Drug Discov Today. 2025 May 27:104390. doi: 10.1016/j.drudis.2025.104390. Online ahead of print.

ABSTRACT

Drug repurposing leverages existing drugs for new therapeutic uses, offering significant opportunities but facing challenges such as financial and regulatory barriers and the need for robust evidence for an efficient clinical development plan. This paper examines the critical steps in drug repurposing and their role in improving study success rates. It also highlights the support infrastructure provided by the University College London (UCL) Repurposing Therapeutic Innovation Network (TIN), as a partnership model to address these challenges through diverse expertise, enterprise insight, and tailored guidance. By fostering collaborations and offering structured support, the Repurposing TIN aims to accelerate repurposing efforts and deliver patient benefits. We invite potential collaborators to join us in advancing drug repurposing through innovative and strategic approaches.

PMID:40441598 | DOI:10.1016/j.drudis.2025.104390

Regul Toxicol Pharmacol. 2025 May 27:105864. doi: 10.1016/j.yrtph.2025.105864. Online ahead of print.

ABSTRACT

Modified new drugs are pivotal in advancing innovative therapies through repurposing existing therapeutic agents. The regulatory framework, including the pertinent regulations and policies, plays a crucial role in shaping the development and evolution of these drugs. This retrospective study systematically compared the regulatory approvals of modified new drugs via the 505(b)(2) new drug application (NDA) pathway in the United States (US) and Class 2 NDA pathway in China from 2017 to 2023, which focused on distinctions in registration classifications, availability, therapeutic indications, dosage forms, modifications, clinical advantages and clinical study designs. The findings indicate that the US has more detailed and comprehensive classification systems, as well as a higher number of approvals (417 vs. 99). Moreover, the modified new drugs approved in China still exhibit significant gaps in indication distribution, dosage forms, and modifications compared to those in the US. Notably, a greater proportion of confirmatory clinical studies were conducted for Class 2 NDAs (81.4%) than 505(b)(2) NDAs (41.0%), with a significant difference in the use of active controls (48.6% in China vs. 26.4% in the US, P=0.002). Additionally, the combination of emerging technologies in modified new drugs presents both technical and regulatory challenges for authorities. It raises worthwhile questions about how regulators will evaluate medical products developed with entirely new technologies. Therefore, it is recommended that Chinese regulators refine registration classifications, reassess the positioning of modified new drugs, and expand the definition of clinical advantage within the policy and regulatory framework. These measures are essential for addressing unmet medical needs and fostering a conducive ecosystem for the advancement of modified new drugs.

PMID:40441284 | DOI:10.1016/j.yrtph.2025.105864

Br J Clin Pharmacol. 2025 Jun;91(6):1842-1852. doi: 10.1111/bcp.70004.

ABSTRACT

AIMS: Phenoconversion, a genotype-phenotype mismatch, challenges a successful implementation of personalized medicine. The aim of this study was to detect and determine phenoconversion using the solanidine metabolites 3,4-seco-solanidine-3,4-dioic acid (SSDA) and 4-OH-solanidine as diet-derived cytochrome P450 2D6 (CYP2D6) biomarkers in a geriatric, multimorbid cohort with high levels of polypharmacy.

METHODS: Blood samples and data of geriatric, multimedicated patients were collected during physician counsel (CT: NCT05247814). Solanidine and its metabolites were determined via liquid chromatography/tandem mass spectrometry and used for CYP2D6 phenotyping. CYP2D6 genotyping was performed and activity scores (AS) were assigned. Complete medication intake was assessed. A shift of the AS predicted via genotyping as measured by phenotyping was calculated.

RESULTS: Solanidine and its metabolites were measured in 88 patients with complete documentation of drug use. Patients had a median age of 83 years (interquartile range [IQR] 77-87) and the majority (70.5%, n = 62) were female. Patients took a median of 15 (IQR 12-17) medications. The SSDA/solanidine metabolic ratio correlated significantly with the genotyping-derived AS (P < .001) and clearly detected poor metabolizers. In the model adjusted for age, sex, Charlson Comorbidity Index and estimated glomerular filtration rate each additional CYP2D6 substrate/inhibitor significantly lowered the expected AS by 0.53 (95% confidence interval 0.85-0.21) points in patients encoding functional CYP2D6 variants (R2 = 0.242).

