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Record W2016589057 · doi:10.1039/c2mb25398d

Effect of IR laser on myoblasts: a proteomic study

2013· article· en· W2016589057 on OpenAlexaff
Monica Monici, Francesca Cialdai, Francesco Ranaldi, Paolo Paoli, Francesca Boscaro, Gloriano Moneti, Anna Caselli

Bibliographic record

VenueMolecular BioSystems · 2013
Typearticle
Languageen
FieldMedicine
TopicLaser Applications in Dentistry and Medicine
Canadian institutionsIONICS Mass Spectrometry (Canada)
FundersEnte Cassa di Risparmio di FirenzeFondazione Cassa di Risparmio di Pistoia e Pescia
KeywordsMyoDC2C12MyocyteCytoskeletonCell biologyProteomeCellBiologyCell growthCellular differentiationProteomicsMyoD ProteinBioinformaticsBiochemistryMyogenesisGene

Abstract

fetched live from OpenAlex

Laser therapy is used in physical medicine and rehabilitation to accelerate muscle recovery and in sports medicine to prevent damages produced by metabolic disturbances and inflammatory reactions after heavy exercise. The aim of this research was to get insight into possible benefits deriving from the application of an advanced IR laser system to counteract deficits of muscle energy metabolism and stimulate the recovery of hypotrophic tissue. We studied the effect of IR laser treatment on proliferation, differentiation, cytoskeleton organization and global protein expression in C2C12 myoblasts. We found that laser treatment induced a decrease in the cell proliferation rate without affecting cell viability, while leading to cytoskeletal rearrangement and expression of the early differentiation marker MyoD. The differential proteome analysis revealed the up-regulation and/or modulation of many proteins known to be involved in cell cycle regulation, cytoskeleton organization and differentiation.

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 score0.004

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.004
GPT teacher head0.272
Teacher spread0.268 · 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

Citations35
Published2013
Admission routes1
Has abstractyes

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