Potential of serum metabolites for diagnosing post-stroke cognitive impairment
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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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".