Comparative Proteomic Profiling of Blood Plasma Revealed Marker Proteins Involved in Temporal Lobe Epilepsy
Bibliographic record
Abstract
Temporal lobe epilepsy has various origins, involving or not involving structural changes in brain tissue. The mechanisms of epileptogenesis are associated with cell regulation and signaling disruptions expressed in varied levels of proteins. The blood plasma proteomic profiling of temporal lobe epilepsy patients (including magnetic resonance imaging (MRI)-positive and MRI-negative ones) and healthy volunteers using mass spectrometry and label-free quantification revealed a list of differently expressed proteins. Several apolipoproteins (APOA1, APOD, and APOA4), serpin protease inhibitors (SERPINA3, SERPINF1, etc.), complement components (C9, C8, and C1R), and a total of 42 proteins were found to be significantly upregulated in the temporal lobe epilepsy group. A classification analysis of these proteins according to their biological functions, as well as a review of the published sources, disclosed the predominant involvement of the processes mostly affected during epilepsy such as neuroinflammation, intracellular signaling, lipid metabolism, and oxidative stress. The presence of several proteins related to the corresponding compensatory mechanisms has been noted. After further validation, the newly identified temporal lobe epilepsy biomarker candidates may be used as epilepsy diagnostic tools, in addition to other less specific methods such as electroencephalography or MRI.
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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 | 0.001 |
| 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".