Association between biochemical and hematologic factors with COVID-19 using data mining methods.

BACKGROUND AND AIM: Coronavirus disease (COVID-19) is an infectious disease that can spread very rapidly with important public health impacts. The prediction of the important factors related to the patient's infectious diseases is helpful to health care workers. The aim of this research was to select the critical feature of the relationship between demographic, biochemical, and hematological characteristics, in patients with and without COVID-19 infection. METHOD: A total of 13,170 participants in the age range of 35-65 years were recruited. Decision Tree (DT), Logistic Regression (LR), and Bootstrap Forest (BF) techniques were fitted into data. Three models were considered in this study, in model I, the biochemical features, in model II, the hematological features, and in model II, both biochemical and homological features were studied. RESULTS: In Model I, the BF, DT, and LR algorithms identified creatine phosphokinase (CPK), blood urea nitrogen (BUN), fasting blood glucose (FBG), total bilirubin, body mass index (BMI), sex, and age, as important predictors for COVID-19. In Model II, our BF, DT, and LR algorithms identified BMI, sex, mean platelet volume (MPV), and age as important predictors. In Model III, our BF, DT, and LR algorithms identified CPK, BMI, MPV, BUN, FBG, sex, creatinine (Cr), age, and total bilirubin as important predictors. CONCLUSION: The proposed BF, DT, and LR models appear to be able to predict and classify infected and non-infected people based on CPK, BUN, BMI, MPV, FBG, Sex, Cr, and Age which had a high association with COVID-19.

Strategy of targeting the tumor microenvironment via inhibition of fibroblast/fibrosis remodeling new era to cancer chemo-immunotherapy resistance.

The use of repurposing drugs that may have neoplastic and anticancer effects increases the efficiency and decrease resistance to chemotherapy drugs through a biochemical and mechanical transduction mechanisms through modulation of fibroblast/fibrosis remodeling in tumor microenvironment (TME). Interestingly, fibroblast/fibrosis remodeling plays a vital role in mediating cancer metastasis and drug resistance after immune chemotherapy. The most essential hypothesis for induction of chemo-immunotherapy resistance is via activation of fibroblast/fibrosis remodeling and preventing the infiltration of T cells after is mainly due to the interference between cytoskeleton, mechanical, biochemical, metabolic, vascular, and remodeling signaling pathways in TME. The structural components of the tumor that can be targeted in the fibroblast/fibrosis remodeling include the depletion of the TME components, targeting the cancer-associated fibroblasts and tumor associated macrophages, alleviating the mechanical stress within the ECM, and normalizing the blood vessels. It has also been found that during immune-chemotherapy, TME injury and fibroblast/fibrosis remodeling causes the up-regulation of inhibitory signals and down-regulation of activated signals, which results in immune escape or chemo-resistance of the tumor. In this regard, repurposing or neo-adjuvant drugs with various transduction signaling mechanisms, including anti-fibrotic effects, are used to target the TME and fibroblast/fibrosis signaling pathway such as angiotensin 2, transforming growth factor-beta, physical barriers of the TME, cytokines and metabolic factors which finally led to the reverse of the chemo-resistance. Consistent to many repurposing drugs such as pirfenidone, metformin, losartan, tranilast, dexamethasone and pentoxifylline are used to decrease immune-suppression by abrogation of TME inhibitory signal that stimulates the immune system and increases efficiency and reduces resistance to chemotherapy drugs. To overcome immunosuppression based on fibroblast/fibrosis remodeling, in this review, we focus on inhibitory signal transduction, which is the physical barrier, alleviates mechanical stress and prevents mechano-metabolic activation.