Sci Rep. 2025 May 19;15(1):17301. doi: 10.1038/s41598-025-02359-3.

ABSTRACT

Through an in-depth analysis of ertapenem-associated adverse events (AEs) in the FDA Adverse Event Reporting System (FAERS) database, this study provides a reference for monitoring and safety management of ertapenem. Data from the FAERS database from Q1 2004 to Q1 2024 were analyzed via four nonproportional analysis techniques, including the reporting odds ratio (ROR). Gender, age, and sensitivity analyses were conducted for a more detailed assessment of ertapenem-associated signals. A total of 2,931 reports with ertapenem as the primary suspected drug were collected, covering 27 system organ classes (SOCs). The two SOCs with the strongest signals were nervous system disorders and psychiatric disorders, with overall stronger signals in individuals aged ≥ 65 years. The most frequently reported AEs were confusional state (n = 265) and convulsions (n = 214). Among the strongest signals were oropharyngeal edema (ROR = 191.05, 95% CI: 60.76-601.35) and granulomatous dermatitis (ROR = 150.49, 95% CI: 55.9-405.15). Eleven AEs not listed on the FDA label were identified. The top 20 AEs were predominantly associated with nervous system and psychiatric disorders, with a median time to onset ranging from 3.5 to 8.5 days. This study highlights the neuropsychiatric risks of ertapenem, providing strong evidence for its safety assessment and emphasizing the need for monitoring and individualized management in high-risk patients. Ertapenem, FAERS, Adverse events, Drug safety, Disproportionality analysis.

PMID:40389541 | DOI:10.1038/s41598-025-02359-3

Medicine (Baltimore). 2025 May 16;104(20):e42411. doi: 10.1097/MD.0000000000042411.

ABSTRACT

Currently, there remains a significant gap in effective pharmacologic interventions for neonatal respiratory distress syndrome (NRDS). To address this critical unmet medical need, we aimed to systematically identify novel therapeutic targets and preventive strategies through comprehensive integration and analysis of multiple publicly accessible datasets. In this study, we employed an integrative approach combining druggable genome data, cis-expression quantitative trait loci (cis-eQTL) from human blood and lung tissues, and genome-wide association study summary statistics for neonatal respiratory distress. We performed two-sample Mendelian randomization (TSMR) analysis to investigate potential causal relationships between druggable genes and neonatal respiratory distress. To strengthen causal inference, we performed Bayesian co-localization analyses. Furthermore, we conducted phenome-wide Mendelian randomization (Phe-MR) to systematically evaluate potential side effects and alternative therapeutic indications associated with the identified candidate drug targets. Finally, we interrogated existing drug databases to identify actionable pharmacological agents targeting the identified genes. All 3 genes (LTBR, NAAA, CSNK1G2) were analyzed by Bayesian co-localization (PH4 > 75%). CSNK1G2 (lung eQTL, odds ratio [OR]: 0.419, 95% CI: 0.185-0.948, P = .037; blood eQTL, OR: 4.255, 95% CI: 1.346-13.455, P = .014; Gtex whole blood eQTL, OR: 4.966, 95% CI: 1.104-22.332, P = .037). LTBR (lung eQTL, OR: 0.550, 95% CI: 0.354-0.856, P = .008; blood eQTL, OR: 0.347, 95% CI: 0.179-0.671, P = .002; Gtex whole blood eQTL, OR: 0.059, 95% CI: 0.0.007-0.478, P = .008). NAAA (lung eQTL, OR: 0.717, 95% CI: 0.555-0.925, P = .011; Gtex whole blood eQTL, OR: 0.660, 95% CI: 0.476-0.913, P = .012). Drug repurposing analyses support the possibility that etanercept and asciminib hydrochloride may treat neonatal respiratory distress by activating LTBR. This study demonstrated that LTBR, NAAA, and CSNK1G2 may serve as promising biomarkers and therapeutic targets for NRDS.

PMID:40388790 | DOI:10.1097/MD.0000000000042411

Circulation. 2025 May 20;151(20):1449-1450. doi: 10.1161/CIRCULATIONAHA.125.074556. Epub 2025 May 19.

NO ABSTRACT

PMID:40388510 | DOI:10.1161/CIRCULATIONAHA.125.074556

J Vis Exp. 2025 May 2;(219). doi: 10.3791/67831.

