SARS-CoV-2 Brain Regional Detection, Histopathology, Gene Expression, and Immunomodulatory Changes in Decedents with COVID-19
Bibliographic record
Abstract
Brains of 42 COVID-19 decedents and 107 non-COVID-19 controls were studied. RT-PCR screening of 16 regions from 20 COVID-19 autopsies found SARS-CoV-2 E gene viral sequences in 7 regions (2.5% of 320 samples), concentrated in 4/20 subjects (20%). Additional screening of olfactory bulb (OB), amygdala (AMY) and entorhinal area for E, N1, N2, RNA-dependent RNA polymerase, and S gene sequences detected one or more of these in OB in 8/21 subjects (38%). It is uncertain whether these RNA sequences represent viable virus. Significant histopathology was limited to 2/42 cases (4.8%), one with a large acute cerebral infarct and one with hemorrhagic encephalitis. Case-control RNAseq in OB and AMY found more than 5000 and 700 differentially expressed genes, respectively, unrelated to RT-PCR results; these involved immune response, neuronal constituents, and olfactory/taste receptor genes. Olfactory marker protein-1 reduction indicated COVID-19-related loss of OB olfactory mucosa afferents. Iba-1-immunoreactive microglia had reduced area fractions in cerebellar cortex and AMY, and cytokine arrays showed generalized downregulation in AMY and upregulation in blood serum in COVID-19 cases. Although OB is a major brain portal for SARS-CoV-2, COVID-19 brain changes are more likely due to blood-borne immune mediators and trans-synaptic gene expression changes arising from OB deafferentation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".