Sci Rep. 2025 Apr 3;15(1):11514. doi: 10.1038/s41598-025-96285-z.

ABSTRACT

To address the issue of low efficiency in feature extraction and model training when traditional deep learning methods handle long time-series data, this paper proposes a Time-Series Lightweight Transformer (TSL-Transformer) model. According to the data characteristics of bearing fault diagnosis tasks, the model makes lightweight improvements to the traditional Transformer model, and focuses on adjusting the encoder module (core feature extraction module), introducing multi-head attention mechanism and feedforward neural network to efficiently extract complex features of vibration signals. Considering the rich temporal features present in vibration signals, a Long Short-Term Memory (LSTM) module is introduced in parallel to the encoder module of the improved lightweight Transformer model. This enhancement further strengthens the model's ability to capture temporal features, thereby improving diagnostic accuracy. Experimental results demonstrate that the proposed TSL-Transformer model achieves a fault diagnosis accuracy of 99.2% on the CWRU dataset. Through dimensionality reduction and visualization analysis using the t-SNE method, the effectiveness of different network structures within the proposed TSL-Transformer model is elucidated.

PMID:40181056 | DOI:10.1038/s41598-025-96285-z

Sci Rep. 2025 Apr 3;15(1):11475. doi: 10.1038/s41598-025-96540-3.

ABSTRACT

Knowledge Tracing (KT) assesses students' mastery of specific knowledge concepts and predicts their problem-solving abilities by analyzing their interactions with intelligent tutoring systems. Although recent years have seen significant improvements in tracking accuracy with the introduction of deep learning and graph neural network techniques, existing research has not sufficiently focused on the impact of difficulty on knowledge state. The text understanding difficulty and knowledge concept difficulty of programming problems are crucial for students' responses; thus, accurately assessing these two types of difficulty and applying them to knowledge state prediction is a key challenge. To address this challenge, we propose a Difficulty aware Programming Knowledge Tracing via Large Language Models(DPKT) to extract the text understanding difficulty and knowledge concept difficulty of programming problems. Specifically, we analyze the relationship between knowledge concept difficulty and text understanding difficulty using an attention mechanism, allowing for dynamic updates to students' s. This model combines an update gate mechanism with a graph attention network, significantly improving the assessment accuracy of programming problem difficulty and the spatiotemporal reflection capability of knowledge state. Experimental results demonstrate that this model performs excellently across various language datasets, validating its application value in programming education. This model provides an innovative solution for programming knowledge tracing and offers educators a powerful tool to promote personalized learning.

PMID:40181055 | DOI:10.1038/s41598-025-96540-3

Sci Rep. 2025 Apr 3;15(1):11515. doi: 10.1038/s41598-025-96005-7.

ABSTRACT

Fragmentation size is an important indicator for evaluating blasting effectiveness. To address the limitations of conventional blasting fragmentation size prediction methods in terms of prediction accuracy and applicability, this study proposes an NRBO-CNN-LSSVM model for predicting mean fragmentation size, which integrates Convolutional Neural Networks (CNN), Least Squares Support Vector Machines (LSSVM), and the Newton-Raphson Optimizer (NRBO). The study is based on a database containing 105 samples derived from both previous research and field collection. Additionally, several machine learning prediction models, including CNN-LSSVM, CNN, LSSVM, Support Vector Machine (SVM), and Support Vector Regression (SVR), are developed for comparative analysis. The results showed that the NRBO-CNN-LSSVM model achieved remarkable prediction accuracy on the training dataset, with a coefficient of determination (R2) as high as 0.9717 and a root mean square error (RMSE) as low as 0.0285. On the test set, the model maintained high prediction accuracy, with an R2 value of 0.9105 and an RMSE of 0.0403. SHapley Additive exPlanations (SHAP) analysis revealed that the modulus of elasticity (E) was a key variable influencing the prediction of mean fragmentation size. Partial Dependence Plots (PDP) analysis further disclosed a significant positive correlation between the modulus of elasticity (E) and mean fragmentation size. In contrast, a distinct negative correlation was observed between the powder factor (Pf) and mean fragmentation size. To enhance the convenience of the model in practical applications, we developed an interactive Graphical User Interface (GUI), allowing users to input relevant variables and obtain instant prediction results.

