MétaCan
Menu
Back to cohort
Record W2054353317 · doi:10.1097/mpa.0b013e3181f690ff

Interstitial Cells of Cajal

2010· article· en· W2054353317 on OpenAlexaff
Xuan‐Yu Wang, Nicholas E. Diamant, Jan D. Huizinga

Bibliographic record

VenuePancreas · 2010
Typearticle
Languageen
FieldMedicine
TopicGastrointestinal motility and disorders
Canadian institutionsToronto Western HospitalMcMaster University
Fundersnot available
KeywordsInterstitial cell of CajalEndoplasmic reticulumBasal laminaCaveolaePathologyBiologyInterstitial cellMyocyteAnatomyUltrastructureImmunohistochemistryCell biologyMedicineSignal transduction

Abstract

fetched live from OpenAlex

OBJECTIVES: Ramon y Cajal discovered interstitial cells in the pancreas associated with intrinsic nerves. It was our aim to provide evidence for or against the hypothesis that the pancreatic duct harbors interstitial cells of Cajal (ICCs) that may function as pacemakers for duct motility. METHODS: We used immunohistochemistry using c-Kit as the ICC marker and protein gene product 9.5 for nerves. Electron microscopy further characterized the cells and their interrelationships. RESULTS: c-Kit-positive cells were associated with smooth muscle cells and nerve fibers of the duct wall and were rich in mitochondria, rough endoplasmic reticulum, and intermediate filaments; they possessed occasional caveolae and had a discontinuous basal lamina. They were connected by small gap junctions to each other and to smooth muscle cells. c-Kit-positive cells around large blood vessels were similar. c-Kit-positive cells within acini were similar in structure but were not associated with smooth muscle cells. CONCLUSIONS: The c-Kit-positive cells around the main duct were identified as ICCs and have the morphological criteria to likely function as pacemaker cells for the previously observed spontaneous rhythmic pancreatic duct contractions. Interstitial cells of Cajal around the large blood vessels likely affect vessel wall rhythmicity.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.598
Threshold uncertainty score0.586

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)0.0010.000

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 head0.006
GPT teacher head0.237
Teacher spread0.231 · 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 designBench or experimental
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
Published2010
Admission routes1
Has abstractyes

Explore more

Same venuePancreasSame topicGastrointestinal motility and disordersFrench-language works237,207