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Record W4390339367 · doi:10.3390/ijms25010436

Somatostatin and Somatostatin Receptors in Tumour Biology

2023· article· en· W4390339367 on OpenAlexafffund
Ujendra Kumar

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2023
Typearticle
Languageen
FieldMedicine
TopicNeuroendocrine Tumor Research Advances
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsSomatostatinSomatostatin receptorBiologyReceptorSomatostatin receptor 2Endocrine systemImmune systemCancer researchIn vivoSomatostatin receptor 1HormoneCell growthInternal medicineEndocrinologyCell biologyMedicineImmunologyBiochemistry

Abstract

fetched live from OpenAlex

Somatostatin (SST), a growth hormone inhibitory peptide, is expressed in endocrine and non-endocrine tissues, immune cells and the central nervous system (CNS). Post-release from secretory or immune cells, the first most appreciated role that SST exhibits is the antiproliferative effect in target tissue that served as a potential therapeutic intervention in various tumours of different origins. The SST-mediated in vivo and/or in vitro antiproliferative effect in the tumour is considered direct via activation of five different somatostatin receptor subtypes (SSTR1-5), which are well expressed in most tumours and often more than one receptor in a single cell. Second, the indirect effect is associated with the regulation of growth factors. SSTR subtypes are crucial in tumour diagnosis and prognosis. In this review, with the recent development of new SST analogues and receptor-specific agonists with emerging functional consequences of signaling pathways are promising therapeutic avenues in tumours of different origins that are discussed.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.376
Teacher spread0.352 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations36
Published2023
Admission routes2
Has abstractyes

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Same venueInternational Journal of Molecular SciencesSame topicNeuroendocrine Tumor Research AdvancesFrench-language works237,207