PMID:40181054 | DOI:10.1038/s41598-025-96005-7

Sci Rep. 2025 Apr 3;15(1):11413. doi: 10.1038/s41598-025-95640-4.

ABSTRACT

The potential of the sequence in Coronary Angiography (CA) frames for diagnosing coronary artery disease (CAD) has been largely overlooked. Our study aims to reveal the "Sequence Value" embedded within these frames and to explore methods for its application in diagnostics. We conduct a survey via Amazon Mturk (Mechanical Turk) to evaluate the effectiveness of Sequence Restoration Capability in indicating CAD. Furthermore, we develop a self-supervised deep learning model to automatically assess this capability. Additionally, we ensure the robustness of our results by differently selecting coronary angiographies/modules for statistical analysis. Our self-supervised deep learning model achieves an average AUC of 80.1% across five-fold validation, demonstrating robustness against static data noise and efficiency, with calculations completed within 30 s. This study uncovers significant insights into CAD diagnosis through the sequence value in coronary angiography. We successfully illustrate methodologies for harnessing this potential, contributing valuable knowledge to the field.

PMID:40181050 | DOI:10.1038/s41598-025-95640-4

Nat Commun. 2025 Apr 3;16(1):3193. doi: 10.1038/s41467-025-58023-x.

ABSTRACT

The biological functions of extragenic enhancer RNAs and their impact on disease risk remain relatively underexplored. In this work, we develop in silico models of genetically regulated expression of enhancer RNAs across 49 cell and tissue types, characterizing their degree of genetic control. Leveraging the estimated genetically regulated expression for enhancer RNAs and canonical genes in a large-scale DNA biobank (N > 70,000) and high-resolution Hi-C contact data, we train a deep learning-based model of pairwise three-dimensional chromatin contact frequency for enhancer-enhancer and enhancer-gene pairs in cerebellum and whole blood. Notably, the use of genetically regulated expression of enhancer RNAs provides substantial tissue-specific predictive power, supporting a role for these transcripts in modulating spatial chromatin organization. We identify schizophrenia-associated enhancer RNAs independent of GWAS loci using enhancer RNA-based TWAS and determine the causal effects of these enhancer RNAs using Mendelian randomization. Using enhancer RNA-based TWAS, we generate a comprehensive resource of tissue-specific enhancer associations with complex traits in the UK Biobank. Finally, we show that a substantially greater proportion (63%) of GWAS associations colocalize with causal regulatory variation when enhancer RNAs are included.

PMID:40180945 | DOI:10.1038/s41467-025-58023-x

Nat Commun. 2025 Apr 3;16(1):3205. doi: 10.1038/s41467-025-58588-7.

ABSTRACT

Degeneracy in the genetic code allows many possible DNA sequences to encode the same protein. Optimizing codon usage within a sequence to meet organism-specific preferences faces combinatorial explosion. Nevertheless, natural sequences optimized through evolution provide a rich source of data for machine learning algorithms to explore the underlying rules. Here, we introduce CodonTransformer, a multispecies deep learning model trained on over 1 million DNA-protein pairs from 164 organisms spanning all domains of life. The model demonstrates context-awareness thanks to its Transformers architecture and to our sequence representation strategy that combines organism, amino acid, and codon encodings. CodonTransformer generates host-specific DNA sequences with natural-like codon distribution profiles and with minimum negative cis-regulatory elements. This work introduces the strategy of Shared Token Representation and Encoding with Aligned Multi-masking (STREAM) and provides a codon optimization framework with a customizable open-access model and a user-friendly Google Colab interface.

PMID:40180930 | DOI:10.1038/s41467-025-58588-7

Diagn Interv Imaging. 2025 Apr 3:S2211-5684(25)00067-1. doi: 10.1016/j.diii.2025.03.007. Online ahead of print.

