BackgroundSeveral 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 MethodsHaCaT 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.ResultsMetformin 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.ConclusionsMetformin 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.

Viral sepsis has been proposed as an accurate term to describe all multisystemic dysregulations and clinical findings in severe and critically ill COVID-19 patients. The adoption of this term may help the implementation of more accurate strategies of early diagnosis, prognosis, and in-hospital treatment. We accurately quantified 110 metabolites using targeted metabolomics, and 13 cytokines/chemokines in plasma samples of 121 COVID-19 patients with different levels of severity, and 37 non-COVID-19 individuals. Analyses revealed an integrated host-dependent dysregulation of inflammatory cytokines, neutrophil activation chemokines, glycolysis, mitochondrial metabolism, amino acid metabolism, polyamine synthesis, and lipid metabolism typical of sepsis processes distinctive of a mild disease. Dysregulated metabolites and cytokines/chemokines showed differential correlation patterns in mild and critically ill patients, indicating a crosstalk between metabolism and hyperinflammation. Using multivariate analysis, powerful models for diagnosis and prognosis of COVID-19 induced sepsis were generated, as well as for mortality prediction among septic patients. A metabolite panel made of kynurenine/tryptophan ratio, IL-6, LysoPC a C18:2, and phenylalanine discriminated non-COVID-19 from sepsis patients with an area under the curve (AUC (95%CI)) of 0.991 (0.986–0.995), with sensitivity of 0.978 (0.963–0.992) and specificity of 0.920 (0.890–0.949). The panel that included C10:2, IL-6, NLR, and C5 discriminated mild patients from sepsis patients with an AUC (95%CI) of 0.965 (0.952–0.977), with sensitivity of 0.993(0.984–1.000) and specificity of 0.851 (0.815–0.887). The panel with citric acid, LysoPC a C28:1, neutrophil-lymphocyte ratio (NLR) and kynurenine/tryptophan ratio discriminated severe patients from sepsis patients with an AUC (95%CI) of 0.829 (0.800–0.858), with sensitivity of 0.738 (0.695–0.781) and specificity of 0.781 (0.735–0.827). Septic patients who survived were different from those that did not survive with a model consisting of hippuric acid, along with the presence of Type II diabetes, with an AUC (95%CI) of 0.831 (0.788–0.874), with sensitivity of 0.765 (0.697–0.832) and specificity of 0.817 (0.770–0.865).

Despite the existing research, the etiology of rheumatoid arthritis (RA), an autoimmune disease remains poorly understood with early and accurate diagnosis difficult to achieve. MicroRNAs (miRNAs) play an important role in biological processes as modulators of transcription and translation. Previous studies have demonstrated a downregulation of several genes in early RA stages and in addition, miRNAs may serve as early biomarkers of subclinical changes in early RA.When comparing the four groups (ANOVA P<0.01, fold change >4), we found 253 differentially expressed miRNAs. Of these, 97 miRNAs were identified as overexpressed in early rheumatoid arthritis. The validation of miRNA microarray expression was performed in a set by RT-qPCR and showed strong agreement with microarray expression data. The putative targets of overexpressed microRNAs in early RA were significantly enriched in apoptosis, tolerance loss and Wnt pathways.Moreover, ROC analysis showed values of AUC 0.76 and p<0.05 for miR361-5p, identifying this miRNA as a potential biomarker of disease.We identified specific microRNAs associated with early rheumatoid arthritis and proposed them as early biomarkers of disease. Our results provide novel insight into immune disease physiopathology and describe unreported microRNAs in RA with potential for clinical use.

Type 2 diabetes mellitus (DM2) is strongly associated with other comorbidities such as obesity, atherosclerosis, and hypertension. Obesity is associated with sustained low-grade inflammatory response due to the production of proinflammatory cytokines. This inflammatory process promotes the differentiation of some myeloid cells, including myeloid-derived suppressor cells (MDSCs). In this study, two groups of individuals were included: DM2 patients and non-DM2 individuals with similar characteristics. Immunolabeling of CD15+ CD14-and CD33+ HLA-DR-/low was performed from whole peripheral blood, and samples were analyzed by flow cytometry, and frequencies of MDSCs and the relationship of these with clinical variables, cytokine profile (measured by cytometric bead array), and anthropometric variables were analyzed. The frequency of CD33+ HLA-DR-/low MDSCs (that produce IL-10 and TGF-β, according to an intracellular detection) is higher in patients with DM2 (P < 0:05), and there is a positive correlation between the frequency of CD15+ CD14-and CD33+ HLA-DR-/low MDSC phenotypes. DM2 patients have an increased concentration of serum IL-5 (P < 0:05). Also, a negative correlation between the frequency of CD15+ CD14-MDSCs and LDL cholesterol was found. Our group of DM2 patients have an increased frequency of mononuclear MDSC CD33+ HLA-DR-/low that produce TGF-β and IL-10. These cytokines have been associated with immune modulation and reduced T cell responses. DM2 and non-DM2 subjects show a similar cytokine profile, but the DM2 patients have an increased concentration of IL-5.

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: Diabetes is a chronic disease characterized by marked alterations in the metabolism of glucose andby high con-centrations of glucose in the blood due to a decreased insulin production or resistance to the action of this hormone in pe-ripheral tissues. The International Diabetes Federation estimates a global incidence of diabetes of about 10% in the adult population (20 -79 years old), some 430 million cases reported worldwide in 2018. It is well documented that people with diabetes have a higher susceptibility to infectious diseases and therefore show higher morbidity and mortality compared to the non-diabetic population. Given that the innate immune response plays a fundamental role in protecting against invading pathogens through a myriad of humoral and cellular mechanisms, the present work makes a comprehensive review of the innate immune alterations in patients with type 2 diabetes mellitus (T2D) as well as a brief description of the molecular events leading or associated to such conditions.We show that in these patients a compromised innate immune response in-creases susceptibility to infections.

Differences in clinical manifestations, immune response, metabolic alterations, and outcomes (including disease severity and mortality) between men and women with COVID-19 have been reported since the pandemic outbreak, making it necessary to implement sex-specific biomarkers for disease diagnosis and treatment. This study aimed to identify sex-associated differences in COVID-19 patients by means of a genetic algorithm (GALGO) and machine learning, employing support vector machine (SVM) and logistic regression (LR) for the data analysis. Both algorithms identified kynurenine and hemoglobin as the most important variables to distinguish between men and women with COVID-19. LR and SVM identified C10:1, cough, and lysoPC a 14:0 to discriminate between men with COVID-19 from men without, with LR being the best model. In the case of women with COVID-19 vs. women without, SVM had a higher performance, and both models identified a higher number of variables, including 10:2, lysoPC a C26:0, lysoPC a C28:0, alpha-ketoglutaric acid, lactic acid, cough, fever, anosmia, and dysgeusia. Our results demonstrate that differences in sexes have implications in the diagnosis and outcome of the disease. Further, genetic and machine learning algorithms are useful tools to predict sex-associated differences in COVID-19.