Background To analyze the health-related quality of life associated with the conversion of dialysis modality among end-stage renal disease patients in China. Methods Patients were recruited from hospitals and a dialysis center in Kunshan, China. Patients converting from continuous ambulatory peritoneal dialysis to automated peritoneal dialysis were recruited as the observation group (n = 64), and patients continuing with continuous ambulatory peritoneal dialysis treatment were included in the control group (n = 64) after matching in this retrospective cohort study. Their health-related quality of life was measured using the kidney disease quality of life instrument in 2019 and 2020, respectively. Baseline socio-demographic characteristics and clinical data were collected in 2019. The before-and-after cross-group comparisons of subscale scores of two groups were conducted using a Student‘s t-test. Multiple linear regression models were fitted to identify the factors associated with the change of each scale. Results The health-related quality of life scores of the two groups was comparable in baseline, while the observation group had higher scores in Physical Component Summary (51.92 ± 7.50), Kidney Disease Component Summary (81.21 ± 8.41), Symptoms (90.76 ± 6.30), Effects (82.86 ± 11.42), and Burden (69.04 ± 15.69) subscales after one year. In multivariate regression analysis, the change of Physical Component Summary was significantly associated with conversion to APD (β = 11.54, 95% CI [7.26–15.82]); the change of Mental Component Summary with higher education (β = − 5.96, 95% CI [−10.18–−1.74]) and CCI (>2) (β = 5.39, 95% CI [1.05–9.73]); the change of Kidney Disease Component Summary with conversion to APD (β = 15.95, 95% CI [10.19–21.7]) and age (>60 years) (β = − 7.36, 95% CI [−14.11–−0.61]); the change of Symptoms with CCI (>2) (β = 7.96, 95% CI [1.49–14.44]); the change of Effects with conversion to APD (β = 19.23, 95% CI [11.57–26.88]); and the change of Burden with conversion to APD (β = 22.40, 95% CI [13.46–31.34]), age (>60 years) (β = − 12.12, 95% CI [−22.59–−1.65]), and higher education (β = − 10.38, 95% CI [−19.79–−0.98]). Conclusions The conversion of dialysis modality had a significant impact on the scores of most subscales. Patients converting from continuous ambulatory peritoneal dialysis to automated peritoneal dialysis generally had improved health-related quality of life scores.
Small molecule modulators of protein-protein interactions (PPIs) have emerged as promising drug candidates with potential applications in anticancer, antiviral, and antimicrobial therapies. While recent virtual screening methods have demonstrated promising results in identifying modulators for specific PPI targets, challenges remain in predicting modulators for novel PPI targets and vice versa. For the first time, we directly predict PPI-modulator interaction through a binary classification task. We construct a benchmark dataset comprising PPIs information and active/inactive modulators data. Moreover, we develop a novel deep learning framework MultiPPIMI that leverages multimodal representations of PPI targets and modulators, and incorporates a bilinear attention network to capture inter-molecule interactions between them. Experiments conducted under both in-domain and cross-domain settings demonstrate the impressive performance and generalization capabilities of MultiPPIMI. The hit rate for identifying modulators of PPIs is greatly improved by combining deep learning with molecular docking. We believe that this work represents a significant step forward in the development of PPI-targeted therapeutics, offering new insights into predicting and understanding the interactions between small molecules and PPIs.
IntroductionThe COVID-19 pandemic has posed a major burden on healthcare and economic systems across the globe for over 3 years. Even though vaccines are available, the pathogenesis is still unclear. Multiple studies have indicated heterogeneity of immune responses to SARS-CoV-2, and potentially distinct patient immune types that might be related to disease features. However, those conclusions are mainly inferred by comparing the differences of pathological features between moderate and severe patients, some immunological features may be subjectively overlooked.MethodsIn this study, the relevance scores(RS), reflecting which features play a more critical role in the decision-making process, between immunological features and the COVID-19 severity are objectively calculated through neural network, where the input features include the immune cell counts and the activation marker concentrations of particular cell, and these quantified characteristic data are robustly generated by processing flow cytometry data sets containing the peripheral blood information of COVID-19 patients through PhenoGraph algorithm.ResultsSpecifically, the RS between immune cell counts and COVID-19 severity with time indicated that the innate immune responses in severe patients are delayed at the early stage, and the continuous decrease of classical monocytes in peripherial blood is significantly associated with the severity of disease. The RS between activation marker concentrations and COVID-19 severity suggested that the down-regulation of IFN-γ in classical monocytes, Treg, CD8 T cells, and the not down-regulation of IL_17a in classical monocytes, Tregs are highly correlated with the occurrence of severe disease. Finally, a concise dynamic model of immune responses in COVID-19 patients was generalized.DiscussionThese results suggest that the delayed innate immune responses in the early stage, and the abnormal expression of IL-17a and IFN-γ in classical monocytes, Tregs, and CD8 T cells are primarily responsible for the severity of COVID-19.
Objectives: To examine whether moral hazard may exist under unsupervised home-based online applications, leading to more assistive technology devices (ATDs) and larger per capita expenditures on ATDs than under supervised community center-based online applications. Methods: Using the data from the Assistive Devices Resource Centre in Shanghai, descriptive statistics were estimated for the sociodemographics of applicants. Multiple linear regression and logistic regression were used to test the effect of the introduction of home-based online applications. Results: In 2015-2016, there were marked increases of 22.3% in the total number of ATDs and 27.2% in the total expenditure on ATDs compared with 2013-2014. The per capita number and expenditure also demonstrated an increasing trend. More devices were applied for in 2015-2016 than in 2013-2014, yielding a higher expenditure per capita (P < .001). Interestingly, with an invisible price, more devices were applied for at home than in community centers (P < .001), but the expenditure per capita was smaller (P < .001). Conclusions: The introduction of online applications increased the number of ATDs per capita. The home-based Huang Xiaojing and Sun Mei contributed equally to this work.
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