Quantitative-profiling of neurotransmitter abnormalities in the disease progression of experimental diabetic encephalopathy rat
Bibliographic record
Abstract
Diabetic encephalopathy (DE) is one of the most prevalent chronic complications of diabetes mellitus (DM), with neither effective prevention nor proven therapeutic regimen. This study aims to uncover the potential dysregulation pattern of the neurotransmitters in a rat model of streptozotocin (STZ)-induced experimental DE. For that purpose, male Sprague-Dawley (SD) rats were treated with a single intraperitoneal injection of STZ. Cognitive performance was detected with the Morris water maze (MWM) test. Serum, cerebrospinal fluid (CSF), and brain tissues were collected to measure the levels of neurotransmitters. Compared with the control rats, the acetylcholine (ACh) levels in serum, CSF, hippocampus, and cortex were all significantly down-regulated as early as 6 weeks in the STZ treatment group. In contrast, the glutamate (Glu) levels were decreased in CSF and the hippocampus, but unaffected in the serum and cortex of STZ-treated rats. As for γ-aminobutyric acid (GABA), it was down-regulated in serum, but up-regulated in CSF, hippocampus, and the cortex in the STZ-treated group. The mRNA expressions of neurotransmitter-related rate limiting enzymes (including AChE, GAD1, and GAD2) and pro-inflammatory cytokines (including IL-1β and TNF-α) were all increased in the DE rats. Our data suggest that DM induces isoform-dependent and tissue-specific neurotransmitter abnormalities, and that neuroinflammation may underlay the nervous system dysfunction observed in the progression of DE.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".