Sci Rep. 2025 Apr 16;15(1):13105. doi: 10.1038/s41598-025-95961-4.

ABSTRACT

The accurate preclinical prediction of adverse drug reactions (ADRs), such as nausea and vomiting, remains a challenge. The Gastro-Intestinal Pacemaker Activity Drug Database (GIPADD) ( http://www.gutrhythm.com/public_database ) is a new source of electrophysiological big data for drug research. Over the past 2 years, the database has doubled in size, and now contains the electrophysiological profiles of 172 drugs across 11,943 datasets. This study used a state-of-the-art deep-learning model with time-series classification to explore the feasibility of using raw electrophysiological recordings from tissues to predict ADRs. The GIPADD contains the recordings of the electrical activity of various gastrointestinal tissues (stomach, duodenum, ileum, and colon) exposed to a drug at three or more different concentrations, representing the effects of the drug on gastrointestinal pacemaker activity. Each drug in the database is associated with at least 60 recordings. The datasets are divided in a ratio of 8:2 for training and validation. A modified InceptionTime classifier (ICT) was used to predict whether a drug induces ADRs, using data from the SIDER database as the target. Concentrations and tissues were added as covariates and added to the input of the model during forward propagation. We also established a negative control with shuffled target labels, and external validation was conducted using time-shifted recording predictions. The best model for predicting nausea, vomiting, diarrhoea, and constipation achieved by-drug accuracies of 0.87, 0.89, 0.85, and 0.91, respectively; by-drug precision (class 1) of 0.88, 0.90, 0.99, and 0.89, respectively; and area under the receiver operating characteristic curve (AUROC) values of 0.84, 0.87, 0.94, and 0.96, respectively. The best model was an ensemble of five independent ICT classifiers trained on the same dataset. Models trained using shuffled labels (negative controls) exhibited significantly lower accuracy, precision, and AUROC values than models trained using correctly labelled datasets, indicating that ICT classifiers successfully identified latent features in the raw recordings associated with ADRs. The combined benefits of the GIPADD and deep learning may accelerate drug safety testing and drug development by enabling the reliable analysis of electrophysiological drug profiles during the preclinical stage.

PMID:40240387 | DOI:10.1038/s41598-025-95961-4

J Appl Lab Med. 2025 Apr 16:jfaf041. doi: 10.1093/jalm/jfaf041. Online ahead of print.

ABSTRACT

BACKGROUND: Clopidogrel is a widely used antiplatelet agent used to prevent adverse events for patients suffering from acute coronary syndromes and ischemic stroke. As a prodrug, clopidogrel must be converted to the active form through the enzyme cytochrome (CYP) P450 2C19 (among other enzymes). Individuals carrying a loss of function (LOF) allele (i.e., *2 and/or *3) have reduced pharmacologic efficacy. Ticagrelor is an alternative antiplatelet medication that is not a prodrug.

METHODS: We reviewed the Clopidogrel in High-Risk Patients with Acute Nondisabling Cerebrovascular Events (CHANCE2) Trial demonstrating the inferiority of clopidogrel dual therapy with aspirin vs ticagrelor dual therapy to prevent adverse events among patients suffering from a mild stroke among Chinese patients who carried a CYP2C19 LOF. We also summarized the pharmacogenomic testing policies within Chinese clinical laboratories after publication of this trial, and tabulated the CYP2C19 LOF allele frequencies among ancestries, as a criteria for justifying the expense required for establishing pharmacogenomic testing services for other populations.

RESULTS: The CHANCE2 trial showed that stroke patients carrying a CYP2C19 LOF allele(s) had a reduction of 1.6% for recurrent stroke for those treated with ticagrelor vs clopidogrel. The LOF allele frequency was highest among Pacific Island and Western and Central Asian (e.g., Han Chinese) patients and lowest among European, Latin, and Hispanic Latino patients.

CONCLUSIONS: Pharmacogenomic testing for CYP2C19 variants is more economically justified for laboratories that serve a population enriched with CYP2C19 LOF alleles, than populations exhibiting a lower allele frequency. Within a clinical laboratory offering testing, restricting testing to certain populations is not ethical.

