Molecular Targets for Diagnostics and Therapeutics of Severe Acute Respiratory Syndrome (SARS-CoV)
Bibliographic record
Abstract
PURPOSE: The large number of deaths in a short period of time due to the spread of severe acute respiratory syndrome (SARS) infection led to the unparalleled collaborative efforts world wide to determine and characterize the new coronavirus (SARS-CoV). The full genome sequence was determined within weeks of the first outbreak by the Canadian group with international collaboration. As per the World Health Organization (WHO), the continual lack of a rapid laboratory test to aid the early diagnosis of suspected cases of SARS makes this area a priority for future research. To prevent deaths in the future, early diagnosis and therapy of this infectious disease is of paramount importance. METHODS: This review describes the specific molecular targets for diagnostics and therapeutics of viral infection. RESULTS: The three major diagnostic methods available for SARS includes viral RNA detection by reverse transcription polymerase chain reaction (RT-PCR), virus induced antibodies by immunofluorescence assay (IFA) or by enzyme linked immunosorbant assay (ELISA) of nucleocapsid protein (NP). The spike glycoprotein of SARS-CoV is the major inducer of neutralizing antibodies. The receptor binding domain (RBD) in the S1 region of the spike glycoprotein contains multiple conformational epitopes that induces highly potent neutralizing antibodies. The genetically engineered attenuated form of the virus or viral vector vaccine encoding for the SARS-CoV spike glycoprotein has been shown to elicit protective immunity in vaccinated animals. CONCLUSION: NP is the preferred target for routine detection of SARS-CoV infection by ELISA which is an economical method compared to other methods. The RBD of the spike glycoprotein is both a functional domain for cell receptor binding and also a major neutralizing determinant of SARS-CoV. The progress in evaluating a therapeutic or vaccine would depend on the avail ability of clinically relevant animal model.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".