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.

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.

RER-YOLO: improved method for surface defect detection of aluminum ingot alloy based on YOLOv5.

Aluminum ingot alloy is one of the commonly used materials in industrial production and intelligent manufacturing, whose quality directly affects the performance of aluminum processed products. Therefore, the inspection of surface defects of aluminum ingot alloy is extremely valuable for actual industrial engineering. Aiming at the issues of low detecting precision and the slowly processing rate thatexisted in the traditional target detection methods for aluminum ingot alloy dataset, the YOLOv5-based improvement model RER-YOLO is proposed. Firstly, the aluminum ingot alloy dataset is coped with the image pretreatment methods of rotation, translation, contrast and brightness transformations in a random combination so as to boost the capacity of generalization for model training. Secondly, a multi-scale characteristic extraction network block (Res2Net) is utilized to take the place of the C3 block in the previous YOLOv5 to augment the model's ability that can accurately extract rich features. Finally, an over-parameterization-based re-parameterized convolutional block is utilized in place of the 3×3 convolutional blocks in the Res2Net residual block and baseline model, enlarging the search space of the network and boosting the model's fitting ability while maintaining inference rate. The comparison experimental results demonstrate that the RER-YOLO reaches a mean average precision of 75.1% on the aluminum ingot alloy dataset, which is higher 4.9% than the conventional YOLOv5 and does not increase the inference delay. It also improves the detection accuracy by 12.7% for burr defects, which are fewer in number in the dataset and the defect features are difficult to extract. It can be seen that the presented model in this study has an important reference value towards detecting surface defects in aluminum ingot alloy.

Associations between sleep problems and cardiometabolic risk in maintenance hemodialysis patients: A multicenter study.

The incidence of cardiovascular disease is increasing around the world, and it is one of the main causes of death in chronic kidney diseases patients. It is urgent to early identify the factors of cardiometabolic risk. Sleep problems have been recognized as a risk factor for cardiometabolic risk in both healthy people and chronic patients. However, the relationship between sleep problems and cardiometabolic risk has not been clearly explored in hemodialysis patients. This study aimed to investigate the relationship between sleep problems and cardiometabolic risk in 3025 hemodialysis patients by a multicenter study. After adjusting for confounders, binary logistic regression models showed that hemodialysis patients reported sleep duration greater than 7 h were more likely to be with hypertension, hyperglycemia, hypertriglyceridemia, and hypercholesterolemia. Patients reported sleep duration less than 7 h were more likely to be with hypertriglyceridemia and hypercholesterolemia, but the risks of hyperglycemia and Low HDL-cholesterol were decreased. Poor sleep quality was negatively correlated to low HDL cholesterol and hypertriglyceridemia. Moreover, gender-based differences were explained.

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.

Three-dimensional printing technique aids screw insertion into the sustentaculum tali of the internal fixation of intra-articular calcaneal fractures.

INTRODUCTION: Treating complex calcaneus fractures remains challenging. This study evaluated the influence of 3D printing and simulation on precision screw insertion into the calcaneus sustentaculum tali (ST). HYPOTHESIS: 3D printing and simulation improve the treatment for calcaneal fracture. PATIENTS AND METHODS: This retrospective cohort study included 85 patients admitted with 93 Sanders type II-IV intra-articular fractures from January 2015 to June 2020. Multi-slice computed tomography (MSCT) images were used in the conventional group, and MSCT data were used to construct a 3D model of the calcaneus to simulate screw insertion and verify parameter accuracy in the 3D group. RESULTS: The designed parameters (upward and backward oblique angles and screw-path length) were similar to the actual values in the 3D group (p=0.428,0.287,0.585) but not in the conventional group (p=0.01,0.002,0.023). The Maryland foot functional score, accuracy rate, and average screw number were higher and operative time was shorter in the 3D group (p=0.005,0.007,0.000,0.000). DISCUSSION: Preoperative simulation using the 3D printing model helped guide the screws into the ST more accurately, lending better-quality treatment for Sanders type II-IV calcaneal fractures. LEVEL OF PROOF: III; Retrospective case-control study.

