Using artificial intelligence algorithms to predict the overall survival of hemodialysis patients during the COVID-19 pandemic: A prospective cohort study.

BACKGROUND: Hemodialysis (HD) patients are a vulnerable population at high risk for severe complications from COVID-19. The impact of partial COVID-19 vaccination on the survival of HD patients remains uncertain. This prospective cohort study was designed to use artificial intelligence algorithms to predict the survival impact of partial COVID-19 vaccination in HD patients. METHODS: A cohort of 433 HD patients was used to develop machine-learning models based on a subset of clinical features assessed between July 1, 2021, and April 29, 2022. The patient cohort was randomly split into training (80%) and testing (20%) sets for model development and evaluation. Machine-learning models, including categorical boosting (CatBoost), light gradient boosting machines (LightGBM), RandomForest, and extreme gradient boosting models (XGBoost), were applied to evaluate their discriminative performance using the patient cohorts. RESULTS: Among these models, LightGBM achieved the highest F1 score of 0.95, followed by CatBoost, RandomForest, and XGBoost, with area under the receiver operating characteristic curve values of 0.94 on the testing dataset. The SHapley Additive explanation summary plot derived from the XGBoost model indicated that key features such as age, albumin, and vaccination details had a significant impact on survival. Furthermore, the fully vaccinated group exhibited higher levels of anti-spike (S) receptor-binding domain antibodies. CONCLUSION: This prospective cohort study involved using artificial intelligence algorithms to predict overall survival in HD patients during the COVID-19 pandemic. These predictive models assisted in identifying high-risk individuals and guiding vaccination strategies for HD patients, ultimately improving overall prognosis. Further research is warranted to validate and refine these predictive models in larger and more diverse populations of HD patients.

Granulocyte Colony-Stimulating Factor Improves Endothelial Progenitor Cell-Mediated Neovascularization in Mice with Chronic Kidney Disease.

Patients with chronic kidney disease (CKD) have a higher prevalence of peripheral arterial disease (PAD), and endothelial progenitor cells (EPCs) play a pivotal role. We examined the impact of granulocyte colony-stimulating factor (G-CSF) on EPC function in response to tissue ischemia. Eight-week-old male C57BL/6J male mice were divided into sham operation and subtotal nephrectomy (SNx) groups, received hindlimb ischemic operation after seven weeks, then randomly received G-CSF or PBS intervention for four weeks with weekly follow-ups. SNx mice had significantly reduced limb reperfusion, decreased plasma EPC mobilization, and impaired angiogenesis in ischemic hindlimbs compared to the control group. However, G-CSF increased IL-10 and reversed these adverse changes. Additionally, ischemia-associated protein expressions, including IL-10, phospho-STAT3, VEGF, and phospho-eNOS, were significantly downregulated in the ischemic hindlimbs of SNx mice versus control, but these trends were reversed by G-CSF. Furthermore, in cultured EPCs, G-CSF significantly attenuated the decrease in EPC function initiated by indoxyl sulfate through IL-10. Overall, we discovered that G-CSF can improve EPC angiogenic function through a hypoxia/IL-10 signaling cascade and impede neovascular growth in response to ischemia of SNx mice. Our results highlight G-CSF's potential to restore angiogenesis in CKD patients with PAD via EPC-based methods.

[Effects of Exogenous Microorganism Inoculation on Efficiency and Bacterial Community Structure of Sludge Composting].

In this study, municipal sludge, sawdust, and mushroom residues were used as raw materials for composting, and thermophiles and white-rot fungi were added into the compost in stages. By measuring physicochemical factors, including temperature, pH value, organic matter, water-soluble organic carbon, moisture content, total nitrogen, NH4+-N, NO3--N, and germination index during the composting process, the effect of exogenous bacteria inoculation on the efficiency of compost was determined. By means of high-throughput sequencing technology, the variation of bacterial community structure and the impact of exogenous bacteria inoculation on bacterial community structure during sludge composting were also investigated. The results showed that the inoculation extended the high temperature duration, decreased the nitrogen loss, and accelerated the decomposition and detoxification of the compost. During the entire period of composting, the structure of bacterial community changed significantly. There was low similarity of bacterial community structure among different stages of the same composting, but high similarity was observed in different composting of the same period. The inoculation of thermophilic bacteria improved the abundance of bacterial community and increased the proportions of dominant genera in thermophilic phase, but changed no species of dominant genera. Canonical correspondence analysis showed that pH had the highest influence on the structure of bacterial community and that temperature possessed positive correlation with nine genera.