Eur Arch Otorhinolaryngol. 2025 Aug 7. doi: 10.1007/s00405-025-09592-6. Online ahead of print.

ABSTRACT

PURPOSE: The aim of this article was to conduct a systematic review to evaluate the role and reliability of artificial intelligence (AI) and machine learning (ML) in the diagnosis, management, and potential treatment of pediatric upper airway obstruction (UAO).

METHODS: This PRISMA-based review searched PubMed, Scopus, and Web of Science for English-language studies on pediatric UAO (≤ 18 years) using AI/ML. Non-original works, unrelated topics, mixed-age studies, and those without AI/ML were excluded.

RESULTS: Out of 76 identified articles, 27 were included in the review. Most studies on AI and ML focused on pediatric obstructive sleep apnea (OSA), particularly diagnosis and severity classification.Convolutional Neural Networks (CNNs) were the most common approach, used in 29% of studies. The most frequent input modality was nocturnal blood oxygen saturation (SpO₂) signals (44%), followed by clinical parameters (14.8%), electrocardiography (ECG) (7.4%), and polysomnography (PSG) data (7.4%). Model performance varied based on input data and study design. Advanced methods for OSA show high accuracy: deep learning (88.8%), actigraphy/oximetry (96%), and smartphone oximeters (> 79%). The Sunrise algorithm reached 100% sensitivity for severe OSA. Limitations across current studies include heterogeneous patient populations, small sample sizes, and a predominant focus on obstructive sleep apnea (OSA), which may restrict the generalizability of the findings.

CONCLUSIONS: In pediatric sleep medicine, ML models have focused on diagnosis mainly using physiological signalsand XGBoost/Support Vector Machines (SVM) for clinical data. No studies addressed treatment or monitoring, and challenges like data diversity, validation, and feasibility remain.

PMID:40775390 | DOI:10.1007/s00405-025-09592-6

Alzheimers Res Ther. 2025 Aug 7;17(1):182. doi: 10.1186/s13195-025-01827-2.

ABSTRACT

BACKGROUND: Alzheimer's disease (AD) is the principal cause of dementia and requires the early diagnosis of people with mild cognitive impairment (MCI) who are at high risk of progressing. Early diagnosis is imperative for optimizing clinical management and selecting proper therapeutic interventions. Structural magnetic resonance imaging (MRI) markers have been widely investigated for predicting the conversion of MCI to AD, and recent advances in deep learning (DL) methods offer enhanced capabilities for identifying subtle neurodegenerative changes over time.

METHODS: We selected 228 MCI participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) who had at least three T1-weighted MRI scans within 18 months of baseline. MRI volumes underwent bias correction, segmentation, and radiomics feature extraction. A 3D residual network (ResNet3D) was trained using a pairwise ranking loss to capture single-timepoint risk scores. Longitudinal analyses were performed by extracting deep convolutional neural network (CNN) embeddings and gray matter radiomics for each scan, which were put into a time-aware long short-term memory (LSTM) model with an attention mechanism.

RESULTS: A single-timepoint ResNet3D model achieved modest performance (c-index ~ 0.70). Incorporating longitudinal MRI files or downstream survival models led to a pronounced prognostic improvement (c-index:0.80-0.90), but was not further improved by longitudinal radiomics data. Time-specific classification within two- and three-year and five-year windows after the last MRI acquisition showed high accuracy (AUC > 0.85). Several radiomics, including gray matter surface to volume and elongation, emerged as the most predictive features. Each SD change in the gray matter surface to volume change within the last visit was associated with an increased risk of developing AD (HR: 1.50; 95% CI: 1.25-1.79).

CONCLUSIONS: These findings emphasize the value of structural MRI within the advanced DL architectures for predicting MCI-to-AD conversion. The approach may enable earlier risk stratification and targeted interventions for individuals most likely to progress. limitations in sample size and computational resources warrant larger, more diverse studies to confirm these observations and explore additional improvements.

