Interpretable machine learning model for predicting acute kidney injury in critically ill patients.

BACKGROUND: This study aimed to create a method for promptly predicting acute kidney injury (AKI) in intensive care patients by applying interpretable, explainable artificial intelligence techniques. METHODS: Population data regarding intensive care patients were derived from the Medical Information Mart for Intensive Care IV database from 2008 to 2019. Machine learning (ML) techniques with six methods were created to construct the predicted models for AKI. The performance of each ML model was evaluated by comparing the areas under the curve (AUC). Local Interpretable Model-Agnostic Explanations (LIME) method and Shapley Additive exPlanation values were used to decipher the best model. RESULTS: According to inclusion and exclusion criteria, 53,150 severely sick individuals were included in the present study, of which 42,520 (80%) were assigned to the training group, and 10,630 (20%) were allocated to the validation group. Compared to the other five ML models, the eXtreme Gradient Boosting (XGBoost) model greatly predicted AKI following ICU admission, with an AUC of 0.816. The top four contributing variables of the XGBoost model were SOFA score, weight, mechanical ventilation, and the Simplified Acute Physiology Score II. An AKI and Non-AKI cases were predicted separately using the LIME algorithm. CONCLUSION: Overall, the constructed clinical feature-based ML models are excellent in predicting AKI in intensive care patients. It would be constructive for physicians to provide early support and timely intervention measures to intensive care patients at risk of AKI.

Exploring the nexus between fatigue, body composition, and muscle strength in hemodialysis patients.

BACKGROUND: Fatigue is a relatively prevalent condition among hemodialysis patients, resulting in diminished health-related quality of life and decreased survival rates. The purpose of this study was to investigate the relationship between fatigue and body composition in hemodialysis patients. METHODS: This cross-sectional study included 92 patients in total. Fatigue was measured by Functional Assessment of Chronic Illness Therapy - Fatigue (FACIT-F) (cut-off ≤ 34). Body composition was measured based on quantitative computed tomography (QCT), parameters including skeletal muscle index (SMI), intermuscular adipose tissue (IMAT), and bone mineral density (BMD). Handgrip strength was also collected. To explore the relationship between fatigue and body composition parameters, we conducted correlation analyses and binary logistic regression. RESULTS: The prevalence of fatigue was 37% (n = 34), abnormal bone density was 43.4% (n = 40). There was a positive correlation between handgrip strength and FACIT-F score (r = 0.448, p < 0.001). Age (r = - 0.411, p < 0.001), IMAT % (r = - 0.424, p < 0.001), negatively associated with FACIT-F score. Multivariate logistic regression analysis shows that older age, lower serum phosphorus, higher IMAT% are associated with a high risk of fatigue. CONCLUSION: The significantly increased incidence and degree of fatigue in hemodialysis patients is associated with more intermuscular adipose tissue in paraspinal muscle.

Development and validation of a nomogram for predicting in-hospital death in cirrhotic patients with acute kidney injury.

BACKGROUND: The purpose of this study was to develop a nomogram for predicting in-hospital mortality in cirrhotic patients with acute kidney injury (AKI) in order to identify patients with a high risk of in-hospital death early. METHODS: This study collected data on cirrhotic patients with AKI from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Multivariate logistic regression was used to identify confounding factors related to in-hospital mortality, which were then integrated into the nomogram. The concordance index (C-Index) was used to evaluate the accuracy of the model predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram. RESULTS: The final study population included 886 cirrhotic patients with AKI, and 264 (29.8%) died in the hospital. After multivariate logistic regression, age, gender, cerebrovascular disease, heart rate, respiration rate, temperature, oxygen saturation, hemoglobin, blood urea nitrogen, serum creatinine, international normalized ratio, bilirubin, urine volume, and sequential organ failure assessment score were predictive factors of in-hospital mortality. In addition, the nomogram showed good accuracy in estimating the in-hospital mortality of patients. The calibration plots showed the best agreement with the actual presence of in-hospital mortality in patients. In addition, the AUC and DCA curves showed that the nomogram has good prediction accuracy and clinical value. CONCLUSIONS: We have created a prognostic nomogram for predicting in-hospital death in cirrhotic patients with AKI, which may facilitate timely intervention to improve prognosis in these patients.

