Interaction network of differentially expressed genes between Kashin-Beck disease and osteoarthritis
Bibliographic record
Abstract
Objective To investigate the data of gene expression microarray by protein interaction network analysis, establish an interaction network of differentially expressed genes between Kashin-Beck disease (KBD) and osteoarthritis (OA) and choose the central nodes of the network. Methods The articular cartilage samples of degrees Ⅱ° and Ⅲ° KBD and OA patients were selected according to the national diagnosis criteria for KBD and the Western Ontario and McMaster Universities (WOMAC) for OA. Chondrocytes of 8 patients with KBD and 7 with OA were selected. About 1 000 different genes detected by gene expression microarray were inputted into STRING 9.1 database online for analysis and establishment of the interaction network. The interaction data were imported into Cytoscape 3.2.1 software for screening the central nodes of the network. KEGG database was exploited for pathway analysis and functional study of the central node genes, Real-time PCR (RT-PCR) was used for verification. Results The protein products of 334 differentially expressed genes between KBD and OA had interrelation, forming a complicated interaction network. About 150 central nodes were selected by Cytoscape 3.2.1 that involved in more than ten signal pathways involved in mitochondria, bone metabolism and inflammatory cytokine. Conclusion The interaction network of the differentially expressed genes between KBD and OA, especially the central nodes of this network, can provide clues to the mechanism and early diagnosis and molecular targeted therapy of KBD and OA. Key words: Kashin-Beck disease; Osteoarthritis; Interaction network; Central nodes
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".