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Development of vitex-derived polymer nanofibers using electrochemical sensors for the treatment of polycystic ovarian syndrome in rats as an animal model

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Biosensors and Bioelectronics: X 2025年 22卷

作者： Mahmoud, Zaid H. Hussein, Uday Abdul-Reda Aljbory, Najwa Alnajar, Mohammed Jawad Maleknia, Laleh Mirani, Abolfazl kianfar, Ehsan college of Sciences University of Diyala chemistry Department Iraq College of Pharmacy University of Al-Ameed Iraq AlSafwa University College Iraq Department of Medical Laboratories Technology AL-Nisour University College Baghdad Iraq Department of Biomedical Engineering Islamic Azad University South Tehran Branch Tehran Iran Biomedical Engineering Research Center Clinical Sciences Institute Baqiyatallah University of Medical Sciences Tehran Iran Young Researchers and Elite Club Gachsaran Branch Islamic Azad University Gachsaran Iran

Background and objectives: Nanofibers have a high specific surface area and small pores, which increases the possibility of drug uptake. In this study, chasteberry-containing nanofibers were prepared by electrospinning to investigate their potential for polycystic follicles after induction of polycystic ovarian syndrome (PCOS). Materials and methods: Chasteberry-containing nanofibers were identified using Fourier-transform infrared spectroscopy (FTIR), Field Emission Scanning Electron Microscopy (FESEM), and thermal gravimetric analysis (TGA) tests, as well as drug release and antibacterial tests. In this study, 42 Sprague-Dawley rats with regular menstrual cycles were divided into five groups: control group (healthy rats), polycarbonate (PC) group (treated with 1 mg/kg letrozole for 28 days to induce PCOS syndrome), and Poly terephthalate (PT) group (treated with PCOS for 30 days). Chasteberry was included at 1, 2, 4 wt%. To induce PCOS, the polyterephthalate (PT) and polycarbonate (PC) groups were administered 1 mg/kg letrozole by oral gavage for 28 days. The polyterephthalate (PT) group was treated with different doses of chasteberry-containing nanofibers (1, 2, 4 wt%). Results: The results showed that the drug was well incorporated into the fibers and released slowly and sustainedly from the nanofibers for 30 days. Ex vivo results showed that by taking letrozole, the polycarbonate (PC) group had larger cystic follicles with a very thin granulosa layer than the control group. This is the cause of ovarian anovulation or oligo-ovulation and induction of polycystic ovarian syndrome. In the polyterephthalate (PT) group, which was administered nanofibers containing chasteberry nanomedicine subcutaneously, the number of cystic follicles decreased and the number of various follicles indicating ovulation increased in these groups. Conclusion: Subcutaneous administration of Vitex nanofibers at a dose of 4 wt% can improve polycystic ovarian syndrome by decreasing cystic fo

关键词： Fourier transform infrared spectroscopy

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Air Curtains for Landing Gear Noise Reduction

SSRN

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SSRN 2025年

作者： Carroll, Daniel Pott-Polenske, Michael Bennett, Gareth J. Department of Mechanical Manufacturing and Biomedical Engineering Trinity College Dublin The University of Dublin D02 PN40 Ireland Institute of Aerodynamics and Flow Technology DLR German Aerospace Center Braunschweig38108 Germany

This paper examines the implementation of air curtain nozzles to reduce the aerodynamic noise radiated from a modified scaled LAGOON landing gear and a detailed trailing-arm Main Landing Gear representing a generic Bizjet wing mounted gear provided by Dassault Aviation and Safran Landing Systems. At approach to landing, landing gear noise is still a significant contributor to environmental noise in the vicinity of airports. Progress is being made with ambitious projects which aim to develop significantly reconfigured aircraft architectures to reduce airframe noise. The current project examines a noise abatement concept which could be retrofitted to existing landing gear configurations as an interim measure or as part of a longer term solution. Flow control in the form of "air curtain" nozzles which create a fluidic shield or virtual fairing are examined. The air curtain concept was examined as a low test-readiness-level (TRL), high-risk, high-gain technology tested in the EU funded H2020 collaborative research project: INVENTOR, InnoVative dEsign of iNstalled airframe componenTs for aircraft nOise Reduction. The nozzles operate at high pressure with rows of choked-flow sub-millimeter holes. Narrow and broadband noise attenuation was achieved, with reductions of over 7 dB measured. The air curtains were examined in DLR’s AWB aeroacoustic facility in Braunschweig, Germany. © 2025, The Authors. All rights reserved.

