A new perspective on HIV: effects of HIV on brain-heart axis

A Retrospective Analysis of Central and Peripheral Metabolic Characteristics in Patients with Cryptococcal Meningitis

INTRODUCTION

Most current treatment strategies and investigations on cryptococcal meningitis (CM) focus primarily on the central nervous system (CNS), often overlooking the complex interplay between the CNS and the peripheral system. This study aims to explore the characteristics of central and peripheral metabolism in patients with CM.

METHODS

Patients diagnosed with CM as per the hospital records of the Fourth People's Hospital of Nanning were retrospectively analyzed. Patients were divided into two groups, non-structural damage of the brain (NSDB) and structural damage of the brain (SDB), according to the presence of brain lesions as detected with imaging. Based on the presence of enlarged cerebral ventricles, the cases in the SDB group were classified into non-ventriculomegaly (NVM) and ventriculomegaly (VM). Various parameters of cerebrospinal fluid (CSF) and peripheral blood (PB) were analyzed.

RESULTS

A significant correlation was detected between CSF and PB parameters. The levels of CSF-adenosine dehydrogenase (ADA), CSF-protein, CSF-glucose, and CSF-chloride ions were significantly correlated with the levels of PB-aminotransferase, PB-bilirubin, PB-creatinine (Cr), PB-urea nitrogen, PB-electrolyte, PB-protein, and PB-lipid. Compared with NSDB, the levels of CSF-glucose were significantly decreased in the SDB group, while the levels of CSF-lactate dehydrogenase (LDH) and CSF-protein were significantly increased in the SDB group. In the SDB group, the levels of PB-potassium, PB-hemoglobin(Hb), and PB-albumin were significantly decreased in the patients with VM, while the level of PB-urea nitrogen was significantly increased in these patients.

CONCLUSION

Metabolic and structural alterations in the brain may be associated with peripheral metabolic changes.

Association between lactate metabolism‑related molecules and venous thromboembolism: A study based on bioinformatics and an in vitro model

Venous thromboembolism (VTE) is characterized by a high recurrence rate and adverse consequences, including high mortality. Damage to vascular endothelial cells (VECs) serves a key role in VTE and lactate (LA) metabolism is associated with VEC damage. However, the pathogenesis of VTE and the role of lactate metabolism-related molecules (LMRMs) remain unclear. Based on the GSE48000 dataset, the present study identified differentially expressed (DE-)LMRMs between healthy individuals and those with VTE. Thereafter, LMRMs were used to establish four machine learning models, namely, the random forest, support vector machine and generalized linear model (GLM) and eXtreme gradient boosting. To verify disease prediction efficiency of the models, nomograms, calibration curves, decision curve analyses and external datasets were used. The optimal machine learning model was used to predict genes involved in disease and an in vitro oxygen-glucose deprivation (OGD) model was used to detect the survival rate, LA levels and LMRM expression levels of VECs. A total of four DE-LMRMs, solute carrier family 16 member 1 (SLC16A1), SLC16A7, SLC16A8 and SLC5A12 were obtained and GLM was identified as the best performing model based on its ability to predict differential expression of the embigin, lactate dehydrogenase B, SLC16A1, SLC5A12 and SLC16A8 genes. Additionally, SLC16A1, SLC16A7 and SLC16A8 served key roles in VTE and the OGD model demonstrated a significant decrease in VEC survival rate as well as a significant increase and decrease in intracellular LA and SLC16A1 expression levels in VECs, respectively. Thus, LMRMs may be involved in VTE pathogenesis and be used to build accurate VTE prediction models. Further, it was hypothesized that the observed increase in intracellular LA levels in VECS was associated with the decrease in SLC16A1 expression. Therefore, SLC16A1 expression may be an essential target for VTE treatment.