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.

Associations of Whole Blood Zinc Levels with Coronary Artery Calcification and Future Cardiovascular Events in CKD Patients.

This study was conducted to compare the differences of the whole blood zinc concentration in patients with chronic kidney disease (CKD) as compared to healthy controls, and to explore the correlations of the whole blood zinc level with coronary artery calcification (CAC) and cardiovascular event (CVE) in CKD patients. A total of 170 CKD patients and 62 healthy controls were recruited. The whole blood zinc concentration was determined in using atomic absorption spectroscopy (AAS) method. The degrees of CAC were evaluated by Agatston score based on computed tomography (CT). Regular follow-up visits were performed to record the incidence of CVE, and risk factors were analyzed by COX proportional hazard model and Kaplan-Meier survival curve. There were statistically significant lower zinc levels in CKD patients than in healthy population. The prevalence of CAC was 58.82% in CKD patients. Correlation analysis showed that dialysis duration, intact parathyroid hormone (iPTH), alkaline phosphatase (ALP), 25-hydroxyvitamin D3 (25(OH)D3), neutrophil-lymphocyte ratio (NLR), total cholesterol (TC), and high-sensitive C-reactive protein (Hs-CRP) were positively correlated with CAC, while albumin (ALB), hemoglobin (Hb), and zinc levels were negatively associated with CAC. Further COX proportional hazard model demonstrated that moderate to severe CAC, NLR, phosphate, 25(OH)D3, iPTH, and high-density lipoprotein (HDL) were associated with an increased risk for CVE, while zinc levels, Hb, and ALB were inversely associated with a reduced risk for CVE. Kaplan-Meier curve showed that low zinc (zinc < 86.62 µmol/L) patients and moderate to severe CAC patients had lower survival respectively. Our study found the lower levels of zinc and higher prevalence of CAC in CKD patients; the low zinc is involved in the high incidence rate of moderate to severe CAC and CVE in CKD patients.

The prevalence and influencing factors of frailty in patients with chronic kidney disease: a systematic review and meta-analysis.

BACKGROUND: To investigate the prevalence and influencing factors of frailty and pre-frailty in chronic kidney disease (CKD) patients and thereby provide a scientific basis for effective avoidance of frailty in patients with CKD. METHODS: PubMed, EMBASE, Web of Science, EBSCO, Cochrane Library, CNKI, VIP, CBMdisc, and Wanfang databases were searched for relevant studies published till December 31, 2021. The summary results were described as odds ratios (ORs) or standardized mean differences (SMDs) with 95% confidence intervals (CIs). A meta-analysis was performed using StataSE12.0. RESULTS: Fifteen published studies, which enrolled a total of 3294 CKD patients, met the inclusion criteria. The combined prevalence of frailty in CKD patients was 38.1% (95% CI 29.7-46.5%) and pre-frailty was 37.9% (95% CI 32.7-43.1%). The main factors influencing frailty in CKD patients were age (SMD 0.524, 95% CI 0.326-0.723), diastolic blood pressure (SMD - 0.294, 95% CI - 0.518 to - 0.071), body mass index (BMI) (SMD - 0.267, 95% CI - 0.471 to - 0.064), grip strength (SMD - 0.929, 95% CI - 1.233 to - 0.626), hemoglobin level (SMD - 0.346, 95% CI - 0.448 to - 0.243), serum albumin level (SMD - 0.533, 95% CI - 0.655 to - 0.411), Charlson Comorbidity Index (SMD 0.421, 95% CI 0.150-0.692), multiple medications (SMD 0.625, 95% CI 0.354-0.895), Mini-Mental State Examination (MMSE) score (SMD - 0.563, 95% CI - 0.846 to - 0.280), and female (OR 2.391, 95% CI 1.236-4.627). CONCLUSION: Frailty is common in CKD patients. The prevalence of frailty among CKD patients was related to age, diastolic blood pressure, BMI, grip strength, hemoglobin and serum albumin levels, Charlson Comorbidity Index, multiple medications, MMSE score, and female.