关键词： Landing gear (aircraft)

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A digital image correlation method for strain measurement using higher-order X-ray optical field components

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Optics and Laser technology 2025年 189卷

作者： Li, Zixin Su, Yong Li, Jia Yuan, Yajun Xiao, Yu Xu, Feng CAS Key Laboratory of Mechanical Behaviour and Design of Materials Department of Modern Mechanics CAS Center for Excellence in Complex System Mechanics University of Science and Technology of China Anhui Hefei230027 China School of Biomedical Engineering Anhui Medical University Hefei230032 China

To achieve highly sensitive characterization of internal material strain fields, this study proposes a digital image correlation strain field measurement method based on higher-order X-ray optical field *** method utilizes random gratings to modulate the X-ray optical field, extracts the information of higher-order field components during material deformation based on the discrepancy between the theoretical transmission model and the experimental measurement, and combines the extracted information with Digital Image Correlation (DIC) technology to achieve precise measurement of internal material strain *** to the experimental comparison and verification, the relative displacement difference between the deformation field distribution obtained by the proposed method and the results of the traditional absorption phase method in the non-boundary and stress concentration area proves that the correlation information of the higher order components can truly reflect the deformation characteristics of the ***, compared to traditional methods, this method demonstrates higher sensitivity in strain field characterization. Particularly in the measurement of shear strain at crack tips, its computational results align more closely with linear elastic fracture mechanics theoretical predictions, providing a novel experimental approach for studying material damage evolution and failure mechanisms. © 2025 Elsevier Ltd

关键词： Crack tips

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Tumor cell-derived N-acetyl-aspartyl-glutamate reshapes the tumor microenvironment to facilitate breast cancer metastasis

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Science Bulletin 2025年第7期70卷 1126-1138页

作者： Jie Xia Lixing Zhang Wucheng Zhu Juchuanli Tu Xilei Peng Qiaodan Deng Siqin Li Xueyan He Haonan Dong Cuicui Liu Xian Chen Jiahui Xu Wei Ma Yi Xiao Wen Liu Guohong Hu Yi-Zhou Jiang Ceshi Chen Xiu-Wu Bian Zhi-Ming Shao Suling Liu Fudan University Shanghai Cancer Center&Institutes of Biomedical Sciences State Key Laboratory of Genetic EngineeringCancer InstitutesDepartment of OncologyKey Laboratory of Breast Cancer in ShanghaiThe Shanghai Key Laboratory of Medical EpigeneticsShanghai Key Laboratory of Radiation OncologyThe International Co-laboratory of Medical Epigenetics and MetabolismMinistry of Science and TechnologyShanghai Medical CollegeFudan UniversityShanghai 200032China Jinfeng Laboratory Chongqing 401329China Jiangsu Key Lab of Cancer Biomarkers Prevention and TreatmentCollaborative Innovation Center for Cancer MedicineNanjing Medical UniversityNanjing 211166China Key Laboratory of Breast Cancer in Shanghai Department of Breast SurgeryFudan University Shanghai Cancer Center and Cancer InstituteShanghai Medical CollegeFudan UniversityShanghai 200032China Key Laboratory of Breast Cancer in Shanghai Department of Breast SurgeryFudan University Shanghai Cancer CenterShanghai 200032China Department of Oncology Shanghai Medical CollegeFudan UniversityShanghai 200032China Precision Cancer Medical Center Fudan University Shanghai Cancer CenterShanghai 201315China School of Pharmaceutical Sciences Faculty of Medicine and Life SciencesXiamen UniversityXiamen 361102China Shanghai Institute of Nutrition and Health University of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200032China Key Laboratory of Animal Models and Human Disease Mechanisms of Chinese Academy of Sciences and Yunnan Province Kunming Institute of ZoologyKunming 650201China Academy of Biomedical Engineering&The Third Affiliated Hospital Kunming Medical UniversityKunming 650500China Institute of Pathology and Southwest Cancer Centre Southwest HospitalThe Third Military Medical UniversityChongqing 400038China

