J Chem Inf Model. 2025 May 16. doi: 10.1021/acs.jcim.5c00435. Online ahead of print.

ABSTRACT

Drug repositioning, which identifies novel therapeutic applications for existing drugs, offers a cost-effective alternative to traditional drug development. However, effectively capturing the complex relationships between drugs and diseases remains challenging. We present HGCL-DR, a novel heterogeneous graph contrastive learning framework for drug repositioning that effectively integrates global and local feature representations through three key components. First, we introduce an improved heterogeneous graph contrastive learning approach to model drug-disease relationships. Second, for local feature extraction, we employ a bidirectional graph convolutional network with a subgraph generation strategy in the bipartite drug-disease association graph, while utilizing a graph diffusion process to capture long-range dependencies in drug-drug and disease-disease relation graphs. Third, for global feature extraction, we leverage contrastive learning in the heterogeneous graph to enhance embedding consistency across different feature spaces. Extensive experiments on four benchmark data sets using 10-fold cross-validation demonstrate that HGCL-DR consistently outperforms state-of-the-art baselines in both AUPR, AUROC, and F1-score metrics. Ablation studies confirm the significance of each proposed component, while case studies on Alzheimer's disease and breast neoplasms validate HGCL-DR's practical utility in identifying novel drug candidates. These results establish HGCL-DR as an effective approach for computational drug repositioning.

PMID:40377926 | DOI:10.1021/acs.jcim.5c00435

Urologiia. 2025 May;(2):121-127.

ABSTRACT

OBJECTIVE: to assess the needs and attitudes of urologists regarding the use of technologies related to artificial intelligence, particularly the web platform "Sechenov.AI_nephro", in the surgical treatment of patients with renal parenchymal neoplasms.

MATERIALS AND METHODS: a qualitative study was conducted through in-depth interviews. A questionnaire was developed for the interviews, including 14 categories of questions covering various aspects of the use of artificial intelligence (AI) aimed at optimizing preoperative planning for patients with renal parenchymal neoplasms. The study involved 8 urologists with extensive experience in the surgical treatment of patients with renal parenchymal neoplasms.

RESULTS: the survey results highlight the growing interest in the implementation of AI technologies in medical practice.

CONCLUSION: in-depth interviews among urologists in Russia showed that there is a high interest in AI developments in urological practice. At the same time, successful integration of technologies requires overcoming several obstacles, including training specialists and ensuring data security. The "Sechenov.AI_nephro" platform has the potential to become an important tool in optimizing preoperative planning, but its success will depend on the readiness of physicians for new technologies and support from the medical community.

PMID:40377592

Nucleic Acids Res. 2025 May 16:gkaf414. doi: 10.1093/nar/gkaf414. Online ahead of print.

ABSTRACT

We present the CellHit web server (https://cellhit.bioinfolab.sns.it/), a web-based platform designed to predict and analyze cancer patients' responsiveness to drugs using transcriptomic data. By leveraging extensive pharmacogenomics datasets from the Genomics of Drug Sensitivity in Cancer v1 and v2 (GDSC) and Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) and transcriptomic data from the Cancer Cell Line Encyclopedia (CCLE) and The Cancer Genome Atlas Program (TCGA). CellHit integrates a computational pipeline for preprocessing, gene imputation, and robust alignment between patient and cell line transcriptomic data with pre-trained SOTA models for drug sensitivity prediction. The pipeline employs batch correction, enhanced Celligner methodology, and Parametric UMAP for stable and actionable alignment. The intuitive interface requires no programming expertise, offering interactive visualizations, including low-dimensional embeddings and drug sensitivity heatmaps for the input transcriptomic samples. Results feature contextual metadata, SHAP-based feature importance, and transcriptomic neighbors from reference datasets, simplifying interpretation and hypothesis generation. CellHit provides precomputed predictions across TCGA samples and offers the ability to run custom analyses online on input samples, democratizing precision oncology by enabling rapid, interpretable predictions accessible the research community.

PMID:40377071 | DOI:10.1093/nar/gkaf414

Stat Med. 2025 May;44(10-12):e70082. doi: 10.1002/sim.70082.

