Evolution of organic phosphor through precision regulation of nonradiative decay.

Development of single-component organic phosphor attracts increasing interest due to its wide applications in optoelectronic technologies. Theoretically, activating efficient intersystem crossing (ISC) via 1(π, π*) to 3(π, π*) transitions, rather than 1(n, π*) → 3(π, π*) transitions, is an alternative access to purely organic phosphors but remains challenging. Herein, we designed and successfully synthesized the sila-8-membered ring fused biaryl benzoskeleton by transition metal catalysis, which served as a new organic phosphor with efficient 1(π, π*) to 3(π, π*) ISC. We first found that such a compound exhibits a record-long phosphorescence lifetime of 6.5 s at low temperature for single-component organic systems. Then, we developed two strategies to tune their decay channels to evolve such nonemissive molecules into bright phosphors with elongated lifetimes at room temperature: 1) Physic-based design, where quantitative analyses of electron-phonon coupling led us to reveal and hinder the major nonradiative channels, thus lighted up room temperature phosphorescence (RTP) with a lifetime of 480 ms at 298 K; 2) chemical geometry-driven molecular engineering, where a geometry-based descriptor ΔΘT1-S0/ΘS0 was developed for rational screening RTP candidates and further improved the RTP lifetime to 794 ms. This study clearly shows the power of interdiscipline among synthetic methodology, physics-based rational design, and computational modeling, which represents a paradigm for the development of an organic emitter.

A non-negative spike-and-slab lasso generalized linear stacking prediction modeling method for high-dimensional omics data.

BACKGROUND: High-dimensional omics data are increasingly utilized in clinical and public health research for disease risk prediction. Many previous sparse methods have been proposed that using prior knowledge, e.g., biological group structure information, to guide the model-building process. However, these methods are still based on a single model, offen leading to overconfident inferences and inferior generalization. RESULTS: We proposed a novel stacking strategy based on a non-negative spike-and-slab Lasso (nsslasso) generalized linear model (GLM) for disease risk prediction in the context of high-dimensional omics data. Briefly, we used prior biological knowledge to segment omics data into a set of sub-data. Each sub-model was trained separately using the features from the group via a proper base learner. Then, the predictions of sub-models were ensembled by a super learner using nsslasso GLM. The proposed method was compared to several competitors, such as the Lasso, grlasso, and gsslasso, using simulated data and two open-access breast cancer data. As a result, the proposed method showed robustly superior prediction performance to the optimal single-model method in high-noise simulated data and real-world data. Furthermore, compared to the traditional stacking method, the proposed nsslasso stacking method can efficiently handle redundant sub-models and identify important sub-models. CONCLUSIONS: The proposed nsslasso method demonstrated favorable predictive accuracy, stability, and biological interpretability. Additionally, the proposed method can also be used to detect new biomarkers and key group structures.

A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data.

BACKGROUND: Survival prediction using high-dimensional molecular data is a hot topic in the field of genomics and precision medicine, especially for cancer studies. Considering that carcinogenesis has a pathway-based pathogenesis, developing models using such group structures is a closer mimic of disease progression and prognosis. Many approaches can be used to integrate group information; however, most of them are single-model methods, which may account for unstable prediction. METHODS: We introduced a novel survival stacking method that modeled using group structure information to improve the robustness of cancer survival prediction in the context of high-dimensional omics data. With a super learner, survival stacking combines the prediction from multiple sub-models that are independently trained using the features in pre-grouped biological pathways. In addition to a non-negative linear combination of sub-models, we extended the super learner to non-negative Bayesian hierarchical generalized linear model and artificial neural network. We compared the proposed modeling strategy with the widely used survival penalized method Lasso Cox and several group penalized methods, e.g., group Lasso Cox, via simulation study and real-world data application. RESULTS: The proposed survival stacking method showed superior and robust performance in terms of discrimination compared with single-model methods in case of high-noise simulated data and real-world data. The non-negative Bayesian stacking method can identify important biological signal pathways and genes that are associated with the prognosis of cancer. CONCLUSIONS: This study proposed a novel survival stacking strategy incorporating biological group information into the cancer prognosis models. Additionally, this study extended the super learner to non-negative Bayesian model and ANN, enriching the combination of sub-models. The proposed Bayesian stacking strategy exhibited favorable properties in the prediction and interpretation of complex survival data, which may aid in discovering cancer targets.

Peritoneal Phosphate Clearance: Determinants and Association With Mortality.

