Autoantigen Microarray for High-throughput Autoantibody Profiling in Systemic Lupus Erythematosus.

Genomics Proteomics Bioinformatics. 2015 Aug;13(4):210-8

Authors: Zhu H, Luo H, Yan M, Zuo X, Li QZ

Abstract
Systemic lupus erythematosus (SLE) is a complex autoimmune disease characterized by the production of autoantibodies to a broad range of self-antigens. Profiling the autoantibody repertoire using array-based technology has emerged as a powerful tool for the identification of biomarkers in SLE and other autoimmune diseases. Proteomic microarray has the capacity to hold large number of self-antigens on a solid surface and serve as a high-throughput screening method for the determination of autoantibody specificities. The autoantigen arrays carrying a wide variety of self-antigens, such as cell nuclear components (nucleic acids and associated proteins), cytoplasmic proteins, phospholipid proteins, cell matrix proteins, mucosal/secreted proteins, glomeruli, and other tissue-specific proteins, have been used for screening of autoantibody specificities associated with different manifestations of SLE. Arrays containing synthetic peptides and molecular modified proteins are also being utilized for identification of autoantibodies targeting to special antigenic epitopes. Different isotypes of autoantibodies, including IgG, IgM, IgA, and IgE, as well as other Ig subtypes, can be detected simultaneously with multi-color labeled secondary antibodies. Serum and plasma are the most common biologic materials for autoantibody detection, but other body fluids such as cerebrospinal fluid, synovial fluid, and saliva can also be a source of autoantibody detection. Proteomic microarray as a multiplexed high-throughput screening platform is playing an increasingly-important role in autoantibody diagnostics. In this article, we highlight the use of autoantigen microarrays for autoantibody exploration in SLE.

PMID: 26415621 [PubMed - indexed for MEDLINE]

Molecular cytopathology for thyroid nodules: A review of methodology and test performance.

Cancer Cytopathol. 2016 Jan;124(1):14-27

Authors: Nishino M

Abstract
Advances in the molecular characterization of thyroid cancers have fueled the development of genetic and gene expression-based tests for thyroid fine-needle aspirations. Collectively, these tests are designed to improve the diagnostic certainty of thyroid cytology. This review summarizes the early published experience with the commercially available versions of these tests: the Afirma Gene Expression Classifier, ThyGenX (formerly miRInform)/ThyraMIR, and ThyroSeq. Key differences in testing approaches and issues regarding test performance and interpretation are also discussed.

PMID: 26348024 [PubMed - indexed for MEDLINE]

RNA-Seq: Improving Our Understanding of Retinal Biology and Disease.

Cold Spring Harb Perspect Med. 2015;5(9):a017152

Authors: Farkas MH, Au ED, Sousa ME, Pierce EA

Abstract
Over the past several years, rapid technological advances have allowed for a dramatic increase in our knowledge and understanding of the transcriptional landscape, because of the ability to study gene expression in greater depth and with more detail than previously possible. To this end, RNA-Seq has quickly become one of the most widely used methods for studying transcriptomes of tissues and individual cells. Unlike previously favored analysis methods, RNA-Seq is extremely high-throughput, and is not dependent on an annotated transcriptome, laying the foundation for novel genetic discovery. Additionally, RNA-Seq derived transcriptomes provide a basis for widening the scope of research to identify potential targets in the treatment of retinal disease.

PMID: 25722474 [PubMed - indexed for MEDLINE]

Argo: enabling the development of bespoke workflows and services for disease annotation.

