Diagnostic ability for common skin diseases among general practitioners working in community health service centers in Shanghai, China: a cross-sectional study.

Objective: Primary care general practitioners (GPs) play a crucial role in common skin diseases (CSDs) diagnosis and treatment for community residents. This study investigates their clinical diagnostic ability for CSDs and influencing factors among GPs in Shanghai.

Methods: In 2023, we recruited 5745 GPs in Shanghai, and online survey was conducted among 5745 GPs with written informed consents.

Honokiol enhances the sensitivity of cetuximab in KRAS mutant colorectal cancer through destroying SNX3-retromer complex.

: the proto-oncogene is frequently mutated in colorectal cancer (CRC), leading to inherent resistance against monoclonal antibodies targeting the epidermal growth factor receptor (EGFR), such as cetuximab. Therefore, addressing the primary resistance and expanding the indications for target therapy have become critical challenges. : the screening of a natural product library against KRAS mutant CRC cells was conducted, leading to the discovery of a small molecule compound that sensitive to the KRAS mutation site.

Natural products for enhancing the sensitivity or decreasing the adverse effects of anticancer drugs through regulating the redox balance.

Redox imbalance is reported to play a pivotal role in tumorigenesis, cancer development, and drug resistance. Severe oxidative damage is a general consequence of cancer cell responses to treatment and may cause cancer cell death or severe adverse effects. To maintain their longevity, cancer cells can rescue redox balance and enter a state of resistance to anticancer drugs.

β-Elemene Reverses Gefitinib Resistance in NSCLC Cells by Inhibiting lncRNA H19-Mediated Autophagy.

Lung cancer is a leading cause of mortality worldwide, especially among Asian patients with non-small cell lung cancer (NSCLC) who have epidermal growth factor receptor (EGFR) mutations. Initially, first-generation EGFR tyrosine kinase inhibitors (TKIs) are commonly administered as the primary treatment option; however, encountering resistance to these medications poses a significant obstacle. Hence, it has become crucial to address initial resistance and ensure continued effectiveness.

The Underlying Structure of Preventive Behaviors and Related Factors During the COVID-19 Pandemic: A Comprehensive Network Analysis.

Background: Various strategies against COVID-19 have been adopted in different countries, with vaccination and mask-wearing being widely used as self-preventive interventions. However, the underlying structure of these behaviors and related factors remain unclear.

Purpose: In this study, we aimed to explore the network structure of preventive behaviors during the COVID-19 pandemic and their underlying factors, incorporating age and sex in the network.

Changes of Serum C-Reactive Protein Level in Patients With Depressive Disorders After Treatment With Agomelatine Combined With Aerobic Exercise and Its Significance.

Objective: Depressive disorders constitute a series of debilitating diseases. This study investigated the therapeutic effect of agomelatine (AG) combined with aerobic exercise (AE) on patients with moderate-severe depression (MSD) and the changes of the serum C-reactive protein (CRP) level in patients after treatment as well as its significance.

Methods: A total of 178 MSD patients were randomly assigned to the AG group (N = 90) and AG + AE group (N = 88).

More factors, better understanding: model verification and construct validity study on the community of inquiry in MOOC.

This study aimed to verify the applicability of the community of inquiry (CoI) survey instrument in MOOC involving 1,186 college students from 11 different disciplines in China. Exploratory factor analysis was used to explore potential factor structure models, and confirmatory factor analysis was utilized to verify the four-factor structure obtained from exploratory factor analysis. The original three- and new six-factor structure models were also included in the study.

An exploration on COVID-19 vaccination motivation patterns from the perspective of the Chaxu culture in metropolis of China: A multi-center study.

Objectives: Chaxugeju is a very special Chinese culture following a self-centered and outward expanding social network, which might be a significant culture factor for vaccination behavior. This study aimed to identify the motivation pattern in China, and paid special focus on socio-economic status (SES), region, and migration.

Methods: We used a latent class analysis, with a sample of 12,432 participants collected in China from April to June, to identify the COVID-19 vaccination motivation patterns.

Concerns about and stimuli of COVID-19 vaccination hesitancy among diverse occupational groups in metropolitan areas of China: a cross-sectional study.

