Front Psychiatry. 2025 Mar 18;16:1452557. doi: 10.3389/fpsyt.2025.1452557. eCollection 2025.

ABSTRACT

Neurological disorders (e.g., Alzheimer's disease and Parkinson's disease) and mental disorders (e.g., depression and anxiety), pose huge challenges to global public health. The pathogenesis of these diseases can usually be attributed to many factors, such as genetic, environmental and socioeconomic status, which make the diagnosis and treatment of the diseases difficult. As research on the diseases advances, so does the body of medical data. The accumulation of such data provides unique opportunities for the basic and clinical study of these diseases, but the vast and diverse nature of the data also make it difficult for physicians and researchers to precisely extract the information and utilize it in their work. A powerful tool to extract the necessary knowledge from large amounts of data is knowledge graph (KG). KG, as an organized form of information, has great potential for the study neurological and mental disorders when it is paired with big data and deep learning technologies. In this study, we reviewed the application of KGs in common neurological and mental disorders in recent years. We also discussed the current state of medical knowledge graphs, highlighting the obstacles and constraints that still need to be overcome.

PMID:40171303 | PMC:PMC11958944 | DOI:10.3389/fpsyt.2025.1452557

Front Oncol. 2025 Mar 18;15:1525650. doi: 10.3389/fonc.2025.1525650. eCollection 2025.

ABSTRACT

In recent years, with the rapid advancement of computer science, artificial intelligence has found extensive applications and has been the subject of significant research within the healthcare industry, particularly in areas such as medical imaging, diagnostics, biomedical engineering, and health data analytics. Artificial intelligence has also made considerable inroads in the diagnosis and treatment of thyroid cancer. This study aims to evaluate the progress, current hotspots, and potential future directions of research on artificial intelligence in the field of thyroid cancer through a bibliometric analysis. This study retrieved literature on the application of artificial intelligence in thyroid cancer from 2004 to 2024 from the Web of Science Core Collection (WoSCC) database. A retrospective bibliometric analysis and visualization study of the filtered data were conducted using VOSviewer, CiteSpace, and the Bibliometrix package in R software. A total of 956 articles from 70 countries/regions were included. China had the highest number of publications, with Shanghai Jiao Tong University (China) being the most prolific research institution. The most prolific author was Wei, X. (n=14), while Haugen, B. R. was the most co-cited author (n=297). The Frontiers in Oncology (35 articles, IF=3.5, Q1) was the most frequently publishing journal, and Thyroid (cited 1,705 times) was the most co-cited journal. Keywords such as 'ultrasound,' 'deep learning,' and 'diagnosis' indicate research hotspots in this field. This study provides a comprehensive exposition of the current advancements, emerging trends, and future directions of artificial intelligence in thyroid cancer research. It serves as a valuable resource for clinicians and researchers, offering a systematic understanding of key focal areas in the field, thereby assisting in the identification and determination of future research trajectories.

PMID:40171256 | PMC:PMC11958942 | DOI:10.3389/fonc.2025.1525650

Front Endocrinol (Lausanne). 2025 Mar 18;16:1485311. doi: 10.3389/fendo.2025.1485311. eCollection 2025.

ABSTRACT

OBJECTIVE: To systematically review and meta-analyze the effectiveness of deep learning algorithms applied to optical coherence tomography (OCT) and retinal images for the detection of diabetic retinopathy (DR).

METHODS: We conducted a comprehensive literature search in multiple databases including PubMed, Cochrane library, Web of Science, Embase and IEEE Xplore up to July 2024. Studies that utilized deep learning techniques for the detection of DR using OCT and retinal images were included. Data extraction and quality assessment were performed independently by two reviewers. Meta-analysis was conducted to determine pooled sensitivity, specificity, and diagnostic odds ratios.

RESULTS: A total of 47 studies were included in the systematic review, 10 were meta-analyzed, encompassing a total of 188268 retinal images and OCT scans. The meta-analysis revealed a pooled sensitivity of 1.88 (95% CI: 1.45-2.44) and a pooled specificity of 1.33 (95% CI: 0.97-1.84) for the detection of DR using deep learning models. All of the outcome of deep learning-based optical coherence tomography ORs ≥0.785, indicating that all included studies with artificial intelligence assistance produced good boosting results.

