IEEE J Biomed Health Inform. 2025 Apr 25;PP. doi: 10.1109/JBHI.2025.3564360. Online ahead of print.

ABSTRACT

Drug repositioning (DR) has emerged as an effective method of identifying new indications for existing drugs. Many DR methods have demonstrated superior performance. However, most of them utilize a limited number of biological entities, ignoring the critical role of other entities in addressing data sparsity as well as improving model generalization capabilities. In addition, fully capturing high-order information of biological data still needs to be fully explored. To address above issues, a model based on transformer and enhanced multi-view contrastive learning (TEMCL) is proposed for predicting drug-disease associations (DDAs). Firstly, transformer is employed to obtain high-order features of nodes from similarity information. Secondly, based on similarity matrices and association matrices of nodes, two different types of views are constructed, i.e., homogeneous hypergraphs and heterogeneous association graphs. Among them, to alleviate sparsity problem existing in heterogeneous graphs, protein nodes as well as meta-path enhancement strategy are introduced. Thirdly, hypergraph convolutional network and heterogeneous graph transformer are used to extract node features on above two types of views, respectively. Contrastive learning is applied to obtain more representative features. Finally, multilayer perceptron (MLP) is used for predicting DDAs. Experiments show that TEMCL outperforms existing methods on DR task, exhibiting superior performance. In addition, case studies further demonstrate the effectiveness of this model. TEMCL provides new insights for identifying novel DDAs.

PMID:40279215 | DOI:10.1109/JBHI.2025.3564360

IEEE Trans Image Process. 2025 Apr 25;PP. doi: 10.1109/TIP.2025.3562740. Online ahead of print.

ABSTRACT

Webly-supervised fine-grained visual classification (WSL-FGVC) aims to learn similar sub-classes from cheap web images, which suffers from two major issues: label noises in web images and subtle differences among fine-grained classes. However, existing methods for WSL-FGVC only focus on suppressing noise at image-level, but neglect to mine cues at pixel-level to distinguish the subtle differences among fine-grained classes. In this paper, we propose a bag-level top-down attention framework, which could tackle label noises and mine subtle cues simultaneously and integrally. Specifically, our method first extracts high-level semantic information from a bag of images belonging to the same class, and then uses the bag-level information to mine discriminative regions in various scales of each image. Besides, we propose to derive attention weights from attention maps to weight the bag-level fusion for a robust supervision. We also propose an attention loss on self-bag attention and cross-bag attention to facilitate the learning of valid attention. Extensive experiments on four WSL-FGVC datasets, i.e., Web-Aircraft, Web-Bird, Web-Car, and WebiNat-5089, demonstrate the effectiveness of our method against the state-of-the-art methods.

PMID:40279222 | DOI:10.1109/TIP.2025.3562740

Clin Transl Sci. 2025 May;18(5):e70230. doi: 10.1111/cts.70230.

ABSTRACT

Pharmacogenetic (PGx) testing can individualize pharmacotherapy, but many current panels lack inclusivity for diverse populations and are often cost-prohibitive for medically underserved communities. This study aimed to develop and validate GatorPGx Plus, a low-cost, preemptive PGx panel tailored for diverse patient populations. Pharmacogenes were selected based on the drug/drug classes potentially influenced by their variants, the clinical severity of drug-gene interactions, or the strength of guideline recommendations or emerging evidence. Variants within the pharmacogenes were included if their allele frequencies were approximately 1% or greater in any major ancestral population. The panel was validated for accuracy, precision, and analytical sensitivity and applied to 124 participants from an ongoing pharmacogenetic clinical implementation trial. To reduce costs, a high-throughput platform was chosen, laboratory technician hands-on time was minimized, and result translation and reporting were automated. The panel comprised tests for 62 variants in 14 genes/gene regions, including a CYP2D6 copy number assay. It demonstrated 100% concordance with reference methods. The average turnaround time between test order and results was 14.3 (±6.4) days. Among the 124 genotyped trial participants (mean age 60 years, 57.3% female), 99% had at least one non-normal function (less common or higher-risk) phenotype. The most frequently identified non-normal function phenotypes were in CYP2C19 (69.4%). CYP2D6 *17, *29, and CYP2C19 *9 were captured at higher frequencies than reported in European populations. GatorPGx Plus is a low per-test cost, clinically validated, preemptive PGx panel that effectively captures key variants in a mixed-ancestry population, underscoring its potential clinical utility in diverse, medically underserved populations.