PMID:40238818 | DOI:10.1093/jalm/jfaf041

Am J Respir Cell Mol Biol. 2025 Apr 16. doi: 10.1165/rcmb.2024-0225OC. Online ahead of print.

ABSTRACT

The respiratory tract possesses a highly regulated innate defence system which includes cilia-mediated mucociliary clearance (MCC). Efficient MCC relies on appropriate hydration of airway surfaces, which is controlled by a blend of transepithelial sodium and liquid absorption, and anion and liquid secretion The latter is primarily mediated by the cystic fibrosis transmembrane conductance regulator (CFTR) anion channel. Succinate is derived from parasites, microorganisms and inflammatory cells, and its concentration increases in the airway surface liquid (ASL) during infections, activating the G-protein coupled succinate receptor (SUCNR1), which acts as a succinate sensor. Since MCC is tightly regulated by second messengers we tested the hypothesis that succinate signalling was linked to CFTR activity. We observed that SUCNR1 activation stimulated anion secretion, increased mucus transport, and induced tracheal constriction in mouse airways. In the CftrΔF508/ΔF508 mouse, increased mucus transport and tracheal constriction were observed, while succinate-induced electrogenic anion secretion remained unaffected. Stimulation of normal human bronchial epithelial cells (HBECs) with succinate activated CFTR-dependent anion secretion and increased ASL height. Moreover, when HBECs derived from ΔF508-CF individuals lacked succinate-induced anion secretion, unless incubated with elexacaftor-tezacaftor-ivacaftor (ETI), which restored succinate-induced anion secretion, confirming the tight relationship between SUCNR1 signalling and CFTR function. We have identified a novel mechanism for regulating CFTR/MCC activation which is defective in CF airways. We propose that succinate acts as a danger molecule that alerts the airways to the presence of pathogens leading to a flushing out of the airways.

PMID:40239014 | DOI:10.1165/rcmb.2024-0225OC

Comput Biol Med. 2025 Apr 15;191:110124. doi: 10.1016/j.compbiomed.2025.110124. Online ahead of print.

ABSTRACT

The increasing digitalization of multi-modal data in medicine and novel artificial intelligence (AI) algorithms opens up a large number of opportunities for predictive models. In particular, deep learning models show great performance in the medical field. A major limitation of such powerful but complex models originates from their 'black-box' nature. Recently, a variety of explainable AI (XAI) methods have been introduced to address this lack of transparency and trust in medical AI. However, the majority of such methods have solely been evaluated on single data modalities. Meanwhile, with the increasing number of XAI methods, integrative XAI frameworks and benchmarks are essential to compare their performance on different tasks. For that reason, we developed BenchXAI, a novel XAI benchmarking package supporting comprehensive evaluation of fifteen XAI methods, investigating their robustness, suitability, and limitations in biomedical data. We employed BenchXAI to validate these methods in three common biomedical tasks, namely clinical data, medical image and signal data, and biomolecular data. Our newly designed sample-wise normalization approach for post-hoc XAI methods enables the statistical evaluation and visualization of performance and robustness. We found that the XAI methods Integrated Gradients, DeepLift, DeepLiftShap, and GradientShap performed well over all three tasks, while methods like Deconvolution, Guided Backpropagation, and LRP-α1-β0 struggled for some tasks. With acts such as the EU AI Act the application of XAI in the biomedical domain becomes more and more essential. Our evaluation study represents a first step towards verifying the suitability of different XAI methods for various medical domains.

PMID:40239236 | DOI:10.1016/j.compbiomed.2025.110124

J Am Acad Orthop Surg Glob Res Rev. 2025 Apr 10;9(4). doi: 10.5435/JAAOSGlobal-D-24-00405. eCollection 2025 Apr 1.

ABSTRACT

BACKGROUND: A comprehensive review on the application of artificial intelligence (AI) within spine surgery as a specialty remains lacking.

