Natural Killer Cell Dysfunction and Its Role in COVID-19
Bibliographic record
Abstract
When facing an acute viral infection, our immune systems need to function with finite precision to enable the elimination of the pathogen, whilst protecting our bodies from immune-related damage. In many instances however this "perfect balance" is not achieved, factors such as ageing, cancer, autoimmunity and cardiovascular disease all skew the immune response which is then further distorted by viral infection. In SARS-CoV-2, although the vast majority of COVID-19 cases are mild, as of 24 August 2020, over 800,000 people have died, many from the severe inflammatory cytokine release resulting in extreme clinical manifestations such as acute respiratory distress syndrome (ARDS) and hemophagocytic lymphohistiocytosis (HLH). Severe complications are more common in elderly patients and patients with cardiovascular diseases. Natural killer (NK) cells play a critical role in modulating the immune response and in both of these patient groups, NK cell effector functions are blunted. Preliminary studies in COVID-19 patients with severe disease suggests a reduction in NK cell number and function, resulting in decreased clearance of infected and activated cells, and unchecked elevation of tissue-damaging inflammation markers. SARS-CoV-2 infection skews the immune response towards an overwhelmingly inflammatory phenotype. Restoration of NK cell effector functions has the potential to correct the delicate immune balance required to effectively overcome SARS-CoV-2 infection.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".