Interdiscip Sci. 2025 Jun 2. doi: 10.1007/s12539-025-00720-8. Online ahead of print.

ABSTRACT

Accurate pulmonary nodule detection in CT imaging remains challenging due to fragmented feature integration in conventional deep learning models. This paper proposes SPCF-YOLO, a real-time detection framework that synergizes hierarchical feature fusion with anatomical context modeling. First, the space-to-depth convolution (SPDConv) module preserves fine-grained features in low-resolution images through spatial dimension reorganization. Second, the shared feature pyramid convolution (SFPConv) module is designed to dynamically extract multi-scale contextual information using multi-dilation-rate convolutional layers. Incorporating a small object detection layer aims to improve sensitivity to small nodules. This is achieved in combination with the improved pyramid squeeze attention (PSA) module and the improved contextual transformer (CoTB) module, which enhance global channel dependencies and reduce feature loss. The model achieves 82.8% mean average precision (mAP) and 82.9% F1 score on LUNA16 at 151 frames per second (representing improvements of 17.5% and 82.9% over YOLOv8 respectively), demonstrating real-time clinical viability. Cross-modality validation on SIIM-COVID-19 shows 1.5% improvement, confirming robust generalization.

PMID:40455403 | DOI:10.1007/s12539-025-00720-8

Interdiscip Sci. 2025 Jun 2. doi: 10.1007/s12539-025-00713-7. Online ahead of print.

ABSTRACT

With the advantages of reducing biochemical experiments and enabling the rapid screening of potential druggable compounds, accurate computational methods are essential for predicting Drug-Target affinity (DTA). Current deep learning-based DTA prediction methods predominantly concentrate on single-modal information from drugs or targets. In this article, we propose a new multi-modal DTA prediction method, MGSDTA, to integrate graph features and sequence features of drug molecules and target proteins. We extract features from the drug molecular graphs and target protein graphs, meanwhile, we extract sequence features using continuous embeddings generated by advanced self-supervised pre-trained models, Mol2vec and ProtVec, for drug substructures and target subsequences respectively. Finally, they are integrated with a weighted fusion module for DTA prediction. Experiments on benchmark datasets indicate that the performance of MGSDTA exceeds single-modal methods based solely on sequences or graphs.

PMID:40455402 | DOI:10.1007/s12539-025-00713-7

Interdiscip Sci. 2025 Jun 2. doi: 10.1007/s12539-025-00716-4. Online ahead of print.

ABSTRACT

Non-coding RNAs (ncRNAs) are one of the components of epigenetic mechanisms that regulates gene expression. Studying ncRNA-protein interactions (NPI) can help to explore a wide range of biological features and related diseases. Traditional NPI research methods often require expensive equipment, a lot of time and labor. With the abundant samples accumulated from traditional experiments, remarkable progress has been made in the study of NPI by computational methods. Heterogeneous graph neural network is a deep learning method that synthesizes heterogeneous types of data as well as network topology. In this study, we propose an NPI-HetGNN model for NPI prediction based on heterogeneous graph neural networks. Firstly, initial features are constructed by integrating the sequence properties of ncRNA and protein data as well as the topology of heterogeneous connections. Then, the multilevel homogeneous subgraph is obtained and its semantic information is aggregated by metapath walking. At the same time, the homogeneous node information is fused within the subgraph metapath. To enhance feature extraction ability of the network, an energy-constrained self-attention module is introduced. Due to the lack of wet lab validation conditions, this study adopts computational verification. The performance of the NPI-HetGNN model on four benchmark datasets is experimentally verified. Ablation experiments also confirmed the comprehensiveness and validity of our model design. The experimental results show that comparing with six state-of-the-art methods, our NPI-HetGNN achieves very satisfactory results on all four datasets.

PMID:40455400 | DOI:10.1007/s12539-025-00716-4

Leuk Lymphoma. 2025 Jun 2:1-13. doi: 10.1080/10428194.2025.2507198. Online ahead of print.

