Effects of a nutritional intervention on impaired behavior and cognitive function in an emphysematous murine model of COPD with endotoxin-induced lung inflammation

Linkage of CDC42 and T-helper cell ratio with anxiety, depression and quality of life in ST-elevation myocardial infarction

Objective: To investigate the correlations between CDC42 and T-cell subsets concerning anxiety, depression and quality of life in ST-elevation myocardial infarction patients undergoing percutaneous coronary intervention. Methods: Sera from 156 participants were analyzed for CDC42 levels and Th1, Th2, Th17 and Treg cells. Results: CDC42 correlated with reduced Th1/Th2 and Th17/Treg ratios, lower anxiety and depression, and higher EuroQol visual analog scale (EQ-VAS) score. The Th17/Treg ratio correlated with elevated anxiety, depression, EuroQol-5 dimensions score and decreased EQ-VAS score. The Th1/Th2 ratio was positively related to the EQ-VAS score. Conclusion: CDC42 correlates with reduced Th1/Th2 and Th17/Treg ratios, reduced anxiety and depression, and improved quality of life in ST-elevation myocardial infarction patients undergoing percutaneous coronary intervention.

ApoA1, ApoB, ApoA1/B for Pathogenic Prediction of Chronic Obstructive Pulmonary Disease Complicated by Acute Lower Respiratory Tract Infection: A Cross-Sectional Study

Purpose

To investigate the value of apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB), and ApoA1/B ratio in pathogenic diagnosis of chronic obstructive pulmonary disease (COPD) complicated by acute lower respiratory tract infection, assisting comprehensive disease assessment.

Patients and Methods

The study enrolled 171 COPD patients with acute lower respiratory tract infections, 35 COPD patients without acute lower respiratory tract infections, and 41 healthy controls. Correlation analysis and binary logistic regression were used to assess the roles of various factors in COPD with acute lower respiratory tract infections. Receiver operating characteristic (ROC) curves were plotted and area under curves (AUC) values were calculated to evaluate the predictive performance.

Results

Infections were the cause of alterations in ApoA1, ApoB and ApoA1/B index. In correlation analysis for pathogenic diagnosis of COPD complicated by acute lower respiratory infections, age, ApoA1, ApoA1/B ratio, lymphocyte count (LYMPH), neutrophil count (NEUT), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and endotoxin were significantly correlated. For predicting COPD complicated by acute lower respiratory tract bacterial infection, ApoA1 had the highest area under the ROC curve (AUC: 0.889), with sensitivity and specificity of 82.9% and 83.9%, respectively. The combination of NEUT and ApoA1 improved the prediction efficacy (AUC: 0.909; sensitivity/specificity: 85.1%/85.7%).

Conclusion

ApoA1, ApoB, and ApoA1/B ratio are good indicators for predicting pathogens in COPD complicated by acute lower respiratory tract infection, especially ApoA1 which has high predictive value.

Microglial morphometric analysis: so many options, so little consistency

Quantification of microglial activation through morphometric analysis has long been a staple of the neuroimmunologist's toolkit. Microglial morphological phenomics can be conducted through either manual classification or constructing a digital skeleton and extracting morphometric data from it. Multiple open-access and paid software packages are available to generate these skeletons via semi-automated and/or fully automated methods with varying degrees of accuracy. Despite advancements in methods to generate morphometrics (quantitative measures of cellular morphology), there has been limited development of tools to analyze the datasets they generate, in particular those containing parameters from tens of thousands of cells analyzed by fully automated pipelines. In this review, we compare and critique the approaches using cluster analysis and machine learning driven predictive algorithms that have been developed to tackle these large datasets, and propose improvements for these methods. In particular, we highlight the need for a commitment to open science from groups developing these classifiers. Furthermore, we call attention to a need for communication between those with a strong software engineering/computer science background and neuroimmunologists to produce effective analytical tools with simplified operability if we are to see their wide-spread adoption by the glia biology community.