Database (Oxford). 2016;2016

Authors: Batista-Navarro R, Carter J, Ananiadou S

Abstract
Argo (http://argo.nactem.ac.uk) is a generic text mining workbench that can cater to a variety of use cases, including the semi-automatic annotation of literature. It enables its technical users to build their own customised text mining solutions by providing a wide array of interoperable and configurable elementary components that can be seamlessly integrated into processing workflows. With Argo's graphical annotation interface, domain experts can then make use of the workflows' automatically generated output to curate information of interest.With the continuously rising need to understand the aetiology of diseases as well as the demand for their informed diagnosis and personalised treatment, the curation of disease-relevant information from medical and clinical documents has become an indispensable scientific activity. In the Fifth BioCreative Challenge Evaluation Workshop (BioCreative V), there was substantial interest in the mining of literature for disease-relevant information. Apart from a panel discussion focussed on disease annotations, the chemical-disease relations (CDR) track was also organised to foster the sharing and advancement of disease annotation tools and resources.This article presents the application of Argo's capabilities to the literature-based annotation of diseases. As part of our participation in BioCreative V's User Interactive Track (IAT), we demonstrated and evaluated Argo's suitability to the semi-automatic curation of chronic obstructive pulmonary disease (COPD) phenotypes. Furthermore, the workbench facilitated the development of some of the CDR track's top-performing web services for normalising disease mentions against the Medical Subject Headings (MeSH) database. In this work, we highlight Argo's support for developing various types of bespoke workflows ranging from ones which enabled us to easily incorporate information from various databases, to those which train and apply machine learning-based concept recognition models, through to user-interactive ones which allow human curators to manually provide their corrections to automatically generated annotations. Our participation in the BioCreative V challenges shows Argo's potential as an enabling technology for curating disease and phenotypic information from literature.Database URL: http://argo.nactem.ac.uk.

PMID: 27189607 [PubMed - as supplied by publisher]

Identifying a biomarker network for corticosteroid resistance in asthma from bronchoalveolar lavage samples.

Mol Biol Rep. 2016 May 17;

Authors: Vargas JE, Porto BN, Puga R, Stein RT, Pitrez PM

Abstract
Corticosteroid resistance (CR) is a major barrier to the effective treatment of severe asthma. Hence, a better understanding of the molecular mechanisms involved in this condition is a priority. Network analysis is an emerging strategy to explore this complex heterogeneous disorder at system level to identify a small own network for CR in asthma. Gene expression profile of GSE7368 from bronchoalveolar lavage (BAL) of CR in subjects with asthma was downloaded from the gene expression omnibus (GEO) database and compared to BAL of corticosteroid-sensitive (CS) patients. DEGs were identified by the Limma package in R language. In addition, DEGs were mapped to STRING to acquire protein-protein interaction (PPI) pairs. Topological properties of PPI network were calculated by Centiscape, ClusterOne and BINGO. Subsequently, text-mining tools were applied to design one own cell signalling for CR in asthma. Thirty-five PPI networks were obtained; including a major network consisted of 370 nodes, connected by 777 edges. After topological analysis, a minor PPI network composed by 48 nodes was indentified, which is composed by most relevant nodes of major PPI network. In this subnetwork, several receptors (EGFR, EGR1, ESR2, PGR), transcription factors (MYC, JAK), cytokines (IL8, IL6, IL1B), one chemokine (CXCL1), one kinase (SRC) and one cyclooxygenase (PTGS2) were described to be associated with inflammatory environment and steroid resistance in asthma. We suggest a biomarker network composed by 48 nodes that could be potentially explored with diagnostic or therapeutic use.

PMID: 27188427 [PubMed - as supplied by publisher]

On the unsupervised analysis of domain-specific Chinese texts.

Proc Natl Acad Sci U S A. 2016 May 16;

Authors: Deng K, Bol PK, Li KJ, Liu JS

Abstract
With the growing availability of digitized text data both publicly and privately, there is a great need for effective computational tools to automatically extract information from texts. Because the Chinese language differs most significantly from alphabet-based languages in not specifying word boundaries, most existing Chinese text-mining methods require a prespecified vocabulary and/or a large relevant training corpus, which may not be available in some applications. We introduce an unsupervised method, top-down word discovery and segmentation (TopWORDS), for simultaneously discovering and segmenting words and phrases from large volumes of unstructured Chinese texts, and propose ways to order discovered words and conduct higher-level context analyses. TopWORDS is particularly useful for mining online and domain-specific texts where the underlying vocabulary is unknown or the texts of interest differ significantly from available training corpora. When outputs from TopWORDS are fed into context analysis tools such as topic modeling, word embedding, and association pattern finding, the results are as good as or better than that from using outputs of a supervised segmentation method.

PMID: 27185919 [PubMed - as supplied by publisher]

Exploring mechanisms of Panax notoginseng saponins in treating coronary heart disease by integrating gene interaction network and functional enrichment analysis.

