Anal Chem. 2025 Jan 30. doi: 10.1021/acs.analchem.4c06057. Online ahead of print.

ABSTRACT

Off-axis integrated cavity output spectroscopy (OA-ICOS) allows the laser to be reflected multiple times inside the cavity, increasing the effective absorption path length and thus improving sensitivity. However, OA-ICOS systems are affected by various types of noise, and traditional filtering methods offer low processing efficiency and perform limited feature extraction. Deep learning models enable us to extract important features from large-scale, complex spectral data and analyze them efficiently and accurately. We propose a carbon dioxide (CO2) sensor operating in the near-infrared spectral region (1.602 μm) based on OA-ICOS and deep learning models. A radiofrequency (RF) noise source is employed to reduce the cavity-mode noise in OA-ICOS and thus improve the signal-to-noise ratio (SNR). A time-series-based neural network, known as the informer, is employed for filtering CO2 spectral time series. After filtering, spectral features are directly extracted from the filtered spectral data and CO2 concentrations are predicted using a multilayer perceptron (MLP) model. Our results showed that the SNR attained using informer filtering approximately double those obtained using traditional filtering methods (Savitzky-Golay filtering, Kalman filtering, and wavelet threshold). The linear correlation coefficient (R2) between measured concentrations and standard concentrations was increased from 79.74% (obtained by using the absorption-peak-fitting method) to 98.52% (obtained by using the proposed MLP model). Moreover, the detection limit of the CO2 sensor using the MLP model reached 1.38 ppm at 224.4 s, a 3.79-fold improvement compared to that obtained by using the absorption-peak-fitting method. Our results demonstrate the feasibility of integrating deep learning methods in the field of spectroscopy-based sensing and provide a promising approach for spectral data processing.

PMID:39882837 | DOI:10.1021/acs.analchem.4c06057

J Chem Theory Comput. 2025 Jan 30. doi: 10.1021/acs.jctc.4c01602. Online ahead of print.

ABSTRACT

Despite its importance in understanding biology and computer-aided drug discovery, the accurate prediction of protein ionization states remains a formidable challenge. Physics-based approaches struggle to capture the small, competing contributions in the complex protein environment, while machine learning (ML) is hampered by the scarcity of experimental data. Here, we report the development of pKa ML (KaML) models based on decision trees and graph attention networks (GAT), exploiting physicochemical understanding and a new experiment pKa database (PKAD-3) enriched with highly shifted pKa's. KaML-CBtree significantly outperforms the current state of the art in predicting pKa values and ionization states across all six titratable amino acids, notably achieving accurate predictions for deprotonated cysteines and lysines─a blind spot in previous models. The superior performance of KaMLs is achieved in part through several innovations, including the separate treatment of acid and base, data augmentation using AlphaFold structures, and model pretraining on a theoretical pKa database. We also introduce the classification of protonation states as a metric for evaluating pKa prediction models. A meta-feature analysis suggests a possible reason for the lightweight tree model to outperform the more complex deep learning GAT. We release an end-to-end pKa predictor based on KaML-CBtree and the new PKAD-3 database, which facilitates a variety of applications and provides the foundation for further advances in protein electrostatic research.

PMID:39882632 | DOI:10.1021/acs.jctc.4c01602

Front Med (Lausanne). 2025 Jan 15;11:1505692. doi: 10.3389/fmed.2024.1505692. eCollection 2024.

ABSTRACT

INTRODUCTION: In recent years, the development of artificial intelligence (AI) technologies, including machine learning, deep learning, and large language models, has significantly supported clinical work. Concurrently, the integration of artificial intelligence with the medical field has garnered increasing attention from medical experts. This study undertakes a dynamic and longitudinal bibliometric analysis of AI publications within the healthcare sector over the past three decades to investigate the current status and trends of the fusion between medicine and artificial intelligence.

METHODS: Following a search on the Web of Science, researchers retrieved all reviews and original articles concerning artificial intelligence in healthcare published between January 1993 and December 2023. The analysis employed Bibliometrix, Biblioshiny, and Microsoft Excel, incorporating the bibliometrix R package for data mining and analysis, and visualized the observed trends in bibliometrics.