ABSTRACT

Monoclonal antibody (mAb)-based therapies targeting CD19, CD20, and CD22 have revolutionized B-ALL treatment, offering precision and reduced systemic toxicity by engaging immune mechanisms to eliminate leukemic cells. This review synthesizes literature from PubMed, Web of Science, and ClinicalTrials.gov (2000-2024), focusing on clinical outcomes and resistance mechanisms. Bispecific T-cell engagers (e.g. blinatumomab) and CD22-directed antibody-drug conjugates (e.g. inotuzumab ozogamicin) demonstrate robust efficacy in relapsed/refractory disease. Advances in antibody engineering, such as Fc optimization, nanobodies, and humanization, enhance tumor targeting and therapeutic safety. Persistent challenges include antigen escape, stromal-mediated resistance, and treatment-related toxicities. Combinatorial approaches integrating mAbs with CAR-T cells or checkpoint inhibitors show promise in overcoming resistance pathways. Emerging technologies like artificial intelligence and deep learning are transforming antibody design by predicting epitope binding, enabling de novo protein engineering, and streamlining affinity maturation. These innovations accelerate the development of next-generation therapies, underscoring the evolving potential of precision immunotherapy of B-ALL.

PMID:40455243 | DOI:10.1080/10428194.2025.2507198

Chaos. 2025 Jun 1;35(6):063101. doi: 10.1063/5.0264041.

ABSTRACT

The "bad" Jaulent-Miodek (JM) equation serves to describe the motion of non-viscous shallow water wave packets in a flat-bottomed domain subject to shear forces. The "bad" JM equation exhibits poor properties, characterized by the linear instability of nonlinear waves on the zero-plane background, rendering it challenging to solve through traditional analytical and numerical methods. In this paper, two classic physics-driven deep learning approaches, namely, Physics-Informed Neural Networks (PINN) and Physics and Equality-Constrained Artificial Neural Networks (PECANN), are combined into a two-stage "PINN+PECANN" neural network to address the nonlinear wave evolution on the zero-plane background for the "bad" JM equation. The two-stage "PINN+PECANN" neural network method employs PINN in the first stage to pre-train the neural network, followed by fine-tuning of the network parameters using PECANN in the second stage. This approach not only correctly obtains solutions to the "bad" JM equation but also enhances computational efficiency. Specifically, we present the evolutionary behavior of nonlinear waves for the common initial values of the "bad" JM equation: Gauss wave packets, sech wave packets, and rational wave packets. Furthermore, the nonlinear interactions between two Gauss, sech, rational wave packets are provided. The results in this paper validate the advantages of physics-driven deep learning methods in solving equations with poor properties and open up a new pathway for obtaining unstable solutions of nonlinear equations.

PMID:40455205 | DOI:10.1063/5.0264041

Methods Mol Biol. 2025;2927:195-220. doi: 10.1007/978-1-0716-4546-8_11.

ABSTRACT

The COVID crisis has accelerated the integration of artificial intelligence (AI) in drug discovery and omics research, providing novel avenues to tackle intricate issues in virology research. AI has lately enabled significant breakthroughs in a wide range of biological disciplines, including genetic variant interpretation, protein structure prediction, disease detection, and pharmaceutical creation. It has prominently assumed a pivotal role in virology research, with generative AI at the forefront of innovation. Generative AI (GAI) is a subset of AI that majorly focuses on creating new data or content resembling existing data through learning underlying patterns and relationships. It has revolutionized virology/omics study by generating synthetic data to augment limited datasets, predicting protein structures, identifying gene regulatory networks, and assisting in drug discovery through virtual screening, accelerating advancements in genomics, proteomics, and metabolomics research. This chapter aims to discuss the basic concept of generative models and their current and future scope in virology.

PMID:40455159 | DOI:10.1007/978-1-0716-4546-8_11

Clin Oral Investig. 2025 Jun 2;29(6):328. doi: 10.1007/s00784-025-06410-5.

ABSTRACT

OBJECTIVE: The aim of this study was to develop convolutional neural network (CNN)-based systems to diagnose calculus, plaque, gingival hyperplasia and gingival inflammation in intraoral images from orthodontic patients.

MATERIALS AND METHODS: A dataset of 1,000 lateral and frontal intraoral images from orthodontic patients was used to develop CNN-based models. Periodontology specialists annotated areas of dental calculus, plaque, gingival inflammation, and gingival hyperplasia on the teeth and gingiva. The dataset was divided into training (80%), validation (10%), and test (10%) sets for model development. The YOLOv8 and hybrid U-Net + ResNet50 models were examined. Their performance was evaluated on the basis of accuracy, precision, recall, F1 score, Tversky loss, intersection over union, mean average precision, Dice coefficient, and Cohen's kappa.

