Substance P/ Neurokinin-1 Receptor, Trigeminal Ganglion, Latency, and Coronavirus Infection-Is There Any Link?
Bibliographic record
Abstract
Novel Severe Acute Respiratory Syndrome-Corona Virus-2 infection (SARS-CoV-2) is an acute respiratory and infectious disease. This perspective aims to provide a basic understanding of the inflammation caused by SARS-CoV-2 and its relation to the trigeminal ganglion (TG). The virus enters through the mucous membranes of the orofacial region and reaches the TG, where it resides and takes control of its peptides including Substance P (SP). SP is the main neuropeptide, neuromodulator, and neuro-hormone of TG, associated with nociception and inflammation under noxious stimulus. SP release is triggered and, consequently, affects the immune cells and blood vessels to release the mediators for inflammation. Hence, cytokine storm is initiated and causes respiratory distress, bronchoconstriction, and death in complicated cases. Neurokinin-1 Receptor (NK-1R) is the receptor for SP and its antagonists, along with glucocorticoids, may be used to alleviate the symptoms and treat this infection by blocking this nociceptive pathway. SP seems to be the main culprit involved in the triggering of inflammatory pathways in SARS-CoV-2 infection. It may have a direct association with cardio-respiratory rhythm, sleep-wake cycle, nociception, and ventilatory responses and regulates many important physiological and pathological functions. Its over-secretion should be blocked by NK-1R antagonist. However, experimental work leading to clinical trials are mandatory for further confirmation. Here, it is further proposed that there is a possibility of latency in SARS-CoV-2 virus infection if it is acting through TG, which is the main site for other viruses that become latent.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".