CONCLUSIONS: Phenotyping of CYP2D6 activity by measurement of diet-derived biomarkers elucidates phenoconversion in geriatric patients. These results might serve as a prerequisite for the validation and establishment of a bedside method to measure CYP2D6 activity in multimorbid patients for successful application of personalized drug prescribing.

PMID:40441673 | DOI:10.1111/bcp.70004

Nat Rev Microbiol. 2025 May 29. doi: 10.1038/s41579-025-01193-8. Online ahead of print.

ABSTRACT

Pseudomonas aeruginosa has long served as a model organism in microbiology, particularly for studies on gene expression, quorum sensing, antibiotic resistance, virulence and biofilm formation. Its genetic tractability has advanced the understanding of complex regulatory networks and experimental evolution. The versatility of this bacterium stems from its genomic variability, metabolic flexibility and phenotypic diversity, enabling it to thrive in diverse environments, both as a harmless saprophyte and an opportunistic human pathogen. P. aeruginosa can cause acute and chronic human infections, particularly in patients with underlying immune deficiencies. Its intrinsic antibiotic tolerance and resistance, together with its ability to produce multiple virulence factors while rapidly adapting to infection conditions, pose a major clinical challenge. In this Review, we explore key features contributing to the ecological and pathogenic versatility of P. aeruginosa. We examine the molecular mechanisms and ecological and evolutionary implications of quorum sensing and biofilm formation. We explore the virulence strategies and in vivo fitness determinants, as well as the evolutionary dynamics and global epidemiology of P. aeruginosa, with a focus on antimicrobial resistance. Finally, we discuss emerging strategies to control P. aeruginosa infections and address outstanding questions in the field.

PMID:40442328 | DOI:10.1038/s41579-025-01193-8

Clin Exp Allergy. 2025 May 29. doi: 10.1111/cea.70089. Online ahead of print.

ABSTRACT

BACKGROUND: Gaining a better understanding of the risk factors for severe anaphylaxis represents a crucial challenge for physicians. This survey aimed to analyse cases of severe food anaphylaxis and assess potential risk factors for severity.

METHODS: We retrospectively analysed food anaphylaxis cases recorded by the French-speaking Allergy-Vigilance Network (2002-2021) and compared the main characteristics of grade 3 (Ring classification) and grade 4 cases using univariate and multivariate statistical analyses.

RESULTS: Of the 2621 food anaphylaxis cases reported, 731 (27.9%) were considered severe (grade 3, n = 687 [94%] and grade 4, n = 44 [6%]; 19 deaths). Overall, 56.1% of cases were adults (mean age: 28.3 years) and 53.7% were male. The most frequent triggers were peanut (13.9%), wheat (9.4%), cashew (5.8%), shrimp (5.3%), and cow's milk (4.6%). More grade 4 anaphylaxis cases occurred in children than in adults (26 vs. 18; p = 0.01). In univariate analysis, individuals with grade 4 anaphylaxis were more likely to have a history of allergy to the culprit food (71.1% vs. 42.1%; p < 0.001), asthma diagnosis (59.5% vs. 30.4%; p < 0.001), and peanut as the culprit food (34.1% vs. 12.6%; p < 0.001). In multivariate analysis, factors predictive of grade 4 anaphylaxis were asthma diagnosis (OR [95% CI]: 3.41 [1.56-7.44]; p = 0.002) and peanut as the culprit trigger (OR [95% CI]: 3.46 [1.28-9.34]; p = 0.014).

CONCLUSIONS: Our data highlight the risk factors for severe food anaphylaxis, notably a history of asthma and peanut as the culprit food. These individuals should benefit from personalised management strategies.

PMID:40441889 | DOI:10.1111/cea.70089

BMC Med Educ. 2025 May 29;25(1):801. doi: 10.1186/s12909-025-07396-0.

ABSTRACT

BACKGROUND: Artificial intelligence (AI) has gained significant attention and progress in various scientific fields, especially medicine. Since its introduction in the 1950s, AI has advanced remarkably, supporting innovations like diagnostic tools and healthcare technologies. Despite these developments, challenges such as ethical concerns and limited integration in regions like Syria emphasize the importance of increasing awareness and conducting more targeted studies.

METHODS: A cross-sectional study was conducted to evaluate medical students' preparedness and readiness to use AI technologies in the medical field using the Medical Artificial Intelligence Readiness Scale for Medical Students (MAIRS_MS). The scale comprises 22 items divided into 4 domains: ethics, vision, ability, and cognition, with responses rated on a five-point Likert scale, higher scores indicate greater readiness. Data were collected through electronic and paper questionnaires distributed over a period of 20 days.

