Astrocyte reactivity: a key mediator of Alzheimer's disease risk following critical illness
Bibliographic record
Abstract
Survivors of a critical illness are at high risk of developing neurodegeneration and long-term cognitive impairment. Although common, this condition is poorly understood. Astrocyte reactivity, a heterogeneous response of challenged astrocytes, is a common characteristic of Alzheimer's disease. Here, we aimed to evaluate the impact of critical diseases on astrocyte reactivity, neurodegeneration, and cognition. Individuals from an intensive care unit (n=53) and healthy controls (n=44) aged 40 years or older were recruited. The Simplified Acute Physiology Score III (SAPS III) was used to predict mortality in ICU patients. GFAP and NfL levels in plasma were measured with SIMOA. Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MOCA) were used for cognitive screening. Statistical analyses were conducted using generalized linear models, which accounted for age and sex. The critical disease group presented higher GFAP (F=15.094, p<0.001) and NfL levels (F=12.32, p<0.001) compared to controls. GFAP levels were positively associated with SAPS III (β=0.0351, 95%CI 0.011, 0.0588, p=0.004), while NfL levels were not (β=0.00347, 95%CI -0.027–0.0339, p=0.824). MMSE (F=5.371, p=0.029) and MOCA (F=11.75, p=0.002) showed a significant difference between groups; however, neither test was associated with biomarker levels. Critical diseases influence astrocyte reactivity and neurodegeneration processes, suggesting that they may be associated with neuropathological processes. Astrocyte reactivity biomarkers were associated with mortality prediction scores, highlighting the crucial role astrocytes have in mediating brain-periphery communication.
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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 | 0.001 | 0.002 |
| 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".