TOP-EVs: Technology of Protein delivery through Extracellular Vesicles is a versatile platform for intracellular protein delivery.

Extracellular vesicles (EVs) have emerged as biocompatible drug delivery vehicles due to their native ability to deliver bioactive cargo to recipient cells. However, the application of EVs as a therapeutic delivery vehicle is hampered by effective methods for endogenously loading target proteins inside EVs and unloading proteins after delivery to recipient cells. Most EV-based engineered loading methods have a limited delivery efficiency owing to their inefficient endosomal escape or cargo release from the intraluminal attachment from the EV membrane. Here, we describe the 'Technology Of Protein delivery through Extracellular Vesicles' (TOP-EVs) as a tool for efficient intracellular delivery of target proteins mediated via EVs. The vesicular stomatitis virus glycoprotein and the rapamycin-heterodimerization of the FKBP12/T82L mutant FRB proteins were both important for the effective protein delivery through TOP-EVs. We showed that TOP-EVs could efficiently deliver Cre recombinase and CRISPR/Cas9 ribonucleoprotein complex in vitro. Moreover, our results demonstrated that the capacity of TOP-EVs to deliver intracellular proteins in recipient cells was not an artifact of plasmid contamination or direct plasmid loading into EVs. Finally, we showed that TOP-EVs could successfully mediate intracellular protein delivery in the liver in vivo. Taken together, TOP-EVs are a versatile platform for efficient intracellular protein delivery in vitro and in vivo, which can be applied to advance the development of protein-based therapeutics.

A U-shaped association between baseline neutrophil count and COVID-19-related mortality: A retrospective cohort study.

Several descriptive studies have reported that higher neutrophil count (NC) may be correlated with poor prognosis in patients with confirmed COVID-19 infection. However, the findings from these studies are limited by methodology and data analysis. This study is a cohort study. We nonselectively and consecutively collected a total of 663 participants in a Chinese hospital from January 7 to February 28. Standardized and two-piecewise Cox regression model were employed to evaluate the association between baseline neutrophil count (bNC), neutrophil count change rate (NCR), and death. bNC had a U-shaped association with death. In the range of 0.1 to ≤1.49 × 109 /L (hazard ratio [HR] = 0.19, 95% confidence interval [CI] = 0.05-0.66) and >3.55 × 109 /L of bNC (HR = 2.82, 95% CI = 1.19-6.67), the trends on bNC with mortality were opposite. By recursive algorithm, the bNC at which the risk of the death was lower in the range of >1.49 to ≤3.55 × 109 /L (HR = 13.64, 95% CI = 0.25-74.71). In addition, we find that NCRs (NCR1 and NCR2) are not associated with COVID-19-related deaths. Compared with NCR, bNC has the potential to be used for early risk stratification in patients with COVID-19 infection. The relationship between bNC and mortality was U-shaped. The safe range of bNC was 1.64-4.0 × 109 /L. Identifying the correlation may be helpful for early risk stratification and medical decision-making.

Association between the non-HDL-cholesterol-to-HDL-cholesterol ratio and the risk of gallbladder polyp formation among men: a retrospective cohort study.

