Journal of biophotonics最新文献

Research on the Effect of the Energy Distribution of Dual-Beam Laser 980 and 1064 nm on Skin Tissue Soldering Performance. 980和1064 nm双光束激光能量分布对皮肤组织焊接性能影响的研究。

Journal of biophotonics Pub Date : 2025-06-02 DOI: 10.1002/jbio.202500143

Jun Huang, Mintao Yan, Yanyu Li, Yuxin Chen, Kehong Wang

引用次数: 0

Hyperspectral Imaging for Predicting Bladder Cancer Grading: A Novel Diagnostic Approach. 高光谱成像预测膀胱癌分级：一种新的诊断方法。

Journal of biophotonics Pub Date : 2025-06-02 DOI: 10.1002/jbio.202500161

Jinfeng Hu, Xiuqing Fu, Yanli Zhang, Mengqiu Zhang, Yihan Zhao, Xiaoqing Yang

引用次数: 0

Hyperspectral Imaging for Rapid Detection of Common Infected Bacteria Based on Fluorescence Effect. 基于荧光效应的高光谱成像快速检测常见感染细菌。

Journal of biophotonics Pub Date : 2025-05-30 DOI: 10.1002/jbio.202500164

Lin Tao, Decheng Wu, Hao Tang, Wendan Liu, Xudong Fu, Zheng Hu, Dengchao Huang, Lianyang Zhang

{"title":"Hyperspectral Imaging for Rapid Detection of Common Infected Bacteria Based on Fluorescence Effect.","authors":"Lin Tao, Decheng Wu, Hao Tang, Wendan Liu, Xudong Fu, Zheng Hu, Dengchao Huang, Lianyang Zhang","doi":"10.1002/jbio.202500164","DOIUrl":"https://doi.org/10.1002/jbio.202500164","url":null,"abstract":"The rapid and accurate detection of bacterial infections in wounds is crucial for clinical diagnosis. Traditional methods, such as bacterial culture and polymerase chain reaction (PCR), are invasive and time-consuming. In this study, we propose a non-invasive detection method for common bacteria in wound infections, combining fluorescence hyperspectral imaging (FHSI) with deep learning algorithms. FHSI technology captures fluorescence data from culture plates for eight bacterial species, extracting spectral features within the 420-700 nm wavelength range. To manage the complex spatial and spectral data, we developed a Spatial-Spectral Multi-Scale Attention Network (SSMA-Net). Our method achieves an impressive 98.52% accuracy in bacterial classification under various growth conditions and 98.71% accuracy in species-level identification, with classification possible at bacterial concentrations as low as 104 CFU/mL. These results underscore the effectiveness of FHSI and deep learning for rapid, non-invasive bacterial typing, offering substantial potential for clinical applications.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500164"},"PeriodicalIF":0.0,"publicationDate":"2025-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144188727","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

Empirical Mode Decomposition and Grassmann Manifold-Based Cervical Cancer Detection. 基于经验模态分解和Grassmann流形的宫颈癌检测。

Journal of biophotonics Pub Date : 2025-05-28 DOI: 10.1002/jbio.202400584

Sidharthenee Nayak, Bhaswati Singha Deo, Mayukha Pal, Prasanta K Panigrahi, Asima Pradhan

{"title":"Empirical Mode Decomposition and Grassmann Manifold-Based Cervical Cancer Detection.","authors":"Sidharthenee Nayak, Bhaswati Singha Deo, Mayukha Pal, Prasanta K Panigrahi, Asima Pradhan","doi":"10.1002/jbio.202400584","DOIUrl":"https://doi.org/10.1002/jbio.202400584","url":null,"abstract":"Cervical cancer is a prevalent malignancy affecting the female reproductive system and is recognized as a prominent factor to female mortality on a global scale. Timely and precise detection of various stages of cervical cancer plays a crucial role in enhancing the chances of successful treatment and extending patient survival. Fluorescence spectroscopy stands out as a highly sensitive method for identifying biochemical alterations associated with cancer and numerous other pathological conditions. In our study, empirical mode decomposition (EMD) and Grassmann manifold (GM) learning are explored for reliable cancer detection using fluorescence spectral signals collected from 110 subjects representing various categories of the human cervix. Initially, EMD is used to decompose the signal into several multi-feature intrinsic mode functions (IMFs) on a spectral scale. Each IMF demonstrates uniqueness by capturing the inherent frequency characteristics within the signal, thus facilitating the extraction of signal features. The GM representation of IMFs is employed for investigating the non-linear subspace structure within spectral signals, which is subsequently followed by a low-rank representation to transform and analyze the spectral signals. The GM allows for the extraction of relevant information, reduction of dimensionality, and exploration of complex relationships within data, ultimately contributing to improved diagnosis. Mutual information is further used for feature selection to reduce the number of features and hence the computational cost. When the selected features were employed for classification, the Random Forest (RF) classifier attained a high five-fold validation accuracy of 99% and exhibited a minimal standard deviation of 0.02. Other state-of-the-art machine learning classifiers were also used and compared with the RF model.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202400584"},"PeriodicalIF":0.0,"publicationDate":"2025-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144164359","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

