Circulating microRNAs as markers for scrub typhus-associated acute kidney injury
Bibliographic record
Abstract
BACKGROUND: Circulating microRNAs (miRNAs) are potential biomarkers for various kidney diseases. In this study, we aimed to identify a circulating miRNA signature for detecting acute kidney injury (AKI) in scrub typhus. METHODS: We prospectively enrolled 40 patients with scrub typhus (20 with AKI, AKI group; 20 without AKI, non-AKI group) and 20 healthy volunteers (the HV group). Thereafter, we performed microarray analysis to assess the serum miRNA profiles of all the participants. Then, to identify miRNAs predictive of scrub typhus-associated AKI, we compared miRNA profiles among these three groups. RESULTS: The proportions of miRNAs, small nucleolar RNAs, and small Cajal body-specific ribonucleoproteins were higher in patients with scrub typhus than in the HVs. Further, relative to the HVs, we identified 120 upregulated and 449 downregulated miRNAs in the non-AKI group and 101 upregulated and 468 downregulated miRNAs in the AKI group. We also identified 11 and 110 upregulated and downregulated miRNAs, respectively, in the AKI group relative to the non-AKI group, and among these miRNAs, we noted 14 miRNAs whose levels were significantly upregulated or downregulated in the AKI group relative to their levels in the HV and non-AKI groups. Biological pathway analysis of these 14 miRNAs indicated their potential involvement in various pathways associated with tumor necrosis factor alpha. CONCLUSION: We identified miRNAs associated with AKI in patients with scrub typhus that have predictive potential for AKI. Thus, they can be used as surrogate markers for the detection of scrub typhus-associated AKI.
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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 | 0.000 |
| 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) | 0.001 | 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".