BACKGROUND: Dialytic phosphate removal is a cornerstone of the management of hyperphosphatemia in peritoneal dialysis (PD) patients, but the influencing factors on peritoneal phosphate clearance (PPC) are incompletely understood. Our objective was to explore clinically relevant factors associated with PPC in patients with different PD modality and peritoneal transport status and the association of PPC with mortality. METHODS: This is a cross-sectional and prospective observational study. Four hundred eighty-five PD patients were enrolled and divided into 2 groups according to PPC. All-cause mortality was evaluated after followed-up for at least 3 months. RESULTS: High PPC group showed lower mortality compared with Low PPC group by Kaplan-Meier analysis and log-rank test. Both multivariate linear regression and multivariate logistic regression revealed that high transport status, total effluent dialysate volume per day, continuous ambulatory PD (CAPD), and protein in total effluent dialysate volume appeared to be positively correlated with PPC; body mass index (BMI) and the normalized protein equivalent of total nitrogen appearance (nPNA) were negatively correlated with PPC. Besides PD modality and membrane transport status, total effluent dialysate volume showed a strong relationship with PPC, but the correlation differed among PD modalities. CONCLUSIONS: Higher PPC was associated with lower all-cause mortality risk in PD patients. Higher PPC correlated with CAPD modality, fast transport status, higher effluent dialysate volume and protein content, and with lower BMI and nPNA.

Mizoribine combined with steroids and dietary sodium restriction on the treatment of primary membranous nephropathy: a prospective study.

BACKGROUND: We aimed to initially explore the efficiency and safety of mizoribine (MZR) combined with steroids and dietary sodium restriction on the treatment of primary membranous nephropathy (MN) compared with cyclophosphamide (CPM)-based steroids. METHODS: Patients with primary MN were enrolled. According to the therapy, they were divided into the MZR combined with steroids and dietary sodium restriction group (N = 30) and CPM-based steroids group (N = 30). Both groups were followed up for 1 year to monitor safety and efficacy. RESULTS: Compared with the CPM-based steroids group, the MZR combined with steroids and dietary sodium restriction group had significantly lower daily sodium intake, serum sodium, blood pressure (BP), and 24 h urine protein (all P < 0.05). Conversely, plasma albumin and complete remission rate in the MZR group were higher at the 12th follow-up (40.39 ± 5.14 g/L vs. 37.63 ± 5.40 g/L; 86.67% vs. 66.67%; all P < 0.05). These two groups showed similar adverse events rates (20.00% vs. 26.67%, P = 0.54). CONCLUSION: This study demonstrates that MZR combined with steroids and dietary sodium restriction is superior to CPM-based steroids in terms of complete remission and 24 h urine protein in patients with primary MN.

Near-Infrared Light-Controlled Activation of Adhesive Peptides Regulates Cell Adhesion and Multidifferentiation in Mesenchymal Stem Cells on an Up-Conversion Substrate.

Cell adhesion and differentiation can be regulated through material engineering, but current methods have low temporal and spatial accuracy to control invivo. Here, we developed an up-conversion nanoparticle (UCNP) substrate to regulate cell adhesion and multidifferentiation in mesenchymal stem cells (MSCs) by near-infrared (NIR) light. First, the cell-adhesive peptide Arg-Gly-Asp (RGD) was conjugated on the surface of UCNPs, and the photocleavage 4-(hydroxymethyl)-3-nitrobenzoic acid (ONA) was connected to RGD. Then, the photoactivated UCNPs were linked to cover glass to form UCNP-substrate. Under the NIR, the up-convert UV from UCNPs triggered the release of ONA and exposed RGD to change the cell-matrix interactions dynamically for cell adhesion and spreading. Moreover, MSCs cultured on UCNP-substrate could be specifically induced to multidifferentiate adipocytes or osteoblasts via different power and periods of NIR irradiation in vitro and in vivo. Our work demonstrates a new way to control cell adhesion and multidifferentiation by light for regeneration medicine.

The Synergistic Value of Time-Averaged Serum Albumin and Globulin in Predicting the First Peritonitis in Incident Peritoneal Dialysis Patients.

