Gene Set Enrichment Analyses Revealed Several Affected Pathways in Niemann-Pick Disease Type C Fibroblasts
Bibliographic record
Abstract
Niemann-pick type C (NPC) disease is characterized by endosomal and lysosomal accumulation of lipids, impaired tubulovesicular trafficking, and neurodegeneration leading to premature death. Current treatment options are limited to mainly symptomatic treatments. Thus, new and efficient drug targets are needed, and therefore we performed a Gene Set Enrichment Analysis (GSEA) on NPC and healthy fibroblasts to identify globally affected pathways in NPC that could serve as targets for later drug discovery programs. Cell lines were characterized by analyzing cellular concentrations of cholesterol, its precursors and metabolites, as well as cellular plant sterol levels. Gene expression analyses were performed with Sentrix Human-8 Expression BeadChips, analyzing 23,000 transcripts. Pathway analysis of the expression data was performed using the GSEA method. Twenty-seven upregulated and 33 downregulated pathways emerged as globally affected in the GSEA analysis. These pathways included, for example, mitochondrial pathway, caspase cascade, as well as prostaglandin and leukotriene metabolism. Based on the present results and earlier published data, anti-inflammatory and antiapoptotic treatment could be beneficial in NPC.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".