ABSTRACT

PURPOSE: The purpose of this study was to evaluate the efficacy of a deep learning (DL)-based computer-aided detection (CAD) system for the detection of abnormalities on chest X-rays performed in an emergency department setting, where readers have access to relevant clinical information.

MATERIALS AND METHODS: Four hundred and four consecutive chest X-rays performed over a two-month period in patients presenting to an emergency department with respiratory symptoms were retrospectively collected. Five readers (two radiologists, three emergency physicians) with access to clinical information were asked to identify five abnormalities (i.e., consolidation, lung nodule, pleural effusion, pneumothorax, mediastinal/hilar mass) in the dataset without assistance, and then after a 2-week period, with the assistance of a DL-based CAD system. The reference standard was a chest X-ray consensus review by two experienced radiologists. Reader performances were compared between the reading sessions, and interobserver agreement was assessed using Fleiss' kappa test.

RESULTS: The dataset included 118 occurrences of the five abnormalities in 103 chest X-rays. The CAD system improved sensitivity for consolidation, pleural effusion, and nodule, with respective absolute differences of 8.3 % (95 % CI: 3.8-12.7; P < 0.001), 7.9 % (95 % CI: 1.7-14.1; P = 0.012), and 29.5 % (95 % CI: 19.8-38.2; P < 0.001), respectively. Specificity was greater than 89 % for all abnormalities and showed a minimal but significant decrease with DL for nodules and mediastinal/hilar masses (-1.8 % [95 % CI: -2.7 - -0.9]; P < 0.001 and -0.8 % [95 % CI: -1.5 - -0.2]; P = 0.005). Inter-observer agreement improved with DL, with kappa values ranging from 0.40 [95 % CI: 0.37-0.43] for mediastinal/hilar mass to 0.84 [95 % CI: 0.81-0.87] for pneumothorax.

CONCLUSION: Our results suggest that DL-assisted reading increases the sensitivity for detecting important chest X-ray abnormalities in the emergency department, even when clinical information is available to the radiologist.

PMID:40180796 | DOI:10.1016/j.diii.2025.03.007

AAPS J. 2025 Apr 3;27(3):73. doi: 10.1208/s12248-025-01059-0.

ABSTRACT

The Blood-Brain Barrier (BBB) is a selective barrier between the Central Nervous System (CNS) and the peripheral system, regulating the distribution of molecules. BBB permeability has been crucial in CNS-targeting drug development, such as glioblastoma-related drug discovery. In addition, more CNS diseases still present significant challenges, for instance, neurological disorders like Alzheimer's Disease (AD) and drug abuse. Conversely, cannabinoid drugs that do not cross the BBB are needed to avoid off-target CNS psychotropic effects. In vitro and in vivo experiments measuring BBB permeability are costly and low throughput. Computational pharmacoanalytics modeling, particularly using deep-learning Graph Neural Networks (GNNs), offers a promising alternative. GNNs excel at capturing intricate relationships in graph-based information, such as small molecular structures. In this study, we developed GNNs model for BBB permeability using the graph representation of drugs. The GNNs were compared with other algorithms using molecular fingerprints or physical-chemical descriptors. With a dataset of 1924 molecules, the best GNNs model, a convolutional graph neural network using a normalized Laplacian matrix (GCN_2), achieved a precision of 0.94, recall of 0.96, F1 score of 0.95, and MCC score of 0.77. This outperformed other machine learning algorithms with molecular fingerprints. The findings indicate that the graphic representation of small molecules combined with GNNs architecture is powerful in predicting BBB permeability with high accuracy and recall. The developed GNNs model can be utilized in the initial screening stage for new drug development.

PMID:40180695 | DOI:10.1208/s12248-025-01059-0

J Imaging Inform Med. 2025 Apr 3. doi: 10.1007/s10278-025-01467-w. Online ahead of print.