RESULTS: The mean classification accuracy was 0.96 for the YOLOv8 model and 0.93 for the U-Net + ResNet50 model. On the basis of the Dice coefficient, the models performed best in detecting gingival hyperplasia (YOLOv8: 0.78, U-Net + ResNet50: 0.79) and worst in detecting dental calculus (YOLOv8:0.48, U-Net + ResNet50:0.53). Cohen's kappa coefficient was highest for classifying gingival hyperplasia (YOLOv8: 0.785, U-Net + ResNet50: 0.790). The precision exceeded 0.72 across all the classifications, with the greatest precision in classifying gingival inflammation.

CONCLUSION: Deep learning-based systems can serve as decision support tools by offering rapid and objective evaluations of dental calculus, plaque, gingival inflammation, and gingival hyperplasia. Nonetheless, the definitive diagnostic conclusion should be based on the clinician's specialized expertise and professional judgment.

CLINICAL RELEVANCE: The integration of CNN-based diagnostic models into clinical workflows has the potential to facilitate early periodontal diagnosis and improve accessibility to periodontal assessments in orthodontic patients.

PMID:40455084 | DOI:10.1007/s00784-025-06410-5

Transl Vis Sci Technol. 2025 Jun 2;14(6):3. doi: 10.1167/tvst.14.6.3.

ABSTRACT

PURPOSE: Standard deep learning (DL) models often suffer significant performance degradation on out-of-distribution (OOD) data, where test data differs from training data, a common challenge in medical imaging due to real-world variations.

METHODS: We propose a unified self-censorship framework as an alternative to the standard DL models for glaucoma classification using deep evidential uncertainty quantification. Our approach detects OOD samples at both the dataset and image levels. Dataset-level self-censorship enables users to accept or reject predictions for an entire new dataset based on model uncertainty, whereas image-level self-censorship refrains from making predictions on individual OOD images rather than risking incorrect classifications. We validated our approach across diverse datasets.

RESULTS: Our dataset-level self-censorship method outperforms the standard DL model in OOD detection, achieving an average 11.93% higher area under the curve (AUC) across 14 OOD datasets. Similarly, our image-level self-censorship model improves glaucoma classification accuracy by an average of 17.22% across 4 external glaucoma datasets against baselines while censoring 28.25% more data.

CONCLUSIONS: Our approach addresses the challenge of generalization in standard DL models for glaucoma classification across diverse datasets by selectively withholding predictions when the model is uncertain. This method reduces misclassification errors compared to state-of-the-art baselines, particularly for OOD cases.

TRANSLATIONAL RELEVANCE: This study introduces a tunable framework that explores the trade-off between prediction accuracy and data retention in glaucoma prediction. By managing uncertainty in model outputs, the approach lays a foundation for future decision support tools aimed at improving the reliability of automated glaucoma diagnosis.

PMID:40455037 | DOI:10.1167/tvst.14.6.3

J Chem Inf Model. 2025 Jun 2. doi: 10.1021/acs.jcim.5c00538. Online ahead of print.

ABSTRACT

Predictive models hold considerable promise in enabling the faster discovery of safer, more efficacious therapeutics. To better understand and improve the performance of small-molecule predictive models for drug discovery, we conduct multiple experiments with deep learning and traditional machine learning approaches, leveraging our large internal data sets as well as publicly available data sets. The experiments include assessing performance on random, temporal, and reverse-temporal data ablation tasks as well as tasks testing model extrapolation to different property spaces. We identify factors that contribute to the higher performance of predictive models built using graph neural networks compared to traditional methods such as XGBoost and random forest. These insights were successfully used to develop a scaling relationship that explains 81% of the variance in model performance across various assays and data regimes. This relationship can be used to estimate the performance of models for ADMET (absorption, distribution, metabolism, excretion, and toxicity) end points, as well as for drug discovery assay data more broadly. The findings offer guidance for further improving model performance in drug discovery.

PMID:40454949 | DOI:10.1021/acs.jcim.5c00538

Rheumatology (Oxford). 2025 Jun 2:keaf299. doi: 10.1093/rheumatology/keaf299. Online ahead of print.

