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Obesity can induce dysbiosis in the gut microbiota and is considered a separate risk factor for kidney function decline. Nonetheless, the precise function of intestinal microorganisms in facilitating the connection between obesity and kidney function decline remains uncertain. Hence, the objective of this study was to investigate the alterations in the gut microbiota composition that take place during obesity and their correlations with renal function utilizing a rat model. For 20 weeks, 25 Sprague-Dawley rats were fed either a high-fat diet (HFD) or a normal-fat normal diet (ND). Physiological indices, peripheral plasma, kidney tissue, and colon contents were collected for comparison between groups. Metagenomic analysis of intestinal flora was performed. The HFD group demonstrated significantly increased levels of creatinine and urea nitrogen in the peripheral blood. Additionally, the HFD rats exhibited a significantly larger glomerular diameter compared to the ND group, accompanied by the presence of glomerulosclerosis, tubular vacuolar transformation, and other pathological changes in certain glomeruli. Metagenomics analysis revealed a notable rise in the prevalence of the Firmicutes phylum within the HFD group, primarily comprising the genus. Functional analysis indicated that the gut microbiota in the HFD group primarily correlated with infectious diseases, signal transduction, and signaling molecules and interactions. This study provides evidence that the consumption of a HFD induces modifications in the composition and functionality of the gut microbiome in rats, which may serve as a potential mechanism underlying the relationship between obesity and the progression of kidney function decline.

Increased myocardial T1 values on cardiovascular MRI (CMRI) have been shown to be a surrogate marker for myocardial fibrosis. The use of CMRI in patients on hemodialysis (HD) remains limited. This research aimed to explore the characteristics of native T1 values in HD patients and identify factors related to T1 values. A total of thirty-two patients on HD and fourteen healthy controls were included in this study. All participants underwent CMRI. Using modified Look-Locker inversion recovery (MOLLI) sequence, native T1 mapping was achieved. Native CMRI T1 values were compared between the two groups. In order to analyze the relationship between T1 values and clinical parameters, correlation analysis was performed in patients on HD. Patients on HD exhibited elevated global native T1 values compared to control subjects. In the HD group, the global native T1 value correlated positively with intact parathyroid hormone (iPTH) (  = 0.418,  = 0.017) and negatively with triglycerides ( = -0.366,  = 0.039). Moreover, the global native T1 value exhibited a positive correlation with the left ventricular end-diastolic volume indexed to body surface area (BSA;  = 0.528,  = 0.014), left ventricular end-systolic volume indexed to BSA (  = 0.506,  = 0.019), and left ventricular mass indexed to BSA (  = 0.600,  = 0.005). A negative correlation was observed between the global native T1 value and ejection fraction (  = 0.-0.551,  = 0.010). The global native T1 value was prolonged in HD patients compared with controls. In the HD group, the global T1 value correlated strongly with iPTH, triglycerides, and cardiac structural and functional parameters.

Background Numerous studies have investigated the effects of exercise on depression and anxiety in breast cancer survivors, yet the results remain inconsistent. The aim of this study was to investigate the effects of exercise on depression and anxiety in breast cancer survivors and to ascertain the optimal exercise regimen for this patient population. Methods A comprehensive search was conducted across Embase, PubMed, the Cochrane Library, Web of Science, and Scopus until 28 October 2023. A meta‐analysis was conducted to quantify the standardized mean difference (SMD) and 95% confidence interval (CI). Results A total of 25 studies met the inclusion criteria. Exercise significantly alleviated depression (SMD, −0.63, p < 0.0001) and anxiety (SMD, −0.49, p = 0.0002) in breast cancer survivors. Specifically, aerobic exercise (SMD, −0.44, p = 0.03) and multicomponent training (SMD, −0.86, p = 0.002) were found to be particularly effective in alleviating depression. However, only multicomponent training significantly alleviated anxiety (SMD, −0.66, p = 0.003) in breast cancer survivors. Additionally, multicomponent training conducted for ≥ 3 times per week (depression, SMD, −1.22, p = 0.006; anxiety, SMD, −0.93, p = 0.004) and ≤ 60 min per session (depression, SMD, −1.19, p = 0.002; anxiety, SMD, −0.85, p = 0.005) were deemed most effective in alleviating depression and anxiety in breast cancer survivors. Conclusions Exercise significantly alleviated depression and anxiety in breast cancer survivors, with multicomponent training being the most effective intervention type. This meta‐analysis provides clinicians with evidence to recommend that breast cancer survivors engage in multicomponent training more than three times per week, with each session lasting no more than 60 min, to alleviate depression and anxiety.

