Ana R. Guerra, Luís R. Oliveira, Gonçalo O. Rodrigues, Maria R. Pinheiro, Maria I. Carvalho, Valery V. Tuchin, Luís M. Oliveira
{"title":"Tartrazine for Optical Clearing of Tissues: Stability and Diffusion Issues","authors":"Ana R. Guerra, Luís R. Oliveira, Gonçalo O. Rodrigues, Maria R. Pinheiro, Maria I. Carvalho, Valery V. Tuchin, Luís M. Oliveira","doi":"10.1002/jbio.202500160","DOIUrl":"10.1002/jbio.202500160","url":null,"abstract":"<div>\u0000 \u0000 <p>Measuring the density of tartrazine (TZ) powder allowed to develop a protocol for fast preparation of aqueous solutions with a desired concentration. The stability time of these solutions decreases exponentially with the increase of TZ concentration: solutions with TZ concentrations below 25% remain stable for more than 24 h, while the solution with 60% TZ remains stable only for 35 min. To validate the developed protocol, muscle samples were immersed in the 40% TZ solution and, as expected, the tissue transparency increased smoothly and exponentially during the whole treatment of 30 min. The diffusion time of TZ in ex vivo skeletal muscle was quantitatively determined with high accuracy as <i>τ</i>\u0000 <sub>TZ</sub> = 5.39 ± 0.49 min for sample thickness of 0.5 mm. By measuring the refractive index of TZ solutions during preparation, it will be easier to prepare such solutions in a fast manner for future research on tissue optical clearing.</p>\u0000 </div>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144546660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"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":"10.1002/jbio.202500142","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p>\u0000 </div>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 10","pages":""},"PeriodicalIF":2.0,"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":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Unstained Blood Smear Analysis: A Review of Rule-Based, Machine Learning, and Deep Learning Techniques","authors":"Husnu Baris Baydargil, Thomas Bocklitz","doi":"10.1002/jbio.202500121","DOIUrl":"10.1002/jbio.202500121","url":null,"abstract":"<p>Blood cells are central to oxygen transport, immune defense, and hemostasis. Their number and morphology act as sensitive biomarkers, making accurate segmentation and classification essential for hematological diagnostics. Biophotonic techniques now provide label-free imaging of unstained smears by exploiting intrinsic phase and scattering contrast, yet such images exhibit low optical signal and subtle morphological variation that exacerbate segmentation errors. Label-free modalities nevertheless preserve contrast where dyes fail, motivating renewed interest in unstained workflows. This review analyzes rule-based, machine-learning, and deep-learning approaches for segmenting and classifying label-free blood cells, highlighting performance gains, persistent challenges, and future directions for clinical adoption.</p>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jbio.202500121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144532020","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"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":"10.1002/jbio.202500197","url":null,"abstract":"<div>\u0000 \u0000 <p>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.</p>\u0000 </div>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 10","pages":""},"PeriodicalIF":2.0,"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":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Diagnostic Study of Head and Neck Metastatic Tumors From Different Primary Sites Based on Stacking Machine Learning Methods","authors":"Yifei Liu, Cong Wu, Junpeng Ma, Liang Ma, Chongxuan Tian, Yunze Li, Jinlin Deng, Qize Lv, Wei Li, Miaoqing Zhao","doi":"10.1002/jbio.202500044","DOIUrl":"10.1002/jbio.202500044","url":null,"abstract":"<div>\u0000 \u0000 <p>Metastatic tumors of the head and neck (MTHN) typically indicate advanced disease with a poor prognosis, originating from cells that spread from other body parts. Diagnosis generally relies on slow and error-prone methods like imaging and histopathology. Addressing the need for a faster, more accurate diagnostic method, this study uses hyperspectral imaging to gather detailed cellular data from 208 patients at six primary MTHN sites. Techniques select characteristic spectral bands, and models including SVM, LightGBM, and ResNet are developed. A high-performance classification model, MTHN-SC, employs stacking technology with SVM and LightGBM as base learners and Random Forest as the meta-learner, achieving a diagnostic accuracy of 82.47%, outperforming other models. This research enhances targeted treatment strategies and advances the application of hyperspectral technology in identifying MTHN primary sites.</p>\u0000 </div>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144487540","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
O. V. Angelsky, A. Y. Bekshaev, C. Yu. Zenkova, D. I. Ivanskyi, J. Zheng, Xinzheng Zhang, Yu. Ursuliak
