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Record W2185226620 · doi:10.1038/srep16017

Identification of the active components in Bone Marrow Soup: a mitigator against irradiation-injury to salivary glands

2015· article· en· W2185226620 on OpenAlexafffund
Dongdong Fang, Shen Hu, Younan Liu, Vu-Hung Quan, Jan Seuntjens, Simon D. Tran

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldMedicine
TopicSalivary Gland Disorders and Functions
Canadian institutionsUniversité de MontréalMcGill University
FundersCanadian Institutes of Health ResearchCanada Research ChairsMcGill University
KeywordsBone marrowSalivaAngiogenesisSalineMedicineChemistryPathologyCancer researchBiochemistryInternal medicine

Abstract

fetched live from OpenAlex

In separate studies, an extract of soluble intracellular contents from whole bone marrow cells, named "Bone Marrow (BM) Soup", was reported to either improve cardiac or salivary functions post-myocardial infarction or irradiation (IR), respectively. However, the active components in BM Soup are unknown. To demonstrate that proteins were the active ingredients, we devised a method using proteinase K followed by heating to deactivate proteins and for safe injections into mice. BM Soup and "deactivated BM Soup" were injected into mice that had their salivary glands injured with 15Gy IR. Control mice received either injections of saline or were not IR. Results at week 8 post-IR showed the 'deactivated BM Soup' was no better than injections of saline, while injections of native BM Soup restored saliva flow, protected salivary cells and blood vessels from IR-damage. Protein arrays detected several angiogenesis-related factors (CD26, FGF, HGF, MMP-8, MMP-9, OPN, PF4, SDF-1) and cytokines (IL-1ra, IL-16) in BM Soup. In conclusion, the native proteins (but not the nucleic acids, lipids or carbohydrates) were the therapeutic ingredients in BM Soup for functional salivary restoration following IR. This molecular therapy approach has clinical potential because it is theoretically less tumorigenic and immunogenic than cell therapies.

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)0.001
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 score0.330
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.025
GPT teacher head0.266
Teacher spread0.241 · 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

Citations39
Published2015
Admission routes2
Has abstractyes

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