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, t tests, correlation analyses, and linear regression models, were conducted to assess factors influencing participants' care needs. RESULTS: A total of 530 older adults with UI participated in the survey and mean standardized score for overall care needs was 78.65 (SD = 5.01), with mean scores for each dimension ranging from 70.88 (SD = 10.55) for social participation needs to 82.45 (SD = 7.11) for health education needs. Factors that were found to influence incontinence care needs in older adults included age, literacy level, number of leaks, and type of disease (F = 37.07, adjusted R2 = 0.290, p < 0.001). CONCLUSION: Comprehensive care for older adults with UI, encompassing physiological, psychological, and social aspects, is crucial. It is essential to tailor care to individual needs and characteristics, taking into account factors, such as age and education, to ensure effective care. [Journal of Gerontological Nursing, 50(5), 43-49.].

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. The meta-analysis of all data was conducted using RevMan 5.4 and the evidence was summarized according to GRADE (the grading of recommendation, assessment, development, and evaluation). RESULTS: Twelve randomized control trials consisting of 1,033 women with cervical cancer who had undergone RH were included. Compared with women in the control group, women receiving LFES had improved therapeutic effect (RR = 0.22, 95% CI: 0.16-0.29) and reduced residual urine volume (MD = -32.27, 95% CI: -34.10 to -30.43) and catheter retention time (MD = -4.46, 95% CI: -5.17 to -3.76) following treatment. Muscle strength scores of pelvic floor type I and type II muscle fibers in the LFES group were also higher than in the control group (MD = 1.07, 95% CI: 0.91-1.24). CONCLUSION: LFES may be an effective auxiliary treatment for women with cervical cancer after hysterectomy, which can help reduce the duration of indwelling urethral catheter and residual urine volume.

[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. The purpose of this study is to illuminate the anti-inflammatory mechanism of Aconiti Radix based on the protein interaction network (PIN) analysis on molecular network level. The main anti-inflammatory components (aconitine, hypaconitine and mesaconitine) were chosen in this study to obtain the targets of the components and protein-protein information though databases retrieval and construct the PIN of Aconiti Radix. By a graph theoretic clustering algorithm molecular complex detection(MCODE), 13 modules were identified and analyzed by gene ontology(GO) enrichment. The results showed that the anti-inflammatory mechanism of Aconiti Radix was mainly associated with prostanoid metabolic process and leukocyte chemotaxis mediated by chemokines. In this study, the anti-inflammatory mechanism of Aconiti Radix was elucidated systematically from molecular network level, which provided the scientific basis for the treatment of rheumatic diseases.

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. RESULTS: A total of 510 older adults with urinary incontinence participated. The standardized total care needs score was 78.77 ± 5.03, with variations across dimensions: social participation needs scored (71.16 ± 10.32), daily life care needs (78.80 ± 5.51), medical care needs (77.33 ± 12.17), psychological comfort needs (76.97 ± 6.51) and health education needs scored highest (82.67 ± 6.77). Three distinct profiles emerged: "medium," "high SPN-PCN" and "high DLCN-MCN-HEN". The majority belonged to the "high SPN-PCN" profile. Significant correlations were found with age, education, leaks and frequency of micturitions. CONCLUSION: Research findings showed the existence of three distinct categories, with a notable majority of participants belonging to the "high SPN-PCN" group. The significance of having these classes identified lies in the move away from a one-size-fits-all approach to a more nuanced understanding of care needs. Customized nursing interventions can be devised based on specific factors, such as age, education level, urinary incontinence-related symptoms and potential category. For instance, for the "high SPN-PCN" group, our nursing strategy can encompass heightened psychological support and expanded opportunities for social engagement.Furthermore, in the training and education of healthcare professionals, recognizing and meeting the needs of each potential category of older adults might require more attention. Geriatr Gerontol Int 2024; ••: ••-••.

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. Recently, numerous text mining- and machine learning-based methods have been developed for predicting DDIs. All these methods implicitly utilize the feature of drugs from diverse drug-related properties. However, how to integrate these features more efficiently and improve the accuracy of classification is still a challenge. In this paper, we proposed a novel method (called NMDADNN) to predict the DDI types by integrating five drug-related heterogeneous information sources to extract the unified drug mapping features. NMDADNN first constructs the similarity networks by using the Jaccard coefficient and then implements random walk with restart algorithm and positive pointwise mutual information for extracting the topological similarities. After that, five network-based similarities are unified by using a multimodel deep autoencoder. Finally, NMDADNN implements the deep neural network (DNN) on the unified drug feature to infer the types of DDIs. In comparison with other recent state-of-the-art DNN-based methods, NMDADNN achieves the best results in terms of accuracy, area under the precision-recall curve, area under the ROC curve, F1 score, precision and recall. In addition, many of the promising types of drug-drug pairs predicted by NMDADNN are also confirmed by using the interactions checker tool. These results demonstrate the effectiveness of our NMDADNN method, indicating that NMDADNN has the great potential for predicting DDI types.

[Spatial-temporal trend of grassland net primary production and their driving factors in the Loess Plateau, China]. / é»„åœŸé«˜åŽŸè‰åœ°å‡€åˆçº§ç”Ÿäº§åŠ›æ—¶ç©ºè¶‹åŠ¿åŠå ¶é©±åŠ¨å› ç´ .

