Publications by authors named "J S Jia"

Background: The COVID-19 pandemic has had a profound impact on adolescent mental health, particularly in China. However, there is a lack of research examining the trends in depressive symptom levels among Chinese adolescents before and after the pandemic. This study aims to investigate the changes in depressive symptom levels among Chinese adolescents pre- and post-pandemic and to identify the factors influencing these changes.

View Article and Find Full Text PDF

Rationale: Exhaled breath can be used for early warning of disease, with organic nitrogen compounds, including triethylamine (TEA), being linked to various medical conditions. Surface ionization ion mobility spectrometry (SI-IMS) facilitates the direct detection of TEA in exhaled breath. However, the presence of multiple ionization products of TEA poses challenges for both quantitative and qualitative analyses.

View Article and Find Full Text PDF

Single-atom catalysts (SACs) with nonplanar configurations possess unique capabilities for tailoring the oxygen reduction reaction (ORR) catalytic performance compared with the ones with planar configurations, owing to the additional orbital rearrangement arising from the asymmetric coordination atoms. However, the systematic investigation of these nonplanar SACs has long been hindered by the difficulty in screening feasible nonplanar configurations and precisely controlling the coordination structures. Herein, we demonstrate a combined high-throughput screening and experimental verification of nonplanar SACs (ppy-MN3) for highly active and selective 2e- ORR electrocatalysis.

View Article and Find Full Text PDF

To determine the distributions of serum HBsAg level in treatment-naive or treatment-experienced chronic hepatitis B (CHB) patients. Based on the China Registry of Hepatitis B (CR-HepB), a nationwide hospital-based electronic platform, treatment-naive or treatment-experienced CHB patients who receive nucleos(t)ide analog (NA) therapy were enrolled in our study. We collected patients' clinical characteristics, including demographic, virological and biochemical data.

View Article and Find Full Text PDF

This study aimed to explore a deep learning radiomics (DLR) model based on grayscale ultrasound images to assist radiologists in distinguishing between benign breast lesions (BBL) and malignant breast lesions (MBL). A total of 382 patients with breast lesions were included, comprising 183 benign lesions and 199 malignant lesions that were collected and confirmed through clinical pathology or biopsy. The enrolled patients were randomly allocated into two groups: a training cohort and an independent test cohort, maintaining a ratio of 7:3.

View Article and Find Full Text PDF

A PHP Error was encountered

Severity: Warning

Message: A non-numeric value encountered

Filename: controllers/Author.php

Line Number: 219

Backtrace:

File: /var/www/html/application/controllers/Author.php
Line: 219
Function: _error_handler

File: /var/www/html/index.php
Line: 316
Function: require_once

A PHP Error was encountered

Severity: Warning

Message: A non-numeric value encountered

Filename: libraries/Pagination.php

Line Number: 413

Backtrace:

File: /var/www/html/application/controllers/Author.php
Line: 274
Function: create_links

File: /var/www/html/index.php
Line: 316
Function: require_once