RESULTS: A total of 22,950 documents were collected in this study. From 1993 to 2023, there was a discernible upward trajectory in scientific output within bibliometrics. The United States and China emerged as primary contributors to medical artificial intelligence research, with Harvard University leading in publication volume among institutions. Notably, the rapid expansion of emerging topics such as COVID-19 and new drug discovery in recent years is noteworthy. Furthermore, the top five most cited papers in 2023 were all pertinent to the theme of ChatGPT.

CONCLUSION: This study reveals a sustained explosive growth trend in AI technologies within the healthcare sector in recent years, with increasingly profound applications in medicine. Additionally, medical artificial intelligence research is dynamically evolving with the advent of new technologies. Moving forward, concerted efforts to bolster international collaboration and enhance comprehension and utilization of AI technologies are imperative for fostering novel innovations in healthcare.

PMID:39882522 | PMC:PMC11775008 | DOI:10.3389/fmed.2024.1505692

Heliyon. 2025 Jan 8;11(2):e41802. doi: 10.1016/j.heliyon.2025.e41802. eCollection 2025 Jan 30.

ABSTRACT

Deep Learning (DL) has significantly contributed to the field of medical imaging in recent years, leading to advancements in disease diagnosis and treatment. In the case of Diabetic Retinopathy (DR), DL models have shown high efficacy in tasks such as classification, segmentation, detection, and prediction. However, DL model's opacity and complexity lead to errors in decision-making, particularly in complex cases, making it necessary to estimate the model's uncertainty in predictions. Therefore, there is a need to estimate uncertainty in the model's predictions, which cannot be estimated by classical DL models alone. To address this issue, Bayesian DL methods have been proposed, and their use is increasing in the field. In this paper, we developed a straightforward architecture for the classification of DR using a Convolutional Neural Network (CNN) model. We then applied the Bayesian CNN twice, once using Variational Inference (VI) and once using Monte Carlo dropout (MC-dropout) methods, to the same CNN architecture. This allowed us to gain the posterior predictive distributions for each of them. The performance of the proposed models was evaluated on two benchmark datasets, namely APTOS 2019 and Messidor-2. Experimental findings demonstrated that the proposed models surpassed other state-of-the-art models, achieving a test accuracy of 94.70 % and 77.00 % for CNN, 94.00 % and 86.00 % for BCNN-VI, and 93.30 % and 81.00 % for BCNN-MC-dropout on the APTOS dataset and Messidor-2 dataset, respectively. Finally, we computed the entropy and standard deviation on the obtained predictive distribution to quantify the model uncertainty. This research highlights the potential benefits of using Bayesian DL methods in medical image analysis to improve the accuracy and reliability of diagnosing disease and treatment.

PMID:39882466 | PMC:PMC11774831 | DOI:10.1016/j.heliyon.2025.e41802

Front Neurol. 2025 Jan 15;15:1510522. doi: 10.3389/fneur.2024.1510522. eCollection 2024.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) is a common neurodegenerative disorder worldwide and the using of magnetic resonance imaging (MRI) in the management of AD is increasing. The present study aims to summarize MRI in AD researches via bibliometric analysis and predict future research hotspots.

METHODS: We searched for records related to MRI studies in AD patients from 2004 to 2023 in the Web of Science Core Collection (WoSCC) database. CiteSpace was applied to analyze institutions, references and keywords. VOSviewer was used for the analysis of countries, authors and journals.

RESULTS: A total of 13,659 articles were obtained in this study. The number of published articles showed overall exponential growth from 2004 to 2023. The top country and institution were the United States and the University of California System, accounting for 40.30% and 9.88% of the total studies, respectively. Jack CR from the United States was the most productive author. The most productive journal was the Journal of Alzheimers Disease. Keyword burst analysis revealed that "machine learning" and "deep learning" were the keywords that frequently appeared in the past 6 years. Timeline views of the references revealed that "#0 tau pathology" and "#1 deep learning" are currently the latest research focuses.

CONCLUSION: This study provides an in-depth overview of publications on MRI studies in AD. The United States is the leading country in this field with a concentration of highly productive researchers and high-level institutions. The current research hotspot is deep learning, which is being applied to develop noninvasive diagnosis and safer treatment of AD.