ABSTRACT

RASopathies are genetic syndromes caused by ERK hyperactivation and resulting in multisystemic diseases that can also lead to cancer predisposition. Despite a broad genetic heterogeneity, germline gain-of-function mutations in key regulators of the RAS-MAPK pathway underlie the majority of the cases, and, thanks to advanced sequencing techniques, potentially pathogenic variants affecting the RAS-MAPK pathway continue to be identified. Functional validation of the pathogenicity of these variants, essential for accurate diagnosis, requires fast and reliable protocols, preferably in vivo. Given the scarcity of effective treatments in early childhood, such protocols, especially if scalable in cost-effective animal models, can be instrumental in offering a preclinical ground for drug repositioning/repurposing. Here we describe step-by-step the protocol for rapid generation of transient RASopathy models in zebrafish embryos and direct inspection of live disease-associated ERK activity changes occurring already during gastrulation through real-time multispectral Förster resonance energy transfer (FRET) imaging. The protocol uses a transgenic ERK reporter recently established and integrated with the hardware of commercial microscopes. We provide an example application for Noonan syndrome (NS) zebrafish models obtained by expression of the Shp2D61G. We describe a straightforward method that enables registration of ERK signal change in the NS fish model before and after pharmacological signal modulation by available low-dose MEK inhibitors. We detail how to generate, retrieve, and assess ratiometric FRET signals from multispectral acquisitions before and after treatment and how to cross-validate the results via classical immunofluorescence on whole embryos at early stages. We then describe how, via examining standard morphometric parameters, to query late changes in embryo shape, indicative of a resulting impairment of gastrulation, in the same embryos whose ERK activity is assessed by live FRET at 6 h post fertilization.

PMID:40388378 | DOI:10.3791/67831

Pain. 2025 May 20. doi: 10.1097/j.pain.0000000000003656. Online ahead of print.

ABSTRACT

The peripheral nervous system (PNS) plays a critical role in pathological conditions, including chronic pain disorders, that manifest differently in men and women. To investigate this sexual dimorphism at the molecular level, we integrated quantitative proteomic profiling of human dorsal root ganglia (hDRG) and peripheral nerve tissue into the expanding omics framework of the PNS. Using data-independent acquisition (DIA) mass spectrometry, we characterized a comprehensive proteomic profile, validating tissue-specific differences between the hDRG and peripheral nerve. Through multi-omic analyses and in vitro functional assays, we identified sex-specific molecular differences, with TNFα signalling emerging as a key sexually dimorphic pathway with higher prominence in men. Genetic evidence from genome-wide association studies further supports the functional relevance of TNFα signalling in the periphery, while clinical trial data and meta-analyses indicate a sex-dependent response to TNFα inhibitors. Collectively, these findings underscore a functionally sexual dimorphism in the PNS, with direct implications for sensory and pain-related clinical translation.

PMID:40388638 | DOI:10.1097/j.pain.0000000000003656

Syst Biol. 2025 May 19:syaf037. doi: 10.1093/sysbio/syaf037. Online ahead of print.

ABSTRACT

Batesian mimicry is an impressive example of convergent evolution driven by predation. However, the observation that many mimics only superficially resemble their models despite strong selective pressures is an apparent paradox. Here, we tested the 'perfecting hypothesis', that posits that inaccurate mimicry may represent a transitional stage at the macro-evolutionary scale by performing the hereto largest phylogenetic analysis (in terms of the number of taxa and genetic data) of ant-mimicking spiders across two speciose but independent clades, the jumping spider tribe Myrmarachnini (Salticidae) and the sac spider sub-family Castianeirinae (Corinnidae). We found that accurate ant mimicry evolved in a gradual process in both clades, by an integration of compound traits contributing to the ant-like habitus with each trait evolving at different speeds. Accurate states were highly unstable at the macro-evolutionary scale likely because strong expression of some of these traits comes with high fitness costs. Instead, the inferred global optimum of mimicry expression was at an inaccurate state. This result reverses the onus of explanation from inaccurate mimicry to explaining the exceptional evolution and maintenance of accurate mimicry and highlights that the evolution of Batesian mimicry is ruled by multiple conflicting selective pressures.

PMID:40388318 | DOI:10.1093/sysbio/syaf037

Physiol Genomics. 2025 May 19. doi: 10.1152/physiolgenomics.00183.2024. Online ahead of print.

