Nerve Growth Factor Modulation of the Cavernous Nerve Response to Injury
Bibliographic record
Abstract
INTRODUCTION: Surgical therapies for prostate cancer and other pelvic malignancies often result in neuronal damage and debilitating loss of sexual function due to cavernous nerve (CN) trauma. Advances in the neurobiology of growth factors have heightened clinical interest in the development of protective and regenerative neuromodulatory strategies targeting CN recovery following injury. AIM: The aim of this review was to offer an examination of current and future nerve growth factor (NGF) modulation of the CN response to injury with a focus on brain-derived nerve growth factor (BDNF), growth differentiation factor-5 (GDF-5), and neurturin (NTN). METHODS: Information for this presentation was derived from a current literature search using the National Library of Medicine PubMed Services producing publications relevant to this topic. Search terms included neuroprotection, nerve regeneration, NGFs, neurotrophic factors, BDNF, GDF-5, NTN, and CNs. MAIN OUTCOME MEASURES: Basic science studies satisfying the search inclusion criteria were reviewed. RESULTS: In this session, BDNF, atypical growth factors GDF-5 and NTN, and their potential influence upon CN recovery after injury are reviewed, as are the molecular pathways by which their influence is exerted. CONCLUSIONS: Compromised CN function is a significant cause of erectile dysfunction development following prostatectomy and serves as the primary target for potential neuroprotective or regenerative strategies utilizing NGFs such as BDNF, GDF-5, and NTN, and/or targeted novel therapeutics modulating signaling pathways.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".