The role of statin therapy in the development of kidney disease in patients with type 2 diabetes mellitus (DM) remains uncertain. We aimed to determine the relationships between statin initiation and kidney outcomes in patients with type 2 DM. Through a new-user design, we conducted a multicentre retrospective cohort study using the China Renal Data System database (which includes inpatient and outpatient data from 19 urban academic centres across China). We included patients with type 2 DM who were aged 40 years or older and admitted to hospital between Jan. 1, 2000, and May 26, 2021, and excluded those with pre-existing chronic kidney disease and those who were already on statins or without follow-up at an affiliated outpatient clinic within 90 days after discharge. The primary exposure was initiation of a statin. The primary outcome was the development of diabetic kidney disease (DKD), defined as a composite of the occurrence of kidney dysfunction (estimated glomerular filtration rate [eGFR] < 60 mL/min/1.73 m2 and > 25% decline from baseline) and proteinuria (a urinary albumin-to-creatinine ratio ≥ 30 mg/g and > 50% increase from baseline), sustained for at least 90 days; secondary outcomes included development of kidney function decline (a sustained > 40% decline in eGFR). We used Cox proportional hazards regression to evaluate the relationships between statin initiation and kidney outcomes, as well as to conduct subgroup analyses according to patient characteristics, presence or absence of dyslipidemia, and pattern of dyslipidemia. For statin initiators, we explored the association between different levels of lipid control and outcomes. We conducted analyses using propensity overlap weighting to balance the participant characteristics. Among 7272 statin initiators and 12 586 noninitiators in the weighted cohort, statin initiation was associated with lower risks of incident DKD (hazard ratio [HR] 0.72, 95% confidence interval [CI] 0.62–0.83) and kidney function decline (HR 0.60, 95% CI 0.44–0.81). We obtained similar results to the primary analyses for participants with differing patterns of dyslipidemia, those prescribed different statins, and after stratification according to participant characteristics. Among statin initiators, those with intensive control of high-density lipoprotein cholesterol (LDL-C) (< 1.8 mmol/L) had a lower risk of incident DKD (HR 0.51, 95% CI 0.32–0.81) than those with inadequate lipid control (LDL-C ≥ 3.4 mmol/L). For patients with type 2 DM admitted to and followed up in academic centres, statin initiation was associated with a lower risk of kidney disease development, particularly in those with intensive control of LDL-C. These findings suggest that statin initiation may be an effective and reasonable approach for preventing kidney disease in patients with type 2 DM.

Acute kidney injury (AKI) is prevalent and a leading cause of in-hospital death worldwide. Early prediction of AKI-related clinical events and timely intervention for high-risk patients could improve outcomes. We develop a deep learning model based on a nationwide multicenter cooperative network across China that includes 7,084,339 hospitalized patients, to dynamically predict the risk of in-hospital death (primary outcome) and dialysis (secondary outcome) for patients who developed AKI during hospitalization. A total of 137,084 eligible patients with AKI constitute the analysis set. In the derivation cohort, the area under the receiver operator curve (AUROC) for 24-h, 48-h, 72-h, and 7-day death are 95·05%, 94·23%, 93·53%, and 93·09%, respectively. For dialysis outcome, the AUROC of each time span are 88·32%, 83·31%, 83·20%, and 77·99%, respectively. The predictive performance is consistent in both internal and external validation cohorts. The model can predict important outcomes of patients with AKI, which could be helpful for the early management of AKI. Early prediction of AKI-related clinical events and timely intervention for high-risk patients could improve outcomes. Here, the authors show a deep learning model that can identify patients with acute kidney injury (AKI) who are at high risk of death or dialysis at certain time points.

