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Enregistrement W1656202872 · doi:10.1155/2015/456348

Muscular Dystrophy: Disease Mechanisms and Therapies

2015· editorial· en· W1656202872 sur OpenAlexaff
Sachchida Nand Pandey, Akanchha Kesari, Toshifumi Yokota, Gouri Shankar Pandey

Notice bibliographique

RevueBioMed Research International · 2015
Typeeditorial
Langueen
DomaineBiochemistry, Genetics and Molecular Biology
ThématiqueMuscle Physiology and Disorders
Établissements canadiensUniversity of Alberta
Organismes subventionnairesnon disponible
Mots-clésMuscular dystrophyDiseaseMedicineBioinformaticsComputational biologyBiologyPathologyInternal medicine

Résumé

récupéré en direct d'OpenAlex

Progressive weakness and degeneration of skeletal muscles caused by genetic alterations fall into the category of muscular dystrophy. Muscular dystrophy occurs worldwide and affects all races. The overall incidence of muscular dystrophy varies among forms, as some forms are more common than others. Muscle loss and weakness are not necessarily caused by genetic alteration. Skeletal muscle inactivity, denervation, cancer-associated cachexia, and physiological responses to fasting or malnutrition cause skeletal muscle mass loss through imbalance in synthesis and breakdown of proteins. Several genes have been identified that are directly or indirectly involved in various muscle wasting. Studies performed in human and animal models have substantially contributed to our knowledge of molecular mechanism of muscle degeneration but still these findings are inadequate for developing effective therapy. Therefore, precise dissection of molecular mechanisms provides the way for the development of therapeutic interventions for muscular dystrophies as well as for skeletal muscle loss. In this special issue, we intended to publish research and review articles on exploring molecular mechanisms and target identification for treatment of muscle diseases. This issue will give insight into cellular and molecular mechanisms, activation of signaling pathways, how activation of these pathways causes muscle dysfunction, and subsequent disease symptoms. The review article published in this special issue discusses the animal model for muscular dystrophy associated with dystroglycan (F. Sciandra et al.), followed by an article focusing on inflammation status and nutrition in Duchenne muscular dystrophy (DMD) patients (O. R. Cruz-Guzman et al.). The other three articles are related to cellular and molecular modeling of skeletal muscle loss. These articles describe recent advancement in the skeletal muscle research field as well as possibility for developing tools in therapeutic intervention. A study conducted by E. Guadagnin et al. provides new insight into the role of transforming growth factor beta 1 (TGFβ1) in skeletal muscles. TGFβ1 is recently shown to be a key player in skeletal muscle atrophy and endomysial fibrosis. E. Guadagnin et al. demonstrated that TGFβ1 alone can induce Tyr705 phosphorylation of STAT3 in skeletal muscle cells, and higher pSTAT3 (Tyr705) leads to severe phenotype in transgenic TGFβ1 mice. O. R. Cruz-Guzman et al. have shown that chronic inflammation in patients with DMD may be related to loss of muscle function or to obesity. It is not known whether circulating proinflammatory cytokines such as, IL-6, IL-1, and TNF-α levels are associated with muscle function. Therefore, the purpose of their study was to evaluate whether an association exists between systemic inflammation with muscle function and nutritional status in DMD patient. The study concluded that systemic inflammation is increased in patients with better muscle function and decreases in DMD patients with poorer muscle function; nevertheless, systemic inflammation is similar among different levels of nutritional status in DMD patients. Dystroglycan (DG) is highly expressed in skeletal muscle and served as extracellular matrix receptor. Mutations in components of the DG complex are cause of several muscular dystrophies such as mutations in one of the DG complex genes, DAG1, which has been recently associated with two forms of muscular dystrophy. In this special issue, Sciandra et al. published a review focused on the animal model systems, conditional DG knockout, and knock-in mice development, in order to study the DG function in skeletal muscle as well as in other tissues. Hampered calcium signaling has been often reported in muscular dystrophies, which led to proteolysis of muscle protein induced by calcium ions. The assessment of intracellular calcium event is important for understanding the molecular mechanisms underlying muscular dystrophies. To understand the precise mechanism of calcium signaling in muscle cells, there is a need of robust cellular model. N. Smolina et al. have examined myotubes as a model of adult skeletal muscle for studying evoked calcium release. In addition, the authors also assessed the possibility of this cellular model for studying functional mutation effects through lentiviral transduction. Therefore, primary murine myotubes may serve as a useful cellular model for investigating calcium signaling. C. Baligand et al. identified the key molecular pathways activated during muscle remodeling after spinal cord injury (SCI) and locomotor training in a rat model since molecular events associated with changes in muscle mass after SCI are not known completely. In this study the authors have performed genome wide expression profiling of soleus muscles at multiple time points after SCI in the well-characterize rat model. Their expression data suggest the involvement of TGF-beta/smad3 signaling in association with decrease in muscle mass observed with SCI, while the BMP pathway was activated during treadmill training. This study may provide insight into effects of BMP signaling activation and TGFβ signaling on muscle regeneration with treadmill training in SCI through Smad3 downregulation, providing early indicators of efficient reloading in SCI model. Articles published in this special issue will provide new insights into understanding the pathophysiology and novel therapeutic target identification of skeletal muscle diseases. As the understanding of skeletal muscle diseases improves with time, new findings will further enrich the current knowledge of these diseases, ultimately helping to develop effective therapy.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,001
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,050
Score d'incertitude au seuil0,723

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0010,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0010,000
Intégrité de la recherche0,0010,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,028
Tête enseignante GPT0,366
Écart entre enseignants0,338 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations10
Publié2015
Routes d'admission1
Résumé présentoui

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