BACKGROUND: Dyslipidaemia and male sex are associated with gallbladder polyp (GBP) formation. However, the potential relation between the non-high-density lipoprotein-cholesterol-to-high-density lipoprotein-cholesterol (non-HDL-c/HDL-c) ratio and GBPs in men is unclear. METHODS: A total of 1866 eligible subjects were selected for this retrospective cohort study from Wuhan Union Hospital between April 1, 2013, and November 30, 2014. Clinical and laboratory data of subjects were collected. Patients with GBPs or cholecystectomy at baseline, with missing data for baseline lipid profiles, following abdominal ultrasonography or taking lipid-lowering drugs were excluded. The patients were divided into five groups based on their non-HDL-c/HDL-c ratios, and descriptive analyses of the baseline data were performed. A Cox proportional hazards model was applied to estimate the relationship between the non-HDL-c/HDL-c ratio and GBPs. RESULTS: After a median follow-up of 1 year, 7.34% (n = 137) of the subjects developed GBPs. Compared with subjects without GBPs, those who developed GBPs after follow-up had significantly higher triglyceride (TG) levels and non-HDL-c/HDL-c ratios. The prevalence of GBPs showed a linearity increment with age, peaked in the 30-39 years group, 40-49 years group and 50-59 years group, and then declined slightly. The results of univariate analysis showed that the non-HDL-c/HDL-c ratio (hazard ratio (HR) = 1.29, 95% confidence interval (CI), 1.05-1.60, P = 0.0159) was positively correlated with GBPs. In the fully adjusted Cox regression model, the HRs were 2.24 for quintile 2 (95% CI: 1.13-4.44, P = 0.0203), 1.50 for quintile 3 (95% CI: 0.73-3.10, P = 0.269), 2.52 for quintile 4 (95% CI: 1.26-5.01, P = 0.0087) and 2.13 for quintile 5 (95% CI: 1.04-4.37, P = 0.0397). No interaction was found among the subgroups. CONCLUSIONS: A higher non-HDL-c/HDL-c ratio is independently related to a higher risk of GBP formation in Chinese men. Further research is needed to investigate whether this association exists in different regions and races.

Clinical and Laboratory Predictors of In-hospital Mortality in Patients With Coronavirus Disease-2019: A Cohort Study in Wuhan, China.

BACKGROUND: This study aimed to develop mortality-prediction models for patients with coronavirus disease-2019 (COVID-19). METHODS: The training cohort included consecutive COVID-19 patients at the First People's Hospital of Jiangxia District in Wuhan, China, from 7 January 2020 to 11 February 2020. We selected baseline data through the stepwise Akaike information criterion and ensemble XGBoost (extreme gradient boosting) model to build mortality-prediction models. We then validated these models by randomly collected COVID-19 patients in Union Hospital, Wuhan, from 1 January 2020 to 20 February 2020. RESULTS: A total of 296 COVID-19 patients were enrolled in the training cohort; 19 died during hospitalization and 277 discharged from the hospital. The clinical model developed using age, history of hypertension, and coronary heart disease showed area under the curve (AUC), 0.88 (95% confidence interval [CI], .80-.95); threshold, -2.6551; sensitivity, 92.31%; specificity, 77.44%; and negative predictive value (NPV), 99.34%. The laboratory model developed using age, high-sensitivity C-reactive protein, peripheral capillary oxygen saturation, neutrophil and lymphocyte count, d-dimer, aspartate aminotransferase, and glomerular filtration rate had a significantly stronger discriminatory power than the clinical model (P = .0157), with AUC, 0.98 (95% CI, .92-.99); threshold, -2.998; sensitivity, 100.00%; specificity, 92.82%; and NPV, 100.00%. In the subsequent validation cohort (N = 44), the AUC (95% CI) was 0.83 (.68-.93) and 0.88 (.75-.96) for the clinical model and laboratory model, respectively. CONCLUSIONS: We developed 2 predictive models for the in-hospital mortality of patients with COVID-19 in Wuhan that were validated in patients from another center.

Early decrease in blood platelet count is associated with poor prognosis in COVID-19 patients-indications for predictive, preventive, and personalized medical approach.

BACKGROUND: Changes in platelet count are common in COVID-19 patients. The platelet count reflects the thrombocyte turnover, acting as a sensitive indicator of illness severity that is of great clinical utility to monitor a quickly changing health condition of patients affected by aggressive viral infections. This study aims to investigate the significance of platelet count during the progression of the disease in COVID-19 patients. METHODS: A total of 532 COVID-19 patients were involved in the cohort study from the First People's Hospital of Jiangxia District in Wuhan from January 7, 2020, to February 28, 2020. We collected the clinical characteristics and laboratory data of patients. Patients still hospitalized before February 29, 2020, died on admission, with malignant tumors, previous gastrointestinal surgery, missing baseline platelet count, or platelet count detected only once, were excluded. We used a generalized additive model and generalized additive mixed model to compare trends in platelet count over time among survivors and non-survivors, with an adjustment for potential confounders. RESULTS: During the follow-up, twenty-nine subjects died (mortality rate, 5.45%). The platelets among non-survivors decreased and among survivors increased gradually within 1 week after admission. In addition, the difference between the two groups showed an increasing trend during 1 week after admission. This difference increased by an average of 5.3 × 10^9/L daily. CONCLUSIONS: In the early stage, platelet count can dynamically reflect the pathophysiological changes in COVID-19 patients. Early decrease in platelet count was associated with mortality in patients with COVID-19. Causality, however, cannot be deduced from our data.