PMID:39882364 | PMC:PMC11774745 | DOI:10.3389/fneur.2024.1510522

Digit Discov. 2025 Jan 24. doi: 10.1039/d4dd00219a. Online ahead of print.

ABSTRACT

Plastic pollution, particularly microplastics (MPs), poses a significant global threat to ecosystems and human health, necessitating innovative remediation strategies. Biocompatible and biodegradable plastic-binding peptides (PBPs) offer a potential solution through targeted adsorption and subsequent MP detection or removal from the environment. A challenge in discovering plastic-binding peptides is the vast combinatorial space of possible peptides (i.e., over 1015 for 12-mer peptides), which far exceeds the sample sizes typically reachable by experiments or biophysics-based computational methods. One step towards addressing this issue is to train deep learning models on experimental or biophysical datasets, permitting faster and cheaper evaluations of peptides. However, deep learning predictions are not always accurate, which could waste time and money due to synthesizing and evaluating false positives. Here, we resolve this issue by combining biophysical modeling data from Peptide Binder Design (PepBD) algorithm, the predictive power and uncertainty quantification of evidential deep learning, and metaheuristic search methods to identify high-affinity PBPs for several common plastics. Molecular dynamics simulations show that the discovered PBPs have greater median adsorption free energies for polyethylene (5%), polypropylene (18%), and polystyrene (34%) relative to PBPs previously designed by PepBD. The impact of including uncertainty quantification in peptide design is demonstrated by the increasing improvement in the median adsorption free energy with decreasing uncertainty. This robust framework accelerates peptide discovery, paving the way for effective, bio-inspired solutions to MP remediation.

PMID:39882101 | PMC:PMC11771220 | DOI:10.1039/d4dd00219a

Front Endocrinol (Lausanne). 2025 Jan 14;16:1513929. doi: 10.3389/fendo.2025.1513929. eCollection 2025.

NO ABSTRACT

PMID:39882100 | PMC:PMC11772191 | DOI:10.3389/fendo.2025.1513929

Sovrem Tekhnologii Med. 2024;16(4):5-13. doi: 10.17691/stm2024.16.4.01. Epub 2024 Aug 30.

ABSTRACT

The aim of the study is to develop a method for diagnosing fungal keratitis based on the analysis of photographs of the anterior segment of the eye using deep learning algorithms with subsequent evaluation of sensitivity and specificity of the method on a test data set in comparison with the results of practicing ophthalmologists.

MATERIALS AND METHODS: The study has included the stages of data acquisition, image pre-training and markup, selection of training approach and neural network architecture, training with input data augmentation, validation with hyperparameter correction, evaluation of algorithm performance on a test sample, and determination of sensitivity and specificity of fungal keratitis detection by practicing doctors. A total of 274 anterior segment images were used, including 130 photographs of the eyes affected by fungal keratitis and 144 photographs illustrating normal eyes, keratitis of other etiologies, and various anterior segment pathologies. Photographs taken after the treatment onset, illustrations of keratitis of mixed etiology and corneal perforation were excluded from the study. Images of the training sample were marked up using the VGG Image Annotator web application and then used to train the YOLOv8 convolutional neural network. Images from the test data set were also offered to practicing ophthalmologists to determine the diagnostic accuracy of fungal keratitis.

RESULTS: The sensitivity of the model was 56.0%, the specificity level reached 96.1%, and the proportion of correct answers of the algorithm was 76.5%. The accuracy of image recognition by practicing ophthalmologists was 50.0%, specificity - 41.7%, sensitivity - 57.7%.

CONCLUSION: The study showed the high potential of deep learning algorithms in the diagnosis of fungal keratitis and its advantages in accuracy compared to expert judgment in the absence of metadata. The use of computer vision technologies may find application as a complementary diagnostic method in decision making in complex cases and in telemedicine care settings. Further research is required to compare the developed model with alternative approaches, to expand and standardize databases.

PMID:39881837 | PMC:PMC11773139 | DOI:10.17691/stm2024.16.4.01

Health Inf Sci Syst. 2025 Jan 27;13(1):19. doi: 10.1007/s13755-025-00339-5. eCollection 2025 Dec.

