Impacts of SARS-CoV-2 on brain renin angiotensin system related signaling and its subsequent complications on brain: A theoretical perspective.

Cellular ACE2 (cACE2), a vital component of the renin-angiotensin system (RAS), possesses catalytic activity to maintain AngII and Ang 1-7 balance, which is necessary to prevent harmful effects of AngII/AT2R and promote protective pathways of Ang (1-7)/MasR and Ang (1-7)/AT2R. Hemostasis of the brain-RAS is essential for maintaining normal central nervous system (CNS) function. Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a viral disease that causes multi-organ dysfunction. SARS-CoV-2 mainly uses cACE2 to enter the cells and cause its downregulation. This, in turn, prevents the conversion of Ang II to Ang (1-7) and disrupts the normal balance of brain-RAS. Brain-RAS disturbances give rise to one of the pathological pathways in which SARS-CoV-2 suppresses neuroprotective pathways and induces inflammatory cytokines and reactive oxygen species. Finally, these impairments lead to neuroinflammation, neuronal injury, and neurological complications. In conclusion, the influence of RAS on various processes within the brain has significant implications for the neurological manifestations associated with COVID-19. These effects include sensory disturbances, such as olfactory and gustatory dysfunctions, as well as cerebrovascular and brain stem-related disorders, all of which are intertwined with disruptions in the RAS homeostasis of the brain.

Generalizable machine learning approach for COVID-19 mortality risk prediction using on-admission clinical and laboratory features.

We aimed to propose a mortality risk prediction model using on-admission clinical and laboratory predictors. We used a dataset of confirmed COVID-19 patients admitted to three general hospitals in Tehran. Clinical and laboratory values were gathered on admission. Six different machine learning models and two feature selection methods were used to assess the risk of in-hospital mortality. The proposed model was selected using the area under the receiver operator curve (AUC). Furthermore, a dataset from an additional hospital was used for external validation. 5320 hospitalized COVID-19 patients were enrolled in the study, with a mortality rate of 17.24% (N = 917). Among 82 features, ten laboratories and 27 clinical features were selected by LASSO. All methods showed acceptable performance (AUC > 80%), except for K-nearest neighbor. Our proposed deep neural network on features selected by LASSO showed AUC scores of 83.4% and 82.8% in internal and external validation, respectively. Furthermore, our imputer worked efficiently when two out of ten laboratory parameters were missing (AUC = 81.8%). We worked intimately with healthcare professionals to provide a tool that can solve real-world needs. Our model confirmed the potential of machine learning methods for use in clinical practice as a decision-support system.

The (CF3C(O)NH)(C6H5CH2NH)2P(O) phosphoric triamide as a novel carrier with excellent efficiency for Cu(ii) in a liquid membrane transport system.

Transport of Ag(i), Cd(ii), Co(ii), Cu(ii), Ni(ii), Pb(ii) and Zn(ii) cations across a bulk liquid membrane (BLM) containing N,N'-dibenzyl-N''-(2,2,2-trifluoroacetyl)-phosphoric triamide (PTC) as a new carrier is studied by atomic absorption spectrometry. The results show selective and efficient transport of the copper(ii) cation from aqueous solution in the presence of the other cations. Various factors are optimized in order to obtain maximum transport efficiency. The PTC ligand is characterized by single crystal X-ray diffraction analysis, IR, NMR (19F, 31P, 1H, 13C) and mass spectroscopy. The complex formation reaction between copper(ii) and PTC is studied by a conductometric method, which shows the 1 : 1 stoichiometry for ligand and copper(ii).

Mixture cure model for estimating short-term and long-term colorectal cancer survival.

AIM: We used mixture cure mode to separately investigate the risk factors for long-term and short-term survival of colorectal cancer patients. BACKGROUND: Colorectal cancer (CRC) is the second most common cancer worldwide. In cancer studies, patients' survival is the most important indicator of patients' status. Classical methods in analyzing the survival data usually apply Cox proportional hazard regression. METHODS: The study was performed on 1121 patients diagnosed with colorectal cancer. Mixture cure model with Weibull distribution and logit link function was fitted to data. RESULTS: Odds of long-term survival for rectum cancer patients were lower than for colon cancer patients (OR=0.29(0.09, 0.9)). Also, patients with the advanced stage of the disease had lower odds of long-term survival compared to early-stage patients (OR=0.24(0.06, 0.86)).In the short-term, the hazard of death for people with normal BMI was lower than the underweight group (HR=0.4(0.21, 0.76)). The short-term hazard of death for rectum cancer was about half of the short-term hazard for colon cancer (HR=0.49(0.29, 0.81)). Further, people with moderately (HR=2.11(1.26, 3.55)) and poorly (HR=4.04(2.03, 8.03)) differentiated tumor grade had a higher short-term hazard of death compared to people with well-differentiated grade. CONCLUSION: Predictive variables of colorectal cancer survival showed different effects in short- and long -terms. Site topography was a prognosis for both long-term and short-term survival; BMI and tumor grade were short-term predictors of survival while stage was a long-term predictor of survival.