Maternal undernutrition during pregnancy and lactation affects testicular morphology, the stages of spermatogenic cycle, and the testicular IGF-I system in adult offspring
Bibliographic record
Abstract
Maternal undernutrition decreases sperm production in male offspring, possibly through insulin-like growth factor (IGF-I). To test this hypothesis, we fed pregnant Wistar rats ad libitum with a standard diet (CONTROL) or fed 50% of CONTROL intake, either throughout pregnancy (UNP), lactation (UNL, or both (UNPL). After weaning, male offspring (n = 10 per treatment) were fed a standard diet until postnatal day 160, when testes process for histological and molecular analyses. IGF-I immunostaining area and intensity in the testis were greater (P = 0.003) in the UNPL group compared to CONTROL, but lower in the UNP group (P < 0.0001). Levels of IGF-I receptor transcript were lower in the UNPL and UNL groups, compared to CONTROL. There were more Ki-67-positive germ and Sertoli cells, in all underfed groups than in CONTROL. Compared to CONTROL, frequency of spermatogenic cycle stage VII was lower in all underfed groups, and seminiferous tubule diameter was smaller in UNP and UNPL. Plasma FSH concentrations were greater in UNP male offspring compared to all groups (P = 0.05), whereas inhibin B concentrations were greater in UNP (P = 0.01) and UNL (P = 0.003) than in CONTROL or UNPL. Thus, prenatal undernutrition leads to a decrease in testicular IGF-I levels, whereas of pre- and postnatal undernutrition increased testicular IGF-I levels and decreased amounts of IGF-I receptor mRNA in adult offspring. We conclude that maternal undernutrition during pregnancy and lactation leads to long-lasting effects on adult male offspring testicular morphology, spermatogenesis, and IGF-I testicular system.
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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) | 0.001 | 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".