Prediction of susceptible biomarkers for end stage renal disease among North Indians
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
AIM: Involvement of pro-inflammatory genes has been correlated with basic kidney diseases and end stage renal disease (ESRD). However, results at odds were often noted from such independent association studies. This study proposes a genome wide analysis approach to predict ESRD risk associated genes. METHODS: We included 42 single nucleotide polymorphisms (SNPs) showing association among north Indian ESRD cases and controls. ESRD cases comprised chronic glomerulonephritis (CGN), chronic interstitial nephritis (CIN), hypertension (HTN) and autosomal dominant polycystic kidney disease (ADPKD). Genotyping data obtained from our prior published reports were compared with Genome-Wide Association Studies (GWAS) SNPs retrieved from HapMap and GWASCentral databases using R-statistical package SNPAssoc. Linkage disequilibrium (LD), gene-gene interaction, classification and regression tree (CART) and pathway analysis were carried out in the present study supplemented with IL-6 and TNF-α levels estimation using enzyme linked immunosorbent assay (ELISA). RESULTS: Comparison of genotyping data with GWAS SNPs revealed significant associations for interleukin (IL)1-RN, IL-6, MTHFR, tumour necrosis factor-α (TNF-α) and CCR3 genes with ESRD. Nine SNPs were commonly associated with CGN, CIN, HTN, ADPKD and ESRD. LD (D = 0.9) and gene-gene interaction (P = 0.0002) analyses revealed significant associations for IL-6 and TNF-α genes. In a consistent manner, CART analysis and functional analysis servers predict predisposing effects for TNF-α and IL-6 with ESRD. Finally, higher body circulating levels were observed for mutant TNF-α and IL-6 alleles among ESRD. CONCLUSION: The study indicates significance for IL-6 and TNF-α gene with basic kidney diseases and ESRD. Extensive statistical tests, pathway analysis and functional assays also reflect attenuated level of significance for these SNPs. In future these may be brought from bench side to clinical practice as diagnostic biomarkers upon external and prospective replication and confirmation among other cohorts.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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