ABSTRACT

Diabetic retinopathy (DR) is a significant vision-threatening condition, necessitating accurate and efficient automated screening methods. Traditional deep learning (DL) models struggle to detect subtle lesions and also suffer from high computational complexity. Existing models primarily mimic the primary visual cortex (V1) of the human visual system, neglecting other higher-order processing regions. To overcome these limitations, this research introduces the vision core-adapted network-based crossover osprey algorithm (VCANet-COP) for subtle lesion recognition with better computational efficiency. The model integrates sparse autoencoders (SAEs) to extract vascular structures and lesion-specific features at a pixel level for improved abnormality detection. The front-end network in the VCANet emulates the V1, V2, V4, and inferotemporal (IT) regions to derive subtle lesions effectively and improve lesion detection accuracy. Additionally, the COP algorithm leveraging the osprey optimization algorithm (OOA) with a crossover strategy optimizes hyperparameters and network configurations to ensure better computational efficiency, faster convergence, and enhanced performance in lesion recognition. The experimental assessment of the VCANet-COP model on multiple DR datasets namely Diabetic_Retinopathy_Data (DR-Data), Structured Analysis of the Retina (STARE) dataset, Indian Diabetic Retinopathy Image Dataset (IDRiD), Digital Retinal Images for Vessel Extraction (DRIVE) dataset, and Retinal fundus multi-disease image dataset (RFMID) demonstrates superior performance over baseline works, namely EDLDR, FFU_Net, LSTM_MFORG, fundus-DeepNet, and CNN_SVD by achieving average outcomes of 98.14% accuracy, 97.9% sensitivity, 98.08% specificity, 98.4% precision, 98.1% F1-score, 96.2% kappa coefficient, 2.0% false positive rate (FPR), 2.1% false negative rate (FNR), and 1.5-s execution time. By addressing critical limitations, VCANet-COP provides a scalable and robust solution for real-world DR screening and clinical decision support.

PMID:40180632 | DOI:10.1007/s10278-025-01467-w

Sci Rep. 2025 Apr 3;15(1):11390. doi: 10.1038/s41598-025-90910-7.

ABSTRACT

Pirfenidone is an antifibrotic and anti-inflammatory drug used for the management of idiopathic pulmonary fibrosis. The current oral delivery of PD has multiple drawbacks, including first-pass metabolism and gastrointestinal discomfort. Efforts have been made to create nanostructured lipid carriers (NLCs) using solid lipids, liquid lipids, and surfactants through an emulsification process followed by ultrasonication to achieve sustained drug release. A central composite design (CCD) utilizing response surface methods (RSMs) was employed to develop and optimize the formulation. The assessed characteristics included particle size distribution, surface topography, drug entrapment efficiency, in vitro drug release, and kinetic profiles in animal models. Cytotoxicity experiments were performed on HepG2 and Caco-2 cell lines and compared with that of PD-NLCs. The optimized formulation yielded a particle size of 159.8 ± 3.46 nm and an encapsulation efficiency of 81.4 ± 7.1% after 10 freeze-thaw cycles of homogenized lipid carriers. In vitro tests assessing various tested flow rates revealed that over 95% of the released drug was retrieved. In vitro studies showed that the PD-loaded nanostructured lipid carrier (NLC) was more cytotoxic to HepG2 and Caco-2 cells than a pure aqueous solution of the drug. Using 25% w/w sorbitol as a cryoprotectant, the findings showed no variation in the properties of NLC before and after freeze-drying. PD-NLCs carriers were shown to have better bioavailability, longer retention time in the lung, and a 15.94-targeting factor related to the PD aqueous solution. Hence, the outcomes confirmed the potential of the PD-NLCs formulation to improve the efficacy of the drug in inhalation therapy.

PMID:40181013 | DOI:10.1038/s41598-025-90910-7

Eur Respir J. 2025 Apr 3:2402418. doi: 10.1183/13993003.02418-2024. Online ahead of print.