METHODS: This scoping review was conducted upon PubMed and EMBASE databases according to Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Our analysis focused on publications from January 1, 2020, to March 31, 2024, with a specific focus on AI in the field of spine surgery. Review articles and articles predominantly concerning secondary validation of algorithms, medical physics, electronic devices, biomechanics, preclinical, and with a lack of clinical emphasis were excluded.

RESULTS: One hundred five studies were included after our inclusion/exclusion criteria were applied. Most studies (n = 100) were conducted through supervised learning upon prelabeled data sets. Overall, 38 studies used conventional machine learning methods upon predefined features, whereas 67 used deep learning methods, predominantly for medical image analyses. Only 25.7% of studies (27/105) collected data from more than 1,000 patients for model development and validation. Data originated from only a single center in 72 studies. The most common application was prognostication (38/105), followed by diagnosis (35/105), medical image processing (29/105), and surgical assistance (3/105).

CONCLUSION: The application of AI within the domain of spine surgery has significant potential to advance patient-specific diagnosis, management, and surgical execution.

PMID:40239218 | DOI:10.5435/JAAOSGlobal-D-24-00405

Invest Radiol. 2025 Apr 16. doi: 10.1097/RLI.0000000000001192. Online ahead of print.

ABSTRACT

The past decade has witnessed notable advancements in clinical neuroimaging facilitated by technological innovations and significant scientific discoveries. In conjunction with Investigative Radiology's 60th anniversary, this review examines key contributions from the past 10 years, emphasizing the journal's most accessed articles and their impact on clinical practice and research. Advances in imaging technologies, including photon-counting computed tomography, and innovations in low-field and high-field magnetic resonance imaging systems have expanded diagnostic capabilities. Progress in the development and translation of contrast media and rapid quantitative imaging techniques has further improved diagnostic accuracy. Additionally, the integration of advanced data analysis methods, particularly deep learning and medical informatics, has improved image interpretation and operational efficiency. Beyond technological developments, this review highlights basic neuroscience findings, such as the discovery and characterization of the glymphatic system. These insights have provided a deeper understanding of central nervous system physiology and pathology, bridging the gap between research and clinical applications. This review integrates these advancements to provide an overview of the progress and ongoing challenges in clinical neuroimaging, offering insights into its current state and potential future directions within the broader field of radiology.

PMID:40239043 | DOI:10.1097/RLI.0000000000001192

PLoS One. 2025 Apr 16;20(4):e0319329. doi: 10.1371/journal.pone.0319329. eCollection 2025.

ABSTRACT

Deep learning (DL) has become a powerful tool for the recognition and classification of biological sequences. However, conventional single-architecture models often struggle with suboptimal predictive performance and high computational costs. To address these challenges, we present EnsembleDL-Lipo, an innovative ensemble deep learning framework that combines Convolutional Neural Networks (CNNs) and Deep Neural Networks (DNNs) to enhance the identification of lipocalin sequences. Lipocalins are multifunctional extracellular proteins involved in various diseases and stress responses, and their low sequence similarity and occurrence in the 'twilight zone' of sequence alignment present significant hurdles for accurate classification. These challenges necessitate efficient computational methods to complement traditional, labor-intensive experimental approaches. EnsembleDL-Lipo overcomes these issues by leveraging a set of PSSM-based features to train a large ensemble of deep learning models. The framework integrates multiple feature representations derived from position-specific scoring matrices (PSSMs), optimizing classification performance across diverse sequence patterns. The model achieved superior results on the training dataset, with an accuracy (ACC) of 97.65%, recall of 97.10%, Matthews correlation coefficient (MCC) of 0.95, and area under the curve (AUC) of 0.99. Validation on an independent test set further confirmed the robustness of the model, yielding an ACC of 95.79%, recall of 90.48%, MCC of 0.92, and AUC of 0.97. These results demonstrate that EnsembleDL-Lipo is a highly effective and computationally efficient tool for lipocalin sequence identification, significantly outperforming existing methods and offering strong potential for applications in biomarker discovery.