Optical Method for the Detection of Viral RNA Using an Optical Fiber Sensor. 利用光纤传感器检测病毒RNA的光学方法。

Journal of biophotonics Pub Date : 2025-05-27 DOI: 10.1002/jbio.202500063

Patryk Sokołowski, Paweł Wityk, Joanna Raczak-Gutknecht, Wiktoria Brzezińska, Michał Sobaszek, Paweł Kalinowski, Sebastian Garcia-Galan, Małgorzata Szczerska

引用次数: 0

Design and Analysis of a Novel Nanoscale 2D Photonic Crystal Structure for Enhanced Multi-Disorder Biosensing. 一种新型纳米二维光子晶体结构的设计与分析，用于增强多无序生物传感。

Journal of biophotonics Pub Date : 2025-05-27 DOI: 10.1002/jbio.202500130

K Bhuvaneshwari, B Elizabeth Caroline, J Vidhya, K Sagadevan

引用次数: 0

Near-Infrared Spectroscopy Mapping for Uterine Cancer and Fibroid Detection. 近红外光谱在子宫癌和子宫肌瘤检测中的应用。

Journal of biophotonics Pub Date : 2025-05-24 DOI: 10.1002/jbio.70062

Danyang Cheng, Haiqiu Yang, Arielle S Joasil, Xiaowei Chen, Hanina Hibshoosh, Christine P Hendon

引用次数: 0

A Human Skin Diagnosis System Using Human Skin Reflective Spectrum Matching. 基于人体皮肤反射光谱匹配的人体皮肤诊断系统。

Journal of biophotonics Pub Date : 2025-05-21 DOI: 10.1002/jbio.70064

Munsun Cho, Jiwon Shin, Minyoung Lee, Ye Jin Yang, Yaenyeong Choi, Heemuk Oh, Jun Bae Lee, Sung-Kyu Hong

引用次数: 0

Label-Free and Intelligent Cell Death Recognition Toward Lung Cancer Chemotherapy. 无标签和智能细胞死亡识别在肺癌化疗中的应用

Journal of biophotonics Pub Date : 2025-05-20 DOI: 10.1002/jbio.202500127

Shubin Wei, Guoqing Luo, Zhaoyi Ye, Yueyun Weng, Liye Mei, Yan Jin, Yi Liu, Du Wang, Sheng Liu, Qing Geng, Cheng Lei

{"title":"Label-Free and Intelligent Cell Death Recognition Toward Lung Cancer Chemotherapy.","authors":"Shubin Wei, Guoqing Luo, Zhaoyi Ye, Yueyun Weng, Liye Mei, Yan Jin, Yi Liu, Du Wang, Sheng Liu, Qing Geng, Cheng Lei","doi":"10.1002/jbio.202500127","DOIUrl":"https://doi.org/10.1002/jbio.202500127","url":null,"abstract":"The lack of high-throughput, label-free, and intelligent recognition models for assessing cell death hinders the broad application of cell death analysis in chemotherapy for lung cancer. We propose an intelligent quantitative detection technique for cell deaths. Using high-throughput quantitative phase imaging flow cytometry to capture numerous label-free images and employing convolutional neural networks (CNN) to characterize the heterogeneity and quantitative detection of cell death. We revealed the heterogeneity of cell death through morphology features and achieved interpretability analysis of the CNN using clustering. Finally, the classification reliability of the CNN was validated by extracting features from classified cells. This method, compared with biochemical methods, showed a correlation of 0.92 and 0.91 with autophagy detection (Pearson and Cosine Similarity), and an average error of 12.52% with apoptosis detection. Our approach has the potential to become a valuable tool for studying cell death mechanisms and offers a new perspective for cancer treatment.","PeriodicalId":94068,"journal":{"name":"Journal of biophotonics","volume":" ","pages":"e202500127"},"PeriodicalIF":0.0,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144103438","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

Investigation of Helicobacter pylori Through C₂H₄ and CO₂ Breath Markers Using Photoacoustic Spectroscopy. 利用光声光谱法通过C2H4和CO2呼吸标记物研究幽门螺杆菌。

Journal of biophotonics Pub Date : 2025-05-19 DOI: 10.1002/jbio.202500140

Cristina Popa, Mioara Petrus, Ana Maria Bratu

引用次数: 0