myo-Inositol Oxygenase: A Novel Kidney-Specific Biomarker of Acute Kidney Injury?
Bibliographic record
Abstract
Acute kidney injury (AKI)2 is an increasingly recognized syndrome associated with short-term and long-term morbidity and mortality. Recent studies have demonstrated that even mild AKI portends an increased risk of chronic kidney disease (1). There is thus great impetus to develop novel therapies for treatment of AKI. Several agents are currently being tested in phase I/II clinical trials (2). Despite these ongoing efforts, no effective therapeutic agents have yet emerged on the clinical scene. Some of the reasons for the lack of beneficial therapies in AKI include incomplete understanding of the pathogenesis of this disorder and the absence of early and reliable markers of AKI, which may enable treatment before irreversible injury ensues. The importance given to the development of novel biomarkers is high, as evidenced by the proclamation of the American Society of Nephrology to designate this research endeavor a top priority (3). Biomarkers of AKI have represented an area of active research in the last decade. These biomarkers should identify those at risk, enable a timely diagnosis of AKI (i.e., before the alterations in traditional markers), stratify patients on the basis of prognosis, and improve understanding of the nephron segment(s) affected (4). Markers of renal dysfunction in current use lack both specificity and sensitivity. Serum creatinine is the most widely employed marker of AKI. However, it is a late marker, highly nonspecific to the site or type of injury. It predicts glomerular filtration rate (GFR) only in the steady state and varies with muscle mass and diet. Another biomarker used in the diagnosis of AKI is urine output. Urine output is unreliable except in monitored settings such as intensive care, and it can be altered by administration of fluids and diuretics. Despite their shortcomings, serum creatinine and urine output are the diagnostic biomarkers of AKI included …
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 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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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; both teacher heads agree on what is shown here.
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".