ABSTRACT

Coronary artery disease (CAD) remains the leading cause of death globally, highlighting the critical need for accurate diagnostic tools in medical imaging. Traditional segmentation methods for coronary angiograms often struggle with vessel discontinuity and inaccuracies, impeding effective diagnosis and treatment planning. To address these challenges, we developed the Local Adaptive Segmentation Framework (LASF), enhancing the YOLOv8 architecture with dilation and erosion algorithms to improve the continuity and precision of vascular image segmentation. We further enriched the ARCADE dataset by meticulously annotating both proximal and distal vascular segments, thus broadening the dataset's applicability for training robust segmentation models. Our comparative analyses reveal that LASF outperforms well-known models such as UNet and DeepLabV3Plus, demonstrating superior metrics in precision, recall, and F1-score across various testing scenarios. These enhancements ensure more reliable and accurate segmentation, critical for clinical applications. LASF represents a significant advancement in the segmentation of vascular images within coronary angiograms. By effectively addressing the common issues of vessel discontinuity and segmentation accuracy, LASF stands to improve the clinical management of CAD, offering a promising tool for enhancing diagnostic accuracy and patient outcomes in medical settings.

PMID:39881813 | PMC:PMC11772642 | DOI:10.1007/s13755-025-00339-5

Insights Imaging. 2025 Jan 29;16(1):25. doi: 10.1186/s13244-024-01896-1.

ABSTRACT

PURPOSES: The presence of clinically significant prostate cancer (csPCa) is equivocal for patients with prostate imaging reporting and data system (PI-RADS) category 3. We aim to develop deep learning models for re-stratify risks in PI-RADS category 3 patients.

METHODS: This retrospective study included a bi-parametric MRI of 1567 consecutive male patients from six centers (Centers 1-6) between Jan 2015 and Dec 2020. Deep learning models with double channel attention modules based on MRI (AttenNet) for predicting PCa and csPCa were constructed separately. Each model was first pretrained using 1144 PI-RADS 1-2 and 4-5 images and then retrained using 238 PI-RADS 3 images from three training centers (centers 1-3), and tested using 185 PI-RADS 3 images from the other three testing centers (centers 4-6).

RESULTS: Our AttenNet models achieved excellent prediction performances in testing cohort of center 4-6 with the area under the receiver operating characteristic curves (AUC) of 0.795 (95% CI: [0.700, 0.891]), 0.963 (95% CI: [0.915, 1]) and 0.922 (95% CI: [0.810, 1]) in predicting PCa, and the corresponding AUCs were 0.827 (95% CI: [0.703, 0.952]) and 0.926 (95% CI: [0.846, 1]) in predicting csPCa in testing cohort of center 4 and center 5. In particular, 71.1% to 92.2% of non-csPCa patients were identified by our model in three testing cohorts, who can spare from invasive biopsy or RP procedure.

CONCLUSIONS: Our model offers a noninvasive screening clinical tool to re-stratify risks in PI-RADS 3 patients, thereby reducing unnecessary invasive biopsies and improving the effectiveness of biopsies.

CRITICAL RELEVANCE STATEMENT: The deep learning model with MRI can help to screen out csPCa in PI-RADS category 3.

KEY POINTS: AttenNet models included channel attention and soft attention modules. 71.1-92.2% of non-csPCa patients were identified by the AttenNet model. The AttenNet models can be a screen clinical tool to re-stratify risks in PI-RADS 3 patients.

PMID:39881076 | DOI:10.1186/s13244-024-01896-1

Nat Cancer. 2025 Jan 29. doi: 10.1038/s43018-024-00904-z. Online ahead of print.

