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Record W6981797012

Feline oral neoplasms: a twenty-year retrospective survey and expression of amelogenin and ameloblastin in feline conventional (keratinizing) ameloblastoma and oral squamous cell carcinoma.

2022· dissertation· en· W6981797012 on OpenAlexaboutno aff

Bibliographic record

VenueUniversity Library (University of Saskatchewan) · 2022
Typedissertation
Languageen
FieldArts and Humanities
TopicHistorical Architecture and Urbanism
Canadian institutionsnot available
Fundersnot available
KeywordsAmeloblastomaCATSFibrosarcomaBasal cellImmunohistochemistryEpulisOdontogenic tumorAmelogeninOral cavity
DOInot available

Abstract

fetched live from OpenAlex

Feline oral neoplasms are underrepresented in scientific studies and reviews when compared with similar canine neoplasms. Oral neoplasms include those of the oral cavity, pharynx, gingiva, dental structures (odontogenic neoplasms), tongue, tonsils, and salivary glands. Oral neoplasms are common in cats representing 10-60% of all neoplasms in previous publications. In Chapter 2 of the thesis, 569 surgical biopsies obtained from feline oral cavities submitted for routine diagnostic purposes between January 1998 and December 2019 were reviewed. Twenty-two different neoplasms were found. A majority of neoplasms were malignant (85%). The most frequently diagnosed were: squamous cell carcinoma (68.8%), peripheral odontogenic fibroma (5.3%), fibrosarcoma (4.4%), peripheral giant cell granuloma (3.5%), conventional (keratinizing) ameloblastoma (3.5%), and adenocarcinoma of the salivary gland (2.46%). The current study is the first one of its type conducted in Canada and the second one in North America. Compared to a previous North American study, fewer cases of fibrosarcoma (4.4% vs 12.9 %), and significantly more cases of conventional (keratinizing) ameloblastoma (3.5% vs 0.3%) were reported. Several neoplasms were identified in this study that were not seen in the previous study, these included: plasma cell neoplasm, hemangiosarcoma, and osteoma. Oral squamous cell carcinoma (OSCC) and conventional ameloblastoma (CA) represent two epithelium-derived neoplasms that affect the oral cavity of cats and histologically may look similar. In Chapter 3, two immunohistochemical (IHC) markers, amelogenin and ameloblastin, were compared to determine usefulness in differentiation of the two neoplasms. The expression of amelogenin and ameloblastin has been previously established in the feline tooth bud and canine and human odontogenic tumors. The aim of this study was to characterize the amelogenin and ameloblastin expression profile of OSCC in comparison to CA. Samples from 15 OSCC and 15 CA cases were examined. Amelogenin expression was intranuclear in 15 OSCC cases, with all cases demonstrating high staining intensity. 14 of 15 CA cases demonstrated mild-moderate intranuclear staining intensity. Neither CA nor SCC expressed ameloblastin. Ki67 stained SCC samples had proliferation index 29.80% and CA had proliferation index 16.51%. The difference in staining pattern and intensity of amelogenin and ameloblastin along with proliferation index of Ki76 in OSCC and CA did not help distinguish between the two neoplasia types. The combined conclusions of the investigations are feline oral neoplasms are still an under researched area, ameloblastoma might be more common than previously thought, amelogenin and ameloblastin are not specifically expressed in odontogenic neoplasia, and Ki67 labeling index is not significantly different between OSCC and CA.

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 score0.007
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.170
Teacher spread0.162 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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