SGLT2 inhibition, high-density lipoprotein, and kidney function: a mendelian randomization study.

BACKGROUND: Sodium-glucose cotransporter 2 (SGLT2) inhibition is recognized for its evident renoprotective benefits in diabetic renal disease. Recent data suggest that SGLT2 inhibition also slows down kidney disease progression and reduces the risk of acute kidney injury, regardless of whether the patient has diabetes or not, but the mechanism behind these observed effects remains elusive. The objective of this study is to utilize a mendelian randomization (MR) methodology to comprehensively examine the influence of metabolites in circulation regarding the impact of SGLT2 inhibition on kidney function. METHODS: We used a MR study to obtain associations between genetic proxies for SGLT2 inhibition and kidney function. We retrieved the most recent and comprehensive summary statistics from genome-wide association studies (GWAS) that have been previously published and involved kidney function parameters such as estimated glomerular filtration rate (eGFR), urine albumin-to-creatinine ratio (UACR), and albuminuria. Additionally, we included blood metabolite data from 249 biomarkers in the UK Biobank for a more comprehensive analysis. We performed MR analyses to explore the causal relationships between SGLT2 inhibition and kidney function and two-step MR to discover potential mediating metabolites. RESULTS: The study found that a decrease in HbA1c levels by one standard deviation, which is genetically expected to result in SGLT2 inhibition, was linked to a decreased likelihood of developing type 2 diabetes mellitus (T2DM) (odds ratio [OR] = 0.55 [95% CI 0.35, 0.85], P = 0.007). Meanwhile, SGLT2 inhibition also protects eGFR (ß = 0.05 [95% CI 0.03, 0.08], P = 2.45 × 10- 5) and decreased UACR (-0.18 [95% CI -0.33, -0.02], P = 0.025) and albuminuria (-1.07 [95% CI -1.58, -0.57], P = 3.60 × 10- 5). Furthermore, the study found that of the 249 metabolites present in the blood, only one metabolite, specifically the concentration of small high-density lipoprotein (HDL) particles, was significantly correlated with both SGLT2 inhibition and kidney function. This metabolite was found to play a crucial role in mediating the improvement of renal function through the use of SGLT2 inhibition (ß = 0.01 [95% CI 0.005, 0.018], P = 0.001), with a mediated proportion of 13.33% (95% CI [5.71%, 26.67%], P = 0.020). CONCLUSIONS: The findings of this investigation provide evidence in favor of a genetically anticipated biological linkage between the inhibition of SGLT2, the presence of circulating metabolites, and renal function. The findings demonstrate that the protective effect of SGLT2 inhibition on renal function is mostly mediated by HDL particle concentrations in circulating metabolites. These results offer significant theoretical support for both the preservation of renal function and a better comprehension of the mechanisms underlying SGLT2 inhibition.

Machine learning algorithm for predict the in-hospital mortality in critically ill patients with congestive heart failure combined with chronic kidney disease.

BACKGROUND: The objective of this study was to develop and validate a machine learning (ML) model for predict in-hospital mortality among critically ill patients with congestive heart failure (CHF) combined with chronic kidney disease (CKD). METHODS: After employing least absolute shrinkage and selection operator regression for feature selection, six distinct methodologies were employed in the construction of the model. The selection of the optimal model was based on the area under the curve (AUC). Furthermore, the interpretation of the chosen model was facilitated through the utilization of SHapley Additive exPlanation (SHAP) values and the Local Interpretable Model-Agnostic Explanations (LIME) algorithm. RESULTS: This study collected data and enrolled 5041 patients on CHF combined with CKD from 2008 to 2019, utilizing the Medical Information Mart for Intensive Care Unit. After selection, 22 of the 47 variables collected post-intensive care unit admission were identified as mortality-associated and subsequently utilized in the development of ML models. Among the six models generated, the eXtreme Gradient Boosting (XGBoost) model demonstrated the highest AUC at 0.837. Notably, the SHAP values highlighted the sequential organ failure assessment score, age, simplified acute physiology score II, and urine output as the four most influential variables in the XGBoost model. In addition, the LIME algorithm explains the individualized predictions. CONCLUSIONS: In conclusion, our study accomplished the successful development and validation of ML models for predicting in-hospital mortality in critically ill patients with CHF combined with CKD. Notably, the XGBoost model emerged as the most efficacious among all the ML models employed.