ABSTRACT

OBJECTIVE: Interstitial pneumonia with autoimmune features (IPAF) describes patients with interstitial lung disease (ILD) and autoimmune features without meeting criteria for a specific rheumatic disease. No longitudinal data exist on post-transplant outcomes in IPAF patients. We compared baseline demographics, pre-transplant characteristics, and post-transplant outcomes between IPAF and idiopathic pulmonary fibrosis (IPF) patients undergoing double lung transplantation.

METHODS: We retrospectively analyzed lung transplant recipients with ILD in British Columbia between Jan 1, 2014, and Apr 30, 2024. Diagnoses of IPAF and IPF were made by multidisciplinary review. Continuous variables were analyzed using the Mann-Whitney U test, categorical variables with Fisher's Exact test, and survival using Kaplan-Meier analysis.

RESULTS: We identified 20 IPAF and 64 IPF patients. IPAF patients were more likely female (50% vs 17%, p = 0.006), on pre-transplant immunosuppression (60% vs 6.3%, p < 0.001), and were less likely to receive antifibrotics (20% vs 64%, p < 0.001). No difference was seen in 1-year or cumulative survival, though survival curves diverged over time favouring IPAF. Post-transplant lung function, acute rejection, infection-related hospitalization, malignancy, and chronic lung allograft dysfunction (CLAD) were similar, with non-usual interstitial pneumonia (UIP) IPAF exhibiting a survival advantage over IPF (100% vs 66%, p = 0.044). Explant pathology revealed more UIP patterns in IPF, while IPAF showed nonspecific interstitial pneumonia (NSIP) or unclassifiable patterns.

CONCLUSIONS: Post-transplant survival, lung function, and complication rates were comparable between IPAF and IPF patients at one year and the last follow-up. This is the first study to report both short- and long-term lung transplant outcomes in IPAF patients.

PMID:40455051 | DOI:10.1093/rheumatology/keaf299

J Clin Invest. 2025 Jun 2;135(11):e184158. doi: 10.1172/JCI184158. eCollection 2025 Jun 2.

ABSTRACT

Idiopathic pulmonary fibrosis (IPF) is a devastating interstitial lung disease characterized by the excessive accumulation of activated myofibroblasts that deposit extracellular matrix (ECM) protein, leading to progressive scar formation and mechanical stress. However, the cellular origin and fate of myofibroblasts remain controversial, and the mechanisms by which myofibroblasts sense mechanical cues in the lung are unclear. Here, we report that periostin (Postn) is a reliable and distinctive marker for pulmonary myofibroblasts, while ablation of Postn+ myofibroblasts after injury ameliorated lung fibrosis. PIEZO1 was highly expressed in Postn+ myofibroblast and played a vital role in mechanoactivation of Postn+ myofibroblast and development of lung fibrosis. Conditional deletion of Piezo1 in Postn+ myofibroblasts significantly inhibited lung fibrosis by suppressing myofibroblast activation and proliferation. Loss of Piezo1 led to disruption of actin organization and prevention of Yap/Taz nuclear localization, thus shifting the myofibroblasts from a proliferative state into a stressed and apoptotic state. Furthermore, myofibroblast-specific Yap/Taz deletion fully recapitulated the protective phenotypes of myofibroblast-Piezo1-KO mice. These findings show that periostin marks pulmonary myofibroblasts, and that PIEZO1-mediated mechanosensation is essential for myofibroblast activation in the lung. Targeting PIEZO1 in the periostin-expressing cells is a novel therapeutic option to interfere with fibrotic diseases such as IPF .

PMID:40454481 | DOI:10.1172/JCI184158

Small Methods. 2025 Jun 2:e2401900. doi: 10.1002/smtd.202401900. Online ahead of print.

ABSTRACT

It is shown that regularizing the signal gradient statistics during training of deep-learning models of super-resolution fluorescence microscopy improves the generated images. Specifically, regularizing the images in the training data set is proposed to have gradient and Laplacian statistics closer to those expected for natural-scene images. The BioSR data set of matched pairs of diffraction-limited and super-resolution images is used to evaluate the proposed regularization in a state-of-the-art generative deep-learning model of super-resolution microscopy, the Conditional Variational Diffusion Model (CVDM). Since the proposed regularization is applied as a preprocessing step to the training data, it can be used in conjunction with any supervised machine-learning model. However, its utility is limited to images for which the prior is appropriate, which in the BioSR data set are the images of filamentous structures. The quality and generalization power of CVDM trained with and without the proposed regularization are compared, showing that the new prior yields images with clearer visual detail and better small-scale structure.

