Assessment of Phospholipid Malabsorption by Quantification of Fecal Phospholipid
Bibliographic record
Abstract
ABSTRACT Objectives: The standard methods for quantifying fat absorption involve extraction of fat from fecal samples with heptane, ether and ethanol. These solvents do not quantitatively recover phospholipids. Malabsorption of dietary and biliary phosphatidylcholine could potentially result in choline deficiency. Therefore, the authors developed a method extracting and quantifying fecal phospholipids. Methods: Fecal samples were collected for 72 hours from 18 children with cystic fibrosis and 10 control children. Fat was extracted first with hexane/diethyl ether/ethanol and then with chloroform/methanol. Total fat was quantitated gravimetrically. Phospholipids in extracted fat were separated and quantified using high‐performance liquid chromatography with evaporative light‐scattering detection (HPLC‐ELSD). Phospholipid quantification was validated with a phosphomolybdate colorimetric assay. Results: The combination of solvent systems used in this study significantly improved total fat (p < 0.05) and phospholipid (p < 0.001) extraction compared with either hexane/diethyl ether/ethanol or chloroform/methanol alone. Fecal phospholipid measured by HPLC‐ELSD was significantly correlated with lipid‐soluble phosphorous using the phosphomolybdate assay (r = 0.75, p < 0.001). This method also allows quantification of fecal phosphatidylcholine and lysophosphatidylcholine. Conclusions: Hexane/diethyl ether/ethanol followed by chloroform/methanol extraction of fecal samples and quantification of phospholipids using HPLC‐ELSD is a new method for investigating phospholipid malabsorption.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".