PMID:40238838 | DOI:10.1371/journal.pone.0319329

PLoS One. 2025 Apr 16;20(4):e0320578. doi: 10.1371/journal.pone.0320578. eCollection 2025.

ABSTRACT

Existing research on decision-making of autonomous vehicles (AVs) has mainly focused on normal road sections, with limited exploration of decision-making in complex traffic environments without lane markings. Taking toll plaza diverging area as an example, this study proposes a lateral motion strategy for AVs based on deep reinforcement learning (DRL) algorithms. First, a microscopic simulation platform is developed to simulate the realistic diverging trajectories of human-driven vehicles (HVs), providing AVs with a high-fidelity training environment. Next, a DRL-based self-efficient lateral motion strategy for AVs is proposed, with state and reward functions tailored to the environmental features of the diverging area. Simulation results indicate that the strategy can significantly reduce the diverging time of single vehicles. In addition, considering the long-term coexistence of AVs and HVs, the study further explores how the varying penetration of AVs with self-efficient strategy impacts traffic flow in the diverging area. Findings reveal that a moderate increase in AV penetration can improve overall traffic efficiency and safety. But an excessive penetration of AVs with self-efficient strategy leads to intense competition for limited road resources, further deteriorating operational conditions in the diverging area.

PMID:40238783 | DOI:10.1371/journal.pone.0320578

PLoS One. 2025 Apr 16;20(4):e0318599. doi: 10.1371/journal.pone.0318599. eCollection 2025.

ABSTRACT

BACKGROUND: Quantitative muscle water T2 (T2w) mapping is regarded as a biomarker for disease activity and response to treatment in neuromuscular diseases (NMD). However, the implementation in clinical settings is limited due to long scanning times and low resolution. Using artificial intelligence (AI) to accelerate MR image acquisition offers a possible solution. Combining compressed sensing and parallel imaging with AI-based reconstruction, known as CSAI (SmartSpeed, Philips Healthcare), allows for the generation of high-quality, weighted MR images in a shorter scan time. However, CSAI has not yet been investigated for quantitative MRI. Therefore, in the present work we assessed the performance of CSAI acceleration for T2w mapping compared to standard acceleration with SENSE.

METHODS: T2w mapping of the thigh muscles, based on T2-prepared 3D TSE with SPAIR fat suppression, was performed using standard SENSE (acceleration factor of 2; 04:35 min; SENSE) and CSAI (acceleration factor of 5; 01:57 min; CSAI 5x) in ten patients with facioscapulohumeral muscular dystrophy (FSHD). Subjects were scanned in two consecutive sessions (14 days in between). In each dataset, six regions of interest were placed in three thigh muscles bilaterally. SENSE and CSAI 5x acceleration were compared for i) image quality using apparent signal- and contrast-to-noise ratio (aSNR/aCNR), ii) diagnostic agreement of T2w values, and iii) intra- and inter-session reproducibility.

RESULTS: aSNR and aCNR of SENSE and CSAI 5x scans were not significantly different (p > 0.05). An excellent agreement of SENSE and CSAI 5x T2w values was shown (r = 0.99; ICC = 0.992). T2w mapping with both acceleration methods showed excellent, matching intra-method reproducibility.

CONCLUSION: AI-based acceleration of CS data allows for scan time reduction of more than 50% for T2w mapping in the thigh muscles of NMD patients without compromising quantitative validity.

PMID:40238781 | DOI:10.1371/journal.pone.0318599

PLoS One. 2025 Apr 16;20(4):e0319968. doi: 10.1371/journal.pone.0319968. eCollection 2025.

ABSTRACT

Honey bees play a crucial role in natural ecosystems, mainly through their pollination services. Within a hive, they exhibit intricate social behaviors and communicate among thousands of individuals. Accurate detection and segmentation of honey bees are crucial for automated behavior analysis, as they significantly enhance object tracking and behavior recognition by yielding high-quality results. This study is specifically centered on the detection and segmentation of individual bees, particularly Apis cerana, within a hive environment, employing the Mask R-CNN deep learning model. We used transfer learning weights from our previously trained Apis mellifera model and explored data preprocessing techniques, such as brightness and contrast enhancement, to enhance model performance. Our proposed approach offers an optimal solution with a minimal dataset size and computational time while maintaining high model performance. Mean average precision (mAP) served as the evaluation metric for both detection and segmentation tasks. Our solution for A. cerana segmentation achieves the highest performance with a mAP of 0.728. Moreover, the number of training and validation sets was reduced by 85% compared to our previous study on the A. mellifera segmentation model.

