ORCID Profile
0000-0002-4973-8212
Current Organisations
University of Melbourne
,
St Vincent's Hospital Melbourne
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Publisher: Wiley
Date: 12-2019
DOI: 10.1111/IMJ.14653
Publisher: Springer Science and Business Media LLC
Date: 11-04-2022
DOI: 10.1186/S13075-022-02768-Z
Abstract: Skeletal muscle can be directly affected by systemic sclerosis (SSc) however, a significant burden of SSc-associated myopathy is undetected because clinical parameters such as weakness and creatine kinase (CK) are unreliable biomarkers of muscle involvement. This study presents qualitative and quantitative magnetic resonance imaging (MRI) findings that quantify the prevalence of myopathy and evaluate any association between skeletal and cardiac muscle involvement in SSc. Thirty-two patients with SSc who fulfilled the 2013 American College of Rheumatology/European League Against Rheumatism classification criteria underwent skeletal muscle MRI in addition to cardiac MRI. Skeletal muscles were independently assessed by two musculoskeletal radiologists for evidence of oedema, fatty infiltration and atrophy. Skeletal muscle T2 mapping times and percentage fat fraction were calculated. Linear regression analysis was used to evaluate the clinical and myocardial associations with skeletal muscle oedema and fatty infiltration. Cardiac MRI was performed using post gadolinium contrast imaging and parametric mapping techniques to assess focal and diffuse myocardial fibrosis. Thirteen participants (40.6%) had MRI evidence of skeletal muscle oedema. Five (15.6%) participants had fatty infiltration. There was no association between skeletal muscle oedema and muscle strength, creatine kinase, inflammatory markers or fibroinflammatory myocardial disease. Patients with skeletal muscle oedema had higher T2-mapping times there was a significant association between subjective assessments of muscle oedema and T2-mapping time (coef 2.46, p = 0.02) and percentage fat fraction (coef 3.41, p = 0.02). Diffuse myocardial fibrosis was a near-universal finding, and one third of patients had focal myocardial fibrosis. There was no association between skeletal myopathy detected by MRI and burden of myocardial disease. MRI is a sensitive measure of muscle oedema and systematic assessment of SSc patients using MRI shows that myopathy is highly prevalent, even in patients without symptoms or other signs of muscle involvement. Similarly, cardiac fibrosis is highly prevalent but occurs independently of skeletal muscle changes. These results indicate that novel quantitative MRI techniques may be useful for assessing sub-clinical skeletal muscle disease in SSc.
Publisher: Wiley
Date: 26-05-2020
DOI: 10.1002/ACR2.11132
Abstract: We aimed to evaluate the associations between response to algorithm‐directed treat‐to‐target conventional synthetic disease‐modifying antirheumatic drug therapy and potentially modifiable lifestyle factors, including dietary fish oil supplementation, body mass index (BMI), and smoking history in a rheumatoid arthritis (RA) inception cohort. Patients with RA with a duration of less than 12 months were reviewed every 3 to 6 weeks to adjust therapy according to disease response. All patients received advice to take fish oil supplements, and omega‐3 status was measured as plasma levels of eicosapentaenoic acid (EPA). Lifestyle factors and other variables potentially prognostic for 28‐joint Disease Activity Score (DAS28) remission and DAS28 low disease activity (LDA) at the 12‐month visit were included in multivariable logistic regression models. Of 300 participants, 57.7% reached DAS28 LDA, and 43.7% were in DAS28 remission at 1 year. Increase in plasma EPA was associated with an increase in the odds of being in LDA (adjusted odds ratio [OR] = 1.27 P 0.0001) and remission (adjusted OR = 1.21 P 0.001). There was some evidence that the effect of BMI on LDA might be modified by smoking history. An increase in BMI was associated with a decrease in the odds of being in LDA in current and former smokers but had no impact on LDA in patients who had never smoked. There were no meaningful associations between BMI or smoking history and remission. Omega‐3 status, BMI, and smoking history are potential predictors of outcome in early RA. The possibility of an effect modification by smoking on the predictive value of BMI merits further investigation.
