MOLECULAR CHARACTERIZATION, CLINICAL MANAGEMENT AND DEVELOPMENT OF THE VACCINES AGAINST THE TARGETED VIRAL COMPONENTS OF COVID-19
Bibliographic record
Abstract
Spike proteins on the surface of human corona viruses is important to enhance it's competency to get transmit into other healthy population.Because of it's specific spike protein, the virus got its name corona in 1960s.Afterward, it was renamed as Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV) in 2002 and Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in 2012.It was mortal for old population, new born babies and immune-compromised individuals, who didn't have sufficient immunity or defense system.On February 11, 2020, World Health Organization (WHO) gave the names of COVID-19 and SARS-CoV-2.A characteristic of nCoV-19, which is a cause of COVID-19, was identified as major cause of pneumonia.However, the healthcare professionals worked hard to to stop it's outbreak and transmission all over the world.But, there was no medicines that have been cleared by the FDA to treat COVID-19 successfully.So, the goal of this study is to look at the scientific data that is already available about clinical care and therapy of this disease.Some of the sources that were checked for this study were BioRxiv, medRxiv, Google Scholar, Embase, PsychINFO, WanFang Data, and PubMed.A lot of work went into finding out what medicines could be used to avoid and treat COVID-19 illnesses.Remdesivir, chloroquine, hydroxychloroquine, and immunosuppressant drugs have all been shown to help to fight the virus.Until a treatment for the COVID-19 virus is found, it is best to stay away from other people and follow strict hygiene.Most medicinal treatments still have a lot of unknown effects, and different medicines and vaccines are being trialed and tested succefully to stop prevelance, transmission and develop the symptoms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".