Neurotransmitters are increasingly recognized to play important roles in limiting anti-tumor immunity.N-acetyl-aspartyl-glutamate(NAAG)has been extensively studied in neurological disorders;however,its potential role in restricting anti-tumor immunity has not been ***,we demonstrated that NAAG or its synthetase RimK-like family member B(RIMKLB)significantly disrupted anti-tumor immunity by rewiring the myeloid progenitor differentiation of polymorphonuclear myeloid-derived suppressor cells(PMN-MDSCs),which in turn promoted breast cancer growth and ***,NAAG sustained the tumor immunosuppressive microenvironment by activating an NR2B-containing NMDA receptor(NR2B-NMDAR)-dependent p38-NOTCH1 axis,and subsequently stimulating tumor cell migration and invasion,as well as inducing PMN-MDSC differentiation and *** mouse models,RIMKLB ablation or NMDAR inhibition enhanced the efficacy of anti-PD-1 therapy and suppressed tumor *** analysis of clinical samples revealed that high levels of NAAG and NR2B-NMDAR predicted a poor prognosis in TNBC ***,our findings have uncovered a signaling role for tumor-derived NAAG beyond its classic function as a neurotransmitter that can be targeted pharmacologically to enhance immunotherapy against breast cancer.

关键词： Breast cancer metastasis NAAG PMN-MDSC RIMKLB NMDAR

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Clinician-artificial intelligence collaboration: A win-win solution for efficiency and reliability in atrial fibrillation diagnosis

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Med 2025年 100668页

作者： Zhang, Peng Lin, Fan Ma, Fei Chen, Yuting Liu, Yuhang Feng, Xiaoli Fang, Siyi Zhang, Haowei Xiao, Shuna Yang, Xiangli Li, Dun Wang, Dao Wen Yang, Xiaoyun Li, Qiang Britton Chance Center for Biomedical Photonics Wuhan National Laboratory for Optoelectronics-Hubei Bioinformatics and Molecular Imaging Key Laboratory Department of Biomedical Engineering College of Life Science and Technology Huazhong University of Science and Technology Hubei Wuhan 430074 China MoE Key Laboratory for Biomedical Photonics Collaborative Innovation Center for Biomedical Engineering School of Engineering Sciences Huazhong University of Science and Technology Hubei Wuhan 430074 China Division of Cardiology Department of Internal Medicine Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan 430030 China Cardiovascular Center Liyuan Hospital Affiliated to Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan 430077 China United Imaging Surgical Healthcare Co. Ltd. Hubei Wuhan 430206 China

Background: Given the biases and ethical concerns of AI models, the fully automatic diagnosis of diseases in clinical settings is challenging. In contrast, clinician-AI collaboration is considered essential to ensure the validity and reliability of utilizing AI models in clinical practice. However, effective strategies for clinician-AI collaboration remain largely unexplored. Methods: This study proposed a three-step general clinician-AI collaboration pipeline aimed at improving disease diagnosis efficiency: first, utilizing large real-world clinical datasets to evaluate and clarify clinicians’ diagnostic strengths/weaknesses;second, developing an AI model to complement clinicians’ weakness in disease diagnosis;and finally, proposing a clinician-AI collaboration strategy to leverage the strengths of both AI and clinicians. The effectiveness of this pipeline was validated through a study focusing on clinical paroxysmal atrial fibrillation (PAF) detection, utilizing 24-h Holter recordings from over 30,000 patients. Findings: In PAF detection, clinicians alone required a significant amount of time to identify the data and still overlooked 13.7% of PAF patients but successfully identified all non-atrial fibrillation (AF) patients. Conversely, AI alone rarely missed PAF patients but misidentified 23.3% of non-AF patients as having PAF. After implementing the proposed clinician-AI collaboration strategy, all patients were correctly identified, and clinicians’ workload was reduced by 76.7%. Conclusions: This study improves both the efficiency and reliability of PAF detection, bridging the gap between AI model development and its clinical application, thereby effectively promoting the application of AI models in clinical AF screening. Funding: This study was supported in part by the National Natural Science Foundation of China. © 2025 The Author(s)

