ACS Sens. 2025 Apr 1. doi: 10.1021/acssensors.4c03621. Online ahead of print.

ABSTRACT

Lung cancer exhibits strong heterogeneity, and its early diagnosis and precise subtyping are of great importance, as they can increase the ability to deliver personalized medicines by tailoring therapy regimens. Tissue biopsy, albeit the gold standard, is invasive, costly and provides limited information about the tumor and its molecular landscape. Exosomes, as promising biomarkers for lung cancer, are a heterogeneous collection of membranous vesicles containing tumor-specific information for liquid biopsy to identify lung cancer subtypes. However, the small size, complex structure, and heterogeneous molecular features of exosomes pose significant challenges for their effective isolation and analysis. Herein, we report a deep learning-driven microfluidic chip with surface-enhanced Raman scattering (SERS) readout to characterize the differences in exosomes for the early diagnosis and molecular subtyping of non-small cell lung cancer (NSCLC). This integration comprises a processing unit for exosome capture and enrichment using polystyrene microspheres (PS) binding gold nanocubes (AuNCs) and anti-CD-9 antibody (denoted as PACD), and an optical sensing unit to trap the PACD and detect SERS signals from these exosomes. This system achieved a maximum trapping efficiency of 85%, and could distinguish three different NSCLC cell lines from the normal cell line with an overall accuracy of 97.88% and an area under the curve (AUC) of over 0.95 for each category. This work highlights the combined power of deep learning, SERS, and microfluidics in realizing the capture, detection, and analysis of exosomes from biological matrices, which may pave the way for clinical exosome-based cancer diagnosis and prognostication in the future.

PMID:40167999 | DOI:10.1021/acssensors.4c03621

Radiol Med. 2025 Apr 1. doi: 10.1007/s11547-025-02006-x. Online ahead of print.

ABSTRACT

PURPOSE: This study aims to evaluate the effectiveness of combining transvaginal ultrasound (US)-based radiomics and deep learning model for the accurate differentiation between benign and malignant ovarian tumors in large-scale studies.

MATERIALS AND METHODS: A multicenter retrospective study collected grayscale and color US images of ovarian tumors. Patients were divided into training, internal, and external validation groups. Models including a convolutional neural networks (CNN), optimal radiomics, and a combined model were constructed and evaluated for predictive performance using area under curve (AUC), sensitivity, and specificity. The DeLong test compared model AUCs with O-RADS and expert assessments.

RESULTS: 3193 images from 2078 patients were analyzed. The CNN achieved AUCs of 0.970 (internal) and 0.959 (external), respectively. Optimal radiomic model achieved AUCs of 0.949 (internal) and 0.954 (external), respectively. The combined CNN-radiomics model attained the highest AUC of 0.977 (internal) and 0.972 (external), respectively, outperforming individual models, O-RADS, and expert methods (p < 0.05).

CONCLUSIONS: The combined CNN-radiomics model using transvaginal US images provides more accurate and reliable ovarian tumor diagnosis, enhancing malignancy prediction and offering clinicians a more precise diagnostic tool.

PMID:40167932 | DOI:10.1007/s11547-025-02006-x

Discov Oncol. 2025 Apr 1;16(1):438. doi: 10.1007/s12672-025-02212-z.

ABSTRACT

Artificial intelligence (AI) marks a frontier in histopathologic analysis shift towards the clinic, becoming a mainstream choice to interpret histological images. Surveying studies assessing AI applications in histopathology from 2013 to 2024, we review key methods (including supervised, unsupervised, weakly supervised and transfer learning) in deep learning-based pattern recognition in computational histopathology for diagnostic and prognostic purposes. Deep learning methods also showed utility in identifying a wide range of genetic mutations and standard pathology biomarkers from routine histology. This survey of 41 primary studies also encompasses key regions of AI applicability in histopathology in a multi-cancer review while marking prospects to introduce AI into the clinical setting with key examples including Swarm Learning and Data Fusion.

PMID:40167870 | DOI:10.1007/s12672-025-02212-z

Opt Lett. 2025 Apr 1;50(7):2390-2393. doi: 10.1364/OL.549356.

