Front Digit Health. 2025 Mar 24;7:1507159. doi: 10.3389/fdgth.2025.1507159. eCollection 2025.

ABSTRACT

This paper investigates the importance of Trustworthy Machine Learning (ML) in the context of Multiple Sclerosis (MS) research and care. Due to the complex and individual nature of MS, the need for reliable and trustworthy ML models is essential. In this paper, key aspects of trustworthy ML, such as out-of-distribution generalization, explainability, uncertainty quantification and calibration are explored, highlighting their significance for healthcare applications. Challenges in integrating these ML tools into clinical workflows are addressed, discussing the difficulties in interpreting AI outputs, data diversity, and the need for comprehensive, quality data. It calls for collaborative efforts among researchers, clinicians, and policymakers to develop ML solutions that are technically sound, clinically relevant, and patient-centric.

PMID:40196398 | PMC:PMC11973328 | DOI:10.3389/fdgth.2025.1507159

Front Med (Lausanne). 2025 Mar 24;12:1547588. doi: 10.3389/fmed.2025.1547588. eCollection 2025.

ABSTRACT

BACKGROUND: Early diagnosis of non-union fractures is vital for treatment planning, yet studies using bone morphometric parameters for this purpose are scarce. This study aims to create a fracture micro-CT image dataset, design a deep learning algorithm for fracture segmentation, and develop an early diagnosis model for fracture non-union.

METHODS: Using fracture animal models, micro-CT images from 12 rats at various healing stages (days 1, 7, 14, 21, 28, and 35) were analyzed. Fracture lesion frames were annotated to create a high-resolution dataset. We proposed the Vision Mamba Triplet Attention and Edge Feature Decoupling Module UNet (VM-TE-UNet) for fracture area segmentation. And we extracted bone morphometric parameters to establish an early diagnostic evaluation system for the non-union of fractures.

RESULTS: A dataset comprising 2,448 micro-CT images of the rat fracture lesions with fracture Region of Interest (ROI), bone callus and healing characteristics was established and used to train and test the proposed VM-TE-UNet which achieved a Dice Similarity Coefficient of 0.809, an improvement over the baseline's 0.765, and reduced the 95th Hausdorff Distance to 13.1. Through ablation studies, comparative experiments, and result analysis, the algorithm's effectiveness and superiority were validated. Significant differences (p < 0.05) were observed between the fracture and fracture non-union groups during the inflammatory and repair phases. Key indices, such as the average CT values of hematoma and cartilage tissues, BS/TS and BS/TV of mineralized cartilage, BS/TV of osteogenic tissue, and BV/TV of osteogenic tissue, align with clinical methods for diagnosing fracture non-union by assessing callus presence and local soft tissue swelling. On day 14, the early diagnosis model achieved an AUC of 0.995, demonstrating its ability to diagnose fracture non-union during the soft-callus phase.

CONCLUSION: This study proposed the VM-TE-UNet for fracture areas segmentation, extracted micro-CT indices, and established an early diagnostic model for fracture non-union. We believe that the prediction model can effectively screen out samples of poor fracture rehabilitation caused by blood supply limitations in rats 14 days after fracture, rather than the widely accepted 35 or 40 days. This provides important reference for the clinical prediction of fracture non-union and early intervention treatment.

PMID:40196347 | PMC:PMC11973290 | DOI:10.3389/fmed.2025.1547588

medRxiv [Preprint]. 2025 Mar 25:2025.03.24.25324556. doi: 10.1101/2025.03.24.25324556.

ABSTRACT

Artificial intelligence (AI) applied to single-cell data has the potential to transform our understanding of biological systems by revealing patterns and mechanisms that simpler traditional methods miss. Here, we develop a general-purpose, interpretable AI pipeline consisting of two deep learning models: the Multi- Input Set Transformer++ (MIST) model for prediction and the single-cell FastShap model for interpretability. We apply this pipeline to a large set of routine clinical data containing single-cell measurements of circulating red blood cells (RBC), white blood cells (WBC), and platelets (PLT) to study population fluxes and homeostatic hematological mechanisms. We find that MIST can use these single-cell measurements to explain 70-82% of the variation in blood cell population sizes among patients (RBC count, PLT count, WBC count), compared to 5-20% explained with current approaches. MIST's accuracy implies that substantial information on cellular production and clearance is present in the single-cell measurements. MIST identified substantial crosstalk among RBC, WBC, and PLT populations, suggesting co-regulatory relationships that we validated and investigated using interpretability maps generated by single-cell FastShap. The maps identify granular single-cell subgroups most important for each population's size, enabling generation of evidence-based hypotheses for co-regulatory mechanisms. The interpretability maps also enable rational discovery of a single-WBC biomarker, "Down Shift", that complements an existing marker of inflammation and strengthens diagnostic associations with diseases including sepsis, heart disease, and diabetes. This study illustrates how single-cell data can be leveraged for mechanistic inference with potential clinical relevance and how this AI pipeline can be applied to power scientific discovery.