Diosmin ameliorates renal fibrosis through inhibition of inflammation by regulating SIRT3-mediated NF-κB p65 nuclear translocation.

BACKGROUND: Renal fibrosis is considered an irreversible pathological process and the ultimate common pathway for the development of all types of chronic kidney diseases and renal failure. Diosmin is a natural flavonoid glycoside that has antioxidant, anti-inflammatory, and antifibrotic activities. However, whether Diosmin protects kidneys by inhibiting renal fibrosis is unknown. We aimed to investigate the role of Diosmin in renal interstitial fibrosis and to explore the underlying mechanisms. METHODS: The UUO mouse model was established and gavaged with Diosmin (50 mg/kg·d and 100 mg/kg·d) for 14 days. HE staining, Masson staining, immunohistochemistry, western blotting and PCR were used to assess renal tissue injury and fibrosis. Elisa kits were used to detect the expression levels of IL-1ß, IL-6, and TNF-α and the activity of SIRT3 in renal tissues. In addition, enrichment maps of RNA sequencing analyzed changes in signaling pathways. In vitro, human renal tubular epithelial cells (HK-2) were stimulated with TGF-ß1 and then treated with diosmin (75 µM). The protein and mRNA expression levels of SIRT3 were detected in the cells. In addition, 3-TYP (selective inhibitor of SIRT3) and SIRT3 small interfering RNA (siRNA) were used to reduce SIRT3 levels in HK-2. RESULTS: Diosmin attenuated UUO-induced renal fibrosis and TGF-ß1-induced HK-2 fibrosis. In addition, Diosmin reduced IL-1ß, IL-6, and TNF-α levels in kidney tissues and supernatants of HK-2 medium. Interestingly, Diosmin administration increased the enzymatic activity of SIRT3 in UUO kidneys. In addition, Diosmin significantly increased mRNA and protein expression of SIRT3 in vitro and in vivo. Inhibition of SIRT3 expression using 3-TYP or SIRT3 siRNA abolished the anti-inflammatory effects of diosmin in HK-2 cells. Enrichment map analysis by RNA sequencing indicates that the nuclear factor-kappa B (NF-κB) signaling pathway was inhibited in the Diosmin intervention group. Furthermore, we found that TGF-ß1 increased the nuclear expression of nuclear NF-κB p65 but had little significant effect on the total intracellular expression of NF-κB p65. Additionally, Diosmin reduced TGF-ß1-caused NF-κB p65 nuclear translocation. Knockdown of SIRT3 expression by SIRT3 siRNA increased the nuclear expression of NF-κB p65 and abolished the inhibition effect of Diosmin in NF-κB p65 expression. CONCLUSIONS: Diosmin reduces renal inflammation and fibrosis, which is contributed by inhibiting nuclear translocation of NF-κB P65 through activating SIRT3.

Global, Regional, and National Burden of CKD in Children and Adolescents from 1990 to 2019.

BACKGROUND AND AIMS: CKD is one of the most prevalent non-communicable health concerns in children and adolescents worldwide; however, data on its incidence, prevalence, disability-adjusted life years (DALYs), and trends in the population are limited. We aimed to assess the global, regional, and national trends in CKD burden in children and adolescents. METHODS: In this trend analysis based on the 2019 Global Diseases, Injuries, and Risk Factors Study, CKD incidence, prevalence, and DALYs rates per 100,000 population for children and adolescents were reported at the global, regional, and national levels, as well as the average annual percentage change (AAPC). These global trends were analyzed by age, sex, region, and socio-demographic index (SDI). RESULTS: Globally, the overall incidence of CKD (all stages including KRT) in children and adolescents showed an increasing trend (AAPC 0.44 [95% CI 0.36-0.52]) between 1990 and 2019. Similarly, the overall prevalence of CKD also showed an upward trend (AAPC 0.46 [95% CI 0.42-0.51]). However, the DALYs of CKD showed a continuous decreasing trend (AAPC -1.18[-1.37- -0.99]). The population aged 15-19 years had the largest CKD incidence increase during this period. The largest increase in age-standardized incidence rate (ASIR) was in middle SDI countries (AAPC 0.56 [0.45-0.67]). The relationship between the ASIR and SDI showed an inverse U-shaped correlation while the relationship between the age-standardized DALYs rate (ASDR) and SDI showed an inverse trend with SDI. Among adolescents (15-19 years), the ASIR continued to increase for five causes of CKD, owing to type 2 diabetes mellitus and hypertension. Most of the disease burden was concentrated in countries with a lower SDI. Andean Latin America and Central Latin America showed the largest increases in CKD ASIR between 1990 and 2019. CONCLUSION: The burden of CKD in children and adolescents has increased worldwide, especially in regions and countries with a lower SDI.

