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Transcriptome-wide effects of expansin gene manipulation in etiolated Arabidopsis seedling

Ilias IA, Negishi K, Yasue K, Jomura N, Morohashi K, Baharum SN, et al.

J Plant Res, 2019 Mar;132(2):159-172.
PMID: 30341720 DOI: 10.1007/s10265-018-1067-0

Expansin is a non-enzymatic protein which plays a pivotal role in cell wall loosening by inducing stress relaxation and extension in the plant cell wall. Previous studies on Arabidopsis, Petunia × hybrida, and tomato demonstrated that the suppression of expansin gene expression reduced plant growth but expansin overexpression does not necessarily promotes growth. In this study, both expansin gene suppression and overexpression in dark-grown transgenic Arabidopsis seedlings resulted in reduced hypocotyl length at late growth stages with a more pronounced effect for the overexpression. This defect in hypocotyl elongation raises questions about the molecular effect of expansin gene manipulation. RNA-seq analysis of the transcriptomic changes between day 3 and day 5 seedlings for both transgenic lines found numerous differentially expressed genes (DEGs) including transcription factors and hormone-related genes involved in different aspects of cell wall development. These DEGs imply that the observed hypocotyl growth retardation is a consequence of the concerted effect of regulatory factors and multiple cell-wall related genes, which are important for cell wall remodelling during rapid hypocotyl elongation. This is further supported by co-expression analysis through network-centric approach of differential network cluster analysis. This first transcriptome-wide study of expansin manipulation explains why the effect of expansin overexpression is greater than suppression and provides insights into the dynamic nature of molecular regulation during etiolation.
Left atrial strain: a multi-modality, multi-vendor comparison study

Pathan F, Zainal Abidin HA, Vo QH, Zhou H, D'Angelo T, Elen E, et al.

Eur Heart J Cardiovasc Imaging, 2021 01 01;22(1):102-110.
PMID: 31848575 DOI: 10.1093/ehjci/jez303

AIMS: Left atrial (LA) strain is a prognostic biomarker with utility across a spectrum of acute and chronic cardiovascular pathologies. There are limited data on intervendor differences and no data on intermodality differences for LA strain. We sought to compare the intervendor and intermodality differences between transthoracic echocardiography (TTE) and cardiac magnetic resonance (CMR) derived LA strain. We hypothesized that various components of atrial strain would show good intervendor and intermodality correlation but that there would be systematic differences between vendors and modalities.
METHODS AND RESULTS: We evaluated 54 subjects (43 patients with a clinical indication for CMR and 11 healthy volunteers) in a study comparing TTE- and CMR-derived LA reservoir strain (ƐR), conduit strain (ƐCD), and contractile strain (ƐCT). The LA strain components were evaluated using four dedicated types of post-processing software. We evaluated the correlation and systematic bias between modalities and within each modality. Intervendor and intermodality correlation was: ƐR [intraclass correlation coefficient (ICC 0.64-0.90)], ƐCD (ICC 0.62-0.89), and ƐCT (ICC 0.58-0.77). There was evidence of systematic bias between vendors and modalities with mean differences ranging from (3.1-12.2%) for ƐR, ƐCD (1.6-8.6%), and ƐCT (0.3-3.6%). Reproducibility analysis revealed intraobserver coefficient of variance (COV) of 6.5-14.6% and interobserver COV of 9.9-18.7%.
CONCLUSION: Vendor derived ƐR, ƐCD, and ƐCT demonstrates modest to excellent intervendor and intermodality correlation depending on strain component examined. There are systematic differences in measurements depending on modality and vendor. These differences may be addressed by future studies, which, examine calibration of LA geometry/higher frame rate imaging, semi-quantitative approaches, and improvements in reproducibility.
Fulltext In-hospital risk stratification algorithm of Asian elderly patients

Kasim S, Malek S, Cheen S, Safiruz MS, Ahmad WAW, Ibrahim KS, et al.

