Potential of serum metabolites for diagnosing post-stroke cognitive impairment
Why this work is in the frame
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Bibliographic record
Abstract
Cognitive impairment commonly accompanies clinical syndromes associated with stroke. The identification of laboratory markers of post-stroke cognitive impairment (PSCI) may help detect patients at increased risk of cognitive deterioration and determine the appropriate treatment regimes. A non-targeted metabolomics approach based on ultra-high performance liquid chromatography coupled with Q-TOF mass spectrometry was applied to study PSCI. The stroke patients were significantly distinguishable from the healthy subjects. Stroke patients could be well-stratified based on cognitive impairment. Several differential serum metabolites were further identified for post-stroke non-cognitive impairment (PSNCI) and PSCI patients, suggesting metabolic dysfunction in inflammation, neurotoxicity, bioenergetic homeostasis, oxidative stress, and apoptosis. In total, three serum metabolites (glutamine, kynurenine, and LysoPC(18:2)) were identified as candidate diagnostic biomarkers for PSCI, and their combined use yielded good diagnostic capacity for PSCI by receiver operating characteristic curves. The present metabolomics study provided a novel strategy for stratifying stroke patients with cognitive impairment using serum-based metabolite markers, which could be of great importance in understanding the pathological mechanisms and determining the appropriate treatment regimes of PSCI patients.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it