ACE2 Protein Landscape in the Head and Neck Region: The Conundrum of SARS-CoV-2 Infection
Bibliographic record
Abstract
The coronavirus pandemic raging worldwide since December 2019 is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which invades human cells via the angiotensin-converting enzyme 2 (ACE2) receptor. Although it has already been identified in many organs, ACE2 expression remains largely unknown in the head and neck (HN) sphere. Thus, this study aims to investigate its protein expression in several sites of the upper aerodigestive tract in order to highlight potential routes of infection. We compared ACE2 immunohistochemical expression between 70 paraffin-embedded specimens with two different antibodies and reported the quantified expression in each histological location. Surprisingly, we obtained different results depending on the antibody, an absence of labeling having been observed with a monoclonal antibody raised against the extracellular domain, whereas the polyclonal, against the cytoplasmic part of the protein, revealed enriched ACE2 expression, particularly in sinuses, vocal cords, salivary glands and oral cavity epithelial cells. The interpretation of these discordant results has brought several exciting lines of reflection. In conclusion, this study provides possible routes of entry for the SARS-CoV-2 in HN region and, above all, has led us to encourage caution when studying the ACE2 expression which is currently at the center of all attention.
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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 | 0.000 | 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 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".