RESULTS: The study included 564 medical students from various Syrian universities, of whom 77.8% demonstrated awareness of AI in the medical field. Significant differences in AI awareness were observed based on academic GPA (p = 0.035) and income level (p = 0.016), with higher awareness among students with higher GPA and income levels. Statistically significant differences were found between students aware of AI and those unaware, as well as between students with experience using AI and those without, across all domains of readiness, including cognition (t = -10.319, p < 0.001), ability (t = -11.519, p < 0.001), vision (t = -6.387, p < 0.001), ethics (t = -7.821, p < 0.001), and the overall readiness score (t = -11.354, p < 0.001).

CONCLUSION: Integrating AI into medical education is essential for advancing healthcare in developing countries like Syria. Providing incentives and fostering a culture of continuous learning will equip medical students to leverage AI's benefits while mitigating its drawbacks.

PMID:40442679 | DOI:10.1186/s12909-025-07396-0

BMC Public Health. 2025 May 29;25(1):1977. doi: 10.1186/s12889-025-23156-7.

ABSTRACT

BACKGROUND: Environmental factors have been identified as significant risk factors for scrub typhus. However, the impact of inorganic compounds such as greenhouse gases and air pollutants on the incidence of scrub typhus has not been evaluated.

METHODS: Our study investigated the correlation between greenhouse gases, air pollutants from the global atmospheric emissions database (2005-2018), and reported cases of scrub typhus from the Public Health Science Data Center. First, an early warning method was applied to estimate the epidemic threshold and the grading intensity threshold. Second, four statistical methods were used to assess the correlation and lag effects across different age groups and epidemic periods. Deep learning algorithms were employed to evaluate the predictive effect of environmental factors on the incidence of scrub typhus.

RESULTS: Using the Moving Epidemic Method (MEM) and Treed Distributed Lag Non-Linear Model (TDLNM), we found that the period from April to September is the epidemic season for scrub typhus in China. During this period, BC, CH4, NH3 and PM10 all reach key windows during their respective early warning lag periods. Interaction effects showed that increased CO exposure during the 0-2-month period led to an increased magnitude of the PM10 effect during the 3-7-month period. The Quantile-based G Computation (qgcomp) model revealed age-specific differences in susceptibility to environmental factors. In the Bayesian Kernel Machine Regression (BKMR) model, we identified NOx (RRmax (95% CI) = 103.14 (70.40, 135.87)) and NMVOC as the risk environmental factors for young adults, while CH4 (RRmax (95% CI) = 20.94 (9.26, 32.63)) was significantly associated with scrub typhus incidence in younger populations. For the elderly, N2O and NOx (RRmax (95% CI) = 30.23 (13.78, 46.68)) were identified as susceptibility factors for scrub typhus. The Weighted Quantile Sum (WQS) model revealed a significant risk effect of NOx on scrub typhus during periods of low risk, which are often overlooked (OR (95% CI) = 0.40 (0.23, 0.58)). During periods of medium to high risk, Convolutional Neural Networks (CNN) showed that environmental factors performed well in predicting the incidence of scrub typhus.

CONCLUSIONS: We found that most greenhouse gases and air pollutants increase the risk of contracting scrub typhus, mainly driven by CH4, NOx, and NMVOC. Among these, the primary high-level pollutants have long-term lag effects during the epidemic period. The correlation between environmental factors and scrub typhus incidence varies significantly across different age groups and risk periods. Among them, middle-aged and young individuals are more susceptible to the effects of exposure to mixed air pollutants. CNN algorithm can help develop a comprehensive early warning system for scrub typhus. These findings may have important implications for guiding effective public health interventions in the future. The primary interventions should focus on controlling greenhouse gas emissions and reducing air pollutants, which can, in turn, be used to support infectious disease monitoring systems through environmental monitoring. Moreover, given the cross-sectional approach of our study, these findings need to be confirmed through additional cohort studies.

PMID:40442614 | DOI:10.1186/s12889-025-23156-7

Abdom Radiol (NY). 2025 May 29. doi: 10.1007/s00261-025-04991-z. Online ahead of print.