PMID:40775357 | DOI:10.1186/s13195-025-01827-2

Sci Rep. 2025 Aug 7;15(1):28878. doi: 10.1038/s41598-025-14927-8.

NO ABSTRACT

PMID:40775268 | DOI:10.1038/s41598-025-14927-8

Sci Rep. 2025 Aug 7;15(1):28974. doi: 10.1038/s41598-025-13689-7.

ABSTRACT

Grape disease image recognition is an important part of agricultural disease detection. Accurately identifying diseases allows for timely prevention and control at an early stage, which plays a crucial role in reducing yield losses. This study addresses the problems in grape leaf disease recognition under small-sample conditions, such as the difficulty in capturing multi-scale features, the minuteness of features, and the weak adaptability of traditional data augmentation methods. It proposes a solution that combines a multi-scale feature hybrid fusion architecture with data augmentation. The innovation of this study lies in the following four dimensions: (1) Utilize generative models to enhance the cross-category data balancing ability under small-sample conditions and enrich the sample information in the dataset. (2) Innovatively propose the LVT Block, a multi-scale information perception hybrid module based on the Ghost and Transformer structures. This module can effectively acquire and fuse multi-scale information and global information in the feature map. (3) Use the dense connection method to combine the LVT Block and the MARI Block to propose a new architecture, the DLVT Block. By fusing multi-scale information and global information, it improves the richness of feature information. It also uses the MARI to enhance the model's perception of disease areas and constructs an end-to-end lightweight model, DLVTNet, using the DLVT Block. Experiments show that this method achieves an average recognition rate of 98.48% on the New Plant Diseases Dataset. The number of parameters is reduced to 42.7% of that of MobileNetV4, and it maintains an accuracy of 96.12% in the tomato leaf disease test. This paper embeds pathological features into the generative adversarial process, which can effectively alleviate the problem of insufficient samples in intelligent agricultural detection. It provides a new method system with strong interpretability and excellent generalization performance for disease detection.

PMID:40775261 | DOI:10.1038/s41598-025-13689-7

NPJ Digit Med. 2025 Aug 7;8(1):508. doi: 10.1038/s41746-025-01861-2.

ABSTRACT

The current standard-of-care (SOC) practice for defining the clinical target volume (CTV) for radiation therapy (RT) in patients with glioblastoma still employs an isotropic 1-2 cm expansion of the T2-hyperintensity lesion, without considering the heterogeneous infiltrative nature of these tumors. This study aims to improve RT CTV definition in patients with glioblastoma by incorporating biologically relevant metabolic and physiologic imaging acquired before RT along with a deep learning model that can predict regions of subsequent tumor progression by either the presence of contrast-enhancement or T2-hyperintensity. The results were compared against two standard CTV definitions. Our multi-parametric deep learning model significantly outperformed the uniform 2 cm expansion of the T2-lesion CTV in terms of specificity (0.89 ± 0.05 vs 0.79 ± 0.11; p = 0.004), while also achieving comparable sensitivity (0.92 ± 0.11 vs 0.95 ± 0.08; p = 0.10), sparing more normal brain. Model performance was significantly enhanced by incorporating lesion size-weighted loss functions during training and including metabolic images as inputs.

PMID:40775041 | DOI:10.1038/s41746-025-01861-2

Sci Rep. 2025 Aug 7;15(1):28938. doi: 10.1038/s41598-025-14491-1.

ABSTRACT

During regional seismic monitoring, data is automatically analyzed in real-time to identify events and provide initial locations and magnitudes. Monitoring networks may apply automatic post-processing to small events (M < 3) to add and refine picks and improve the event before analyst review. Recently, machine learning algorithms, particularly for phase picking, have matured enough for use in regional monitoring systems. The Southern California Seismic Network has implemented the deep-learning picker PhaseNet in our event post-processing, resulting in about 2-3 times as many picks, particularly S phases, with slightly better pick accuracy than the previous STA/LTA picker (relative to analyst picks). These improvements have led to better epicenter accuracy. We have also developed an automatic post-processing pipeline (ST-Proc) for sub-network triggers, which are collections of nearby phase picks that the real-time system could not associate into an event. ST-Proc uses PhaseNet to find phase picks and the machine learning algorithm GaMMA to associate events. This pipeline is capable of correctly detecting events in 65-70% of triggers containing events with a low false event rate around 5%. Additionally, the GaMMA-determined epicenters are generally accurate (within a few kilometers of the final). Both pipelines have helped to reduce analyst workload and streamline event processing.

