J Clin Exp Cardiolog. 2025;16(2):935. Epub 2025 Mar 24.

ABSTRACT

As Artificial Intelligence (AI) plays an increasingly prominent role in society, its application in clinical cardiology is gaining traction by providing innovative diagnostic, prognostic, and therapeutic solutions. Electrocardiogram (ECG), as a ubiquitous diagnostic tool in cardiology, has emerged as the leading data source for Deep Learning (DL) applications. A recent study from our group used ECG-based DL model to identify cardiac wall motion abnormalities and outperformed expert human interpretation. Motivated by this work and that of many others, we aim to discuss advances, limitations, future directions, and equity considerations in DL models for ECG-based AI applications.

PMID:40443717 | PMC:PMC12121951

Uncertain Safe Util Mach Learn Med Imaging (2023). 2023 Oct;14291:147-156. doi: 10.1007/978-3-031-44336-7_15. Epub 2023 Oct 7.

ABSTRACT

Clinically-deployed deep learning-based segmentation models are known to fail on data outside of their training distributions. While clinicians review the segmentations, these models do tend to perform well in most instances, which could exacerbate automation bias. Therefore, it is critical to detect out-of-distribution images at inference to warn the clinicians that the model likely failed. This work applies the Mahalanobis distance post hoc to the bottleneck features of a Swin UNETR model that segments the liver on T1-weighted magnetic resonance imaging. By reducing the dimensions of the bottleneck features with principal component analysis, images the model failed on were detected with high performance and minimal computational load. Specifically, the proposed technique achieved 92% area under the receiver operating characteristic curve and 94% area under the precision-recall curve and can run in seconds on a central processing unit.

PMID:40443712 | PMC:PMC12120689 | DOI:10.1007/978-3-031-44336-7_15

IEEE Access. 2025;13:82972-82985. doi: 10.1109/ACCESS.2025.3568641. Epub 2025 May 9.

ABSTRACT

Effective clinical deployment of deep learning models in healthcare demands high generalization performance to ensure accurate diagnosis and treatment planning. In recent years, significant research has focused on improving the generalization of deep learning models by regularizing the sharpness of the loss landscape. Among the optimization approaches that explicitly minimize sharpness, Sharpness-Aware Minimization (SAM) has shown potential in enhancing generalization performance on general domain image datasets. This success has led to the development of several advanced sharpness-based algorithms aimed at addressing the limitations of SAM, such as Adaptive SAM, Surrogate-Gap SAM, Weighted SAM, and Curvature Regularized SAM. These sharpness-based optimizers have shown improvements in model generalization compared to conventional stochastic gradient descent optimizers and their variants on general domain image datasets, but they have not been thoroughly evaluated on medical images. This work provides a review of recent sharpness-based methods for improving the generalization of deep learning networks and evaluates the methods' performance on three medical image datasets, including breast ultrasound, chest X-ray, and colon histopathology images. Our findings indicate that the initial SAM method successfully enhances the generalization of various deep learning models. While Adaptive SAM improves generalization of convolutional neural networks, it fails to do so for vision transformers. Other sharpness-based optimizers, however, do not demonstrate consistent results. The results reveal that contrary to findings in the non-medical domain, SAM is the only recommended sharpness-based optimizer that consistently improves generalization in medical image analysis, and further research is necessary to refine the variants of SAM to enhance generalization performance in this field.

PMID:40443707 | PMC:PMC12121992 | DOI:10.1109/ACCESS.2025.3568641

Proc Mach Learn Res. 2024 Dec;259:248-266.

ABSTRACT

In medical decision-making, clinicians must choose between different time-varying treatment strategies. Counterfactual prediction via g-computation enables comparison of alternative outcome distributions under such treatment strategies. While deep learning can better model high-dimensional data with complex temporal dependencies, incorporating model uncertainty into predicted conditional counterfactual distributions remains challenging. We propose a principled approach to model uncertainty in deep learning implementations of g-computations using approximate Bayesian posterior predictive distributions of counterfactual outcomes via variational dropout and deep ensembles. We evaluate these methods by comparing their counterfactual predictive calibration and performance in decision-making tasks, using two simulated datasets from mechanistic models and a real-world sepsis dataset. Our findings suggest that the proposed uncertainty quantification approach improves both calibration and decision-making performance, particularly in minimizing risks of worst-case adverse clinical outcomes under alternative dynamic treatment regimes. To our knowledge, this is the first work to propose and compare multiple uncertainty quantification methods in machine learning models of g-computation in estimating conditional treatment effects under dynamic treatment regimes.