ABSTRACT

Group sequential designs in clinical trials allow for interim efficacy and futility monitoring. Adjustment for baseline covariates can increase power and precision of estimated effects. However, inconsistently applying covariate adjustment throughout the stages of a group sequential trial can result in inflation of type I error, biased point estimates, and anticonservative confidence intervals. We propose methods for performing correct interim monitoring, estimation, and inference in this setting that avoid these issues. We focus on two-arm trials with simple, balanced randomization and continuous outcomes. We study the performance of our boundary, estimation, and inference adjustments in simulation studies. We end with recommendations about the application of covariate adjustment in group sequential designs.

PMID:40377247 | DOI:10.1002/sim.70082

Int J Comput Assist Radiol Surg. 2025 May 16. doi: 10.1007/s11548-025-03390-5. Online ahead of print.

ABSTRACT

PURPOSE: Trauma-induced rib fractures are a common injury. The number and characteristics of these fractures influence whether a patient is treated nonoperatively or surgically. Rib fractures are typically diagnosed using CT scans, yet 19.2-26.8% of fractures are still missed during assessment. Another challenge in managing rib fractures is the interobserver variability in their classification. Purpose of this study was to develop and assess an automated method that detects rib fractures in CT scans, and classifies them according to the Chest Wall Injury Society (CWIS) classification.

METHODS: 198 CT scans were collected, of which 170 were used for training and internal validation, and 28 for external validation. Fractures and their classifications were manually annotated in each of the scans. A detection and classification network was trained for each of the three components of the CWIS classifications. In addition, a rib number labeling network was trained for obtaining the rib number of a fracture. Experiments were performed to assess the method performance.

RESULTS: On the internal test set, the method achieved a detection sensitivity of 80%, at a precision of 87%, and an F1-score of 83%, with a mean number of FPPS (false positives per scan) of 1.11. Classification sensitivity varied, with the lowest being 25% for complex fractures and the highest being 97% for posterior fractures. The correct rib number was assigned to 94% of the detected fractures. The custom-trained nnU-Net correctly labeled 95.5% of all ribs and 98.4% of fractured ribs in 30 patients. The detection and classification performance on the external validation dataset was slightly better, with a fracture detection sensitivity of 84%, precision of 85%, F1-score of 84%, FPPS of 0.96 and 95% of the fractures were assigned the correct rib number.

CONCLUSION: The method developed is able to accurately detect and classify rib fractures in CT scans, there is room for improvement in the (rare and) underrepresented classes in the training set.

PMID:40377883 | DOI:10.1007/s11548-025-03390-5

Infection. 2025 May 16. doi: 10.1007/s15010-025-02555-3. Online ahead of print.

ABSTRACT

INTRODUCTION: Severe respiratory infections pose a major challenge in clinical practice, especially in older adults. Body composition analysis could play a crucial role in risk assessment and therapeutic decision-making. This study investigates whether obesity or sarcopenia has a greater impact on mortality in patients with severe respiratory infections. The study focuses on the National Pandemic Cohort Network (NAPKON-SUEP) cohort, which includes patients over 60 years of age with confirmed severe COVID-19 pneumonia. An innovative approach was adopted, using pre-trained deep learning models for automated analysis of body composition based on routine thoracic CT scans.

METHODS: The study included 157 hospitalized patients (mean age 70 ± 8 years, 41% women, mortality rate 39%) from the NAPKON-SUEP cohort at 57 study sites. A pre-trained deep learning model was used to analyze body composition (muscle, bone, fat, and intramuscular fat volumes) from thoracic CT images of the NAPKON-SUEP cohort. Binary logistic regression was performed to investigate the association between obesity, sarcopenia, and mortality.

RESULTS: Non-survivors exhibited lower muscle volume (p = 0.043), higher intramuscular fat volume (p = 0.041), and a higher BMI (p = 0.031) compared to survivors. Among all body composition parameters, muscle volume adjusted to weight was the strongest predictor of mortality in the logistic regression model, even after adjusting for factors such as sex, age, diabetes, chronic lung disease and chronic kidney disease, (odds ratio = 0.516). In contrast, BMI did not show significant differences after adjustment for comorbidities.

CONCLUSION: This study identifies muscle volume derived from routine CT scans as a major predictor of survival in patients with severe respiratory infections. The results underscore the potential of AI supported CT-based body composition analysis for risk stratification and clinical decision making, not only for COVID-19 patients but also for all patients over 60 years of age with severe acute respiratory infections. The innovative application of pre-trained deep learning models opens up new possibilities for automated and standardized assessment in clinical practice.