Publisher: Elsevier BV
Date: 06-2021
Publisher: Wiley
Date: 31-01-2019
Publisher: MDPI AG
Date: 03-12-2022
DOI: 10.3390/RS14236137
Abstract: White leaf disease (WLD) is an economically significant disease in the sugarcane industry. This work applied remote sensing techniques based on unmanned aerial vehicles (UAVs) and deep learning (DL) to detect WLD in sugarcane fields at the Gal-Oya Plantation, Sri Lanka. The established methodology to detect WLD consists of UAV red, green, and blue (RGB) image acquisition, the pre-processing of the dataset, labelling, DL model tuning, and prediction. This study evaluated the performance of the existing DL models such as YOLOv5, YOLOR, DETR, and Faster R-CNN to recognize WLD in sugarcane crops. The experimental results indicate that the YOLOv5 network outperformed the other selected models, achieving a precision, recall, mean average precision@0.50 (mAP@0.50), and mean average precision@0.95 (mAP@0.95) metrics of 95%, 92%, 93%, and 79%, respectively. In contrast, DETR exhibited the weakest detection performance, achieving metrics values of 77%, 69%, 77%, and 41% for precision, recall, mAP@0.50, and mAP@0.95, respectively. YOLOv5 is selected as the recommended architecture to detect WLD using the UAV data not only because of its performance, but this was also determined because of its size (14 MB), which was the smallest one among the selected models. The proposed methodology provides technical guidelines to researchers and farmers for conduct the accurate detection and treatment of WLD in the sugarcane fields.
Publisher: Oxford University Press (OUP)
Date: 29-08-2019
Publisher: Springer Science and Business Media LLC
Date: 22-10-2016
Publisher: Elsevier BV
Date: 02-2023
DOI: 10.1016/J.SEMARTHRIT.2022.152137
Abstract: We aimed to quantify the burden of exercise intolerance in systemic sclerosis (SSc) and explore the disease features that contribute to impaired exercise capacity (measured as peak oxygen uptake, peak VO Thirty-three SSc patients with no history of cardiac disease and no active myositis underwent cardiac and skeletal muscle MRI, transthoracic echocardiography, pulmonary function tests and cardiopulmonary exercise testing (CPET). CPET results were compared to an age-, sex-, and weight-matched controls with no overt cardiopulmonary disease. Native T1 and T2-mapping sequences were used to quantify diffuse fibroinflammatory myocardial disease and qualitative assessment of skeletal muscle oedema was performed. The associations between parameters of cardiorespiratory function and skeletal muscle abnormalities and peak VO Exercise capacity was markedly impaired in SSc and significantly reduced when compared to control subjects (percent predicted peak VO SSc is associated with marked functional impairment that is not explained by commonly used parameters of cardiac function such as left ventricular ejection fraction. Rather, only more sensitive measures of organ involvement are associated with impaired exercise tolerance. Our results show diffuse interstitial changes of the myocardium and skeletal muscle affect oxygen uptake and are important contributors to functional limitation in SSc.
Publisher: MDPI AG
Date: 09-2022
Abstract: Sugarcane white leaf phytoplasma (white leaf disease) in sugarcane crops is caused by a phytoplasma transmitted by leafhopper vectors. White leaf disease (WLD) occurs predominantly in some Asian countries and is a devastating global threat to sugarcane industries, especially Sri Lanka. Therefore, a feasible and an effective approach to precisely monitoring WLD infection is important, especially at the early pre-visual stage. This work presents the first approach on the preliminary detection of sugarcane WLD by using high-resolution multispectral sensors mounted on small unmanned aerial vehicles (UAVs) and supervised machine learning classifiers. The detection pipeline discussed in this paper was validated in a sugarcane field located in Gal-Oya Plantation, Hingurana, Sri Lanka. The pixelwise segmented s les were classified as ground, shadow, healthy plant, early symptom, and severe symptom. Four ML algorithms, namely XGBoost (XGB), random forest (RF), decision tree (DT), and K-nearest neighbors (KNN), were implemented along with different python libraries, vegetation indices (VIs), and five spectral bands to detect the WLD in the sugarcane field. The accuracy rate of 94% was attained in the XGB, RF, and KNN to detect WLD in the field. The top three vegetation indices (VIs) for separating healthy and infected sugarcane crops are modified soil-adjusted vegetation index (MSAVI), normalized difference vegetation index (NDVI), and excess green (ExG) in XGB, RF, and DT, while the best spectral band is red in XGB and RF and green in DT. The results revealed that this technology provides a dependable, more direct, cost-effective, and quick method for detecting WLD.