PMID:40775035 | DOI:10.1038/s41598-025-14491-1

BMC Pulm Med. 2025 Aug 7;25(1):378. doi: 10.1186/s12890-025-03836-1.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a progressive and serious lung disease with a poor prognosis and severe clinical and humanistic burden. This systematic literature review and meta-analysis aimed to summarize and quantify the data on IPF incidence and prevalence among adults within the general population and to compare regional differences.

METHODS: Comprehensive searches of MEDLINE®, Embase, and the Cochrane Database of Systematic Reviews were conducted to capture available studies published in English from January 1, 2000, to November 7, 2023, that reported on the incidence or prevalence of IPF. Pooled weighted-mean incidence and prevalence estimates were calculated from studies reporting adequate epidemiological data using a DerSimonian-and-Laird random-effects model.

RESULTS: Of 4,077 records identified, 26 studies were included in the meta-analysis (17 reported both prevalence and incidence, 6 reported incidence only, 3 reported prevalence only). Most studies were retrospective, with study periods ranging from 1984 to 2021. Pooled global incidence per 100,000 (95% confidence interval) was 5.8 (4.8, 6.8; 23 studies). Pooled incidence in Asia was 4.4 (1.6, 7.2; 5 studies), 5.1 (3.9, 6.3; 13 studies) in Europe, and 9.0 (6.9, 11.1; 5 studies) in North America. Pooled prevalence (per 100,000) was 17.7 (14.0, 21.5; 20 studies) globally, 14.8 (7.1, 22.6; 6 studies) in Asia, 14.6 (9.4, 19.7; 9 studies) in Europe, and 27.2 (21.0, 33.4; 6 studies) in North America.

CONCLUSION: This analysis confirms that IPF is a rare condition globally, but substantial heterogeneity exists across studies. Incidence and prevalence were notably high in North America compared with Europe and Asia. This finding may be explained by the use of selective source populations in North American studies, in contrast to the more general populations used in European or Asian studies. Additional contributing factors include variations in case identification algorithms, differences in diagnostic definitions and regional differences in occupational and environmental exposures. While recent multi-societal guidelines have advanced the standardization of the IPF diagnostic process, variability in clinical practice remains a challenge that affects comparisons of incidence and prevalence across regions and over time.

PMID:40775309 | DOI:10.1186/s12890-025-03836-1

Gene. 2025 Aug 5:149717. doi: 10.1016/j.gene.2025.149717. Online ahead of print.

ABSTRACT

BACKGROUND: Idiopathic pulmonary fibrosis (IPF), the most common interstitial lung disease, has a severe prognosis, and its diagnosis is difficult. Long non-coding RNAs (lncRNAs) play crucial roles in the mechanism of IPF and exhibit great potential as biomarkers. Past research found that HOXD antisense growth-associated lncRNA (HAGLR) was elevated in IPF. Therefore, this study assessed the diagnostic utility and function of HAGLR in IPF.

METHOD: HAGLR expression was screened in the Gene Expression Omnibus datasets. Then, the serum specimens and clinical information of 66 patients with IPF, 93 patients with interstitial lung disease (ILD) without IPF, 61 patients with pneumonia, and 58 healthy controls were simultaneously collected. HAGLR expression was tested in all subjects and analyzed using receiver operating characteristic curves to verify the clinical utility of HAGLR. Then, the effects of HAGLR inhibition on fibrosis-related gene and protein expression in a cell model of fibrosis were investigated by quantitative reverse transcription-polymerase chain reaction and western blotting.

