Lung Neutrophilic Recruitment and IL-8/IL-17A Tissue Expression in COVID-19
Bibliographic record
Abstract
The new SARS-CoV-2 virus differs from the pandemic Influenza A virus H1N1 subtype (H1N1pmd09) how it induces a pro-inflammatory response in infected patients. This study aims to evaluate the involvement of SNPs and tissue expression of IL-17A and the neutrophils recruitment in post-mortem lung samples from patients who died of severe forms of COVID-19 comparing to those who died by H1N1pdm09. Twenty lung samples from patients SARS-CoV-2 infected (COVID-19 group) and 10 lung samples from adults who died from a severe respiratory H1N1pdm09 infection (H1N1 group) were tested. The tissue expression of IL-8/IL-17A was identified by immunohistochemistry, and hematoxylin and eosin (H&E) stain slides were used for neutrophil scoring. DNA was extracted from paraffin blocks, and genotyping was done in real time-PCR for two IL17A target polymorphisms. Tissue expression increasing of IL-8/IL-17A and a higher number of neutrophils were identified in samples from the H1N1 group compared to the COVID-19 group. The distribution of genotype frequencies in the IL17A gene was not statistically significant between groups. However, the G allele (GG and GA) of rs3819025 was correlated with higher tissue expression of IL-17A in the COVID-19 group. SARS-CoV-2 virus evokes an exacerbated response of the host’s immune system but differs from that observed in the H1N1pdm09 infection since the IL-8/IL-17A tissue expression, and lung neutrophilic recruitment may be decreased. In SNP rs3819025 (G/A), the G allele may be considered a risk allele in the patients who died for COVID-19.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".