ABSTRACT

The complex pathogenic relationships between idiopathic pulmonary fibrosis (IPF) and its usually associated comorbidities remain poorly understood. While evidence suggests that some comorbidities may directly influence the development or progression of IPF or vice versa, whether these associations are causal or arise independently due to shared risk factors, such as aging, smoking, lifestyle, and genetic susceptibility, is still uncertain. Some comorbidities, such as metabolic syndromes, gastro-esophageal reflux disease, and obstructive sleep apnea, precede the development of IPF. In contrast, others, like pulmonary hypertension or lung cancer, often become apparent after its onset or during its progression. These timing patterns suggest a directional relationship in their associations. The issue is further complicated by the fact that patients often have multiple comorbidities, which may interact and exacerbate one another, creating a vicious cycle. To clarify these correlations, some studies have used causal inference methods (e.g., Mendelian randomisation) and exploration of underlying mechanisms; however, these efforts have not yet generated conclusive insights. In this review, we provide a general overview of the relationship between IPF and its comorbidities, emphasizing the pathogenic mechanisms underlying each comorbidity, potential shared pathobiology with IPF, and, when available, causal insights from Mendelian randomisation studies.

PMID:40180336 | DOI:10.1183/13993003.02418-2024

Respir Med. 2025 Apr 1:108081. doi: 10.1016/j.rmed.2025.108081. Online ahead of print.

ABSTRACT

BACKGROUND: Abnormal α-Klotho (KL) levels play an essential role in the pathogenesis of aging-related lung diseases. However, the correlation between circulating KL levels and aging-related lung diseases has not been determined. This study aimed to determine whether circulating KL levels causally affect aging-related lung diseases using Mendelian randomization (MR).

METHODS: Five KL-associated Single-nucleotide polymorphisms (SNPs) were analyzed using two-sample MR to assess their effects on three aging-related lung diseases: idiopathic pulmonary fibrosis (IPF), chronic obstructive pulmonary disease (COPD), and lung cancer.

RESULTS: Based on a main casual effects model with MR analyses by the inverse variance weighted (IVW) method including multiplicative random-effects model (IVW-mre) and fixed-effects inverse variance-weighted model (IVW-fe), genetically predicted circulating KL levels were negatively related with risk of IPF (Odds ratio (ORIVW-mre), 0.999, 95% CI, 0.999-1.000, PIVW-mre = 0.008; OR IVW-fe, 0.999, 95% CI, 0.999-1.000, PIVW-fe = 0.042). Inversely, the circulating levels of KL displayed no clear association with COPD and lung cancer. No pleiotropy was detected.

CONCLUSIONS: Genetically predicted circulating KL was causally associated with a lower risk of IPF, suggesting a protective effect in preventing IPF risk. Therefore, KL may be a promising target for the prevention and therapeutic intervention in patients with IPF.

PMID:40180194 | DOI:10.1016/j.rmed.2025.108081

Neurobiol Dis. 2025 Apr 1:106888. doi: 10.1016/j.nbd.2025.106888. Online ahead of print.

ABSTRACT

Epilepsy is a severe common neurological disease affecting all ages. Epilepsy with onset before the age of 5 years, designated early-onset epilepsy (EOE), is of special importance. According to previous studies, genetic factors contribute significantly to the pathogenesis of EOE that remains unclear and must be explored. So, a list of 229 well-selected EOE-associated genes expressed in the brain was created for the investigation of genetic factors and molecular mechanisms involved in its pathogenesis. Enrichment analysis showed that among significant pathways were nicotine addiction, GABAergic synapse, synaptic vesicle cycle, regulation of membrane potential, cholinergic synapse, dopaminergic synapse, and morphine addiction. Performing an integrated analysis as well as protein-protein interaction network-based approaches with the use of GO, KEGG, ClueGO, cytoHubba and 3 network metrics, 12 hub genes were identified, seven of which, CDKL5, GABRA1, KCNQ2, KCNQ3, SCN1A, SCN8A and STXBP1, were identified as key genes (via Venn diagram analysis). These key genes are mostly enriched in SNARE interactions in vesicular transport, regulation of membrane potential and synaptic vesicle exocytosis. Clustering analysis of the PPI network via MCODE showed significant functional modules, indicating also other pathways such as N-Glycan biosynthesis and protein N-linked glycosylation, retrograde endocannabinoid signaling, mTOR signaling and aminoacyl-tRNA biosynthesis. Drug-gene interaction analysis identified a number of drugs as potential medications for EOE, among which the non-FDA approved drugs azetukalner (under clinical development), indiplon and ICA-105665 and the FDA approved drugs retigabine, ganaxolone and methohexital.