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.

Could long-term dialysis vintage and abnormal calcium, phosphorus and iPTH control accelerate aging among the maintenance hemodialysis population?

OBJECTIVE: Aging is a complex process of physiological dysregulation of the body system and is common in hemodialysis patients. However, limited studies have investigated the links between dialysis vintage, calcium, phosphorus, and iPTH control and aging. The purpose of the current study was to examine these associations. METHODS: During 2020, a cross-sectional study was conducted in 3025 hemodialysis patients from 27 centers in Anhui Province, China. Biological age was calculated by a formula using chronological age and clinical indicators. The absence of the target range for serum phosphorus (0.87-1.45 mmol/L), corrected calcium (2.1-2.5 mmol/L) and iPTH (130-585 pg/mL) were identified as abnormal calcium, phosphorus, and iPTH control. RESULTS: A total of 1131 hemodialysis patients were included, 59.2% of whom were males (669/1131). The mean (standard deviation) of actual age and biological age were 56.07 (12.79) years and 66.94 (25.88), respectively. The median of dialysis vintage was 4.3 years. After adjusting for the confounders, linear regression models showed patients with abnormal calcium, phosphorus, and iPTH control and on hemodialysis for less than 4.3 years (B = 0.211, p = .002) or on hemodialysis for 4.3 years or more (B = 0.302, p < .001), patients with normal calcium, phosphorus, and iPTH control and on hemodialysis for 4.3 years or more (B = 0.087, p = .013) had a higher biological age. CONCLUSION: Our findings support the hypothesis that long-term hemodialysis and abnormal calcium, phosphorus, and iPTH control may accelerate aging in the hemodialysis population. Further studies are warrant to verify the significance of maintaining normal calcium-phosphorus metabolism in aging.

Influencing factors of cardiac valve calcification (CVC) in patients with chronic kidney disease and the impact of CVC on long-term prognosis: a single-center retrospective study.

Objective: To investigate the effect of cardiac valve calcification (CVC) on the prognosis of patients with chronic kidney disease (CKD). Methods: A total of 343 CKD patients were retrospectively analyzed, and divided into two groups according to the presence or absence of cardiac valve calcification. All patients were followed until death, loss to follow-up, or the end point of the study (December 2021). Results: The incidence of CVC among the 343 CKD patients was 29.7%, including 21 cases of mitral valve calcification, 63 cases of aortic valve calcification, and 18 cases of mitral valve combined with aortic valve calcification. The incidence of CVC in CKD stages 1-2 was 0.3%, 5.2% in CKD stages 3-4, and 24.2% in CKD stage 5 (P < 0.05). Advanced age, higher serum albumin, higher cystatin C and lower uric acid levels were all associated with a higher risk of CVC. After six years of follow-up, 77 patients (22.4%) died. The causes of death were cardiovascular and cerebrovascular diseases in 36 cases (46.7%), infection in 29 cases (37.7%), gastrointestinal bleeding in nine cases (11.7%), and "other" in the remaining three cases (3.9%). A Kaplan Meier survival analysis showed that the overall survival rate of patients with CVC was lower than that of patients without CVC. Conclusion: The incidence of CVC, mainly aortic calcification, is high in patients with CKD. Advanced age, higher serum albumin and higher cystatin C levels were associated with a higher risk of CVC. Hyperuricemia was associated with a lower risk of CVC. The overall survival rate of patients with CVC was lower than that of patients without CVC.

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.

Standard sine motion video-based virtual traceability method for monocular vision low-frequency vibration measurements.