ABSTRACT

OBJECTIVE: The diagnosis of liver fibrosis is usually based on histopathological examination of liver puncture specimens. Although liver puncture is accurate, it has invasive risks and high economic costs, which are difficult for some patients to accept. Therefore, this study uses deep learning technology to build a liver fibrosis diagnosis model to achieve non-invasive staging of liver fibrosis, avoid complications, and reduce costs.

METHODS: This study uses ultrasound examination to obtain pure liver parenchyma image section data. With the consent of the patient, combined with the results of percutaneous liver puncture biopsy, the degree of liver fibrosis indicated by ultrasound examination data is judged. The concept of Fibrosis Contrast Layer (FCL) is creatively introduced in our experimental method, which can help our model more keenly capture the significant differences in the characteristics of liver fibrosis of various grades. Finally, through label fusion (LF), the characteristics of liver specimens of the same fibrosis stage are abstracted and fused to improve the accuracy and stability of the diagnostic model.

RESULTS: Experimental evaluation demonstrated that our model achieved an accuracy of 85.6%, outperforming baseline models such as ResNet (81.9%), InceptionNet (80.9%), and VGG (80.8%). Even under a small-sample condition (30% data), the model maintained an accuracy of 84.8%, significantly outperforming traditional deep-learning models exhibiting sharp performance declines.

CONCLUSION: The training results show that in the whole sample data set and 30% small sample data set training environments, the FCLLF model's test performance results are better than those of traditional deep learning models such as VGG, ResNet, and InceptionNet. The performance of the FCLLF model is more stable, especially in the small sample data set environment. Our proposed FCLLF model effectively improves the accuracy and stability of liver fibrosis staging using non-invasive ultrasound imaging.

PMID:40442504 | DOI:10.1007/s00261-025-04991-z

Pediatr Radiol. 2025 May 29. doi: 10.1007/s00247-025-06266-7. Online ahead of print.

ABSTRACT

BACKGROUND: Cardiovascular magnetic resonance (CMR) cine imaging is the gold standard for assessing ventricular volumes and function. It typically requires two-dimensional (2D) bSSFP sequences and multiple breath-holds, which can be challenging for patients with limited breath-holding capacity. Three-dimensional (3D) cardiovascular magnetic resonance angiography (MRA) usually suffers from lengthy acquisition. Free-running 3D cine imaging with deep learning (DL) reconstruction offers a potential solution by acquiring both cine and angiography simultaneously.

OBJECTIVE: To evaluate the efficiency and accuracy of a ferumoxytol-enhanced 3D cine imaging MR sequence combined with DL reconstruction and Heart-NAV technology in patients with congenital heart disease.

MATERIALS AND METHODS: This Institutional Review Board approved this prospective study that compared (i) functional and volumetric measurements between 3 and 2D cine images; (ii) contrast-to-noise ratio (CNR) between deep-learning (DL) and compressed sensing (CS)-reconstructed 3D cine images; and (iii) cross-sectional area (CSA) measurements between DL-reconstructed 3D cine images and the clinical 3D MRA images acquired using the bSSFP sequence. Paired t-tests were used to compare group measurements, and Bland-Altman analysis assessed agreement in CSA and volumetric data.

RESULTS: Sixteen patients (seven males; median age 6 years) were recruited. 3D cine imaging showed slightly larger right ventricular (RV) volumes and lower RV ejection fraction (EF) compared to 2D cine, with a significant difference only in RV end-systolic volume (P = 0.02). Left ventricular (LV) volumes and EF were slightly higher, and LV mass was lower, without significant differences (P ≥ 0.05). DL-reconstructed 3D cine images showed significantly higher CNR in all pulmonary veins than CS-reconstructed 3D cine images (all P < 0.05).

CONCLUSION: Highly accelerated free-running 3D cine imaging with DL reconstruction shortens acquisition times and provides comparable volumetric measurements to 2D cine, and comparable CSA to clinical 3D MRA.

PMID:40442341 | DOI:10.1007/s00247-025-06266-7

Sci Rep. 2025 May 29;15(1):18783. doi: 10.1038/s41598-025-03778-y.

