Prevalence and risk factors of sarcopenia in patients on maintenance hemodialysis: a retrospective cohort study.

BACKGROUND: This study aimed to explore the prevalence and related risk factors of sarcopenia in patients on maintenance hemodialysis (MHD). METHODS: This cohort study enrolled 165 patients on MHD. The patients were divided into sarcopenia and non-sarcopenia groups based on the presence of sarcopenia or not. Sarcopenia was diagnosed according to the consensus of the Asian Sarcopenia Working Group that considers reduced muscle mass and decreased muscle strength (19). The muscle mass was measured using the multi-frequency bioelectrical impedance (Inbody260) and skeletal muscle index (SMI) was used: <7.0 kg/m2 (male); <5.7 kg/m2 (female) - with muscle mass reduction. The electronic grip dynamometer was used for measuring dominant handgrip strength (HGS) to reflect muscle strength. Male patients with HGS < 28 kg and female patients with HGS < 18 kg were considered with a decrease in muscle strength. The demographic characteristics, laboratory indexes, anthropometrical measurements, body compositions, and InBody score were compared between groups. The multivariate logistic regression was used to explore the risk factors for sarcopenia. RESULTS: Of the 165 patients on MHD, 36 had sarcopenia, and the prevalence was 21.82%. Patients in the sarcopenia group had higher ages and lower body mass index, serum albumin level, circumference of waist, hip, and biceps, handgrip strength, total water content, protein inorganic salt concentrations, skeletal muscle mass, basal metabolic rate, obesity degree, SMI, and body fat content. The multivariate logistic regression showed that age, waist circumference, handgrip strength, and InBody score were influencing factors for sarcopenia in patients on hemodialysis. CONCLUSION: The prevalence of sarcopenia was high in patients on MHD. Higher age, lower waist circumference, lower handgrip strength, and lower InBody score were independent risk factors for sarcopenia in such 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.

Innovative freeze-drying technique in the fabrication of dissolving microneedle patch: Enhancing transdermal drug delivery efficiency.

Microneedle patch (MNP) has become a hot research topic in the field of transdermal drug delivery due to its ability to overcome the stratum corneum barrier. Among the various types of microneedles, dissolving microneedles represent one of the most promising transdermal delivery methods. However, the most used method for preparing dissolving microneedles, namely microfabrication, suffers from issues such as long drying time, susceptibility to humidity, and large batch-to-batch variability, which limit the development of dissolving microneedles. In this study, we report for the first time a method for preparing dissolving microneedles using freeze-drying technology. We screened substrates suitable for freeze-dried microneedle patch (FD-MNP) and used coating technology to enhance the mechanical strength of FD-MNP, allowing them to meet the requirements for skin penetration. We successfully prepared FD-MNP using hyaluronic acid as the substrate and insulin as the model drug. Scanning electron microscopy revealed that the microneedles had a porous structure. After coating, the mechanical strength of the microneedles was 0.61 N/Needle, and skin penetration rate was 97%, with a penetration depth of 215 µm. The tips of the FD-MNP dissolved completely within approximately 60 s after skin penetration, which is much faster than conventional MNP (180 s). In vitro transdermal experiments showed that the FD-MNP shortened the lag time for transdermal delivery of rhodamine 123 and insulin compared to conventional MNP, indicating a faster transdermal delivery rate. Pharmacological experiments showed that the FD-MNP lowered mouse blood glucose levels more effectively than conventional MNP, with a relative pharmacological availability of 96.59 ± 2.84%, higher than that of conventional MNP (84.34 ± 3.87%), P = 0.0095. After storage under 40℃ for two months, the insulin content within the FD-MNP remained high at 95.27 ± 4.46%, which was much higher than that of conventional MNP (58.73 ± 3.71%), P < 0.0001. In conclusion, freeze-drying technology is a highly valuable method for preparing dissolving microneedles with potential applications in transdermal drug delivery.

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.

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 cross-level influence of ethical leadership on employee's OCBE: a two-wave study based on the social identity approach.