PMID:40279185 | DOI:10.1111/cts.70230

J Med Chem. 2025 Apr 25. doi: 10.1021/acs.jmedchem.5c00405. Online ahead of print.

ABSTRACT

Chronic lung infections caused by Pseudomonas aeruginosa and Burkholderia cenocepacia pose a severe threat to immunocompromised patients, particularly those with cystic fibrosis. These pathogens often infect the respiratory tract, and available treatments are limited due to antibiotic resistance. Targeting bacterial lectins involved in biofilm formation and host-pathogen interactions represents a promising therapeutic strategy. In this study, we evaluate the potential of synthetic fucosylamides as inhibitors of the two lectins LecB (P. aeruginosa) and BC2L-C-Nt (B. cenocepacia). Using a suite of biophysical assays, we assessed their binding affinities, identifying three β-fucosylamides as promising dual-target ligands, while crystallography studies revealed the atomic basis of these ligands to interact with both bacterial lectins. The emerged classes of compounds represent a solid starting point for the necessary hit-to-lead optimization for future dual inhibitors aiming at the treatment of coinfections with these two bacterial pathogens.

PMID:40279549 | DOI:10.1021/acs.jmedchem.5c00405

Clin Nucl Med. 2025 Apr 24. doi: 10.1097/RLU.0000000000005928. Online ahead of print.

ABSTRACT

PURPOSE: The aim of this study was to generate and validate artificial delayed-phase technetium-99m methoxyisobutylisonitrile scintigraphy (aMIBI) images from early-phase technetium-99m methoxyisobutylisonitrile scintigraphy (eMIBI) images.

PATIENTS AND METHODS: This retrospective study included patients with hyperparathyroidism who underwent dual-phase technetium-99m methoxyisobutylisonitrile (MIBI) scintigraphy at 2 centers. The patients were divided into a training set (n = 980), an internal test set (n = 100), and an external test set (n = 253). The generation of aMIBI images from eMIBI images was performed using an unpaired image-to-image translation method. Receiver operating characteristic curves and the area under the curves (AUCs) were used to evaluate the diagnostic performance of aMIBI and eMIBI images in identifying hyperfunctioning parathyroid lesions in both the internal and external test sets. In addition, an artificial intelligence (AI)-assisted diagnostic model combining aMIBI and clinical data was evaluated.

RESULTS: The AUCs of aMIBI images were significantly higher than those of eMIBI images (internal test set: 0.944 vs 0.658, P < 0.001; external test set: 0.900 vs 0.761, P < 0.001). The performance of the AI-assisted diagnostic models combining aMIBI images and clinical data was significantly better than those of the aMIBI-only models in both the internal (AUC: 0.974 vs 0.944, P = 0.020) and external (AUC: 0.953 vs 0.900, P < 0.001) test sets.

CONCLUSIONS: The diagnostic performance of aMIBI images in identifying hyperfunctioning parathyroid lesions was significantly superior to that of eMIBI images in patients with hyperparathyroidism. Models combining aMIBI images with clinical information enhanced the diagnostic performance even further.

PMID:40279678 | DOI:10.1097/RLU.0000000000005928

ACS Sens. 2025 Apr 25. doi: 10.1021/acssensors.5c00490. Online ahead of print.