The Causal Effect of Obesity on the Risk of 15 Autoimmune Diseases: A Mendelian Randomization Study.

INTRODUCTION: Observational studies have shown that obesity is a risk factor for various autoimmune diseases. However, the causal relationship between obesity and autoimmune diseases is unclear. Mendelian randomization (MR) was used to investigate the causal effects of obesity on 15 autoimmune diseases. METHODS: MR analysis employed instrumental variables, specifically single-nucleotide polymorphisms associated with obesity measures such as body mass index (BMI), waist circumference, hip circumference, and waist-to-hip ratio. The study utilized UK Biobank and FinnGen data to estimate the causal relationship between obesity and autoimmune diseases. RESULTS: Genetically predicted BMI was associated with risk for five autoimmune diseases. The odds ratio per 1-SD increase in genetically predicted BMI, the OR was 1.28 (95% CI, 1.18-1.09; p < 0.001) for asthma, 1.37 (95% CI, 1.24-1.51; p < 0.001) for hypothyroidism, 1.52 (95% CI, 1.27-1.83; p < 0.001) for psoriasis, 1.22 (95% CI, 1.06-1.40; p = 0.005) for rheumatoid arthritis, and 1.55 (95% CI, 1.32-1.83; p < 0.001) for type 1 diabetes. However, after adjusting for genetic susceptibility to drinking and smoking, the correlation between BMI and rheumatoid arthritis was not statistically significant. Genetically predicted waist circumference, hip circumference, and waist and hip circumference were associated with 6, 6, and 1 autoimmune disease, respectively. CONCLUSION: This study suggests that obesity may be associated with an increased risk of several autoimmune diseases, such as asthma, hypothyroidism, psoriasis, rheumatoid arthritis, and type 1 diabetes.

Identification of basement membrane markers in diabetic kidney disease and immune infiltration by using bioinformatics analysis and experimental verification.

Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease worldwide. Basement membranes (BMs) are ubiquitous extracellular matrices which are affected in many diseases including DKD. Here, the authors aimed to identify BM-related markers in DKD and explored the immune cell infiltration in this process. The expression profiles of three datasets were downloaded from the Gene Expression Omnibus database. BM-related differentially expression genes (DEGs) were identified and Kyoto encyclopaedia of genes and genomes pathway enrichment analysis were applied to biological functions. Immune cell infiltration and immune function in the kidneys of patients with DKD and healthy controls were evaluated and compared using the ssGSEA algorithm. The association of hub genes and immune cells and immune function were explored. A total of 30 BM-related DEGs were identified. The functional analysis showed that BM-related DEGs were notably associated with basement membrane alterations. Crucially, BM-related hub genes in DKD were finally identified, which were able to distinguish patients with DKD from controls. Moreover, the authors observed that laminin subunit gamma 1(LAMC1) expression was significantly high in HK2 cells treated with high glucose. Immunohistochemistry results showed that, compared with those in db/m mouse kidneys, the levels of LAMC1 in db/db mouse kidneys were significantly increased. The biomarkers genes may prove crucial for DKD treatment as they could be targeted in future DKD treatment protocols.

Development and validation of a nomogram for predicting gram-negative bacterial infections in patients with peritoneal dialysis-associated peritonitis.

