Neuroinflammation induces nerve growth factor dependent nociceptor sensitisation in a neonatal rodent model of platinum-based chemotherapy induced neuropathic pain
Bibliographic record
Abstract
Abstract Chemotherapy-induced neuropathic pain (CINP) is a common adverse health related comorbidity that manifests later in life in paediatric patients treated for cancer. CIPN pathology progressively develops over time resulting in a delayed but long-lasting neuropathic pain. Current analgesic strategies are ineffective, aligning closely with our lack of understanding of CINP. Recent studies have indicated alterations in sensory neuronal maturation as component of CINP. The aim of this study was to investigate how cisplatin induces nerve growth factor mediated neuroinflammation and nociceptor sensitisation. In a rodent model of cisplatin induced survivorship pain, there was a significant infiltration of nerve growth factor positive macrophages into the dorsal root ganglia (DRG), demonstrating a robust neuroinflammatory response. Additionally, it was observed that CD11b/F480 positive monocyte/macrophages challenged with cisplatin expressed more NGF. Additionally, DRG derived primary sensory neuron cultures from neonatal mice demonstrated enhanced NGF-dependent TRPV1 mediated nociceptor activity after cisplatin treatment. Increased nociceptor activity was also observed when cultured neurons were treated with conditioned media from cisplatin activated monocyte/macrophages. This elevated nociceptor activity was dose-dependently inhibited by a neutralising monoclonal antibody to NGF. Intraperitoneal administration of NGF neutralising antibody significant reduction in mechanical hypersensitivity was given to mice with cisplatin-induced juvenile survivorship pain there was a as well as suppression of cisplatin induced aberrant nociceptor intraepidermal nerve fibre density. These findings identify the NGF/TrkA signalling pathway as a potential novel therapeutic target for analgesia in adult survivors of childhood cancer.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".