Journal of Biophotonics最新文献_第4页

Texture Analysis of Fibrous Meningioma Using Label-Free Multiphoton Microscopy 利用无标记多光子显微镜分析纤维性脑膜瘤的纹理

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2024-11-28 DOI: 10.1002/jbio.202400241

Na Fang, Linjing Shi, Xiaoli Su, Rong Chen, Liwen Hu, Lianhuang Li, Xingfu Wang, Zanyi Wu, Jianxin Chen

{"title":"Texture Analysis of Fibrous Meningioma Using Label-Free Multiphoton Microscopy","authors":"Na Fang, Linjing Shi, Xiaoli Su, Rong Chen, Liwen Hu, Lianhuang Li, Xingfu Wang, Zanyi Wu, Jianxin Chen","doi":"10.1002/jbio.202400241","DOIUrl":"10.1002/jbio.202400241","url":null,"abstract":"<div>\u0000 \u0000 Fibrous meningiomas, a common type of brain tumor, present surgical challenges due to their variable hardness, which is crucial for complete resection and patient prognosis. This study explores the use of label-free multiphoton microscopy (MPM) for the objective assessment of the texture of fibrous meningiomas. Fresh tumor samples from 20 patients were analyzed using both multichannel and lambda mode MPM, with quantitative image analysis algorithms determining collagen content and multi-peak spectral fitting providing additional optical collagen metrics. The study compared medium and hard fibrous meningiomas, utilizing receiver operating characteristic analysis to evaluate predictive performance. Microstructural features were clearly visualized, enabling accurate diagnosis. Collagen-related parameters significantly differentiated between moderate and hard tumors (p < 0.05). High predictive values were observed for collagen content, collagen-to-NADH-free ratio, and collagen-to-FAD ratio (AUC = 0.748–0.839). A multivariate logistic model combining these biomarkers significantly improved diagnostic accuracy (AUC = 0.907). The findings suggest that MPM, with its ability to visualize and quantify microstructures such as collagen and cells without the need for staining, holds strong potential for rapid, objective, and accurate assessment of tumor texture during neurosurgery. The integration of MPM with multiphoton endoscopy paves the way for potential in vivo applications in the future.\u0000 </div>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 2","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142741870","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}

引用次数: 0

Detecting Collagen by Machine Learning Improved Photoacoustic Spectral Analysis for Breast Cancer Diagnostics: Feasibility Studies With Murine Models 通过机器学习改进光声光谱分析检测胶原蛋白，用于乳腺癌诊断：小鼠模型可行性研究。

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2024-11-26 DOI: 10.1002/jbio.202400371

Jiayan Li, Lu Bai, Yingna Chen, Junmei Cao, Jingtao Zhu, Wenxiang Zhi, Qian Cheng

{"title":"Detecting Collagen by Machine Learning Improved Photoacoustic Spectral Analysis for Breast Cancer Diagnostics: Feasibility Studies With Murine Models","authors":"Jiayan Li, Lu Bai, Yingna Chen, Junmei Cao, Jingtao Zhu, Wenxiang Zhi, Qian Cheng","doi":"10.1002/jbio.202400371","DOIUrl":"10.1002/jbio.202400371","url":null,"abstract":"Collagen, a key structural component of the extracellular matrix, undergoes significant remodeling during carcinogenesis. However, the important role of collagen levels in breast cancer diagnostics still lacks effective in vivo detection techniques to provide a deeper understanding. This study presents photoacoustic spectral analysis improved by machine learning as a promising non-invasive diagnostic method, focusing on exploring collagen as a salient biomarker. Murine model experiments revealed more profound associations of collagen with other cancer components than in normal tissues. Moreover, an optimal set of feature wavelengths was identified by a genetic algorithm for enhanced diagnostic performance, among which 75% were from collagen-dominated absorption wavebands. Using optimal spectra, the diagnostic algorithm achieved 72% accuracy, 66% sensitivity, and 78% specificity, surpassing full-range spectra by 6%, 4%, and 8%, respectively. The proposed photoacoustic methods examine the feasibility of offering valuable biochemical insights into existing techniques, showing great potential for early-stage cancer detection.","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jbio.202400371","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142735435","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}

