Analysis of the frequency of single nucleotide polymorphisms in cytokine genes in patients with New Onset Diabetes After Transplant
Bibliographic record
Abstract
New Onset Diabetes After Transplantation (NODAT) is a serious metabolic complication. While β-cell dysfunction is considered the main contributing factor in the development of NODAT, the precise pathogenesis is not well understood. Cytokines are thought to be involved in the inflammation of islet β-cells in diabetes; however, few studies have investigated this hypothesis in NODAT. A total of 309 kidney transplant recipients (KTRs) were included in this study. An association between kidney transplants, and the development of diabetes after transplant (NODAT) was investigated. Comparison was made between KTRs who develop diabetes (NODAT cases) or did not develop diabetes (control), using key cytokines, IL-6 G (- 174)C, macrophage mediator; IL-4 C (- 490)T, T helper (Th)-2 cytokine profile initiator; Th-1 cytokine profile initiator interferon-γ T (+ 874) A gene and TGF β1 C (+ 869) T gene polymorphisms were investigated. The genes were amplified using well-established polymerase chain reaction (PCR) techniques in our laboratory. Compared to the AA and AT genotypes of interferon gamma (IFNG), there was a strong association between the TT genotype of IFNG and NODAT kidney transplant recipients (KTRs) versus non-NODAT KTRs (p = 0.005). The AA genotype of IFNG was found to be predominant in the control group (p = 0.004). Also, significant variations of IL6 G (- 174) C, IL-4 C (- 590) T, interferon-γ T (+ 874) A gene and transforming growth factor β1 C (+ 869) T may contribute to NODAT. Our data is consistent with theTh-1/T-reg pathway of immunity. Further larger pan Arab studies are required to confirm our findings.
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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) | 0.000 | 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) | 0.002 | 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".