PMID:40443560 | PMC:PMC12121963

Ther Adv Respir Dis. 2025 Jan-Dec;19:17534666251342661. doi: 10.1177/17534666251342661. Epub 2025 May 30.

ABSTRACT

BACKGROUND: Non-idiopathic pulmonary fibrosis interstitial lung diseases (non-IPF ILDs) comprise a broad spectrum of pathologies with varying degrees of inflammation and fibrosis. Progressive fibrosing ILD is associated with significant mortality and limited treatment options. Standard regimens employ multimodal immunosuppression, most commonly prolonged courses of oral corticosteroids (OCS), that are associated with a high risk of adverse effects and limited proven efficacy.

OBJECTIVES: This study investigates the safety, tolerability, and effectiveness of monthly intravenous pulse methylprednisolone (PMP) for the treatment of patients with progressive non-IPF ILD.

DESIGN: Retrospective single-center cohort study of patients at an academic tertiary referral center for ILD between October 2019 and September 2022.

METHODS: All non-IPF ILD patients who received intravenous PMP (1000 mg daily for three consecutive days/month) between October 2019 and September 2022 were included. The decision to treat was based on a multidisciplinary consensus diagnosis following ATS/ERS/JRS/ALAT guidelines and confirmed or at high risk for ILD progression. Treatment continuation was contingent upon pulmonary function test (PFT) improvement (assessed approximately every 3 months), tolerable adverse events, and shared decision making with patients. Effectiveness was measured by a change in forced vital capacity (FVC) and diffusion limit of carbon monoxide (DLCO), with improvement being defined as an absolute increase in either FVC >5% or DLCO >10% from baseline.

RESULTS: Thirty-three patients received PMP at our center. One patient died of an acute exacerbation of ILD. Of the 32 patients included for analysis, 17 (53%) exhibited improved lung function with PMP between PFTs, which was maintained for a median follow-up of 209 days. The regimen was generally well-tolerated, with the most common adverse effects being insomnia and restlessness on infusion days. Advanced disease, indicated by lower FVC, traction bronchiectasis, and oxygen dependence, predicted poor response.

CONCLUSIONS: PMP may offer a safer, better-tolerated, and more effective treatment for progressive non-IPF ILD than prolonged OCS. Notably, a third of fibrotic hypersensitivity pneumonitis patients showed improved FVC after 3 months of PMP, defying expectations of steroid non-responsiveness. However, further well-designed controlled prospective clinical trials are needed to confirm our findings and establish long-term safety.

PMID:40444328 | DOI:10.1177/17534666251342661

Tuberc Respir Dis (Seoul). 2025 May 30. doi: 10.4046/trd.2025.0044. Online ahead of print.

ABSTRACT

Interstitial lung disease (ILD) comprises a heterogeneous group of disorders characterized by interstitial compartment proliferation, inflammatory infiltration, and potential fibrosis with abnormal collagen deposition. Diagnosis requires a multidisciplinary consensus integrating clinical, radiological, and pathological findings. Idiopathic interstitial pneumonia (IIP) includes idiopathic pulmonary fibrosis (IPF), idiopathic nonspecific interstitial pneumonia (NSIP), desquamative interstitial pneumonia (DIP), acute interstitial pneumonia (AIP), and respiratory bronchiolitis-ILD (RB-ILD), each exhibiting distinct prognostic and therapeutic implications. Some non-IPF ILDs progress despite standard treatment, classified as progressive fibrosing interstitial lung disease (PF-ILD) or progressive pulmonary fibrosis (PPF), diagnosed by worsening symptoms, physiological decline, and radiological progression. Nintedanib is conditionally recommended for refractory PPF cases. Combined pulmonary fibrosis and emphysema (CPFE) is characterized by upper-lobe predominant emphysema and lower-lobe fibrosis, frequently complicated by pulmonary hypertension and lung cancer. Interstitial lung abnormality (ILA), observed in both smokers and the general population, is associated with increased mortality and disease risk, warranting further research. Despite advancements, refinement in classification, diagnostic criteria, and therapeutic strategies remains crucial for improving patient outcomes.

