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Record W2001321636 · doi:10.3109/10428194.2013.770847

Pleurocidin-family cationic antimicrobial peptides mediate lysis of multiple myeloma cells and impair the growth of multiple myeloma xenografts

2013· article· en· W2001321636 on OpenAlexafffund
Ashley L. Hilchie, David Conrad, Melanie R. Power Coombs, Tyler Zemlak, Carolyn D. Doucette, Robert Liwski, David W. Hoskin

Bibliographic record

VenueLeukemia & lymphoma/Leukemia and lymphoma · 2013
Typearticle
Languageen
FieldImmunology and Microbiology
TopicAntimicrobial Peptides and Activities
Canadian institutionsDalhousie UniversityQueen Elizabeth II Health Sciences Centre
FundersNatural Sciences and Engineering Research Council of CanadaDalhousie UniversityBeatrice Hunter Cancer Research Institute
KeywordsMultiple myelomaCancer researchMedicineCytotoxic T cellImmune systemMalignancyCancerMelphalanImmunologyInternal medicineBiologyIn vitroBiochemistry

Abstract

fetched live from OpenAlex

Abstract Multiple myeloma is a common hematological malignancy that urgently requires new approaches to treatment, since the disease is not curable using current chemotherapeutic regimens. The aim of this study was to determine whether human and mouse multiple myeloma cells are killed by the pleurocidin-like cationic antimicrobial peptides NRC-03 and NRC-07, previously shown to be active against breast cancer cells. We demonstrate here that NRC-03 and NRC-07 bound to and rapidly killed multiple myeloma cells by causing extensive membrane damage, as well as DNA cleavage. NRC-03 showed greater binding to multiple myeloma cells and a more potent cytotoxic effect than NRC-07. In addition, intratumoral injections of NRC-03 impaired the growth of multiple myeloma xenografts in immune-deficient mice. We conclude that NRC-03 warrants further investigation for its possible use in the treatment of multiple myeloma.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 integrity0.0000.001
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 head0.007
GPT teacher head0.191
Teacher spread0.184 · 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 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

Citations29
Published2013
Admission routes2
Has abstractyes

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