Association of Cognitive Impairment and Peripheral Inflammation in Methamphetamine-dependent Patients: A Cross-sectional Study on Neuroinflammatory Markers TNF-α and IL-6
Bibliographic record
Abstract
Objective: To investigate the cognitive impairment and peripheral inflammation induced by methamphetamine (METH) and their association in METH abusers. Methods: The cross-sectional study included 100 METH-dependent patients and 100 healthy controls. Cognitive screening was conducted using the Thai version of the Montreal Cognitive Assessment (MoCA-T). Thirty normal controls and 30 METH-dependent patients were randomly selected for blood collection to measure inflammatory markers, including tumor necrosis factor (TNF)-α, interleukin (IL)-1β, and IL-6, using a quantitative enzyme-linked immunosorbent assay method. Results: METH-dependent patients had significantly poorer MoCA-T scores and higher levels of blood inflammatory markers compared to healthy controls. Demographic characteristics, METH use patterns, and proinflammatory cytokines were associated with cognitive impairment. The MoCA-T score was negatively associated with plasma TNF-α and IL-6 levels. Conclusion: METH-associated cognitive decline is correlated with elevated plasma levels of TNF-α and IL-6 cytokines, indicating the involvement of specific neuroinflammatory pathways in neurocognitive dysfunction. These insights could pave the way for novel therapeutic strategies aimed at mitigating neuroinflammation, potentially improving outcomes for individuals with METH addiction.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".