Effect of malignant growth and chronic neurogenic pain on neurotrophin levels in rat brain
Bibliographic record
Abstract
Aim. Determination of neurotrophin levels in gray and white matter of the brain in rats with tumor growth associated with chronic neurogenic pain (CNP). Materials and methods . The study included white outbred male rats ( n = 74). In the main group, the CNP model was created (by bilateral sciatic nerve ligation), and after 45 days, M1 sarcoma was transplanted subcutaneously ( n = 11) or into the subclavian vein ( n = 11). Two comparison groups ( n = 13 each) consisted of sham operated animals with M1 sarcoma transplanted subcutaneously and intravenously, but without CNP. Control groups were animals with CNP and sham operated animals. Rats were euthanized on the 21 st day of carcinogenesis. The enzymelinked immunosorbent assay ( ELISA) was used to determine brain levels of brain-derived neurotrophic factor (BDNF) (R&D System, USA & Canada), nerve growth factor (β-NGF), neurotrophin-3 (NT-3), neurotrophin 4/5 (NT-4) (RayBiotech, USA). Results. CNP caused an increase in β-NGF levels in the cortex and white matter and BDNF levels only in white matter of the rat brain. Chronic pain stimulated M1 sarcoma growth in both subcutaneous and intravenous transplantation. The dynamics of neurotrophins levels in brain structures differed depending on the tumor site. Conclusion. Thus, the results demonstrated that in both normal peripheral tumor growth and in tumor growth against the background of CNP, changes in neurotrophin levels in the brain of experimental animals can reflect the body reaction to chronic pain and stress caused by the peripheral tumor growth.
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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".