ABSTRACT

As metabolic byproducts and representative indicators of food spoilage, the monitoring and detection for biogenic amines (BAs) are crucial but challenging for food quality assessment. Here, a strategy is proposed by combining fluorescent probe molecular engineering with a portable detection platform integrating a smartphone and a deep convolutional neural network (DCNN). Four ratiometric fluorescent probes with tunable intramolecular charge transfer (ICT) properties are designed by introducing different electron-withdrawing substituents (-F, -OCH3, -Py, and -CN) to the carbazole. Notably, CNCz exhibits the strongest ICT property and superior sensing performance, with a satisfying detection limit (11 ppb), rapid response (<5 s), and discriminative bathochromic shift (110 nm). Then, a smartphone-based detection platform is fabricated, which enables rapid, visual, and on-site quantitative evaluation of BAs. Furthermore, by integrating DCNN, this platform achieves an impressive 98.5% accuracy in predicting meat freshness. Hereby, this study not only provides a molecular engineering strategy to fine-tune the intrinsic ICT properties to gain high-performance ratiometric fluorescent probes but also presents an intelligent detection platform for BAs and meat freshness with high practical applicability.

PMID:40279659 | DOI:10.1021/acssensors.5c00490

JMIR Med Inform. 2025 Apr 25;13:e64963. doi: 10.2196/64963.

ABSTRACT

BACKGROUND: With the rapid development of artificial intelligence (AI) technology, especially generative AI, large language models (LLMs) have shown great potential in the medical field. Through massive medical data training, it can understand complex medical texts and can quickly analyze medical records and provide health counseling and diagnostic advice directly, especially in rare diseases. However, no study has yet compared and extensively discussed the diagnostic performance of LLMs with that of physicians.

OBJECTIVE: This study systematically reviewed the accuracy of LLMs in clinical diagnosis and provided reference for further clinical application.

METHODS: We conducted searches in CNKI (China National Knowledge Infrastructure), VIP Database, SinoMed, PubMed, Web of Science, Embase, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) from January 1, 2017, to the present. A total of 2 reviewers independently screened the literature and extracted relevant information. The risk of bias was assessed using the Prediction Model Risk of Bias Assessment Tool (PROBAST), which evaluates both the risk of bias and the applicability of included studies.

RESULTS: A total of 30 studies involving 19 LLMs and a total of 4762 cases were included. The quality assessment indicated a high risk of bias in the majority of studies, primary cause is known case diagnosis. For the optimal model, the accuracy of the primary diagnosis ranged from 25% to 97.8%, while the triage accuracy ranged from 66.5% to 98%.

CONCLUSIONS: LLMs have demonstrated considerable diagnostic capabilities and significant potential for application across various clinical cases. Although their accuracy still falls short of that of clinical professionals, if used cautiously, they have the potential to become one of the best intelligent assistants in the field of human health care.

PMID:40279517 | DOI:10.2196/64963

J Phys Chem Lett. 2025 Apr 25:4463-4473. doi: 10.1021/acs.jpclett.5c00714. Online ahead of print.

ABSTRACT

We investigate the linear absorption spectra of the organic photovoltaic nonfullerene acceptor Y6 in chloroform using perturbative and nonperturbative approaches with atomistic details. Direct nonadiabatic semiclassical mapping dynamics reveal population and coherence evolution during and after ultrafast light pulse, revealing dominant absorption to the S1 state and subsequent oscillatory polarization. The simulated spectra accurately reproduce experimental peak positions and broadening, corresponding to transitions from the ground state to the S1, S2, and S6 excited states. Time-dependent radial distribution functions offer atomistic insights into solvent reorganization in response to charge redistribution. These findings enhance the understanding of nonadiabatic dynamics in Y6 and provide a consistent protocol for simulating electronic spectroscopy in condensed-phase systems.

PMID:40279488 | DOI:10.1021/acs.jpclett.5c00714

Bioinformatics. 2025 Apr 25:btaf203. doi: 10.1093/bioinformatics/btaf203. Online ahead of print.

ABSTRACT

SUMMARY: Differentiable folding is an emerging paradigm for RNA design in which a probabilistic sequence representation is optimized via gradient descent. However, given the significant memory overhead of differentiating the expected partition function over all RNA sequences, the existing proof-of-concept algorithm only scales to ≤50 nucleotides. We present JAX-RNAfold, an open-source software package for our drastically improved differentiable folding algorithm that scales to 1,250 nucleotides on a single GPU. Our software permits the natural inclusion of differentiable folding as a module in larger deep learning pipelines, as well as complex RNA design procedures such as mRNA design with flexible objective functions.