BACKGROUND: The prognostic value of serum time-averaged albumin (TA-Alb) and time-averaged globulin (TA-Glo) combination on the peritonitis in peritoneal dialysis (PD) patients is unknown. METHODS: The patients who started PD treatment between July 2013 and 2018 were included. Serum Alb and globulin (Glo) were tested at baseline and monthly during follow-up. TA-Alb and TA-Glo were calculated until first peritonitis occurred or the end of the study. PD patients were divided into 4 groups based on the medians of TA-Alb and TA-Glo separately. Cox regression was conducted to identify the hazard ratios (HRs) of peritonitis among categorical groups. RESULTS: Three hundred and sixty-three patients were included and among them 109 patients experienced first peritonitis. Peritonitis patients had lower baseline Alb, TA-Alb, and TA-Glo levels and ultrafiltration volume. Multivariate cox regression analysis revealed that TA-Alb, TA-Glo, and baseline Alb were significantly associated with first peritonitis. The highest HR existed in Group 1 with lower Alb and lower Glo (HR 4.57, 95% CI 2.36-8.87, p < 0.001) compared with Group 4 with higher Alb and higher Glo. CONCLUSION: Lower TA-Glo is an independent risk factor for the first peritonitis in PD patients. Combined with lower TA-Alb will increase the predictive effect than separate factor alone.

Enantioselective Catalytic Aldehyde α-Alkylation/Semipinacol Rearrangement: Construction of α-Quaternary-δ-Carbonyl Cycloketones and Total Synthesis of (+)-Cerapicol.

An enantioselective aldehyde α-alkylation/semipinacol rearrangement was achieved through organo-SOMO catalysis. The catalytically generated enamine radical cation serves as a carbon radical electrophile that can stereoselectively add to the alkene of an allylic alcohol and initiate ensuing ring-expansion of cyclopropanol or cyclobutanol. This tandem reaction enables the production of a wide range of nonracemic functionalizable α-quaternary-δ-carbonyl cycloketones in high yields and excellent enantioselectivity from simple aldehydes and allylic alcohols. As a key step, the intramolecular reaction was also successfully applied in the asymmetric total synthesis of (+)-cerapicol.

The Association of Longitudinal Serum Uric Acid and All-Cause Mortality in Incident Peritoneal Dialysis Patients.

BACKGROUND: Time-averaged uric acid (TA-UA) value was calculated to investigate the association of longitudinal UA and all-cause mortality in incident peritoneal dialysis (PD) patients. METHODS: Three hundred PD patients were divided into 3 groups based on the serum TA-UA level (Group 1: < 6 mg/dL; Group 2: 6-8 mg/dL; Group 3: ≥8 mg/dL). Hazards ratio (HR) of all-cause mortality was calculated. Logistic regression was conducted to identify the associated clinical factors of lower and higher TA-UA level. RESULTS: Increased HRs for death existed in Group 1 and Group 3 compared with Group 2 (HR 3.24, 95% CI 1.25-8.39, p = 0.016; HR 4.69, 95% CI 1.24-17.72, p = 0.023). Lower residual renal function, lower albumin, and higher high-density lipoprotein cholesterol were related to the lower serum TA-UA. Higher body mass index and higher C-reactive protein were associated with higher serum TA-UA in PD patients. CONCLUSION: Both TA-UA < 6 and ≥8 mg/dL increased the all-cause mortality in incident PD patients.

Effects of Peritoneal Dialysis on Clinical Factors and Functional Performance.

BACKGROUND: We investigated the longitudinal trend of functional performance in peritoneal dialysis (PD) patients over 1 year after PD commencement and its related clinical parameters. METHODS: One hundred and ninety-six PD patients were enrolled in this study. Karnofsky Performance Status Scale(KPSS) scores were used to assess functional performance. Patients were stratified into 3 groups according to the changes in KPSS from baseline to 1 year. A logistic regression analysis was performed to examine the associations of clinical parameters with KPSS changes. RESULTS: Patients with KPSS declined showed older age and higher serum albumin concentration reduction within 1 year than those in KPSS improved and stable changes. Age was the significant risk factor for KPSS decline, while male and diabetes were significantly associated with non-declined KPSS by multivariable logistic regression analysis. CONCLUSION: The main determinants of KPSS trend were age, sex, and diabetes in new PD patients.

ETCNet: An EEG-based motor imagery classification model combining efficient channel attention and temporal convolutional network.