ABSTRACT

To meet rapidly growing communication demands, researchers have focused on structured light-based shift keying techniques. However, higher-order modes are prone to large diffraction divergence and are easily perturbed. In this study, we experimentally demonstrate what we believe to be a novel coherence shaping method for petal-like structures of optical vortices, enabling the generation of non-diffraction interference states between completely coherent and incoherent states. In addition, we propose a coherence shift keying (CSK) scheme enabled by deep learning, and a well-trained model can achieve a high recognition accuracy (>0.997) of interference states under practical conditions, including complex environments. Further experimental validation has confirmed that the minimum achievable visibility-level bandwidth is 0.02. This study provides a new, to the best of our knowledge, platform for low-order structured light mode-based high-capacity and encrypted shift keying communication systems.

PMID:40167728 | DOI:10.1364/OL.549356

Abdom Radiol (NY). 2025 Apr 1. doi: 10.1007/s00261-025-04895-y. Online ahead of print.

ABSTRACT

PURPOSE: To investigate the value of deep learning (DL) in T2-weighted imaging (T2DL) of the bladder regarding acquisition time (TA), image quality, and diagnostic confidence compared to standard T2-weighted turbo-spin-echo (TSE) imaging (T2S).

METHODS: We prospectively enrolled a total of 28 consecutive patients for the evaluation of bladder cancer. T2S and T2DL sequences in three planes were performed for each participant, and acquisition time was compared between the two acquisition protocols. The image evaluation was conducted independently by two radiologists using a 5-point Likert scale for artifacts, noise, overall image quality, and diagnostic confidence, with 5 indicating the best quality. Additionally, T2 scoring based on Vesical Imaging-Reporting and Data System (VI-RADS) was performed by two readers.

RESULTS: Compared to T2S, the acquisition time of T2DL was reduced by 49.4% in the axial and by 43.8% in the coronal and sagittal orientations. The severity and impact of artifacts and noise levels were superior in T2DL versus T2S (both p < 0.05). The overall image quality in T2DL was demonstrated to be higher compared to that in T2S in axial and sagittal imaging (both p < 0.05). The diagnostic confidence and T2 scoring of both sequences in all planes did not differ (p > 0.05).

CONCLUSIONS: Our study preliminarily demonstrated the feasibility of T2-weighted imaging with DL reconstruction of bladder MR in clinical practice. T2DL achieved a reduction in acquisition time, superior lesion detectability, and overall image quality with similar diagnostic confidence and T2 score compared to the standard T2 TSE sequence.

PMID:40167648 | DOI:10.1007/s00261-025-04895-y

Abdom Radiol (NY). 2025 Apr 1. doi: 10.1007/s00261-025-04886-z. Online ahead of print.

ABSTRACT

PURPOSE: To develop and validate a deep learning-based feature ensemble model using multiparametric magnetic resonance imaging (MRI) for predicting tumor budding (TB) grading in patients with rectal cancer (RC).

METHODS: A retrospective cohort of 458 patients with pathologically confirmed rectal cancer (RC) from three institutions was included. Among them, 355 patients from Center 1 were divided into two groups at a 7:3 ratio: the training cohort (n = 248) and the internal validation cohort (n = 107). An additional 103 patients from two other centers served as the external validation cohort. Deep learning models were constructed for T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) based on the CrossFormer architecture, and deep learning features were extracted. Subsequently, a feature ensemble module based on the attention mechanism of Transformer was used to capture spatial interactions between different imaging sequences, creating a multiparametric ensemble model. The predictive performance of each model was evaluated using the area under the curve (AUC), calibration curves, and decision curve analysis (DCA).

RESULTS: The deep learning model based on T2WI achieved AUC values of 0.789 (95% CI: 0.680-0.900) and 0.720 (95% CI: 0.591-0.849) in the internal and external validation cohorts, respectively. The deep learning model based on DWI had AUC values of 0.806 (95% CI: 0.705-0.908) and 0.772 (95% CI: 0.657-0.887) in the internal and external validation cohorts, respectively. The multiparametric ensemble model demonstrated superior performance, with AUC values of 0.868 (95% CI: 0.775-0.960) in the internal validation cohort and 0.839 (95% CI: 0.743-0.935) in the external validation cohort. DeLong test showed that the differences in AUC values among the models were not statistically significant in both the internal and external test sets (P > 0.05). The DCA curve demonstrated that within the 10-80% threshold range, the fusion model provided significantly higher clinical net benefit compared to other models.