Background: This study aimed to develop a nomogram for predicting gram-negative bacterial (GNB) infections in patients with peritoneal dialysis-associated peritonitis (PDAP) to identify patients at high risk for GNB infections. Methods: In this investigation, hospitalization information was gathered retrospectively for patients with PDAP from January 2016 to December 2021. The concatenation of potential biomarkers obtained by univariate logistic regression, LASSO analysis, and RF algorithms into multivariate logistic regression was used to identify confounding factors related to GNB infections, which were then integrated into the nomogram. The concordance index (C-Index) was utilized to assess the precision of the model's predictions. The area under the curve (AUC) and decision curve analysis (DCA) was used to assess the predictive performance and clinical utility of the nomogram. Results: The final study population included 217 patients with PDAP, and 37 (17.1%) patients had gram-negative bacteria due to dialysate effluent culture. After multivariate logistic regression, age, procalcitonin, and hemoglobin were predictive factors of GNB infections. The C-index and bootstrap-corrected index of the nomogram for estimating GNB infections in patients were 0.821 and 0.814, respectively. The calibration plots showed good agreement between the predictions of the nomogram and the actual observation of GNB infections. The AUC of the receiver operating characteristic curve was 0.821, 95% CI: 0.747-0.896, which indicates that the model has good predictive accuracy. In addition, the DCA curve showed that the nomogram had a high clinical value in the range of 1%-94%, which further demonstrated that the nomogram could accurately predict GNB infection in patients with PDAP. Conclusions: We have created a new nomogram for predicting GNB infections in patients with PDAP. The nomogram model may improve the identification of GNB infections in patients with PDAP and contribute to timely intervention to improve patient prognosis.

Machine learning algorithm to predict the in-hospital mortality in critically ill patients with chronic kidney disease.

BACKGROUND: This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in critically ill patients with chronic kidney disease (CKD). METHODS: This study collected data on CKD patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. Six ML approaches were used to build the model. Accuracy and area under the curve (AUC) were used to choose the best model. In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values. RESULTS: There were 8527 CKD patients eligible for participation; the median age was 75.1 (interquartile range: 65.0-83.5) years, and 61.7% (5259/8527) were male. We developed six ML models with clinical variables as input factors. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.860. According to the SHAP values, the sequential organ failure assessment score, urine output, respiratory rate, and simplified acute physiology score II were the four most influential variables in the XGBoost model. CONCLUSIONS: In conclusion, we successfully developed and validated ML models for predicting mortality in critically ill patients with CKD. Among all ML models, the XGBoost model is the most effective ML model that can help clinicians accurately manage and implement early interventions, which may reduce mortality in critically ill CKD patients with a high risk of death.

Analysis of the potential biological mechanisms of diosmin against renal fibrosis based on network pharmacology and molecular docking approach.

BACKGROUND: Interstitial fibrosis is involved in the progression of various chronic kidney diseases and renal failure. Diosmin is a naturally occurring flavonoid glycoside that has antioxidant, anti-inflammatory, and antifibrotic activities. However, whether diosmin protects kidneys by inhibiting renal fibrosis is unknown. METHODS: The molecular formula of diosmin was obtained, targets related to diosmin and renal fibrosis were screened, and interactions among overlapping genes were analyzed. Overlapping genes were used for gene function and KEGG pathway enrichment analysis. TGF-ß1 was used to induce fibrosis in HK-2 cells, and diosmin treatment was administered. The expression levels of relevant mRNA were then detected. RESULTS: Network analysis identified 295 potential target genes for diosmin, 6828 for renal fibrosis, and 150 hub genes. Protein-protein interaction network results showed that CASP3, SRC, ANXA5, MMP9, HSP90AA1, IGF1, RHOA, ESR1, EGFR, and CDC42 were identified as key therapeutic targets. GO analysis revealed that these key targets may be involved in the negative regulation of apoptosis and protein phosphorylation. KEGG indicated that pathways in cancer, MAPK signaling pathway, Ras signaling pathway, PI3K-Akt signaling pathway, and HIF-1 signaling pathway were key pathways for renal fibrosis treatment. Molecular docking results showed that CASP3, ANXA5, MMP9, and HSP90AA1 stably bind to diosmin. Diosmin treatment inhibited the protein and mRNA levels of CASP3, MMP9, ANXA5, and HSP90AA1. Network pharmacology analysis and experimental results suggest that diosmin ameliorates renal fibrosis by decreasing the expression of CASP3, ANXA5, MMP9, and HSP90AA1. CONCLUSIONS: Diosmin has a potential multi-component, multi-target, and multi-pathway molecular mechanism of action in the treatment of renal fibrosis. CASP3, MMP9, ANXA5, and HSP90AA1 might be the most important direct targets of diosmin.

Machine learning algorithm to predict mortality in critically ill patients with sepsis-associated acute kidney injury.

