Care needs for urinary incontinence in older adults: Latent profile analysis.

Aim: This study utilized latent profile analysis to investigate care needs subgroups among older adults with urinary incontinence.

Methods: The "Elderly Urinary Incontinence Care Needs Inventory" surveyed 510 participants in two Guangzhou City hospitals from July 2022 to June 2023. Latent profile analysis created a classification model, and variance and correlation analysis assessed influencing factors.

Care Needs of Older Adults With Urinary Incontinence: A Cross-Sectional Study.

Purpose: To explore care requirements of older adults with urinary incontinence (UI) and contributing factors.

Method: This cross-sectional study used the Older Adults Urinary Incontinence Care Needs Inventory to survey participants with UI in three large-scale tertiary hospitals located in Guangzhou City, China, from January 2023 to November 2023. Statistical analyses, including analysis of variance, tests, correlation analyses, and linear regression models, were conducted to assess factors influencing participants' care needs.

Effectiveness of Low-Frequency Electrical Stimulation for Radical Hysterectomy Women: Systematic Review and Meta-Analysis.

Introduction: The aim of the study was to explore the effects of low-frequency electrical stimulation (LFES) in preventing urinary retention after radical hysterectomy (RH) in women with cervical cancer.

Methods: Seven electronic bibliographic databases were searched from inception to December 25, 2021. The mean difference (MD) or risk ratio (RR) with its corresponding 95% CI was selected as effect size.

Metonymy Processing in Chinese: A Linguistic Context-Sensitive Eye-Tracking Preliminary Study.

Current cognitively oriented research on metaphor proposes that understanding metaphorical expressions is a process of building embodied simulations, which are constrained by past and present bodily experiences. However, it has also been shown that metaphor processing is also constrained by the linguistic context but, to our knowledge, there is no comparable work in the domain of metonymy. As an initial attempt to fill this gap, the present study uses eye-tracking experimentation to explore this aspect of Chinese metonymy processing.

Prediction of the Drug-Drug Interaction Types with the Unified Embedding Features from Drug Similarity Networks.

Drug combination therapies are a promising strategy to overcome drug resistance and improve the efficacy of monotherapy in cancer, and it has been shown to lead to a decrease in dose-related toxicities. Except the synergistic reaction between drugs, some antagonistic drug-drug interactions (DDIs) exist, which is the main cause of adverse drug events. Precisely predicting the type of DDI is important for both drug development and more effective drug combination therapy applications.

Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods.

Background: Identification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target interactions. However, large-scale pharmacological, genomic and chemical datum emerged recently provide new opportunity for further heightening the accuracy of drug-target interactions prediction.

Trend in the nutritional status of children aged 2-7 years in Luoding city, China: A panel study from 2004 to 2013.

To examine trends in the prevalence of wasting, stunting, overweight, and obesity among children in Luoding, a lower-middle-income city in southern China, we collected height, weight and other information on 65,908 pre-school children aged 2 to 7 years from 23 kindergartens, in which health examinations were conducted annually between 2004 and 2013. We used the growth standards of the World Health Organization (WHO) to calculate Z-scores for height and body mass index (BMI), and used the cut-offs recommended by WHO to define wasting, stunting, overweight, and obesity for each child. From 2004 to 2013, the prevalence of overweight increased from 3.

[Anti-inflammatory mechanism analysis of antirheumatic Chinese medicinal herb Aconiti Radix based on protein interaction network].

Inflammation is one of the important risk factors of rheumatic diseases. Aconiti Radix is widely used for the treatment of rheumatism, which has significant anti-inflammatory effects. However, its anti-inflammatory mechanism on molecular level is still not clear.

Some Remarks on Prediction of Drug-Target Interaction with Network Models.

System-level understanding of the relationships between drugs and targets is very important for enhancing drug research, especially for drug function repositioning. The experimental methods used to determine drug-target interactions are usually time-consuming, tedious and expensive, and sometimes lack reproducibility. Thus, it is highly desired to develop computational methods for efficiently and effectively analyzing and detecting new drug-target interaction pairs.

Identifying Drug-Target Interactions with Decision Templates.

Background: During the development process of new drugs, identification of the drug-target interactions wins primary concerns. However, the chemical or biological experiments bear the limitation in coverage as well as the huge cost of both time and money. Based on drug similarity and target similarity, chemogenomic methods can be able to predict potential drug-target interactions (DTIs) on a large scale and have no luxurious need about target structures or ligand entries.

Prediction of drug-target interaction by label propagation with mutual interaction information derived from heterogeneous network.

The identification of potential drug-target interaction pairs is very important, which is useful not only for providing greater understanding of protein function, but also for enhancing drug research, especially for drug function repositioning. Recently, numerous machine learning-based algorithms (e.g.