PMID:40454902 | DOI:10.1002/smtd.202401900

J Phycol. 2025 Jun 2. doi: 10.1111/jpy.70040. Online ahead of print.

ABSTRACT

The transition from interbreeding populations to species continues to represent difficult terrain for phylogenetic investigations. Genotyping entire genomes holds promise for enhancing insights into the process of speciation and evolutionary relationships among recently speciated taxa. Northeast Pacific ribbon kelp was once recognized as four species before they were folded into Alaria marginata based on DNA barcodes, although several lineages continue to be recognized. We used whole genome sequencing to determine whether these lineages represente species. Whole genomes of 69 individuals from five genetically distinctive lineages in the Gulf of Alaska (United States) and Salish Sea (Canada) were analyzed, along with 63 genomes from three other species of Alaria. Our analysis of >3.4 million single nucleotide polymorphisms reaffirmed that organellar and nuclear phylogenetic signals are incongruent in Alaria, producing different topologies among five organellar and six nuclear A. marginata lineages. Lineages appeared to be reproductively isolated, as evidenced by strong clustering and lack of recent admixture across nuclear genomes. Genetic divergence between A. marginata lineages also exceeded intra-lineage divergence, proxied by A. esculenta populations, but fell short of distances observed across other species of Alaria. Despite the genomic data supporting predictions of the biological and genetic species concepts, we encountered inherent limitations in declaring species status. While our work shifts taxonomic conversations toward a genome-scale framework that provides a more comprehensive picture of divergence and connectivity, our work also highlights that philosophical challenges inherent to defining species persist and that integrative approaches continue to be necessary in the genomic era.

PMID:40454788 | DOI:10.1111/jpy.70040

Curr Med Chem. 2025 May 29. doi: 10.2174/0109298673410629250520111827. Online ahead of print.

NO ABSTRACT

PMID:40454496 | DOI:10.2174/0109298673410629250520111827

Neurol Ther. 2025 Jun 2. doi: 10.1007/s40120-025-00752-8. Online ahead of print.

ABSTRACT

Omaveloxolone is approved for the treatment of Friedreich ataxia (FA) in patients aged ≥ 16 years and is under clinical development for pediatric patients. In the MOXIe study, alanine and aspartate aminotransferase (ALT and AST) elevations were among the most common treatment-emergent adverse events (TEAEs) in the omaveloxolone arm and were mild to moderate, generally asymptomatic, transient, and reversible; no patients who received omaveloxolone had laboratory abnormalities that met the Hy's law criteria. Omaveloxolone labels (US and EU) provide guidance for monitoring and managing these elevations. Here, practical use considerations, from experience-based opinions of four FA experts and a hepatologist via semi-structured interviews, are presented. Prior to omaveloxolone initiation, assessment of baseline ALT, AST, and total bilirubin is recommended per label. During treatment, ALT, AST, and total bilirubin should be monitored monthly for the first 3 months and periodically thereafter per label. Reduced frequency of patient monitoring after 3 months is suggested if aminotransferase levels remain normal. Per label, omaveloxolone should be temporarily discontinued if aminotransferases increase to > 5 × the upper limit of normal (ULN) or > 3 × ULN with other evidence of liver dysfunction. Stemming from real-world practical considerations wherein patients are followed up less frequently than in the trial setting, treatment interruption when aminotransferases increase to ≥ 3 × ULN without other signs of hepatic impairment may be considered. When aminotransferase elevations stabilize or resolve, omaveloxolone may be reinitiated with an appropriate increased frequency of monitoring of liver function per label. We propose patients who pause treatment may have testing repeated after 2 weeks, while those with resolving aminotransferase elevations may reinitiate omaveloxolone with stepwise dose titrations and testing every 2 weeks for ≈ 3 months. Use considerations herein may inform decisions on monitoring and managing ALT and AST elevations, which potentially help to encourage the treatment adherence needed to achieve the slowing of FA progression seen in MOXIe.Graphical abstract available for this article.

PMID:40455368 | DOI:10.1007/s40120-025-00752-8

N Engl J Med. 2025 May 31. doi: 10.1056/NEJMoa2503119. Online ahead of print.