Net primary productivity (NPP) of grassland is a key link and important part of the ecosystem's carbon cycle. We estimated the changes of NPP in grasslands of the Loess Plateau with unchanged land use types during 2000-2015 and analyzed its responses to the variation of main climate factors (annual precipitation, annual heavy rainfall, annual effective rainfall days, annual average temperature, annual maximum temperature, annual minimum temperature) using piecewise linear regression and Pearson correlation analysis. The driving factors of grassland NPP were further analyzed by pixel-by-pixel with boosted regression tree analysis. The results showed that annual mean grassland NPP in the Loess Plateau showed an increasing trend during the study period, with 51.3% of the total grassland area showing a significant increasing trend. The average increase rate of annual mean NPP declined from 15.23 g C·m-2·a-1 in 2000-2004 to 3.58 g C·m-2·a-1 in 2004-2015. There was a significant positive correlation between grassland NPP and precipitation, but negative correlation with temperature factors. Annual precipitation was the dominant climatic factor affecting NPP of the whole study area with the highest relative importance. Annual maximum temperature was the dominant driving force of grassland NPP of central Loess Plateau, while annual minimum temperature mainly affected the growth of grassland in high-altitude area of the western Loess Plateau.

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. RESULTS: In this work, based on the assumption that similar drugs tend to interact with similar proteins and vice versa, we developed a novel computational method (namely MKLC-BiRW) to predict new drug-target interactions. MKLC-BiRW integrates diverse drug-related and target-related heterogeneous information source by using the multiple kernel learning and clustering methods to generate the drug and target similarity matrices, in which the low similarity elements are set to zero to build the drug and target similarity correction networks. By incorporating these drug and target similarity correction networks with known drug-target interaction bipartite graph, MKLC-BiRW constructs the heterogeneous network on which Bi-random walk algorithm is adopted to infer the potential drug-target interactions. CONCLUSIONS: Compared with other existing state-of-the-art methods, MKLC-BiRW achieves the best performance in terms of AUC and AUPR. MKLC-BiRW can effectively predict the potential drug-target interactions.

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. OBJECTIVE: In order to reflect the cases that the drugs having variant structures interact with common targets and the targets having dissimilar sequences interact with same drugs. In addition, though several other similarity metrics have been developed to predict DTIs, the combination of multiple similarity metrics (especially heterogeneous similarities) is too naïve to sufficiently explore the multiple similarities. METHOD: In this paper, based on Gene Ontology and pathway annotation, we introduce two novel target similarity metrics to address above issues. More importantly, we propose a more effective strategy via decision template to integrate multiple classifiers designed with multiple similarity metrics. RESULTS: In the scenarios that predict existing targets for new drugs and predict approved drugs for new protein targets, the results on the DTI benchmark datasets show that our target similarity metrics are able to enhance the predictive accuracies in two scenarios. And the elaborate fusion strategy of multiple classifiers has better predictive power than the naïve combination of multiple similarity metrics. CONCLUSION: Compared with other two state-of-the-art approaches on the four popular benchmark datasets of binary drug-target interactions, our method achieves the best results in terms of AUC and AUPR for predicting available targets for new drugs (S2), and predicting approved drugs for new protein targets (S3).These results demonstrate that our method can effectively predict the drug-target interactions. The software package can freely available at https://github.com/NwpuSY/DT_all.git for academic users.

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 3.70% to 7.27% and of obesity increased from 1.04% to 2.08%. Meanwhile, the prevalence of wasting decreased from 0.91% to 0.72% and of stunting decreased from 9.29% to 5.22%. These trends suggest there was still a double burden of nutritional status there. The nutritional interventions focusing on pre-school children should be comprehensively elaborated in lower-middle-income areas such as Luoding.

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. With the explosive growth of different types of omics data, such as genome, pharmacology, phenotypic, and other kinds of molecular networks, numerous computational approaches have been developed to predict Drug-Target Interactions (DTI). In this review, we make a survey on the recent advances in predicting drug-target interaction with network-based models from the following aspects: i) Available public data sources and benchmark datasets; ii) Drug/target similarity metrics; iii) Network construction; iv) Common network algorithms; v) Performance comparison of existing network-based DTI predictors.

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. kernel-based, matrix factorization-based and network-based inference methods) have been developed for predicting drug-target interactions. All these methods implicitly utilize the assumption that similar drugs tend to target similar proteins and yield better results for predicting interactions between drugs and target proteins. To further improve the accuracy of prediction, a new method of network-based label propagation with mutual interaction information derived from heterogeneous networks, namely LPMIHN, is proposed to infer the potential drug-target interactions. LPMIHN separately performs label propagation on drug and target similarity networks, but the initial label information of the target (or drug) network comes from the drug (or target) label network and the known drug-target interaction bipartite network. The independent label propagation on each similarity network explores the cluster structure in its network, and the label information from the other network is used to capture mutual interactions (bicluster structures) between the nodes in each pair of the similarity networks. As compared to other recent state-of-the-art methods on the four popular benchmark datasets of binary drug-target interactions and two quantitative kinase bioactivity datasets, LPMIHN achieves the best results in terms of AUC and AUPR. In addition, many of the promising drug-target pairs predicted from LPMIHN are also confirmed on the latest publicly available drug-target databases such as ChEMBL, KEGG, SuperTarget and Drugbank. These results demonstrate the effectiveness of our LPMIHN method, indicating that LPMIHN has a great potential for predicting drug-target interactions.