PMID:40238729 | DOI:10.1371/journal.pone.0319968

IEEE Trans Ultrason Ferroelectr Freq Control. 2025 Apr 16;PP. doi: 10.1109/TUFFC.2025.3561599. Online ahead of print.

ABSTRACT

Shear wave elastography (SWE) provides quantitative markers for tissue characterization by measuring shear wave speed (SWS), which reflects tissue stiffness. SWE uses an acoustic radiation force pulse sequence to generate shear waves that propagate laterally through tissue with transient displacements. These waves travel perpendicular to the applied force, and their displacements are tracked using high-frame-rate ultrasound. Estimating the SWS map involves two main steps: speckle tracking and SWS estimation. Speckle tracking calculates particle velocity by measuring RF/IQ data displacement between adjacent firings, while SWS estimation methods typically compare particle velocity profiles of samples that are laterally a few millimeters apart. Deep learning (DL) methods have gained attention for SWS estimation, often relying on supervised training using simulated data. However, these methods may struggle with real-world data, which can differ significantly from simulated training data, potentially leading to artifacts in the estimated SWS map. To address this challenge, we propose a physics-inspired learning approach that utilizes real data without known SWS values. Our method employs an adaptive unsupervised loss function, allowing the network to train with real noisy data to minimize the artifacts and improve the robustness. We validate our approach using experimental phantom data and in vivo liver data from two human subjects, demonstrating enhanced accuracy and reliability in SWS estimation compared to conventional and supervised methods. This hybrid approach leverages the strengths of both data-driven and physics-inspired learning, offering a promising solution for more accurate and robust SWS mapping in clinical applications.

PMID:40238602 | DOI:10.1109/TUFFC.2025.3561599

IEEE Trans Pattern Anal Mach Intell. 2025 Apr 16;PP. doi: 10.1109/TPAMI.2025.3562051. Online ahead of print.

ABSTRACT

Although supervised deep normal estimators have recently shown impressive results on synthetic benchmarks, their performance deteriorates significantly in real-world scenarios due to the domain gap between synthetic and real data. Building high-quality real training data to boost those supervised methods is not trivial because point-wise annotation of normals for varying-scale real-world 3D scenes is a tedious and expensive task. This paper introduces PointNorm-Net, the first self-supervised deep learning framework to tackle this challenge. The key novelty of PointNorm-Net is a three-stage multi-modal normal distribution estimation paradigm that can be integrated into either deep or traditional optimization-based normal estimation frameworks. Extensive experiments show that our method achieves superior generalization and outperforms state-of-the-art conventional and deep learning approaches across three real-world datasets that exhibit distinct characteristics compared to the synthetic training data.

PMID:40238601 | DOI:10.1109/TPAMI.2025.3562051

Foods. 2025 Apr 4;14(7):1269. doi: 10.3390/foods14071269.

ABSTRACT

In addition to its flavor and nutritional value, the origin of kelp has become a crucial factor influencing consumer choices. Nevertheless, research on kelp's origin traceability by volatile organic compound (VOC) analysis is lacking, and the application of deep learning in this field remains scarce due to its black-box nature. To address this gap, we attempted to identify the origin of kelp by analyzing its VOCs in conjunction with explainable deep learning. In this work, we identified 115 distinct VOCs in kelp samples using gas chromatography coupled with ion mobility spectroscopy (GC-IMS), of which 68 categories were discernible. Consequently, we developed a comprehensible one-dimensional convolutional neural network (1D-CNN) model that incorporated 107 VOCs exhibiting significant regional disparities (p < 0.05). The model successfully discerns the origin of kelp, achieving perfect metrics across accuracy (100%), precision (100%), recall (100%), F1 score (100%), and AUC (1.0). SHapley Additive exPlanations (SHAP) analysis highlighted the impact of features such as 1-Octen-3-ol-M, (+)-limonene, allyl sulfide-D, 1-hydroxy-2-propanone-D, and (E)-2-hexen-1-al-M on the model output. This research provides deeper insights into how critical product features correlate with specific geographic information, which in turn boosts consumer trust and promotes practical utilization in actual settings.