Monocyte lymphocyte ratio predicts the new-onset of chronic kidney disease: A cohort study.

BACKGROUND AND AIMS: The role of monocyte lymphocyte ratio (MLR) in predicting the risk of chronic kidney disease (CKD) is unclear, although inflammation contributes to the development of CKD. This study aimed to investigate whether elevated MLR predicts new-onset CKD. METHODS: This study enrolled 14,033 consecutively Chinese participants. The primary outcome was the new-onset CKD defined as an estimated glomerular filtration rate <60 mL/min/1.73 m2 or the presence of proteinuria after follow-up. After the descriptive analyses of baseline data, Univariate and multivariate Cox proportional models were used to evaluate the independent relationship between MLR and new-onset CKD. RESULTS: 11,280 participants were included in the final analysis, and 58.44% (n = 6592) of them were male. The mean age was 44.67 ± 12.85 years. After a median follow-up of 1.94 years, 2.55% (n = 288) of participants developed new-onset CKD. MLR was associated with the increased risk of CKD (HR = 16.12, 95% CI = 4.52-57.56, p < 0.0001). After adjustment for age, gender, body mass index, history of hypertension, systolic blood pressure, high-density lipoprotein cholesterol, triglyceride, fasting plasma glucose, uric acid and estimated glomerular filtration rate, MLR remained an independent risk factor for CKD (HR = 8.89, 95%CI = 2.18-36.27, p = 0.0023). CONCLUSION: MLR is an independent predictor of the risk of CKD, which might be expected to better guide early prevention and treatment interventions.

Nomogram prediction for the 3-year risk of type 2 diabetes in healthy mainland China residents.

AIMS: To develop a precise personalized type 2 diabetes mellitus (T2DM) prediction model by cost-effective and readily available parameters in a Central China population. METHODS: A 3-year cohort study was performed on 5557 nondiabetic individuals who underwent annual physical examination as the training cohort, and a subsequent validation cohort of 1870 individuals was conducted using the same procedures. Multiple logistic regression analysis was performed, and a simple nomogram was constructed via the stepwise method. Receiver operating characteristic (ROC) curve and decision curve analyses were performed by 500 bootstrap resamplings to assess the determination and clinical value of the nomogram, respectively. We also estimated the optimal cutoff values of each risk factor for T2DM prediction. RESULTS: The 3-year cumulative incidence of T2DM was 10.71%. We developed simple nomograms that predict the risk of T2DM for females and males by using the parameters of age, BMI, fasting blood glucose (FBG), low-density lipoprotein cholesterol (LDLc), high-density lipoprotein cholesterol (HDLc), and triglycerides (TG). In the training cohort, the area under the ROC curve (AUC) showed statistical accuracy (AUC = 0.863 for female, AUC = 0.751 for male), and similar results were shown in the subsequent validation cohort (AUC = 0.847 for female, AUC = 0.755 for male). Decision curve analysis demonstrated the clinical value of this nomogram. To optimally predict the risk of T2DM, the cutoff values of age, BMI, FBG, systolic blood pressure, diastolic blood pressure, total cholesterol, LDLc, HDLc, and TG were 47.5 and 46.5 years, 22.9 and 23.7 kg/m2, 5.1 and 5.4 mmol/L, 118 and 123 mmHg, 71 and 85 mmHg, 5.06 and 4.94 mmol/L, 2.63 and 2.54 mmol/L, 1.53 and 1.34 mmol/L, and 1.07 and 1.65 mmol/L for females and males, respectively. CONCLUSION: Our nomogram can be used as a simple, plausible, affordable, and widely implementable tool to predict a personalized risk of T2DM for Central Chinese residents. The successful identification of at-risk individuals and intervention at an early stage can provide advanced strategies from a predictive, preventive, and personalized medicine perspective.