PMID:40196278 | PMC:PMC11974774 | DOI:10.1101/2025.03.24.25324556

medRxiv [Preprint]. 2025 Mar 26:2025.03.25.25324627. doi: 10.1101/2025.03.25.25324627.

ABSTRACT

Accurate understanding of biological aging and the impact of environmental stressors is crucial for understanding cardiovascular health and identifying patients at risk for adverse outcomes. Chronological age stands as perhaps the most universal risk predictor across virtually all populations and diseases. While chronological age is readily discernible, efforts to distinguish between biologically older versus younger individuals can, in turn, potentially identify individuals with accelerated versus delayed cardiovascular aging. This study presents a deep learning artificial intelligence (AI) approach to predict age from echocardiogram videos, leveraging 2,610,266 videos from 166,508 studies from 90,738 unique patients and using the trained models to identify features of accelerated and delayed aging. Leveraging multi-view echocardiography, our AI age prediction model achieved a mean absolute error (MAE) of 6.76 (6.65 - 6.87) years and a coefficient of determination (R 2 ) of 0.732 (0.72 - 0.74). Stratification by age prediction revealed associations with increased risk of coronary artery disease, heart failure, and stroke. The age prediction can also identify heart transplant recipients as a discontinuous prediction of age is seen before and after a heart transplant. Guided back propagation visualizations highlighted the model's focus on the mitral valve, mitral apparatus, and basal inferior wall as crucial for the assessment of age. These findings underscore the potential of computer vision-based assessment of echocardiography in enhancing cardiovascular risk assessment and understanding biological aging in the heart.

PMID:40196275 | PMC:PMC11974980 | DOI:10.1101/2025.03.25.25324627

medRxiv [Preprint]. 2025 Mar 25:2025.03.24.25324549. doi: 10.1101/2025.03.24.25324549.

ABSTRACT

The discovery of genetic loci associated with brain architecture can provide deeper insights into neuroscience and improved personalized medicine outcomes. Previously, we designed the Unsupervised Deep learning-derived Imaging Phenotypes (UDIPs) approach to extract endophenotypes from brain imaging using a convolutional (CNN) autoencoder, and conducted brain imaging GWAS on UK Biobank (UKBB). In this work, we leverage a vision transformer (ViT) model due to a different inductive bias and its ability to potentially capture unique patterns through its pairwise attention mechanism. Our approach based on 128 endophenotypes derived from average pooling discovered 10 loci previously unreported by CNN-based UDIP model, 3 of which were not found in the GWAS catalog to have had any associations with brain structure. Our interpretation results demonstrate the ViT's capability in capturing non-local patterns such as left-right hemisphere symmetry within brain MRI data, by leveraging its attention mechanism and positional embeddings. Our results highlight the advantages of transformer-based architectures in feature extraction and representation for genetic discovery.

PMID:40196251 | PMC:PMC11974795 | DOI:10.1101/2025.03.24.25324549

bioRxiv [Preprint]. 2025 Mar 24:2025.03.21.644589. doi: 10.1101/2025.03.21.644589.

