Nanoantidote for repression of acidosis pH promoting COVID-19 infection

Approaching Personalized Medicine: The Use of Machine Learning to Determine Predictors of Mortality in a Population with SARS-CoV-2 Infection

The COVID-19 pandemic demonstrated the need to develop strategies to control a new viral infection. However, the different characteristics of the health system and population of each country and hospital would require the implementation of self-systems adapted to their characteristics. The objective of this work was to determine predictors that should identify the most severe patients with COVID-19 infection. Given the poor situation of the hospitals in the first wave, the analysis of the data from that period with an accurate and fast technique can be an important contribution. In this regard, machine learning is able to objectively analyze data in hourly sets and is used in many fields. This study included 291 patients admitted to a hospital in Spain during the first three months of the pandemic. After screening seventy-one features with machine learning methods, the variables with the greatest influence on predicting mortality in this population were lymphocyte count, urea, FiO2, potassium, and serum pH. The XGB method achieved the highest accuracy, with a precision of >95%. Our study shows that the machine learning-based system can identify patterns and, thus, create a tool to help hospitals classify patients according to their severity of illness in order to optimize admission.

Hydrogen Ion Capturing Hydrogel Microspheres for Reversing Inflammaging

Inflammaging is deeply involved in aging-related diseases and can be destructive during aging. The maintenance of pH balance in the extracellular microenvironment can alleviate inflammaging and repair aging-related tissue damage. In this study, the hydrogen ion capturing hydrogel microsphere (GMNP) composed of mineralized transforming growth factor-β (TGF-β) and catalase (CAT) nanoparticles is developed via biomimetic mineralization and microfluidic technology for blocking the NLRP3 cascade axis in inflammaging. This GMNP can neutralize the acidic microenvironment by capturing excess hydrogen ions through the calcium carbonate mineralization layer. Then, the subsequent release of encapsulated TGF-β and CAT can eliminate both endogenous and exogenous stimulus of NLRP3, thus suppressing the excessive activation of inflammaging. In vitro, GMNP can suppress the excessive activation of the TXNIP/NLRP3/IL-1β cascade axis and enhance extracellular matrix (ECM) synthesis in nucleus pulposus cells. In vivo, GMNP becomes a sustainable and stable niche with microspheres as the core to inhibit inflammaging and promote the regeneration of degenerated intervertebral discs. Therefore, this hydrogen ion-capturing hydrogel microsphere effectively reverses inflammaging by interfering with the excessive activation of NLRP3 in the degenerated tissues.

Sevoflurane upregulates neuron death process-related Ddit4 expression by NMDAR in the hippocampus

Postoperative cognitive dysfunction (POCD) is a serious and common complication induced by anesthesia and surgery. Neuronal apoptosis induced by general anesthetic neurotoxicity is a high-risk factor. However, a comprehensive analysis of general anesthesia-regulated gene expression patterns and further research on molecular mechanisms are lacking. Here, we performed bioinformatics analysis of gene expression in the hippocampus of aged rats that received sevoflurane anesthesia in GSE139220 from the GEO database, found a total of 226 differentially expressed genes (DEGs) and investigated hub genes according to the number of biological processes in which the genes were enriched and performed screening by 12 algorithms with cytoHubba in Cytoscape. Among the screened hub genes, Agt, Cdkn1a, Ddit4, and Rhob are related to the neuronal death process. We further confirmed that these genes, especially Ddit4, were upregulated in the hippocampus of aged mice that received sevoflurane anesthesia. NMDAR, the core target receptor of sevoflurane, rather than GABAAR, mediates the sevoflurane regulation of DDIT4 expression. Our study screened sevoflurane-regulated DEGs and focused on the neuronal death process to reveal DDIT4 as a potential target mediated by NMDAR, which may provide a new target for the treatment of sevoflurane neurotoxicity.

Effect of COVID-19 on Glycemic Control, Insulin Resistance, and pH in Elderly Patients With Type 2 Diabetes

Background The coronavirus disease of 2019 (COVID-19) was spread all over the world, while diabetes mellitus (DM) remains the most prevalent chronic disease worldwide. Aims This study aims to investigate the effect of COVID-19 on glycemic control, insulin resistance (IR), and pH in elderly patients with type 2 diabetes. Methods  A retrospective study was conducted on patients with type 2 DM who were diagnosed with COVID-19 infection in the central hospitals of the Tabuk region. Patient data were collected from September 2021 to August 2022. Four non-insulin-based insulin resistance indexes were calculated for patients: the triglyceride-glucose (TyG) index, the triglyceride glucose-body mass index (TyG-BMI) index, the triglyceride to high-density lipoprotein cholesterol (TG/HDL) ratio, and the metabolic score for insulin resistance (METS-IR). Results Patients showed increased serum fasting glucose and blood HbA1c associated with a high TyG index, TyG-BMI index, TG/HDL ratio, and METS-IR as compared with results before COVID-19. Moreover, during COVID-19, patients revealed a reduction in pH, associated with a reduction in cBase and bicarbonate, and an elevation in PaCO₂ as compared with their results before COVID-19. After complete remission, all patients' results turn back to their level before COVID-19. Conclusions Patients with type 2 DM who catch the COVID-19 infection suffer from dysregulation of glycemic control and elevated insulin resistance associated with a significant reduction in their pH.