CONCLUSION: Compared to single-sequence deep learning models, the attention mechanism-based multiparametric MRI fusion model enables more effective individualized prediction of TB grading in RC patients. It offers valuable guidance for treatment selection and prognostic evaluation while providing imaging-based support for personalized postoperative follow-up adjustments.

PMID:40167646 | DOI:10.1007/s00261-025-04886-z

Abdom Radiol (NY). 2025 Apr 1. doi: 10.1007/s00261-025-04887-y. Online ahead of print.

ABSTRACT

OBJECTIVES: To train and validate segmentation models for automated segmentation of gallbladder cancer (GBC) lesions from contrast-enhanced CT images.

MATERIALS AND METHODS: This retrospective study comprised consecutive patients with pathologically proven treatment naïve GBC who underwent a contrast-enhanced CT scan at four different tertiary care referral hospitals. The training and validation cohort comprised CT scans of 317 patients (center 1). The internal test cohort comprised a temporally independent cohort (n = 29) from center 1 (internal test 1). The external test cohort comprised CT scans from three centers [ (n = 85)]. We trained the state-of-the-art 2D and 3D image segmentation models, SAM Adapter, MedSAM, 3D TransUNet, SAM-Med3D, and 3D-nnU-Net, for automated segmentation of the GBC. The models' performance for GBC segmentation on the test datasets was assessed via dice score and intersection over union (IoU) using manual segmentation as the reference standard.

RESULTS: The 2D models performed better than 3D models. Overall, MedSAM achieved the highest dice and IoU scores on both the internal [mean dice (SD) 0.776 (0.106) and mean IoU 0.653 (0.133)] and external [mean dice (SD) 0.763 (0.098) and mean IoU 0.637 (0.116)] test sets. Among the 3D models, TransUNet showed the best segmentation performance with mean dice (SD) and IoU (SD) of 0.479 (0.268) and 0.356 (0.235) in the internal test and 0.409 (0.339) and 0.317 (0.283) in the external test sets. The segmentation performance was not associated with GBC morphology. There was weak correlation between the dice/IoU and the size of the GBC lesions for any segmentation model.

CONCLUSION: We trained 2D and 3D GBC segmentation models on a large dataset and validated these models on external datasets. MedSAM, a 2D prompt-based foundational model, achieved the best segmentation performance.

PMID:40167645 | DOI:10.1007/s00261-025-04887-y

Abdom Radiol (NY). 2025 Apr 1. doi: 10.1007/s00261-025-04897-w. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) may have the potential to improve existing diagnostic challenges in endometriosis imaging. To better direct future research, this descriptive review summarizes the general landscape of AI applications in endometriosis imaging. Articles from PubMed were selected to represent different approaches to AI applications in endometriosis imaging. Current endometriosis imaging literature focuses on AI applications in ultrasound (US) and magnetic resonance imaging (MRI). Most studies use US data, with MRI studies being limited at present. The majority of US studies employ transvaginal ultrasound (TVUS) data and aim to detect deep endometriosis implants, adenomyosis, endometriomas, and secondary signs of endometriosis. Most MRI studies evaluate endometriosis disease diagnosis and segmentation. Some studies analyze multi-modal methods for endometriosis imaging, combining US and MRI data or using imaging data in combination with clinical data. Current literature lacks generalizability and standardization. Most studies in this review utilize small sample sizes with retrospective approaches and single-center data. Existing models only focus on narrow disease detection or diagnosis questions and lack standardized ground truth. Overall, AI applications in endometriosis imaging analysis are in their early stages, and continued research is essential to develop and enhance these models.

PMID:40167644 | DOI:10.1007/s00261-025-04897-w

JASA Express Lett. 2025 Apr 1;5(4):040801. doi: 10.1121/10.0036312.

ABSTRACT

A generative deep learning framework is introduced for ray-based ocean acoustic tomography (OAT), an inverse problem for estimating sound speed profiles (SSP) based on arrival-times measurements between multiple acoustic transducers, which is typically ill-posed. This framework relies on a robust low-dimensional parametrization of the expected SSP variations using a variational autoencoder and a linear dynamical model as further regularization. This framework was tested using SSP variations simulated by a regional ocean model with submesoscale permitting horizontal resolution and various transducer configurations spanning the upper ocean over short propagation ranges and was found to outperform conventional linear least squares formulations of OAT.