Chin J Integr Med. 2016 May 16;

Authors: Yu G, Wang J

Abstract
OBJECTIVE: To investigate the mechanisms of Panax notoginseng saponins (PNS) in treating coronary heart disease (CHD) by integrating gene interaction network and functional enrichment analysis.
METHODS: Text mining was used to get CHD and PNS associated genes. Gene-gene interaction networks of CHD and PNS were built by the GeneMANIA Cytoscape plugin. Advanced Network Merge Cytoscape plugin was used to analyze the two networks. Their functions were analyzed by gene functional enrichment analysis via DAVID Bioinformatics. Joint subnetwork of CHD network and PNS network was identified by network analysis.
RESULTS: The 11 genes of the joint subnetwork were the direct targets of PNS in CHD network and enriched in cytokine-cytokine receptor interaction pathway. PNS could affect other 85 genes by the gene-gene interaction of joint subnetwork and these genes were enriched in other 7 pathways. The direct mechanisms of PNS in treating CHD by targeting cytokines to relieve the inflammation and the indirect mechanisms of PNS in treating CHD by affecting other 7 pathways through the interaction of joint subnetwork of PNS and CHD network. The genes in the 7 pathways could be potential targets for the immunologic adjuvant, anticoagulant, hypolipidemic, anti-platelet and anti-hypertrophic activities of PNS.
CONCLUSION: The key mechanisms of PNS in treating CHD could be anticoagulant and hypolipidemic which are indicated by analyzing biological functions of hubs in the merged network.

PMID: 27184904 [PubMed - as supplied by publisher]

Celastrol targets IRAKs to block Toll-like receptor 4-mediated nuclear factor-κB activation.

J Integr Med. 2016 May;14(3):203-8

Authors: Shen YF, Zhang X, Wang Y, Cao FF, Uzan G, Peng B, Zhang DH

Abstract
OBJECTIVE: Celastrol has been established as a nuclear factor-κB (NF-κB) activation inhibitor; however, the exact mechanism behind this action is still unknown. Using text-mining technology, the authors predicted that interleukin-1 receptor-associated kinases (IRAKs) are potential celastrol targets, and hypothesized that targeting IRAKs might be one way that celastrol inhibits NF-κB. This is because IRAKs are key molecules for some crucial pathways to activate NF-κB (e.g., the interleukin-1 receptor (IL-1R)/Toll-like receptor (TLR) superfamily).
METHODS: The human hepatocellular cell line (HepG2) treated with palmitic acid (PA) was used as a model for stimulating TLR4/NF-κB activation, in order to observe the potential effects of celastrol in IRAK regulation and NF-κB inhibition. The transfection of small interfering RNA was used for down-regulating TLR4, IRAK1 and IRAK4, and the Western blot method was used to detect changes in the protein expressions.
RESULTS: The results showed that celastrol could effectively inhibit PA-caused TLR4-dependent NF-κB activation in the HepG2 cells; PA also activated IRAKs, which were inhibited by celastrol. Knocking down IRAKs abolished PA-caused NF-κB activation.
CONCLUSION: The results for the first time show that targeting IRAKs is one way in which celastrol inhibits NF-κB activation.

PMID: 27181127 [PubMed - in process]

Text mining patents for biomedical knowledge.

Drug Discov Today. 2016 May 11;

Authors: Rodriguez-Esteban R, Bundschus M

Abstract
Biomedical text mining of scientific knowledge bases, such as Medline, has received much attention in recent years. Given that text mining is able to automatically extract biomedical facts that revolve around entities such as genes, proteins, and drugs, from unstructured text sources, it is seen as a major enabler to foster biomedical research and drug discovery. In contrast to the biomedical literature, research into the mining of biomedical patents has not reached the same level of maturity. Here, we review existing work and highlight the associated technical challenges that emerge from automatically extracting facts from patents. We conclude by outlining potential future directions in this domain that could help drive biomedical research and drug discovery.

PMID: 27179985 [PubMed - as supplied by publisher]

Genetic testing to guide warfarin dosing: Impact of direct oral anticoagulants.

Clin Pharmacol Ther. 2016 May 14;

Authors: Lentz SR

PMID: 27178490 [PubMed - as supplied by publisher]

Therapeutic Application of Pharmacogenomics in Oncology.