ABSTRACT

Accurate classification of midpalatal suture maturation stages is critical for orthodontic diagnosis, treatment planning, and the assessment of maxillary growth. Cone Beam Computed Tomography (CBCT) imaging offers detailed insights into this craniofacial structure but poses unique challenges for deep learning image recognition model design due to its high dimensionality, noise artifacts, and variability in image quality. To address these challenges, we propose a novel technique that highlights key image features through a simple filtering process to improve image clarity prior to analysis, thereby enhancing the learning process and better aligning with the distribution of the input data domain. Our preprocessing steps include region-of-interest extraction, followed by high-pass and Sobel filtering for emphasis of low-level features. The feature extraction integrates Convolutional Neural Networks (CNN) architectures, such as EfficientNet and ResNet18, alongside our novel Multi-Filter Convolutional Residual Attention Network (MFCRAN) enhanced with Discrete Cosine Transform (DCT) layers. Moreover, to better capture the inherent order within the data classes, we augment the supervised training process with a ranking loss by attending to the relationship within the label domain. Furthermore, to adhere to diagnostic constraints while training the model, we introduce a tailored data augmentation strategy to improve classification accuracy and robustness. In order to validate our method, we employed a k-fold cross-validation protocol on a private dataset comprising 618 CBCT images, annotated into five stages (A, B, C, D, and E) by expert evaluators. The experimental results demonstrate the effectiveness of our proposed approach, achieving the highest classification accuracy of 79.02%, significantly outperforming competing architectures, which achieved accuracies ranging from 71.87 to 78.05%. This work introduces a novel and fully automated framework for midpalatal suture maturation classification, marking a substantial advancement in orthodontic diagnostics and treatment planning.

PMID:40442312 | DOI:10.1038/s41598-025-03778-y

Sci Rep. 2025 May 29;15(1):18888. doi: 10.1038/s41598-025-03254-7.

ABSTRACT

This study aimed to determine whether trigeminal neuralgia can be diagnosed using convolutional neural networks (CNNs) based on plain X-ray skull images. A labeled dataset of 166 skull images from patients aged over 16 years with trigeminal neuralgia was compiled, alongside a control dataset of 498 images from patients with unruptured intracranial aneurysms. The images were randomly partitioned into training, validation, and test datasets in a 6:2:2 ratio. Classifier performance was assessed using accuracy and the area under the receiver operating characteristic (AUROC) curve. Gradient-weighted class activation mapping was applied to identify regions of interest. External validation was conducted using a dataset obtained from another institution. The CNN achieved an overall accuracy of 87.2%, with sensitivity and specificity of 0.72 and 0.91, respectively, and an AUROC of 0.90 on the test dataset. In most cases, the sphenoid body and clivus were identified as key areas for predicting trigeminal neuralgia. Validation on the external dataset yielded an accuracy of 71.0%, highlighting the potential of deep learning-based models in distinguishing X-ray skull images of patients with trigeminal neuralgia from those of control individuals. Our preliminary results suggest that plain x-ray can be potentially used as an adjunct to conventional MRI, ideally with CISS sequences, to aid in the clinical diagnosis of TN. Further refinement could establish this approach as a valuable screening tool.

PMID:40442191 | DOI:10.1038/s41598-025-03254-7

Sci Rep. 2025 May 29;15(1):18807. doi: 10.1038/s41598-025-00628-9.

ABSTRACT

Vegetation fires are most common in Southeast Asian (SEA) countries, causing biodiversity loss, habitat destruction, and air pollution. Accurately predicting fire occurrences in SEA remains challenging due to its complex spatiotemporal dynamics. Improved fire predictions enable timely interventions, helping to control and mitigate fires. In this study, we utilize Visible Infrared Imaging Radiometer Suite (VIIRS) satellite-derived fire data alongside six machine learning (ML) and deep learning (DL) models, Simple Persistence, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-Long Short-Term Memory (CNN-LSTM), and Convolutional Long Short-Term Memory (ConvLSTM) to determine the most effective fire prediction model. We evaluated model performance using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2 (coefficient of determination). Our results indicate that the CNN performs best in regions with strong spatial dependencies, such as Brunei, Indonesia, Malaysia, the Philippines, Timor-Leste, and Thailand. Conversely, the ConvLSTM excels in countries with complex spatiotemporal dynamics, like Laos, Myanmar, and Vietnam. The CNN-LSTM hybrid model also performed well in Cambodia, suggesting a need for a balanced approach in areas requiring both spatial and temporal feature extraction. Furthermore, simpler models, such as Simple Persistence and MLP, showed limitations in capturing dynamic patterns and temporal dependencies. Our findings highlight the importance of evaluating various ML and DL models before integrating them into any decision support systems (DSS) for fire management studies. By tailoring models to specific regional fire data, prediction accuracy and responsiveness can be enhanced, ultimately improving fire risk management in Southeast Asia and beyond.

