Predicted hexameric structure of the Agrobacterium VirB4 C terminus suggests VirB4 acts as a docking site during type IV secretion

The Agrobacterium T-DNA transporter belongs to a growing class of evolutionarily conserved transporters, called type IV secretion systems (T4SSs). VirB4, 789 aa, is the largest T4SS component, providing a rich source of possible structural domains. Here, we use a variety of bioinformatics methods to predict that the C-terminal domain of VirB4 (including the Walker A and B nucleotide-binding motifs) is related by divergent evolution to the cytoplasmic domain of TrwB, the coupling protein required for conjugative transfer of plasmid R388 from Escherichia coli. This prediction is supported by detailed sequence and structure analyses showing conservation of functionally and structurally important residues between VirB4 and TrwB. The availability of a solved crystal structure for TrwB enables the construction of a comparative model for VirB4 and the prediction that, like TrwB, VirB4 forms a hexamer. These results lead to a model in which VirB4 acts as a docking site at the entrance of the T4SS channel and acts in concert with VirD4 and VirB11 to transport substrates (T-strand linked to VirD2 or proteins such as VirE2, VirE3, or VirF) through the T4SS.     

Early prediction model for progression and prognosis of severe patients with coronavirus disease 2019

Coronavirus disease 2019 (COVID-19) has been a rampant worldwide health threat and we aimed to develop a model for early prediction of disease progression.This retrospective study included 124 adult inpatients with COVID-19 who presented with severe illness at admission and had a definite outcome (recovered or progressed to critical illness) during February 2020. Eighty-four patients were used as training cohort and 40 patients as validation cohort. Logistic regression analysis and receiver operating characteristic curve (ROC) analysis were used to develop and evaluate the prognostic prediction model.In the training cohort, the mean age was 63.4 ± 1.5 years, and male patients (48, 57%) were predominant. Forty-three (52%) recovered, and 41 (49%) progressed to critical. Decreased lymphocyte count (LC, odds ratio [OR] = 4.40, P = .026), elevated lactate dehydrogenase levels (LDH, OR = 4.24, P = .030), and high-sensitivity C-reactive protein (hsCRP, OR = 1.01, P = .025) at admission were independently associated with higher odds of deteriorated outcome. Accordingly, we developed a predictive model for disease progression based on the levels of the 3 risk factors (LC, LDH, and hsCRP) with a satisfactory performance in ROC analysis (area under the ROC curve [AUC] = 0.88, P < .001) and the best cut-off value was 0.526 with the sensitivity and specificity of 75.0% and 90.7%, respectively. Then, the model was internally validated by leave-one-out cross-validation with value of AUC 0.85 (P < .001) and externally validated in another validation cohort (26 recovered patients and 14 progressed patients) with AUC 0.84 (P < .001).We identified 3 clinical indicators of risk of progression and developed a severe COVID-19 prognostic prediction model, allowing early identification and intervention of high-risk patients being critically illness.     

Preclinical pharmacokinetics of TPN729MA,a novel PDE5 inhibitor,and prediction of its human pharmacokinetics using a PBPK model

Aim:

TPN729MA is a novel selective PDE5 inhibitor currently under clinical development in China for the treatment of erectile dysfunction. In this study we characterized its preclinical pharmacokinetics (PK) and predict its human PK using a physiologically based pharmacokinetic (PBPK) model.

Methods:

The preclinical PK of TPN729MA was studied in rats and dogs. Human clearance (CL) values for TPN729MA were predicted from various allometric methods and from intrinsic CL determined in human liver microsomes. Human PK and plasma concentration versus time profiles of TPN729MA were predicted by using a PBPK model in GastroPlus. Considering the uncertainties in the prediction, a preliminary human study was conducted in 3 healthy male volunteers with an oral dose of 25 mg.

Results:

After a single intravenous administration of TPN729MA at a dose of 1 mg/kg in rats and 3 mg/kg in dogs, the plasma CL was 69.7 mL·min⁻¹·kg⁻¹ in rats and 26.3 mL·min⁻¹·kg⁻¹ in dogs, and the steady-state volumes of distribution (V_ss) were 7.35 L/kg in rats and 6.48 L/kg in dogs. The oral bioavailability of TPN729MA was 10% in rats and above 34% in dogs. Profiles of predicted plasma concentration versus time were similar to those observed in humans at 25 mg, and the predicted T_max, C_max and AUC values were within 2-fold of the observed values.

Conclusion:

TPN729MA demonstrates good preclinical PK. This compound is a valuable candidate for further clinical development. This study shows the benefits of using a PBPK model to predict PK in humans.     

Predicting in-hospital mortality in ICU patients with sepsis using gradient boosting decision tree

Sepsis is a leading cause of mortality in the intensive care unit. Early prediction of sepsis can reduce the overall mortality rate and cost of sepsis treatment. Some studies have predicted mortality and development of sepsis using machine learning models. However, there is a gap between the creation of different machine learning algorithms and their implementation in clinical practice.This study utilized data from the Medical Information Mart for Intensive Care III. We established and compared the gradient boosting decision tree (GBDT), logistic regression (LR), k-nearest neighbor (KNN), random forest (RF), and support vector machine (SVM).A total of 3937 sepsis patients were included, with 34.3% mortality in the Medical Information Mart for Intensive Care III group. In our comparison of 5 machine learning models (GBDT, LR, KNN, RF, and SVM), the GBDT model showed the best performance with the highest area under the receiver operating characteristic curve (0.992), recall (94.8%), accuracy (95.4%), and F1 score (0.933). The RF, SVM, and KNN models showed better performance (area under the receiver operating characteristic curve: 0.980, 0.898, and 0.877, respectively) than the LR (0.876).The GBDT model showed better performance than other machine learning models (LR, KNN, RF, and SVM) in predicting the mortality of patients with sepsis in the intensive care unit. This could be used to develop a clinical decision support system in the future.     