ABSTRACT

Pseudomonas aeruginosa (PA) causes severe respiratory infections utilizing multiple virulence functions. Our previous findings on PA quorum sensing (QS)-regulated small molecule, 2'-aminoacetophenone (2-AA), secreted by the bacteria in infected tissues, revealed its effect on immune and metabolic functions favouring a long-term presence of PA in the host. However, studies on 2-AA's specific effects on bronchial-airway epithelium and pulmonary endothelium remain elusive. To evaluate 2AA's spatiotemporal changes in the human airway, considering endothelial cells as the first point of contact when the route of lung infection is hematogenic, we utilized the microfluidic airway-on-chip lined by polarized human bronchial-airway epithelium and pulmonary endothelium. Using this platform, we performed RNA-sequencing to analyse responses of 2-AA-treated primary human pulmonary microvascular endothelium (HPMEC) and adjacent primary normal human bronchial epithelial (NHBE) cells from healthy female donors and potential cross-talk between these cells. Analyses unveiled specific signaling and biosynthesis pathways to be differentially regulated by 2-AA in epithelial cells, including HIF-1 and pyrimidine signaling, glycosaminoglycan, and glycosphingolipid biosynthesis, while in endothelial cells were fatty acid metabolism, phosphatidylinositol and estrogen receptor signaling, and proinflammatory signaling pathways. Significant overlap in both cell types in response to 2-AA was found in genes implicated in immune response and cellular functions. In contrast, we found that genes related to barrier permeability, cholesterol metabolism, and oxidative phosphorylation were differentially regulated upon exposure to 2-AA in the cell types studied. Murine in-vivo and additional in vitro cell culture studies confirmed cholesterol accumulation in epithelial cells. Results also revealed specific biomarkers associated with cystic fibrosis and idiopathic pulmonary fibrosis to be modulated by 2-AA in both cell types, with the cystic fibrosis transmembrane regulator expression to be affected only in endothelial cells. The 2-AA-mediated effects on healthy epithelial and endothelial primary cells within a microphysiological dynamic environment mimicking the human lung airway enhance our understanding of this QS signaling molecule. This study provides novel insights into their functions and potential interactions, paving the way for innovative, cell-specific therapeutic strategies to combat PA lung infections.

PMID:40196568 | PMC:PMC11974707 | DOI:10.1101/2025.03.21.644589

bioRxiv [Preprint]. 2025 Mar 28:2025.03.24.644676. doi: 10.1101/2025.03.24.644676.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) is a complex neurodegenerative disorder with substantial molecular variability across different brain regions and individuals, hindering therapeutic development. This study introduces PRISM-ML, an interpretable machine learning (ML) framework integrating multiomics data to uncover patient-specific biomarkers, subtissue-level pathology, and drug repurposing opportunities.

METHODS: We harmonized transcriptomic and genomic data of three independent brain studies containing 2105 post-mortem brain samples (1363 AD, 742 controls) across nine tissues. A Random Forest classifier with SHapley Additive exPlanations (SHAP) identified patient-level biomarkers. Clustering further delineated each tissue into subtissues, and network analysis revealed critical "bottleneck" (hub) genes. Finally, a knowledge graph-based screening identified multi-target drug candidates, and a real-world pharmacoepidemiologic study evaluated their clinical relevance.

RESULTS: We uncovered 36 molecularly distinct subtissues, each defined by a set of associated unique biomarkers and genetic drivers. Through network analysis of gene-gene interactions networks, we highlighted 262 bottleneck genes enriched in synaptic, cytoskeletal, and membrane-associated processes. Knowledge graph queries identified six FDA-approved drugs predicted to target multiple bottleneck genes and AD-relevant pathways simultaneously. One candidate, promethazine, demonstrated an association with reduced AD incidence in a large healthcare dataset of over 364000 individuals (hazard ratios ≤ 0.43; p < 0.001). These findings underscore the potential for multi-target approaches, reveal connections between AD and cardiovascular pathways, and offer novel insights into the heterogeneous biology of AD.

CONCLUSIONS: PRISM-ML bridges interpretable ML with multi-omics and systems biology to decode AD heterogeneity, revealing region-specific mechanisms and repurposable therapeutics. The validation of promethazine in real-world data underscores the clinical relevance of multi-target strategies, paving the way for more personalized treatments in AD and other complex disorders.

PMID:40196631 | PMC:PMC11974764 | DOI:10.1101/2025.03.24.644676

Front Pharmacol. 2025 Mar 24;16:1530109. doi: 10.3389/fphar.2025.1530109. eCollection 2025.

