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Record W1985128764 · doi:10.2310/7070.2005.5004

Role of Mast and Goblet Cells in the Pathogenesis of Nasal Polyps

2006· article· en· W1985128764 on OpenAlexvenueno aff
Fatma Kitapçi, Nuray Bayar Muluk, Pınar Atasoy, Can Koç

Bibliographic record

VenueThe Journal of Otolaryngology · 2006
Typearticle
Languageen
FieldMedicine
TopicSinusitis and nasal conditions
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineFunctional endoscopic sinus surgerySeptoplastyNasal polypsEthmoid sinusNasal cavitySinus (botany)Paranasal sinusesPathologyHistopathologyPathogenesisMaxillary sinusSinusitisNoseAnatomySurgeryBiology

Abstract

fetched live from OpenAlex

In this study, the role of mast and goblet cells and eosinophils in the pathogenesis of nasal polyposis was investigated. The study group consisted of 28 adult patients (15 males, 13 females) with nasal polyposis who underwent functional endoscopic sinus surgery (FESS). All patients in the study group were examined with a questionnaire, an otolaryngologic examination, an endoscopic examination with 0 degrees and 30 degrees endoscopes, Waters' graphy, and axial and coronal computed tomography of the paranasal sinuses. The control group consisted of 10 adult patients without nasal polyp (7 males and 3 females) who underwent septoplasty. They gave written approval to enter the study. The polyp specimens from the study group were excised from four regions: the maxillary sinus, ethmoid sinus, sphenoid sinus, and nasal cavity. They were examined at x400 magnification by light microscopy, and only the slides with polypoid tissue were included in the study. Slides including a chronic inflammatory process without polypoid tissue were excluded from the study. The control group was composed of the slides of specimens from the inferior turbinate. Forty slides (10 in each group) in the study group and 10 slides in the control group were included in the study. The surgical specimens from the study and control groups were examined with a histochemical staining technique. In every surgical specimen, the type of epithelium and the numbers of goblet and mast cells and eosinophils were calculated in x400 high-magnification field in 10 areas on light microscopy, as well as the mean number of these cells, and for mast cells separately, cell count in the epithelium and the stromal layer of polyp tissue and total mast cell count, including both epithelial and stromal mast cells, were identified. Goblet cells, mast cells, and inflammation with eosinophils were observed in all sinonasal mucosa. The common epithelial type in the polyp tissue was pseudostratified ciliated cylindric epithelium, which contains goblet cells. Goblet cell numbers in the maxillary, ethmoid, and sphenoid sinuses and nasal cavity were found to be significantly higher than in the control group (p < .05). For total mast cell and eosinophil count, no statistically significant difference was found between all five groups. In each group, there was no statistically significant difference between goblet and mast cells. Increased goblet cells in sinonasal polyps indicated that systemic factors also affect nasal polyposis as much as local factors, such as airflow and mucosal contact. Surgical treatment of sinonasal polyps by FESS causes more sufficient air ventilation in the nasal cavity and paranasal sinuses. Therefore, the goblet cell density will decrease because of the exposure of the mucosal surfaces to the air. In particular, FESS and then the appropriate medical treatment may decrease the recurrence rates and increase the patient's comfort. The significantly increased goblet cell count in the sinonasal mucosa demonstrated the importance of these cells in the pathogenesis of nasal polyposis. Also, mast cells and eosinophils may have a role in the inflammatory processes, leading to nasal polyposis formation.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.227
Teacher spread0.220 · 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

Citations15
Published2006
Admission routes1
Has abstractyes

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