Emergency room prediction of mortality and severe complications in patients with suspected acute myocardial infarction

This study aims at describing the in-hospital prognosis of patientsadmitted with suspected acute myocardial infarction, focusingon the possibility of emergency room prediction of the riskfor death and severe complications. From 7157 consecutive patientswith chest pain or other symptoms suggestive of acute myocardialinfarction in the emergency room, 4690 were hospitalized Ofthese, 246 (5%) died in hospital, with a mortality rate amongthe 921 patients who developed myocardial infarction of 14%,and among those without infarction of 3%. From the clinical history, examination and electrocardiogramin the emergency room, independent predictors of death and deathor any severe complication were determined by logistic regressionanalysis. These included age, initial degree of suspicion ofinfarction, electrocardiographs pattern, history of diabetesmellitus, history of congestive heart failure and on admissionarrhythmias, loss of consciousness, acute congestive heart failure,or unspecific symptoms. From these analyses the probabilityof death or death or any severe complication can be calculated Thus, 18% of patients hospitalized due to suspected acute myocardialinfarction suffered a severe complication or died in hospitalFrom a statistical model it is possible to predict the in-hospitalprognosis of every such patient.     

The role of metabolomic markers for patients with infectious diseases: implications for risk stratification and therapeutic modulation

Introduction: Metabolomics is a rapidly growing area of research. Metabolomic markers can provide information about the interaction of different organ systems, and thereby improve the understanding of physio-pathological processes, disease risk, prognosis and therapy responsiveness in a variety of diseases.

Areas covered: In this narrative review of recent clinical studies investigating metabolomic markers in adult patients presenting with acute infectious disease, we mainly focused on patients with sepsis and lower respiratory tract infections. Currently, there is a growing body of literature showing that single metabolites from distinct metabolic pathways, as well as more complex metabolomic signatures are associated with disease severity and outcome in patients with systemic infections. These pathways include, among others, metabolomic markers of oxidative stress, steroid hormone and amino acid pathways, and nutritional markers.

Expert commentary: Metabolic profiling has great potential to optimize patient management, to provide new targets for individual therapy and thereby improve survival of patients. At this stage, research mainly focused on the identification of new predictive signatures and less on metabolic determinants to predict treatment response. The transition from observational studies to implementation of novel markers into clinical practice is the next crucial step to prove the usefulness of metabolomic markers in patient care.     

Independent validation of the ICU requirement score in a cohort of acutely poisoned adults

Objective: To independently validate the predictive value of the intensive care requirement score (IRS) in unselected poisoned patients.

Design: Retrospective chart review.

Patients and methods: Five hundred and seventeen out of 585 admissions for acute intoxications could be analyzed. Eleven were excluded for a condition already requiring intensive care unit (ICU) support at admission (e.g., preclinical intubation). A further 57 admissions were excluded due to missing data. The IRS was calculated using a point-scoring system including age, Glasgow Coma Scale, heart rate, type of intoxication, and preexisting conditions. It was then compared to a composite endpoint indicating an ICU requirement (death in hospital, vasopressors, need for ventilation). The endpoint and the point-scoring system were identical to the original publication of the score.

Results and conclusion: Twenty-three out of 517 patients had a complicated clinical course as defined by meeting the endpoint definition. Twenty-one out of 23 complicated courses had a positive IRS (defined as greater or equal 6 points), as compared to 255/494 patients with an uncomplicated clinical course (p?相似文献   

基于常见遗传变异和传统风险因素的中国南方汉族人群结直肠癌风险预测模型研究

目的 基于结直肠癌全基因组关联研究(GWAS)发现的易感位点,联合传统风险因素建立中国南方汉族人群结直肠癌风险预测模型。方法 对1 066例结直肠癌患者和3 880例健康对照的21个GWAS候选位点进行基因分型,分析其与结直肠癌易感性之间的关联。通过遗传风险评分(GRS)和加权遗传风险评分(wGRS)计算显著候选位点的联合效应。以不同方式组合遗传风险评分和传统风险因素,构建结直肠癌风险预测模型,并绘制受试者工作特征曲线评价模型优劣性。结果 7个候选位点与结直肠癌易感性显著相关。随着风险评分的升高,人群患结直肠癌的风险也随之升高(GRS:P=0.002 6,wGRS:P<0.000 1),相比于四分位分组中最低一组,GRS和wGRS最高的一组OR值分别为1.33(95%CI:1.12~1.58,P=0.001 0)和1.76(95%CI:1.45~2.14,P<0.000 1)。联合传统风险因素和wGRS的模型为最优模型,其曲线下面积为0.593(95%CI:0.573~0.613)。结论 结直肠癌易感位点间存在显著的联合作用。相比于传统风险因素模型,传统风险因素结合加权遗传风险评分模型能更好预测结直肠癌的患病风险。