Biophotonics discovery最新文献

{"title":"Advancing Optical Techniques for Cancer Diagnosis and Therapy Monitoring.","authors":"Javier A Jo, Narasimhan Rajaram, Srivalleesha Mallidi, Alexandra Walsh","doi":"10.1117/1.BIOS.2.4.042701","DOIUrl":"10.1117/1.BIOS.2.4.042701","url":null,"abstract":"<p><p>The editorial comments on the BIOS Special Section on Optics for Cancer Therapy Monitoring and Diagnosis, which published as a series in Volume 2.</p>","PeriodicalId":519981,"journal":{"name":"Biophotonics discovery","volume":"2 4","pages":"042701"},"PeriodicalIF":0.0,"publicationDate":"2025-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13101060/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147794812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigation of quantitative structural changes in astrocyte cells after laser-induced shockwave.","authors":"Pegah Pouladian, Janelle Ho, Nicolas Perez, Nicole M Wakida, Veronica Gomez-Godinez, Daryl Preece","doi":"10.1117/1.BIOS.2.4.045002","DOIUrl":"10.1117/1.BIOS.2.4.045002","url":null,"abstract":"<p><strong>Significance: </strong>Traumatic brain injury (TBI) arises from external forces impacting the brain, leading to outcomes that range from mild to severe. Despite continuous scientific advancements, TBI remains a significant cause of physical impairment and mortality. In recent years, blast-induced TBI (bTBI) has become more prominent due to the use of explosive devices, necessitating accurate models to simulate and study the effects of shockwaves on brain tissue. To better understand bTBI at the cellular level, various models have been proposed. Laser-induced shockwaves (LISs) have emerged as an effective method to simulate bTBI in a controlled environment by generating shockwaves through pulsed laser-induced plasma formation.</p><p><strong>Aim: </strong>We introduce a cost-efficient method to investigate cellular morphology changes in response to mechanical stress by combining LIS and quantitative phase microscopy (QPM).</p><p><strong>Approach: </strong>QPM, a label-free imaging technique, facilitates quantitative visualization of cellular dynamics. The integration of LIS and QPM enabled the precise assessment of type 1 astrocyte cells under shear stress, revealing both immediate and sustained morphological changes.</p><p><strong>Results: </strong>Key findings include significant alterations in some morphological features such as surface area to volume ratio ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ) immediately post-LIS, which returned to baseline within 2 h, and lasting changes in features such as circularity ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.001</mn></mrow> </math> ), suggesting prolonged cellular adaptation. These insights provide a deeper understanding of how mechanical stimuli affect astrocyte morphology, offering potential pathways for targeted therapeutic strategies in TBI and related neurological disorders.</p><p><strong>Conclusions: </strong>The integrated QPM-LIS approach serves as a powerful tool for studying quantitative cellular dynamics, opening the door to further investigations of astrocyte and other brain cell morphologies in response to mechanical forces, with broad implications for neurological research and therapy development.</p>","PeriodicalId":519981,"journal":{"name":"Biophotonics discovery","volume":"2 4","pages":"045002"},"PeriodicalIF":0.0,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13101065/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147794806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Commercialization Report: DoseOptics LLC.","authors":"Brian W Pogue","doi":"10.1117/1.BIOS.2.4.040502","DOIUrl":"https://doi.org/10.1117/1.BIOS.2.4.040502","url":null,"abstract":"<p><p>This community report discusses the experience of navigating the complex path of commercializing biomedical technologies, offering lessons for the scientific community and industry stakeholders alike.</p>","PeriodicalId":519981,"journal":{"name":"Biophotonics discovery","volume":"2 4","pages":"040502"},"PeriodicalIF":0.0,"publicationDate":"2025-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13227148/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148160183","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of a polarization-sensitive, dual-wavelength wearable photoplethysmography sensor across a range of skin tones.","authors":"Rutendo Jakachira, Wenyuan Yan, Sian C Thomas, Yareli Macias-Sanchez, Lovisa Werner, Joshua A Burrow, Shira Dunsiger, Kimani C Toussaint","doi":"10.1117/1.BIOS.3.1.012509","DOIUrl":"10.1117/1.BIOS.3.1.012509","url":null,"abstract":"<p><strong>Significance: </strong>High-quality photoplethysmography (PPG) signals are essential for accurate extraction of cardiovascular metrics such as heart rate, heart rate variability, and perfusion index (PI). However, signal degradation for individuals with dark skin tones can compromise PPG quality and pose challenges for equitable sensing.</p><p><strong>Aim: </strong>We developed a dual-wavelength, polarization-sensitive PPG device to assess PI across a range of skin tones.