Antibody Dependent Enhancement (ADE) of Infection into Macrophages Validates the Importance of HERV-K102 Particle Production for Pandemic Preparedness
Bibliographic record
Abstract
Historically, macrophages have been long implicated in the control of the severity of infectious diseases. This has been based on the observations of a higher risk of severe disease and death upon rechallenge with viral variants due to antibody dependent enhance-ment (ADE) of infection into macrophages. The question remains as to what can account for this potent heterologous protection in macrophages? Here it is argued that the elusive defense mechanism of M1-like pro-inflammatory macrophages may pertain to a novel virus anti-virus response. This system initiates with high replication of human endogenous retrovirus K102 (HERV-K102), a non-pathogenic, protector foamy retrovirus of humans which generates M1-like pro-inflammatory foamy macrophages, glycolysis, and epigenetic changes, all characteristic of trained immunity. This virus-anti-virus system kills virally infected cells by several mechanisms, amplifies the innate interferon response via ‘viral mimicry’, has many unique components that interfere with exoge-nous virus replication, and may be especially adept at neutralizing enveloped exogenous pandemic viruses, such as SARS-CoV-2 and HIV-1. The goal of this treatise is to introduce the multifaceted HERV-K102 protector system, to illustrate how SARS-CoV-2 may target the HERV-K102 protector system by ADE, and to explore how this innate defense system may be exploited for pandemic preparedness.
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 | 0.001 |
| 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".