ABSTRACT

The diagnostic landscape of brain tumors integrates comprehensive molecular markers alongside traditional histopathological evaluation. DNA methylation and next-generation sequencing (NGS) have become a cornerstone in central nervous system (CNS) tumor classification. A limiting requirement for NGS and methylation profiling is sufficient DNA quality and quantity, which restrict its feasibility. Here we demonstrate NePSTA (neuropathology spatial transcriptomic analysis) for comprehensive morphological and molecular neuropathological diagnostics from single 5-µm tissue sections. NePSTA uses spatial transcriptomics with graph neural networks for automated histological and molecular evaluations. Trained and evaluated across 130 participants with CNS malignancies and healthy donors across four medical centers, NePSTA predicts tissue histology and methylation-based subclasses with high accuracy. We demonstrate the ability to reconstruct immunohistochemistry and genotype profiling on tissue with minimal requirements, inadequate for conventional molecular diagnostics, demonstrating the potential to enhance tumor subtype identification with implications for fast and precise diagnostic workup.

PMID:39880907 | DOI:10.1038/s43018-024-00904-z

Sci Rep. 2025 Jan 29;15(1):3640. doi: 10.1038/s41598-025-86532-8.

ABSTRACT

Air pollution in cities, especially NO2, is linked to numerous health problems, ranging from mortality to mental health challenges and attention deficits in children. While cities globally have initiated policies to curtail emissions, real-time monitoring remains challenging due to limited environmental sensors and their inconsistent distribution. This gap hinders the creation of adaptive urban policies that respond to the sequence of events and daily activities affecting pollution in cities. Here, we demonstrate how city CCTV cameras can act as a pseudo-NO2 sensors. Using a predictive graph deep model, we utilised traffic flow from London's cameras in addition to environmental and spatial factors, generating NO2 predictions from over 133 million frames. Our analysis of London's mobility patterns unveiled critical spatiotemporal connections, showing how specific traffic patterns affect NO2 levels, sometimes with temporal lags of up to 6 h. For instance, if trucks only drive at night, their effects on NO2 levels are most likely to be seen in the morning when people commute. These findings cast doubt on the efficacy of some of the urban policies currently being implemented to reduce pollution. By leveraging existing camera infrastructure and our introduced methods, city planners and policymakers could cost-effectively monitor and mitigate the impact of NO2 and other pollutants.

PMID:39880905 | DOI:10.1038/s41598-025-86532-8

Commun Biol. 2025 Jan 29;8(1):141. doi: 10.1038/s42003-025-07568-0.

ABSTRACT

In mammalian oocytes, large-scale chromatin organization regulates transcription, nuclear architecture, and maintenance of chromosome stability in preparation for meiosis onset. Pre-ovulatory oocytes with distinct chromatin configurations exhibit profound differences in metabolic and transcriptional profiles that ultimately determine meiotic competence and developmental potential. Here, we developed a deep learning pipeline for the non-invasive prediction of chromatin structure and developmental potential in live mouse oocytes. Our Fluorescence prediction and Classification on Bright-field (Fluo-Cast-Bright) pipeline achieved 91.3% accuracy in the classification of chromatin state in fixed oocytes and 85.7% accuracy in live oocytes. Importantly, transcriptome analysis following non-invasive selection revealed that meiotically competent oocytes exhibit a higher expression of transcripts associated with RNA and protein nuclear export, maternal mRNA deadenylation, histone modifications, chromatin remodeling and signaling pathways regulating microtubule dynamics during the metaphase-I to metaphase-II transition. Fluo-Cast-Bright provides fast and non-invasive selection of meiotically competent oocytes for downstream research and clinical applications.

PMID:39880880 | DOI:10.1038/s42003-025-07568-0

J Cheminform. 2025 Jan 29;17(1):12. doi: 10.1186/s13321-024-00945-7.

