Smart therapies against global pandemics: A potential of short peptides
Bibliographic record
Abstract
As we entered the third year of this pandemic, since the World Health Organisation (WHO) declared in March 2020 the novel coronavirus severe acute respiratory syndrome (SARS-CoV2) outbreak as a global pandemic COVID-19 (COronaVIrus Disease 19), we are still fighting with newer and newer viral mutations. The pandemic has passed the grim milestone of over 6.4 million COVID19 deaths, from more than 550 million reported cases thus far. In fact, more than 15 million people can die by the end of this year. It is highly likely that this pandemic will become endemic, while the full evolutionary potential of coronaviruses has yet to be revealed. The next pandemic is coming. A microbe with features of SARS-Middle East respiratory syndrome (MERS) and SARS-CoV-2 could lead to significantly more catastrophic loss of life. The co-evolution with other viruses should not be neglected. According to the WHO, we should expect diverse zoonotic, outbreakprone microbes, including highly pathogenic strains of influenza, Nipah, Ebola, Zika, or hemorrhagic fever viruses. 'It's an evolutionary certainty that there will be another virus with the potential to be more transmittable and deadly than this one', said Tedros Adhanom Ghebreyesus, director-general of the WHO. On the other hand, in both poor countries and regions of armed conflict, where vaccination is hampered, historic diseases are re-emerging, with migration and displacement influencing transmission risk and limiting control, and raising potential for additional outbreaks. Furthermore, there are other looming terrible threats to humanity as damaging as the bubonic plagues, such as bioterrorism or antibiotic resistant microorganisms. In most cases, both effective prevention and treatment options are limited.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".