Brain-computer interface (BCI) enables the control of external devices using signals from the brain, offering immense potential in assisting individuals with neuromuscular disabilities. Among the different paradigms of BCI systems, the motor imagery (MI) based electroencephalogram (EEG) signal is widely recognized as exceptionally promising. Deep learning (DL) has found extensive applications in the processing of MI signals, wherein convolutional neural networks (CNN) have demonstrated superior performance compared to conventional machine learning (ML) approaches. Nevertheless, challenges related to subject independence and subject dependence persist, while the inherent low signal-to-noise ratio of EEG signals remains a critical aspect that demands attention. Accurately deciphering intentions from EEG signals continues to present a formidable challenge. This paper introduces an advanced end-to-end network that effectively combines the efficient channel attention (ECA) and temporal convolutional network (TCN) components for the classification of motor imagination signals. We incorporated an ECA module prior to feature extraction in order to enhance the extraction of channel-specific features. A compact convolutional network model uses for feature extraction in the middle part. Finally, the time characteristic information is obtained by using TCN. The results show that our network is a lightweight network that is characterized by few parameters and fast speed. Our network achieves an average accuracy of 80.71% on the BCI Competition IV-2a dataset.

EEG-VTTCNet: A loss joint training model based on the vision transformer and the temporal convolution network for EEG-based motor imagery classification.

Brain-computer interface (BCI) is a technology that directly connects signals between the human brain and a computer or other external device. Motor imagery electroencephalographic (MI-EEG) signals are considered a promising paradigm for BCI systems, with a wide range of potential applications in medical rehabilitation, human-computer interaction, and virtual reality. Accurate decoding of MI-EEG signals poses a significant challenge due to issues related to the quality of the collected EEG data and subject variability. Therefore, developing an efficient MI-EEG decoding network is crucial and warrants research. This paper proposes a loss joint training model based on the vision transformer (VIT) and the temporal convolutional network (EEG-VTTCNet) to classify MI-EEG signals. To take advantage of multiple modules together, the EEG-VTTCNet adopts a shared convolution strategy and a dual-branching strategy. The dual-branching modules perform complementary learning and jointly train shared convolutional modules with better performance. We conducted experiments on the BCI Competition IV-2a and IV-2b datasets, and the proposed network outperformed the current state-of-the-art techniques with an accuracy of 84.58% and 90.94%, respectively, for the subject-dependent mode. In addition, we used t-SNE to visualize the features extracted by the proposed network, further demonstrating the effectiveness of the feature extraction framework. We also conducted extensive ablation and hyperparameter tuning experiments to construct a robust network architecture that can be well generalized.

EEG-FMCNN: A fusion multi-branch 1D convolutional neural network for EEG-based motor imagery classification.

Motor imagery (MI) electroencephalogram (EEG) signal is recognized as a promising paradigm for brain-computer interface (BCI) systems and has been extensively employed in various BCI applications, including assisting disabled individuals, controlling devices and environments, and enhancing human capabilities. The high-performance decoding capability of MI-EEG signals is a key issue that impacts the development of the industry. However, decoding MI-EEG signals is challenging due to the low signal-to-noise ratio and inter-subject variability. In response to the aforementioned core problems, this paper proposes a novel end-to-end network, a fusion multi-branch 1D convolutional neural network (EEG-FMCNN), to decode MI-EEG signals without pre-processing. The utilization of multi-branch 1D convolution not only exhibits a certain level of noise tolerance but also addresses the issue of inter-subject variability to some extent. This is attributed to the ability of multi-branch architectures to capture information from different frequency bands, enabling the establishment of optimal convolutional scales and depths. Furthermore, we incorporate 1D squeeze-and-excitation (SE) blocks and shortcut connections at appropriate locations to further enhance the generalization and robustness of the network. In the BCI Competition IV-2a dataset, our proposed model has obtained good experimental results, achieving accuracies of 78.82% and 68.41% for subject-dependent and subject-independent modes, respectively. In addition, extensive ablative experiments and fine-tuning experiments were conducted, resulting in a notable 7% improvement in the average performance of the network, which holds significant implications for the generalization and application of the network.

Association of spontaneous abortion with bipolar disorder and major depression based on inverse probability treatment weighting of multigroup propensity scores: Evidence from the UK Biobank.

BACKGROUND: Few studies have explored the association between the number of SAs and bipolar disorder and major depression (BDMD). This study aims to investigate the association between SA and BDMD, and the possible dose-response relationship between them. METHODS: We conducted a cross-sectional study of 13,200 female UK Biobank participants. Participants were classified into BDMD and no-BDMD groups based on their BDMD status. The number of SAs was grouped into non-SA, occasional SA (OSA), and recurrent SA (RSA). Baseline characteristics of the three groups were balanced using inverse probability treatment weighting (IPTW) based on propensity scores. The three-knots restricted cubic spline regression model was utilized to assess the dose-response relationship between the number of SAs and BDMD. RESULTS: The IPTW-adjusted multivariate logistic regression revealed that SA was an independent risk factor for BDMD, with adjusted OR of 1.12 (95 % CI: 1.07-1.19) and 1.32 (95 % CI: 1.25-1.40) in the OSA and RSA groups, respectively. The strength of this association amplified as the number of SAs (P for trend <0.001). There was a nonlinear relationship between the number of SAs and the risk of BDMD, with an approximately inverted L-shaped curve. LIMITATIONS: The information of the SA and BDMD status relied on self-reported by volunteers, and the study sample was mostly of European descent. CONCLUSIONS: Women who reported experiencing multiple SAs are more likely to have BDMD. Therefore, it is imperative to provide psychological care and interventions for women in the postpartum period.

Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using machine learning with stacking.

Our objective is to develop a prognostic model focused on cuproptosis, aimed at predicting overall survival (OS) outcomes among Acute myeloid leukemia (AML) patients. The model utilized machine learning algorithms incorporating stacking. The GSE37642 dataset was used as the training data, and the GSE12417 and TCGA-LAML cohorts were used as the validation data. Stacking was used to merge the three prediction models, subsequently using a random survival forests algorithm to refit the final model using the stacking linear predictor and clinical factors. The prediction model, featuring stacking linear predictor and clinical factors, achieved AUC values of 0.840, 0.876 and 0.892 at 1, 2 and 3 years within the GSE37642 dataset. In external validation dataset, the corresponding AUCs were 0.741, 0.754 and 0.783. The predictive performance of the model in the external dataset surpasses that of the model simply incorporates all predictors. Additionally, the final model exhibited good calibration accuracy. In conclusion, our findings indicate that the novel prediction model refines the prognostic prediction for AML patients, while the stacking strategy displays potential for model integration.

Salviolone protects against high glucose-induced proliferation, oxidative stress, inflammation, and fibrosis of human renal mesangial cells by upregulating membrane metalloendopeptidase expression.

As one of complications of diabetes mellitus, diabetic nephropathy is related to renal dysfunction. Membrane metalloendopeptidase (MME) is associated with the pathogenesis of diabetic nephropathy and exerts a protective function in high glucose (HG)-treated podocytes. Salviolone, one of important bioactive components from Salvia miltiorrhiza, possesses an anti-inflammatory activity. However, the roles of salviolone in renal mesangial cell dysfunction under HG condition remain unknown. The targets of salviolone in diabetic nephropathy were predicted by bioinformatics analysis. Relative mRNA level of MME was detected by qPCR in HG-treated human renal mesangial cells (HRMCs). Cell viability was analyzed using CCK-8 assay. Cell proliferation was investigated by EdU staining. Oxidative stress was evaluated by detection of ROS generation and levels of oxidative stress-related biomarkers. The inflammatory cytokines and fibrosis-related biomarkers were examined by ELISA. Our results showed that MME expression was decreased in diabetic nephropathy and HG-treated HRMCs. Salviolone increased MME level in HG-treated HRMCs. Salviolone mitigated HG-induced HRMC proliferation by increasing MME expression. Salviolone attenuated HG-induced ROS generation, MDA level increase, and SOD activity decrease through upregulating MME expression. Moreover, salviolone suppressed HG-induced increase of levels of TNF-α, IL-1ß, IL-6, fibronectin, and collagen IV through upregulating MME expression. In conclusion, salviolone attenuates proliferation, oxidative stress, inflammation, and fibrosis in HG-treated HRMCs through upregulating MME expression.

Classification Algorithm for Electroencephalogram-based Motor Imagery Using Hybrid Neural Network with Spatio-temporal Convolution and Multi-head Attention Mechanism.

Motor imagery (MI) is a brain-computer interface (BCI) technique in which specific brain regions are activated when people imagine their limbs (or muscles) moving, even without actual movement. The technology converts electroencephalogram (EEG) signals generated by the brain into computer-readable commands by measuring neural activity. Classification of motor imagery is one of the tasks in BCI. Researchers have done a lot of work on motor imagery classification, and the existing literature has relatively mature decoding methods for two-class motor tasks. However, as the categories of EEG-based motor imagery tasks increase, further exploration is needed for decoding research on four-class motor imagery tasks. In this study, we designed a hybrid neural network that combines spatiotemporal convolution and attention mechanisms. Specifically, the data is first processed by spatiotemporal convolution to extract features and then processed by a Multi-branch Convolution block. Finally, the processed data is input into the encoder layer of the Transformer for a self-attention calculation to obtain the classification results. Our approach was tested on the well-known MI datasets BCI Competition IV 2a and 2b, and the results show that the 2a dataset has a global average classification accuracy of 83.3% and a kappa value of 0.78. Experimental results show that the proposed method outperforms most of the existing methods.

