Post Pandemic Problem, is there an animal model suitable to investigate PASC
Bibliographic record
Abstract
Although the COVID-19 pandemic is no longer a global health emergency, many patients still suffer from long-term effects, known as post-acute sequelae of COVID-19 (PASC) or long COVID. Understanding its complex pathophysiology requires animal models replicating the post-acute phase, which may aid in developing, the urgently needed, therapeutics. Our review assessed and summarized 81 studies from 1979 manuscripts. In addition, a second table summarizing the imaging findings of 26 studies related to this topic was added, based on a separate literature search of 797 manuscripts. In humans a SARS-CoV-2 infection, the sequelae and possible development of PASC is heterogenic. The same holds true for experimental animal models. While several models are suitable to address different research questions, no single model can fully replicate all aspects of PASC. Imaging plays a crucial role in visualizing these aspects, especially since questionnaires, the primary diagnostic tool in humans, cannot be used in animals. Thus, imaging allows the investigation of pathophysiology in a controlled setting, offering valuable insights. This review summarizes the available animal models and imaging modalities used in PASC research. Our aim is to provide researchers with guidance on selecting the most appropriate model and imaging technique to address their specific research questions.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".