CONCLUSION: Deep learning-based approaches show high accuracy in detecting diabetic retinopathy from OCT and retinal images, supporting their potential as reliable tools in clinical settings. Future research should focus on standardizing datasets, improving model interpretability, and validating performance across diverse populations.

SYSTEMATIC REVIEW REGISTRATION: https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024575847.

PMID:40171193 | PMC:PMC11958191 | DOI:10.3389/fendo.2025.1485311

Digit Health. 2025 Mar 31;11:20552076251332999. doi: 10.1177/20552076251332999. eCollection 2025 Jan-Dec.

ABSTRACT

OBJECTIVE: Gait analysis plays a pivotal role in evaluating walking abilities, with recent advancements in digital health stressing the importance of efficient data collection methods. This study aims to classify nine gait types including one normal and eight abnormal gaits, using sequential network-based models and diverse feature combinations obtained from insole sensors.

METHODS: The dataset was collected using insole sensors from subjects performing 15 m walking with designated gait types. The sensors incorporated pressure sensors and inertial measurement units (IMUs), along with the center of pressure engineered from the pressure readings. A number of deep learning architectures were evaluated for their ability to classify the gait types, focusing on feature sets including temporal parameters, statistical features of pressure signals, center of pressure data, and IMU data. Ablation studies were also conducted to assess the impact of combining features from different modalities.

RESULTS: Our results demonstrate that models incorporating IMU features outperform those using different combinations of modalities including individual feature sets, with the top-performing models achieving F1-scores of up to 90% in sample-wise classification and 92% in subject-wise classification. Additionally, an ablation study reveals the importance of considering diverse feature modalities, including temporal parameters, statistical features from pressure signals, center of pressure data, and IMU data, for comprehensive gait classification.

CONCLUSION: Overall, this study successfully developed deep sequential models that effectively classify nine different gait types, with the ablation study underscoring the potential for integrating features from diverse domains to enhance clinical applications, such as intervention for gait-related disorders.

PMID:40171146 | PMC:PMC11960168 | DOI:10.1177/20552076251332999

Front Physiol. 2025 Mar 18;16:1511716. doi: 10.3389/fphys.2025.1511716. eCollection 2025.

ABSTRACT

INTRODUCTION: Lung nodule detection is a crucial task for diagnosis and lung cancer prevention. However, it can be extremely difficult to identify tiny nodules in medical images since pulmonary nodules vary greatly in shape, size, and location. Further, the implemented methods have certain limitations including scalability, robustness, data availability, and false detection rate.

METHODS: To overcome the limitations in the existing techniques, this research proposes the Cnidaria Herd Optimization (CHO) algorithm-enabled Bi-directional Long Short-Term Memory (CHSTM) model for effective lung nodule detection. Furthermore, statistical and texture descriptors extract the significant features that aid in improving the detection accuracy. In addition, the FC2R segmentation model combines the optimized fuzzy C-means clustering algorithm and the Resnet -101 deep learning approach that effectively improves the performance of the model. Specifically, the CHO algorithm is modelled using the combination of the induced movement strategy of krill with the time control mechanism of the cnidaria to find the optimal solution and improve the CHSTM model's performance.

RESULTS: According to the experimental findings of a performance comparison between other established methods, the FC2R + CHSTM model achieves 98.09% sensitivity, 97.71% accuracy, and 97.03% specificity for TP 80 utilizing the LUNA-16 dataset. Utilizing the LIDC/IDRI dataset, the proposed approach attained a high accuracy of 97.59%, sensitivity of 96.77%, and specificity of 98.41% with k-fold validation outperforming the other existing techniques.

CONCLUSION: The proposed FC2R + CHSTM model effectively detects lung nodules with minimum loss and better accuracy.

PMID:40171113 | PMC:PMC11959082 | DOI:10.3389/fphys.2025.1511716

J Appl Crystallogr. 2025 Feb 28;58(Pt 2):513-522. doi: 10.1107/S1600576725000974. eCollection 2025 Apr 1.