Predicting monthly hospital outpatient visits based on meteorological environmental factors using the ARIMA model.

Accurate forecasting of hospital outpatient visits is beneficial to the rational planning and allocation of medical resources to meet medical needs. Several studies have suggested that outpatient visits are related to meteorological environmental factors. We aimed to use the autoregressive integrated moving average (ARIMA) model to analyze the relationship between meteorological environmental factors and outpatient visits. Also, outpatient visits can be forecast for the future period. Monthly outpatient visits and meteorological environmental factors were collected from January 2015 to July 2021. An ARIMAX model was constructed by incorporating meteorological environmental factors as covariates to the ARIMA model, by evaluating the stationary [Formula: see text], coefficient of determination [Formula: see text], mean absolute percentage error (MAPE), and normalized Bayesian information criterion (BIC). The ARIMA [Formula: see text] model with the covariates of [Formula: see text], [Formula: see text], and [Formula: see text] was the optimal model. Monthly outpatient visits in 2019 can be predicted using average data from past years. The relative error between the predicted and actual values for 2019 was 2.77%. Our study suggests that [Formula: see text], [Formula: see text], and [Formula: see text] concentration have a significant impact on outpatient visits. The model built has excellent predictive performance and can provide some references for the scientific management of hospitals to allocate staff and resources.

Correlation between meteorological factors and vitamin D status under different season.

Pregnant women with low vitamin D levels tend to have poor clinical outcomes. Meteorological factors were associated with vitamin D. Here, we aimed to study the current status of 25-Hydroxy vitamin D (25(OH)D) concentrations in pregnant women in Kunshan city and investigate the meteorological factors associated with 25(OH)D levels under different seasons. The correlation between meteorological factors and 25(OH)D levels was estimated by cross-correlation analysis and multivariate logistic regression. A restrictive cubic spline method was used to estimate the non-linear relationship. From 2015 to 2020, a total of 22,090 pregnant women were enrolled in this study. Pregnant women with 25(OH)D concentrations below 50 nmol/l represent 65.85% of the total study population. There is a positive correlation between temperature and 25(OH)D. And there is a protective effect of the higher temperature on vitamin D deficiency. However, in the subgroup analysis, we found that in autumn, high temperatures above 30 °C may lead to a decrease in 25(OH)D levels. This study shows that vitamin D deficiency in pregnant women may widespread in eastern China. There is a potential inverted U-shaped relationship between temperature and 25(OH)D levels, which has implications for understanding of vitamin D changes under different seasons.

Effects and Clinical Value of Peritoneal Dialysis on Water and Water Balance, Adverse Reactions, Quality of Life, and Clinical Prognosis in Patients with Decompensated Chronic Nephropathy: A Systematic Review and Meta-Analysis.

Objective: To systematically evaluate the effects of peritoneal dialysis and hemodialysis on renal function and quality of life in patients with end-stage renal disease. An evidence-based medical rationale would be provided for peritoneal dialysis or hemodialysis treatment in patients with end-stage renal disease. Methods: The PubMed, EMBASE, ScienceDirect, Cochrane Library, China National Knowledge Infrastructure (CNKI), China VIP Database, Wanfang, and China Biomedical Literature Database (CBM) online databases were searched. Comparisons on the effects of peritoneal dialysis on renal function and quality of life were taken between patients with end-stage renal disease (RD). The data were extracted independently by two researchers. The bias-risk-included literatures were assessed according to the Cochrane manual 5.1.0 standard. RevMan 5.4 statistical software was used to analyze the collected data via meta-analysis. Results: Seven RCT articles were finally included. A total of 745 samples were analyzed via meta-analysis. The obvious heterogeneities of serum creatinine (Scr) and blood urea nitrogen (BUN) were discovered (P < 0.00001) in the selective investigations. According to the results of this analysis, it was indicated that the renal function of patients with end-stage renal disease treated by peritoneal dialysis was significantly better than that of hemodialysis. According to the meta-analysis, there was obvious heterogeneity of life quality among the included research data. It was indicated that the score of quality of life of patients with end-stage renal disease treated by peritoneal dialysis was significantly better than that of hemodialysis. Conclusion: Compared with hemodialysis in the treatment of end-stage renal disease, the renal function and quality of life of patients with peritoneal dialysis are better than those of hemodialysis. More further studies and follow-up with higher methodological quality and longer intervention time are still needed for further verification.