Minimizing neonatal hypothyroidism induced by lithium exposure through breast milk
Bibliographic record
Abstract
BACKGROUND: Lithium-induced hypothyroidism in the neonate is a growing concern for lactating mothers. Maternal hypothyroidism in the postpartum period could lead to hypothyroidism in the infant via maternal compromised thyroid hormones (likely T4) in breast milk, and lithium in breast milk could have a direct effect on the neonatal thyroid axis over lithium carbonate direct administration. METHODS: We have studied the effects of lithium exposure on neonatal pups through two different modes of exposure: direct oral administration of lithium carbonate and indirect exposure of lithium from breast milk from dams. Furthermore, dams were supplemented with two different iodine dosages in both control and lithium-treated groups. We employed Enzyme-linked immunosorbent assay, inductively coupled plasma mass and atomic absorption spectrometry to assess hormone profiles and intrathyroidal elemental content. RESULTS: Interestingly, lithium administered directly to pups from control mothers (average dose 900 mg/50 kg per day), did not affect their weight, thyroid hormones, blood urea, and intrathyroidal iodine content despite traces of lithium found in their blood and thyroid. The iodine pathway in the presence of lithium content in both thyroid follicular cells and lactocyte has been hypothesized. The results also demonstrate that lithium administration in lactating dams alters thyroid hormones (T4) and blood urea in both dams and pups, which could be reversed by iodine supplement. The mechanism for supplemented iodine uptake in the presence of lithium is hypothesized. CONCLUSION: In future, supplementing iodine may be potentially useful in clinical practices to address the neonate concerns of lactating mothers and their infants either caused by prolonged lithium medication or maternal iodine deficiency.
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".