Human respiratory coronaviruses : neuroinvasive, neurotropic and potentially neurovirulent pathogens.
Bibliographic record
Abstract
In humans, viral infections of the respiratory tract are a leading cause of morbidity and mortality worldwide. Among the various respiratory viruses, coronaviruses are important ubiquitous pathogens of humans and animals. Since the late 1960's, human coronaviruses (HCoV) are recognized pathogens of the upper respiratory tract, being mainly associated with mild pathologies such as the common cold. However, in vulnerable populations, (newborns, infants, the elderly and immune-compromised individuals), they can affect the lower respiratory tract, leading to pneumonia, exacerbations of asthma, respiratory distress syndrome or even severe acute respiratory syndrome (SARS). For almost three decades now, the scientific literature has also demonstrated that HCoV are neuroinvasive and neurotropic: neurons are often the target cell in the central nervous system (CNS), inducing neurodegeneration and eventually death. Moreover, HCoV can contribute to an overactivation of the immune system that could lead to autoimmunity in the CNS of susceptible individuals. Given all these properties, it has been suggested that HCoV could be associated with the triggering or the exacerbation of human neurological diseases for which the etiology remains unknown or poorly understood.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".