PMID:40238501 | DOI:10.3390/foods14071269

Am J Respir Cell Mol Biol. 2025 Apr 16. doi: 10.1165/rcmb.2024-0614OC. Online ahead of print.

ABSTRACT

The quest for innovative pharmacologic interventions in idiopathic pulmonary fibrosis (IPF) is a challenging journey. The complexity of the disease demands a comprehensive approach targeting multiple cell types and pathways. This study examined the antifibrotic properties of nerandomilast, a preferential phosphodiesterase 4B inhibitor, focusing on its effects on myofibroblasts (MF)s and endothelial cells. Using cytokine-stimulated human IPF lung fibroblasts (IPF-HLF) and RNA-seq, we assessed the effect nerandomilast has on MF contractility, MF markers and differentiation mechanisms. In addition, using human microvascular endothelial cells, endothelial barrier integrity and monocyte adhesion were assessed in a 3D microfluidic chip. Our results show that nerandomilast significantly inhibited MF contractility and marker expression in cytokine-stimulated IPF-HLF cells. Treatment with nerandomilast significantly activated cAMP-associated pathways and G-protein-coupled receptor (GPCR) signaling events while inhibiting mitogen-activated protein kinase (MAPK) signaling pathways and transforming growth factor beta (TGFβ) signaling. Nerandomilast also significantly reduced microvascular permeability in cytokine-stimulated human lung microvascular endothelial cells. Finally, in an adeno-associated virus-human diphtheria toxin receptor/diphtheria toxin mouse model of acute lung injury, nerandomilast significantly inhibited total protein in lavage, total macrophages, neutrophils, cell count and VCAM-1 expression. In summary, our results demonstrate that nerandomilast induces the dedifferentiation of human IPF lung MFs and diminishes their contractility in vitro by interfering with TGFβ, MAPK phosphatase-1 and GPCR signaling pathways. It also mitigates vascular dysfunction by strengthening endothelial junctions and inhibiting adhesion protein expression. These findings highlight nerandomilast's potential therapeutic use in IPF by providing insights into its cellular and molecular actions. This article is open access and distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives License 4.0 (http://creativecommons.org/licenses/by-nc-nd/4.0/).

PMID:40239038 | DOI:10.1165/rcmb.2024-0614OC

Am J Respir Cell Mol Biol. 2025 Apr 16. doi: 10.1165/rcmb.2024-0433OC. Online ahead of print.

ABSTRACT

Infection by influenza A virus (IAV) and other viruses causes disease exacerbations in chronic obstructive pulmonary disease (COPD). Immune responses are blunted in COPD, a deficit compounded by current standard-of-care glucocorticosteroids (GCS) to further predispose patients to life-threatening infections. The immunosuppressive effects of elevated transforming growth factor-beta (TGF-β) in COPD may amplify lung inflammation during infections whilst advancing fibrosis. In the current study, we investigated potential repurposing of pirfenidone, currently used as an anti-fibrotic for idiopathic pulmonary fibrosis, as a non-steroidal treatment for viral exacerbations of COPD. Murine models of lung-specific TGF-β overexpression or chronic cigarette smoke exposure with IAV infection were used. Pirfenidone was administered daily by oral gavage commencing pre-or post-infection, while inhaled pirfenidone and GCS treatment pre-infection were also compared. Tissue and bronchoalveolar lavage were assessed for viral replication, inflammation and immune responses. Overexpression of TGF-β enhanced severity of IAV infection contributing to unrestrained airway inflammation. Mechanistically, TGF-β reduced innate immune responses to IAV by blunting interferon regulated gene (IRG) expression and suppressing production of anti-viral proteins. Prophylactic pirfenidone administration opposed these actions of TGF-β, curbing IAV infection and airway inflammation associated with TGF-β overexpression and cigarette smoke-induced COPD. Notably, inhaled pirfenidone caused greater inhibition of viral loads and inflammation than inhaled GCS. These proof-of-concept studies demonstrate that repurposing pirfenidone and employing a preventative strategy may yield substantial benefit over anti-inflammatory GCS in COPD. Pirfenidone can mitigate damaging virus exacerbations without attendant immunosuppressive actions and merits further investigation, particularly as an inhaled formulation.

