Aptamer Profiling of A549 Cells Infected with Low-Pathogenicity and High-Pathogenicity Influenza Viruses
Bibliographic record
Abstract
Influenza A viruses (IAVs) are important animal and human emerging and re-emerging pathogens that are responsible for yearly seasonal epidemics and sporadic pandemics. IAVs cause a wide range of clinical illnesses, from relatively mild infections by seasonal strains, to acute respiratory distress during infections with highly pathogenic avian IAVs (HPAI). For this study, we infected A549 human lung cells with lab prototype A/PR/8/34 (H1N1) (PR8), a seasonal H1N1 (RV733), the 2009 pandemic H1N1 (pdm09), or with two avian strains, an H5N1 HPAI strain or an H7N9 strain that has low pathogenicity in birds but high pathogenicity in humans. We used a newly-developed aptamer-based multiplexed technique (SOMAscan®) to examine >1300 human lung cell proteins affected by the different IAV strains, and identified more than 500 significantly dysregulated cellular proteins. Our analyses indicated that the avian strains induced more profound changes in the A549 global proteome compared to all tested low-pathogenicity H1N1 strains. The PR8 strain induced a general activation, primarily by upregulating many immune molecules, the seasonal RV733 and pdm09 strains had minimal effect upon assayed molecules, and the avian strains induced significant downregulation, primarily in antimicrobial response, cardiovascular and post-translational modification systems.
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".