Brain structural connectomic topology predicts medication response in youth with bipolar disorder: A randomized clinical trial.

Background: Response to pharmacotherapy varies considerably among youths with bipolar disorder (BD) and is poorly predicted by clinical or demographic features. It can take several weeks to determine whether medication for BD is clinically effective. Although neuroimaging biomarkers are promising predictors, few studies examined the predictive value of the brain connectomic topology.

Machine learning for predicting in-hospital mortality in elderly patients with heart failure combined with hypertension: a multicenter retrospective study.

Background: Heart failure combined with hypertension is a major contributor for elderly patients (≥ 65 years) to in-hospital mortality. However, there are very few models to predict in-hospital mortality in such elderly patients. We aimed to develop and test an individualized machine learning model to assess risk factors and predict in-hospital mortality in in these patients.

The identification and prediction of atrial fibrillation in coronary artery disease patients: a multicentre retrospective study based on Bayesian network.

Background: Atrial fibrillation (AF) coexisting with coronary artery disease (CAD) remains a prevailing issue that often results in poor short- and long-term patient outcomes. Screening has been proposed as a method to increase AF detection rates and reduce the incidence of poor prognosis through early intervention. Nevertheless, due to the cost implications and uncertainty over the benefits of a systematic screening programme, the International Task Force currently recommends against screening.

Predicting the Risk of Loneliness in Children and Adolescents: A Machine Learning Study.

Background: Loneliness is increasingly emerging as a significant public health problem in children and adolescents. Predicting loneliness and finding its risk factors in children and adolescents is lacking and necessary, and would greatly help determine intervention actions.

Objective: This study aimed to find appropriate machine learning techniques to predict loneliness and its associated risk factors among schoolchildren.

Perturbed saliva microbiome is gender-specific in patients with oral lichen planus.

Objective: To understand the gender characteristics of oral lichen planus (OLP) by identifying the gender-specific salivary microbiome and its potential biomarkers.

Methods: A gender-based study was undertaken, commencing with the collection of saliva samples, followed by 16S rRNA gene sequencing, to explore the differences in the composition of saliva microbiome in OLP patients (40 males and 56 females) and healthy controls (40 males and 56 females), respectively.

Results: Both male and female OLP patients had significant differences in saliva microbiome composition from healthy controls, especially in female patients.

Drug repurposing to tackle parainfluenza 3 based on multi-similarities and network proximity analysis.

Given that there is currently no clinically approved drug or vaccine for parainfluenza 3 (PIV3), we applied a drug repurposing method based on disease similarity and chemical similarity to screen 2,585 clinically approved chemical drugs using PIV3 potential drugs BCX-2798 and zanamivir as our controls. Twelve candidate drugs were obtained after being screened with good disease similarity and chemical similarity ( > 0.50, > 0.

Assessment of Eye Care Apps for Children and Adolescents Based on the Mobile App Rating Scale: Content Analysis and Quality Assessment.

Background: In China, the current situation of myopia among children and adolescents is very serious. Prevention and control of myopia are inhibited by the lack of medical resources and the low awareness about eye care. Nevertheless, mobile apps provide an effective means to solve these problems.

Radiomics Nomograms Based on Multi-sequence MRI for Identifying Cognitive Impairment and Predicting Cognitive Progression in Relapsing-Remitting Multiple Sclerosis.

Rationale And Objectives: To build radiomics nomograms based on multi-sequence MRI to facilitate the identification of cognitive impairment (CI) and prediction of cognitive progression (CP) in patients with relapsing-remitting multiple sclerosis (RRMS).

Materials And Methods: We retrospectively included two RRMS cohorts with multi-sequence MRI and Symbol Digit Modalities Test (SDMT) data: dataset1 (n = 149, for training and validation) and dataset2 (n = 29, for external validation). 80 patients of dataset1 had a 2-year follow-up SDMT.

Screening chondrocyte necroptosis-related genes in the diagnosis and treatment of osteoarthritis.

Background: Osteoarthritis (OA) is the most common form of joint diseases, with hallmark of cartilage degeneration. Recent studies have shown that the pathogenesis of OA is associated with chondrocyte necroptosis.

Methods: In this study, we used single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing data to analyze necroptosis regulation in OA chondrocytes.

A Real-World Study on the Short-Term Efficacy of Amlodipine in Treating Hypertension Among Inpatients.

Purpose: Hospitalized hypertensive patients rely on blood pressure medication, yet there is limited research on the sole use of amlodipine, despite its proven efficacy in protecting target organs and reducing mortality. This study aims to identify key indicators influencing the efficacy of amlodipine, thereby enhancing treatment outcomes.

Patients And Methods: In this multicenter retrospective study, 870 hospitalized patients with primary hypertension exclusively received amlodipine for the first 5 days after admission, and their medical records contained comprehensive blood pressure records.

Validity of self-assessment pubertal Tanner stages by realistic color images and Pubertal Development Scale in a longitudinal cohort study.