AVAILABILITY AND IMPLEMENTATION: JAX-RNAfold is hosted on GitHub (https://github.com/rkruegs123/jax-rnafold) and can be installed locally as a Python package. All source code is also archived on Zenodo (https://doi.org/10.5281/zenodo.15003072).

CONTACT: Please email max.ward@uwa.edu.au with any questions.

SUPPLEMENTARY INFORMATION: Please refer to the online-only Supplementary Material.

PMID:40279486 | DOI:10.1093/bioinformatics/btaf203

PLoS One. 2025 Apr 25;20(4):e0317514. doi: 10.1371/journal.pone.0317514. eCollection 2025.

ABSTRACT

Global awareness of sustainable development has heightened interest in green buildings as a key strategy for reducing energy consumption and carbon emissions. Accurate prediction of energy consumption plays a vital role in developing effective energy management and conservation strategies. This study addresses these challenges by proposing an advanced deep learning framework that integrates Time-Dependent Variational Autoencoder (TD-VAE) with Adaptive Gated Self-Attention GRU (AGSA-GRU). The framework incorporates self-attention mechanisms and Multi-Task Learning (MTL) strategies to capture long-term dependencies and complex patterns in energy consumption time series data, while simultaneously optimizing prediction accuracy and anomaly detection. Experiments on two public green building energy consumption datasets validate the effectiveness of our proposed approach. Our method achieves a prediction accuracy of 93.2%, significantly outperforming traditional deep learning methods and existing techniques. ROC curve analysis demonstrates our model's robustness, achieving an Area Under the Curve (AUC) of 0.91 while maintaining a low false positive rate (FPR) and high true positive rate (TPR). This study presents an efficient solution for green building energy consumption prediction, contributing significantly to energy conservation, emission reduction, and sustainable development in the construction industry.

PMID:40279377 | DOI:10.1371/journal.pone.0317514

PLoS One. 2025 Apr 25;20(4):e0314837. doi: 10.1371/journal.pone.0314837. eCollection 2025.

ABSTRACT

The accurate prediction of RNA secondary structure, and pseudoknots in particular, is of great importance in understanding the functions of RNAs since they give insights into their folding in three-dimensional space. However, existing approaches often face computational challenges or lack precision when dealing with long RNA sequences and/or pseudoknots. To address this, we propose a divide-and-conquer method based on deep learning, called DivideFold, for predicting the secondary structures including pseudoknots of long RNAs. Our approach is able to scale to long RNAs by recursively partitioning sequences into smaller fragments until they can be managed by an existing model able to predict RNA secondary structure including pseudoknots. We show that our approach exhibits superior performance compared to state-of-the-art methods for pseudoknot prediction and secondary structure prediction including pseudoknots for long RNAs. The source code of DivideFold, along with all the datasets used in this study, is accessible at https://evryrna.ibisc.univ-evry.fr/evryrna/dividefold/home.

PMID:40279361 | DOI:10.1371/journal.pone.0314837

Curr Opin Ophthalmol. 2025 Apr 22. doi: 10.1097/ICU.0000000000001150. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: The integration of artificial intelligence (AI) in the diagnosis and management of anterior segment diseases has rapidly expanded, demonstrating significant potential to revolutionize clinical practice.

RECENT FINDINGS: AI technologies, including machine learning and deep learning models, are increasingly applied in the detection and management of a variety of conditions, such as corneal diseases, refractive surgery, cataract, conjunctival disorders (e.g., pterygium), trachoma, and dry eye disease. By analyzing large-scale imaging data and clinical information, AI enhances diagnostic accuracy, predicts treatment outcomes, and supports personalized patient care.

