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Record W2913765198 · doi:10.5430/jst.v9n1p13

The impact of proliferation and cancer stem cell upon the resistence to chemotherapy in salivary mucopepidermoid carcinoma

2019· article· en· W2913765198 on OpenAlexvenueno aff
Doaa Esmaeil, Rehab Allah Ahmed, Mohamed Mourad, Essam Taher M.A. Gaballah

Bibliographic record

VenueJournal of Solid Tumors · 2019
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Tumors Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsMalignancyMultiple drug resistanceChemotherapyMucoepidermoid carcinomaMedicineCancerPathologyLymph nodeProliferation MarkerKi-67CarcinomaStem cellCancer stem cellOncologyCancer researchInternal medicineBiologyImmunohistochemistryDrug resistance

Abstract

fetched live from OpenAlex

Background and aim: Mucoepidermoid carcinoma is a common salivary tumor that affects both adults and children. Proliferation is one of the most fundamental biological processes of growth and maintenance of tissue homeostasis. CD-44 may be used as an indicator of aggressive behavior of some human malignancy. Multidrug resistance is one of the major obstacles for successful cancer chemotherapy. The present study was carried out for evaluation of the biological rules and the clinicopathological significance of Ki-67, CD-44 and MDR-1 expression in the different histopathological grades of MECs.Patients and methods: Eighty paraffin embedded MEC tissues were collected and classified to three groups according to their histological grades. Tissue sections were stained with Ki-67, CD-44 and MDR-1 then examined microscopically and analyzedstatistically.Result: High grade MEC cases showed the highest expression for Ki-67, CD-44 and MDR-1. Additionally, significant differences were found between the histopathological grades as well as between lymph node stages of the studied cases and the expression of the three utilized markers.Conclusion: Ki-67, CD-44 and MDR-1can be used to evaluate the degree of differentiation and to predict the prognosis of MECs, furthermore, high grade MEC cases with high proliferative indices might be resistant to chemotherapy.

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 score0.014
Threshold uncertainty score0.224

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 studies0.0000.000
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 head0.015
GPT teacher head0.302
Teacher spread0.287 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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