The importance of organizational citizenship behavior for the environment (OCBE) has received increasing attention in recent years because organizations face increasing pressure from environmental deterioration. The purpose of this study is to use social identity theory to construct a cross-level theoretical model of ethical leadership on OCBE, and to explore the cross-level influential mechanisms of ethical leadership on OCBE. Data collection was conducted via a two-wave distribution of leader-employee paired questionnaires in 20 manufacturing companies in China. In the first wave, data about OCBE and team environmental atmosphere were collected from leaders. Subsequently, 2 months later, we conducted the second wave of data collection when data about ethical leadership and leader identity were obtained from their employees. The results showed that at the individual level, ethical leadership has a significant positive impact on employees' OCBE, and such relationship is partially mediated by employees' leader identity and positively moderated by team environmental atmosphere across levels. At the team level, ethical leadership has a significant positive impact on employees' OCBE, and such relationship is completely mediated by team environmental atmosphere. This study investigates the cross-level influential mechanism of ethical leadership on OCBE in China and provides theoretical guidance for enterprises to promote OCBE effectively.

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.

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.

Quantification of peptide components in cinobufacini injection and toad skin by ultra-high-performance liquid chromatography/triple quadrupole mass spectrometry.

Cinobufacini injection is commonly used in the clinical treatment of tumors and hepatitis B, but the quality is uneven. Currently, the main focus of its quality assessment is on steroids and alkaloids. Based on a previous study, we screened four peptides with high reproducibility, responsiveness, and specificity. This research was the first to develop an ultra-high-performance liquid chromatography/triple quadrupole mass spectrometry approach for evaluating the quality of cinobufacini preparations from the peptide perspective. In this study, we have identified 230 peptides in cinobufacini injection by Q-Exactive mass spectrometry, which contains species-specific peptides. Then, we used ultra-high-performance liquid chromatography/triple quadrupole mass spectrometry to establish a quantitative method for species-specific peptides and carried out method validation. The result revealed that four peptides were linear in a specific range, and had great reproducibility, accuracy, and stability. Eventually, we evaluated the quality of eight batches of cinobufacini injections and 26 batches of toad skins using the total content of target peptides as the criterion. The outcomes demonstrated that the quality of cinobufacini injection is generally stable and the toad skin from Shandong is of the best quality. In conclusion, the quantitative approach that focuses on peptides will offer innovative perspectives on assessing the quality of cinobufacini preparations.

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.

Increased hemoglobin concentration and related factors in maintenance hemodialysis patients in Anhui, China.

To investigate the hemoglobin (Hb) concentration and related factors among maintenance hemodialysis (MHD) patients in Anhui province in 2020, so as to compare with the results in 2014. The cases of 3025 MHD patients were investigated in 27 hemodialysis centers of Anhui province from January 2020 to December 2020. The data of age, sex, primary disease, dialysis age, dialysis mode, drug use and laboratory tests were collected and analyzed. Compared with the survey in 2014, the average Hb level of MHD patients in Anhui province was increased (107.41 ± 20.40 g/L vs 100.2 ± 28.1 g/L), the anemia prevalence was decreased (65.9% vs 82.4%), and the percentage of patients with standard Hb level was increased significantly (47.1% vs 32.9%). Compared with low-Hb patients (Hb < 110 g/L), patients with Hb ≥ 110 g/L had lower age, higher proportion of males, longer dialysis age, higher levels of serum Alb, creatinine, total cholesterol, triglyceride, low density lipoprotein, calcium, phosphorus, magnesium, and lower high-density lipoprotein (P < .05). The multivariate logistic regression analysis results showed that male, longer duration of dialysis therapy, treatment with iron, higher triglyceride and albumin were protective factors of anemia, but older age was independent risk factors. The anemia treatment in MHD patients in Anhui province was significantly improved. Male, long dialysis age, use of iron, high serum albumin and triglyceride levels may be protective factors for Hb reaching standard level, and old age may be an independent risk factor.

Research on highway traffic flow prediction model and decision-making method.

