, Luís R Oliveira, Gonçalo O Rodrigues, Maria R Pinheiro, Maria I Carvalho, Valery V Tuchin, Luís M Oliveira

引用次数: 0

DeepLabV3+ With Convolutional Triplet Attention and Histopathology-Guided Voting for Hyperspectral Image Segmentation of Serous Ovarian Cancer. 基于卷积三重关注和组织病理学引导投票的DeepLabV3+用于浆液性卵巢癌高光谱图像分割。

Journal of biophotonics Pub Date : 2025-06-30 DOI: 10.1002/jbio.202500142

Wenrui Tang, Lijun Wei, Zhenfeng Mo, Jiahao Wang, Xuan Zhang, Siqi Zhu, Lvfen Gao

{"title":"DeepLabV3+ With Convolutional Triplet Attention and Histopathology-Guided Voting for Hyperspectral Image Segmentation of Serous Ovarian Cancer.","authors":"Wenrui Tang, Lijun Wei, Zhenfeng Mo, Jiahao Wang, Xuan Zhang, Siqi Zhu, Lvfen Gao","doi":"10.1002/jbio.202500142","DOIUrl":"https://doi.org/10.1002/jbio.202500142","url":null,"abstract":"Deep learning has been extensively applied in medical image analysis, providing healthcare professionals with more efficient and accurate diagnostic information. Among these advanced semantic segmentation models, the baseline DeepLabV3+ model is more adept at processing low-dimensional data such as RGB images, but its performance on high-dimensional data like hyperspectral images is suboptimal, limiting its generalization and discriminative capabilities. We propose a highly innovative hybrid architecture integrating a Convolutional Triplet Attention Module (CTAM) to capture cross-dimensional spectral-spatial dependencies and a Histopathology-Guided Voting Mechanism (HVM) to incorporate WHO diagnostic criteria. The results demonstrate that our model can accurately differentiate and localize low-grade and high-grade serous ovarian cancer tissues, with an accuracy of 92.7% and 90.2%, respectively. Furthermore, our performance exceeds the pathologist's consensus (85.4%) and surpasses state-of-the-art models (e.g., U-Net, PAN, FPN) by a significant margin of over 20% in LGSC classification, rigorously validating its scientific superiority.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500142"},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unstained Blood Smear Analysis: A Review of Rule-Based, Machine Learning, and Deep Learning Techniques. 未染血涂片分析：基于规则、机器学习和深度学习技术综述。

Journal of biophotonics Pub Date : 2025-06-30 DOI: 10.1002/jbio.202500121

Husnu Baris Baydargil, Thomas Bocklitz

引用次数: 0

Automated Classification of Rheumatoid Arthritis and Knee Synovitis From Hyperspectral Reflectance Data. 基于高光谱反射数据的类风湿性关节炎和膝关节滑膜炎的自动分类。

Journal of biophotonics Pub Date : 2025-06-25 DOI: 10.1002/jbio.202500197

Shuwang Sun, Zhengyu Wang, Minmin Yu, Yihan Zhao, Yihui He, Lining Zhao

{"title":"Automated Classification of Rheumatoid Arthritis and Knee Synovitis From Hyperspectral Reflectance Data.","authors":"Shuwang Sun, Zhengyu Wang, Minmin Yu, Yihan Zhao, Yihui He, Lining Zhao","doi":"10.1002/jbio.202500197","DOIUrl":"https://doi.org/10.1002/jbio.202500197","url":null,"abstract":"Accurate differentiation between rheumatoid arthritis (RA) and knee synovitis (KS) is essential for guiding optimal treatment, yet conventional histopathology often relies on subjective interpretation and offers limited insight into tissue biochemistry. Here, we introduce TransCNN, a novel multimodal framework that integrates hyperspectral imaging (HSI) with deep learning to achieve objective, high-precision diagnosis. Reflectance-mode HSI across the 400-1000 nm spectrum was performed on 95 synovial tissue specimens. Spectral data were denoised using Savitzky-Golay filtering and distilled via principal component analysis to enhance feature separability. TransCNN employs convolutional neural networks to capture intricate spatial morphology and Transformer layers to model global spectral correlations, producing a unified spectral-spatial representation. On an independent validation set, TransCNN achieved 91% accuracy, 89% F1-score, 90% recall, and 89% precision, substantially surpassing traditional approaches. These findings demonstrate that TransCNN provides a noninvasive, highly sensitive tool for pathological diagnosis, facilitating more reliable, data-driven decision-making in rheumatologic practice.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e70081"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144499953","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0