引用次数: 0

Measuring Metabolic Changes in Cancer Cells Using Two-Photon Fluorescence Lifetime Imaging Microscopy and Machine-Learning Analysis 利用双光子荧光寿命成像显微镜和机器学习分析测量癌细胞的代谢变化

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2024-11-25 DOI: 10.1002/jbio.202400426

Jiaxin Zhang, Horst Wallrabe, Karsten Siller, Brian Mbogo, Thomas Cassidy, Shagufta Rehman Alam, Ammasi Periasamy

{"title":"Measuring Metabolic Changes in Cancer Cells Using Two-Photon Fluorescence Lifetime Imaging Microscopy and Machine-Learning Analysis","authors":"Jiaxin Zhang, Horst Wallrabe, Karsten Siller, Brian Mbogo, Thomas Cassidy, Shagufta Rehman Alam, Ammasi Periasamy","doi":"10.1002/jbio.202400426","DOIUrl":"10.1002/jbio.202400426","url":null,"abstract":"Two-photon (2P) fluorescence lifetime imaging microscopy (FLIM) was used to track cellular metabolism with drug treatment of auto-fluorescent coenzymes NAD(P)H and FAD in living cancer cells. Simultaneous excitation at 800 nm of both coenzymes was compared with traditional sequential 740/890 nm plus another alternative of 740/800 nm, before and after adding doxorubicin in an imaging time course. Changes of redox states at single cell resolution were compared by three analysis methods: our recently introduced fluorescence lifetime redox ratio (FLIRR: NAD(P)H-a\u0000 2%/FAD-a\u0000 1%), machine-learning (ML) algorithms using principal component (PCA) and non-linear multi-Feature autoencoder (AE) analysis. While all three led to similar biological conclusions (early drug response), the ML models provided statistically the most robust significant results. The advantage of the single 800 nm excitation of both coenzymes for metabolic imaging in above mentioned analysis is demonstrated.","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jbio.202400426","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142717230","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}

引用次数: 0

Subtype-Specific Detection in Stage Ia Breast Cancer: Integrating Raman Spectroscopy, Machine Learning, and Liquid Biopsy for Personalised Diagnostics Ia 期乳腺癌的亚型特异性检测：整合拉曼光谱、机器学习和液体活检，实现个性化诊断。

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2024-11-25 DOI: 10.1002/jbio.202400427

Kevin Saruni Tipatet, Katie Hanna, Liam Davison-Gates, Mario Kerst, Andrew Downes

引用次数: 0

Enhancing Spatial Resolution of Three-Dimensional Pulse Waves Through Advanced Photometric Stereo Techniques 通过先进的光度立体技术提高三维脉冲波的空间分辨率。

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2024-11-25 DOI: 10.1002/jbio.202400448

Jiuai Sun, Kai Li, Zengkai Li, Mingji Zhang, Zhonghang Wu, Dida Zhang

引用次数: 0

Urine Analysed by FTIR, Chemometrics and Machine Learning Methods in Determination Spectroscopy Marker of Prostate Cancer in Urine 利用傅立叶变换红外光谱、化学计量学和机器学习方法分析尿液，确定尿液中前列腺癌的光谱标记。

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2024-11-21 DOI: 10.1002/jbio.202400278

Przemysław Mitura, Wiesław Paja, Bartosz Klebowski, Paweł Płaza, Krzyszof Bar, Grzegorz Młynarczyk, Joanna Depciuch