PMID:40442135 | DOI:10.1038/s41598-025-00628-9

Nat Commun. 2025 May 29;16(1):4994. doi: 10.1038/s41467-025-60333-z.

ABSTRACT

Recent advancements in single-cell technologies have enabled comprehensive characterization of cellular states through transcriptomic, epigenomic, and proteomic profiling at single-cell resolution. These technologies have significantly deepened our understanding of cell functions and disease mechanisms from various omics perspectives. As these technologies evolve rapidly and data resources expand, there is a growing need for computational methods that can integrate information from different modalities to facilitate joint analysis of single-cell multi-omics data. However, integrating single-cell omics datasets presents unique challenges due to varied feature correlations and technology-specific limitations. To address these challenges, we introduce scMODAL, a deep learning framework tailored for single-cell multi-omics data alignment using feature links. scMODAL integrates datasets with limited known positively correlated features, leveraging neural networks and generative adversarial networks to align cell embeddings and preserve feature topology. Our experiments demonstrate scMODAL's effectiveness in removing unwanted variation, preserving biological information, and accurately identifying cell subpopulations across diverse datasets. scMODAL not only advances integration tasks but also supports downstream analyses such as feature imputation and feature relationship inference, offering a robust solution for advancing single-cell multi-omics research.

PMID:40442129 | DOI:10.1038/s41467-025-60333-z

J Chem Inf Model. 2025 May 29. doi: 10.1021/acs.jcim.5c00575. Online ahead of print.

ABSTRACT

Dysfunctions of the dopamine D2 and D3 receptors (D2 and D3) are implicated in neuropsychiatric conditions such as Parkinson's disease, schizophrenia, and substance use disorders (SUDs). Evidence indicates that D3-selective ligands can effectively modulate reward pathways, offering potential in treating SUDs with reduced side effects. However, the high homology between D2 and D3 presents challenges in developing subtype-selective ligands, crucial for elucidating receptor-specific functions and developing targeted therapeutics. Here, to facilitate selective ligand discovery, we leveraged ligand-based quantitative structure-activity relationship (QSAR) modeling approaches to predict binding affinity at D2 and D3, as well as ligand selectivity for D3. We first queried training data from the ChEMBL database and performed a systematic curation process to enhance the data quality. We then developed QSAR models using eXtreme Gradient Boosting, random forest, and deep neural network (DNN) algorithms, with DNN benefiting from a novel hyperparameter optimization protocol. All models exhibited strong predictive performance, with DNN-based models slightly but consistently outperforming the tree-based models. Integrating predictions from all algorithms into a consensus metric further improved the accuracy and robustness. Notably, our selectivity models outperformed the affinity models, likely due to noise cancellation achieved by subtracting the binding affinities of the two receptors. The Shapley Additive explanations analysis revealed key pharmacophoric and physicochemical features critical for receptor affinity and selectivity, while molecular docking of representative D3-selective compounds highlighted the structural basis of D3 selectivity. These findings provide a robust framework for modeling QSARs at D2 and D3, advancing the rational design of targeted therapeutics for these receptors.

PMID:40442044 | DOI:10.1021/acs.jcim.5c00575

Obstet Gynecol Sci. 2025 May 29. doi: 10.5468/ogs.25041. Online ahead of print.

ABSTRACT

Artificial intelligence (AI) and machine learning (ML) are transforming cervical cancer research and offering advancements in diagnosis, prognosis, screening, and treatment. This review explores ML applications with particular emphasis on prediction models. A comprehensive literature search identified studies using ML for survival prediction, risk assessment, and treatment optimization. ML-driven prognostic models integrate clinical, histopathological, and genomic data to improve survival prediction and patient stratification. Screening methods, including deep-learning-based cytology analysis and HPV detection, enhance accuracy and efficiency. ML-driven imaging techniques facilitate early and precise cancer diagnosis, whereas risk prediction models assess susceptibility based on demographic and genetic factors. AI also optimizes treatment planning by predicting therapeutic responses and guiding personalized interventions. Despite significant progress, challenges remain regarding data availability, model interpretability, and clinical implementation. Standardized datasets, external validation, and cross-disciplinary collaborations are crucial for implementing ML innovations in clinical settings. Subsequent investigations should prioritize joint initiatives among data scientists, healthcare providers, and health authorities to translate AI innovations into real-world applications and to enhance the impact of ML on cervical cancer care. By synthesizing recent developments, this review highlights the potential of ML to improve clinical outcomes and shaping the future of cervical cancer management.