PMID:40167492 | DOI:10.1121/10.0036312

J Synchrotron Radiat. 2025 May 1. doi: 10.1107/S1600577525001833. Online ahead of print.

ABSTRACT

In bone-imaging research, in situ synchrotron radiation micro-computed tomography (SRµCT) mechanical tests are used to investigate the mechanical properties of bone in relation to its microstructure. Low-dose computed tomography (CT) is used to preserve bone's mechanical properties from radiation damage, though it increases noise. To reduce this noise, the self-supervised deep learning method Noise2Inverse was used on low-dose SRµCT images where segmentation using traditional thresholding techniques was not possible. Simulated-dose datasets were created by sampling projection data at full, one-half, one-third, one-fourth and one-sixth frequencies of an in situ SRµCT mechanical test. After convolutional neural networks were trained, Noise2Inverse performance on all dose simulations was assessed visually and by analyzing bone microstructural features. Visually, high image quality was recovered for each simulated dose. Lacunae volume, lacunae aspect ratio and mineralization distributions shifted slightly in full, one-half and one-third dose network results, but were distorted in one-fourth and one-sixth dose network results. Following this, new models were trained using a larger dataset to determine differences between full dose and one-third dose simulations. Significant changes were found for all parameters of bone microstructure, indicating that a separate validation scan may be necessary to apply this technique for microstructure quantification. Noise present during data acquisition from the testing setup was determined to be the primary source of concern for Noise2Inverse viability. While these limitations exist, incorporating dose calculations and optimal imaging parameters enables self-supervised deep learning methods such as Noise2Inverse to be integrated into existing experiments to decrease radiation dose.

PMID:40167487 | DOI:10.1107/S1600577525001833

J Clin Lab Anal. 2025 Apr 1:e70026. doi: 10.1002/jcla.70026. Online ahead of print.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive and irreversible interstitial lung disease with a complex pathogenesis involving multiple immune cells. This study investigates the relationship between immune cells and IPF using Mendelian randomization (MR) analysis.

METHODS: A two-sample MR analysis was performed using genome-wide association studies (GWAS) and immune cell databases by R software. We analyzed data from 1028 European individuals with IPF, focusing on 731 immune traits. The primary method of analysis was inverse variance weighting (IVW), supplemented with sensitivity analyses, including MR-Egger regression and MR-PRESSO, to detect and correct for pleiotropy.

RESULTS: The MR analysis identified six immune panels and 23 immune traits significantly associated with IPF, including five traits that increase and 18 traits that decrease IPF risk. Notable traits increasing IPF risk included switched memory B-cells (OR = 1.27, p = 0.0029) and IgD- CD38dim B-cells (OR = 1.08, p = 0.0449). Traits associated with a reduced IPF risk included central memory CD4+ T-cells (%CD4+, OR = 0.96, p = 0.0489), CD20 on naive-mature B-cells (OR = 0.94, p = 0.0499), and CD33br HLA-DR+ absolute count (AC) (OR = 0.93, p = 0.0489). There was no significant causal relationship between IPF disease and some immune traits (p > 0.05).

CONCLUSION: This study suggests a potential causal link between specific immune cell traits and the development of IPF, providing new insights into the disease's immunological mechanisms. Future research should focus on validating these findings in larger, more diverse populations to inform drug development and therapeutic strategies.

PMID:40167279 | DOI:10.1002/jcla.70026

Methods Mol Biol. 2025;2914:275-302. doi: 10.1007/978-1-0716-4462-1_20.