AAPS J. 2016 May 13;

Authors: Zhang Y, Somtakoune SD, Cheung C, Listiawan M, Feng X

Abstract
Personalizing cancer treatment has been proved to be difficult for healthcare providers due to the nature of chemotherapies which includes narrow therapeutic indices, severe and potential life-threatening toxicities, and variable response rates and efficacies. Studies in pharmacogenomics (PGx) may help guide clinicians to personalize treatment for cancer patients. Implementing PGx in cancer treatment may offer choices to anticipate differences in drug response, resistance, efficacy, and toxicity within chemotherapeutic agents and targeted immune biologic agents. This can be used to achieve optimization of treatment regimens based on patients' variability. Many of the cancer treatment agents are biologics targeting specific antigens expressed on cancer cells, or blocking stimulators and signal transduction pathways of tumor growth, or enhance anticancer immune responses. It is now crucial for clinicians to understand the important association of clinically important biomarker polymorphisms with the clinical benefits of cancer therapies. By identifying specific PGx biomarker polymorphisms present in cancer cells, physicians can select and tailor a patient's treatment based on his or her genetic profile. PGx-guided cancer treatment may have the ability to improve the survival of patients while avoiding the unnecessary cost due to unresponsive treatment and toxicities of that patients experience.

PMID: 27178043 [PubMed - as supplied by publisher]

Eosinophilic cystitis and haematuria: Case report of a rare disease and common presentation.

Int J Surg Case Rep. 2016 May 6;24:43-45

Authors: Chia D

Abstract
INTRODUCTION: Eosinophilic cystitis is a rare inflammatory condition of the bladder that can cause haematuria. The aetiology is unknown and clinical presentation is difficult to distinguish from other causes of haematuria. Diagnosis is confirmed by biopsy. In this case, a patient with haematuria is diagnosed with eosinohpilic cystitis after presenting to hospital. He was commenced on antibiotics for a presumed urinary tract infection with no resolution of haematuria and symptoms. After diagnosis he was commenced on treatment with resolution of symptoms.
CASE PRESENTATION: A 73-year-old male presents with first episode of haematuria. He was initially diagnosed with a urinary tract infection and commenced on antibiotics with no resolution. After further investigations including a cystoscopy and bladder biopsy, he was diagnosed with eosinophilic cystitis. He was treated with steroids improving his symptoms.
CONCLUSION: Eosinophilic cystitis is a rare disease of the bladder which is difficult to distinguish from other causes of haematuria, and is often misdiagnosed. Bladder biopsy is necessary for diagnosis. Early diagnosis is important, and it is through a combination of non-operative and operative interventions such as biopsy. Natural history is difficult to predict as it is difficult to determine is a patient will have a benign course with resolution with or without treatment, or result in a chronic course which may result in bladder damage and renal failure. This case highlights the importance of investigating haematuria that is unresponsive to initial empiric treatment such as antibiotics. It is important to refer to a Urologist for further investigation to rule out a sinister cause, but to also obtain a diagnosis, leading to definitive treatment.

PMID: 27179336 [PubMed - as supplied by publisher]

Primary extraskeletal Ewing sarcoma of the stomach: a rare disease in an uncommon location.

Clin Imaging. 2016 Apr 5;40(5):843-845

Authors: Maxwell AW, Wood S, Dupuy DE

Abstract
We report the case of a 63-year-old female undergoing evaluation of symptomatic anemia, gastroesophageal reflux disease, and abdominal pain. After a thorough diagnostic workup, a large, ulcerated mass was identified in the patient's stomach, and surgical pathology in combination with molecular analysis yielded a diagnosis of primary extraskeletal Ewing sarcoma. In our report, we discuss the epidemiologic, clinicopathologic, and radiographic features of this rare disease and provide a review of the existing literature.

PMID: 27179157 [PubMed - as supplied by publisher]

Peritoneal lymphomatosis confused with peritoneal carcinomatosis due to the previous history of gastric cancer: a case report.

Clin Imaging. 2016 Mar 29;40(5):837-839

Authors: Choi WY, Kim JH, Choi SJ, Park J, Park YH, Lim JH, Lee MH, Kim CS, Yi HG

Abstract
Peritoneal lymphomatosis is a very rare disease of extranodal involvement of malignant lymphoma that is occasionally confounded with other peritoneal diseases. Herein, we reported the case of a 59-year-old woman who presented with massive ascites with prior history of stomach perforation during endoscopic procedure to treat early gastric cancer. Imaging studies showed massive ascites and tumor infiltration in the omentum and peritoneal wall. Initially, relapsed gastric cancer with peritoneal seeding was suspected based on the patient's history and imaging findings. However, final diagnosis was confirmed by ascites cytology as peritoneal lymphomatosis of diffuse large B-cell lymphoma unlike prior clinical information.