The low-frequency vibration exists in building structures, mechanical devices, instrument manufacturing, and other fields, and is the key to modal analysis, steady-state control, and precision machining. At present, the monocular vision (MV) method has gradually become the primary choice to measure the low-frequency vibration because of its distinctive advantages in efficiency, non-contact, simplicity, flexibility, low cost, etc. Although many literature reports have demonstrated that this method has the capability to reach high measurement repeatability and resolution, its metrological traceability and uncertainty evaluation are difficult to be unified. In this study, a novel, to the best of our knowledge, virtual traceability method is presented to evaluate the measurement performance of the MV method for the low-frequency vibration. This presented method achieves traceability by adopting the standard sine motion videos and the precise position error correction model. Simulations and experiments confirm that the presented method can evaluate the amplitude and phase measurement accuracy of the MV-based low-frequency vibration in the frequency range from 0.01 to 20 Hz.

Dialysis vintage is associated with a high prevalence and severity of unpleasant symptoms in patients on hemodialysis.

Background: The burden of physical and emotional symptoms caused by somatic illness is present in most dialysis patients. However, it's unclear how symptom burden varies among patients with different dialysis vintages. We sought to examine differences in the prevalence and severity of unpleasant symptoms in hemodialysis patients with diverse dialysis vintage cohorts.Methods: This cross-sectional study included patients on maintenance hemodialysis at the Second Hospital of Anhui Medical University. We used the Dialysis Symptom Index (DSI) to determine the associated unpleasant symptoms, which is a validated survey to assess symptom burden/severity (higher scores indicate more severe symptoms), over June 2022 - September 2022.Results: We studied 146 patients: 35 (24%) had a dialysis vintage of ≤12 months (group 1) and 111 (76%) had a dialysis vintage of >12 months (group 2). Concerning Group 1 patients, the prevalence and severity of unpleasant symptoms were significantly higher in Group 2, the most common individual symptoms included feeling tired or lack of energy and trouble falling asleep (i.e., 75-85% of patients in each group), with dialysis vintage being an independent influencing factor (adjusted OR, 0.19; 95% CI, 0.16 to 0.23). Lower hemoglobin levels, iron stores, and dialysis adequacy levels are correlated with longer dialysis vintage.Conclusion: We observed a high prevalence of unpleasant symptoms and symptom clusters in a diverse dialysis vintages hemodialysis cohort. Further studies are needed to accurately and routinely define the symptom burden of chronic patients with chronic kidney disease (CKD).

LncRNA GAS5 as an Inflammatory Regulator Acting through Pathway in Human Lupus.

AIM: To investigate the contribution of GAS5 in the pathogenesis of SLE. BACKGROUND: Systemic Lupus Erythematosus (SLE) is characterized by aberrant activity of the immune system, leading to variable clinical symptoms. The etiology of SLE is multifactor, and growing evidence has shown that long noncoding RNAs (lncRNAs) are related to human SLE. Recently, lncRNA growth arrest-specific transcript 5 (GAS5) has been reported to be associated with SLE. However, the mechanism between GAS5 and SLE is still unknown. OBJECTIVE: Find the specific mechanism of action of lncRNA GAS5 in SLE. METHODS: Collecting samples of the SLE patients, Cell culture and treatment, Plasmid construction, and transfection, Quantitative real-time PCR analysis, Enzyme-linked immunosorbent assay (ELISA), Cell viability analysis, Cell apoptosis analysis, Western blot. RESULTS: In this research, we investigated the contribution of GAS5 in the pathogenesis of SLE. We confirmed that, compared to healthy people, the expression of GAS5 was significantly decreased in peripheral monocytes of SLE patients. Subsequently, we found that GAS5 can inhibit the proliferation and promote the apoptosis of monocytes by over-expressing or knocking down the expression of GAS5. Additionally, the expression of GAS5 was suppressed by LPS. Silencing GAS5 significantly increased the expression of a group of chemokines and cytokines, including IL-1ß, IL-6, and THFα, which were induced by LPS. Furthermore, it was identified the involvement of GAS5 in the TLR4-mediated inflammatory process was through affecting the activation of the MAPK signaling pathway. CONCLUSION: In general, the decreased GAS5 expression may be a potential contributor to the elevated production of a great number of cytokines and chemokines in SLE patients. And our research suggests that GAS5 contributes a regulatory role in the pathogenesis of SLE, and may provide a potential target for therapeutic intervention.

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.