This study aimed to establish and validate a machine learning (ML) model for predicting in-hospital mortality in patients with sepsis-associated acute kidney injury (SA-AKI). This study collected data on SA-AKI patients from 2008 to 2019 using the Medical Information Mart for Intensive Care IV. After employing Lasso regression for feature selection, six ML approaches were used to build the model. The optimal model was chosen based on precision and area under curve (AUC). In addition, the best model was interpreted using SHapley Additive exPlanations (SHAP) values and Local Interpretable Model-Agnostic Explanations (LIME) algorithms. There were 8129 sepsis patients eligible for participation; the median age was 68.7 (interquartile range: 57.2-79.6) years, and 57.9% (4708/8129) were male. After selection, 24 of the 44 clinical characteristics gathered after intensive care unit admission remained linked with prognosis and were utilized developing ML models. Among the six models developed, the eXtreme Gradient Boosting (XGBoost) model had the highest AUC, at 0.794. According to the SHAP values, the sequential organ failure assessment score, respiration, simplified acute physiology score II, and age were the four most influential variables in the XGBoost model. Individualized forecasts were clarified using the LIME algorithm. We built and verified ML models that excel in early mortality risk prediction in SA-AKI and the XGBoost model performed best.

Environmental factors influencing the risk of ANCA-associated vasculitis.

Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a group of diseases characterized by inflammation and destruction of small and medium-sized blood vessels. Clinical disease phenotypes include microscopic polyangiitis (MPA), granulomatosis with polyangiitis (GPA), and eosinophilic granulomatosis with polyangiitis (EGPA). The incidence of AAV has been on the rise in recent years with advances in ANCA testing. The etiology and pathogenesis of AAV are multifactorial and influenced by both genetic and environmental factors, as well as innate and adaptive immune system responses. Multiple case reports have shown that sustained exposure to silica in an occupational environment resulted in a significantly increased risk of ANCA positivity. A meta-analysis involving six case-control studies showed that silica exposure was positively associated with AAV incidence. Additionally, exposure to air pollutants, such as carbon monoxide (CO), is a risk factor for AAV. AAV has seasonal trends. Studies have shown that various environmental factors stimulate the body to activate neutrophils and expose their own antigens, resulting in the release of proteases and neutrophil extracellular traps, which damage vascular endothelial cells. Additionally, the activation of complement replacement pathways may exacerbate vascular inflammation. However, the role of environmental factors in the etiology of AAV remains unclear and has received little attention. In this review, we summarized the recent literature on the study of environmental factors, such as seasons, air pollution, latitude, silica, and microbial infection, in AAV with the aim of exploring the relationship between environmental factors and AAV and possible mechanisms of action to provide a scientific basis for the prevention and treatment of AAV.

Early Humoral Responses of Hemodialysis Patients After Inactivated SARS-CoV-2 Vaccination.

Purpose: To detect antibody responses to inactivated severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) vaccine in patients undergoing hemodialysis and to investigate vaccine-related adverse events. Patients and Methods: A total of 120 hemodialysis (HD) patients and 24 healthy controls (HCs) who had not been previously infected with SARS-CoV-2 and had received their first dose of the inactivated vaccine (CoronaVac; Sinovac Biotech Ltd) were recruited for this study. All participants were scheduled to receive a second dose of inactivated SARS-CoV-2 vaccine. Serum-specific immunoglobulin M (IgM) and immunoglobulin G (IgG) antibodies against the SARS-CoV-2 were detected at least 14 days after the second dose of vaccine using a commercial kit. Positive and negative results were defined as a sample/cutoff (S/CO) ratio≥1.00 and <1.00, respectively. Vaccination-related adverse events were assessed using a standardized questionnaire. Results: There were no significant differences regarding the seroprevalences of IgG and IgM antibodies against SARS-CoV-2 and the self-reported vaccination-related adverse events between HD patients and HCs. The analysis results for HD patients suggest that 82 (68.3%) and 27 (22.5%) tested positive for IgG and IgM, respectively. The levels of IgG were higher than IgM levels (P<0.0001). In addition, the IgG-positive group had significantly higher serum albumin levels than the IgG-negative group (P<0.05). Only mild vaccine-related adverse events were observed in two patients (1.66%) and in one healthy individual (4.2%). Conclusion: The seroprevalences of IgG and IgM antibodies against SARS-CoV-2 and vaccination-related adverse effects are similar between HD and HCs. The inactivated SARS-CoV-2 vaccine is effective and safe in inducing near-term immunity in hemodialysis patients.