{"title":"Comprehensive Investigation of the Eye-Cornea Structure Based on the Extended Techniques of Polarization-Sensitive Optical Coherence Tomography","authors":"O. V. Angelsky, A. Y. Bekshaev, C. Yu. Zenkova, D. I. Ivanskyi, J. Zheng, Xinzheng Zhang, Yu. Ursuliak","doi":"10.1002/jbio.202500101","DOIUrl":"10.1002/jbio.202500101","url":null,"abstract":"<div>\u0000 \u0000 <p>We present a universal technique for noninvasive investigation of thin multilayer optically transparent tissues based on polarization-sensitive optical coherence tomography. To reach higher diagnostic accuracy, we revisit the model of the cornea structure and reconsider the physical features of the interaction of light with the tissue structural elements. In the scheme proposed, the probing beam is algorithmically adjustable such that the <i>x</i>-polarized radiation impinges each consecutive structural layer; the object beam is formed by the reflection and back-scattering. Its characteristics are found analytically and numerically within the framework of the polarized Monte-Carlo model and the Jones matrix formalism. A modified Mach–Zehnder interferometer with orthogonal polarization channels enables the elimination of the object-signal depolarization caused by stochastic scattering and facilitates evaluation of the refractive indices and birefringence of tissue elements. The technique permits spatial scanning of the object, providing a complete 3D mapping with a submicrometer resolution in the longitudinal and transverse directions.</p>\u0000 </div>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144487539","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Md Mobarak Karim, Achuth Nair, Manmohan Singh, Maryam Hatami, Salavat R. Aglyamov, Kirill V. Larin
{"title":"Depth-Resolved Attenuation Coefficient Quantification During Murine Embryonic Brain Development","authors":"Md Mobarak Karim, Achuth Nair, Manmohan Singh, Maryam Hatami, Salavat R. Aglyamov, Kirill V. Larin","doi":"10.1002/jbio.202500212","DOIUrl":"10.1002/jbio.202500212","url":null,"abstract":"<div>\u0000 \u0000 <p>Brain development is a highly regulated process with significant morphological and functional transformations during early embryogenesis. Here, we quantified the optical attenuation coefficient (OAC) during murine embryonic brain development with a focus on crucial areas, including the forebrain, midbrain, and hindbrain from embryonic day (E)9.5 to E13.5. At earlier developmental stages, the estimation of OAC in these regions is comparatively low due to the low cell density and more straightforward pattern of extracellular matrix (ECM) composition, which results in minimal scattering and signal attenuation. However, as the embryo grows (by E13.5), increased ECM density and vascularization, along with the formation of blood vessels, contribute to enhanced signal attenuation, thereby reducing light penetration. As a result of gradual changes in cellular composition, tissue architecture, and extracellular matrix density, the study's findings demonstrate an increasing trend in OAC across the midbrain, hindbrain, and forebrain during embryonic development from E9.5 to E13.5.</p>\u0000 </div>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144478277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Begum Kara Gulay, Nilufer Zengin, Fatih Emre Ozturk, Vesile Ozturk, Cagdas Guducu, Neslihan Demirel
{"title":"Identification of Migraine Subtypes Using Functional Near-Infrared Spectroscopy Data: A Domain-Based Feature Extraction","authors":"Begum Kara Gulay, Nilufer Zengin, Fatih Emre Ozturk, Vesile Ozturk, Cagdas Guducu, Neslihan Demirel","doi":"10.1002/jbio.202500120","DOIUrl":"10.1002/jbio.202500120","url":null,"abstract":"<div>\u0000 \u0000 <p>Migraine diagnosis relies on subjective patient reports and International Headache Society guidelines, leading to misdiagnoses. In clinical practice, objective, reliable diagnostic tools are needed. To address this, the study proposes a framework utilizing functional near-infrared spectroscopy (fNIRS) to distinguish healthy individuals, interictal migraine patients with and without aura. The approach focuses on prefrontal cortex (PFC) activity, extracting features from oxyhemoglobin, deoxyhemoglobin, and total hemoglobin in time, frequency, and time-frequency domains. XGBoost applied to time-frequency features of oxyhemoglobin in the left PFC demonstrated outstanding performance, achieving 92% balanced accuracy, 89% sensitivity, 95% specificity, and 89% F1 score. Non-invasive fNIRS with Machine Learning offers a promising, cost-effective alternative to traditional diagnostic methods, enhancing early and accurate diagnosis, leading to better-targeted treatments and improved outcomes. The study provides a strong foundation for future research and clinical applications in migraine diagnosis.</p>\u0000 </div>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144478278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Use of Mitochondrial Delayed Luminescence Measurements From Brain Tissue to Optimize Photobiomodulation Prarameters in Alzheimer's Disease Mice","authors":"Hong Bae Kim, Chang Kyu Sung","doi":"10.1002/jbio.202500151","DOIUrl":"10.1002/jbio.202500151","url":null,"abstract":"<p>The therapeutic potential of photobiomodulation (PBM) for Alzheimer's disease (AD) was evaluated by examining β-amyloid accumulation, microglial activation, and memory function. Mitochondrial delayed luminescence (m-DL), an indirect mitochondrial marker, was measured by irradiating 2 J/cm<sup>2</sup> of 808 nm near-infrared light to the exposed brain surface of anesthetized 5XFAD mice. Based on m-DL findings, behavioral PBM was applied transcranially to the intact scalp using the same fluence at 30, 40, and 80 Hz with respective duty cycles and durations. The 80 Hz setting produced the longest m-DL decay time and selectively improved recognition memory. Immunofluorescence revealed a significant 0.27-fold decrease in β-amyloid and 0.13-fold decrease in microglial activation without changes in neuronal density. Limitations include the small sample size, short duration, and the need to validate m-DL with established bioenergetic assays. Despite these, findings suggest that optimized PBM may offer a promising noninvasive intervention for AD, warranting further long-term investigation.</p>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 10","pages":""},"PeriodicalIF":2.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jbio.202500151","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144478279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}