ABSTRACT

BACKGROUND: On the basis of phase 2 studies, trastuzumab deruxtecan was approved for patients with human epidermal growth factor receptor 2 (HER2)-positive metastatic gastric cancer or gastroesophageal junction adenocarcinoma who had previously received trastuzumab-based therapy. Ramucirumab plus paclitaxel is also a standard second-line treatment option regardless of HER2 status.

METHODS: We conducted an international, randomized, phase 3 trial comparing second-line trastuzumab deruxtecan at a dose of 6.4 mg per kilogram of body weight with ramucirumab plus paclitaxel in patients with HER2-positive metastatic gastric cancer or gastroesophageal junction adenocarcinoma confirmed on tumor biopsy conducted after the patient had progression while receiving trastuzumab-based therapy. The primary end point was overall survival. Secondary end points included progression-free survival, confirmed objective response (complete or partial response lasting ≥4 weeks), disease control, duration of response, and safety.

RESULTS: Among 494 patients who had undergone randomization, overall survival was significantly longer with trastuzumab deruxtecan than with ramucirumab plus paclitaxel (median, 14.7 vs. 11.4 months; hazard ratio for death, 0.70; 95% confidence interval, 0.55 to 0.90; P = 0.004). Significant results were also seen with regard to progression-free survival (hazard ratio for disease progression or death, 0.74; 95% CI, 0.59 to 0.92) and confirmed objective response (in 44.3% of the patients in the trastuzumab deruxtecan group vs. 29.1% of those in the ramucirumab-paclitaxel group). The incidence of drug-related adverse events of any grade was 93.0% with trastuzumab deruxtecan and 91.4% with ramucirumab plus paclitaxel; the incidence of drug-related adverse events of grade 3 or higher was 50.0% and 54.1%, respectively. Adjudicated drug-related interstitial lung disease or pneumonitis occurred in 13.9% of the patients who received trastuzumab deruxtecan (grade 1 or 2 in 33 patients and grade 3 in 1) and in 1.3% of those who received ramucirumab plus paclitaxel (grade 3 in 2 patients and grade 5 in 1).

CONCLUSIONS: Trastuzumab deruxtecan led to significantly longer overall survival than ramucirumab plus paclitaxel among patients with HER2-positive metastatic gastric cancer or gastroesophageal junction adenocarcinoma. Adverse events were common in both groups. Events of interstitial lung disease or pneumonitis with trastuzumab deruxtecan, a known risk, were mainly low-grade. (Funded by Daiichi Sankyo and AstraZeneca; DESTINY-Gastric04 ClinicalTrials.gov number, NCT04704934.).

PMID:40454632 | DOI:10.1056/NEJMoa2503119

iScience. 2025 Apr 18;28(5):112488. doi: 10.1016/j.isci.2025.112488. eCollection 2025 May 16.

ABSTRACT

Steroid hormones regulate cell physiology and immune function, with dysregulated steroidogenesis promoting cancer progression by supporting tumor growth and suppressing anti-tumor immunity. Targeting CYP11A1, the first and rate-limiting enzyme in steroid biosynthesis, has shown promise in cancer therapy, but safe and effective inhibitors remain an unmet need. Undertaking in silico structure-based drug repurposing approach, we found posaconazole as an inhibitor of CYP11A1. The docking pose analysis showed that posaconazole can form multiple hydrogen bonds and hydrophobic interactions with the key residues at the binding site and the cofactor, stabilizing the protein-ligand complex. We validated its inhibition efficiency in cell-based assays. In a mouse model of lung metastasis, we demonstrated that posaconazole restricts metastasis by stimulating anti-tumor immunity. These findings highlight posaconazole's potential as a research tool to study steroidogenesis and as a candidate for further preclinical and clinical evaluation in pathologies associated with local steroidogenesis, such as steroidogenic tumors.

PMID:40454094 | PMC:PMC12124671 | DOI:10.1016/j.isci.2025.112488

J Alzheimers Dis. 2025 Jun 2:13872877251344572. doi: 10.1177/13872877251344572. Online ahead of print.