Publisher: MDPI AG
Date: 25-02-2022
DOI: 10.3390/RS14051140
Abstract: The use of satellite-based Remote Sensing (RS) is a well-developed field of research. RS techniques have been successfully utilized to evaluate the chlorophyll content for the monitoring of sugarcane crops. This research provides a new framework for inferring the chlorophyll content in sugarcane crops at the canopy level using unmanned aerial vehicles (UAVs) and spectral vegetation indices processed with multiple machine learning algorithms. Studies were conducted in a sugarcane field located in Sugarcane Research Institute (SRI, Uda Walawe, Sri Lanka), with various fertilizer applications over the entire growing season from 2020 to 2021. An UAV with multispectral camera was used to collect the aerial images to generate the vegetation indices. Ground measurements of leaf chlorophyll were used as indications for fertilizer status in the sugarcane field. Different machine learning (ML) algorithms were used ground-truthing data of chlorophyll content and spectral vegetation indices to forecast sugarcane chlorophyll content. Several machine learning algorithms such as MLR, RF, DT, SVR, XGB, KNN and ANN were applied in two ways: before feature selection (BFS) by training the algorithms with all twenty-four (24) vegetation indices with five (05) spectral bands and after feature selection (AFS) by training algorithms with fifteen (15) vegetation indices. All the algorithms with both BFS and AFS methods were compared with an estimated coefficient of determination (R2) and root mean square error (RMSE). Spectral indices such as RVI and DVI were shown to be the most reliable indices for estimating chlorophyll content in sugarcane fields, with coefficients of determination (R2) of 0.94 and 0.93, respectively. XGB model shows the highest validation score (R2) and lowest RMSE in both methods of BFS (0.96 and 0.14) and AFS (0.98 and 0.78), respectively. However, KNN and SVR algorithms show the lowest validation accuracy than other models. According to the results, the AFS validation score is higher than BFS in MLR, SVR, XGB and KNN. Even though, validation score of the ANN model is decreased in AFS. The findings demonstrated that the use of multispectral UAV could be utilized to estimate chlorophyll content and measure crop health status over a larger sugarcane field. This methodology will aid in real-time crop nutrition management in sugarcane plantations by reducing the need for conventional measurement of sugarcane chlorophyll content.
Publisher: AVES YAYINCILIK A.Ş.
Date: 28-10-2020
Publisher: Oxford University Press (OUP)
Date: 08-02-2022
DOI: 10.1093/RHEUMATOLOGY/KEAC065
Abstract: Cardiac complications of SSc are a leading cause of SSc-associated death. Cardiac imaging for identifying substrate abnormality may be useful in predicting risk of cardiac arrhythmias or future cardiac failure. The aim of this study was to quantify the burden of asymptomatic fibro-inflammatory myocardial disease using cardiac magnetic resonance imaging (CMR) and assess the relationship between asymptomatic myocardial fibrosis and cardiac arrhythmias in SSc. Thirty-two patients with SSc with no documented history of pulmonary vascular or heart disease underwent CMR with gadolinium and 24-h ambulatory ECG. Focal myocardial fibrosis was assessed using post-gadolinium imaging and diffuse fibro-inflammatory myocardial disease quantified using T1- and T2-mapping. CMR results were compared with an age- and sex-matched control group. Post-gadolinium focal fibrosis was prevalent in SSc but not controls (30% vs 0%, p & 0.01).. T1-mapping values (as a marker of diffuse fibrosis) were greater in SSc than controls [saturated recovery single-shot acquisition (SASHA): 1584 ms vs 1515 ms, P & 0.001 shortened Modified look locker sequence (ShMOLLI): 1218 ms vs 1138 ms, p & 0.001]. More than one-fifth (22.6%) of the participants had ventricular arrhythmias on ambulatory ECG, but no associations between focal or diffuse myocardial fibrosis and arrhythmias were evident. In SSc patients without evidence of overt cardiac disease, a high burden of myocardial fibrosis and arrhythmias was identified. However, there was no clear association between focal or diffuse myocardial fibrosis and arrhythmias, suggesting CMR may have limited use as a screening tool to identify SSc patients at risk of future significant arrhythmias.
No related grants have been discovered for Zoe Brown.