PMID:40239009 | DOI:10.1165/rcmb.2024-0433OC

Comput Biol Med. 2025 Apr 15;191:110185. doi: 10.1016/j.compbiomed.2025.110185. Online ahead of print.

ABSTRACT

BACKGROUND AND OBJECTIVE: The Gene Ontology (GO) project has been pivotal in providing a structured framework for characterizing genes and annotating them to specific biological concepts. While traditional gene annotation primarily focuses on mapping genes to GO terms, descriptors of biological concepts, there is a growing need for tools facilitating reverse querying. This paper introduces GOReverseLookup, a novel tool designed to identify over- or underrepresented genes in researcher-defined states of interest (phenotypes), described by sets of GO terms. GOReverseLookup supplements the existing power of Gene Ontology by the possibility of orthologous gene querying across several databases, such as Ensembl and UniProtKB. This combination allows for a more nuanced identification of significant genes across a range of cross-species research contexts.

METHODS: GOReverseLookup queries genes associated with input GO terms. Bundles of GO terms encapsulate user-defined states of interest, e.g., angiogenesis. In the second stage of the analysis, all GO terms associated with each gene are fetched, and finally, the statistical relevance of the genes being involved in one (or all) of the defined states of interests is computed.

RESULTS: The two presented use cases illustrate its utility in discovering genes related to rheumatoid arthritis and genes linked with chronic inflammation and tumorigenesis. In both cases, GOReverseLookup discovered a substantial number of genes significantly associated with the aforementioned states of interest.

CONCLUSIONS: GOReverseLookup proves to be a valuable resource for unraveling the genetic basis of phenotypes, with diverse practical potentials in functional genomics, systems biology, and drug discovery. We anticipate that GOReverseLookup will significantly aid in identifying potential gene targets during the initial research phases.

PMID:40239235 | DOI:10.1016/j.compbiomed.2025.110185

G3 (Bethesda). 2025 Apr 16:jkaf083. doi: 10.1093/g3journal/jkaf083. Online ahead of print.

ABSTRACT

Cassava (Manihot esculenta Grantz) is a vital staple crop for millions of people, particularly in Sub-Saharan Africa, where it is a primary source of food and income. However, cassava production is threatened by several viral diseases, including cassava brown streak disease, which causes severe damage to the edible storage roots. Current cassava varieties in Africa lack effective resistance to this disease, leading to significant crop losses. We investigated the genetic diversity of cassava and identified new sources of resistance to the viruses causing cassava brown streak disease. The cassava line, COL40, from a South American germplasm collection showed broad-spectrum resistance against all known strains of the viruses that cause this disease. To further understand the genetic basis of this resistance, we sequenced the genome of COL40 and produced a high-quality, haplotype-resolved genome assembly. This genomic resource provides new insights into cassava's genetic architecture, particularly in regions associated with disease resistance. The sequence reveals significant structural variation, including transposable elements, inversions, and deletions, which may contribute to the resistance phenotype. The reference genome assembly presented here will provide a valuable genomic resource for studying the cassava brown streak resistance and will help in accelerating breeding efforts to introduce virus resistance into African cassava varieties. By identifying genetic variants linked to resistance, future breeding programs can develop cassava cultivars that are more resilient to viral threats, enhancing food security and livelihoods for smallholder farmers across regions affected by the disease.

