The clinical use of urinary mitochondrial <scp>DNA</scp> in adult surgical critical care patients with acute kidney injury
Bibliographic record
Abstract
Acute kidney injury (AKI) affects 47% of adult surgical critical care patients (ASCCPs). AKI is induced through a common oxidative stress pathway resulting in mitochondrial and tubular cell injury with increased urinary mitochondrial DNA (UmtDNA) excretion. UmtDNA is an emerging and readily sampled novel biomarker for varied surgical critical care cohorts. This review aimed to determine the clinical use of UmtDNA genes (ND1 and COX3) in AKI in ASCCPs. PubMed, MEDLINE and Web of Science databases were searched. Eligibility criteria were based on the patient/problem, intervention, comparison and outcome framework. Methodological quality of studies was assessed with the Newcastle-Ottawa Quality Assessment Scale. WebPlot Digitizer version 4.4 was used to extract UmtDNA data from graphs and UmtDNA ratios were statistically analysed with PRISM version 9.1.0 (GraphPad Software). Six human studies (n = 391) with three translational murine models (n = 112) satisfied inclusion criteria. One sample t test suggested significantly high UmtDNA-ND1 ratios in progressive/severe AKI (or delayed renal transplant graft function) to no AKI (or immediate renal transplant graft function) and increased UmtDNA-COX3 ratios approached significance. Sensitivities and specificities for UmtDNA ranged from 68% to 85% and 52% to 83.6%, respectively, comparable with new biomarkers, neutrophil gelatinase-associated lipocalin and kidney injury molecule-1. Weak correlation was observed with serum creatinine. These findings were complemented in translational murine AKI experiments with significantly elevated ND1 and COX3. From bench to clinical practice, UmtDNA appears to be a promising novel biomarker of progressive/severe AKI (or delayed graft function). Large prospective, multi-centre studies reporting standardised UmtDNA findings should clarify use of UmtDNA in ASCCP-AKI management.
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.
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 | 0.013 | 0.041 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".