{"title":"Urine Analysed by FTIR, Chemometrics and Machine Learning Methods in Determination Spectroscopy Marker of Prostate Cancer in Urine","authors":"Przemysław Mitura, Wiesław Paja, Bartosz Klebowski, Paweł Płaza, Krzyszof Bar, Grzegorz Młynarczyk, Joanna Depciuch","doi":"10.1002/jbio.202400278","DOIUrl":"10.1002/jbio.202400278","url":null,"abstract":"<div>\u0000 \u0000 Prostate-specific antigen (PSA) is the most commonly used marker of prostate cancer. However, nearly 25% of men with elevated PSA levels do not have cancer and nearly 20% of patients with prostate cancer have normal serum PSA levels. Therefore, in this study, Fourier transform infrared (FTIR) spectroscopy was investigated as a new tool for detection of prostate cancer from urine. Obtained results showed higher levels of glucose, urea and creatinine in urine collected from patients with prostate cancer than that in control. Principal component analysis (PCA) was not noticed possibility of differentiation urine collected from healthy and nonhealthy patients. However, machine learning algorithms showed 0.90 accuracy and precision of FTIR in detection of prostate cancer from urine. We showed that wavenumbers at 1614 cm−1 and 2972 cm−1 were candidates for prostate cancer spectroscopy markers. Importantly, these FTIR markers correlated with Gleason score, PSA and mpMRI PI-RADS category.\u0000 </div>","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142690104","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}

引用次数: 0

A Deep Learning-Based Approach to Characterize Skull Physical Properties: A Phantom Study 基于深度学习的头骨物理特性表征方法：模型研究

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2024-11-14 DOI: 10.1002/jbio.202400131

Deepika Aggrawal, Loïc Saint-Martin, Rayyan Manwar, Amanda Siegel, Dan Schonfeld, Kamran Avanaki

{"title":"A Deep Learning-Based Approach to Characterize Skull Physical Properties: A Phantom Study","authors":"Deepika Aggrawal, Loïc Saint-Martin, Rayyan Manwar, Amanda Siegel, Dan Schonfeld, Kamran Avanaki","doi":"10.1002/jbio.202400131","DOIUrl":"10.1002/jbio.202400131","url":null,"abstract":"Transcranial ultrasound imaging is a popular method to study cerebral functionality and diagnose brain injuries. However, the detected ultrasound signal is greatly distorted due to the aberration caused by the skull bone. The aberration mechanism mainly depends on thickness and porosity, two important skull physical characteristics. Although skull bone thickness and porosity can be estimated from CT or MRI scans, there is significant value in developing methods for obtaining thickness and porosity information from ultrasound itself. Here, we extracted various features from ultrasound signals using physical skull-mimicking phantoms of a range of thicknesses with embedded porosity-mimicking acoustic mismatches and analyzed them using machine learning (ML) and deep learning (DL) models. The performance evaluation demonstrated that both ML- and DL-trained models could predict the physical characteristics of a variety of skull phantoms with reasonable accuracy. The proposed approach could be expanded upon and utilized for the development of effective skull aberration correction methods.","PeriodicalId":184,"journal":{"name":"Journal of Biophotonics","volume":"18 1","pages":""},"PeriodicalIF":2.0,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/jbio.202400131","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142635089","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}

引用次数: 0

Raman Spectroscopic Analysis of Urinary Creatine and Phosphate in Athletes: Pre- and Post-Training Assessment 运动员尿液肌酸和磷酸盐的拉曼光谱分析：训练前和训练后评估

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2024-11-12 DOI: 10.1002/jbio.202400210

Letícia C. S. Santos, Landulfo Silveira Jr, Marcos T. T. Pacheco

引用次数: 0

Feasibility Study of Label-Free Raman Spectroscopy for Parathyroid Gland Identification 无标签拉曼光谱鉴定甲状旁腺的可行性研究

IF 2 3区物理与天体物理

Journal of Biophotonics Pub Date : 2024-11-11 DOI: 10.1002/jbio.202400220

Hao Wang, Sisi Guo, Ruoyu Zhang, Jing Yao, Wen Tian, Jianfeng Wang

引用次数: 0

SFNet: Spatial and Frequency Domain Networks for Wide-Field OCT Angiography Retinal Vessel Segmentation SFNet：用于宽视场 OCT 血管造影视网膜血管分割的空间和频率域网络。