关键词： artificial intelligence in medicine atrial fibrillation atrial fibrillation detection atrial fibrillation screening clinician-AI collaboration deep learning diagnostic AI model electrocardiogram Holter monitoring segmentation model Translation to patients

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Reducing lead requirements for wearable ECG: Chest lead reconstruction with 1D-CNN and Bi-LSTM

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Informatics in Medicine Unlocked 2025年 53卷

作者： Hebiguchi, Kazuki Togo, Hiroyoshi Hirata, Akimasa Department of Electrical and Mechanical Engineering Nagoya Institute of Technology Nagoya 466-8555 Japan Office of Innovation and Entrepreneurship Keio University Japan Center of Biomedical Physics and Information Technology Nagoya Institute of Technology Nagoya 466-8555 Japan

Wearable ECG devices encounter significant challenges in replicating the diagnostic capabilities of standard 12-lead ECGs, primarily due to the complexity of electrode placement and the need for specialized equipment. This study aims to develop a deep learning model capable of reconstructing complete 12-lead ECG waveforms using a minimal number of chest leads, thereby optimizing lead configurations for wearable ECG systems. Leveraging the PTB-XL ECG dataset, we preprocessed the signals to eliminate noise and trained a model integrating 1D convolutional layers with a Bi-directional Long Short-Term Memory (Bi-LSTM) architecture. Reconstruction performance was assessed using Pearson's correlation coefficient and root mean squared error (RMSE) across various input lead configurations, ranging from single to quintuple inputs. Our preprocessing and network architecture effectively capture both spatial and temporal features. The model achieved its highest reconstruction accuracy for leads located near the input leads, with performance gradually diminishing for more distant leads. Notably, the transitional zone between leads V₃ and V₄ presented reconstruction challenges due to polarity shifts. While increasing the number of input leads enhanced reconstruction accuracy and reduced variability, the improvements plateaued beyond the use of double input leads. Among configurations, double input leads, particularly those with two intervening leads between input pairs, offered an optimal balance between reconstruction accuracy and model complexity. This study highlights that accurate reconstruction of 12-lead ECG is achievable with only two input leads, providing a balance between diagnostic accuracy and reduced electrode requirements. These findings offer valuable insights for designing wearable ECG systems capable of reliable monitoring with fewer electrodes. © 2025 The Author(s)

关键词： Bi-LSTM Diagnosis ECG Waveform reconstruction Wearable devices

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TextAI 3.0 (Multimodal): multimodal sentiment analysis using attention-enabled ensemble-based deep learning in hyperbolic space

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International Journal of Information technology (Singapore) 2025年 1-16页