Introduction: To date, the reliability of pubertal development self-assessment tools is questioned, and very few studies have explored the comparison between these tools in longitudinal studies. Hence, this study aimed to examine the reliability of pubertal development self-assessment using realistic color images (RCIs) and the Pubertal Development Scale (PDS) in a longitudinal cohort study.

Methods: Our longitudinal study recruited 1,429 participants (695 boys and 734 girls), aged 5.

A preliminary prediction model of pediatric Mycoplasma pneumoniae pneumonia based on routine blood parameters by using machine learning method.

Background: The prevalence and severity of pediatric Mycoplasma pneumoniae pneumonia (MPP) poses a significant threat to the health and lives of children. In this study, we aim to systematically evaluate the value of routine blood parameters in predicting MPP and develop a robust and generalizable ensemble artificial intelligence (AI) model to assist in identifying patients with MPP.

Methods: We collected 27 features, including routine blood parameters and hs-CRP levels, from patients admitted to The Affiliated Dazu's Hospital of Chongqing Medical University with or without MPP between January, 2023 and January, 2024.

Enhanced choroid plexus segmentation with 3D UX-Net and its association with disease progression in multiple sclerosis.

Background: The choroid plexus (CP) is suggested to be closely associated with the neuroinflammation of multiple sclerosis (MS). Segmentation based on deep learning (DL) could facilitate rapid and reproducible volume assessment of the CP, which is crucial for elucidating its role in MS.

Purpose: To develop a reliable DL model for the automatic segmentation of CP, and further validate its clinical significance in MS.

Toward a Medication Information Literacy Indicator System for Older Adults: A Delphi Study.

Background: The safety of medication use among older adults is a growing concern, given the aging population. Despite widespread attention, the exploration of medication literacy in older adults, particularly from the perspective of information literacy, is in its nascent stages.

Methods: This study utilized the existing literature to define medication information literacy (MIL) as a theoretical framework.

The safety and efficacy of myomectomy in the treatment of recurrent uterine fibroids after HIFU.

Objective: To evaluate the safety and efficacy of myomectomy for recurrent uterine fibroids (UFs) after high-intensity focused ultrasound (HIFU) ablation.

Methods: This was a retrospective study. Patients who underwent abdominal myomectomy (AM) and laparoscopic myomectomy (LM) from January 2018 to December 2021 at the Three Gorges Hospital of Chongqing University were included.

The Nucleolar Protein C1orf131 Is a Novel Gene Involved in the Progression of Lung Adenocarcinoma Cells through the AKT Signalling Pathway.

Lung adenocarcinoma (LUAD) is the most widespread cancer in the world, and its development is associated with complex biological mechanisms that are poorly understood. Here, we revealed a marked upregulation in the mRNA level of C1orf131 in LUAD samples compared to non-tumor tissue samples in The Cancer Genome Atlas (TCGA). Depletion of C1orf131 suppressed cell proliferation and growth, whereas it stimulated apoptosis in LUAD cells.

A dynamic nomogram for predicting in-hospital major adverse cardiovascular and cerebrovascular events in patients with both coronary artery disease and atrial fibrillation: a multicenter retrospective study.

Background And Objective: Patients with both coronary artery disease (CAD) and atrial fibrillation (AF) are at a high risk of major adverse cardiovascular and cerebrovascular events (MACCE) during hospitalization. Accurate prediction of MACCE can help identify high-risk patients and guide treatment decisions. This study was to elaborate and validate a dynamic nomogram for predicting the occurrence of MACCE during hospitalization in Patients with CAD combined with AF.

Cross-Modality Reference and Feature Mutual-Projection for 3D Brain MRI Image Super-Resolution.

High-resolution (HR) magnetic resonance imaging (MRI) can reveal rich anatomical structures for clinical diagnoses. However, due to hardware and signal-to-noise ratio limitations, MRI images are often collected with low resolution (LR) which is not conducive to diagnosing and analyzing clinical diseases. Recently, deep learning super-resolution (SR) methods have demonstrated great potential in enhancing the resolution of MRI images; however, most of them did not take the cross-modality and internal priors of MR seriously, which hinders the SR performance.

Cross-sectional and longitudinal evaluation of white matter microstructure damage and cognitive correlations by automated fibre quantification in relapsing-remitting multiple sclerosis patients.

The purpose of this study was to characterize whole-brain white matter (WM) fibre tracts by automated fibre quantification (AFQ), capture subtle changes cross-sectionally and longitudinally in relapsing-remitting multiple sclerosis (RRMS) patients and explore correlations between these changes and cognitive performance A total of 114 RRMS patients and 71 healthy controls (HCs) were enrolled and follow-up investigations were conducted on 46 RRMS patients. Fractional anisotropy (FA), mean diffusion (MD), axial diffusivity (AD), and radial diffusivity (RD) at each node along the 20 WM fibre tracts identified by AFQ were investigated cross-sectionally and longitudinally in entire and pointwise manners. Partial correlation analyses were performed between the abnormal metrics and cognitive performance.