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

The combined impact of neutrophil-percentage-to-albumin ratio and depressive symptoms on mortality in US arthritis patients: insights from NHANES (2005-2018)

Background

The neutrophil-to-albumin ratio (NPAR) reflects inflammation and nutritional status, while depression significantly impacts survival in chronic disease patients. This study examines the independent and combined effects of NPAR and depressive symptoms on all-cause and cardiovascular mortality in arthritis patients.

Methods

We analyzed a nationally representative sample of people with arthritisaged 40 and older from NHANES (2005-2018). NPAR assessed inflammation and nutritional status, while depressive symptoms were measured by PHQ-9. Weighted Cox regression examined the independent and joint associations of NPAR and PHQ-9 with all-cause and cardiovascular disease (CVD) mortality.

Results

Our analysis indicated that higher NPAR levels combined with lower depressive symptoms (PHQ-9 < 10) significantly increased all-cause and CVD mortality risks in arthritis patients. In this group, the hazard ratio (HR) for all-cause mortality was 2.087, with a similarly elevated CVD mortality risk (HR = 2.614), underscoring NPAR's predictive strength in non-depressed individuals. Among those with higher depressive symptoms, while elevated NPAR was still associated with increased mortality, its impact on CVD mortality was less marked, highlighting the need for further research into the NPAR-depression interaction.

Conclusion

This study identifies NPAR as a key predictor of mortality in arthritis patients, particularly those with fewer depressive symptoms. NPAR significantly predicts all-cause and CVD mortality, underscoring its value as an inflammation and nutrition biomarker. Integrating NPAR in clinical practice could enhance individualized risk assessment and intervention for arthritis patients.

Desmosine: The Rationale for Its Use as a Biomarker of Therapeutic Efficacy in the Treatment of Pulmonary Emphysema

Desmosine and isodesmosine (DID) are elastin-specific crosslinking amino acids that play a critical role in maintaining the structural integrity of elastic fibers, and their levels in body fluids may serve as biomarkers for alveolar wall injury. To support this concept, we present studies demonstrating the use of DID to detect elastic fiber damage that reflects distention and the rupture of airspaces. The emergence of airspace enlargement may be modeled by a percolation network describing the effect of changing proportions of intact and weak elastic fibers on the transmission of mechanical forces in the lung. Following the unraveling and fragmentation of weakened elastic fibers, the release of DID may correlate with an increasing alveolar diameter and provide an endpoint for clinical trials of novel agents designed to treat pulmonary emphysema. The limitations of the DID measurements related to specificity and reproducibility are also addressed, particularly regarding sample source and analytical techniques. Standardizing protocols to isolate and quantify DID may increase the use of this biomarker for the early detection of alveolar wall injury, which permits timely therapeutic intervention.

The Potential Role of Cigarette Smoke, Elastic Fibers, and Secondary Lung Injury in the Transition of Pulmonary Emphysema to Combined Pulmonary Fibrosis and Emphysema

Combined pulmonary fibrosis and emphysema (CPFE) is a distinct syndrome associated with heavy smoking. The fibrotic component of the disease is generally believed to be superimposed on previously existing pulmonary emphysema, but the mechanisms responsible for these changes remain poorly understood. To better understand the pathogenesis of CPFE, we performed a series of experiments that focused on the relationships between lung elastic fibers, cigarette smoke, and secondary lung injury. The results indicate that even brief smoke exposure predisposes the lung to additional forms of lung injury that may cause alveolar wall fibrosis. The proinflammatory activity of smoke-induced structural alterations in elastic fibers may contribute to this process by enhancing secondary lung inflammation, including acute exacerbations of chronic obstructive pulmonary disease. Furthermore, the levels of the unique elastin crosslinks, desmosine and isodesmosine, in blood, urine, and sputum may serve as biomarkers for the transition from pulmonary emphysema to interstitial fibrosis. While the long-term effects of these inflammatory reactions were not examined, the current studies provide insight into the potential relationships between elastic fiber injury, cigarette smoke, and secondary lung injury. Determining the mechanisms involved in combined pulmonary emphysema and fibrosis and developing a sensitive biomarker for this type of lung injury may permit timely therapeutic intervention that could mitigate the high risk of respiratory failure associated with this condition.

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.