PMID:40443216 | DOI:10.4046/trd.2025.0044

J Sci Food Agric. 2025 May 30. doi: 10.1002/jsfa.14400. Online ahead of print.

ABSTRACT

BACKGROUND: The present study aimed to measure bioavailability (BA) indicators, including epithelial barrier function, apparent permeability (Papp) and efflux ratio, of 84 types of phytochemicals using Caco-2 cell and to develop predictive model systems using machine learning with a quantitative structure-property relationship (QSPR) model based on BA indicators and an Isomeric Simplified Molecular Input Line Entry System (SMILES). Analysis of phytochemicals was carried out with a validated HPLC analytical method.

RESULTS: With these BA indicators, Isomeric SMILES including information such as the stereochemistry, chemical structure and properties of phytochemicals was encoded to molecular descriptors using PaDEL-Descriptor and alvaDesc. The validity of the dataset was verified using principal component analysis, leverage plot and Williams plot. In the case of transepithelial electrical resistance (TEER), R2 Train is 0.86, root mean square error (RMSE)Train is 55.25, R2 Test is 0.63 and RMSETest is 74.77, respectively. Regarding the Papp, the model demonstrated strong performance on the training set with RMSETrain of 4.54 × 10-6 and R2 Train of 0.95 with the test set results (RMSETest = 6.23 × 10-6 and R2 Test = 0.91). For the efflux ratio, the modle explains 92% of the variance with RMSETrain of 0.39, R2 Train of 0.92, R2 Test of 0.85 and RMSETest of 0.71.

CONCLUSION: The present study suggests that a prediction system for bioavailability, including TEER, Papp and efflux ratio, can be developed using a QSPR model, which could contribute to advancements in discover of functional ingredients and drugs. © 2025 The Author(s). Journal of the Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.

PMID:40444409 | DOI:10.1002/jsfa.14400

J Cell Sci. 2025 May 30:jcs.263830. doi: 10.1242/jcs.263830. Online ahead of print.

ABSTRACT

Mitochondrial homeostasis is ensured through communication between diverse cellular organelles, including mitochondria, the endoplasmic reticulum (ER), lysosomes, and endosomes. While mitofusins regulate mitochondrial networks and ER contacts, their role in endosomal-mitochondrial interactions remains unclear. Previously, we reported that endosomal ubiquitin ligase, RFFL-positive vesicles are associated with damaged mitochondria and prime the organelle for PRKN recruitment. Now, we establish that RFFL is a ubiquitin ligase for MFN2. Using electron microscopy and confocal imaging analyses, we demonstrate that RFFL knockout cells exhibit enlarged mitochondrial morphology. RFFL interacts at an endogenous level with MFN2 and contributes to its ubiquitination upon mitochondrial damage. Recombinant RFFL interacts and ubiquitinates MFN2 protein in vitro. Furthermore, exogenous RFFL in a ligase-dependent manner specifically reduces the exogenous protein levels of both MFN1 and MFN2, but not that of DRP1, and also perturbs lipid homeostasis. Importantly, we show that hyperfused mitochondria morphology reported with expression of pathogenic disease mutants of MFN2 (T206I and R364W) of Charcot-Marie-Tooth disease type 2A can be rescued by RFFL co-expression. The study unravels novel mechanisms involving endosomal ubiquitin ligases in mitochondrial networks.

PMID:40444323 | DOI:10.1242/jcs.263830

Chem Sci. 2025 May 28. doi: 10.1039/d5sc01371b. Online ahead of print.

ABSTRACT

Thalidomide, lenalidomide, and their derivatives mimic glutarimide and aspartimide protein modifications that give rise to a motif recognized by the E3 ligase substrate adapter cereblon (CRBN). These cyclic imides have a chiral center that, given the biological significance of chirality, may influence CRBN's function and therapeutic applications. Here, we systematically examine cyclic imides in small molecules, peptides, and proteins to assess their racemization, CRBN engagement, ternary complex formation in vitro, and resulting degradation outcomes in cells. While the thalidomide-binding domain of CRBN consistently favors the (S)-stereoisomer across all cyclic imide small molecule ligands and engineered proteins, we find that, in some cases, the (R)-stereoisomer can bind to CRBN, either enhancing or hindering the eventual target engagement and degradation. Lenalidomide and its derivatives racemize more rapidly (t 50%ee = 4-5 h) than the C-terminal cyclic imide under non-enzymatic conditions. These findings highlight that although the (S)-stereoisomer of the cyclic imide is the primary ligand for the thalidomide-binding domain of CRBN, the (R)-stereoisomer, if present, has the potential to contribute to CRBN-dependent cellular activity.