Objectives: In this study, we aimed to identify concerns and stimuli regarding COVID-19 vaccination acceptance and to compare the findings by occupation.

Methods: We conducted a cross-sectional study of individuals vaccinated against COVID-19 between 1 April and 30 June 2021 in four metropolitan areas of China. A total of 20 863 participants completed questionnaires, 20 767 of which were eligible for analysis.

Effect of teacher autonomy support on the online self-regulated learning of students during COVID-19 in China: The chain mediating effect of parental autonomy support and students' self-efficacy.

Background: Self-regulated learning (SRL) ability is the key determinant of the success of full-time online learning. Thus, exploring the influencing factors of SRL and their influencing mechanisms is necessary to improve this ability among K-12 students.

Objectives: The purpose of this study was to investigate the influence mechanism of teacher autonomy support on students' online SRL by examining the structural relationship among teacher autonomy support, parental autonomy support, students' self-efficacy, and students' online SRL.

Discriminant Subspace Low-Rank Representation Algorithm for Electroencephalography-Based Alzheimer's Disease Recognition.

Alzheimer's disease (AD) is a chronic progressive neurodegenerative disease that often occurs in the elderly. Electroencephalography (EEG) signals have a strong correlation with neuropsychological test results and brain structural changes. It has become an effective aid in the early diagnosis of AD by exploiting abnormal brain activity.

Accelerate the process of getting vaccinated: factors associated with consideration of and accessibility to COVID-19 vaccination in metropolises of China.

Background: Rapid mutation of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus is sweeping the world and delaying the full reopening of society. Acceleration of the vaccination process may be the key element in winning the race against this virus. We examine factors associated with personal considerations of and accessibility to the corona virus disease 2019 (COVID-19) vaccination in metropolises of China.

iArm: Design an Educational Robotic Arm Kit for Inspiring Students' Computational Thinking.

Educational robotics is an effective carrier of information technology education, making its way into classrooms. However, the design of the educational robotic arm kit and the study on the effect of robotic arms on students' thinking literacy remain to be completed. In this paper, iArm, a 6-DOF robotic arm consisting of a drive chassis, an arm body, and end tools, is presented.

An Online Weighted Bayesian Fuzzy Clustering Method for Large Medical Data Sets.

With the rapid development of artificial intelligence, various medical devices and wearable devices have emerged, enabling people to collect various health data of themselves in hospitals or other places. This has led to a substantial increase in the scale of medical data, and it is impossible to import these data into memory at one time. As a result, the hardware requirements of the computer become higher and the time consumption increases.

Ag-In-Zn-S Quantum Dot-Dominated Interface Kinetics in Ag-In-Zn-S/NiFe LDH Composites toward Efficient Photoassisted Electrocatalytic Water Splitting.

Photoassisted electrocatalysis (P-EC) emerges as a rising star for hydrogen production by embedding photoactive species in electrocatalysts, for which the interfacial structure design and charge transfer kinetics of the multifunctional catalysts remain a great challenge. Herein, Zn-AgInS quantum dots (ZAIS QDs) were embedded into 2D NiFe layered double hydroxide nanosheets through a simple hydrothermal treatment to form 0D/2D composite catalysts for P-EC. With evidence from transient photovoltage spectroscopy, we acquired a clear and fundamental understanding on the kinetics of charge extraction time and extraction amount in the 0D/2D heterojunctions that was proved to play a key role in P-EC.

Research on Emotion Analysis of Chinese Literati Painting Images Based on Deep Learning.

Starting from a pure-image perspective, using machine learning in emotion analysis methods to study artwork is a new cross-cutting approach in the field of literati painting and is an effective supplement to research conducted from the perspectives of aesthetics, philosophy, and history. This study constructed a literati painting emotion dataset. Five classic deep learning models were used to test the dataset and select the most suitable model, which was then improved upon for literati painting emotion analysis based on accuracy and model characteristics.

A Novel Paradigm to Design Personalized Derived Images of Art Paintings Using an Intelligent Emotional Analysis Model.