ABSTRACT

Impaired intestinal epithelial barrier function is closely associated with the pathogenesis of ulcerative colitis (UC). Atractylenolide-I (AT-I), a major sesquiterpene derived from the herb Atractylodes macrocephala Koidz., has been reported to alleviate DSS-induced colitis in mice. This study aims to investigated the protective effects of AT-1 on intestinal epithelial barrier function and elucidate it's underlying mechanisms. In vivo, an acute colitis model was established in mice, and transcriptomic analysis to identify differentially expressed genes. In vitro, overexpression plasmids and recombinant protein were used to evaluate their effects on intestinal barrier function, and further analysis of its potential mechanisms.The study found that AT-1 ameliorate DSS-induced acute ulcerative colitis, exhibiting protective effects on the intestinal barrier. Transcriptomic analysis revealed that AT-1 significantly modulated the expression of S100A8 and S100A9. Further investigations indicated that S100A9, rather than S100A8, mediated the expression of tight junction proteins, meanwhile, AT-1 reduces neutrophil activation and subsequent release of S100A9. Mechanistically, recombinant human S100A9 protein was found to induce a decrease in intracellular Ca2+ concentration, while AT-1 regulated the expression of tight junction proteins via modulation of the AMPK/mTOR signaling pathway. AT-1 enhances the recovery of DSS-induced intestinal barrier dysfunction by regulating the recombinant human S100A9 protein-mediated AMPK/mTOR signaling pathway. This study provides new insights into the pathogenesis of ulcerative colitis and suggests potential therapeutic strategies for its treatment.

PMID:40196359 | PMC:PMC11973269 | DOI:10.3389/fphar.2025.1530109

J Microbiol. 2025 Mar;63(3):e2501021. doi: 10.71150/jm.2501021. Epub 2025 Mar 28.

ABSTRACT

Existing microbial engineering strategies-encompassing metabolic engineering, systems biology, and systems metabolic engineering-have significantly enhanced the potential of microbial cell factories as sustainable alternatives to the petrochemical industry by optimizing metabolic pathways. Recently, systems metabolic engineering, which integrates tools from synthetic biology, enzyme engineering, omics technology, and evolutionary engineering, has been successfully developed. By leveraging modern engineering strategies within the Design-Build-Test-Learn (DBTL) cycle framework, these advancements have revolutionized the biosynthesis of valuable compounds. This review highlights recent progress in the metabolic engineering of Corynebacterium glutamicum, a versatile microbial platform, achieved through various approaches from traditional metabolic engineering to advanced systems metabolic engineering, all within the DBTL cycle. A particular focus is placed C5 platform chemicals derived from L-lysine, one of the key amino acid production pathways of C. glutamicum. The development of DBTL cycle-based metabolic engineering strategies for this process is discussed.

PMID:40195836 | DOI:10.71150/jm.2501021

Nat Microbiol. 2025 Apr 7. doi: 10.1038/s41564-025-01978-w. Online ahead of print.

ABSTRACT

Microbial genome-wide association studies (GWAS) have uncovered numerous host genetic variants associated with gut microbiota. However, links between host genetics, the gut microbiome and specific cellular contexts remain unclear. Here we use a computational framework, scBPS (single-cell Bacteria Polygenic Score), to integrate existing microbial GWAS and single-cell RNA-sequencing profiles of 24 human organs, including the liver, pancreas, lung and intestine, to identify host tissues and cell types relevant to gut microbes. Analysing 207 microbial taxa and 254 host cell types, scBPS-inferred cellular enrichments confirmed known biology such as dominant communications between gut microbes and the digestive tissue module and liver epithelial cell compartment. scBPS also identified a robust association between Collinsella and the central-veinal hepatocyte subpopulation. We experimentally validated the causal effects of Collinsella on cholesterol metabolism in mice through single-nuclei RNA sequencing on liver tissue to identify relevant cell subpopulations. Mechanistically, oral gavage of Collinsella modulated cholesterol pathway gene expression in central-veinal hepatocytes. We further validated our approach using independent microbial GWAS data, alongside single-cell and bulk transcriptomic analyses, demonstrating its robustness and reproducibility. Together, scBPS enables a systematic mapping of the host-microbe crosstalk by linking cell populations to their interacting gut microbes.

PMID:40195537 | DOI:10.1038/s41564-025-01978-w

EMBO J. 2025 Apr 7. doi: 10.1038/s44318-025-00423-2. Online ahead of print.