RESULTS: HAGLR expression was higher in patients with IPF than in healthy controls, patients with ILD without IPF, and patients with pneumonia. The ROC curve analysis illustrated that HAGLR can distinguish patients with IPF from healthy controls. A model combining clinical items (including age, gender, routine blood test, tumor biomarkers, and cytokines), with HAGLR displayed good clinical value, with an are under the curve of 0.994, sensitivity of 100.0% and specificity of 91.4%. Upon HAGLR inhibition, fibrosis proteins were downregulated.

CONCLUSION: HAGLR has utility in the auxiliary diagnosis of IPF, as it can differentiate IPF from other conditions. HAGLR inhibition could alleviate fibrosis at the cellular level.

PMID:40774525 | DOI:10.1016/j.gene.2025.149717

Semin Respir Crit Care Med. 2025 Aug 7. doi: 10.1055/a-2657-9494. Online ahead of print.

ABSTRACT

Lung cancer is the leading cause of cancer deaths worldwide, claiming more lives than other age-related and screen-detectable cancers. Cigarette smoking remains the most important risk factor. However, despite common perceptions, risk is not related solely to cigarette smoking. Several vulnerable and special populations experience a disproportionate burden of lung cancer, often complicated by overlapping medical issues, diagnostic challenges, and treatment limitations. This review highlights four populations (people with HIV, persons who are immunocompromised, lung cancer in nonsmoking women, and individuals with interstitial lung disease [ILD]) who experience unique risks that impact early detection, diagnosis, and management of lung cancer. Three of these populations are frequently underrepresented in clinical trials, yet they may be at elevated risk due to chronic inflammation, immune dysregulation, or previous medical therapies. Individuals with HIV have a significantly increased incidence of lung cancer, often presenting at younger ages and with more advanced disease. Similarly, patients who are immunocompromised following organ or stem cell transplantation are at heightened risk due to prolonged immune dysfunction and prior exposures to toxic therapies. Individuals with ILD, especially idiopathic pulmonary fibrosis (IPF), have an increased risk of developing lung cancer, which is challenging to detect with imaging given architectural distortion and even more challenging to treat given limited pulmonary reserve. We also highlight women, as there has been a striking trend of rising incidence of lung cancer among women worldwide, particularly among those who have never smoked. The intersection of these risks with traditional lung cancer risk factors like tobacco smoking highlights a critical need for increased awareness, improved risk stratification, and adapted screening strategies that take these complexities into account. In this review, we explore the epidemiology, clinical presentation, and early detection and management challenges unique to each population, underscoring the necessity of precision approaches to support individualized care.

PMID:40774326 | DOI:10.1055/a-2657-9494

Respir Investig. 2025 Aug 6;63(5):1012-1017. doi: 10.1016/j.resinv.2025.07.021. Online ahead of print.

ABSTRACT

BACKGROUND: Interstitial lung disease (ILD) prognostication incorporates clinical history, pulmonary function testing (PFTs), and chest CT pattern classifications. The machine learning classifier, Fibresolve, includes a model to help detect CT patterns associated with idiopathic pulmonary fibrosis (IPF). We developed and tested new Fibresolve software to predict outcomes in patients with ILD.

METHODS: Fibresolve uses a transformer (ViT) algorithm to analyze CT imaging that additionally embeds PFTs, age, and sex to produce an overall risk score. The model was trained to optimize risk score in a dataset of 602 subjects designed to maximize predictive performance via Cox proportional hazards. Validation was completed with the first hazard ratio assessment dataset, then tested in a second datatest set.

RESULTS: 61 % of 220 subjects died in the validation set's study period, whereas 40 % of the 407 subjects died in the second dataset's. The validation dataset's mortality hazard ratio (HR) was 3.66 (95 % CI: 2.09-6.42) and 4.66 (CI: 2.47-8.77) for the moderate and high-risk groups. In the second dataset, Fibresolve was a predictor of mortality at initial visit, with a HR of 2.79 (1.73-4.49) and 5.82 (3.53-9.60) in the moderate and high-risk groups. Similar predictive performance was seen at follow-up visits, as well as with changes in the Fibresolve scores over sequential visits.