ABSTRACT

Recent advancements in X-ray sources and detectors have dramatically increased data generation, leading to a greater demand for automated data processing. This is particularly relevant for real-time grazing-incidence wide-angle X-ray scattering (GIWAXS) experiments which can produce hundreds of thousands of diffraction images in a single day at a synchrotron beamline. Deep learning (DL)-based peak-detection techniques are becoming prominent in this field, but rigorous benchmarking is essential to evaluate their reliability, identify potential problems, explore avenues for improvement and build confidence among researchers for seamless integration into their workflows. However, the systematic evaluation of these techniques has been hampered by the lack of annotated GIWAXS datasets, standardized metrics and baseline models. To address these challenges, we introduce a comprehensive framework comprising an annotated experimental dataset, physics-informed metrics adapted to the GIWAXS geometry and a competitive baseline - a classical, non-DL peak-detection algorithm optimized on our dataset. Furthermore, we apply our framework to benchmark a recent DL solution trained on simulated data and discover its superior performance compared with our baseline. This analysis not only highlights the effectiveness of DL methods for identifying diffraction peaks but also provides insights for further development of these solutions.

PMID:40170972 | PMC:PMC11957406 | DOI:10.1107/S1600576725000974

Stat Biosci. 2025 Apr;17(1):132-150. doi: 10.1007/s12561-023-09394-6. Epub 2023 Oct 28.

ABSTRACT

Estimating treatment effects is of great importance for many biomedical applications with observational data. Particularly, interpretability of the treatment effects is preferable for many biomedical researchers. In this paper, we first provide a theoretical analysis and derive an upper bound for the bias of average treatment effect (ATE) estimation under the strong ignorability assumption. Derived by leveraging appealing properties of the weighted energy distance, our upper bound is tighter than what has been reported in the literature. Motivated by the theoretical analysis, we propose a novel objective function for estimating the ATE that uses the energy distance balancing score and hence does not require the correct specification of the propensity score model. We also leverage recently developed neural additive models to improve interpretability of deep learning models used for potential outcome prediction. We further enhance our proposed model with an energy distance balancing score weighted regularization. The superiority of our proposed model over current state-of-the-art methods is demonstrated in semi-synthetic experiments using two benchmark datasets, namely, IHDP and ACIC, as well as is examined through the study of the effect of smoking on the blood level of cadmium using NHANES.

PMID:40170916 | PMC:PMC11957463 | DOI:10.1007/s12561-023-09394-6

Health Data Sci. 2025 Apr 1;5:0256. doi: 10.34133/hds.0256. eCollection 2025.

ABSTRACT

Background: Multimodal large language models (LLMs) have shown potential in various health-related fields. However, many healthcare studies have raised concerns about the reliability and biases of LLMs in healthcare applications. Methods: To explore the practical application of multimodal LLMs in skin disease identification, and to evaluate sex and age biases, we tested the performance of 2 popular multimodal LLMs, ChatGPT-4 and LLaVA-1.6, across diverse sex and age groups using a subset of a large dermatoscopic dataset containing around 10,000 images and 3 skin diseases (melanoma, melanocytic nevi, and benign keratosis-like lesions). Results: In comparison to 3 deep learning models (VGG16, ResNet50, and Model Derm) based on convolutional neural network (CNN), one vision transformer model (Swin-B), we found that ChatGPT-4 and LLaVA-1.6 demonstrated overall accuracies that were 3% and 23% higher (and F1-scores that were 4% and 34% higher), respectively, than the best performing CNN-based baseline while maintaining accuracies that were 38% and 26% lower (and F1-scores that were 38% and 19% lower), respectively, than Swin-B. Meanwhile, ChatGPT-4 is generally unbiased in identifying these skin diseases across sex and age groups, while LLaVA-1.6 is generally unbiased across age groups, in contrast to Swin-B, which is biased in identifying melanocytic nevi. Conclusions: This study suggests the usefulness and fairness of LLMs in dermatological applications, aiding physicians and practitioners with diagnostic recommendations and patient screening. To further verify and evaluate the reliability and fairness of LLMs in healthcare, experiments using larger and more diverse datasets need to be performed in the future.

PMID:40170800 | PMC:PMC11961048 | DOI:10.34133/hds.0256

Eur Rev Med Pharmacol Sci. 2025 Mar;29(3):123-134. doi: 10.26355/eurrev_202503_37125.