PMID:40377852 | DOI:10.1007/s15010-025-02555-3

Insights Imaging. 2025 May 16;16(1):107. doi: 10.1186/s13244-025-01966-y.

ABSTRACT

OBJECTIVES: This study aimed to develop and validate a clinical-radiomics deep learning (DL) model based on MRI for endometrial cancer (EC) molecular subtypes classification.

METHODS: This multicenter retrospective study included EC patients undergoing surgery, MRI, and molecular pathology diagnosis across three institutions from January 2020 to March 2024. Patients were divided into training, internal, and external validation cohorts. A total of 386 handcrafted radiomics features were extracted from each MR sequence, and MoCo-v2 was employed for contrastive self-supervised learning to extract 2048 DL features per patient. Feature selection integrated selected features into 12 machine learning methods. Model performance was evaluated with the AUC.

RESULTS: A total of 526 patients were included (mean age, 55.01 ± 11.07). The radiomics model and clinical model demonstrated comparable performance across the internal and external validation cohorts, with macro-average AUCs of 0.70 vs 0.69 and 0.70 vs 0.67 (p = 0.51), respectively. The radiomics DL model, compared to the radiomics model, improved AUCs for POLEmut (0.68 vs 0.79), NSMP (0.71 vs 0.74), and p53abn (0.76 vs 0.78) in the internal validation (p = 0.08). The clinical-radiomics DL Model outperformed both the clinical model and radiomics DL model (macro-average AUC = 0.79 vs 0.69 and 0.73, in the internal validation [p = 0.02], 0.74 vs 0.67 and 0.69 in the external validation [p = 0.04]).

CONCLUSIONS: The clinical-radiomics DL model based on MRI effectively distinguished EC molecular subtypes and demonstrated strong potential, with robust validation across multiple centers. Future research should explore larger datasets to further uncover DL's potential.

CRITICAL RELEVANCE STATEMENT: Our clinical-radiomics DL model based on MRI has the potential to distinguish EC molecular subtypes. This insight aids in guiding clinicians in tailoring individualized treatments for EC patients.

KEY POINTS: Accurate classification of EC molecular subtypes is crucial for prognostic risk assessment. The clinical-radiomics DL model outperformed both the clinical model and the radiomics DL model. The MRI features exhibited better diagnostic performance for POLEmut and p53abn.

PMID:40377781 | DOI:10.1186/s13244-025-01966-y

Environ Monit Assess. 2025 May 16;197(6):657. doi: 10.1007/s10661-025-14066-7.

ABSTRACT

Remote sensing (RS) data is extensively used in the observation and management of surface water and the detection of water bodies for studying ecological and hydrological processes. Small waterbodies are often neglected because of their tiny presence in the image, but being very large in numbers, they significantly impact the ecosystem. However, the detection of small waterbodies in satellite images is challenging because of their varying sizes and tones. In this work, a geospatial artificial intelligence (GeoAI) approach is proposed to detect small water bodies in RS images and generate a spatial map of it along with area statistics. The proposed approach aims to detect waterbodies of different shapes and sizes including those with vegetation cover. For this purpose, a deep neural network (DNN) is trained using the Indian Space Research Organization's (ISRO) Cartosat-3 multispectral satellite images, which effectively extracts the boundaries of small water bodies with a mean precision of 0.92 and overall accuracy over 96%. A comparative analysis with other popular existing methods using the same data demonstrates the superior performance of the proposed method. The proposed GeoAI approach efficiently generates a map of small water bodies automatically from the input satellite image which can be utilized for monitoring and management of these micro water resources.

PMID:40377752 | DOI:10.1007/s10661-025-14066-7

Curr Opin Neurol. 2025 May 19. doi: 10.1097/WCO.0000000000001378. Online ahead of print.

ABSTRACT

PURPOSE OF REVIEW: To summarize recent advancements in artificial intelligence-driven lesion segmentation and novel neuroimaging modalities that enhance the identification and characterization of multiple sclerosis (MS) lesions, emphasizing their implications for clinical use and research.