Sci Rep, 2022 Oct 20;12(1):17592.
PMID: 36266376 DOI: 10.1038/s41598-022-18839-9

Limited research has been conducted in Asian elderly patients (aged 65 years and above) for in-hospital mortality prediction after an ST-segment elevation myocardial infarction (STEMI) using Deep Learning (DL) and Machine Learning (ML). We used DL and ML to predict in-hospital mortality in Asian elderly STEMI patients and compared it to a conventional risk score for myocardial infraction outcomes. Malaysia's National Cardiovascular Disease Registry comprises an ethnically diverse Asian elderly population (3991 patients). 50 variables helped in establishing the in-hospital death prediction model. The TIMI score was used to predict mortality using DL and feature selection methods from ML algorithms. The main performance metric was the area under the receiver operating characteristic curve (AUC). The DL and ML model constructed using ML feature selection outperforms the conventional risk scoring score, TIMI (AUC 0.75). DL built from ML features (AUC ranging from 0.93 to 0.95) outscored DL built from all features (AUC 0.93). The TIMI score underestimates mortality in the elderly. TIMI predicts 18.4% higher mortality than the DL algorithm (44.7%). All ML feature selection algorithms identify age, fasting blood glucose, heart rate, Killip class, oral hypoglycemic agent, systolic blood pressure, and total cholesterol as common predictors of mortality in the elderly. In a multi-ethnic population, DL outperformed the TIMI risk score in classifying elderly STEMI patients. ML improves death prediction by identifying separate characteristics in older Asian populations. Continuous testing and validation will improve future risk classification, management, and results.
Fulltext Validation of the general Framingham Risk Score (FRS), SCORE2, revised PCE and WHO CVD risk scores in an Asian population

Kasim SS, Ibrahim N, Malek S, Ibrahim KS, Aziz MF, Song C, et al.

Lancet Reg Health West Pac, 2023 Jun;35:100742.
PMID: 37424687 DOI: 10.1016/j.lanwpc.2023.100742

BACKGROUND: Cardiovascular risk prediction models incorporate myriad CVD risk factors. Current prediction models are developed from non-Asian populations, and their utility in other parts of the world is unknown. We validated and compared the performance of CVD risk prediction models in an Asian population.
METHODS: Four validation groups were extracted from a longitudinal community-based study dataset of 12,573 participants aged ≥18 years to validate the Framingham Risk Score (FRS), Systematic COronary Risk Evaluation 2 (SCORE2), Revised Pooled Cohort Equations (RPCE), and World Health Organization cardiovascular disease (WHO CVD) models. Two measures of validation are examined: discrimination and calibration. Outcome of interest was 10-year risk of CVD events (fatal and non-fatal). SCORE2 and RPCE performances were compared to SCORE and PCE, respectively.
FINDINGS: FRS (AUC = 0.750) and RPCE (AUC = 0.752) showed good discrimination in CVD risk prediction. Although FRS and RPCE have poor calibration, FRS demonstrates smaller discordance for FRS vs. RPCE (298% vs. 733% in men, 146% vs. 391% in women). Other models had reasonable discrimination (AUC = 0.706-0.732). Only SCORE2-Low, -Moderate and -High (aged <50) had good calibration (X2 goodness-of-fit, P-value = 0.514, 0.189, 0.129, respectively). SCORE2 and RPCE showed improvements compared to SCORE (AUC = 0.755 vs. 0.747, P-value <0.001) and PCE (AUC = 0.752 vs. 0.546, P-value <0.001), respectively. Almost all risk models overestimated 10-year CVD risk by 3%-1430%.
INTERPRETATION: In Malaysians, RPCE are evaluated be the most clinically useful to predict CVD risk. Additionally, SCORE2 and RPCE outperformed SCORE and PCE, respectively.
FUNDING: This work was supported by the Malaysian Ministry of Science, Technology, and Innovation (MOSTI) (Grant No: TDF03211036).