PMID:40180227 | DOI:10.1016/j.nbd.2025.106888

Mol Cell Proteomics. 2025 Apr 1:100960. doi: 10.1016/j.mcpro.2025.100960. Online ahead of print.

ABSTRACT

Nsun5 assumes a pivotal role in the regulation of RNA methylation, and its deficiency has been linked to the advancement of hepatocellular carcinoma, gliomas, tetralogy of Fallot, cognitive deficits in Williams-Beuren syndrome (WBS), and brain development. This underscores Nsun5's significant involvement in the nervous system. In this study, we present evidence of Nsun5's influence on the structure of the primary somatosensory cortex. Through comprehensive multi-omics analyses, we unveil a spectrum of systematically altered genes and proteins, collectively engaged in the orchestration of translation, neurotransmitter metabolism, nerve conduction, synaptic transmission, and other functions. Notably, there are discernible changes in molecules associated with pain sensation, strongly indicating that Nsun5 deficiency undermines pain-related behavior. This study establishes a clear link between Nsun5 deficiency and transcriptional and proteomic changes, as well as neurotransmitter expression within the primary somatosensory cortex, and uncovers its novel role in impaired pain perception.

PMID:40180179 | DOI:10.1016/j.mcpro.2025.100960

Biotechnol Adv. 2025 Apr 1:108574. doi: 10.1016/j.biotechadv.2025.108574. Online ahead of print.

ABSTRACT

Human settlements on the Moon, crewed missions to Mars and space tourism will become a reality in the next few decades. Human presence in space, especially for extended periods of time, will therefore steeply increase. However, despite more than 60 years of spaceflight, the mechanisms underlying the effects of the space environment on human physiology are still not fully understood. Animals, ranging in complexity from flies to monkeys, have played a pioneering role in understanding the (patho)physiological outcome of critical environmental factors in space, in particular altered gravity and cosmic radiation. The use of animals in biomedical research is increasingly being criticized because of ethical reasons and limited human relevance. Driven by the 3Rs concept, calling for replacement, reduction and refinement of animal experimentation, major efforts have been focused in the past decades on the development of alternative methods that fully bypass animal testing or so-called new approach methodologies. These new approach methodologies range from simple monolayer cultures of individual primary or stem cells all up to bioprinted 3D organoids and microfluidic chips that recapitulate the complex cellular architecture of organs. Other approaches applied in life sciences in space research contribute to the reduction of animal experimentation. These include methods to mimic space conditions on Earth, such as microgravity and radiation simulators, as well as tools to support the processing, analysis or application of testing results obtained in life sciences in space research, including systems biology, live-cell, high-content and real-time analysis, high-throughput analysis, artificial intelligence and digital twins. The present paper provides an in-depth overview of such methods to replace or reduce animal testing in life sciences in space research.

PMID:40180136 | DOI:10.1016/j.biotechadv.2025.108574

Int J Biol Macromol. 2025 Apr 1:142469. doi: 10.1016/j.ijbiomac.2025.142469. Online ahead of print.

ABSTRACT

Pancreatic cancer (PC) is one of the deadliest cancers, characterized by a poor prognosis. Currently, there are no screening programs for the early detection of PC, and existing diagnostic methods are primarily limited to high-risk individuals. Biomarkers such as CA19-9 have not significantly improved early diagnosis, making the identification of new potential biomarkers crucial for routine clinical practice. Among the candidate biomarkers, miRNAs have been most extensively studied due to their role in regulating gene expression (either as oncomiRs or tumor suppressor miRNAs) and their potential for minimally invasive analysis through liquid biopsy techniques. This review aims to summarize the current literature on blood-circulating miRNAs and their diagnostic value in PC detection, considering the context of CA19-9 and benign pancreatic diseases. The data from the collected studies were curated through both statistical and bioinformatics analyses to identify the most promising miRNAs with optimal diagnostic accuracy for PC detection and to assess their role in the molecular processes leading to tumor development.

