Prenatal Exposures to LTP-Patterned Magnetic Fields: Quantitative Effects on Specific Limbic Structures and Acquisition of Contextually Conditioned Fear
Bibliographic record
Abstract
Weak (<1 microT) complex magnetic fields (CMFs) may exert their behavioral influences through the hippocampus by resonating by accident or design with intrinsic electrical patterns. Rats were exposed prenatally to one of four intensities of a CMF (either <5 nanoTesla [nT], 10-50 nT, 50-500 nT, or 500-1000 nT) designed to interact with the process of Long-Term Potentiation (LTP) in the hippocampus. Rats then underwent testing in the forced swim, open field, and fear-conditioning procedures. The cell densities of all amygdaloid nuclei, specific hypothalamic structures, and the major regions of the hippocampus were quantified. Results showed that acquisition of conditioned fear was strongly inhibited in animals exposed to LTP-CMFs. Rats exposed to intensities above 10 nT showed decreased cell density in the CA2 fields of the hippocampus; more neurons were present in the CA1 fields of rats exposed to the 10-50 nT intensities compared to all other groups. A decrease in cell density in the medial preoptic nucleus was linearly dependent on field intensity. In the forced-swim test, swimming was decreased in rats that had been exposed to low (10-50 nT) and medium intensity (50-500 nT) LTP-CMFs in a manner consistent with monoamine modulation. In the open field, exposed rats were indistinguishable from controls. These findings support the hypothesis that continuous exposure during prenatal development to CMFs designed to simulate intrinsic LTP within the hippocampus can affect adult behaviors specific to this structure and produce quantitative alterations in neuronal density.
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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 | 0.001 |
| 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".