ABSTRACT

The interstitial lung disease (ILD) field is rapidly expanding as new insights highlight novel mechanisms and procedures that influence epidemiology, diagnosis, and treatment. The aim of this review is to report on recent advancements and future perspectives in clinical management and research in ILD, particularly in idiopathic pulmonary fibrosis (IPF), for the most common ILD. Whilst high-resolution computed tomography (HRCT) remains the gold standard for diagnosis, we focus on newer diagnostic techniques, including IPF genome analysis and epigenetics, biomarkers, bronchoscope robotic navigation, and transbronchial lung cryo biopsy for improving diagnostic accuracy. Further, we report IPF associated with pulmonary hypertension Group 3 and scores for defining disease progression. Positron emission tomography/computed tomography, treatment with prostacyclin and antifibrotic drugs, and lung transplantation as potential treatments for end-stage IPF are discussed. Lastly, we discuss contemporary perspectives on interstitial lung abnormalities (ILA), IPF associated with lung cancer, and the use of artificial intelligence (AI) for ILD diagnosis and monitoring.

GRAPHICAL ABSTRACT: https://www.europeanreview.org/wp/wp-content/uploads/123-134.jpg.

PMID:40171787 | DOI:10.26355/eurrev_202503_37125

iScience. 2025 Mar 4;28(4):112146. doi: 10.1016/j.isci.2025.112146. eCollection 2025 Apr 18.

ABSTRACT

Epidermal growth factor receptor (Egfr)-driven signaling regulates fundamental homeostatic processes. Dysregulated signaling via Egfr is implicated in numerous disease pathologies and distinct Egfr-associated disease etiologies are known to be tissue-specific. The molecular basis of this tissue-specificity remains poorly understood. Most studies of Egfr signaling to date have been performed in vitro or in tissue-specific mouse models of disease, which has limited insight into Egfr signaling patterns in healthy tissues. Here, we carried out integrated phosphoproteomic, proteomic, and transcriptomic analyses of signaling changes across various mouse tissues in response to short-term stimulation with the Egfr ligand Egf. We show how both baseline and Egf-stimulated signaling dynamics differ between tissues. Moreover, we propose how baseline phosphorylation and total protein levels may be associated with clinically relevant tissue-specific Egfr-associated phenotypes. Altogether, our analyses illustrate tissue-specific effects of Egf stimulation and highlight potential links between underlying tissue biology and Egfr signaling output.

PMID:40171493 | PMC:PMC11960661 | DOI:10.1016/j.isci.2025.112146

RSC Adv. 2025 Apr 1;15(13):9884-9890. doi: 10.1039/d4ra07801b. eCollection 2025 Mar 28.

ABSTRACT

This study focuses on the creation and application of an advanced impedimetric immunosensor designed for the sensitive detection of lymphoma cancer cells. The sensor was developed by modifying a glassy carbon electrode (GCE) with gold nanoparticles (AuNPs) and 3,3'-dithiodipropionic acid di(N-hydroxysuccinimide ester) boronic acid (AuNPs@DTSP-BA), followed by the attachment of rituximab monoclonal antibody. Incorporating the boronic acid (BA) component enabled effective oriented immobilization of the antibody, thereby improving the performance of the biosensor. Various spectroscopic techniques were used to characterize the immunosensor. The developed immunosensor demonstrated the ability to detect lymphoma cancer cells across a wide linear range of 100 to 50 000 cells per mL, with a detection sensitivity of 64 cells per mL.

PMID:40171289 | PMC:PMC11959537 | DOI:10.1039/d4ra07801b

Cancer Discov. 2025 Apr 2;15(4):685-701. doi: 10.1158/2159-8290.CD-24-0861.

ABSTRACT

Viewing the hallmarks as a sequence of adaptations captures the "why" behind the "how" of the molecular changes driving cancer. This eco-evolutionary view distils the complexity of cancer progression into logical steps, providing a framework for understanding all existing and emerging hallmarks of cancer and developing therapeutic interventions.

PMID:40170539 | DOI:10.1158/2159-8290.CD-24-0861

Sheng Wu Gong Cheng Xue Bao. 2025 Mar 25;41(3):1152-1178. doi: 10.13345/j.cjb.250065.