ABSTRACT

Background: Risk factors for cardiac arrhythmias that can cause sudden death and heart failure include genetics, age, lifestyle, and other environmental factors. Objectives: The study assessed electrocardiography (ECG) traits in BXD mice and explored associated quantitative trait loci (QTLs). Methods: Five-minute electrocardiograms were recorded in 44 BXD strains at 4-5 months of age (n≥5 mice/sex/strain). ECG and arrhythmia traits were associated with echocardiography, blood pressure, genome and heart transcriptome data followed by expression QTL mapping. Results: A significant variability in ECG parameters and arrhythmias were recorded among BXDs. Among male BXDs, QRS duration was significantly associated with increased left ventricular internal diameter (LVID) and reduced ejection fraction and fractional shortening, while premature ventricular contractions (PVCs) were correlated with LVID, LV volumes and pulmonary vein peak pressure. In female BXDs, PVCs and premature atrial contractions (PACs) significantly related with right ventricular ID and cardiac output. One significant QTL associated with QTc and JT durations was identified on Chromosome (Chr) 3 in male BXDs, while Chr 9 locus was suggestive for association with QTc and QT intervals in female mice. Gon4l was predicted as a strong candidate gene associated with repolarization abnormalities including short or long QT syndromes in humans. Conclusions: Study results suggested an influence of genetic background on expression of ECG parameters and arrhythmias based on significant variations of those traits between mouse strains of the BXD family. We conclude that murine BXD family can serve as a valuable reference for systems biology and comparative predictions of arrhythmia disorders.

PMID:40388294 | DOI:10.1152/physiolgenomics.00183.2024

Expert Opin Drug Discov. 2025 May 19. doi: 10.1080/17460441.2025.2508866. Online ahead of print.

ABSTRACT

INTRODUCTION: Artificial intelligence (AI) has emerged as a transformative tool in drug discovery, particularly in virtual screening (VS), which is a crucial initial step in identifying potential drug candidates. This article highlights the significance of AI in revolutionizing both ligand-based virtual screening (LBVS) and structure-based virtual screening (SBVS) approaches, streamlining and enhancing the drug discovery process.

AREAS COVERED: The authors provide an overview of AI applications in drug discovery, with a focus on LBVS and SBVS approaches utilized in prospective cases where new bioactive molecules were identified and experimentally validated. Discussion includes the use of AI in quantitative structure-activity relationship (QSAR) modeling for LBVS, as well as its role in enhancing SBVS techniques such as molecular docking and molecular dynamics simulations. The article is based on literature searches on all studies published up to March 2025.

EXPERT OPINION: AI is rapidly transforming VS in drug discovery, by leveraging increasing amounts of experimental data and expanding its scalability. These innovations promise to enhance efficiency and precision across both LBVS and SBVS approaches, yet challenges such as data curation, rigorous and prospective validation of new models, and efficient integration with experimental methods remain critical for realizing AI's full potential in drug discovery.

PMID:40388244 | DOI:10.1080/17460441.2025.2508866

Rhinology. 2025 May 19. doi: 10.4193/Rhin25.044. Online ahead of print.

ABSTRACT

BACKGROUND: Sinonasal pathology can be complex and requires a systematic and meticulous approach. Artificial Intelligence (AI) has the potential to improve diagnostic accuracy and efficiency in sinonasal imaging, but its clinical applicability remains an area of ongoing research. This systematic review evaluates the methodologies and clinical relevance of AI in detecting sinonasal pathology through radiological imaging.

METHODOLOGY: Key search terms included "artificial intelligence," "deep learning," "machine learning," "neural network," and "paranasal sinuses,". Abstract and full-text screening was conducted using predefined inclusion and exclusion criteria. Data were extracted on study design, AI architectures used (e.g., Convolutional Neural Networks (CNN), Machine Learning classifiers), and clinical characteristics, such as imaging modality (e.g., Computed Tomography (CT), Magnetic Resonance Imaging (MRI)).

RESULTS: A total of 53 studies were analyzed, with 85% retrospective, 68% single-center, and 92.5% using internal databases. CT was the most common imaging modality (60.4%), and chronic rhinosinusitis without nasal polyposis (CRSsNP) was the most studied condition (34.0%). Forty-one studies employed neural networks, with classification as the most frequent AI task (35.8%). Key performance metrics included Area Under the Curve (AUC), accuracy, sensitivity, specificity, precision, and F1-score. Quality assessment based on CONSORT-AI yielded a mean score of 16.0 ± 2.