ABSTRACT

Cholesteryl esters (CEs) and triacylglycerols (TAGs) are stored in lipid droplets (LDs), but their compartmentalisation is not well understood. Here, we established a hyperspectral stimulated Raman scattering microscopy system to identify and quantitatively assess CEs and TAGs in individual LDs of human cells. We found that nuclear envelope-associated lipid droplets (NE-LDs) were enriched in cholesteryl esters compared to lipid droplets in the cytoplasm. Correlative light-volume-electron microscopy revealed that NE-LDs projected towards the cytoplasm and associated with type II nuclear envelope (NE) invaginations. The nuclear envelope localization of sterol O-acyltransferase 1 (SOAT1) contributed to NE-LD generation, as trapping of SOAT1 to the NE further increased their number. Upon stimulation by the pro-inflammatory cytokine TNFα, the number of NE-LDs moderately increased. Moreover, TNFα-induced NF-κB nuclear translocation was fine-tuned by SOAT1: increased SOAT1 activity and NE-LDs associated with faster NF-κB translocation, whereas reduced SOAT1 activity and NE-LDs associated with slower NF-κB translocation. Our findings suggest that the NE is enriched in CEs and that cholesterol esterification can modulate nuclear translocation.

PMID:40195500 | DOI:10.1038/s44318-025-00423-2

Nat Commun. 2025 Apr 7;16(1):3290. doi: 10.1038/s41467-025-57982-5.

ABSTRACT

One of Earth's largest carbon fluxes is driven by particles made from photosynthetically fixed matter, which aggregate and sink into the deep ocean. While biodegradation is known to reduce this vertical flux, the biophysical processes that control particle sinking speed are not well understood. Here, we use a vertical millifluidic column to video-track single particles and find that biogels scavenged by particles during sinking significantly reduce the particles' sinking speed, slowing them by up to 45% within one day. Combining observations with a mathematical model, we determine that the mechanism for this slowdown is a combination of increased drag due to the formation of biogel tendrils and increased buoyancy due to the biogel's low density. Because biogels are pervasive in the ocean, we propose that by slowing the sinking of organic particles they attenuate the vertical carbon flux in the ocean.

PMID:40195314 | DOI:10.1038/s41467-025-57982-5

Nat Commun. 2025 Apr 7;16(1):3318. doi: 10.1038/s41467-025-58485-z.

ABSTRACT

Genetically encoded calcium ion (Ca2+) indicators (GECIs) are widely-used molecular tools for functional imaging of Ca2+ dynamics and neuronal activities with single-cell resolution. Here we report the design and development of two far-red fluorescent GECIs, FR-GECO1a and FR-GECO1c, based on the monomeric far-red fluorescent proteins mKelly1 and mKelly2. FR-GECOs have excitation and emission maxima at ~596 nm and ~644 nm, respectively, display large responses to Ca2+ in vitro (ΔF/F0 = 6 for FR-GECO1a, 18 for FR-GECO1c), are bright under both one-photon and two-photon illumination, and have high affinities (apparent Kd = 29 nM for FR-GECO1a, 83 nM for FR-GECO1c) for Ca2+. FR-GECOs offer sensitive and fast detection of single action potentials in neurons, and enable in vivo all-optical manipulation and measurement of cellular activities in combination with optogenetic actuators.

PMID:40195305 | DOI:10.1038/s41467-025-58485-z

Neurology. 2025 Apr 8;104(7_Supplement_1):3899. doi: 10.1212/WNL.0000000000211311. Epub 2025 Apr 7.

ABSTRACT

OBJECTIVE: This study aims to evaluate condition-specific knowledge among patients with rare neuroimmune disorders.

BACKGROUND: Rare neuroimmune disorders (RNDs) include conditions such as neuromyelitis optica spectrum disorder (NMOSD), myelin oligodendrocyte glycoprotein antibody associated disease (MOGAD), acute disseminated encephalomyelitis (ADEM), idiopathic optic neuritis (ON) and transverse myelitis (TM). These conditions share significant phenotypic overlap, which makes communication of the diagnosis and relapse risk challenging for neurologists. This may predispose patients to have an incomplete understanding of their condition and long-term prognosis. In this study, we sought to understand the condition-specific knowledge in patients with RNDs.

