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Record W2026328092 · doi:10.1155/2014/426710

Effect of Nonviral Plasmid Delivered Basic Fibroblast Growth Factor and Low Intensity Pulsed Ultrasound on Mandibular Condylar Growth: A Preliminary Study

2014· article· en· W2026328092 on OpenAlexafffund
Harmanpreet Kaur, Hasan Uludağ, Tarek El‐Bialy

Bibliographic record

VenueBioMed Research International · 2014
Typearticle
Languageen
FieldEngineering
TopicUltrasound and Hyperthermia Applications
Canadian institutionsUniversity of Alberta
FundersUniversity of AlbertaWomen and Children's Health Research InstituteChildren's Health Research Institute
KeywordsLow-intensity pulsed ultrasoundBasic fibroblast growth factorCondyleChemistryGrowth factorMandible (arthropod mouthpart)AndrologyEndocrinologyUltrasoundInternal medicineAnatomyBiologyTherapeutic ultrasoundMedicineBiochemistry

Abstract

fetched live from OpenAlex

OBJECTIVE: Basic fibroblast growth factor (bFGF) is an important regulator of tissue growth. Previous studies have shown that low intensity pulsed ultrasound (LIPUS) stimulates bone growth. The objective of this study was to evaluate the possible synergetic effect of LIPUS and local injection of nonviral bFGF plasmid DNA (pDNA) on mandibular growth in rats. DESIGN: Groups were control, blank pDNA, bFGF pDNA, LIPUS, and bFGF pDNA + LIPUS. Treatments were performed for 28 days. Significant increase was observed in mandibular height and condylar length in LIPUS groups. MicroCT analysis showed significant increase in bone volume fraction in bFGF pDNA + LIPUS group. Histomorphometric analysis showed increased cell count and condylar proliferative and hypertrophic layers widths in bFGF pDNA group. RESULTS: Current study showed increased mandibular condylar growth in either bFGF pDNA or LIPUS groups compared to the combined group that showed only increased bone volume fraction. CONCLUSION: It appears that there is an additive effect of bFGF + LIPUS on the mandibular growth.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.396
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.014
GPT teacher head0.275
Teacher spread0.262 · 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

Citations21
Published2014
Admission routes2
Has abstractyes

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