PMID:40443985 | PMC:PMC12117711 | DOI:10.1039/d5sc01371b

Plant Commun. 2025 May 28:101389. doi: 10.1016/j.xplc.2025.101389. Online ahead of print.

ABSTRACT

Sugar regulation of hormonal signaling is crucial for optimizing growth under normal conditions and survival under environmental stresses. Previous studies indicate that sugar starvation causes the degradation of BRASSINAZOLE RESISTANT 1 (BZR1), the master transcription factor of the brassinosteroid (BR) signaling pathway, to inhibit growth. The molecular connection between sugar signaling and BZR1 degradation remains unknown. To identify the proteins that mediate starvation-induced BZR1 degradation, here, we performed a quantitative proteomic analysis of BZR1 interactome under starvation and identified UBIQUITIN PROTEIN LIGASE 3 (UPL3) as a sugar-regulated protein that mediates BZR1 degradation and regulates growth and survival according to sugar availability. The upl3 mutants show increased BZR1 accumulation and seedling size compared to the wild type when grown under sugar-limiting conditions but not when grown on sugar-containing media, indicating UPL3 mediates BZR1 degradation and growth inhibition under sugar-limiting conditions. While increasing growth under short-term starvation, the upl3 mutations substantially reduced survival after long-term starvation treatment. The increased-growth phenotype of upl3 is also observed when Target Of Rapamycin (TOR) is inactivated but not when BR synthesis is blocked, consistent with UPL3 regulating BZR1 degradation downstream of sugar-TOR signaling. Further, the UPL3 protein level is increased post-transcriptionally by starvation and TOR inhibition but decreased by sugar treatment. Our study identifies UPL3 as a key molecular link for sugar regulation of BR signaling. Sugar-TOR signaling inhibits UPL3 to promote BZR1 accumulation and growth, thereby optimizing growth and survival according to sugar availability.

PMID:40443036 | DOI:10.1016/j.xplc.2025.101389

Int J Immunopathol Pharmacol. 2025 Jan-Dec;39:3946320251343943. doi: 10.1177/03946320251343943. Epub 2025 May 29.

ABSTRACT

OBJECTIVE: To investigate the comprehensive landscape of hepatotoxic adverse events (AEs) associated with immune checkpoint inhibitors (ICIs), with a special focus on evaluating the potential risk of lethal hepatotoxic AEs.

INTRODUCTION: The widespread adoption of ICIs has markedly improved the prognosis for patients with advanced cancer. However, this therapeutic advance is accompanied by the risk of immune-related adverse events (irAEs), especially hepatotoxic AEs, which particularly affect patients with pre-existing liver diseases or hepatobiliary cancers.

METHODS: Reports in the FAERS database from Q1 2014 to Q3 2024 were collected. The characteristics of ICI-related hepatotoxic AEs were summarized. Disproportionality analysis was conducted to calculate reported odds ratios for assessing signals of hepatotoxic AEs. Additionally, logistic regression was employed to evaluate patient-related factors contributing to an increased risk of these AEs.

RESULTS: Hepatotoxic AEs increased yearly and occurred primarily in patients with hepatobiliary tumors. CTLA-4 inhibitors exhibited the highest incidence of AEs. In contrast, PD-1 inhibitors had the shortest median time to AE onset. Abnormal hepatic function was a common AE, whereas Stauffer's syndrome was identified as a rare AE. The risk of hepatotoxic AEs was notably elevated by combination immunotherapy and the concurrent use of specific drugs. Despite variations in the safety profiles of different ICI regimens, these differences did not significantly influence the risk of fatal hepatotoxicity. Furthermore, older men who experienced other AEs were found to be at higher risk for developing fatal hepatotoxicity.

CONCLUSION: The safety profiles of different ICIs vary widely, necessitating individualized drug selection based on patient-specific factors.

PMID:40443110 | DOI:10.1177/03946320251343943

Plant Methods. 2025 May 29;21(1):72. doi: 10.1186/s13007-025-01386-5.