CONCLUSIONS: AI shows promise in improving sinonasal imaging interpretation. However, as existing research is predominantly retrospective and single-center, further studies are needed to evaluate AI's generalizability and applicability. More research is also required to explore AI's role in treatment planning and post-treatment prediction for clinical integration.

PMID:40388840 | DOI:10.4193/Rhin25.044

Proc Natl Acad Sci U S A. 2025 May 27;122(21):e2412261122. doi: 10.1073/pnas.2412261122. Epub 2025 May 19.

ABSTRACT

Enhancing the reliability and reproducibility of optical microscopy by reducing specimen irradiance continues to be an important biotechnology target. As irradiance levels are reduced, however, the particle nature of light is heightened, giving rise to Poisson noise, or photon sparsity that restricts only a few (0.5%) image pixels to comprise a photon. Photon sparsity can be addressed by collecting approximately 200 photons per pixel; this, however, requires long acquisitions and, as such, suboptimal imaging rates. Here, we introduce near-zero photon bioimaging, a method that operates at kHz rates and 10,000-fold lower irradiance than standard microscopy. To achieve this level of performance, we uniquely combined a judiciously designed epifluorescence microscope enabling ultralow background levels and AI that learns to reconstruct biological images from as low as 0.01 photons per pixel. We demonstrate that near-zero photon bioimaging captures the structure of multicellular and subcellular features with high fidelity, including features represented by nearly zero photons. Beyond optical microscopy, the near-zero photon bioimaging paradigm can be applied in remote sensing, covert applications, and biomedical imaging that utilize damaging or quantum light.

PMID:40388622 | DOI:10.1073/pnas.2412261122

PLoS One. 2025 May 19;20(5):e0310230. doi: 10.1371/journal.pone.0310230. eCollection 2025.

ABSTRACT

The rapid growth of Android applications has led to an increase in security threats, while traditional detection methods struggle to combat advanced malware, such as polymorphic and metamorphic variants. To address these challenges, this study introduces a hybrid deep learning model (DBN-GRU) that integrates Deep Belief Networks (DBN) for static analysis and Gated Recurrent Units (GRU) for dynamic behavior modeling to enhance malware detection accuracy and efficiency. The model extracts static features (permissions, API calls, intent filters) and dynamic features (system calls, network activity, inter-process communication) from Android APKs, enabling a comprehensive analysis of application behavior.The proposed model was trained and tested on the Drebin dataset, which includes 129,013 applications (5,560 malware and 123,453 benign).Performance evaluation against NMLA-AMDCEF, MalVulDroid, and LinRegDroid demonstrated that DBN-GRU achieved 98.7% accuracy, 98.5% precision, 98.9% recall, and an AUC of 0.99, outperforming conventional models.In addition, it exhibits faster preprocessing, feature extraction, and malware classification times, making it suitable for real-time deployment.By bridging static and dynamic detection methodologies, the DBN-GRU enhances malware detection capabilities while reducing false positives and computational overhead.These findings confirm the applicability of the proposed model in real-world Android security applications, offering a scalable and high-performance malware detection solution.

PMID:40388500 | DOI:10.1371/journal.pone.0310230

PLoS One. 2025 May 19;20(5):e0313692. doi: 10.1371/journal.pone.0313692. eCollection 2025.

ABSTRACT

Surveillance systems are integral to ensuring public safety by detecting unusual incidents, yet existing methods often struggle with accuracy and robustness. This study introduces an advanced framework for anomaly recognition in surveillance, leveraging deep learning to address these challenges and achieve significant improvements over current techniques. The framework begins with preprocessing input images using histogram equalization to enhance feature visibility. It then employs two DCNNs for feature extraction: a novel 63-layer CNN, "Up-to-the-Minute-Net," and the established Inception-Resnet-v2. The features extracted by both models are fused and optimized through two sophisticated feature selection techniques: Dragonfly and Genetic Algorithm (GA). The optimization process involves rigorous experimentation with 5- and 10-fold cross-validation to evaluate performance across various feature sets. The proposed approach achieves an unprecedented 99.9% accuracy in 5-fold cross-validation using the GA optimizer with 2500 selected features, demonstrating a substantial leap in accuracy compared to existing methods. This study's contribution lies in its innovative combination of deep learning models and advanced feature optimization techniques, setting a new benchmark in the field of anomaly recognition for surveillance systems and showcasing the potential for practical real-world applications.