Aims About 20–40% patients with type 2 diabetes mellitus (T2DM) had an increased risk of developing diabetic nephropathy (DN). Dipeptidyl peptidase-4 inhibitors (DPP-4i) were recommended for treatment of T2DM, while the impact of DPP-4i on renal function remained unclear. This study aimed to explore the effect of DPP-4i on renal parameter of estimated glomerular filtration rate (eGFR) and albumin-to-creatinine ratio (ACR) in T2DM. Methods A systematic search was performed across PubMed, Embase and Cochrane Library. A fixed or random-effects model was used for quantitative synthesis according to the heterogeneity, which was assessed with I 2 index. Sensitivity analysis and publication bias were performed with standard methods, respectively. Results A total of 17 randomized controlled trials were identified. Administration of DPP-4i produced no significant effect on eGFR (WMD, -0.92 mL/min/1.73m 2 , 95% CI, -2.04 to 0.19) in diabetic condition. DPP-4i produced a favorable effect on attenuating ACR (WMD, -2.76 mg/g, 95% CI, -5.23 to -0.29) in patients with T2DM. The pooled estimate was stable based on the sensitivity test. No publication bias was observed according to Begg’s and Egger’s tests. Conclusions Treatment with DPP-4i preserved the renal parameter of eGFR in diabetic condition. Available evidences suggested that administration of DPP-4i produced a favorable effect on attenuating ACR in patients with T2DM. International Prospective Register for Systematic Review (PROSPERO) number CRD.42020144642.

The physics governing the fluid dynamics of bio-inspired flapping wings is effectively characterized by partial differential equations (PDEs). Nevertheless, the process of discretizing these equations at spatiotemporal scales is notably time consuming and resource intensive. Traditional PDE-based computations are constrained in their applicability, which is mainly due to the presence of numerous shape parameters and intricate flow patterns associated with bionic flapping wings. Consequently, there is a significant demand for a rapid and accurate solution to nonlinear PDEs, to facilitate the analysis of bionic flapping structures. Deep learning, especially physics-informed deep learning (PINN), offers an alternative due to its great nonlinear curve-fitting capability. In the present work, a hybrid coarse-data-driven physics-informed neural network model (HCDD-PINN) is proposed to improve the accuracy and reliability of predicting the time evolution of nonlinear PDEs solutions, by using an order-of-magnitude-coarser grid than traditional computational fluid dynamics (CFDs) require as internal training data. The architecture is devised to enforce the initial and boundary conditions, and incorporate the governing equations and the low-resolution spatiotemporal internal data into the loss function of the neural network, to drive the training. Compared to the original PINN with no internal data, the training and predicting dynamics of HCDD-PINN with different resolutions of coarse internal data are analyzed on the problem relevant to the two-dimensional unsteady flapping wing, which involves unsteady flow features and moving boundaries. Additionally, a hyper-parametrical study is conducted to obtain an optimal model for the problem under consideration, which is then utilized for investigating the effects of the snapshot and fraction of the coarse internal data on the HCDD-PINN’s performances. The results show that the proposed framework has a sufficient stability and accuracy for solving the considered biomimetic flapping-wing problem, and its great potential means that it can be considered as an alternative to accelerate or replace traditional CFD solvers in the future. The interested variables of the flow field at any instant can be rapidly obtained by the trained HCDD-PINN model, which is superior to the traditional CFD method that usually needs to be re-run. For the three-dimensional and optimization problems of flapping wings, the advantages of the proposed method are supposedly even more apparent.