ABSTRACT

G protein-coupled receptors (GPCRs) play vital roles in various physiological processes, making them attractive drug discovery targets. Meanwhile, deep learning techniques have revolutionized drug discovery by facilitating efficient tools for expediting the identification and optimization of ligands. However, existing models for the GPCRs often focus on single-target or a small subset of GPCRs or employ binary classification, constraining their applicability for high throughput virtual screening. To address these issues, we introduce AiGPro, a novel multitask model designed to predict small molecule agonists (EC50) and antagonists (IC50) across the 231 human GPCRs, making it a first-in-class solution for large-scale GPCR profiling. Leveraging multi-scale context aggregation and bidirectional multi-head cross-attention mechanisms, our approach demonstrates that ensemble models may not be necessary for predicting complex GPCR states and small molecule interactions. Through extensive validation using stratified tenfold cross-validation, AiGPro achieves robust performance with Pearson's correlation coefficient of 0.91, indicating broad generalizability. This breakthrough sets a new standard in the GPCR studies, outperforming previous studies. Moreover, our first-in-class multi-tasking model can predict agonist and antagonist activities across a wide range of GPCRs, offering a comprehensive perspective on ligand bioactivity within this diverse superfamily. To facilitate easy accessibility, we have deployed a web-based platform for model access at https://aicadd.ssu.ac.kr/AiGPro . Scientific Contribution We introduce a deep learning-based multi-task model to generalize the agonist and antagonist bioactivity prediction for GPCRs accurately. The model is implemented on a user-friendly web server to facilitate rapid screening of small-molecule libraries, expediting GPCR-targeted drug discovery. Covering a diverse set of 231 GPCR targets, the platform delivers a robust, scalable solution for advancing GPCR-focused therapeutic development. The proposed framework incorporates an innovative dual-label prediction strategy, enabling the simultaneous classification of molecules as agonists, antagonists, or both. Each prediction is further accompanied by a confidence score, offering a quantitative measure of activity likelihood. This advancement moves beyond conventional models focusing solely on binding affinity, providing a more comprehensive understanding of ligand-receptor interactions. At the core of our model lies the Bi-Directional Multi-Head Cross-Attention (BMCA) module, a novel architecture that captures forward and backward contextual embeddings of protein and ligand features. By leveraging BMCA, the model effectively integrates structural and sequence-level information, ensuring a precise representation of molecular interactions. Results show that this approach is highly accurate in binding affinity predictions and consistent across diverse GPCR families. By unifying agonist and antagonist bioactivity prediction into a single model architecture, we bridge a critical gap in GPCR modeling. This enhances prediction accuracy and accelerates virtual screening workflows, offering a valuable and innovative solution for advancing GPCR-targeted drug discovery.

PMID:39881398 | DOI:10.1186/s13321-024-00945-7

BMC Oral Health. 2025 Jan 29;25(1):153. doi: 10.1186/s12903-025-05431-6.

ABSTRACT

BACKGROUND: The severity of furcation involvement (FI) directly affected tooth prognosis and influenced treatment approaches. However, assessing, diagnosing, and treating molars with FI was complicated by anatomical and morphological variations. Cone-beam computed tomography (CBCT) enhanced diagnostic accuracy for detecting FI and measuring furcation defects. Despite its advantages, the high cost and radiation dose associated with CBCT equipment limited its widespread use. The aim of this study was to evaluate the performance of the Vision Transformer (ViT) in comparison with several commonly used traditional deep learning (DL) models for classifying molars with or without FI on panoramic radiographs.

METHODS: A total of 1,568 tooth images obtained from 506 panoramic radiographs were used to construct the database and evaluate the models. This study developed and assessed a ViT model for classifying FI from panoramic radiographs, and compared its performance with traditional models, including Multi-Layer Perceptron (MLP), Visual Geometry Group (VGG)Net, and GoogLeNet.

RESULTS: Among the evaluated models, the ViT model outperformed all others, achieving the highest precision (0.98), recall (0.92), and F1 score (0.95), along with the lowest cross-entropy loss (0.27) and the highest accuracy (92%). ViT also recorded the highest area under the curve (AUC) (98%), outperforming the other models with statistically significant differences (p < 0.05), confirming its enhanced classification capability. The gradient-weighted class activation mapping (Grad-CAM) analysis on the ViT model revealed the key areas of the images that the model focused on during predictions.

CONCLUSION: DL algorithms can automatically classify FI using readily accessible panoramic images. These findings demonstrate that ViT outperforms the tested traditional models, highlighting the potential of transformer-based approaches to significantly advance image classification. This approach is also expected to reduce both the radiation dose and the financial burden on patients while simultaneously improving diagnostic precision.

PMID:39881302 | DOI:10.1186/s12903-025-05431-6

BMC Med Educ. 2025 Jan 29;25(1):143. doi: 10.1186/s12909-025-06716-8.