SUMMARY: As AI models continue to evolve, particularly with the use of large models and generative AI techniques, they will further refine diagnosis and treatment planning. While challenges remain, including issues related to data diversity and model interpretability, AI's integration into ophthalmology promises to improve healthcare outcomes, making it a cornerstone of data-driven medical practice. The continued development and application of AI will undoubtedly transform the future of anterior segment ophthalmology, leading to more efficient, accurate, and individualized care.

PMID:40279352 | DOI:10.1097/ICU.0000000000001150

IEEE Trans Biomed Eng. 2025 Apr 25;PP. doi: 10.1109/TBME.2025.3562067. Online ahead of print.

ABSTRACT

Sleep spindles, which are key biomarkers of non-rapid eye movement stage 2 sleep, play a crucial role in predicting outcomes for patients with acute disorders of consciousness (ADOC). However, several critical challenges remain in spindle detection: 1) the limited use of automated spindle detection in ADOC; 2) the difficulty in identifying low-frequency spindles in patient populations; and 3) the lack of effective tools for quantitatively analyzing the relationship between spindle density and patient outcomes. To address these challenges, we propose a novel Deep Learning-Augmented algorithm for automated sleep spindle detection in ADOC patients. This method combines Convolutional Neural Networks with decision tree-assisted validation, using wavelet transform principles to enhance detection accuracy and sensitivity, especially for the slow spindles commonly found in ADOC patients. Our approach not only demonstrates superior performance and reliability but also has the potential to significantly improve diagnostic precision and guide treatment strategies when integrated into clinical practice. Our algorithm was evaluated on the Montreal Archive of Sleep Studies - Session 2 (MASS SS2, n = 19), achieving average F1 scores of 0.798 and 0.841 compared to annotations from two experts. On a self-recorded dataset from ADOC patients (n = 24), it achieved an F1 score of 0.745 compared to expert annotations. Additionally, our analysis using the Spearman correlation coefficient revealed a moderate positive correlation between sleep spindle density and 28-day Glasgow Outcome Scale scores in ADOC patients. This suggests that spindle density could serve as a prognostic marker for predicting clinical outcomes and guiding personalized patient care.

PMID:40279237 | DOI:10.1109/TBME.2025.3562067

IEEE Trans Biomed Eng. 2025 Apr 25;PP. doi: 10.1109/TBME.2025.3564567. Online ahead of print.

ABSTRACT

A transfer function approach has recently proven effective for calibrating deep learning (DL) algorithms in quantitative ultrasound (QUS), addressing data shifts at both the acquisition and machine levels. Expanding on this approach, we develop a strategy to acquire the functionality of a DL model from one ultrasound machine and implement it on another in a black-box setting, in the context of QUS. This demonstrates the ease with which the functionality of a DL model can be transferred between machines. While the proposed approach can also assist regulatory bodies in comparing and approving DL models, it also highlights the security risks associated with deploying such models in a commercial scanner for clinical use. The method is a black-box unsupervised domain adaptation technique that integrates the transfer function approach with an iterative schema. It does not utilize any information related to model internals but it solely relies on the availability of an input-output interface. Additionally, we assume the availability of unlabeled data from a testing machine. This scenario could become relevant as companies begin deploying their DL functionalities for clinical use. In the experiments, we used a SonixOne and a Verasonics machine. The model was trained on SonixOne data, and its functionality was then transferred to the Verasonics machine. The proposed method successfully transferred the functionality to the Verasonics machine, achieving a remarkable 98 percent classification accuracy in a binary decision task. This study underscores the need to establish security measures prior to deploying DL models in clinical settings.

PMID:40279236 | DOI:10.1109/TBME.2025.3564567

Adv Sci (Weinh). 2025 Apr 25:e2408173. doi: 10.1002/advs.202408173. Online ahead of print.

