Urinary bile acid profile in children with inborn errors of bile acid metabolism and chronic cholestasis; screening technique using electrospray tandem mass-spectrometry (ES/MS/MS).
Bibliographic record
Abstract
BACKGROUND: It is well known that urine becomes the major route for bile acid excretion in liver diseases and thus we examined bile acid profile in urine obtained from normal children and children having chronic liver diseases using electrospray tandem mass spectrometry (ES/MS/MS). MATERIAL/METHODS: Bile acid were extracted from 5 ml of urine obtained from five healthy children or from twenty patients with various liver diseases including patients with unknown chronic liver diseases, Zellweger syndrome, peroxisomal bifunctional protein deficiency disease, tyrosinema type 1, biliary atresia, and patients with progressive familial intrahepatic cholestasis (PFIC) of undetermined type. Identification and quantification of bile acids were achieved in 5 minutes using electrospray tandem mass spectrometry (ES/MS/MS). RESULTS: Urinary bile acid excretion increased in liver diseases an average of 100 times as compared to control values. There was a specific profile for different liver disease which confirms the pathology of the disease and could be used for its diagnosis. The results also show that the ions used for the diagnosis of oxo-steroid reductase deficiency disease were present in other chronic liver diseases suggesting that these atypical bile acids may not be a result of an inborn error of bile acid metabolism. CONCLUSIONS: The urinary bile acid profile obtained in this study by ES/MS/MS can be of use for the diagnosis of certain chronic liver diseases.
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 | 0.000 | 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) | 0.001 | 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".