With the development of artificial intelligence (AI), it is imperative to combine design methods with new technologies. From the perspective of the personalized design of derived images of art paintings, this study analyzes the new user demand generated by the current situation and background of personalized design, puts forward a new method of derivative design based on AI emotion analysis, verifies the feasibility of the new method by constructing a personalized design system of derived images of art paintings driven by facial emotion features, and explores the method of combining AI emotion recognition, emotion analysis, and personalized design. This study provides new ideas for the design of art derivatives for the future with massive personalized demand.

Optimized Projection and Fisher Discriminative Dictionary Learning for EEG Emotion Recognition.

Electroencephalogram (EEG)-based emotion recognition (ER) has drawn increasing attention in the brain-computer interface (BCI) due to its great potentials in human-machine interaction applications. According to the characteristics of rhythms, EEG signals usually can be divided into several different frequency bands. Most existing methods concatenate multiple frequency band features together and treat them as a single feature vector.

Development and Validation of a Nomogram to Predict Frailty Progression in Nonfrail Chinese Community-Living Older Adults.

Objective: Frailty state progression is common among older adults, so it is necessary to identify predictors to implement individualized interventions. We aimed to develop and validate a nomogram to predict frailty progression in community-living older adults.

Design: Prospective cohort study.

Brain Tumor MR Image Classification Using Convolutional Dictionary Learning With Local Constraint.

Brain tumor image classification is an important part of medical image processing. It assists doctors to make accurate diagnosis and treatment plans. Magnetic resonance (MR) imaging is one of the main imaging tools to study brain tissue.

Polyaniline/Carbon Dots Composite as a Highly Efficient Metal-Free Dual-Functional Photoassisted Electrocatalyst for Overall Water Splitting.

Photoassisted electrocatalytic (P-EC) water splitting for H production has received much attention. Here, we report a metal-free bifunctional photoassisted catalyst of a polyaniline/carbon dots (PANI/CDs) composite for overall water splitting. In a neutral electrolyte, under visible light, the overpotentials of the oxygen evolution reaction (OER) and hydrogen evolution reaction (HER) for PANI/CDs/NF are reduced by 150 and 65 mV to reach the current densities of 30 and 20 mA cm, respectively.

A Novel Brain MRI Image Segmentation Method Using an Improved Multi-View Fuzzy -Means Clustering Algorithm.

The brain magnetic resonance imaging (MRI) image segmentation method mainly refers to the division of brain tissue, which can be divided into tissue parts such as white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF). The segmentation results can provide a basis for medical image registration, 3D reconstruction, and visualization. Generally, MRI images have defects such as partial volume effects, uneven grayscale, and noise.

Online learning satisfaction in higher education during the COVID-19 pandemic: A regional comparison between Eastern and Western Chinese universities.

Student satisfaction is of great significance in online learning, but few studies have explored its determinants in emerging countries. This study investigated the determinants of university students' satisfaction with online learning platforms in China through applying the Technology Satisfaction Model during the COVID-19 pandemic, when an unprecedented amount of learning began to take place online due to the closure of educational institutions. A total of 928 students from five universities in four Chinese provinces or municipalities were surveyed through a purposive sampling technique and analyzed through structural equation modeling and the Rasch model.

Response to "A design framework for enhancing engagement in student-centered learning: own it, learn it, and share it": a design perspective.

This essay is a response to the special issue call on the theme of In this essay, the author first described the needs of student-centered learning that emerged from the current full-scale online teaching and learning practice due to the pandemic. With these needs, the author revisited the published article of (Lee and Hannafin, in Educ Technol Res Dev 64(4):707-734, 2016) discussed its value, application, and future development.

Auto-Weighted Multi-View Discriminative Metric Learning Method With Fisher Discriminative and Global Structure Constraints for Epilepsy EEG Signal Classification.

Metric learning is a class of efficient algorithms for EEG signal classification problem. Usually, metric learning method deals with EEG signals in the single view space. To exploit the diversity and complementariness of different feature representations, a new uto-weighted ulti-view iscriminative etric earning method with Fisher discriminative and global structure constraints for epilepsy EEG signal classification called AMDML is proposed to promote the performance of EEG signal classification.