ABSTRACT

Biomanufacturing has emerged as a crucial driving force for efficient material conversion through engineered cells or cell-free systems. However, the intrinsic spatiotemporal heterogeneity, complexity, and dynamic characteristics of these processes pose significant challenges to systematic understanding, optimization, and regulation. This review summarizes essential methodologies for multi-omics data acquisition and analyses for bioprocesses and outlines modelling approaches based on multi-omics data. Furthermore, we explore practical applications of multi-omics and modelling in fine-tuning process parameters, improving fermentation control, elucidating stress response mechanisms, optimizing nutrient supplementation, and enabling real-time monitoring and adaptive adjustment. The substantial potential offered by integrating multi-omics with computational modelling for precision bioprocessing is also discussed. Finally, we identify current challenges in bioprocess optimization and propose the possible solutions, the implementation of which will significantly deepen understanding and enhance control of complex bioprocesses, ultimately driving the rapid advancement of biomanufacturing.

PMID:40170317 | DOI:10.13345/j.cjb.250065

Sheng Wu Gong Cheng Xue Bao. 2025 Mar 25;41(3):1112-1132. doi: 10.13345/j.cjb.240966.

ABSTRACT

The metabolic reactions in cells, whether spontaneous or enzyme-catalyzed, form a highly complex metabolic network closely related to cellular physiological metabolic activities. The reconstruction of cellular physiological metabolic network models aids in systematically elucidating the relationship between genotype and growth phenotype, providing important computational biology tools for precisely characterizing cellular physiological metabolic activities and green biomanufacturing. This paper systematically introduces the latest research progress in different types of cellular physiological metabolic network models, including genome-scale metabolic models (GEMs), kinetic models, and enzyme-constrained genome-scale metabolic models (ecGEMs). Additionally, our paper discusses the advancements in the automated construction of GEMs and strategies for condition-specific GEM modeling. Considering artificial intelligence offers new opportunities for the high-precision construction of cellular physiological metabolic network models, our paper summarizes the applications of artificial intelligence in the development of kinetic models and enzyme-constrained models. In summary, the high-quality reconstruction of the aforementioned cellular physiological metabolic network models will provide robust computational support for future research in quantitative synthetic biology and systems biology.

PMID:40170315 | DOI:10.13345/j.cjb.240966

Sheng Wu Gong Cheng Xue Bao. 2025 Mar 25;41(3):1052-1078. doi: 10.13345/j.cjb.250061.

ABSTRACT

Biomanufacturing harnesses engineered cells for the large-scale production of biochemicals, biopharmaceuticals, biofuels, and biomaterials, playing a vital role in mitigating global environmental crises, achieving carbon peaking and neutrality, and driving the green transformation of the economy and society. The effective design and construction of these engineered cells require precise and comprehensive computational models. Recent technological breakthroughs including high-throughput sequencing, mass spectrometry, spectroscopy, and microfluidic devices, coupled with advances in data science, artificial intelligence, and automation, have enabled the rapid acquisition of large-scale biological datasets, thereby facilitating a deeper understanding of cellular dynamics and the construction of mechanism-based models with enhanced accuracy. This review systematically summarises the mathematical frameworks employed in cellular modelling. It begins by evaluating prevalent mathematical paradigms, such as network topology analyses, stochastic processes, and kinetic equations, critically assessing their applicability across various contexts. The discussion then categorises modelling strategies for specific cellular processes, including cellular growth and division, morphogenesis, DNA replication, transcriptional regulation, metabolism, signal transduction, and quorum sensing. We also examine the recent progress in developing whole-cell models through the integration of diverse cellular processes. The review concludes by addressing key challenges such as data scarcity, unknown mechanisms, multi-dimensional data integration, and exponentially escalating computational complexity. Overall, this work consolidates the mathematical models for the precise simulation of cellular processes, thereby enhancing our understanding of the molecular mechanisms governing cellular functions and contributing to the future design and optimisation of engineered organisms.

PMID:40170312 | DOI:10.13345/j.cjb.250061

Sheng Wu Gong Cheng Xue Bao. 2025 Mar 25;41(3):901-916. doi: 10.13345/j.cjb.240653.

