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Record W2031343000 · doi:10.4021/gr284w

Alterations of Mast Cells in the Esophageal Mucosa of the Patients With Non-Erosive Reflux Disease

2011· article· en· W2031343000 on OpenAlexvenueno aff
Yue Yu

Bibliographic record

VenueGastroenterology Research · 2011
Typearticle
Languageen
FieldMedicine
TopicGastroesophageal reflux and treatments
Canadian institutionsnot available
FundersNatural Science Foundation of Anhui Province
KeywordsNerdMedicineDegranulationPathologyHigh-power fieldRefluxGastroenterologyEsophagusVacuolizationInternal medicineUltrastructureImmunohistochemistryGERDDisease

Abstract

fetched live from OpenAlex

BACKGROUND: Mast cells (MCs) are widely distributed in the gastrointestinal tract, which could be involved in visceral hypersensitivity and gut dysmotility. Whether esophageal MCs play a role in non-erosive reflux disease (NERD) has yet to be determined. The aim of this study was to characterize esophageal MCs distribution, degranulation, and ultrastructure. METHODS: The esophageal mucosa at 5 cm above the end of esophagus was obtained from 26 NERD and 14 healthy volunteers (control) by gastroscopy. Immunohistochemistry was performed and average MC counts per high-power field (HPF) and the percentage of degranulated MCs were obtained. The ultrastructure of MCs was observed by transmission electron microscope (TEM). RESULTS: More MCs were observed in NERD (7.23 ± 2.41 cells/HPF) as compared with controls (3.79 ± 1.67 cells/HPF) (P < 0.01) and the percentage of degranulated MCs in NERD was also significantly higher than controls (26.85 ± 8.79% vs 11.5 ± 4.18%, P < 0.01). Under TEM, more Golgi apparatus, mitochondria and endoplasmic reticulum were found in MCs in patients with NERD. Special secreting particles were also found in cytoplasm, more vacuoles were left after MCs degranulation in patients with NERD. CONCLUSIONS: Our results indicate that increased numbers of MCs and MCs activation may be involved in the pathogenesis of NERD.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.267

Codex and Gemma teacher scores by category

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

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 head0.041
GPT teacher head0.306
Teacher spread0.264 · 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 teacher head, 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

Citations16
Published2011
Admission routes1
Has abstractyes

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