</p><p><strong>Approach: </strong>To evaluate the impact of polarization on PPG signal quality, we recorded PI for co-polarized (polarized illumination and parallel-aligned polarized detection) and cross-polarized conditions (polarized illumination and orthogonally aligned polarized detection) at 655 and 940 nm in participants representing light, medium, and brown skin tone categories. Skin tone classification was based on the individual typology angle values derived from the CIE L*b* color space measurements.</p><p><strong>Results: </strong>At 940 nm, light from the cross-polarized light channel significantly increases PI ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ). At 655 nm, cross-polarization presents a statistically significantly enhanced PI ( <math><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ) relative to light from the co-polarized illumination condition, although the magnitude of the improvement decreases with lighter skin tone, indicating a possible interaction between skin tone and polarization. This improvement was consistent across all skin tones.</p><p><strong>Conclusions: </strong>Our results suggest that the cross-polarized condition improves PPG signal quality by reducing the influence of superficial scattering and enhancing deeper vascular signals. This approach may be especially beneficial for individuals with darker skin tones and offers a promising path toward more robust and inclusive physiological monitoring using PPG-based technologies.</p>","PeriodicalId":519981,"journal":{"name":"Biophotonics discovery","volume":"3 1","pages":"012509"},"PeriodicalIF":0.0,"publicationDate":"2025-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13052480/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147794555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optical methods for cuffless blood pressure measurements.","authors":"Ariane Garrett, Ana Perez, Rivka Ayalon, John Forman, Naomi Hamburg, David Boas, Darren Roblyer","doi":"10.1117/1.BIOS.3.1.010901","DOIUrl":"10.1117/1.BIOS.3.1.010901","url":null,"abstract":"<p><strong>Significance: </strong>Accurate, noninvasive, and continuous blood pressure (BP) monitoring without a cuff is a long-standing goal in biomedical engineering, having the potential to revolutionize hypertension care. Significant effort has gone toward the development of an optical cuffless BP device, primarily using the finger photoplethysmography (PPG) signal. Despite this effort, optical-based BP monitors have yet to achieve widespread adoption primarily due to accuracy concerns. Recently, new optical techniques such as multiwavelength PPG, imaging PPG, and speckle contrast optical spectroscopy/speckle plethysmography (SCOS/SPG) have shown promise toward improved performance.</p><p><strong>Aim: </strong>We aim to review methods in optical cuffless BP estimation.</p><p><strong>Approach: </strong>We first review prior literature in finger PPG, wrist PPG, multiwavelength PPG, imaging PPG, and SCOS/SPG strategies for BP estimation. We then review the current state of commercial cuffless BP devices and accuracy standards for new devices and provide an outlook for future work.</p><p><strong>Results: </strong>Most of the effort toward the development of an optical cuffless BP monitor has centered on the use of the finger PPG signal. The methods used to quantify device performance vary widely, hindering cross-device evaluation. To mitigate this issue, future works should adhere to recently published consensus standards for performance evaluation. Emerging techniques such as imaging PPG, multiwavelength PPG, and SCOS/SPG increase the depth of physiological information used for BP monitoring and may improve cuffless BP estimation going forward.</p><p><strong>Conclusion: </strong>Optical methods for cuffless BP monitoring hold enormous promise for the healthcare and consumer spaces, but further improvements in both technology and device characterization are needed to achieve widespread adoption.</p>","PeriodicalId":519981,"journal":{"name":"Biophotonics discovery","volume":"3 1","pages":"010901"},"PeriodicalIF":0.0,"publicationDate":"2025-10-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13052489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147794597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lighting the Way: Anita Mahadevan-Jansen's Journey Through Biophotonics and Beyond.","authors":"Darren Roblyer","doi":"10.1117/1.BIOS.2.4.040501","DOIUrl":"https://doi.org/10.1117/1.BIOS.2.4.040501","url":null,"abstract":"<p><p>Anita Mahadevan-Jansen discusses her experiences of discovery and translation in biophotonics. The interview highlights her groundbreaking research as well as her impact as a mentor and leader in biomedical optics.</p>","PeriodicalId":519981,"journal":{"name":"Biophotonics discovery","volume":"2 4","pages":"040501"},"PeriodicalIF":0.