ABSTRACT

Maize is the most produced crop in the world, exceeding wheat and rice production. However, its yield is often affected by various leaf diseases. Early identification of maize leaf disease through easily accessible tool is required to increase the yield of maize. Recently, researchers have attempted to detect and classify maize leaf diseases using Deep Learning algorithms. However, to the best of the researcher's knowledge, nearly all the studies are concentrated on developing an offline model that can detect maize diseases. But, those models are not easily accessible to individuals and don't provide immediate feedback and monitoring. Thus, in this study, we developed a novel real-time, user-friendly maize leaf disease detection and classification mobile application. The VGG16, AlexNet, and ResNet50 models were implemented and compared their performance on maize disease detection and classification. A total of 4188 images of blight, common_rust, grey_leaf_spot, and healthy were used to train each model. Data augmentation techniques were applied to the dataset to increase the size of the dataset, which can also reduce model overfitting. Weighted cross-entropy loss was also employed to mitigate class-imbalance problems. After training, VGG16 achieved 95% of testing accuracy, AlexNet achieved 91%, and ResNet50 achieved 72% of testing accuracy. The VGG16 model outperformed the other models in terms of accuracy. Consequently, we deployed the VGG16 model into a mobile application to provide real-time disease detection and classification tool for farmers, extension officers, agribusiness managers, and policy-makers. The developed application will enhance early disease detection, decision making, and contribute to better crop management and food security.

PMID:40442806 | DOI:10.1186/s13007-025-01386-5

Plant Methods. 2025 May 30;21(1):73. doi: 10.1186/s13007-025-01398-1.

ABSTRACT

BACKGROUND: Rice is one of the major food crops in the world, and the monitoring of its growth condition is of great significance for guaranteeing food security and promoting sustainable agricultural development. Leaf area index (LAI) is a key indicator for assessing the growth condition and yield potential of rice, and the traditional methods for obtaining LAI have problems such as low efficiency and large error. With the development of remote sensing technology, unmanned aerial multispectral remote sensing combined with deep learning technology provides a new way for efficient and accurate estimation of LAI in rice.

RESULTS: In this study, a multispectral camera mounted on a UAV was utilized to acquire rice canopy image data, and rice LAI was uniformly estimated over multiple periods by the multilayer perceptron (MLP) and convolutional neural network (CNN) models in deep learning. The results showed that the CNN model based on five-band reflectance images (490, 550, 670, 720, and 850 nm) as input after feature screening exhibited high estimation accuracy at different growth stages. Compared with the traditional MLP model with multiple vegetation indices as inputs, the CNN model could better process the original multispectral image data, effectively avoiding the problem of vegetation index saturation, and improving the accuracies by 4.89, 5.76, 10.96, 1.84 and 6.01% in the rice tillering, jointing, booting, and heading periods, respectively, and the overall accuracy was improved by 6.01%. Moreover, the model accuracies (MLP and CNN) before and after variable screening showed noticeable changes. Conducting variable screening contributed to a substantial improvement in the accuracy of rice LAI estimation.

CONCLUSIONS: UAV multispectral remote sensing combined with CNN technology provides an efficient and accurate method for the unified multi-period estimation of rice LAI. Moreover, the generalization ability and adaptability of the model were further improved by rational variable screening and data enhancement techniques. This study can provide a technical support for precision agriculture and a more accurate solution for rice growth monitoring. More feature extraction and variable screening methods can be further explored in future studies by optimizing the model structure to improve the accuracy and stability of the model.

PMID:40442795 | DOI:10.1186/s13007-025-01398-1

Sci Rep. 2025 May 29;15(1):18810. doi: 10.1038/s41598-025-03393-x.

ABSTRACT

Growing rates of chronic wound occurrence, especially in patients with diabetes, has become a recent concerning trend. Chronic wounds are difficult and costly to treat, and have become a serious burden on health care systems worldwide. Innovative deep learning methods for the detection and monitoring of such wounds have the potential to reduce the impact to patients and clinicians. We present a novel multimodal segmentation method which allows for the introduction of patient metadata into the training workflow whereby the patient data are expressed as Gaussian random fields. Our results indicate that the proposed method improved performance when utilising multiple models, each trained on different metadata categories. Using the Diabetic Foot Ulcer Challenge 2022 test set, when compared to the baseline results (intersection over union = 0.4670, Dice similarity coefficient = 0.5908) we demonstrate improvements of +0.0220 and +0.0229 for intersection over union and Dice similarity coefficient respectively. This paper presents the first study to focus on integrating patient data into a chronic wound segmentation workflow. Our results show significant performance gains when training individual models using specific metadata categories, followed by average merging of prediction masks using distance transforms. All source code for this study is available at: https://github.com/mmu-dermatology-research/multimodal-grf.