ABSTRACT

STUDY OBJECTIVE: Student-centered learning and unconventional teaching modalities are gaining popularity in medical education. One notable approach involves engaging students in producing creative projects to complement the learning of preclinical topics. A systematic review was conducted to characterize the impact of creative project-based learning on metacognition and knowledge gains in medical students.

METHODS: A systematic search was conducted using MEDLINE and Embase via Ovid, PubMed, CINAHL, Web of Science, Cochrane CENTRAL, and Scopus from January 1st, 1995, to July 6th, 2023. Studies using quantitative, qualitative, or mixed-methods approaches that explored the impact of creative project-based lessons on medical students' educational outcomes were included. Two investigators independently screened the titles and abstracts and extracted data from included articles. A narrative synthesis was conducted to summarize study designs and outcome measures. Content analysis was conducted to generate codes and themes. Study quality was assessed using the Mixed Methods Appraisal Tool in view of the range of study types employed.

RESULTS: The review included 17 studies published between 2010 to 2022. These studies implemented various creative project interventions such as handicraft models, drawings, and concept maps covering multiple topics, including anatomy, histopathology, and fundamental sciences. The identified themes of Enhanced Learning, Collaborative Learning, and Deep Learning led to further themes of Student Engagement, Student Disengagement, and Faculty Engagement. Collaborative learning involves students working in teams and benefitting from effective mentorship. Creative projects facilitated deep learning objectives via interdisciplinary learning and promoted new ways of perceiving concepts. Learning was enhanced through increased interactivity, high conceptual fidelity and improved knowledge retention.

CONCLUSION: Creative projects undertaken by medical students exhibit attributes that facilitate the acquisition of collaborative and deep learning objectives through self-directed learning, cognitive load modulation, and metacognitive behaviours. Faculty mentorship and group learning amongst peers facilitate these processes, although challenges such as high task demands, cognitive and emotional intensiveness, and mismatch with students; professional identities remain. Overall, students and faculty received these interventions well, thus, warranting further exploration for uses in medical curricula.

TRIAL REGISTRATION: Not applicable as this study is a systematic review.

PMID:39881268 | DOI:10.1186/s12909-025-06716-8

BMC Bioinformatics. 2025 Jan 29;26(1):33. doi: 10.1186/s12859-025-06047-x.

ABSTRACT

BACKGROUND: Single-cell RNA sequencing (scRNA-seq) has transformed biological research by offering new insights into cellular heterogeneity, developmental processes, and disease mechanisms. As scRNA-seq technology advances, its role in modern biology has become increasingly vital. This study explores the application of deep learning to single-cell data clustering, with a particular focus on managing sparse, high-dimensional data.

RESULTS: We propose the SMD deep learning model, which integrates nonlinear dimensionality reduction techniques with a porous dilated attention gate component. Built upon a convolutional autoencoder and informed by the negative binomial distribution, the SMD model efficiently captures essential cell clustering features and dynamically adjusts feature weights. Comprehensive evaluation on both public datasets and proprietary osteosarcoma data highlights the SMD model's efficacy in achieving precise classifications for single-cell data clustering, showcasing its potential for advanced transcriptomic analysis.

CONCLUSION: This study underscores the potential of deep learning-specifically the SMD model-in advancing single-cell RNA sequencing data analysis. By integrating innovative computational techniques, the SMD model provides a powerful framework for unraveling cellular complexities, enhancing our understanding of biological processes, and elucidating disease mechanisms. The code is available from https://github.com/xiaoxuc/scSMD .

PMID:39881248 | DOI:10.1186/s12859-025-06047-x

BMC Cancer. 2025 Jan 29;25(1):173. doi: 10.1186/s12885-025-13582-6.

ABSTRACT

BACKGROUND: To develop and test the performance of a fully automated system for classifying renal tumor subtypes via deep machine learning for automated segmentation and classification.