ABSTRACT

Cerebrospinal fluid (CSF) is a source of valuable information concerning brain disorders. The technical advances of high throughput omics platforms to analyze body fluids can generate a huge amount of data, whose translation of the biological meaning can be a challenge. Several bioinformatics tools have emerged to help handle this data from a systems biology perspective. Herein, we describe a step-by-step tutorial for CSF proteome data analysis in the set of neurodegenerative diseases using: (i) ShinyGO webtool to perform functional enrichment analysis envisioning the characterization of the biological pathways and processes deregulated in neurodegenerative diseases including Alzheimer's and Parkinson's diseases; (ii) Cytoscape to map disease-specific proteins based on evidence from proteomics; (iii) DisGeNET to identify the proteins more strongly and more specifically associated with neurodegenerative diseases to date; (iv) STRING to identify putative therapeutic targets through a combined protein-protein interaction and network topological analyses. This step-by-step guide might help researchers to better characterize disease pathogenesis and to identify putative disease biomarkers and therapeutic targets.

PMID:40167925 | DOI:10.1007/978-1-0716-4462-1_20

Metabolomics. 2025 Apr 1;21(2):48. doi: 10.1007/s11306-025-02247-x.

NO ABSTRACT

PMID:40167843 | DOI:10.1007/s11306-025-02247-x

Semin Immunopathol. 2025 Apr 1;47(1):23. doi: 10.1007/s00281-025-01049-6.

ABSTRACT

Barrier organs such as the gastrointestinal tract, lungs, and skin are colonized by diverse microbial strains, including bacteria, viruses, and fungi. These microorganisms, collectively known as the commensal microbiota, play critical roles in maintaining health by defending against pathogens, metabolizing nutrients, and providing essential metabolites. In the gut, commensal-derived antigens are frequently sensed by the intestinal immune system. Maintaining tolerance toward these beneficial microbial species is crucial, as failure to do so can lead to chronic inflammatory conditions like inflammatory bowel disease (IBD) and can even affect systemic immune or metabolic health. The immune system carefully regulates responses to commensals through various mechanisms, including the induction of anti-inflammatory CD4⁺ T cell responses. Foxp3⁺ regulatory T cells (Foxp3+ Tregs) and Type 1 regulatory T cells (Tr1) play a major role in promoting tolerance, as both cell types can produce the anti-inflammatory cytokine IL-10. In addition to these regulatory T cells, effector T cell subsets, such as Th17 cells, also adopt anti-inflammatory functions within the intestine in response to the microbiota. This process of anti-inflammatory CD4+ T cell induction is heavily influenced by the microbiota and their metabolites. Microbial metabolites affect intestinal epithelial cells, promoting the secretion of anti-inflammatory mediators that create a tolerogenic environment. They also modulate intestinal dendritic cells (DCs) and macrophages, inducing a tolerogenic state, and can interact directly with T cells to drive anti-inflammatory CD4⁺ T cell functionality. The disrupted balance of these signals may result in chronic inflammation, with broader implications for systemic health. In this review, we highlight the intricate interplays between commensal microorganisms and the immune system in the gut. We discuss how the microbiota influences the differentiation of commensal-specific anti-inflammatory CD4⁺ T cells, such as Foxp3⁺ Tregs, Tr1 cells, and Th17 cells, and explore the mechanisms through which microbial metabolites modulate these processes. We further discuss the innate signals that prime and commit these cells to an anti-inflammatory fate.

PMID:40167791 | DOI:10.1007/s00281-025-01049-6

Funct Integr Genomics. 2025 Apr 1;25(1):78. doi: 10.1007/s10142-025-01588-z.

ABSTRACT

Metabolic engineering of lipids in crops presents a promising strategy to enhance resilience against environmental stressors while improving nutritional quality. By manipulating key enzymes in lipid metabolism, introducing novel genes, and utilizing genome editing technologies, researchers have improved crop tolerance to abiotic stresses such as drought, salinity, and extreme temperatures. Additionally, modified lipid pathways contribute to resistance against biotic stresses, including pathogen attacks and pest infestations. Engineering multiple stress-resistance traits through lipid metabolism offers a holistic approach to strengthening crop resilience amid changing environmental conditions. Beyond stress tolerance, lipid engineering enhances the nutritional profile of crops by increasing beneficial lipids such as omega-3 fatty acids, vitamins, and antioxidants. This dual approach not only improves crop yield and quality but also supports global food security by ensuring sustainable agricultural production. Integrating advanced biotechnological tools with a deeper understanding of lipid biology paves the way for developing resilient, nutrient-rich crops capable of withstanding climate change and feeding a growing population.