CONCLUSION: Fibresolve predicts mortality by automatically assessing combined CT, PFTs, age, and sex into a ViT model. The new software algorithm affords accurate prognostication and demonstrates the ability to detect clinical changes over time.

PMID:40774166 | DOI:10.1016/j.resinv.2025.07.021

Plant Cell Rep. 2025 Aug 7;44(9):195. doi: 10.1007/s00299-025-03577-9.

ABSTRACT

The grapevine VviEPFL9-2 paralog is specifically expressed during leaf expansion and its knockout provide a phenotype with superior adaptation to environmental stresses via reduced stomatal density. In Arabidopsis stomatal initiation relies on the transcription factor SPEECHLESS, which is positively regulated by AtEPFL9, a peptide of the epidermal patterning factor family. In grapevine, two EPFL9 paralogs exist but despite a structural similarity, their specific function remains unclear. In this study, we investigated their distinct functional roles and the extent to which reduced stomatal density (SD) may be beneficial for grapevine in terms of water use. We combined expression analysis of the two paralogs in untreated and ABA-treated leaves with the functional characterization of the two genes using grapevine epfl9-1 and epfl9-2 mutants. A physiological analysis of epfl9-2 mutants under different environmental conditions was also performed. We showed that VviEPFL9-1 is exclusively expressed in leaf primordia, whereas VviEPFL9-2 plays a predominant role in fine-tuning SD during the leaf expansion. An epfl9-2 mutant line with 84% lower SD than wild type, exhibited a significant improvement in intrinsic water-use efficiency under both well-watered and water-stressed conditions, with little trade-off in photosynthesis. When the reduction in SD was close to 60%, photosynthetic rate and stomatal conductance were comparable to WT. Our results provide compelling evidence that VviEPFL9-2 knockout determines a significant reduction in stomatal density without a major impact on photosynthesis which may help optimize the adverse impacts of climate change on viticulture.

PMID:40775479 | DOI:10.1007/s00299-025-03577-9

Nat Commun. 2025 Aug 7;16(1):7291. doi: 10.1038/s41467-025-62101-5.

ABSTRACT

Diabetic kidney disease (DKD) progression is not well understood. Using high-throughput proteomics, biostatistical, pathway and machine learning tools, we examine the urinary Complement proteome in two prospective cohorts with type 1 or 2 diabetes and advanced DKD followed for 1,804 person-years. The top 5% urinary proteins representing multiple components of the Complement system (C2, C5a, CL-K1, C6, CFH and C7) are robustly associated with 10-year kidney failure risk, independent of clinical covariates. We confirm the top proteins in three early-to-moderate DKD cohorts (2,982 person-years). Associations are especially pronounced in advanced kidney disease stages, similar between the two diabetes types and far stronger for urinary than circulating proteins. We also observe increased Complement protein and single cell/spatial RNA expressions in diabetic kidney tissue. Here, our study shows Complement engagement in DKD progression and lays the groundwork for developing biomarker-guided treatments.

PMID:40775226 | DOI:10.1038/s41467-025-62101-5

Nat Methods. 2025 Aug 7. doi: 10.1038/s41592-025-02744-w. Online ahead of print.

ABSTRACT

Cellular interactions are of fundamental importance, orchestrating organismal development, tissue homeostasis and immunity. Recently, powerful methods that use single-cell genomic technologies to dissect physically interacting cells have been developed. However, these approaches are characterized by low cellular throughput, long processing times and high costs and are typically restricted to predefined cell types. Here we introduce Interact-omics, a cytometry-based framework to accurately map cellular landscapes and cellular interactions across all immune cell types at ultra-high resolution and scale. We demonstrate the utility of our approach to study kinetics, mode of action and personalized response prediction of immunotherapies, and organism-wide shifts in cellular composition and cellular interaction dynamics following infection in vivo. Our scalable framework can be applied a posteriori to existing cytometry datasets or incorporated into newly designed cytometry-based studies to map cellular interactions with a broad range of applications from fundamental biology to applied biomedicine.