DESIGN/METHODS: A questionnaire was developed to assess condition-specific knowledge in patients with RNDs by a group of neuroimmunologists. An initial version of the test was administered to five individuals with RNDs, who provided feedback via semistructured interviews. The final version of the test included fifteen questions covering localization, symptoms, etiology, and relapse risk. The test was administered virtually to subjects via a Redcap survey. Subjects also completed a demographic questionnaire, the Medical Term Recognition Test (METER) for health literacy assessment, and the Patient Determined Disease Steps (PDDS).

RESULTS: Ninety-two subjects completed the test of knowledge, and eighty-nine completed all procedures. The study population was largely female (73%), and 68% completed 16+ years of education. Individuals with MOGAD and NMOSD (n=45) scored higher on the test (median score 87%) compared to individuals with idiopathic conditions (n=45; median score 73%). Analysis for the correlation of test scores with age, duration since diagnosis, health literacy, and self-reported disability are ongoing.

CONCLUSIONS: Individuals with idiopathic conditions (TM, ON, ADEM) scored lower on our test when compared to the better-characterized conditions of NMOSD and MOGAD. Individuals with idiopathic conditions may benefit from targeted education about their diagnosis and relapse risk. Disclaimer: Abstracts were not reviewed by Neurology® and do not reflect the views of Neurology® editors or staff. Disclosure: Ms. Mahale has nothing to disclose. Dr. Sguigna has received personal compensation in the range of $500-$4,999 for serving as a Consultant for EMD Serono. Dr. Sguigna has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Genentech. Dr. Sguigna has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Horizon Therapeutics. The institution of Dr. Sguigna has received research support from Genentech. The institution of Dr. Sguigna has received research support from Clene Nanomedicine. The institution of Dr. Sguigna has received research support from The International Progressive Multiple Sclerosis Alliance through the National Multiple Sclerosis Society. The institution of Dr. Sguigna has received research support from PCORI. The institution of Dr. Sguigna has received research support from DOD/CDMRP. The institution of Dr. Sguigna has received research support from Alexion. Dr. Sguigna has received intellectual property interests from a discovery or technology relating to health care. Dr. Tardo has received personal compensation in the range of $500-$4,999 for serving as a Consultant for EMD Serono. Dr. Tardo has received personal compensation in the range of $500-$4,999 for serving on a Speakers Bureau for NeurologyLive. Dr. Tardo has received personal compensation in the range of $500-$4,999 for serving as a Panel member with CanDoMS. Dr. Tardo has a non-compensated relationship as a Tardo with The MOG Project that is relevant to AAN interests or activities. Dr. Nguyen has nothing to disclose. Dr. DeFiebre has received personal compensation for serving as an employee of Siegel Rare Neuroimmune Association. Dr. Blackburn has received personal compensation in the range of $500-$4,999 for serving on a Scientific Advisory or Data Safety Monitoring board for TG Therapeutics.

PMID:40194445 | DOI:10.1212/WNL.0000000000211311

Pharmacogenomics. 2025 Apr 7:1-8. doi: 10.1080/14622416.2025.2490465. Online ahead of print.

ABSTRACT

Pharmacogenomics (PGx) is an evolving field that integrates genetic information into clinical decision-making to optimize drug therapy and minimize adverse drug reactions (ADRs). Its application in rare disease (RD) drug development is promising, given the genetic basis of many RDs and the need for precision medicine approaches. Despite significant advancements, challenges persist in developing effective therapies for RDs due to small patient populations, genetic heterogeneity, and limited surrogate biomarkers. The Orphan Drug Act in the U.S. has incentivized RD drug development. However, the traditional drug approval process is constrained by logistical and economic challenges, necessitating innovative PGx-driven strategies. Identifying genetic biomarkers in the early drug development stages can optimize dose selection, enhance therapeutic efficacy, and reduce ADRs. Case studies such as eliglustat for Gaucher disease and ivacaftor for cystic fibrosis demonstrate the efficacy of PGx-guided treatment strategies. Integrating PGx into global drug development requires the harmonization of regulatory policies and increased diversity in genetic research. Artificial intelligence (AI) tools further enhance genetic analysis, disease prediction, and clinical decision-making. Modernizing drug labeling with PGx information is critical to ensuring safe and effective druguse. Collectively, PGx offers transformative potential in RD therapeutics by facilitating personalized medicine approaches and addressing unmet medical needs.

