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Record W2896166955 · doi:10.3389/fnint.2018.00052

Neutrophil-Mediated Endogenous Analgesia Contributes to Sex Differences in Oral Cancer Pain

2018· article· en· W2896166955 on OpenAlexfundno aff
Nicole N. Scheff, Aditi Bhattacharya, Edward Dowse, Richard X. Dang, John C. Dolan, Susanna Wang, Hyesung Kim, Donna G. Albertson, Brian L. Schmidt

Bibliographic record

VenueFrontiers in Integrative Neuroscience · 2018
Typearticle
Languageen
FieldMedicine
TopicCancer, Stress, Anesthesia, and Immune Response
Canadian institutionsnot available
FundersNational Institute of Dental and Craniofacial ResearchNational Cancer InstituteNational Institutes of HealthYork University
KeywordsMedicineCancerEndogenous opioidNociceptionCancer painEndogenyImmune system(+)-NaloxoneAnalgesicInternal medicineImmunologyOpioidAnesthesiaReceptor

Abstract

fetched live from OpenAlex

The incidence of oral cancer in the United States is increasing, especially in young people and women. Patients with oral cancer report severe functional pain. Using a patient cohort accrued through the New York University Oral Cancer Center and immune-competent mouse models, we identify a sex difference in the prevalence and severity of oral cancer pain. A neutrophil-mediated endogenous analgesic mechanism is present in male mice with oral cancer. Local naloxone treatment potentiates cancer mediator-induced orofacial nociceptive behavior in male mice only. Tongues from male mice with oral cancer have significantly more infiltrating neutrophils compared to female mice with oral cancer. Neutrophils isolated from the cancer-induced inflammatory microenvironment express beta-endorphin and met-enkephalin. Furthermore, neutrophil depletion results in nociceptive behavior in male mice. These data suggest a role for sex-specific, immune cell-mediated endogenous analgesia in the treatment of oral cancer pain.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.030
GPT teacher head0.283
Teacher spread0.253 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations44
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Integrative NeuroscienceSame topicCancer, Stress, Anesthesia, and Immune ResponseFrench-language works237,207