ABSTRACT
COVID-19, a global-pandemic binds human-lung-ACE2. ACE2 causes vasodilatation. ACE2 works in balance with ACE1. The vaso-status maintains blood-pressure/vascular-health which is demolished in Covid-19 manifesting aldosterone/salt-deregulations/inflammations/endothelial-dysfunctions/hyper-hypo- tension, sepsis/hypovolemic-shock and vessel-thrombosis/coagulations. Here, nigellidine, an indazole-alkaloid was analyzed by molecular-docking for binding to different Angiotensin-binding-proteins (enzymes, ACE1(6en5)/ACE2(4aph)/receptors, AT1(6os1)/AT2(5xjm)) and COVID-19 spike-glycoprotein(6vsb). Nigellidine strongly binds to the spike-protein at the hinge-region/active-site-opening which may hamper proper-binding of nCoV2-ACE2 surface. Nigellidine effectively binds in the Angiotensin- II binding-site/entry-pocket (-7.54 kcal/mol, -211.76, Atomic-Contact-Energy; ACE-value) of ACE2 (Ki 8.68 and 8.3 µmol) in comparison to known-binder EGCG (-4.53) and Theaflavin-di-gallate (-2.85). Nigellidine showed strong-binding (Ki, 50.93 µmol/binding-energy -5.48 kcal/mol) to mono/multi-meric ACE1. Moreover, it binds Angiotensin-receptors, AT1/AT2 (Ki, 42.79/14.22 µmol, binding-energy, -5.96/-6.61 kcal/mol) at active-sites, respectively. This article reports the novel binding of nigellidine and subsequent blockage of angiotensin-binding proteins. The ACEs-blocking could restore Angiotensin-level, restrict vaso-turbulence in Covid patients and receptor-blocking might stop inflammatory/vascular impairment. Nigellidine may slowdown the vaso-fluctuations due to Angiotensin-deregulations in Covid patients. Angiotensin II-ACE2 binding (ACE-value -294.81) is more favorable than nigellidine-ACE2. Conversely, nigellidine-ACE1 binding-energy/Ki is lower than nigellidine-ACE2 values indicating a balanced-state between constriction-dilatation. Moreover, nigellidine binds to the viral-spike, closer-proximity to its ACE2 binding-domain. Taken together, Covid patients/elderly-patients, comorbid-patients (with hypertensive/diabetic/cardiac/renal-impairment, counting >80% of non-survivors) could be greatly benefited.
Subject(s)
Angiotensin-Converting Enzyme 2/metabolism , COVID-19/metabolism , Nigella sativa , Peptidyl-Dipeptidase A/metabolism , Plant Extracts/metabolism , Receptor, Angiotensin, Type 1/metabolism , Receptor, Angiotensin, Type 2/metabolism , Angiotensin-Converting Enzyme 2/chemistry , COVID-19/pathology , COVID-19/prevention & control , Comorbidity , Computer Simulation/trends , Drug Evaluation, Preclinical/methods , Humans , Molecular Docking Simulation/methods , Peptidyl-Dipeptidase A/chemistry , Plant Extracts/isolation & purification , Plant Extracts/therapeutic use , Protein Binding/physiology , Protein Structure, Secondary , Protein Structure, Tertiary , Receptor, Angiotensin, Type 1/chemistry , Receptor, Angiotensin, Type 2/chemistry , SARS-CoV-2/drug effects , SARS-CoV-2/metabolismABSTRACT
Technology-enhanced simulation has emerged as a great educational tool for pediatric education. Indeed, it represents an effective method to instruct on technical and non-technical skills, employed by a large number of pediatric training programs. However, this unique pandemic era posed new challenges also on simulation-based education. Beyond the mere facing of the clinical and societal impacts, it is fundamental to take advantage from the current changes and investigate innovative approaches to improve the education of pediatric healthcare professionals. To this aim, we herein lay down the main pillars that should support the infrastructure of the future technology-enhanced simulation.
Subject(s)
Clinical Competence , Computer Simulation/trends , Education, Medical, Graduate/methods , Pediatrics/education , Simulation Training/trends , Child , HumansABSTRACT
Growing evidence suggests that T cells may play a critical role in combating severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Hence, COVID-19 vaccines that can elicit a robust T cell response may be particularly important. The design, development and experimental evaluation of such vaccines is aided by an understanding of the landscape of T cell epitopes of SARS-CoV-2, which is largely unknown. Due to the challenges of identifying epitopes experimentally, many studies have proposed the use of in silico methods. Here, we present a review of the in silico methods that have been used for the prediction of SARS-CoV-2 T cell epitopes. These methods employ a diverse set of technical approaches, often rooted in machine learning. A performance comparison is provided based on the ability to identify a specific set of immunogenic epitopes that have been determined experimentally to be targeted by T cells in convalescent COVID-19 patients, shedding light on the relative performance merits of the different approaches adopted by the in silico studies. The review also puts forward perspectives for future research directions.