MATERIALS AND METHODS: The model was developed using computed tomography (CT) images of pathologically proven renal tumors collected from a prospective cohort at a medical center between March 2016 and December 2020. A total of 561 renal tumors were included: 233 clear cell renal cell carcinomas (RCCs), 82 papillary RCCs, 74 chromophobe RCCs, and 172 angiomyolipomas. Renal tumor masks manually drawn on contrast-enhanced CT images were used to develop a 3D U-Net-based deep learning model for fully automated tumor segmentation. After segmentation, the entire classification pipeline, including feature extraction and subtype classification, was conducted without any manual intervention. Both conventional radiological features (Hounsfield units, HUs) and radiomic features extracted from areas predicted by the deep learning models were used to develop an algorithm for classifying renal tumor subtypes via a random forest classifier. The performance of the segmentation model was evaluated using the Dice similarity coefficient, while the classification model was assessed based on accuracy, sensitivity, and specificity.

RESULTS: For tumors larger than 4 cm, the Dice similarity coefficient (DSC) for automated segmentation was 0.83, while for tumors smaller than 4 cm, the DSC was 0.65. The classification accuracy (ACC) for distinguishing RCC subtypes was 0.77 for tumors larger than 4 cm and 0.68 for tumors smaller than 4 cm. Additionally, the accuracy for benign versus malignant classification was 0.85.

CONCLUSIONS: Our automatic segmentation and classifier model showed promising results for renal tumor segmentation and classification.

PMID:39881216 | DOI:10.1186/s12885-025-13582-6

Sci Rep. 2025 Jan 30;15(1):3734. doi: 10.1038/s41598-025-88210-1.

ABSTRACT

Infertility has emerged as a significant global health concern. Assisted reproductive technology (ART) assists numerous infertile couples in conceiving, yet some experience repeated, unsuccessful cycles. This study aims to identify the pivotal clinical factors influencing the success of fresh embryo transfer of in vitro fertilization (IVF). We introduce a novel Non-negative Matrix Factorization (NMF)-based Ensemble algorithm (NMFE). By combining feature matrices from NMF, accelerated multiplicative updates for non-negative matrix factorization (AMU-NMF), and the generalized deep learning clustering (GDLC) algorithm. NMFE exhibits superior accuracy and reliability in analyzing the in vitro fertilization and embryo transfer (IVF-ET) dataset. The dataset comprises 2238 cycles and 85 independent clinical features, categorized into 13 categories based on feature correlation. Subsequently, the NMFE model was trained and reached convergence. Then the features of 13 categories were sequentially masked to analyze their individual effects on IVF-ET live births. The NMFE analysis highlights the significant influence of therapeutic interventions, Embryo transfer outcomes, and ovarian response assessment on live births of IVF-ET. Therapeutic interventions, including ovarian stimulation protocols, ovulation stimulation drugs, and pre-and intra-stimulation cycle acupuncture play prominent roles. However, their impacts on the IVF-ET model are reduced, suggesting a potential synergistic effect when combined. Conversely, factors like basic information, diagnosis, and obstetric history have a lesser influence. The NMFE algorithm demonstrates promising potential in assessing the influence of clinical features on live births in IVF fresh embryo transfer.

PMID:39881210 | DOI:10.1038/s41598-025-88210-1

Sci Rep. 2025 Jan 29;15(1):3648. doi: 10.1038/s41598-024-77602-4.

ABSTRACT

The opioid crisis has disproportionately affected U.S. veterans, leading the Veterans Health Administration to implement opioid prescribing guidelines. Veterans who receive care from both VA and non-VA providers-known as dual-system users-have an increased risk of Opioid Use Disorder (OUD). The interaction between dual-system use and demographic and clinical factors, however, has not been previously explored. We conducted a retrospective study of 856,299 patient instances from the Washington DC and Baltimore VA Medical Centers (2012-2019), using a deep neural network (DNN) and explainable Artificial Intelligence to examine the impact of dual-system use on OUD and how demographic and clinical factors interact with it. Of the cohort, 146,688(17%) had OUD, determined through Natural Language Processing of clinical notes and ICD-9/10 diagnoses. The DNN model, with a 78% area under the curve, confirmed that dual-system use is a risk factor for OUD, along with prior opioid use or other substance use. Interestingly, a history of other drug use interacted negatively with dual-system use regarding OUD risk. In contrast, older age was associated with a lower risk of OUD but interacted positively with dual-system use. These findings suggest that within the dual-system users, patients with certain risk profiles warrant special attention.

PMID:39881142 | DOI:10.1038/s41598-024-77602-4