In order to solve the problem of traffic congestion in a certain area, this paper develops a set of traffic optimization decision system. For analyzing the actual traffic conditions and calculating the traffic volume, density and traffic speed, a traffic prediction model is established and updated iteratively to modify the prediction model parameters. Based on this model, the congestion degree is estimated at the current road section, thus, an intelligent decision-making and the coordinated optimization methods are proposed. Moreover, this paper implements some application experiments on the isometric road of a three-intersection and obtains better prediction results of traffic density and traffic speed on the three-section highway. At the same time, compared with other existing prediction methods, the prediction model presented in this paper not only has higher accuracy, shorter prediction time and stronger anti-interference ability, but also has better effect on vehicle diversion. In addition, it also greatly relieves the traffic pressure on the road, maximizes the complementary advantages between intersections, and balances the good cooperation between each intersection.

New opportunities and challenges of natural products research: When target identification meets single-cell multiomics.

Natural products, and especially the active ingredients found in traditional Chinese medicine (TCM), have a thousand-year-long history of clinical use and a strong theoretical basis in TCM. As such, traditional remedies provide shortcuts for the development of original new drugs in China, and increasing numbers of natural products are showing great therapeutic potential in various diseases. This paper reviews the molecular mechanisms of action of natural products from different sources used in the treatment of inflammatory diseases and cancer, introduces the methods and newly emerging technologies used to identify and validate the targets of natural active ingredients, enumerates the expansive list of TCM used to treat inflammatory diseases and cancer, and summarizes the patterns of action of emerging technologies such as single-cell multiomics, network pharmacology, and artificial intelligence in the pharmacological studies of natural products to provide insights for the development of innovative natural product-based drugs. Our hope is that we can make use of advances in target identification and single-cell multiomics to obtain a deeper understanding of actions of mechanisms of natural products that will allow innovation and revitalization of TCM and its swift industrialization and internationalization.

Bufotenines-loaded liposome exerts anti-inflammatory, analgesic effects and reduce gastrointestinal toxicity through altering lipid and bufotenines metabolism.

Bufotenines, a natural component from toad venom, showed great potential for development as a novel anti-inflammation and analgesia agent, but the potential toxicity limited its clinic use. In this paper, bufotenines-loaded liposome was prepared and optimized. Then, the therapeutic effects and drug safety of bufotenines-liposome were investigated against inflammation and pain on animal models, with a focus on gastrointestinal toxicity. Bufotenines and its liposome significantly increased paw withdrawal mechanical threshold (PWMT) in Von Frey test and hot paw withdrawal latency (HPWL) in hot-plate test. Moreover, intestinal absorption in vitro and pathological analysis in vivo showed that total bufotenines-loaded liposome significantly reduced the gastrointestinal irritation through reducing exposure of total bufotenines on intestinal tissue. High-sensitivity lipidomics analysis revealed the effect of total bufotenines-loaded liposome were be related to the down-regulation of inflammatory mediators from cyclooxygenase (COX) and lipoxygenase (LOX), the up-regulation of cytochrome P450 (CYP450), and other pathways, thus regulating lipid metabolism pathway and ultimately reducing gastrointestinal irritation. This study shows that liposome-loaded bufotenines has anti-inflammatory, analgesic effects and achieves toxicity reduction. These results provide systematic evidences for efficacy and safety of toad venom active ingredients.

[Research strategies and methods for precious animal medicine substitutes].

Animal medicine is a large category of Chinese medicinecommonly used in clinical practice and has important scientific and therapeutic value. Animal medicine isscarcer than herbal medicine. In recent years, with the vigorous development of traditional Chinese medicine(TCM),the contradiction between the increasing industrial demand andsupply of scarce and even endangered medicinal animals has become increasingly prominent. The continuous lack of medicinal animal resources affects the clinical demandandalso causes serious damage to the ecological environment. Only relying on artificial breeding is not enough to alleviate the current condition of depletion. In the face of this dilemma, it is a major challenge for the current industrial development to protect animal resources and meet clinical and industrial needs with "available medicines". The application of substitutes for animal medicines isthe key focus to alleviate this problem, and it is also the key scientific issue to be solved urgently in the modernization of TCM. This paper summarizedand reviewedthe history, current situation, strategies, and methods of animal medicinesubstitution and put forward the point of view of "similar chemical characteristics, similar efficacy, and higher safety" to provide references for scientific substitution and resource protection of rare animals.

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.