PMID:40442267 | DOI:10.1038/s41598-025-03393-x

Sci Rep. 2025 May 29;15(1):18821. doi: 10.1038/s41598-025-03313-z.

ABSTRACT

With the advancement of deep learning, robotic grasping has seen widespread application in fields, becoming a critical component in enhancing automation. Accurate and efficient grasping capabilities not only significantly boost productivity but also ensure safety and reliability in complex and dynamic environments. However, current approaches, particularly those based on convolutional neural networks (CNNs), often neglect the hierarchical information inherent in the data and lead to challenges in complex environments with abundant background information. Moreover, these methods struggle to capture long-range dependencies and non-local self-similarity, critical for accurate grasp detection. To address these issues, we propose GraspFormer, a novel method for robotic grasp detection. GraspFormer features a unique Encoder-Decoder framework that incorporates a Grasp Transformer Block designed to model long-range dependencies while avoiding background interference. Our approach also designs hierarchical information-guided self-attention (HIGSA) and an adaptive deep channel modulator (DCM) to enhance feature interactions and competition. Extensive experiments demonstrate that GraspFormer achieves performance comparable to state-of-the-art methods. The code is available at https://github.com/shine793/Hierarchical-Information-guided-Robotic-Grasp-Detection .

PMID:40442259 | DOI:10.1038/s41598-025-03313-z

Sci Rep. 2025 May 29;15(1):18858. doi: 10.1038/s41598-025-03208-z.

ABSTRACT

In the era of digitization, online digital advertising is one of the best techniques for modern marketing. This makes advertisers rely heavily on accurate user interest and behavior modelling to deliver precise advertisement impressions and increase click-through rates. The classic approach to model user interests has often required the use of predefined feature sets which are typically stagnant and not representative of temporal changes and trends in user behavior. While recent advances in deep learning offer potential solutions to these obstacles, many existing approaches fail to address the sequential nature of user interactions. In this paper, we propose an optimized Bi-Directional Long Short-Term Memory (Bi-LSTM) based user interest modeling approach together with an Updated version of Parrot Optimizer (UPO) to enhance performance. It treats the user activity as a temporal sequence which well fits the changing nature of user interest and preferences over time. The proposed approach is evaluated on two important tasks: predicting the probability that a user will click on an ad and predicting the probability that a user will click on a particular type of ad campaign. Simulation results demonstrate that the proposed method provides superior results than the static set-based approaches and achieves significant improvements on both user ad responses predictions and user ad clicks at the campaign level. The research also enhances the efficiency of user interest modeling with significant implications for online advertising, recommendation systems, and personalized marketing.

PMID:40442252 | DOI:10.1038/s41598-025-03208-z

BMC Med. 2025 May 30;23(1):321. doi: 10.1186/s12916-025-04127-6.

ABSTRACT

BACKGROUND: Insulin is known to be associated with a higher risk of coronary artery disease (CAD), but molecular mechanisms remain unclear. This study aimed to explore protein-mediated pathways linking fasting insulin to CAD using Mendelian randomization (MR).

METHODS: This MR study examined the association between fasting insulin and CAD using genome-wide association study (GWAS) data from MAGIC and CARDIoGRAMplusC4D. To investigate underlying mechanisms, a two-step proteome-wide MR analysis was conducted. First, associations of fasting insulin with 2940 circulating proteins were assessed using GWAS of proteomics from UKB-PPP. Proteins affected by insulin were then analyzed for their association with CAD risk. Proteins selected in both steps were considered as potential mediators. Sensitivity analyses to test whether associations are robust to pleiotropy and replication using other GWAS data, including GWAS of proteomics from deCODE and GWAS of CAD from FinnGen Biobank, were performed.

