Thermal Taste and Anti-Aspiration Drugs: A Novel Drug Discovery Against Pneumonia
Bibliographic record
Abstract
Despite the development of strong antibiotics, the pneumonia death is increasing all over the world in these decades. Among the people who died of pneumonia, the majority were 65 years old or over. Although pneumonia is recently categorized into several entities, aspiration pneumonia includes all entities. Therefore, targeting dysphagia and aspiration to treat pneumonia is a promising strategy and anti-aspiration drugs will be a part of pneumonia treatment. The swallowing reflex in elderly people was temperature-sensitive and the improvement of swallowing reflex by temperature stimuli could be mediated by the thermosensing TRP channels at pharynx. The administration of capsaicin as an agonist stimulus of TRPV1, a warm temperature receptor, decreased the delay in swallowing reflex. Red wine polyphenols improved swallowing reflex by enhancing TRPV1 response. Food with menthol, agonist of TRPM8 which is a cold temperature receptor, also decreased the delay in swallowing reflex. Olfactory stimulation such as black pepper was useful to improve the swallowing reflex for people with low ADL levels or with decreased consciousness. Thus, recent advancement of geriatrics found several anti-aspiration drugs such as thermosensing TRP channel agonists, black pepper odor, amantadine, cilostazol, theophylline and angiotensin- converting enzymes inhibitors. Thermosensing TRP channel agonists include capsaicin, capsiate, menthol, and red wine polyphenols. Controls of swallowing are mediated by various stages of neural system from peripheral sensory nerves to the entire cerebral cortex. Each anti-aspiration drug acts on various sites of neural axis of swallowing reflex. The combination of various anti-aspiration drugs may improve dysphagia and prevent aspiration pneumonia.
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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) | 0.001 | 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 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".