MyMedR

Displaying all 4 publications

Abstract:

Sort:

The role of infarct transmural extent in infarct extension: A computational study

Leong CN, Lim E, Andriyana A, Al Abed A, Lovell NH, Hayward C, et al.

Int J Numer Method Biomed Eng, 2017 Feb;33(2).
PMID: 27043925 DOI: 10.1002/cnm.2794

Infarct extension, a process involving progressive extension of the infarct zone (IZ) into the normally perfused border zone (BZ), leads to continuous degradation of the myocardial function and adverse remodelling. Despite carrying a high risk of mortality, detailed understanding of the mechanisms leading to BZ hypoxia and infarct extension remains unexplored. In the present study, we developed a 3D truncated ellipsoidal left ventricular model incorporating realistic electromechanical properties and fibre orientation to examine the mechanical interaction among the remote, infarct and BZs in the presence of varying infarct transmural extent (TME). Localized highly abnormal systolic fibre stress was observed at the BZ, owing to the simultaneous presence of moderately increased stiffness and fibre strain at this region, caused by the mechanical tethering effect imposed by the overstretched IZ. Our simulations also demonstrated the greatest tethering effect and stress in BZ regions with fibre direction tangential to the BZ-remote zone boundary. This can be explained by the lower stiffness in the cross-fibre direction, which gave rise to a greater stretching of the IZ in this direction. The average fibre strain of the IZ, as well as the maximum stress in the sub-endocardial layer, increased steeply from 10% to 50% infarct TME, and slower thereafter. Based on our stress-strain loop analysis, we found impairment in the myocardial energy efficiency and elevated energy expenditure with increasing infarct TME, which we believe to place the BZ at further risk of hypoxia. Copyright © 2016 John Wiley & Sons, Ltd.
Fulltext Durable mechanical circulatory support across the Asia-Pacific region

Sivathasan C, Hayward C, Jansz P, Sibal AK, ChenChen, Cally HKL, et al.

J Heart Lung Transplant, 2020 11;39(11):1195-1198.
PMID: 32950381 DOI: 10.1016/j.healun.2020.08.022
Fulltext Development of an E-learning System for the Endoscopic Diagnosis of Early Gastric Cancer: An International Multicenter Randomized Controlled Trial

Yao K, Uedo N, Muto M, Ishikawa H, Cardona HJ, Filho ECC, et al.

EBioMedicine, 2016 Jul;9:140-147.
PMID: 27333048 DOI: 10.1016/j.ebiom.2016.05.016

BACKGROUND: In many countries, gastric cancer is not diagnosed until an advanced stage. An Internet-based e-learning system to improve the ability of endoscopists to diagnose gastric cancer at an early stage was developed and was evaluated for its effectiveness.
METHODS: The study was designed as a randomized controlled trial. After receiving a pre-test, participants were randomly allocated to either an e-learning or non-e-learning group. Only those in the e-learning group gained access to the e-learning system. Two months after the pre-test, both groups received a post-test. The primary endpoint was the difference between the two groups regarding the rate of improvement of their test results.
FINDINGS: 515 endoscopists from 35 countries were assessed for eligibility, and 332 were enrolled in the study, with 166 allocated to each group. Of these, 151 participants in the e-learning group and 144 in the non-e-learning group were included in the analysis. The mean improvement rate (standard deviation) in the e-learning and non-e-learning groups was 1·24 (0·26) and 1·00 (0·16), respectively (P<0·001).
INTERPRETATION: This global study clearly demonstrated the efficacy of an e-learning system to expand knowledge and provide invaluable experience regarding the endoscopic detection of early gastric cancer (R000012039).
Fulltext Identifying the Common Genetic Basis of Antidepressant Response

Pain O, Hodgson K, Trubetskoy V, Ripke S, Marshe VS, Adams MJ, et al.

Biol Psychiatry Glob Open Sci, 2022 Apr;2(2):115-126.
PMID: 35712048 DOI: 10.1016/j.bpsgos.2021.07.008

BACKGROUND: Antidepressants are a first-line treatment for depression. However, only a third of individuals experience remission after the first treatment. Common genetic variation, in part, likely regulates antidepressant response, yet the success of previous genome-wide association studies has been limited by sample size. This study performs the largest genetic analysis of prospectively assessed antidepressant response in major depressive disorder to gain insight into the underlying biology and enable out-of-sample prediction.
METHODS: Genome-wide analysis of remission (n remit = 1852, n nonremit = 3299) and percentage improvement (n = 5218) was performed. Single nucleotide polymorphism-based heritability was estimated using genome-wide complex trait analysis. Genetic covariance with eight mental health phenotypes was estimated using polygenic scores/AVENGEME. Out-of-sample prediction of antidepressant response polygenic scores was assessed. Gene-level association analysis was performed using MAGMA and transcriptome-wide association study. Tissue, pathway, and drug binding enrichment were estimated using MAGMA.
RESULTS: Neither genome-wide association study identified genome-wide significant associations. Single nucleotide polymorphism-based heritability was significantly different from zero for remission (h 2 = 0.132, SE = 0.056) but not for percentage improvement (h 2 = -0.018, SE = 0.032). Better antidepressant response was negatively associated with genetic risk for schizophrenia and positively associated with genetic propensity for educational attainment. Leave-one-out validation of antidepressant response polygenic scores demonstrated significant evidence of out-of-sample prediction, though results varied in external cohorts. Gene-based analyses identified ETV4 and DHX8 as significantly associated with antidepressant response.
CONCLUSIONS: This study demonstrates that antidepressant response is influenced by common genetic variation, has a genetic overlap schizophrenia and educational attainment, and provides a useful resource for future research. Larger sample sizes are required to attain the potential of genetics for understanding and predicting antidepressant response.