RECENT FINDINGS: Artificial intelligence, particularly deep learning approaches, are revolutionizing MS lesion assessment and segmentation, improving accuracy, reproducibility, and efficiency. Artificial intelligence-based tools now enable automated detection not only of T2-hyperintense white matter lesions, but also of specific lesion subtypes, including gadolinium-enhancing, central vein sign-positive, paramagnetic rim, cortical, and spinal cord lesions, which hold diagnostic and prognostic value. Novel neuroimaging techniques such as quantitative susceptibility mapping (QSM), χ-separation imaging, and soma and neurite density imaging (SANDI), together with PET, are providing deeper insights into lesion pathology, better disentangling their heterogeneities and clinical relevance.

SUMMARY: Artificial intelligence-powered lesion segmentation tools hold great potential for improving fast, accurate and reproducible lesional assessment in the clinical scenario, thus improving MS diagnosis, monitoring, and treatment response assessment. Emerging neuroimaging modalities may contribute to advance the understanding MS pathophysiology, provide more specific markers of disease progression, and novel potential therapeutic targets.

PMID:40377692 | DOI:10.1097/WCO.0000000000001378

Eur Radiol. 2025 May 16. doi: 10.1007/s00330-025-11673-3. Online ahead of print.

ABSTRACT

OBJECTIVES: Clinical assessment of lymphedema, particularly for lymphedema severity and fluid-fibrotic lesions, remains challenging with traditional methods. We aimed to develop and validate a deep learning segmentation tool for automated tissue component analysis in lower extremity CT scans.

MATERIALS AND METHODS: For development datasets, lower extremity CT venography scans were collected in 118 patients with gynecologic cancers for algorithm training. Reference standards were created by segmentation of fat, muscle, and fluid-fibrotic tissue components using 3D slicer. A deep learning model based on the Unet++ architecture with an EfficientNet-B7 encoder was developed and trained. Segmentation accuracy of the deep learning model was validated in an internal validation set (n = 10) and an external validation set (n = 10) using Dice similarity coefficient (DSC) and volumetric similarity (VS). A graphical user interface (GUI) tool was developed for the visualization of the segmentation results.

RESULTS: Our deep learning algorithm achieved high segmentation accuracy. Mean DSCs for each component and all components ranged from 0.945 to 0.999 in the internal validation set and 0.946 to 0.999 in the external validation set. Similar performance was observed in the VS, with mean VSs for all components ranging from 0.97 to 0.999. In volumetric analysis, mean volumes of the entire leg and each component did not differ significantly between reference standard and deep learning measurements (p > 0.05). Our GUI displays lymphedema mapping, highlighting segmented fat, muscle, and fluid-fibrotic components in the entire leg.

CONCLUSION: Our deep learning algorithm provides an automated segmentation tool enabling accurate segmentation, volume measurement of tissue component, and lymphedema mapping.

KEY POINTS: Question Clinical assessment of lymphedema remains challenging, particularly for tissue segmentation and quantitative severity evaluation. Findings A deep learning algorithm achieved DSCs > 0.95 and VS > 0.97 for fat, muscle, and fluid-fibrotic components in internal and external validation datasets. Clinical relevance The developed deep learning tool accurately segments and quantifies lower extremity tissue components on CT scans, enabling automated lymphedema evaluation and mapping with high segmentation accuracy.

PMID:40377677 | DOI:10.1007/s00330-025-11673-3

Urologiia. 2024 Dec;(6):5-11.

ABSTRACT

AIM: to explore the development of a computer-aided diagnosis (CAD) system based on deep learning (DL) neural networks aimed at minimizing human error in PI-RADS grading and supporting medical decision-making.

MATERIALS AND METHODS: This retrospective multicenter study included a cohort of 136 patients, comprising 108 cases of PCa (PI-RADS score 4-5) and 28 cases of benign conditions (PI-RADS score 1-2). The 3D U-Net architecture was applied to process T2-weighted images (T2W), diffusion-weighted images (DWI), and dynamic contrast-enhanced images (DCE). Statistical analysis was conducted using Python libraries to assess diagnostic performance, including sensitivity, specificity, Dice similarity coefficients, and the area under the receiver operating characteristic curve (AUC).

RESULTS: The DL-CAD system achieved an average accuracy of 78%, sensitivity of 60%, and specificity of 84% for detecting lesions in the prostate. The Dice similarity coefficient for prostate segmentation was 0.71, and the AUC was 81.16%. The system demonstrated high specificity in reducing false-positive results, which, after further optimization, could help minimize unnecessary biopsies and overtreatment.

