Microarray and Bioinformatics Analysis of Gene Expression in Experimental Membranous Nephropathy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Passive Heymann nephritis (PHN), the best characterized animal model of experimental membranous nephropathy, is characterized by subepithelial immune deposits, podocyte foot processes effacement and massive proteinuria beginning 4 days following disease induction. Although single genes involved in PHN have been studied, no whole genome-wide expression analysis of kidney tissue has been performed. METHODS: Microarray analysis was performed to identify gene expression changes in PHN rat kidneys during the onset of proteinuria. RESULTS: Our results showed that 234 transcripts were differentially expressed in diseased animals compared to controls. Genes exclusively upregulated in diseased animals were mainly required for cell structure and motility, immunity and defense, cell cycle, and developmental processes. The single most increased gene was transgelin (Tagln) showing a 70-fold upregulation in animals with PHN. Protein-protein interaction analysis revealed the following four processes of major relevance in disease manifestation: (i) DNA damage and repair; (ii) changes in the extracellular matrix; (iii) deregulation of cytokines and growth factors, as well as (iv) rearrangements of the cytoskeleton. CONCLUSION: We show for the first time the complex interplay between multiple different genes in experimental membranous nephropathy, supporting a role for genomic approaches to better understanding and defining specific disease processes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 it