PMID:40194983 | DOI:10.1080/14622416.2025.2490465

Pharmacogenomics. 2025 Apr 7:1-8. doi: 10.1080/14622416.2025.2490465. Online ahead of print.

ABSTRACT

Pharmacogenomics (PGx) is an evolving field that integrates genetic information into clinical decision-making to optimize drug therapy and minimize adverse drug reactions (ADRs). Its application in rare disease (RD) drug development is promising, given the genetic basis of many RDs and the need for precision medicine approaches. Despite significant advancements, challenges persist in developing effective therapies for RDs due to small patient populations, genetic heterogeneity, and limited surrogate biomarkers. The Orphan Drug Act in the U.S. has incentivized RD drug development. However, the traditional drug approval process is constrained by logistical and economic challenges, necessitating innovative PGx-driven strategies. Identifying genetic biomarkers in the early drug development stages can optimize dose selection, enhance therapeutic efficacy, and reduce ADRs. Case studies such as eliglustat for Gaucher disease and ivacaftor for cystic fibrosis demonstrate the efficacy of PGx-guided treatment strategies. Integrating PGx into global drug development requires the harmonization of regulatory policies and increased diversity in genetic research. Artificial intelligence (AI) tools further enhance genetic analysis, disease prediction, and clinical decision-making. Modernizing drug labeling with PGx information is critical to ensuring safe and effective druguse. Collectively, PGx offers transformative potential in RD therapeutics by facilitating personalized medicine approaches and addressing unmet medical needs.

PMID:40194983 | DOI:10.1080/14622416.2025.2490465

AJNR Am J Neuroradiol. 2025 Apr 7:ajnr.A8768. doi: 10.3174/ajnr.A8768. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Perfusion and perfusion-related parameter maps obtained using dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are both useful for clinical diagnosis and research. However, using both DSC and DCE MRI in the same scan session requires two doses of gadolinium contrast agent. The objective was to develop deep-learning based methods to synthesize DSC-derived parameter maps from DCE MRI data.

MATERIALS AND METHODS: Independent analysis of data collected in previous studies was performed. The database contained sixty-four participants, including patients with and without brain tumors. The reference parameter maps were measured from DSC MRI performed following DCE MRI. A conditional generative adversarial network (cGAN) was designed and trained to generate synthetic DSC-derived maps from DCE MRI data. The median parameter values and distributions between synthetic and real maps were compared using linear regression and Bland-Altman plots.

RESULTS: Using cGAN, realistic DSC parameter maps could be synthesized from DCE MRI data. For controls without brain tumors, the synthesized parameters had distributions similar to the ground truth values. For patients with brain tumors, the synthesized parameters in the tumor region correlated linearly with the ground truth values. In addition, areas not visible due to susceptibility artifacts in real DSC maps could be visualized using DCE-derived DSC maps.

CONCLUSIONS: DSC-derived parameter maps could be synthesized using DCE MRI data, including susceptibility-artifact-prone regions. This shows the potential to obtain both DSC and DCE parameter maps from DCE MRI using a single dose of contrast agent.

ABBREVIATIONS: cGAN=conditional generative adversarial network; Ktrans=volume transfer constant; rCBV=relative cerebral blood volume; rCBF=relative cerebral blood flow; Ve=extravascular extracellular volume; Vp=plasma volume.

PMID:40194853 | DOI:10.3174/ajnr.A8768

AJNR Am J Neuroradiol. 2025 Apr 7:ajnr.A8770. doi: 10.3174/ajnr.A8770. Online ahead of print.

ABSTRACT

BACKGROUND AND PURPOSE: Spinal cord injury (SCI) in the pediatric population presents a unique challenge in diagnosis and prognosis due to the complexity of performing clinical assessments on children. Accurate evaluation of structural changes in the spinal cord is essential for effective treatment planning. This study aims to evaluate structural characteristics in pediatric patients with SCI by comparing cross-sectional area (CSA), anterior-posterior (AP) width, and right-left (RL) width across all vertebral levels of the spinal cord between typically developing (TD) and participants with SCI. We employed deep learning techniques to utilize these measures for detecting SCI cases and determining their injury severity.