PMID:40388481 | DOI:10.1371/journal.pone.0313692

PLoS One. 2025 May 19;20(5):e0324047. doi: 10.1371/journal.pone.0324047. eCollection 2025.

ABSTRACT

This study aims to develop a predictive hybrid model for a grid-connected PV system with DC-DC optimizers, designed to operate in extreme altitude conditions at 3800 m above sea level. This approach seeks to address the "curse of dimensionality" by reducing model complexity and improving its accuracy by combining the recursive feature removal (RFE) method with advanced regularization techniques, such as Lasso, Ridge, and Bayesian Ridge. The research used a photovoltaic system composed of monocrystalline modules, DC-DC optimizers and a 3000 W inverter. The data obtained from the system were divided into training and test sets, where RFE identified the most relevant variables, eliminating the reactive power of AC. Subsequently, the three regularization models were trained with these selected variables and evaluated using metrics such as precision, mean absolute error, mean square error and coefficient of determination. The results showed that RFE - Bayesian Ridge obtained the highest accuracy (0.999935), followed by RFE - Ridge, while RFE - Lasso had a slightly lower performance and also obtained an exceptionally low MASE (0.0034 for Bayesian and Ridge, compared to 0.0065 for Lasso). All models complied with the necessary statistical validations, including linearity, error normality, absence of autocorrelation and homoscedasticity, which guaranteed their reliability. This hybrid approach proved effective in optimizing the predictive performance of PV systems under challenging conditions. Future work will explore the integration of these models with energy storage systems and smart control strategies to improve operational stability. In addition, the application of the hybrid model in extreme climates, such as desert or polar areas, will be investigated, as well as its extension through deep learning techniques to capture non-linear relationships and increase adaptability to abrupt climate variations.

PMID:40388424 | DOI:10.1371/journal.pone.0324047

PLoS One. 2025 May 19;20(5):e0317519. doi: 10.1371/journal.pone.0317519. eCollection 2025.

ABSTRACT

This study significantly contributes to the sphere of educational technology by deploying state-of-the-art machine learning and deep learning strategies for meaningful changes in education. The hybrid stacking approach did an excellent implementation using Decision Trees, Random Forest, and XGBoost as base learners with Gradient Boosting as a meta-learner, which managed to record an accuracy of 90%. That indeed puts into great perspective the huge potential it possesses for accuracy measures while predicting in educational setups. The CNN model, which predicted with an accuracy of 89%, showed quite impressive capability in sentiment analysis to acquire further insight into the emotional status of the students. RCNN, Random Forests, and Decision Trees contribute to the possibility of educational data complexity with valuable insight into the complex interrelationships within ML models and educational contexts. The application of the bagging XGBoost algorithm, which attained a high accuracy of 88%, further stamps its utility toward enhancement of academic performance through strong robust techniques of model aggregation. The dataset that was used in this study was sourced from Kaggle, with 1205 entries of 14 attributes concerning adaptability, sentiment, and academic performance; the reliability and richness of the analytical basis are high. The dataset allows rigorous modeling and validation to be done to ensure the findings are considered robust. This study has several implications for education and develops on the key dimensions: teacher effectiveness, educational leadership, and well-being of the students. From the obtained information about student adaptability and sentiment, the developed system helps educators to make modifications in instructional strategy more efficiently for a particular student to enhance effectiveness in teaching. All these aspects could provide critical insights for the educational leadership to devise data-driven strategies that would enhance the overall school-wide academic performance, as well as create a caring learning atmosphere. The integration of sentiment analysis within the structure of education brings an inclusive, responsive attitude toward ensuring students' well-being and, thus, a caring educational environment. The study is closely aligned with sustainable ICT in education objectives and offers a transformative approach to integrating AI-driven insights with practice in this field. By integrating notorious ML and DL methodologies with educational challenges, the research puts the basis for future innovations and technology in this area. Ultimately, it contributes to sustainable improvement in the educational system.

PMID:40388422 | DOI:10.1371/journal.pone.0317519

PLoS One. 2025 May 19;20(5):e0316043. doi: 10.1371/journal.pone.0316043. eCollection 2025.