PMID:40441737 | DOI:10.5468/ogs.25041

BMC Pulm Med. 2025 May 29;25(1):271. doi: 10.1186/s12890-025-03736-4.

ABSTRACT

BACKGROUND: Patients with anti-neutrophil cytoplasmic antibody (ANCA)-positive interstitial lung disease (ILD) but without evidence of systemic vasculitis have been reported in studies and are classified as isolated ANCA-positive idiopathic interstitial pneumonia (IIP). However, the clinical significance of ANCA, particularly myeloperoxidase (MPO) -ANCA in IIP remains poorly understood. This study aims to investigate the differences between ANCA-positive and ANCA-negative IIP patients and further explore the impact of MPO-ANCA on clinical manifestations and prognostic outcomes.

METHODS: We reviewed 408 ILD patients with available ANCA results from January 2012 to September 2021. 61 patients diagnosed with microscopic polyangiitis-associated ILD were not included in the analysis. A comparative analysis was performed between 61 isolated ANCA-positive IIP patients (ANCA-IIP group) and 286 ANCA-negative IIP patients (IIP group). We further conducted subgroup analyses based on the status of MPO-ANCA.

RESULTS: Baseline clinical characteristics, pulmonary function tests, radiological features and all-cause mortality were similar between ANCA-IIP and IIP groups. When comparing the MPO-ANCA-IIP group with the IIP group and the non-MPO-ANCA-IIP group separately, a higher proportion of fibrotic features was observed on imaging (P = 0.004 vs IIP group; P = 0.031 vs non-MPO-ANCA-IIP group). After one year of treatment, the MPO-ANCA-IIP group showed a significantly greater decline in pulmonary function parameters compared to both the IIP group and the non-MPO-ANCA-IIP group. The frequency of pulmonary function decline was significantly higher in the MPO-ANCA-IIP group compared to the non-MPO-ANCA-IIP group (P = 0.026). Additionally, MPO-ANCA was not found to be statistically associated with mortality among patients with IIP.

CONCLUSION: ANCA-IIP patients had similar clinical characteristics and prognoses with IIP patients. MPO-ANCA-IIP patients had more prominent fibrosis on imaging and a greater decline in pulmonary function following treatment. Special attention should be paid to MPO-ANCA positivity during the diagnosis and treatment of IIP patients.

TRIAL REGISTRATION: ClinicalTrials.gov: NCT04413149, May 2020.

PMID:40442650 | DOI:10.1186/s12890-025-03736-4

Medicine (Baltimore). 2025 May 30;104(22):e42671. doi: 10.1097/MD.0000000000042671.

ABSTRACT

It has been demonstrated that bactericidal/permeability-increasing-fold-containing family B member 1 (BPIFB1) is highly expressed in idiopathic pulmonary fibrosis (IPF) and is linked to a dismal prognosis; nevertheless, its molecular role and connection to lung function in IPF are still unknown. The Gene Expression Omnibus database provided the data. Functional enrichment analyses, Kaplan-Meier, Cox regression, and the Pearson correlation coefficient were applied. Studies and single-cell ribonucleic acid sequencing research verified the impact of BPIFB1 on IPF. BPIFB1 was highly expressed in differentiating ciliated cells and MUC5B + cells, and single-cell sequencing and transcriptome analysis revealed that it was up-regulated in the lungs of IPF; however, blood BPIFB1 expression was similar in the control group and IPF. In addition, we identified 24 genes that interact with BPIFB1 and the top 5 transcription factors that target these genes as reported by the hTFtarget database. Clinical samples show that in the IPF, BPIFB1 is inversely correlated with lung function. According to bioinformatics analysis, elevated levels of BPIFB1 expression might be correlated with lung fibroblast differentiation and collagen synthesis. BPIFB1 may be a useful indicator of the prognosis and severity of the IPF. BPIFB1 may be associated with lung fibroblast differentiation and collagen production.

PMID:40441200 | DOI:10.1097/MD.0000000000042671

BMC Pregnancy Childbirth. 2025 May 29;25(1):629. doi: 10.1186/s12884-025-07752-4.

ABSTRACT

BACKGROUND: Cesarean scar pregnancy (CSP) is a life-threatening condition with a rising incidence in China. The pathogenesis of CSP remains poorly understood, partly due to the limited availability of comprehensive datasets constrained by spatiotemporal factors.