PMID:40775086 | DOI:10.1038/s41592-025-02744-w

Nat Commun. 2025 Aug 7;16(1):7272. doi: 10.1038/s41467-025-62214-x.

ABSTRACT

YKYY017 is a SARS-CoV-2 membrane fusion inhibitor. We report efficacy and safety of inhaled YKYY017 for COVID-19 patients with mild to moderate illness from a phase 2 trial (ChiCTR2300075467). 239 patients aged 18-75 years with mostly mild COVID-19 were randomly allocated to receive aerosol inhalation of 10 or 20 mg YKYY017 or placebo once daily. The primary endpoint is the change in SARS-CoV-2 viral load from baseline to Day 4. The mean (±SE) differences in viral load change from baseline were -0.48 ± 0.27 log10 copies/mL (95% CI, -1.01 to 0.06) for the 20 mg group and -0.27 ± 0.27 log10 copies/mL (95% CI, -0.79 to 0.26) for the 10 mg group, compared to the placebo group. Viral load changes at visits other than Day 4 did not differ significantly from placebo in either the 10 or 20 mg YKYY017 groups. The time to sustained symptom recovery was shorter in the 20 mg YKYY017 group (median 117.53, 95%CI 95.33 to 141.45 hours) than in the placebo group (median 143.00, 95%CI 139.17 to 186.87 hours; HR 1.552, 95%CI 1.089 to 2.214, p = 0.0151), whereas the 10 mg YKYY017 group showed a similar but not statistically significant trend compared to placebo (p = 0.0833). The time to sustained symptom alleviation was shorter in both the 20 and 10 mg YKYY017 groups than in the placebo group. The adverse events were mostly mild to moderate. The primary outcome was not met. Following a supplementary phase 1b trial, we are planning another phase 2/3 trial using a twice-daily 20 mg YKYY017 regimen to further assess efficacy and safety.

PMID:40775020 | DOI:10.1038/s41467-025-62214-x

Genome Res. 2025 Aug 7. doi: 10.1101/gr.280586.125. Online ahead of print.

ABSTRACT

Evolutionary rate covariation (ERC) is an established comparative genomics method that identifies sets of genes sharing patterns of sequence evolution, which suggests shared function. Whereas many functional predictions of ERC have been empirically validated, its predictive power has hitherto been limited by its inability to tackle the large numbers of species in contemporary comparative genomics data sets. This study introduces ERC2.0, an enhanced methodology for studying ERC across phylogenies with hundreds of species and tens of thousands of genes. ERC2.0 improves upon previous iterations of ERC in algorithm speed, normalizing for heteroskedasticity, and normalizing correlations via Fisher transformations. These improvements have resulted in greater statistical power to predict biological function. In exemplar yeast and mammalian data sets, we demonstrate that the predictive power of ERC2.0 is improved relative to the previous method, ERC1.0, and that further improvements are obtained by using larger yeast and mammalian phylogenies. We attribute the improvements to both the larger data sets and improved rate normalization. We demonstrate that ERC2.0 has high predictive accuracy for known annotations and can predict the functions of genes in nonmodel systems. Our findings underscore the potential for ERC2.0 to be used as a single-pass computational tool in candidate gene screening and functional predictions.

PMID:40774815 | DOI:10.1101/gr.280586.125

Biochim Biophys Acta Rev Cancer. 2025 Aug 5:189408. doi: 10.1016/j.bbcan.2025.189408. Online ahead of print.