ABSTRACT

The adoption of deep learning in ECG diagnosis is often hindered by the scarcity of large, well-labeled datasets in real-world scenarios, leading to the use of transfer learning to leverage features learned from larger datasets. Yet the prevailing assumption that transfer learning consistently outperforms training from scratch has never been systematically validated. In this study, we conduct the first extensive empirical study on the effectiveness of transfer learning in multi-label ECG classification, by investigating comparing the fine-tuning performance with that of training from scratch, covering a variety of ECG datasets and deep neural networks. Firstly, We confirm that fine-tuning is the preferable choice for small downstream datasets; however, it does not necessarily improve performance. Secondly, the improvement from fine-tuning declines when the downstream dataset grows. With a sufficiently large dataset, training from scratch can achieve comparable performance, albeit requiring a longer training time to catch up. Thirdly, fine-tuning can accelerate convergence, resulting in faster training process and lower computing cost. Finally, we find that transfer learning exhibits better compatibility with convolutional neural networks than with recurrent neural networks, which are the two most prevalent architectures for time-series ECG applications. Our results underscore the importance of transfer learning in ECG diagnosis, yet depending on the amount of available data, researchers may opt not to use it, considering the non-negligible cost associated with pre-training.

PMID:40388401 | DOI:10.1371/journal.pone.0316043

PLoS One. 2025 May 19;20(5):e0320426. doi: 10.1371/journal.pone.0320426. eCollection 2025.

ABSTRACT

Monitoring the remaining food in patients' trays is a routine activity in healthcare facilities as it provides valuable insights into the patients' dietary intake. However, estimating food leftovers through visual observation is time-consuming and biased. To tackle this issue, we have devised an efficient deep learning-based approach that promises to revolutionize how we estimate food leftovers. Our first step was creating the LeFoodSet dataset, a pioneering large-scale open dataset explicitly designed for estimating food leftovers. This dataset is unique in its ability to estimate leftover rates and types of food. To the best of our knowledge, this is the first comprehensive dataset for this type of analysis. The dataset comprises 524 image pairs representing 34 Indonesian food categories, each with images captured before and after consumption. Our prediction models employed a combined visual feature extraction and late fusion approach utilizing soft parameter sharing. Here, we used multi-task (MT) models that simultaneously predict leftovers and food types in training. In the experiments, we tested the single task (ST) model, the ST Model with Ground Truth (ST-GT), the MT model, and the MT model with Inter-task Connection (MT-IC). Our AI-based models, particularly the MT and MT-IC models, have shown promising results, outperforming human observation in predicting leftover food. These findings show the best with the ResNet101 model, where the Mean Average Error (MAE) of leftover task and food classification accuracy task is 0.0801 and 90.44% in the MT Model and 0.0817 and 92.56% in the MT-IC Model, respectively. It is proved that the proposed solution has a bright future for AI-based approaches in medical and nursing applications.

PMID:40388400 | DOI:10.1371/journal.pone.0320426

N Engl J Med. 2025 May 19. doi: 10.1056/NEJMoa2503643. Online ahead of print.

ABSTRACT

BACKGROUND: Nerandomilast (BI 1015550) is an orally administered preferential inhibitor of phosphodiesterase 4B with antifibrotic and immunomodulatory properties. Nerandomilast has been shown to slow the progression of idiopathic pulmonary fibrosis, but an assessment of its effects in other types of progressive pulmonary fibrosis is needed.

METHODS: In a phase 3, double-blind trial, we randomly assigned patients with progressive pulmonary fibrosis in a 1:1:1 ratio to receive nerandomilast at a dose of 18 mg twice daily, nerandomilast at a dose of 9 mg twice daily, or placebo, with stratification according to background therapy (nintedanib vs. none) and fibrotic pattern on high-resolution computed tomography (usual interstitial pneumonia-like pattern vs. other patterns). The primary end point was the absolute change from baseline in the forced vital capacity (FVC), measured in milliliters, at week 52.

RESULTS: A total of 1176 patients received at least one dose of nerandomilast or placebo, of whom 43.5% were taking background nintedanib therapy at baseline. The adjusted mean change in the FVC at week 52 was -98.6 ml (95% confidence interval [CI], -123.7 to -73.4) in the nerandomilast 18-mg group, -84.6 ml (95% CI, -109.6 to -59.7) in the nerandomilast 9-mg group, and -165.8 ml (95% CI, -190.5 to -141.0) in the placebo group. The adjusted difference between the nerandomilast 18-mg group and the placebo group was 67.2 ml (95% CI, 31.9 to 102.5; P<0.001), and the adjusted difference between the nerandomilast 9-mg group and the placebo group was 81.1 ml (95% CI, 46.0 to 116.3; P<0.001). The most frequent adverse event was diarrhea, reported in 36.6% of the patients in the nerandomilast 18-mg group, 29.5% of those in the nerandomilast 9-mg group, and 24.7% of those in the placebo group. Serious adverse events occurred in similar percentages of patients in the trial groups.

