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Fulltext Ganoderma boninense Disease Detection by Near-Infrared Spectroscopy Classification: A Review

Mohd Hilmi Tan MIS, Jamlos MF, Omar AF, Dzaharudin F, Chalermwisutkul S, Akkaraekthalin P

Sensors (Basel), 2021 Apr 27;21(9).
PMID: 33925576 DOI: 10.3390/s21093052

Ganoderma boninense (G. boninense) infection reduces the productivity of oil palms and causes a serious threat to the palm oil industry. This catastrophic disease ultimately destroys the basal tissues of oil palm, causing the eventual death of the palm. Early detection of G. boninense is vital since there is no effective treatment to stop the continuing spread of the disease. This review describes past and future prospects of integrated research of near-infrared spectroscopy (NIRS), machine learning classification for predictive analytics and signal processing towards an early G. boninense detection system. This effort could reduce the cost of plantation management and avoid production losses. Remarkably, (i) spectroscopy techniques are more reliable than other detection techniques such as serological, molecular, biomarker-based sensor and imaging techniques in reactions with organic tissues, (ii) the NIR spectrum is more precise and sensitive to particular diseases, including G. boninense, compared to visible light and (iii) hand-held NIRS for in situ measurement is used to explore the efficacy of an early detection system in real time using ML classifier algorithms and a predictive analytics model. The non-destructive, environmentally friendly (no chemicals involved), mobile and sensitive leads the NIRS with ML and predictive analytics as a significant platform towards early detection of G. boninense in the future.
Fulltext Development and Validation of Creatinine-Based Estimates of the Glomerular Filtration Rate Equation from 99mTc-DTPA Imaging in the Malaysian Setting

Md Ralib A, Mohd Hanafiah FN, Abd Rashid I, Abd Rahim MS, Dzaharudin F, Mat Nor MB

Int J Nephrol, 2021;2021:3465472.
PMID: 34540290 DOI: 10.1155/2021/3465472

Introduction: Accurate assessment of glomerular filtration rate (GFR) is very important for diagnostic and therapeutic intervention. Clinically, GFR is estimated from plasma creatinine using equations such as Cockcroft-Gault, Modification of Diet in Renal Disease, and Chronic Kidney Disease-Epidemiology Collaboration (CKD-EPI) equations. However, these were developed in the Western population. To the best of our knowledge, there was no equation that has been developed specifically in our population.
Objectives: We developed a new equation based on the gold standard of 99mTc-DTPA imaging measured GFR. We then performed an internal validation by comparing the bias, precision, and accuracy of the new equation and the other equations with the gold standard of 99mTc-DTPA imaging measured GFR.
Methods: This was a cross-sectional study using the existing record of patients who were referred for 99mTc-DTPA imaging at the Nuclear Medicine Centre, International Islamic University Malaysia. As this is a retrospective study utilising routinely collected data from the existing pool of data, the ethical committee has waived the need for informed consent.
Results: Data of 187 patients were analysed from January 2016 to March 2021. Of these, 94 were randomised to the development cohort and 93 to the validation cohort. A new equation of eGFR was determined as 16.637 ∗ 0.9935Age ∗ (SCr/23.473)-0.45159. In the validation cohort, both CKD-EPI and the new equation had the highest correlation to 99mTc-DTPA with a correlation coefficient of 0.81 (p < 0.0001). However, the new equation had the least bias and was the most precise (mean bias of -3.58 ± 12.01) and accurate (P30 of 64.5% and P50 of 84.9%) compared to the other equations.
Conclusion: The new equation which was developed specifically using our local data population was the most accurate and precise, with less bias compared to the other equations. Further study validating this equation in the perioperative and intensive care patients is needed.

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