ABSTRACT

BackgroundAlzheimer's disease (AD) is a major neurodegenerative disorder with limited treatment options.ObjectiveThis study aimed to identify novel therapeutic targets for AD using proteome-wide Mendelian randomization (MR) and colocalization analyses.MethodsWe conducted a large-scale, proteome-wide MR analysis using data from two extensive genome-wide association studies (GWASs) of plasma proteins: the UK Biobank Pharma Proteomics Project (UKB-PPP) and the deCODE Health Study. We extracted genetic instruments for plasma proteins from these studies and utilized AD summary statistics from European Bioinformatics Institute GWAS Catalog. Colocalization analysis assessed whether identified associations were due to shared causal variants. Phenome-wide association studies and drug repurposing analyses were performed to assess potential side effects and identify existing drugs targeting the identified proteins.ResultsOur MR analysis identified significant associations between genetically predicted levels of 9 proteins in the deCODE dataset and 17 proteins in the UKB-PPP dataset with AD risk after Bonferroni correction. Four proteins (BCAM, CD55, CR1, and GRN) showed consistent associations across both datasets. Colocalization analysis provided strong evidence for shared causal variants between GRN, CR1, and AD. PheWAS revealed minimal potential side effects for CR1 but suggested possible pleiotropic effects for GRN. Drug repurposing analysis identified several FDA-approved drugs targeting CR1 and GRN with potential for AD treatment.ConclusionsThis study identifies GRN and CR1 as promising therapeutic targets for AD. These findings provide new directions for AD drug development, but further research and clinical trials are warranted to validate the therapeutic potential of these targets.

PMID:40452368 | DOI:10.1177/13872877251344572

Arch Suicide Res. 2025 Jun 2:1-20. doi: 10.1080/13811118.2025.2509653. Online ahead of print.

ABSTRACT

OBJECTIVE: Suicide rates continue to rise, particularly among young adults, with college student-athletes representing a specific subgroup of concern. The aim of this systematic review was to clarify the individual and environmental risk factors for suicide specific to U.S. college student-athletes.

METHOD: Databases searched included the State University of New York (SUNY) libraries, Google Scholar, Web of Science, PsychINFO, Semantic Scholar, and PubMed. No date restrictions were applied, resulting in 112 articles and reports included in this review. Studies examining U.S. student-athletes participating in intercollegiate athletics within the context of suicide, including ideation, actions, or attempts, met the inclusion criteria for this thematic review. The PRISMA framework guided the literature selection and content review.

RESULTS: Risk factors included the convergence of academic and athletic pressure, toxic team culture, barriers to accessing services, complexities of the athlete identity, and experiences of injury.

CONCLUSION: Given these unique risk factors, approaches to suicide prevention, intervention, and postvention for U.S. college student-athletes should include mandated suicide training for college athletic department personnel, routine mental health screening for athletes, improved access to mental health services, and the implementation of collaborative multidisciplinary care.

PMID:40454444 | DOI:10.1080/13811118.2025.2509653

Adv Funct Mater. 2025 Feb 12;35(7):2411334. doi: 10.1002/adfm.202411334. Epub 2024 Sep 27.

ABSTRACT

Cancer stem cells (CSCs), harboring stem cell-like properties involving self-renewal and aberrant differentiation potential, have been known to be one of the determining factors that contribute to therapeutic resistance and tumor recurrence. However, much remains to be understood about the reprogramming network leading to the generation of CSCs driven by chemotherapy. In this study, guided by bioinformatics study, we uncover and provide deeper insight into the CSC enrichment mechanism driven by cisplatin (CDDP) treatment. We discover that CDDP can repopulate the level of CSC by activating AKT1 oncogenic pathway that is further enhanced by COX-2 inflammatory signaling. Simultaneously blocking these two pathways can synergistically restrain the number of CSCs. Under the guidance of a series of advanced hierarchical computational modeling, including molecular docking, molecular dynamics (MD) simulation and binding free energy analysis, MK-2206 is selected as the AKT1 inhibitor to achieve optimal codelivery of CDDP, MK-2206 and 5-ASA (COX-2 inhibitor) through the use of 5-ASA-derivatized dual functional immunostimulatory nanocarrier (PASA). This triple combination (PASA/CDDP/MK-2206) significantly reduces tumor burden in both orthotopic and metastatic lung cancer models. Mechanistic studies show that this improved therapeutic activity is due to elimination of CSCs and reversal of the immunosuppressive tumor microenvironment. Our study suggests that PASA/CDDP/MK-2206 may represent a simple and effective lung cancer therapy via reversing CSCs-associated chemoresistance.

PMID:40452781 | PMC:PMC12124824 | DOI:10.1002/adfm.202411334