作者： Singh, Jaskaran Sanga, Prabhav Khanna, Narendra N. Laird, John R. Singh, Inder M. Kalra, Mannudeep K. Mantella, Laura E. Johri, Amer M. Faa, Gavino Tiwari, Ekta Singh, Rajesh Abbas, Mohamed Saba, Luca Suri, Jasjit S. Stroke Monitoring and Diagnostic Division AtheroPoint™ Roseville 95661 CA United States Bharati Vidyapeeth’s College of Engineering New Delhi India Department of Cardiology Indraprastha APOLLO Hospitals New Delhi 110001 India Department of Neurology and Stroke Program University of Maryland School of Medicine Baltimore United States Department of Radiology Harvard Medical School Boston 02115 MA United States Department of Biomedical and Molecular Sciences Queen’s University Kingston ON Canada Division of Cardiology Department of Medicine Queen’s University Kingston ON Canada Department of Pathology University of Cagliari Cagliari Italy Department of Computer Science Visvesvaraya National Institute of Technology (VNIT) Nagpur 440010 India Department of Research and Innovation Uttaranchal Institute of Technology Uttaranchal University Dehradun 248007 India Central Labs King Khalid University P.O. Box 960 AlQura’a Abha Saudi Arabia Electrical Engineering Department College of Engineering King Khalid University Abha 61421 Saudi Arabia Department of Neurology University of Cagliari Cagliari Italy Department of ECE Idaho State University Pocatello 83209 ID United States Department of CE Graphic Era Deemed to Be University Dehradun 248002 India University Center for Research and Development Chandigarh University Mohali India Symbiosis Institute of Technology Symbiosis International (Deemed University) Nagpur Campus Pune India Department of Innovation Global Biomedical Technologies Inc. Roseville 95661 CA United States

The rising prevalence of depression presents a significant global health challenge, underscoring the need for effective early detection through sentiment analysis. Traditional multimodal sentiment analysis methods, which rely on Euclidean space for data representation, often fail to capture the complex hierarchical relationships and non-linear structures inherent in multimodal data. To address this limitation, we propose a novel multimodal architecture that operates in hyperbolic space and incorporates attention-based ensemble learning to enhance feature representation and model performance. We developed and evaluated ten AI models—five utilizing Euclidean space and five leveraging hyperbolic space—on the Spoken Language Understanding Evaluation (SLUE) benchmark dataset. The models were assessed using cross-validation techniques to ensure robustness, and statistical analyses were conducted to validate the findings. Our results indicate that hyperbolic-based multimodal models achieve a 2.32% improvement in accuracy compared to their Euclidean counterparts, while the integration of self-attention mechanisms further contributes an average accuracy gain of 0.96%. Additionally, we observed a 3.76% decline in performance when reducing the training-to-test data ratio from 90:10 to 50:50, highlighting the importance of data distribution in model training. These findings confirm that attention-enhanced multimodal models in hyperbolic space offer superior representations for sentiment analysis. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2025.

关键词： Attention-enabled Deep learning Euclidean space Hyperbolic space Multimodal Sentiment analysis

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Semi-Supervised Learning, Foundation Models and Image Processing for Pleural Line Detection and Segmentation in Lung Ultrasound

SSRN

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SSRN 2025年

作者： Valensi, David Lupu, Lior Adam, Dan Topilsky, Yan Biomedical Engineering Department Technion Israel Institute of Technology Haifa3200003 Israel Department of Cardiology Tel Aviv Sourasky Medical Center Tel Aviv6423906 Israel Sackler School of Medicine Tel Aviv University Tel Aviv6997801 Israel

During the COVID-19 pandemic, lung ultrasound (LUS) has gained prominence due to its rapid, point-of-care application without ionizing radiation. LUS reveals anatomical features and ultrasonic artifacts, including the pleural line, which aid in diagnosing lung diseases such as COVID-19. Our research focuses on developing algorithms for automatic detection and segmentation of the pleural line in LUS scans while employing phased array probes. For detection, we explored image processing, deep neural networks (Faster RCNN and YOLOv8), and semi-supervised learning enhanced by object tracking. We experimented with various input options for Faster RCNN, including raw frames, consecutive frames, and temporal statistics. The YOLOv8 model was trained and applied in a semi-supervised learning technique using pseudo-labels that were created by the BoT-SORT object tracker. For segmentation, we utilized two foundation models (Segment Anything Model and MedSAM) in zero-shot and fine-tuning pipelines, using YOLOv8x predictions as bounding box prompts. We also investigated the impact of input image cropping on model performance. Results showed that deep neural networks outperformed image processing for detection (Average Precision@0.5 of 0.89 vs 0.52), with semi-supervised learning further improving performance (AP@0.5 of 0.95). For segmentation, zero-shot performance of SAM and MedSAM was similar (mean Dice scores of 0.62 and 0.61), with improvements achieved through input cropping and fine-tuning (mean Dice score of 0.77 for fine-tuned MedSAM). © 2025, The Authors. All rights reserved.