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13227147/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148160265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-time processing of high-throughput quantitative phase microscopy data using a Jetson Orin Nano.","authors":"Wan Wang, Robert E Highland, Justin Fan, David A Miller, Adam Wax","doi":"10.1117/1.BIOS.3.1.012902","DOIUrl":"10.1117/1.BIOS.3.1.012902","url":null,"abstract":"<p><strong>Significance: </strong>Quantitative phase microscopy (QPM) is a holographic imaging technique often applied to studying cell morphology. To advance QPM for clinical applications, high-throughput implementations have been developed to allow imaging of thousands of cells at a time.</p><p><strong>Aim: </strong>To meet the needs of processing raw data and creating QPM holographic images, higher throughput processing methods are needed. Here, we report on the use of a system-on-module approach for QPM data processing.</p><p><strong>Approach: </strong>We have developed a real-time processing pipeline that leverages the parallel processing capabilities of the NVIDIA Jetson Orin Nano to implement processing of cell data. We demonstrate this pipeline on a holographic cytometry (HC) system, a high-throughput QPM implementation. The CUDA processing algorithm enables the generation of QPM data from raw interferograms followed by phase unwrapping, cell segmentation, and refocusing.</p><p><strong>Result: </strong>We captured, processed, and analyzed 107,631 red blood cell images. The processing speed reaches 1200 cells/s in the speed test. Benchmarking shows that real-time refocusing maintains a high degree of structural similarity to the traditional refocusing method.</p><p><strong>Conclusion: </strong>The result demonstrates that our pipeline could accelerate the statistical analysis of cell populations. We expect this study to benefit the development of a portable, low-cost HC system.</p>","PeriodicalId":519981,"journal":{"name":"Biophotonics discovery","volume":"3 1","pages":"012902"},"PeriodicalIF":0.0,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13052479/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147794588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimization of a mobile imaging system to aid in evaluating patients with oral lesions in a dental care setting.","authors":"Ruchika Mitbander, Jennifer Carns, Richard A Schwarz, Sean Anderson, Geethanjali Vipulanandan, Miriam Hernandez, Amelia E Hartzell, Jun Jiao, Ilona Rutkowska, Safia Durab, Sahar Sadeghi, Ashlee D Clayton, Nancy Bass, Loganayaki Anandasivam, Rachel A Giese, Ann M Gillenwater, Nadarajah Vigneswaran, Rebecca Richards-Kortum","doi":"10.1117/1.BIOS.2.4.042307","DOIUrl":"10.1117/1.BIOS.2.4.042307","url":null,"abstract":"<p><strong>Significance: </strong>Oral cancer is often diagnosed at an advanced stage even though the oral cavity is easily accessible for examination. There is a need for tools to support early detection and help non-expert clinicians make appropriate referral recommendations.</p><p><strong>Aim: </strong>We aim to evaluate a mobile detection of oral cancer (mDOC) smartphone-based autofluorescence imaging system by collecting data in a representative, low-prevalence population and optimizing a referral algorithm suitable for deployment in community dental settings.</p><p><strong>Approach: </strong>Data were collected from patients at a community dental clinic using the mDOC system, which captures white light and autofluorescence images. A multi-input deep learning algorithm was developed using both previously acquired and newly collected data in a rehearsal training method. Performance was evaluated on training, validation, and test sets using expert referral decision as the ground truth. Metrics included the Brier Skill Score, area under the ROC curve (AUC-ROC), sensitivity, and specificity.</p><p><strong>Results: </strong>Data from 252 anatomic sites across 50 patients were collected, with five sites in four patients referred to an oral cancer specialist based on expert review. The best-performing algorithm achieved a Brier Skill Score of 0.126. It reached an AUC-ROC of 0.986 on the validation set and an AUC-ROC of 0.778 on the test set. Sensitivity and specificity on the test set were 60.0% and 88.0%, respectively.</p><p><strong>Conclusions: </strong>These findings demonstrate the potential of the mDOC system and referral algorithm to assist in early detection and triage of oral mucosal lesions in low-prevalence, community dental clinic settings.</p>","PeriodicalId":519981,"journal":{"name":"Biophotonics discovery","volume":"2 4","pages":"042307"},"PeriodicalIF":0.0,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13101062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147794776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deuterium-oxide-assisted stimulated Raman scattering microscopy: metabolic imaging for precision medicine.","authors":"Yuhui Li, Shuhua Yue","doi":"10.1117/1.BIOS.2.4.040901","DOIUrl":"10.1117/1.BIOS.2.4.040901","url":null,"abstract":"<p><strong>Significance: </strong>Metabolomics is transforming personalized medicine by enabling the customization of treatment plans based on the unique metabolic profiles of individual patients. However, general metabolomics technologies are destructive and lack spatial resolution, which limits their application in live-cell analysis and dynamic imaging studies. Furthermore, these methods typically lack sufficient spatial resolution, which limits their capacity to capture metabolic heterogeneity at the single-cell or subcellular level. These limitations hinder the study of spatially and temporally dynamic metabolic processes in diseases such as cancer, metabolic, and neurological disorders.