PMID:40180095 | DOI:10.1016/j.ijbiomac.2025.142469

Med. 2025 Mar 27:100642. doi: 10.1016/j.medj.2025.100642. Online ahead of print.

ABSTRACT

BACKGROUND: Adverse drug events (ADEs) are the fourth leading cause of death in the US and cost billions of dollars annually in increased healthcare costs. However, few machine-readable databases of ADEs exist, limiting our capacity to study drug safety on a broader, systematic scale. Recent advances in natural language processing methods, such as BERT models, present an opportunity to accurately extract relevant information from unstructured biomedical text.

METHODS: We fine-tune a PubMedBERT model to extract ADE terms from text in FDA Structured Product Labels for prescription drugs. Here, we present OnSIDES (on-label side effects resource), a compiled, machine-friendly database of drug-ADE pairs generated with this method. We further utilize this method to extract pediatric-specific ADEs, serious ADEs from labels' "Boxed Warnings" section, and ADEs from drug labels of other major nations-the UK, the European Union, and Japan-to build a complementary OnSIDES-INTL database. To present OnSIDES' potential applications, we leverage the database to predict novel drug targets and indications, analyze enrichment of ADEs across drug classes, and predict novel ADEs from chemical compound structures.

FINDINGS: We achieve an F1 score of 0.90, AUROC of 0.92, and AUPR of 0.95 at extracting ADEs from the labels' "Adverse Reactions" section. OnSIDES contains over 3.6 million drug-ADE pairs for 3,233 unique drug ingredient combinations extracted from 47,211 labels.

CONCLUSIONS: OnSIDES can be used as a comprehensive resource to study and enhance drug safety.

FUNDING: R35GM131905 to N.P.T.; T32GM145440 to H.Y.C.; and T15LM007079 to U.G., M.Z., and K.L.B.

PMID:40179876 | DOI:10.1016/j.medj.2025.100642

Eur J Obstet Gynecol Reprod Biol. 2025 Mar 30;310:113947. doi: 10.1016/j.ejogrb.2025.113947. Online ahead of print.

ABSTRACT

BACKGROUND: Polycystic ovary syndrome (PCOS) is an endocrine-metabolic disorder; however, the current guidelines do not adequately address the metabolic aspect. By gathering patients' perspectives, this survey investigates potential issues with the current diagnostic process to identify key points that need addressing in the future.

METHOD: A survey comprising of 49 multiple-choice question was distributed to members of the Italian PCOS community NoiPCOS, including topics such as demographics, PCOS diagnosis experience, symptom management, quality of life, and access to information about PCOS.

RESULTS: 769 women aged 18-40 responded to the survey. 72.2% of responders were employed and perceived their socio-economic status as "good". PCOS diagnosis was primarily obtained in adolescence (35.1%) or late adolescence (33.6 %), with the most common symptoms being polycystic ovaries (85.8%), irregular menses (80.4%), and hirsutism (64.1%). Moreover, PCOS symptoms were seen to severely impact the mental health for 64.7% of responders. Treatments prescribed for PCOS were diet (49.5%), exercise (46.9%), metformin (27.6%), hormonal contraception (26.4%), and myo-inositol and D-chiro-inositol (25.2%). When accessing information about PCOS, women often relied on unofficial sources (i.e. internet sources) rather guidance from their physician.

CONCLUSION: Findings of this survey highlight that a thorough update of PCOS diagnostic criteria is required, which should consider the endocrine and metabolic aspects of the syndrome. Such revision should enable a more accurate, precise diagnosis that translates to effective therapy. Finally, any reconsideration of the PCOS guidelines should increase the perceived reliability by patients of medical care, reducing the communication gap between specialists and patients.

PMID:40179473 | DOI:10.1016/j.ejogrb.2025.113947

Korean J Anesthesiol. 2025 Apr 4. doi: 10.4097/kja.24815. Online ahead of print.

ABSTRACT

BACKGROUND: We hypothesized that intranasal combination of dexmedetomidine (2 μg/kg) and ketamine (3 mg/kg) (IN DEXKET) improves the success rate of sedation in pediatric patients compared with chloral hydrate (CH; 50 mg/kg).

