Near‐infrared spectroscopy characterizes vitamin deficiencies and infection status during pregnancy (804.1)
Bibliographic record
Abstract
Background: Near‐infrared (NIR) spectroscopy can identify spectral changes resulting from viral infections but has not been used to examine vitamin deficiencies or infection status during pregnancy. Objectives: To determine if vitamin status (sufficient/deficient) for A, D, B12, and folate, and presence/absence of respiratory, skin, oral, urogenital infections were reflected in NIR of maternal serum in 2nd (n=90) and 3rd trimester (n=100) in rural Panama. Methods: Eight functional groups (1600‐2400nm) were selected (CH, SH, POH, ROH, amide, amine, lactate and glucose). Values for each functional group and their ratios were compared by trimester between mothers with/without each deficiency, and with/without each infection using Mann‐Whitney U‐test. Results: Serum profiles differed by vitamin status in the 2nd trimester for vitamins A (amide:lactate, amine:lactate, lactate:glucose), B12 (POH:amide, ROH:amide) and folate (CH:ROH, SH:ROH). Differences were also observed for oral (CH:SH) and respiratory (amine:glucose) infections. In the third trimester, differences with vitamin D status involved both glucose and amine whereas folic acid deficiency involved differences only in amine. Only lactate differed between women with and without respiratory infections. Conclusion: NIR spectroscopy identified spectral differences due to nutrient deficiencies and infections. Grant Funding Source : SENACYT, NSERC
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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) | 0.003 | 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".