关键词： Lung cancer

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Computational Assessment of Hemodynamics in Asymmetric-type Lesion of Idealized Coronary Stenoses

arXiv

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arXiv 2025年

作者： Oyejide, Ayodele Abodunrin, Oluwatosin Ige, Ebenezer Awonusi, Adetokunbo Department of Biomedical Engineering College of Engineering Afe Babalola University Ado-Ekiti360231 Nigeria Euromed Research Center Euromed Engineering Faculty Euromed University of Fes Morocco Department of Mechanical Engineering Rochester Institute of Technology NY14623 United States Department of Mechanical and Aerospace Engineering Oklahoma State University StillwaterOK-74078 United States

Coronary artery stenosis, characterized by the narrowing of the lumen, significantly affects blood flow and contributes to the progression of cardiovascular diseases. This study investigates the hemodynamics of coronary artery models with varying stenosis configurations, all maintaining an 80% lumen reduction, to determine how differences in morphology influence flow behavior and mechanical stresses. We employed computational fluid dynamics to analyze five idealized geometries with (10% & 70%), (20% & 60%), (30% & 50%), (40% & 40%), and (0% & 80%) stenosis configurations. Through physiological pulsatile flow conditions, we evaluated key hemodynamic pattern including velocity profiles, wall shear stress, and pressure distribution. Our results reveal that despite the same degree of lumen reduction, each stenosis configuration produced distinct flow patterns and hemodynamic profiles. Asymmetric configurations, such as 10% & 70% and 20% & 60%, exhibited pronounced flow disruptions and higher wall shear stress at the stenosis throats, while symmetric configurations, such as 40% & 40%, demonstrated more uniform flow and reduced vortex. Our findings challenge the practice of generalizing results across stenosis configurations without accounting for morphological variations, which is prevalent in many CFD studies using idealized models. This study emphasize the importance of considering stenosis-specific morphology in CFD analyses and clinical interpretations to enhance the accuracy of diagnostic tools, improve personalized treatment planning, and guide the design of medical devices such as stents. © 2025, CC BY.

关键词： Shear stress

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Prion propensity of Betacoronaviruses including SARS-CoV-2

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Heliyon 2025年第4期11卷 e42199页

作者： Haddad, Hazem Al-Zyoud, Walid Princess Haya Biotechnology Center Jordan University of Science and Technology Irbid 22110 Jordan Department of Biomedical Engineering School of Applied Medical Sciences German Jordanian University Amman 11180 Jordan

Prions are considered as sub-viral protein particles that have exceptional ability for multiple structural or functional conformational changes, that any might affect the regulation of viral infections. The aim of this study is to utilize two computational platforms to predict the prion-forming potential of the spike protein (S) in Betacoronavirus, including SARS-CoV-2 clades. The abovementioned computational platforms included two algorithms;the Prion Aggregation Prediction Algorithm (PAPA) and the Supervised Machine Learning Algorithm Called Prion RANKing and Classification (pRANK) have been adopted due to their high classifier performance proteome-wide when compared with other algorithms, such as PLAAC-LLR and prionW. The findings of this study imply the propensity of some Betacorona viruses, including the Wild type of SARS-CoV-2 and some variants, specifically as Gamma and Delta, to develop prion-like sequence which can act as a regulator for viral pathogenicity or as a biochemical threat. © 2025 The Authors

关键词： Betacoronavirus Biosecurity PAPA pRANK Prion QN propensity SARS-CoV-2

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