The COVID-19 pandemic is currently spreading widely around the world, causing huge threats to public safety and global society. This study analyzes the spatiotemporal pattern of the COVID-19 pandemic in China, reveals China’s epicenters of the pandemic through spatial clustering, and delineates the substantial effect of distance to Wuhan on the pandemic spread. The results show that the daily new COVID-19 cases mostly occurred in and around Wuhan before March 6, and then moved to the Grand Bay Area (Shenzhen, Hong Kong and Macau). The total COVID-19 cases in China were mainly distributed in the east of the Huhuanyong Line, where the epicenters accounted for more than 60% of the country’s total in/on 24 January and 7 February, half in/on 31 January, and more than 70% from 14 February. The total cases finally stabilized at approximately 84,000, and the inflection point for Wuhan was on 14 February, one week later than those of Hubei (outside Wuhan) and China (outside Hubei). The generalized additive model-based analysis shows that population density and distance to provincial cities were significantly associated with the total number of the cases, while distances to prefecture cities and intercity traffic stations, and population inflow from Wuhan after 24 January, had no strong relationships with the total number of cases. The results and findings should provide valuable insights for understanding the changes in the COVID-19 transmission as well as implications for controlling the global COVID-19 pandemic spread.

As an important data source, the Building Information Model (BIM) plays an important role in modern building safety management. Numerous studies have closely examined automatic compliance inspections for building safety and the safety management of dangerous projects. However, the value of the BIM has not been fully exploited in evidence-based practices of building safety. To address this limitation, this paper proposes an ontology-based Evidence/Risk-Based Safety (ERBS) management framework for divisional and subdivisional works with high risk, which includes: (1) BIM data extraction based on dynamo; (2) creation of an ontology based on building information and the ERBS management process model; (3) converting BIM data and evidence into ontology individuals; and (4) integrating the ontology through semantic web technology and using the Semantic Web Rule Language (SWRL) to conduct rule-based reasoning on the ontology. A case study shows that the framework is effective for the ERBS management of divisional and subdivisional works with high risk. The framework proposed in this study provides effective safety management methods for high-risk projects that can be applied in wider engineering practice in the future.

To compare the efficacy and safety of ticagrelor versus clopidogrel in stroke patients who were loss-of-function (LOF) carriers stratified by age and renal function. Patients in the CHANCE-2 trial were randomized to ticagrelor-aspirin or clopidogrel-aspirin treatment. The primary efficacy outcome was occurrence of a new stroke within 90 days, while bleeding was assessed for safety. Patients were categorized based on age and estimated glomerular filtration rate (eGFR). In patients with eGFR >90 mL/min/1.73 m , ticagrelor-aspirin was associated with a significantly lower risk of the subsequent stroke within 90 days compared with the clopidogrel-aspirin in those aged over 65 years (HR 0.53, 95% CI 0.33-0.85,  = 0.008) and under 65 years (HR, 0.67, 95% CI, 0.47-0.96,  = 0.03). While in those with eGFR 60-89 mL/min/1.73 m , ticagrelor did not show superiority over clopidogrel in reducing stroke regardless of age category (age ≥ 65: HR 1.14, 95% CI 0.71-1.84,  = 0.59; age < 65: HR 0.40, 95% CI 0.12-1.33,  = 0.13). The incidence of mild bleeding events was higher with ticagrelor-aspirin treatment in those aged < 65 years with eGFR ≥90 mL/min/1.73 m (HR 3.33, 95% CI 2.18-5.10,  < 0.001) and in those aged ≥ 65 years with eGFR <60mL/min/1.73 m (HR 8.68, 95% CI 1.06-71.1,  = 0.04). Elderly patients with normal renal function appear to benefit from ticagrelor compared with clopidogrel. Both younger patients with normal renal function and those with advanced age and renal insufficiency are prone to mild bleeding.