Neutrophil-to-lymphocyte ratio in relation to the risk of all-cause mortality and cardiovascular events in patients with chronic kidney disease: a systematic review and meta-analysis.

AIM: To systematically evaluate the relationship between the neutrophil-to-lymphocyte ratio (NLR) and the risk of all-cause mortality or cardiovascular events in patients with chronic kidney disease (CKD). METHODS: PubMed, Embase, and Web of Science databases were searched for cohort studies that were published since the databases were launched, until 1 April 2020. We selected papers according to specific inclusion and exclusion criteria, extracted data, and evaluated the quality of the citations. Data from eligible studies were used to calculate the combined hazard ratios (HRs) and 95% confidence intervals (CI). RESULTS: The search identified 1048 potentially eligible records, and 10 studies (n = 1442) were selected. Eight studies reported all-cause mortality, and two studies reported cardiovascular events. The combined HR of all-cause mortality was 1.45 (95% CI 1.20-1.75) and the HR of cardiovascular events was 1.52 (95% CI 1.33-1.72) when NLR was considered as a categorical variable. Similarly, the association between NLR and all-cause mortality was confirmed (HR 1.35; 95% CI 1.23-1.48) when NLR was used as a continuous variable. CONCLUSION: NLR is a predictor of all-cause mortality and cardiovascular events in patients with chronic kidney disease.

miR-124 regulates liver cancer stem cells expansion and sorafenib resistance.

Liver cancer stem cells (CSCs) contribute to tumorigenesis, progression, recurrence and drug resistance of hepatocellular carcinoma (HCC). However, the underlying mechanism for liver CSCs expansion remains unclear. Herein, we report that miR-124 is downregulated in liver CSCs and associated with the poor prognosis of HCC. Functional studies revealed that a forced expression of miR-124 inhibits liver CSCs self-renew and tumorigenesis. Conversely, miR-124 knockdown promotes liver CSCs self-renew and tumorigenesis. Mechanistically, miR-124 directly target Caveolin-1 (CAV1) via its mRNA 3'UTR in liver CSCs. Furthermore, miR-124 expression determines the responses of hepatoma cells to sorafenib treatment. The analysis of patient cohort and patient-derived xenografts (PDXs) further demonstrated that miR-124 may predict sorafenib benefits in HCC patients. In conclusion, our findings revealed the crucial role of the miR-124 in liver CSCs expansion and sorafenib response, rendering miR-124 an optimal target for the prevention and intervention in HCC.

miR-552 promotes ovarian cancer progression by regulating PTEN pathway.

BACKGROUND: Increasing researches have demonstrated the critical functions of MicroRNAs (miRNAs) in the progression of malignant tumors, including ovarian cancer. It was reported that miR-552 was an important oncogene in both breast cancer and colorectal cancer. However, the role of miR-552 in ovarian cancer (OC) remains to be elucidated. METHODS: RT-PCR and western blot analysis were used to detect the expression of miR-552 and PTEN. The impact of miR-552 on ovarian cancer proliferation and metastasis was investigated in vitro. The prognostic value of miR-552 was evaluated using the online bioinformatics tool Kaplan-Meier plotter. RESULTS: In the present study, we for first found that miR-552 was upregulated in ovarian cancer, especially in metastatic and recurrence ovarian cancer. Forced miR-552 expression promotes the growth and metastasis of ovarian cancer cells. Consistently, miR-552 interference inhibits the proliferation and metastasis of ovarian cancer cells. Mechanically, bioinformatics and luciferase reporter analysis identified Phosphatase and tension homolog (PTEN) as a direct target of miR-552. miR-552 downregulated the PTEN mRNA and protein expression in ovarian cancer cells. Furthermore, the PTEN siRNA abolishes the discrepancy of growth and metastasis capacity between miR-552 mimic ovarian cells and control cells. More importantly, upregulation of miR-552 predicts the poor prognosis of ovarian cancer patients. CONCLUSION: Our findings revealed that miR-552 could promote ovarian cancer cells progression by targeting PTEN signaling and might therefore be useful to predict patient prognosis.