Olfactory Bulb and Amygdala Gene Expression Changes in Subjects Dying with COVID-19
Bibliographic record
Abstract
ABSTRACT In this study we conducted RNA sequencing on two brain regions (olfactory bulb and amygdala) from subjects who died from COVID-19 or who died of other causes. We found several-fold more transcriptional changes in the olfactory bulb than in the amygdala, consistent with our own work and that of others indicating that the olfactory bulb may be the initial and most common brain region infected. To some extent our results converge with pseudotime analysis towards common processes shared between the brain regions, possibly induced by the systemic immune reaction following SARS-CoV-2 infection. Changes in amygdala emphasized upregulation of interferon-related neuroinflammation genes, as well as downregulation of synaptic and other neuronal genes, and may represent the substrate of reported acute and subacute COVID-19 neurological effects. Additionally, and only in olfactory bulb, we observed an increase in angiogenesis and platelet activation genes, possibly associated with microvascular damages induced by neuroinflammation. Through coexpression analysis we identified two key genes ( CAMK2B for the synaptic neuronal network and COL1A2 for the angiogenesis/platelet network) that might be interesting potential targets to reverse the effects induced by SARS-CoV-2 infection. Finally, in olfactory bulb we detected an upregulation of olfactory and taste genes, possibly as a compensatory response to functional deafferentation caused by viral entry into primary olfactory sensory neurons. In conclusion, we were able to identify transcriptional profiles and key genes involved in neuroinflammation, neuronal reaction and olfaction induced by direct CNS infection and/or the systemic immune response to 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 | 0.000 |
| 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".