Gene expression changes in thalamus and inferior colliculus associated with inflammation, cellular stress, metabolism and structural damage in thiamine deficiency
Bibliographic record
Abstract
Identification of gene expression changes that promote focal neuronal death and neurological dysfunction can further our understanding of the pathophysiology of these disease states and could lead to new pharmacological and molecular therapies. Impairment of oxidative metabolism is a pathogenetic mechanism underlying neuronal death in many chronic neurodegenerative diseases as well as in Wernicke's encephalopathy (WE), a disorder induced by thiamine deficiency (TD). To identify functional pathways that lead to neuronal damage in this disorder, we have examined gene expression changes in the vulnerable thalamus and inferior colliculus of TD rats using Affymetrix Rat Genome GeneChip analysis in combination with gene ontology and functional categorization assessment utilizing the NetAffx GO Mining Tool. Of the 15 927 transcripts analysed, 125 in thalamus and 141 in inferior colliculus were more abundantly expressed in TD rats compared with control animals. In both regions, the major functional categories of transcripts that were increased in abundance after TD were those associated with inflammation (approximately 33%), stress (approximately 20%), cell death and repair ( approximately 26%), and metabolic perturbation (approximately 19%), together constituting approximately 98% of all transcripts up-regulated. These changes occurred against a background of neuronal cell loss and reactive astro- and microgliosis in both structures. Our results indicate that (i) TD produces changes in gene expression that are consistent with the observed dysfunction and pathology, and (ii) similar alterations in expression occur in thalamus and inferior colliculus, brain regions previously considered to differ in pathology. These findings provide important new insight into processes responsible for lesion development in TD, and possibly WE.
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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 | 0.000 |
| 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".