CONCLUSION: The DL-CAD system shows potential in supporting clinical decision-making for patients with clinically significant PCa by improving diagnostic accuracy, particularly in minimizing intra- and inter-observer variability. Despite its high specificity, improvements in sensitivity and segmentation accuracy are needed, which could be achieved by using larger datasets and advanced deep learning techniques. Further multicenter validation is required for accelerated integration of this system into clinical practice.

PMID:40377545

Curr Med Imaging. 2025 May 15. doi: 10.2174/0115734056360665250506115221. Online ahead of print.

ABSTRACT

BACKGROUND: Due to differences in subjective experience and professional level among doctors, as well as inconsistent diagnostic criteria, there are issues with the accuracy and reliability of single imaging diagnosis results for knee joint injuries.

OBJECTIVE: To address these issues, magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound (US) are adopted in this article for ensemble learning, and deep learning (DL) is combined for automatic analysis.

METHODS: By steps such as image enhancement, noise elimination, and tissue segmentation, the quality of image data is improved, and then convolutional neural networks (CNN) are used to automatically identify and classify injury types. The experimental results show that the DL model exhibits high sensitivity and specificity in the diagnosis of different types of injuries, such as anterior cruciate ligament tear, meniscus injury, cartilage injury, and fracture.

RESULTS: The diagnostic accuracy of anterior cruciate ligament tear exceeds 90%, and the highest diagnostic accuracy of cartilage injury reaches 95.80%. In addition, compared with traditional manual image interpretation, the DL model has significant advantages in time efficiency, with a significant reduction in average interpretation time per case. The diagnostic consistency experiment shows that the DL model has high consistency with doctors' diagnosis results, with an overall error rate of less than 2%.

CONCLUSION: The model has high accuracy and strong generalization ability when dealing with different types of joint injuries. These data indicate that combining multiple imaging technologies and the DL algorithm can effectively improve the accuracy and efficiency of diagnosing sports injuries of knee joints.

PMID:40377156 | DOI:10.2174/0115734056360665250506115221

Nucleic Acids Res. 2025 May 16:gkaf392. doi: 10.1093/nar/gkaf392. Online ahead of print.

ABSTRACT

Antisense oligonucleotides (ASOs) are a promising class of gene therapies that can modulate the gene expression. However, designing ASOs manually is resource-intensive and time-consuming. To address this, we introduce a user-friendly web server for ASOptimizer, a deep learning-based computational framework for optimizing ASO sequences and chemical modifications. Given a user-provided ASO sequence, the web server systematically explores modification sites within the nucleic acid and returns a ranked list of promising modification patterns. With an intuitive interface requiring no expertise in deep learning tools, the platform makes ASOptimizer easily accessible to the broader research community. The web server is freely available at https://asoptimizer.s-core.ai/.

PMID:40377084 | DOI:10.1093/nar/gkaf392

J Chem Phys. 2025 May 21;162(19):194104. doi: 10.1063/5.0256659.

ABSTRACT

Reaction-diffusion equations are commonly used to model a diverse array of complex systems, including biological, chemical, and physical processes. Typically, these models are phenomenological, requiring the fitting of parameters to experimental data. In the present work, we introduce a novel formalism to construct reaction-diffusion models that is grounded in the principle of maximum entropy. This new formalism aims to incorporate various types of experimental data, including ensemble currents, distributions at different points in time, or moments of such. To this end, we expand the framework of Schrödinger bridges and maximum caliber problems to nonlinear interacting systems. We illustrate the usefulness of the proposed approach by modeling the evolution of (i) a morphogen across the fin of a zebrafish and (ii) the population of two varieties of toads in Poland, so as to match the experimental data.

PMID:40377200 | DOI:10.1063/5.0256659

Elife. 2025 May 16;12:RP89862. doi: 10.7554/eLife.89862.

