Bioinformatic prediction of the molecular links between Alzheimer's disease and diabetes mellitus.

Background: Alzheimer's disease (AD) and type 2 diabetes mellitus (DM2) are chronic degenerative diseases with complex molecular processes that are potentially interconnected. The aim of this work was to predict the potential molecular links between AD and DM2 from different sources of biological information. Materials and Methods: In this work, data mining of nine databases (DisGeNET, Ensembl, OMIM, Protein Data Bank, The Human Protein Atlas, UniProt, Gene Expression Omnibus, Human Cell Atlas, and PubMed) was performed to identify gene and protein information that was shared in AD and DM2. Next, the information was mapped to human protein-protein interaction (PPI) networks based on experimental data using the STRING web platform. Then, gene ontology biological process (GOBP) and pathway analyses with EnrichR showed its specific and shared biological process and pathway deregulations. Finally, potential biomarkers and drug targets were predicted with the Metascape platform. Results: A total of 1,551 genes shared in AD and DM2 were identified. The highest average degree of nodes within the PPI was for DM2 (average = 2.97), followed by AD (average degree = 2.35). GOBP for AD was related to specific transcriptional and translation genetic terms occurring in neurons cells. The GOBP and pathway information for the association AD-DM2 were linked mainly to bioenergetics and cytokine signaling. Within the AD-DM2 association, 10 hub proteins were identified, seven of which were predicted to be present in plasma and exhibit pharmacological interaction with monoclonal antibodies in use, anticancer drugs, and flavonoid derivatives. Conclusion: Our data mining and analysis strategy showed that there are a plenty of biological information based on experiments that links AD and DM2, which could provide a rational guide to design further diagnosis and treatment for AD and DM2.

COVID-19 Outcome Prediction by Integrating Clinical and Metabolic Data using Machine Learning Algorithms

ABSTRACT Background: The coronavirus disease (COVID-19) is an infectious disease caused by the SARS-CoV-2 virus and is responsible for nearly 6 million deaths worldwide in the past 2 years. Machine learning (ML) models could help physicians in identifying high-risk individuals. Objectives: To study the use of ML models for COVID-19 prediction outcomes using clinical data and a combination of clinical and metabolic data, measured in a metabolomics facility from a public university. Methods: A total of 154 patients were included in the study. "Basic profile" was considered with clinical and demographic variables (33 variables), whereas in the "extended profile," metabolomic and immunological variables were also considered (156 characteristics). A selection of features was carried out for each of the profiles with a genetic algorithm (GA) and random forest models were trained and tested to predict each of the stages of COVID-19. Results: The model based on extended profile was more useful in early stages of the disease. Models based on clinical data were preferred for predicting severe and critical illness and death. ML detected trimethylamine N-oxide, lipid mediators, and neutrophil/lymphocyte ratio as important variables. Conclusion: ML and GAs provided adequate models to predict COVID-19 outcomes in patients with different severity grades.

Differences in Cytokine Production during Aging and Its Relationship with Antimicrobial Peptides Production.

Aging is a major health issue due to the increased susceptibility of elderly people to infectious, autoimmune, and cardiovascular diseases. Innate immunity is an important mechanism to avoid primary infections; therefore, decreasing of its activity may lead to development of infections. Antimicrobial peptides (AMPs) are effector molecules of innate immunity that can eliminate microbial invaders. The role that cytokines play in the regulation of these innate immune mechanisms needs to be explored. Serum determinations of Th1, Th2, and Th17 cytokines were performed in order to evaluate their association with AMPs human beta-defensin (HBD)-2 and LL-37 in young adults, elder adults, and elder adults with recurrent infections. Our results showed differences in interleukin (IL)-10 and IL-6 among the different groups. Inverse correlations in serum cytokine levels and HBD-2 production were identified for IL-10, IL-2, IL-4, tumor necrosis factor-α, and IL-6. Also inverse correlations were identified for IL-10, IL-4, and cathelicidin (LL-37). Such results could impact the development of immunomodulators that promote AMP production to prevent and/or contain infectious diseases in this population.

Metformin Induces Cell Cycle Arrest, Reduced Proliferation, Wound Healing Impairment In Vivo and Is Associated to Clinical Outcomes in Diabetic Foot Ulcer Patients.

BACKGROUND: Several epidemiological studies in diabetic patients have demonstrated a protective effect of metformin to the development of several types of cancer. The underlying mechanisms of such phenomenon is related to the effect of metformin on cell proliferation among which, mTOR, AMPK and other targets have been identified. However, little is known about the role that metformin treatment have on other cell types such as keratinocytes and whether exposure to metformin of these cells might have serious repercussions in wound healing delay and in the development of complications in diabetic patients with foot ulcers or in their exacerbation. MATERIAL AND METHODS: HaCaT Cells were exposed to various concentrations of metformin and cell viability was evaluated by a Resazurin assay; Proliferation was also evaluated with a colony formation assay and with CFSE dilution assay by flow cytometry. Cell cycle was also evaluated by flow cytometry by PI staining. An animal model of wound healing was used to evaluate the effect of metformin in wound closure. Also, an analysis of patients receiving metformin treatment was performed to determine the effect of metformin treatment on the outcome and wound area. Statistical analysis was performed on SPSS v. 18 and GraphPad software v.5. RESULTS: Metformin treatment significantly reduces cell proliferation; colony formation and alterations of the cell cycle are observed also in the metformin treated cells, particularly in the S phase. There is a significant increase in the area of the wound of the metformin treated animals at different time points (P<0.05). There is also a significant increase in the size and wound area of the patients with diabetic foot ulcers at the time of hospitalization. A protective effect of metformin was observed for amputation, probably associated with the anti inflammatory effects reported of metformin. CONCLUSIONS: Metformin treatment reduces cell proliferation and reduces wound healing in an animal model and affects clinical outcomes in diabetic foot ulcer patients. Chronic use of this drug should be further investigated to provide evidence of their security in association with DFU.

Tuberculin skin test and interferon-gamma release assay values are associated with antimicrobial peptides expression in polymorphonuclear cells during latent tuberculous infection

It has been reported that patients with progressive tuberculosis (TB) express abundant amounts of the antimicrobial peptides (AMPs) cathelicidin (LL-37) and human neutrophil peptide-1 (HNP-1) in circulating cells, whereas latent TB infected donors showed no differences when compared with purified protein derivative (PPD) and QuantiFERON®-TB Gold (QFT)-healthy individuals. The aim of this study was to determine whether LL-37 and HNP-1 production correlates with higher tuberculin skin test (TST) and QFT values in TB household contacts. Twenty-six TB household contact individuals between 26-58 years old TST and QFT positive with at last two years of latent TB infection were recruited. AMPs production by polymorphonuclear cells was determined by flow cytometry and correlation between TST and QFT values was analysed. Our results showed that there is a positive correlation between levels of HNP-1 and LL-37 production with reactivity to TST and/or QFT levels. This preliminary study suggests the potential use of the expression levels of these peptides as biomarkers for progression in latent infected individuals.