CONCLUSIONS: In patients with progressive pulmonary fibrosis, treatment with nerandomilast led to a smaller decline in the FVC than placebo over a period of 52 weeks. (Funded by Boehringer Ingelheim; FIBRONEER-ILD ClinicalTrials.gov number, NCT05321082.).

PMID:40388329 | DOI:10.1056/NEJMoa2503643

medRxiv [Preprint]. 2025 May 7:2025.05.05.25326966. doi: 10.1101/2025.05.05.25326966.

ABSTRACT

Type 2 diabetes (T2D) is epidemiologically associated with a wide range of non-cardiovascular comorbidities, yet their shared etiology has not been fully elucidated. Leveraging eight non-overlapping mechanistic clusters of T2D genetic profiles, each representing distinct biological pathways, we investigate putative causal links between cluster-stratified T2D genetic predisposition and 21 non-cardiovascular comorbidities. Most of the identified putative causal effects are driven by distinct T2D genetic clusters. For example, the risk-increasing effects of T2D genetic predisposition on cataracts and erectile dysfunction are primarily attributed to obesity and glucose regulation mechanisms, respectively. When surveyed in populations across the globe, we observe opposing effect directions for depression, asthma and chronic obstructive pulmonary disease between populations. We identify a putative causal link between T2D genetic predisposition and osteoarthritis. To underscore the translational potential of our findings, we intersect high-confidence effector genes for osteoarthritis with targets of T2D-approved drugs and identify metformin as a potential candidate for drug repurposing in osteoarthritis.

PMID:40385452 | PMC:PMC12083600 | DOI:10.1101/2025.05.05.25326966

Am J Manag Care. 2025 May;31(5):240-244. doi: 10.37765/ajmc.2025.89673.

ABSTRACT

OBJECTIVES: To quantify the magnitude of altruism value as applied to a hypothetical new treatment for a rare, severe pediatric disease: Duchenne muscular dystrophy (DMD).

STUDY DESIGN: Prospective survey of individuals not planning to have children in the future.

METHODS: A survey was administered to US adults (aged ≥ 21 years) not intending to have a child in the future to elicit willingness to pay (WTP) for government insurance coverage for a new hypothetical DMD treatment that improves mortality and morbidity relative to the current standard of care. A multiple random staircase design was used to identify an indifference point between status quo government insurance coverage and coverage with additional cost in taxes that would cover the treatment if unrelated individuals had a child with DMD. Altruism value was calculated as respondents' mean WTP.

RESULTS: Among 215 respondents, 54.9% (n = 118) were aged 25 to 44 years and 80.0% (n = 172) were women. Mean WTP for insurance coverage of the hypothetical DMD treatment for others was $80.01 (95% CI, $41.64-$118.37) annually, or $6.67 monthly, after adjustment to account for disease probability overestimation. The adjusted altruism value was higher than the ex ante per-person value using traditional cost-effectiveness approaches ($45.30/year). Without adjusting, individuals were willing to pay $799.11 annually ($66.59 monthly).

CONCLUSIONS: Despite no possibility of accruing health benefits directly for themselves or their children, individuals had a high WTP for government insurance coverage of a novel treatment for this rare, severe pediatric disease.

PMID:40387711 | DOI:10.37765/ajmc.2025.89673

Clin Pharmacol Ther. 2025 Jun;117(6):1562-1576. doi: 10.1002/cpt.3675.

ABSTRACT

Over the last three decades, transporters have become increasingly recognized for their important roles in clinical pharmacology. As gatekeepers of drug absorption, disposition and targeting, transporters in the intestine, liver, kidney and blood brain barrier have been the subject of many clinical pharmacology studies. A seminal work published in 2001 was among the first studies to shift the focus of pharmacogenomic research from drug metabolizing enzymes to drug transporters, demonstrating that pharmacogenomic factors in genes in addition to drug metabolizing enzymes, and in particular, in transporter genes, could play an important role in interindividual variation in pharmacokinetics of drugs.

PMID:40388108 | DOI:10.1002/cpt.3675