PMID: 27179155 [PubMed - as supplied by publisher]

Rare case of neurinoma of the facial nerve.

Braz J Otorhinolaryngol. 2015 Mar-Apr;81(2):226-7

Authors: Passos IM, Massuda ET, Hyppolito MA, Colli BO, Damico TA

PMID: 25649138 [PubMed - indexed for MEDLINE]

Computational Systems Biology Approach Predicts Regulators and Targets of microRNAs and Their Genomic Hotspots in Apoptosis Process.

Mol Biotechnol. 2016 May 13;

Authors: Alanazi IO, Ebrahimie E

Abstract
Novel computational systems biology tools such as common targets analysis, common regulators analysis, pathway discovery, and transcriptomic-based hotspot discovery provide new opportunities in understanding of apoptosis molecular mechanisms. In this study, after measuring the global contribution of microRNAs in the course of apoptosis by Affymetrix platform, systems biology tools were utilized to obtain a comprehensive view on the role of microRNAs in apoptosis process. Network analysis and pathway discovery highlighted the crosstalk between transcription factors and microRNAs in apoptosis. Within the transcription factors, PRDM1 showed the highest upregulation during the course of apoptosis, with more than 9-fold expression increase compared to non-apoptotic condition. Within the microRNAs, MIR1208 showed the highest expression in non-apoptotic condition and downregulated by more than 6 fold during apoptosis. Common regulators algorithm showed that TNF receptor is the key upstream regulator with a high number of regulatory interactions with the differentially expressed microRNAs. BCL2 and AKT1 were the key downstream targets of differentially expressed microRNAs. Enrichment analysis of the genomic locations of differentially expressed microRNAs led us to the discovery of chromosome bands which were highly enriched (p < 0.01) with the apoptosis-related microRNAs, such as 13q31.3, 19p13.13, and Xq27.3 This study opens a new avenue in understanding regulatory mechanisms and downstream functions in the course of apoptosis as well as distinguishing genomic-enriched hotspots for apoptosis process.

PMID: 27178576 [PubMed - as supplied by publisher]

A heuristic model for working memory deficit in schizophrenia.

Biochim Biophys Acta. 2016 May 10;

Authors: Qi Z, Yu GP, Tretter F, Pogarell O, Grace AA, Voit EO

Abstract
BACKGROUND: The life of schizophrenia patients is severely affected deficits in working memory. In various brain regions, the reciprocal interactions between excitatory glutamatergic neurons and inhibitory GABAergic neurons are crucial. Other neurotransmitters, in particular dopamine, serotonin, acetylcholine, and norepinephrine, modulate the local balance between glutamate and GABA and therefore regulate the function of brain regions. Persistent alterations in the balances between the neurotransmitters can result in working memory deficits.
METHODS: Here we present a heuristic computational model that accounts for interactions among neurotransmitters across various brain regions. The model is based on the concept of a neurochemical interaction matrix at the biochemical level and combines this matrix with a mobile model representing physiological dynamic balances among neurotransmitter systems associated with working memory.
RESULTS: The comparison of clinical and simulation results demonstrates that the model output is qualitatively very consistent with the available data. In addition, the model captured how perturbations migrated through different neurotransmitters and brain regions. Results showed that chronic administration of ketamine can cause a variety of imbalances, and application of an antagonist of the D2 receptor in PFC can also induce imbalances but in a very different manner.
CONCLUSIONS: The heuristic computational model permits a variety of assessments of genetic, biochemical, and pharmacological perturbations and serves as an intuitive tool for explaining clinical and biological observations.
GENERAL SIGNIFICANCE: The heuristic model is more intuitive than biophysically detailed models. It can serve as an important tool for interdisciplinary communication and even for psychiatric education of patients and relatives. This article is part of a Special Issue entitled "System Genetics" Guest Editor: Dr. Yudong Cai and Dr. Tao Huang.