ABSTRACT

DNA repair is involved in the cellular response to alkylating agents used for the treatment of various cancers, decreasing the damages induced by the compounds and thus limiting the efficacy of the drugs. The inhibition of DNA repair should therefore increase the cytotoxic effect of alkylating agents, and this has been suggested as a therapeutic approach to increase clinical success. In this review, we focus on proteins involved in Nucleotide Excision Repair (NER) with a particular emphasis on the heterodimer ERCC1/XPF, and give an overview of preclinical and clinical studies underlying this therapeutic approach, as well as details on studies and compounds with notable activities. We also discuss the use of computer-aided methods to develop small molecule inhibitors targeting NER-related proteins, with a focus on structure-based virtual screening, and reflect on future perspectives on this topic. Although interesting results are obtained on cell models with various molecules, we believe new efforts are needed in order to validate the proof of concept in vivo and to translate the use of NER inhibitors in cancer patients.

PMID:40774469 | DOI:10.1016/j.bbcan.2025.189408

Cell Syst. 2025 Aug 1:101367. doi: 10.1016/j.cels.2025.101367. Online ahead of print.

ABSTRACT

Clostridioides difficile (C. difficile) colonizes up to 40% of community-dwelling adults without causing disease but can eventually lead to infection (C. difficile infection [CDI]). There has been a lack of focus on how to prevent colonization and facilitate the successful clearance of C. difficile prior to the emergence of CDI. We show that microbial community-scale metabolic models (MCMMs) accurately predict C. difficile colonization susceptibility in vitro and in vivo, offering mechanistic insights into microbiota-specific interactions involving metabolites like succinate, trehalose, and ornithine. MCMMs reveal distinct C. difficile metabolic niches-two growth-associated and one non-growth-associated-observed across 15,204 individuals from five cohorts. We further demonstrate that MCMMs can predict personalized C. difficile growth suppression by a probiotic cocktail designed to replace fecal microbiota transplants (FMTs) for the treatment of recurrent CDI, and we identify new probiotic targets for future validation. MCMMs represent a powerful framework for predicting pathogen colonization and assessing probiotic efficacy across diverse microbiota contexts. A record of this paper's transparent peer review process is included in the supplemental information.

PMID:40774255 | DOI:10.1016/j.cels.2025.101367

Cell. 2025 Jul 29:S0092-8674(25)00803-7. doi: 10.1016/j.cell.2025.07.014. Online ahead of print.

ABSTRACT

Discrete genomic units can recombine into composite transposons that transcribe and transpose as single units, but their regulation and function are not fully understood. We report that composite transposons harbor bivalent histone marks, with activating and repressive marks in distinct regions. Genome-wide CRISPR-Cas9 screening, using a reporter driven by the hominid-specific composite transposon SVA (SINE [short interspersed nuclear element]-VNTR [variable number of tandem repeats]-Alu) in human cells, identified diverse genes that modify bivalent histone marks to regulate SVA transcription. SVA transcripts are critical for SVA's cis-regulatory function in selectively contacting and activating long-range gene expression. Remarkably, a subset of bivalent SVAs is activated during erythropoiesis to boost multiple erythroid gene expression, and knocking down these SVAs leads to deficient erythropoiesis. The RNA-dependent cis-regulatory function of SVA activates genes for myelopoiesis and can contribute to aging-associated myeloid-biased hematopoiesis. These results reveal that the cis-regulatory functions of composite transposons are bivalently regulated to control cell fate transitions in development and aging.

PMID:40774253 | DOI:10.1016/j.cell.2025.07.014

Int J Clin Pharm. 2025 Aug 7. doi: 10.1007/s11096-025-01977-1. Online ahead of print.

ABSTRACT

BACKGROUND: The potentially inappropriate medications (PIM)-China criteria, published in 2017, require updates to reflect new therapeutic evidence and address limitations such as outdated medications and condition-specific considerations.

AIM: This study aimed to develop an updated version of the PIM-China criteria through a modified Delphi consensus methodology, ensuring evidence-based and clinically relevant recommendations for older adults in China.

METHOD: A literature review of six PIM criteria (Beers, STOPP, FORTA, EU(7)-PIM, Japan and Korea criteria) and relevant literature (2018-2023) informed a preliminary list of PIMs. A multidisciplinary panel of 33 experts, comprising 12 physicians and 21 pharmacists, evaluated 210 candidate criteria over three Delphi rounds. Statistical measures were used to validate consensus, including Kendall's W, coefficient of variation (CV), and expert authority coefficient (Cr). Cr values ≥ 0.80 indicated high reliability, while Kendall's W > 0.20 signified moderate to strong agreement.