PMID:40239025 | DOI:10.1093/g3journal/jkaf083

STAR Protoc. 2025 Apr 15;6(2):103761. doi: 10.1016/j.xpro.2025.103761. Online ahead of print.

ABSTRACT

Transit time is a key in vivo metric of gastrointestinal (GI) motility, which is a physiologic readout of cellular communication within the enteric system. Here, we present a protocol to characterize longitudinal gut motility in mice. We describe steps for transit testing, whole-mount immunostaining, and tissue harvest. We then detail procedures for image processing and manual cell counting. This protocol seeks to minimize inter-trial variability while assessing cellular and molecular features that may underpin motility differences between experimental conditions. For complete details on the use and execution of this protocol, please refer to Frith et al.1.

PMID:40238633 | DOI:10.1016/j.xpro.2025.103761

J Med Internet Res. 2025 Apr 16;27:e68757. doi: 10.2196/68757.

ABSTRACT

Digital health interventions (DHIs) have the potential to improve health care and health promotion. However, there is a lack of guidance in the literature for the development, refinement, and prioritization of key performance indicators (KPIs) for the evaluation of DHIs. This paper presents a 4-stage process used in the Gravitate Health project based on stakeholder consultation and consensus for this purpose. The Gravitate Health consortium, which comprises private and public partners from across Europe and the United States, is developing innovative digital health solutions in the form of Federated Open-Source Platform and G-lens to present users with individualized digital information about their medicines. The first stage of this was the consultative process for the development of KPIs involving stakeholder (Gravitate Health project leads) consultations at the planning stages of the project. This resulted in the formation of an extensive list of KPIs organized into 7 categories. The second stage was conducting a scoping review, which confirmed the need for extensive stakeholder consultation in all stages of the KPI development, refinement, and prioritization process. The third stage was a period of further consultation with all consortium members, which resulted in the elimination of 1 category of KPIs. The fourth stage involved using the Delphi technique for refining and prioritizing the remaining 6 categories of KPIs. It is unusual to use this methodology in a nonresearch exercise, but it provided a clear consultative framework and structure that facilitated the achievement of consensus within a large consortium of 250 members on a substantial list of KPIs for the project. Consortium members ranked the relevance and importance of each KPI. The final list of KPIs provides substantial indicators sensitive to the needs of a broad group of stakeholders that are being used to capture real-world data in developing and evaluating DHIs.

PMID:40239207 | DOI:10.2196/68757

J Neurooncol. 2025 Apr 16. doi: 10.1007/s11060-025-05004-1. Online ahead of print.

ABSTRACT

PURPOSE: This retrospective study aims to show a real-life single-center experience with clinical management of relapsed pediatric ependymomas using results from comprehensive molecular profiling.

METHODS: Eight relapsed ependymomas were tested by whole exome sequencing, RNA sequencing, phosphoproteomic arrays, array comparative genome hybridization, and immunohistochemistry staining for PD-L1 expression and treated with an individualized approach implementing targeted inhibitors, immunotherapy, antiangiogenic metronomic treatment, or other agents. Treatment efficacy was evaluated using progression-free survival (PFS), overall survival (OS), survival after relapse (SAR), and PFS ratios.

RESULTS: Genomic analyses did not reveal any therapeutically actionable alterations. Surgery remained the cornerstone of patient treatment, supplemented by adjuvant radiotherapy. Empiric agents were chosen quite frequently, often involving drug repurposing. In six patients, prolonged PFS after relapse was seen because of immunotherapy, MEMMAT, or empiric agents and is reflected in the PFS ratio ≥ 1. The 5-year OS was 88%, the 10-year OS was 73%, the 2-year SAR was 88%, and the 5-year SAR was 66%.

CONCLUSION: We demonstrated the feasibility and good safety profile. Promising was the effect of immunotherapy on ZFTA-positive ependymomas. However, further research is required to establish the most effective approach for achieving sustained remission in these patients.

PMID:40238025 | DOI:10.1007/s11060-025-05004-1