PMID: 27177811 [PubMed - as supplied by publisher]

A novel procedure on next generation sequencing data analysis using text mining algorithm.

BMC Bioinformatics. 2016;17(1):213

Authors: Zhao W, Chen JJ, Perkins R, Wang Y, Liu Z, Hong H, Tong W, Zou W

Abstract
BACKGROUND: Next-generation sequencing (NGS) technologies have provided researchers with vast possibilities in various biological and biomedical research areas. Efficient data mining strategies are in high demand for large scale comparative and evolutional studies to be performed on the large amounts of data derived from NGS projects. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining.
METHODS: We report a novel procedure to analyse NGS data using topic modeling. It consists of four major procedures: NGS data retrieval, preprocessing, topic modeling, and data mining using Latent Dirichlet Allocation (LDA) topic outputs. The NGS data set of the Salmonella enterica strains were used as a case study to show the workflow of this procedure. The perplexity measurement of the topic numbers and the convergence efficiencies of Gibbs sampling were calculated and discussed for achieving the best result from the proposed procedure.
RESULTS: The output topics by LDA algorithms could be treated as features of Salmonella strains to accurately describe the genetic diversity of fliC gene in various serotypes. The results of a two-way hierarchical clustering and data matrix analysis on LDA-derived matrices successfully classified Salmonella serotypes based on the NGS data. The implementation of topic modeling in NGS data analysis procedure provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.
CONCLUSION: The implementation of topic modeling in NGS data analysis provides a new way to elucidate genetic information from NGS data, and identify the gene-phenotype relationships and biomarkers, especially in the era of biological and medical big data.

PMID: 27177941 [PubMed - as supplied by publisher]

Naked-eye fingerprinting of single nucleotide polymorphisms on psoriasis patients.

Nanoscale. 2016 May 13;

Authors: Valentini P, Marsella A, Tarantino P, Mauro S, Baglietto S, Congedo M, Paolo Pompa P

Abstract
We report a low-cost test, based on gold nanoparticles, for the colorimetric (naked-eye) fingerprinting of a panel of single nucleotide polymorphisms (SNPs), relevant for the personalized therapy of psoriasis. Such pharmacogenomic tests are not routinely performed on psoriasis patients, due to the high cost of standard technologies. We demonstrated high sensitivity and specificity of our colorimetric test by validating it on a cohort of 30 patients, through a double-blind comparison with two state-of-the-art instrumental techniques, namely reverse dot blotting and sequencing, finding 100% agreement. This test offers high parallelization capabilities and can be easily generalized to other SNPs of clinical relevance, finding broad utility in diagnostics and pharmacogenomics.

PMID: 27174795 [PubMed - as supplied by publisher]

Ciliary neurotrophic factor upregulates follistatin and Pak1, causes overexpression of muscle differentiation related genes and downregulation of established atrophy mediators in skeletal muscle.

Metabolism. 2016 Jun;65(6):915-25

Authors: Tsompanidis A, Vafiadaki E, Blüher S, Kalozoumi G, Sanoudou D, Mantzoros CS

Abstract
INTRODUCTION: The Ciliary Neurotrophic Factor (CNTF) is a pluripotent cytokine with anorexigenic actions in the hypothalamus that improves insulin sensitivity, increases energy expenditure and induces weight loss. Since CNTF also has an established myotrophic role, we sought to examine whether skeletal muscle contributes to the CNTF-induced metabolic improvement and identify the molecular mechanisms mediating these effects.
METHODS: We used a mouse model of diet-induced obesity, to which high or low CNTF doses were administered for 7days. Whole transcriptome expression levels were analyzed in dissected soleus muscles using microarrays and data were then confirmed using qRT-PCR.
RESULTS: We demonstrate that CNTF administration significantly downregulates leptin, while it upregulates follistatin and Pak1; a molecule associated with insulin sensitization in skeletal muscle. A significant overexpression of muscle differentiation related genes and downregulation of established atrophy mediators was observed.
CONCLUSIONS: The overall gene expression changes suggest an indirect, beneficial effect of CNTF on metabolism, energy expenditure and insulin sensitivity, exerted by the pronounced stimulation of muscle growth, with similarities to the described effect of follistatin and the activation of the Akt pathway in skeletal muscle.

PMID: 27173470 [PubMed - in process]