OBJECTIVE: This study aimed to explore key regulatory molecules and mechanisms involved in CSP through a multi-omics approach.

METHODS: Proteomic analysis was performed on decidual and villous tissues from clinical patients (n = 6, including 3 CSP cases and 3 controls). Gene expression datasets (n = 9) were obtained from the GEO and SRA databases. Bioinformatics analyses were conducted using DAVID, Metascape, and STRING, with transcription factor prediction performed via the JASPAR database. Data analysis was conducted using SPSS 27, with a significance threshold set at P < 0.05.

RESULTS: CSP shared differentially expressed genes (DEGs) with cesarean delivery (CD) and embryo implantation (EI). Enrichment analysis revealed that biological processes and KEGG pathways related to adhesion, with Integrin Subunit Beta 3 (ITGB3), Integrin Subunit Alpha 2b (ITGA2B), and Vitronectin (VTN) playing significant roles. ITGB3 expression was significantly downregulated following CD compared to spontaneous delivery, followed by upregulation in subsequent pregnancies. The transcription factor GATA4 was identified as a key regulator of ITGB3, potentially contributing to CSP pathogenesis.

CONCLUSION: Our findings suggest that CSP development is closely associated with CD and EI, with ITGB3 and its regulatory network playing a crucial role. GATA4-mediated regulation of ITGB3 may represent an important molecular mechanism contributing to CSP formation.

PMID:40442657 | DOI:10.1186/s12884-025-07752-4

J Assist Reprod Genet. 2025 May 29. doi: 10.1007/s10815-025-03518-1. Online ahead of print.

ABSTRACT

The intersection of aging and reproductive decline presents a significant challenge in human health, with fertility rates decreasing sharply in later life for both sexes. This review delves into the intricate relationship between germ cells, the fundamental units of reproduction, and their surrounding microenvironment, known as the niche. Emphasizing that reproductive longevity is not solely determined by the intrinsic properties of germ cells, but rather by the complex interplay with their niche, a dynamic system that changes with age. We highlight evidence from model organisms like Drosophila and C. elegans demonstrating how age-related changes in niche signaling impact germ cell function. A systems biology approach, integrating multi-omics data (genetics, epigenetics, cellular behavior), is crucial to fully understanding this complex interaction. Specifically, we discuss the role of epigenetic modifications, such as DNA methylation and histone acetylation, in modulating niche-germ cell communication. This approach offers a comprehensive view of the aging reproductive system and opens up avenues for therapeutic interventions aimed at modulating the niche and potentially extending reproductive lifespan. Future research focused on unraveling the specific molecular mechanisms underlying the niche-germ cell interaction will be pivotal in developing strategies to combat age-related reproductive decline.

PMID:40442411 | DOI:10.1007/s10815-025-03518-1

J Thromb Haemost. 2025 May 27:S1538-7836(25)00339-3. doi: 10.1016/j.jtha.2025.05.020. Online ahead of print.

ABSTRACT

BACKGROUND: Increased body mass index (BMI) is associated with an increased risk of venous thrombosis. However, recent data has highlighted that visceral adipose tissue (VAT) volume may be a better marker of cardiometabolic risk.

OBJECTIVES: To investigate the relationship between VAT volume and VTE risk and explore whether increased VAT volumes is associated with VTE risk.

METHODS: We performed a cross-sectional study utilising MRI imaging data from the UK Biobank (UKB). The association between VTE incidence and VAT measured by MRI imaging from 39,144 UKB patients was analysed by ridge regression accounting for covariates including age and sex.

RESULTS: VAT volume, as measured by MRI, was demonstrated to be associated with an increased risk of VTE [OR 4.020 (95%CI: 3.752 - 4.287) per dm3]. Moreover, we observed a significant association of VAT volume with VTE risk in both those who were overweight [VAT high; OR 1.589 (95%CI: 1.317 - 1.860), VAT medium; OR 1.303 (95%CI:1.054 -1.552)] and obese [VAT high; OR 3.222 (95%CI: 2.971 - 3.473)]. Notably, the strongest association of VAT was observed in those with obesity.

CONCLUSION: These data demonstrate for the first time that VAT volume is associated with an increased risk of VTE and importantly has a stronger association with VTE risk as compared to BMI.

PMID:40441356 | DOI:10.1016/j.jtha.2025.05.020