RESULTS: Genetically predicted insulin was associated with a higher risk of CAD (odds ratio 1.79, 95% confidence interval 1.34 to 2.40). At a false discovery rate of 0.05, insulin affected 355 proteins, ten of which were both increased by insulin and linked to a higher risk of CAD. After sensitivity and replication analyses, PLA2G7, GZMA, LDLR, AGRP, and HHEX were identified as reliable mediators. Mediation analyses using non-pleiotropic instruments showed that PLA2G7, GZMA, LDLR, and AGRP explained 19.50%, 6.91%, 19.31%, and 29.66% of insulin's total effect on CAD, respectively.

CONCLUSIONS: This study identified five protein mediators linking insulin to CAD. These proteins could be considered as potential targets to mitigate insulin-related cardiovascular risk, providing novel insights for drug repurposing.

PMID:40442727 | DOI:10.1186/s12916-025-04127-6

J Biomed Res. 2025 May 30:1-12. doi: 10.7555/JBR.39.20250166. Online ahead of print.

ABSTRACT

The comorbidity of skin and gastrointestinal tract (GIT) diseases, primarily driven by the gut-skin axis (GSA), is well-known. However, the genetic contribution to the GSA remains unclear. Here, using genome-wide association study (GWAS) summary statistics from European populations, we performed genome-wide pleiotropic analysis to investigate the shared genetic basis and causal associations between skin and GIT diseases. We observed extensive genetic correlations and overlaps between skin and GIT diseases. A total of 298 pleiotropic loci were identified, 75 of which were colocalized, and 61 exhibited pleiotropic effects across multiple trait pairs, including 2p16.1 ( PUS10), 6p21.32 ( HLA-DRB1), 10q21.2 ( ZNF365), and 19q13.11 ( SLC7A10). Additionally, five novel loci were identified based on the pleiotropic analysis, with RORA at 15q22.2 validated by the latest inflammatory bowel disease GWAS. Gene-based analysis found 394 unique pleiotropic genes, which were enriched in GSA-associated tissues and immune system, whereas protein-protein interaction analysis further revealed the GPCR-cAMP, chromatin remodeling, JAK-STAT, and HLA-mediated immunity pathways coregulate GSA comorbidity. Notably, the JAK-STAT pathway showed strong potential in drug repurposing, with Adalimumab targeting TNF and Ustekinumab targeting IL-12B already used to treat both skin and GIT diseases. Finally, Mendelian randomization analysis suggested five significant causal associations, and subsequent mediation analysis introduced three potential microbiota-GIT-skin pathways. Taken together, our study suggested that the shared genetic factors between skin and GIT diseases are widely distributed across the genome. These findings will improve our understanding of the genetic basis of GSA and offer significant implications for simultaneously treating skin and GIT diseases.

PMID:40441863 | DOI:10.7555/JBR.39.20250166

Exp Parasitol. 2025 May 27:108966. doi: 10.1016/j.exppara.2025.108966. Online ahead of print.

ABSTRACT

Leishmaniasis is a parasitic disease caused by protozoa of the genus Leishmania, the conventional treatments are expensives, high adverse reactions and long-term parenteral administration This study aimed to evaluate the therapeutic potential of the antiviral Oseltamivir (Osv) in microemulsion in the topical treatment of cutaneous leishmaniasis in BALB/c mice infected with Leishmania major. After infection, the mice were divided into five groups (Control, Amphotericin B 3%, Osv 0.5%, Osv 1% and Osv 1%+Amphotericin B 1.5%) and treated for 21 days. Clinical parameters, such as body weight and lesion size, in addition to parasite load, hematological, biochemical and histopathological analyses were evaluated. A significant reduction in the parasite load was observed in the groups treated with Oseltamivir and Amphotericin B (70% to 76.5%), when compared to the control group (95%). Clinical evaluation showed fewer lesions in the treatment groups compared to the control group. Although Amphotericin B alone caused liver and kidney toxicity, treatment with Oseltamivir, alone or in combination with Amphotericin B, did not show any toxicity. In histopathological examination, the groups treated with Oseltamivir showed lower degrees of histopathological alterations. Thus, Oseltamivir, as monotherapy or in combination with Amphotericin B, proved to be effective and safe, representing a promising alternative in the treatment of cutaneous leishmaniasis.

PMID:40441373 | DOI:10.1016/j.exppara.2025.108966