PMID:40167787 | DOI:10.1007/s10142-025-01588-z

Adv Sci (Weinh). 2025 Apr 1:e2412921. doi: 10.1002/advs.202412921. Online ahead of print.

ABSTRACT

HDAC6 is integral to the regulation of primary cilia, which are specialized structures that serve as crucial signaling hubs for cellular communication and environmental response. These ciliary functions are essential for maintaining cellular homeostasis and orchestrating developmental processes. Dysregulation of HDAC6 activity is implicated in ciliopathies, a group of disorders characterized by defective ciliary structure or function, resulting in widespread organ involvement and significant morbidity. This review provides a comprehensive examination of the molecular dynamics of HDAC6 in the context of ciliogenesis and ciliopathies, emphasizing its dual role in the deacetylation of microtubules and regulation of the ciliary axoneme. Furthermore, HDAC6 interacts with key signaling molecules, modulating processes ranging from cell cycle regulation to inflammatory responses, which highlights its central role in cellular physiology and pathology. The therapeutic potential of HDAC6 inhibitors has been explored, with promising results in various disease models, including retinal and renal ciliopathies, highlighting their ability to restore normal ciliary function. This analysis not only underscores the critical importance of HDAC6 in maintaining ciliary integrity but also illustrates how targeting the HDAC6-cilia axis could provide a groundbreaking approach to treating these complex disorders. In doing so, this review sets the stage for future investigations into HDAC6-targeted therapies, potentially transforming the clinical management of ciliopathies and significantly improving patient outcomes.

PMID:40167251 | DOI:10.1002/advs.202412921

Circulation. 2025 Apr 1. doi: 10.1161/CIRCULATIONAHA.121.058621. Online ahead of print.

ABSTRACT

BACKGROUND: Gene mutations are responsible for a sizeable proportion of cases of heart failure. However, the number of patients with any specific mutation is small. Repositioning of existing US Food and Drug Administration-approved compounds to target specific mutations is a promising approach to efficient identification of new therapies for these patients.

METHODS: The National Institutes of Health Library of Integrated Network-Based Cellular Signatures database was interrogated to identify US Food and Drug Administration-approved compounds that demonstrated the ability to reverse the transcriptional effects of LMNA knockdown. Top hits from this screening were validated in vitro with patient-specific induced pluripotent stem cell-derived cardiomyocytes combined with force measurement, gene expression profiling, electrophysiology, and protein expression analysis.

RESULTS: Several angiotensin receptor blockers were identified from our in silico screen. Of these, olmesartan significantly elevated the expression of sarcomeric genes and rate and force of contraction and ameliorated arrhythmogenic potential. In addition, olmesartan exhibited the ability to reduce phosphorylation of extracellular signal-regulated kinase 1 in LMNA-mutant induced pluripotent stem cell-derived cardiomyocytes.

CONCLUSIONS: In silico screening followed by in vitro validation with induced pluripotent stem cell-derived models can be an efficient approach to identifying repositionable therapies for monogenic cardiomyopathies.

PMID:40166828 | DOI:10.1161/CIRCULATIONAHA.121.058621

medRxiv [Preprint]. 2025 Mar 24:2025.03.19.25324271. doi: 10.1101/2025.03.19.25324271.

ABSTRACT

BACKGROUND: Randomized controlled trials (RCTs) provide the highest level of clinical evidence but are often limited by cost, time, and ethical constraints. Emulating RCTs using real-world data (RWD) offers a complementary approach to evaluate the treatment effect in a real clinical setting. This study aims to replicate clinical trials based on Mayo Clinic Platform (MCP) electronic health records (EHRs) and emulation frameworks. In this study, we address two key questions: (1) whether clinical trials can be feasibly replicated using the MCP, and (2) whether trial emulation produces consistent conclusions based on real clinical data compared to the original randomized controlled trials RCTs.

METHODS: We conducted a retrospective observational study with an adaption of trial emulation. To assess feasibility, we applied a refined filtering method to identify trials suitable for emulation. The emulation protocol was carefully designed on top of the original RCT protocol to balance scientific rigor and practical feasibility. To minimize potential selection bias and enhance comparability between groups, we employed propensity score matching (PSM) as a statistical adjustment method.