ABSTRACT

A wide variety of human diseases are associated with loss of microbial diversity in the human gut, inspiring a great interest in the diagnostic or therapeutic potential of the microbiota. However, the ecological forces that drive diversity reduction in disease states remain unclear, rendering it difficult to ascertain the role of the microbiota in disease emergence or severity. One hypothesis to explain this phenomenon is that microbial diversity is diminished as disease states select for microbial populations that are more fit to survive environmental stress caused by inflammation or other host factors. Here, we tested this hypothesis on a large scale, by developing a software framework to quantify the enrichment of microbial metabolisms in complex metagenomes as a function of microbial diversity. We applied this framework to over 400 gut metagenomes from individuals who are healthy or diagnosed with inflammatory bowel disease (IBD). We found that high metabolic independence (HMI) is a distinguishing characteristic of microbial communities associated with individuals diagnosed with IBD. A classifier we trained using the normalized copy numbers of 33 HMI-associated metabolic modules not only distinguished states of health vs IBD, but also tracked the recovery of the gut microbiome following antibiotic treatment, suggesting that HMI is a hallmark of microbial communities in stressed gut environments.

PMID:40377187 | DOI:10.7554/eLife.89862

Ann N Y Acad Sci. 2025 May 16. doi: 10.1111/nyas.15373. Online ahead of print.

ABSTRACT

Recursion, the neuro-computational operation of nesting a signal or pattern within itself, lies at the structural basis of language. Classically considered absent in the vocal repertoires of nonhuman animals, whether recursion evolved step-by-step or saltationally in humans is among the most fervent debates in cognitive science since Chomsky's seminal work on syntax in the 1950s. The recent discovery of self-embedded vocal motifs in wild (nonhuman) great apes-Bornean male orangutans' long calls-lends initial but important support to the notion that recursion, or at least temporal recursion, is not uniquely human among hominids and that its evolution was based on shared ancestry. Building on these findings, we test four necessary predictions for a gradual evolutionary scenario in wild Sumatran female orangutans' alarm calls, the longest known combinations of consonant-like and vowel-like calls among great apes (excepting humans). From the data, we propose third-order self-embedded isochrony: three hierarchical levels of nested isochronous combinatoric units, with each level exhibiting unique variation dynamics and information content relative to context. Our findings confirm that recursive operations underpin great ape call combinatorics, operations that likely evolved gradually in the human lineage as vocal sequences became longer and more intricate.

PMID:40376956 | DOI:10.1111/nyas.15373

Discov Oncol. 2025 May 16;16(1):786. doi: 10.1007/s12672-025-02502-6.

ABSTRACT

BACKGROUND: Drug-induced second tumors (DIST) refer to new primary cancers that develop during or after the treatment of an initial cancer due to the long-term effects of medications. As a severe long-term adverse event, DIST has gained widespread attention globally in recent years. With the increasing prevalence of cancer treatments and the prolonged survival of patients, drug-induced second tumors have become more prominent and pose a significant public health challenge. However, most existing studies have focused on individual drugs or small patient cohorts, lacking large-scale, real-world data evaluations. Particularly, the potential second-tumor risk of new drugs remains underexplored.

OBJECTIVE: This study aims to systematically assess the adverse event signals between drugs and second tumors using the U.S. FDA Adverse Event Reporting System (FAERS) database, employing disproportionality analysis (DPA) methods. It particularly focuses on uncovering drugs that have not clearly labeled second-tumor risks.

METHODS: Data from the FDA Adverse Event Reporting System (FAERS), covering reports from its inception to the third quarter of 2024, was retrieved. After data standardization, four disproportionality methods were used: Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Bayesian Confidence Propagation Neural Network (BCPNN), and Multi-item Gamma Poisson Shrinker (MGPS). These methods assessed the correlation between azacitidine and adverse drug events (ADEs). Additionally, the Weibull Shape Parameter (WSP) was used to analyze the characteristic patterns of time-to-onset curves. Newly discovered signals were verified against FDA drug labels to confirm their novelty. The Weibull analysis was conducted to examine the temporal aspects of adverse event occurrences.

RESULTS: Since 2004, drug-induced tumor events have been increasing annually, with a total of 7597 drug-related tumor adverse events recorded. A total of 250 drugs were identified as having potential risk signals. High-incidence populations were primarily aged between 65 and 85 years, with a higher proportion of individuals with a body weight ≥ 90 kg. The most frequent occurrence was observed in patients with Chronic Myeloid Leukemia (13.36%). Among the top 5 drugs with the highest number of reported drug-induced second tumor adverse events, IMATINIB (906 reports), RUXOLITINIB (554 reports), PALBOCICLIB (552 reports), OCTREOTIDE (399 reports), and DOXORUBICIN (380 reports) were identified. Among these, PALBOCICLIB, OCTREOTIDE, and DOXORUBICIN are drugs for which the risk of drug-induced second tumors is not explicitly mentioned in their labels. A total of 76 drugs were identified through four disproportionality algorithms (ROR, PRR, MGPS, BCPNN), with a minimum time to drug-induced tumor occurrence of 5 years, exhibiting an early failure-type curve.