RESULTS: The updated criteria consist of 154 items, a 57% increase from 2017, including 100 individual medications or drug classes and 54 condition-specific PIMs. Notable additions include recommendations addressing drug-drug interactions, renal function adjustments, and alternative treatments. Consensus improved significantly across rounds, with Kendall's W increasing from 0.145 to 0.271 for individual PIMs and 0.118 to 0.360 for condition-specific PIMs (P < 0.05). Cr reached 0.85, reflecting the panel's high authority.

CONCLUSION: The updated 2024 PIM-China criteria enhance prescribing safety and clinical relevance by incorporating new evidence and expert consensus. These criteria are vital for reducing adverse drug events, optimizing prescribing practices, and improving healthcare for older adults in China.

PMID:40775482 | DOI:10.1007/s11096-025-01977-1

Sci Rep. 2025 Aug 7;15(1):28886. doi: 10.1038/s41598-025-13786-7.

ABSTRACT

Vortioxetine is an antidepressant approved for the treatment of major depressive disorder (MDD). Given its widespread post-marketing clinical use, it is essential to explore its real-world safety. Reports were extracted from the FDA Adverse Event Reporting System (FAERS) from the third quarter of 2013 to the first quarter of 2025. Four disproportionality analysis methods, commonly used in pharmacovigilance to evaluate the relative reporting frequency of adverse events (AEs), were employed to identify AE signals associated with vortioxetine. These included the Reporting Odds Ratio (ROR), Proportional Reporting Ratio (PRR), Multi-item Gamma Poisson Shrink (MGPS), and Bayesian Confidence Propagation Neural Network (BCPNN). The median was used to describe the time to onset (TTO) of AEs, and Weibull distribution was employed to assess the trend of AE occurrence over time. In addition, sensitivity analyses were conducted to ensure the robustness of the findings. A total of 13,613 individual case safety reports (ICSRs) involving 34,156 AEs were analyzed. Females accounted for 60.9% of the reports, while males represented 26.5%. The median age of patients was 42 years (interquartile range: 26-59 years), with most cases (34.1%) in the 18-65 age group. The United States contributed the highest proportion of reports (77.4%). Common AEs included nausea (n = 2042, ROR = 5.11, PRR = 4.86, EBGM = 4.85, IC = 2.28), anxiety (n = 781, ROR = 5.3, PRR = 5.2, EBGM = 5.18, IC = 2.37 ), vomiting (n = 773, ROR = 3.23, PRR = 3.17, EBGM = 3.17, IC = 1.66), headache (n = 670, ROR = 1.96, PRR = 1.94, EBGM = 1.94, IC = 0.96), and somnolence (n = 212, ROR = 2, PRR = 1.99, EBGM = 1.99, IC = 0.99). Notably, several AEs not listed on the drug label, such as tinnitus (n = 79, ROR = 3.24, PRR = 3.24, EBGM = 3.23, IC = 1.69), urinary retention (n = 62, ROR = 3.57, PRR = 3.57, EBGM = 3.56, IC = 1.83), prolonged QT interval (n = 62, ROR = 3.14, PRR = 3.13, EBGM = 3.13, IC = 1.64), and restless legs syndrome (n = 48, ROR = 5.08, PRR = 5.08, EBGM = 5.06, IC = 2.34) were also identified. Most AEs occurred within the first month of treatment, with a median onset time of 15 days. Sensitivity analyses confirmed the consistency of these findings. This study provides new insights into the safety of vortioxetine and offers preliminary safety evidence. In addition, the findings may inform updates to prescribing information and guide post-marketing safety surveillance. However, the spontaneous nature of the FAERS database precludes establishing a causal relationship between vortioxetine and the reported AEs. Further prospective studies are needed to validate our findings.

PMID:40775011 | DOI:10.1038/s41598-025-13786-7