PSXVII-35 ICP-MS based ionotyping reveals altered ionome in the serum, urine, and milk of pre-milk fever (MF), MF, and post-MF dairy cows.
Bibliographic record
Abstract
The objectives of this study were: 1) to investigate ionome in three body fluids (i.e., serum, urine, and milk) of pre-milk fever (MF), MF, and post-MF dairy cows; and 2) to identify newly predictive serum/urine and diagnostic serum/urine/milk mineral biomarkers for MF. In this study, inductively coupled plasma mass spectrometry (ICP-MS) based ionomics was performed to targetedly analyze minerals in serum, urine, and milk (only at the disease wk) samples from 6 cows with MF and 20 healthy controls (CON) at -8 wks, -4 wks, disease diagnosis, +4 wks, and +8 wks relative to parturition. MetaboAnalyst 3.0 was employed for statistical analyses and biomarker analysis. Results showed that around five (out of 18) minerals were elevated in the serum of MF cows at each tested time point. Particularly, serum levels of chromium were continuously higher in both pre-MF (i.e., 3.93 vs 3.11 at -8 wks, P = 0.02; 3.79 vs 3.16 at -4 wks, P < 0.01; unit: μM) and MF cows (i.e., 3.96 vs 3.27 at the disease wk, P = 0.01) compared with CON ones. Other minerals such as aluminum, manganese, and arsenic were also elevated in the serum at early stages (i.e., at -8 and -4 wks; P < 0.05) of MF. Moreover, besides calcium (MF 37,431 vs CON 27,260 μM; P = 0.02), other two minerals (i.e., aluminum and cobalt, P < 0.05) were increased in the milk of MF cows. Moreover, four (two in serum and two in urine) predictive mineral-biomarker models for MF were developed. Overall, our ionomics data showed that MF cows had altered ionome in pre-MF, MF, and post-MF cows. MF is more complicated than hypocalcemia. ICP-MS based ionomics sheds new light on our understanding of altered mineral metabolism and identification of new biomarkers for MF in dairy cows.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".