MATERIALS AND METHODS: Sixty-one pediatric participants (ages 6-18), including 20 with chronic SCI and 41 TD, were enrolled and scanned using a 3T MRI scanner. All SCI participants underwent the International Standards for Neurological Classification of Spinal Cord Injury (ISNCSCI) test to assess their neurological function and determine their American Spinal Injury Association (ASIA) Impairment Scale (AIS) category. T2-weighted MRI scans were utilized to measure CSA, AP width, and RL widths along the entire cervical and thoracic cord. These measures were automatically extracted at every vertebral level of the spinal cord using the SCT toolbox. Deep convolutional neural networks (CNNs) were utilized to classify participants into SCI or TD groups and determine their AIS classification based on structural parameters and demographic factors such as age and height.

RESULTS: Significant differences (p<0.05) were found in CSA, AP width, and RL width between SCI and TD participants, indicating notable structural alterations due to SCI. The CNN-based models demonstrated high performance, achieving 96.59% accuracy in distinguishing SCI from TD participants. Furthermore, the models determined AIS category classification with 94.92% accuracy.

CONCLUSIONS: The study demonstrates the effectiveness of integrating cross-sectional structural imaging measures with deep learning methods for classification and severity assessment of pediatric SCI. The deep learning approach outperforms traditional machine learning models in diagnostic accuracy, offering potential improvements in patient care in pediatric SCI management.

ABBREVIATIONS: SCI = Spinal Cord Injury, TD = Typically Developing, CSA = Cross-Sectional Area, AP = Anterior-Posterior, RL = Right-Left, ASIA = American Spinal Injury Association, AIS = American Spinal Injury Association, CNN = Convolutional Neural Network.

PMID:40194851 | DOI:10.3174/ajnr.A8770

J Mol Biol. 2025 Apr 5:169131. doi: 10.1016/j.jmb.2025.169131. Online ahead of print.

ABSTRACT

With the rise of small interfering RNA (siRNA) as a therapeutic tool, effective siRNA design is crucial. Current methods often emphasize sequence-related features, overlooking structural information. To address this, we introduce ENsiRNA, a multimodal approach utilizing a geometric graph neural network to predict the efficacy of both standard and modified siRNA. ENsiRNA integrates sequence features from a pretrained RNA language model, structural characteristics, and thermodynamic data or chemical modification to enhance prediction accuracy. Our results indicate that ENsiRNA outperforms existing methods, achieving over a 13% improvement in Pearson Correlation Coefficient (PCC) for standard siRNA across various tests. For modified siRNA, despite challenges associated with RNA folding methods, ENsiRNA still demonstrates competitive performance in different datasets. This novel method highlights the significance of structural information and multimodal strategies in siRNA prediction, advancing the field of therapeutic design.

PMID:40194620 | DOI:10.1016/j.jmb.2025.169131

Int J Biol Macromol. 2025 Apr 5:142871. doi: 10.1016/j.ijbiomac.2025.142871. Online ahead of print.

ABSTRACT

The accurate prediction of inhibitor-kinase binding affinity is pivotal for advancing drug development and precision medicine. In this study, we developed predictive models for human kinases, including cyclin-dependent kinases (CDKs), mitogen-activated protein kinases (MAP kinases), glycogen synthase kinases (GSKs), CDK-like kinases (CMGC kinase group) and receptor tyrosine kinases (RTKs)-key regulators of cellular signaling and disease progression. These kinases serve as primary drug targets in cancer and other critical diseases. To enhance affinity prediction precision, we introduce an innovative multimodal fusion model, KinNet. The model integrates the GraphKAN network, which effectively captures both local and global structural features of drug molecules. Furthermore, it leverages kernel functions and learnable activation functions to dynamically optimize node and edge feature representations. Additionally, the model incorporates the Conv-Enhanced Mamba module, combining Conv1D's ability to capture local features with Mamba's strength in processing long sequences, facilitating comprehensive feature extraction from protein sequences and molecular fingerprints. Experimental results confirm that the KinNet model achieves superior prediction accuracy compared to existing approaches, underscoring its potential to elucidate inhibitor-kinase binding mechanisms. This model serves as a robust computational framework to support drug discovery and the development of kinase-targeted therapies.

PMID:40194581 | DOI:10.1016/j.ijbiomac.2025.142871