RESULTS: Based on our predefined search criteria targeting phase 3 trials focused on drug repurposing for heart failure patients, we initially identified 27 eligible trials. After a two-step manual review of the original eligibility criteria and extraction of the patient cohorts based on MCP visualizer, we further narrowed our selection to the WARCEF trial, as it provided an adequate sample size for the emulation within the MCP. The experiment compares the WARCEF trial and a simulation study on Aspirin vs. Warfarin. The original study (smaller sample) found no significant difference (HR = 1.016, p < 0.91). The simulation (larger sample) showed a slightly higher HR (1.161) with borderline significance (p < 0.052, CI: 0.999-1.350), suggesting a possible increased risk with Warfarin, though not conclusive.

CONCLUSION: RCT emulation enhances real-world evidence (RWE) for clinical decision-making but faces limitations from confounding, missing data, and cohort biases. Future research should explore machine learning-driven patient matching and scalable RCT emulation. This study supports the integration of RWE into evidence-based medicine.

PMID:40166580 | PMC:PMC11957179 | DOI:10.1101/2025.03.19.25324271

medRxiv [Preprint]. 2025 Mar 23:2025.03.22.25324223. doi: 10.1101/2025.03.22.25324223.

ABSTRACT

Drug development is a long and costly process, and repurposing existing drugs for use toward a different disease or condition may serve as a cost-effective alternative. As drug targets with genetic support have a doubled success rate, genetics-informed drug repurposing holds promise in translating genetic findings into therapeutics. In this study, we developed a Genetics Informed Network-based Drug Repurposing via in silico Perturbation (GIN-DRIP) framework and applied the framework to repurpose drugs for type-2 diabetes (T2D). In GIN-DRIP for T2D, it integrates multi-level omics data to translate T2D GWAS signals into a genetics-informed network that simultaneously encodes gene importance scores and a directional effect (up/down) of risk genes for T2D; it then bases on the GIN to perform signature matching with drug perturbation experiments to identify drugs that can counteract the effect of T2D risk alleles. With this approach, we identified 3 high-confidence FDA-approved candidate drugs for T2D, and validated telmisartan, an anti-hypertensive drug, in our EHR data with over 3 million patients. We found that telmisartan users were associated with a reduced incidence of T2D compared to users of other anti-hypertensive drugs and non-users, supporting the therapeutic potential of telmisartan for T2D. Our framework can be applied to other diseases for translating GWAS findings to aid drug repurposing for complex diseases.

PMID:40166562 | PMC:PMC11957187 | DOI:10.1101/2025.03.22.25324223

medRxiv [Preprint]. 2025 Mar 17:2025.03.17.25324106. doi: 10.1101/2025.03.17.25324106.

ABSTRACT

BACKGROUND: Complex diseases may share portions of their polygenic architectures which can be leveraged to identify drug targets with low off-target potential or repurposable candidates. However, the literature lacks methods which can make these inferences at scale using publicly available data.

METHODS: We introduce a Bayesian model to estimate the polygenic structure of a trait using only gene-based association test statistics from GWAS summary data and returns gene-level posterior risk probabilities (PRPs). PRPs were used to infer shared polygenicity between 496 trait pairs and we introduce measures that can prioritize drug targets with low off-target effects or drug repurposing potential.

RESULTS: Across 32 traits, we estimated that 69.5 to 97.5% of disease-associated genes are shared between multiple traits, and the estimated number of druggable genes that were only associated with a single disease ranged from 1 (multiple sclerosis) to 59 (schizophrenia). Estimating the shared genetic architecture of ALS with all other traits identified the KIT gene as a potentially harmful drug target because of its deleterious association with triglycerides, but also identified TBK1 and SCN11B as putatively safer because of their non-association with any of the other 31 traits. We additionally found 21 genes which are candidate repourposable targets for Alzheimer's disease (AD) (e.g., PLEKHA1, PPIB ) and 5 for ALS (e.g., GAK, DGKQ ).

CONCLUSIONS: The sets of candidate drug targets which have limited off-target potential are generally smaller compared to the sets of pleiotropic and putatively repurposable drug targets, but both represent promising directions for future experimental studies.

PMID:40166559 | PMC:PMC11957083 | DOI:10.1101/2025.03.17.25324106