METHODS: This prospective, two-center, single-blinded, randomized controlled trial involved 136 pediatric patients (aged < 7 years) requiring procedural sedation. The participants were randomized to receive CH or IN DEXKET via a mucosal atomizer device. The primary outcome was the success rate of sedation (Pediatric Sedation State Scale, scores 1-3) within 15 min. The secondary outcomes included sedation failure at 30 min and overall complications of first-attempt sedation.

RESULTS: After excluding eight patients, 128 were included (CH = 66, IN DEXKET = 62). IN DEXKET showed a similar sedation success rate (75.8% [47/62] vs. 66.7% [44/66]; P = 0.330) but a lower complication rate (3.2% [2/62] vs. 16.7% [11/66]; P = 0.017) than CH. In the subgroup analysis for patients aged < 1 year, IN DEXKET showed a reduced complication rate than CH (2.6% [1/38] vs. 22.9% [8/35]; P = 0.012). In the subgroup analysis of children aged 1-7 years, IN DEXKET showed a higher sedation success rate within 15 min (79.2% [19/24] vs. 51.6% [16/31]; P = 0.049) and a lower sedation failure after 30 min (0% vs. 29.0% [9/31]; P = 0.003) than CH.

CONCLUSIONS: The intranasal combination of dexmedetomidine (2 μg/kg) and ketamine (3 mg/kg) is a safe and effective alternative to CH (50 mg/kg) for sedation in pediatric patients aged < 7 years.

PMID:40180590 | DOI:10.4097/kja.24815

Lung Cancer. 2025 Mar 12;203:108478. doi: 10.1016/j.lungcan.2025.108478. Online ahead of print.

ABSTRACT

OBJECTIVES: To evaluate the safety and effectiveness of entrectinib, an orally-administered potent multi-kinase inhibitor, for the treatment of proto-oncogene tyrosine-protein kinase-1 (ROS1) gene fusion-positive, unresectable, advanced/recurrent non-small cell lung cancer (NSCLC) in Japan.

MATERIALS AND METHODS: Patients with ROS1 gene fusion-positive, unresectable, advanced/recurrent NSCLC who initiated entrectinib therapy were enrolled in this all-case post marketing surveillance between February 21, 2020 and November 30, 2021. Outcomes were to identify the: (1) type and onset of initial cognitive disorder and ataxia during entrectinib therapy; (2) status of treatment and outcome of drug-related cognitive disorder and ataxia events; (3) incidence of other adverse drug reactions (ADRs) of safety concern: cognitive disorder and/or ataxia, cardiac disorder (excluding QT interval prolongation), QT interval prolongation, syncope, and interstitial lung disease; (4) incidence of serious adverse events (AEs) and ADRs; and (5) effectiveness.

RESULTS: Of the 276 patients who initiated entrectinib, 269 and 260 were included in the safety and effectiveness analysis sets, respectively. Cognitive disorder/ataxia was the most common ADR of safety concern, occurring in 72 patients (26.8 %). The median time to onset of initial cognitive disorder/ataxia symptoms was 2.0 days. Overall, entrectinib dose reduction, interruption, or discontinuation occurred in 9.7 %, 28.3 %, and 15.2 % of patients, respectively. Most ADRs of safety concern were manageable; 86.9 % of patients with ADRs were recovered/recovering. Serious AEs were reported in 42.8 % of patients. The overall response rate (ORR) was 38.8 % and median time to treatment failure was 6.4 months. ORR was 70.8 % versus 26.8 % to 34.7 % with entrectinib as first-line versus second- or later-line treatment, and 65.3 % versus 28.2 % in patients without versus with a history of tyrosine kinase inhibitor treatment.

CONCLUSIONS: Consistent with clinical trials, entrectinib is tolerable and effective in Japanese patients with ROS1 gene fusion-positive, unresectable, advanced/recurrent NSCLC.

STUDY REGISTRATION: UMIN Clinical Trials Registry (UMIN000046619).

PMID:40179540 | DOI:10.1016/j.lungcan.2025.108478