ABSTRACT

Discovering dominant patterns and exploring dynamic behaviors especially critical state transitions and tipping points in high-dimensional time-series data are challenging tasks in study of real-world complex systems, which demand interpretable data representations to facilitate comprehension of both spatial and temporal information within the original data space. This study proposes a general and analytical ultralow-dimensionality reduction method for dynamical systems named spatial-temporal principal component analysis (stPCA) to fully represent the dynamics of a high-dimensional time-series by only a single latent variable without distortion, which transforms high-dimensional spatial information into one-dimensional temporal information based on nonlinear delay-embedding theory. The dynamics of this single variable is analytically solved and theoretically preserves the temporal property of original high-dimensional time-series, thereby accurately and reliably identifying the tipping point before an upcoming critical transition. Its applications to real-world datasets such as individual-specific heterogeneous ICU records demonstrate the effectiveness of stPCA, which quantitatively and robustly provides the early-warning signals of the critical/tipping state on each patient.

PMID:40279642 | DOI:10.1002/advs.202408173

Phys Rev Lett. 2025 Apr 11;134(14):148401. doi: 10.1103/PhysRevLett.134.148401.

ABSTRACT

Biomolecules stochastically occupy different configurations that correspond to distinct functional states. Changing biochemical inputs such as rate constants alters the output probability distribution of configurations, and thus constitutes a form of computation. In the cell, such computations are often coupled to thermodynamic forces such as ATP hydrolysis that drive systems far from equilibrium, resulting in energy expenditure even during times when computations are not being performed. The information-theoretic advantage of this costly computational paradigm is unclear. Here we introduce a theoretical framework showing how much the thermodynamic force enables changes in probability distributions, quantified by the information gain, beyond what is possible at equilibrium. Using this framework, we derive a general expression relating the force to the maximum information gain in an arbitrary computation, revealing how small input changes can exponentially alter outputs. We numerically show that biomolecular systems can closely approach this universal bound, illustrating how energy expenditure is needed to achieve the information processing capabilities observed in nature.

PMID:40279610 | DOI:10.1103/PhysRevLett.134.148401

Sci Adv. 2025 Apr 25;11(17):eadt9712. doi: 10.1126/sciadv.adt9712. Epub 2025 Apr 25.

ABSTRACT

Coordinated cell cycle regulation is essential for homeostasis, with most cells in the body residing in quiescence (G0). Many pathologies arise due to disruptions in tissue-specific G0, yet little is known about the temporal-spatial mechanisms that establish G0 and its signaling hub, primary cilia. Mechanistic insight is limited by asynchronous model systems and failure to connect context-specific, transient mechanisms to function. To address this gap, we developed STAMP (synchronized temporal-spatial analysis via microscopy and phosphoproteomics) to track changes in cellular landscape occurring throughout G0 transition and ciliogenesis. We synchronized ciliogenesis and G0 transition in two cell models and combined microscopy with phosphoproteomics to order signals for further targeted analyses. We propose that STAMP is broadly applicable for studying temporal-spatial signaling in many biological contexts. The findings revealed through STAMP provide critical insight into healthy cellular functions often disrupted in pathologies, paving the way for targeted therapeutics.

PMID:40279433 | DOI:10.1126/sciadv.adt9712

Annu Rev Immunol. 2025 Apr;43(1):693-722. doi: 10.1146/annurev-immunol-090122-042631.

ABSTRACT

The immune system, critical for human health and implicated in many diseases, defends against pathogens, monitors physiological stress, and maintains tissue and organismal homeostasis. It exhibits substantial variability both within and across individuals and populations. Recent technological and conceptual progress in systems human immunology has provided predictive insights that link personal immune states to intervention responses and disease susceptibilities. Artificial intelligence (AI), particularly machine learning (ML), has emerged as a powerful tool for analyzing complex immune data sets, revealing hidden patterns across biological scales, and enabling predictive models for individualistic immune responses and potentially personalized interventions. This review highlights recent advances in deciphering human immune variation and predicting outcomes, particularly through the concepts of immune setpoint, immune health, and use of the immune system as a window for measuring health. We also provide a brief history of AI; review ML modeling approaches, including their applications in systems human immunology; and explore the potential of AI to develop predictive models and personal immune state embeddings to detect early signs of disease, forecast responses to interventions, and guide personalized health strategies.

PMID:40279304 | DOI:10.1146/annurev-immunol-090122-042631