Neonatal tactile stimulation reverses alterations in fine structure of small, but not large myelinated fibers, from the optic nerve of iron-deficient rats: A size-based selectivity
Bibliographic record
Abstract
Iron is the most common micronutrient deficiency in the world and it is most prevalent in young children, exposing their developing brain to inadequate iron levels. The damage related to neuroanatomical parameters is not reversed after iron treatment. However, evidence suggest that tactile stimulation (TS) may offer great therapeutic efficacy in cases of nutritional disorders postnatally, since the brain is remarkably responsive to its interaction with the environment. Recently, we shown that neonatal iron deficient rats achieved some remedial effect by exposing them to TS treatment early in life, reinforcing the fact that the TS approach is a positive enriching experience, therefore, here we ask whether exposure to TS treatment, could also be employed to prevent fine structural changes in the fibers from optic nerve of rats maintained on an iron-deficient diet during brain development. To elucidate the protective effect of tactile stimulation, our methods resulted in 10,859 analyzed fibers, divided into small and large fibers. We found that iron deficiency led to a decreased axon, fiber and myelin size of small fibers, however, TS completely reversed the iron-decifiency-induced alteration on those fiber measurements. Large fibers were disproportionately affected by iron deficiency and there was no remediating effect due to tactile stimulation treatment. The present study adds new information regarding different alterations between small and large fibers due to diet and TS, which suggest a size-based selectivity. These results emphasize the concept that compromised brain development can be mitigated at an early age by environmental factors, such as tactile stimulation.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".