CONCLUSION: This study, based on large-scale real-world data, reveals the potential associations between drugs and second tumors, especially highlighting the risks of some new drugs. The findings provide valuable insights for drug safety monitoring and have significant public health implications. By uncovering previously unrecognized potential risks, this research lays the groundwork for further advancements in pharmacovigilance.

PMID:40377769 | DOI:10.1007/s12672-025-02502-6

Tissue Barriers. 2025 May 16:2503523. doi: 10.1080/21688370.2025.2503523. Online ahead of print.

ABSTRACT

Vascular endothelial barrier disruption is a critical determinant of morbidity and mortality in sepsis. Whole blood represents a key source of paracrine signaling molecules inducing vascular endothelial barrier disruption in sepsis. This study analyzes whole-genome transcriptome data from sepsis patients' whole blood available in the NCBI GEO database to identify paracrine mediators of vascular endothelial barrier dysfunction, uncovering novel insights that may guide drug repositioning strategies. This study identifies the regulated expression of paracrine signaling molecules TFPI, MMP9, PROS1, JAG1, S1PR1, and S1PR5 which either disrupt or protect vascular endothelial barrier function in sepsis and could serve as potential targets for repositioning existing drugs. Specifically, TFPI (barrier protective), MMP9 (barrier destructive), PROS1 (barrier protective), and JAG1 (barrier destructive) are upregulated, while S1PR1 (barrier protective) and S1PR5 (barrier protective) are downregulated. Our observations highlight the importance of considering both protective and disruptive mediators in the development of therapeutic strategies to restore endothelial barrier integrity in septic patients. Identifying TFPI, MMP9, PROS1, JAG1, S1PR1, and S1PR5 as druggable paracrine regulators of vascular endothelial barrier function in sepsis could pave the way for precision medicine approaches, enabling personalized treatments that target specific mediators of endothelial barrier disruption to improve patient outcomes in sepsis.

PMID:40376886 | DOI:10.1080/21688370.2025.2503523

Recenti Prog Med. 2025 May;116(5):310-321. doi: 10.1701/4495.44951.

ABSTRACT

INTRODUCTION: Duchenne muscular dystrophy (DMD) is a rare disease that causes a progressive loss of muscle function in males, presenting at the age of two years, and involving respiratory and heart function starting from teenage years. This retrospective observational study has identified patients potentially affected by DMD and described their utilization of healthcare resources and direct healthcare costs charged to the Italian National Healthcare Service (INHS).

METHODS: From the Foundation Ricerca e Salute (ReS), through a specific algorithm based only on administrative healthcare data of 5.4 million inhabitants in 2021 (index date), male patients aged <30 years potentially affected by DMD were identified. Comorbidities at baseline, and utilization of healthcare resources and direct healthcare costs charged to the INHS during the year following index date, were described.

RESULTS: In 2021, 120 male patients aged <30 years were identified as potentially affected with DMD (2.2/100,000 inhabitants; 16.1/100,000 males aged <30 years). Chronic airway disease and cardiomyopathy were found in 19.2% and 15.0% of patients, respectively. During follow-up: 41.7% of patients were treated with deflazacort, 2.5% with ataluren and about one third with cardiac drugs; 29.2% and 42.5% were admitted to overnight and day hospitalization, respectively, mainly due to neurological, cardiac, and respiratory diseases; 12.5% accessed the emergency department, mainly for traumatisms and fractures; 70.8% received local outpatient specialist care, half of which were specialist visits, and about 15% cardiac diagnostics. On average, the per capita annual total cost charged to the -INHS was € 6713; ataluren accounted for more than half of this expenditure. After having excluded the dispensation of ataluren during follow-up, the mean per capita total cost was € 2548, more than half of which due to hospitalizations.

CONCLUSIONS: This study of administrative data has identified patients potentially affected by DMD, a rare disease, from a large sample of INHS beneficiaries, and assessed their healthcare pathway. This is useful for regulatory purposes and for improved access to emerging innovative therapies.

PMID:40376903 | DOI:10.1701/4495.44951