</p><p><strong>Aim: </strong>Our review summarizes advances in deuterium-oxide-assisted stimulated Raman scattering ( <math> <mrow> <msub><mrow><mi>D</mi></mrow> <mrow><mn>2</mn></mrow> </msub> <mi>O</mi> <mtext>-</mtext> <mi>SRS</mi></mrow> </math> ) microscopy, with a focus on its capabilities for real-time, high-resolution metabolic imaging. We discuss how this technology enables real-time tracking of biosynthetic processes in living systems, contributing to the advancement of precision diagnostics and therapeutic strategies, highlighting its recent progress in tumor metabolism monitoring, and drug sensitivity testing for infectious diseases, as well as its future potential applications.</p><p><strong>Approach: </strong>We outline the nonlinear optical principles of SRS, the deuterium oxide ( <math> <mrow><msub><mi>D</mi> <mn>2</mn></msub> <mi>O</mi></mrow> </math> ) labeling strategy that generates carbon-deuterium (C-D) vibrational signals in newly synthesized macromolecules, and key applications in tracking lipid, protein, and nucleic acid biosynthesis.</p><p><strong>Results: </strong><math> <mrow> <msub><mrow><mi>D</mi></mrow> <mrow><mn>2</mn></mrow> </msub> <mi>O</mi> <mtext>-</mtext> <mi>SRS</mi></mrow> </math> enables real-time, quantitative imaging of metabolic activity with subcellular resolution and minimal perturbation, offering chemical and spatial insight into metabolic dynamics.</p><p><strong>Conclusions: </strong><math> <mrow> <msub><mrow><mi>D</mi></mrow> <mrow><mn>2</mn></mrow> </msub> <mi>O</mi> <mtext>-</mtext> <mi>SRS</mi></mrow> </math> microscopy represents a significant advancement in live metabolic imaging, offering a powerful tool for investigating disease-related metabolic dysfunction. This technology enables high-resolution, real-time metabolic imaging in living systems. As a result, <math> <mrow> <msub><mrow><mi>D</mi></mrow> <mrow><mn>2</mn></mrow> </msub> <mi>O</mi> <mtext>-</mtext> <mi>SRS</mi></mrow> </math> is expected to promote the transformation and implementation of basic metabolic research into clinical applications.</p>","PeriodicalId":519981,"journal":{"name":"Biophotonics discovery","volume":"2 4","pages":"040901"},"PeriodicalIF":0.0,"publicationDate":"2025-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13101068/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147794770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating machine learning algorithms to classify label-free images of pancreatic neuroendocrine neoplasms.","authors":"Noelle Daigle, Shuyuan Guan, Suzann Duan, Thomas G Knapp, Eung-Joo Lee, Ali Azhdarinia, Sukhen C Ghosh, Solmaz AghaAmiri, Servando Hernandez Vargas, Naruhiko Ikoma, Jeannelyn Estrella, Martin J Schnermann, Tobias Else, Michelle Kang Kim, Juanita L Merchant, Travis W Sawyer","doi":"10.1117/1.BIOS.2.4.045001","DOIUrl":"10.1117/1.BIOS.2.4.045001","url":null,"abstract":"<p><strong>Significance: </strong>Pancreatic neuroendocrine neoplasms (PNENs) are an uncommon cancer whose incidence rate has increased dramatically in recent years. Surgery is the only potentially curative treatment, which relies on both preoperative tumor localization and postoperative margin definition using histopathological examination for decision making. If pathology could be automated, valuable time and resources could be saved.</p><p><strong>Aim: </strong>In this study, we investigate the ability of machine learning (ML) with handcrafted features, as well as deep learning, to classify label-free microscopy images of PNENs as a first step toward automated pathology of such tumors.</p><p><strong>Approach: </strong>Patient samples of two different preparation types were imaged, and ML and convolutional neural networks (CNNs) were developed to test the ability of such algorithms to classify PNENs.</p><p><strong>Results: </strong>Our classification algorithms were able to distinguish PNENs from normal tissue with high accuracy using multiphoton microscopy (MPM) images, regardless of sample preparation. Using a combined FFPE and fixed frozen dataset, we achieved an AUC value of 0.793 and an accuracy of 80.6% with ML, and an AUC value of 0.977 and an accuracy of 96.43% using CNNs.</p><p><strong>Conclusions: </strong>Label-free MPM combined with deep learning can provide fast, accurate classification of PNENs. With the ability to assess margins rapidly and potentially automatically, both disease recurrence and the need for resections after initial surgery could be reduced.</p>","PeriodicalId":519981,"journal":{"name":"Biophotonics discovery","volume":"2 4","pages":"045001"},"PeriodicalIF":0.0,"publicationDate":"2025-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13101064/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147794819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}