MATERIALS AND METHODS: Between 16-April-2020 to 30-April- 2020, patients with suspected or confirmed for COVID-19 indicated for in-patient treatment with hydroxychloroquine with or without lopinavir-ritonavir to the Sarawak General Hospital were monitored with KardiaMobile smartphone electrocardiogram (AliveCor®, Mountain View, CA) or standard 12-lead electrocardiogram. The baseline and serial QTc intervals were monitored till the last dose of medications or until the normalization of the QTc interval.
RESULTS: Thirty patients were treated with hydroxychloroquine, and 20 (66.7%) patients received a combination of hydroxychloroquine and lopinavir-ritonavir therapy. The maximum QTc interval was significantly prolonged compared to baseline (434.6±28.2msec vs. 458.6±47.1msec, p=0.001). The maximum QTc interval (456.1±45.7msec vs. 464.6±45.2msec, p=0.635) and the delta QTc (32.6±38.5msec vs. 26.3±35.8msec, p=0.658) were not significantly different between patients on hydroxychloroquine or a combination of hydroxychloroquine and lopinavir-ritonavir. Five (16.7%) patients had QTc of 500msec or more. Four (13.3%) patients required discontinuation of hydroxychloroquine and 3 (10.0%) patients required discontinuation of lopinavirritonavir due to QTc prolongation. However, no torsade de pointes was observed.
CONCLUSIONS: QTc monitoring using smartphone electrocardiogram was feasible in COVID-19 patients treated with hydroxychloroquine with or without lopinavir-ritonavir. The usage of hydroxychloroquine and lopinavir-ritonavir resulted in QTc prolongation, but no torsade de pointes or arrhythmogenic death was observed.
METHODS: We obtained the validity and reliability evidence for the SAS-M-SF using a group of 307 pre-university students in Universiti Putra Malaysia (UPM), Serdang, Selangor, Malaysia with a mean age of 18.4±0.2 years (70.4% female and 29.6% male). A questionnaire containing the Malay version of Smartphone Addiction Scale (SAS-M), the Malay version of the short form Smartphone Addiction Scale (SAS-M-SF), and the Malay version of the Internet Addiction Test (IAT-M) was administered on the adolescents.
RESULTS: The SAS-M-SF displayed good internal consistency (Cronbach's α=0.80). Using principle component analysis, we identified a 4-factor SAS-M-SF model. A significant correlation between the SAS-M-SF and the IAT-M was found, lending support for concurrent validity. The prevalence of smartphone addiction was 54.5% based on cut-off score of ≥36 with a sensitivity of 70.2% and a specificity of 72.5%.
CONCLUSIONS: The 10-item SAS-M-SF is a valid and reliable screening tool for smartphone addiction among adolescents. The scale can help clinicians or educators design appropriate intervention and prevention programs targeting smartphone addiction in adolescents at clinical or school settings.
METHOD: A prospective cross-sectional study was conducted using the validated Smartphone Addiction Scale-Malay version (SAS-M) questionnaire. One-way ANOVA was used to determine the correlation between the PSU among the students categorised by their ethnicity, hand dominance and by their field of study. MLR analysis was applied to predict PSU based on socio-demographic data, usage patterns, psychological factors and fields of study.
RESULTS: A total of 1060 students completed the questionnaire. Most students had some degree of problematic usage of the smartphone. Students used smartphones predominantly to access SNAs, namely Instagram. Longer duration on the smartphone per day, younger age at first using a smartphone and underlying depression carried higher risk of developing PSU, whereas the field of study (science vs. humanities based) did not contribute to an increased risk of developing PSU.
CONCLUSION: Findings from this study can help better inform university administrators about at- risk groups of undergraduate students who may benefit from targeted intervention designed to reduce their addictive behaviour patterns.
PURPOSE: The purpose of this virtual analysis study was to compare the accuracy and precision of 3-dimensional (3D) ear models generated by scanning gypsum casts with a smartphone camera and a desktop laser scanner.
MATERIAL AND METHODS: Six ear casts were fabricated from green dental gypsum and scanned with a laser scanner. The resultant 3D models were exported as standard tessellation language (STL) files. A stereophotogrammetry system was fabricated by using a motorized turntable and an automated microcontroller photograph capturing interface. A total of 48 images were captured from 2 angles on the arc (20 degrees and 40 degrees from the base of the turntable) with an image overlap of 15 degrees, controlled by a stepper motor. Ear 1 was placed on the turntable and captured 5 times with smartphone 1 and tested for precision. Then, ears 1 to 6 were scanned once with a laser scanner and with smartphones 1 and 2. The images were converted into 3D casts and compared for accuracy against their laser scanned counterparts for surface area, volume, interpoint mismatches, and spatial overlap. Acceptability thresholds were set at <0.5 mm for interpoint mismatches and >0.70 for spatial overlap.
RESULTS: The test for smartphone precision in comparison with that of the laser scanner showed a difference in surface area of 774.22 ±295.27 mm2 (6.9% less area) and in volume of 4228.60 ±2276.89 mm3 (13.4% more volume). Both acceptability thresholds were also met. The test for accuracy among smartphones 1, 2, and the laser scanner showed no statistically significant differences (P>.05) in all 4 parameters among the groups while also meeting both acceptability thresholds.
CONCLUSIONS: Smartphone cameras used to capture 48 overlapping gypsum cast ear images in a controlled environment generated 3D models parametrically similar to those produced by standard laser scanners.
OBJECTIVES: The aim of this study is to develop a colorimetric sensor to detect Hg2+ in water sources using HRP inhibitive assay. The system can be incorporated with a mobile app to make it practical for a prompt in-situ analysis.
METHODS: HRP enzyme was pre-incubated with different concentration of Hg2+ at 37°C for 1 hour prior to the addition of chromogen. The mix of PBS buffer, 4-AAP and phenol which act as a chromogen was then added to the HRP enzyme and was incubated for 20 minutes. Alcohol was added to stop the enzymatic reaction, and the change of colour were observed and analyse using UV-Vis spectrophotometer at 520 nm wavelength. The results were then analysed using GraphPad PRISM 4 for a non-linear regression analysis, and using Mathematica (Wolfram) 10.0 software for a hierarchical cluster analysis. The samples from spectroscopy measurement were directly used for dynamic light scattering (DLS) evaluation to evaluate the changes in HRP size due to Hg2+ malfunctionation. Finally, molecular dynamic simulations comparing normal and malfunctioned HRP were carried out to investigate structural changes of the HRP using YASARA software.
RESULTS: Naked eye detection and data from UV-Vis spectroscopy showed good selectivity of Hg2+ over other metal ions as a distinctive color of Hg2+ is observed at 0.5 ppm with the IC50 of 0.290 ppm. The mechanism of Hg2+ inhibition towards HRP was further validated using a dynamic light scattering (DLS) and molecular dynamics (MD) simulation to ensure that there is a conformational change in HRP size due to the presence of Hg2+ ions. The naked eye detection can be quantitatively determined using a smartphone app namely ColorAssist, suggesting that the detection signal does not require expensive instruments to be quantified.
CONCLUSION: A naked-eye colorimetric sensor for mercury ions detection was developed. The colour change due to the presence of Hg2+ can be easily distinguished using an app via a smartphone. Thus, without resorting to any expensive instruments that are mostly laboratory bound, Hg2+ can be easily detected at IC50 value of 0.29 ppm. This is a promising alternative and practical method to detect Hg2+ in the environment.