ABSTRACT

Biomanufacturing is an advanced manufacturing method that integrates biology, chemistry, and engineering. It utilizes renewable biomass and biological organisms as production media to scale up the production of target products through fermentation. Compared with petrochemical routes, biomanufacturing offers significant advantages in reducing CO2 emissions, lowering energy consumption, and cutting costs. With the development of systems biology and synthetic biology and the accumulation of bioinformatics data, the integration of information technologies such as artificial intelligence, large models, and high-performance computing with biotechnology is propelling biomanufacturing into a data-driven era. This paper reviews the latest research progress on databases, knowledge bases, and large language models for biomanufacturing. It explores the development directions, challenges, and emerging technical methods in this field, aiming to provide guidance and inspiration for scientific research in related areas.

PMID:40170304 | DOI:10.13345/j.cjb.240653

Sheng Wu Gong Cheng Xue Bao. 2025 Mar 25;41(3):I-VIII. doi: 10.13345/j.cjb.250197.

ABSTRACT

Biomanufacturing is one of important strategies for sustainable development, China places significant emphasis on the development of biomanufacturing, and the national and local governments have successively introduced special policies for biomanufacturing, and vigorously developing biomanufacturing has become an unstoppable trend. At present, with the rapid development of systems biology and synthetic biology, biological big data and information technology are deeply integrating with biotechnology. Novel theories, methods and technologies for the design, creation and application of biological systems are constantly emerging, which promoted the development of biomanufacturing into the era of artificial intelligence (AI). In order to grasp the innovation and development of AI-driven biomanufacturing, we publish this special issue to review the opportunities, challenges, and development status of AI-driven biomanufacturing from aspects such as AI-driven enabling technologies, intelligent design and construction of biological parts, circuits and artificial cells, as well as intelligent bioprocess control and optimization, and look forward to the future developments. This will provide valuable references for effectively promoting technological innovation and industrial development in the field of biomanufacturing.

PMID:40170303 | DOI:10.13345/j.cjb.250197

Clin Transl Med. 2025 Apr;15(4):e70290. doi: 10.1002/ctm2.70290.

ABSTRACT

Single-cell omics has emerged as a powerful tool for elucidating cellular heterogeneity in health and disease. Parallel advances in artificial intelligence (AI), particularly in pattern recognition, feature extraction and predictive modelling, now offer unprecedented opportunities to translate these insights into clinical applications. Here, we propose single-cell -omics-based Disease Predictor through AI (scDisPreAI), a unified framework that leverages AI to integrate single-cell -omics data, enabling robust disease and disease-stage prediction, alongside biomarker discovery. The foundation of scDisPreAI lies in assembling a large, standardised database spanning diverse diseases and multiple disease stages. Rigorous data preprocessing, including normalisation and batch effect correction, ensures that biological rather than technical variation drives downstream models. Machine learning pipelines or deep learning architectures can then be trained in a multi-task fashion, classifying both disease identity and disease stage. Crucially, interpretability techniques such as SHapley Additive exPlanations (SHAP) values or attention weights pinpoint the genes most influential for these predictions, highlighting biomarkers that may be shared across diseases or disease stages. By consolidating predictive modelling with interpretable biomarker identification, scDisPreAI may be deployed as a clinical decision assistant, flagging potential therapeutic targets for drug repurposing and guiding tailored treatments. In this editorial, we propose the technical and methodological roadmap for scDisPreAI and emphasises future directions, including the incorporation of multi-omics, standardised protocols and prospective clinical validation, to fully harness the transformative potential of single-cell AI in precision medicine.

PMID:40170267 | DOI:10.1002/ctm2.70290

Afr J Prim Health Care Fam Med. 2025 Mar 31;17(1):e1-e4. doi: 10.4102/phcfm.v17i1.4929.

ABSTRACT

Prescribing cascades contribute to the increasing prevalence of polypharmacy and its associated risks, where a drug-induced adverse event is misinterpreted as a new condition and treated with additional medications. Notable cascades include the use of anticholinergics leading to cognitive impairment, dyspepsia or constipation, which then prompt prescriptions for dementia medications, proton pump inhibitors or laxatives, respectively. Similarly, calcium channel blockers and gabapentinoids often induce oedema, resulting in unnecessary diuretic use. Strategies for prevention include careful review of adverse effects, deprescribing where appropriate and clinician education to improve symptom interpretation and prescribing practices. Recognising these cascades can mitigate unnecessary interventions and improve patient outcomes.

PMID:40171689 | DOI:10.4102/phcfm.v17i1.4929