Asian Journal Of Medical Technology

VIGNA RADIATA (MUNG BEANS) AS AN ALTERNATIVE CULTURE MEDIUM FOR TRYPTICASE SOY AGAR

Fri, 31 May 2024 00:00:00 +0000

High costs of commercial culture media poses challenge in microbiology research, driving a quest for cost-effective culture mediums. The study investigates the potential of Mung beans (Vigna radiata) as a potential alternative culture medium for Trypticase Soy Agar (TSA). This study utilized a quantitative experimental research design with the use of Absolute Growth Index (AGI) Scale and Centers for Disease Control and Prevention (CDC) categorized characteristics of colony morphologies. Mung beans are ground into powder, mixed with 1.5% agar, and processed similarly to TSA. Four-quadrant streaking is performed, followed by incubation at 37°C for 24 hours. Test microorganisms include Escherichia coli, Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans. Colony growth from both the formulated Mung Bean Agar (MBA) and TSA were scored according to AGI and compared with the use of two-way ANOVA. Colony morphology observations reveal that Mung Bean Agar (MBA) produces pinpoint, smooth, grayish, opaque, punctiform colonies with entire margins, whereas TSA yields small/medium, yellow, mucoid, transparent, circular colonies with entire margins. Statistical analysis shows no significant difference in AGI between MBA and TSA. In conclusion, MBA can serve as a cost-effective alternative to TSA for microbiological culture, offering a similar growth performance while being more economically feasible.

HARNESSING THE POWER OF ARTIFICIAL INTELLIGENCE FOR ENHANCED POINT-OF-CARE QUALITY CONTROL IN HEALTHCARE

Fri, 31 May 2024 00:00:00 +0000

Artificial intelligence (AI) is increasingly being used to improve the quality control of point-of-care diagnostics. This is caused by a number of factors, including the following: 1. AI can accelerate and improve testing accuracy. In comparison to humans, AI technology can review data more quickly and precisely, reducing errors and improving overall quality assurance. 2. When it comes to improving POC test findings on healthcare issues such as infectious diseases or medical crises such as heart attacks, AI can be used for sophisticated predictive analytics and modeling that aid in better decision-making. 3. Artificial intelligence facilitates process automation, increasing productivity and lowering labor costs in labor-intensive tasks such as testing and analyzing samples collected at point-of-care facilities. 4. The use of AI enables organizations implementing these solutions to gain insights from large volumes of raw diagnostic data generated faster and more accurately, allowing them to build solid frameworks around preventive care initiatives and significantly influence public health outcomes.5. Artificial intelligence (AI) has been demonstrated to be a useful tool for real-time monitoring systems that identify any problems with test results early so that they can be corrected before affected patients receive inaccurate diagnoses or treatment plans based on false information provided by diagnostic tests performed at points of care such as clinics or hospitals.

Using these technologies would allow healthcare organizations to spend less on labor while still receiving exact diagnoses and rapid treatment delivery at a fraction of the cost that manual approaches required earlier.

OCCUPATIONAL RISK ASSESSMENT AND DETERMINATION OF THE NEED FOR INTERNAL EXPOSURE MONITORING OF RADIATION WORKERS AT INSTITUT KANSER NEGARA

Fri, 31 May 2024 00:00:00 +0000

Ionizing radiation exposure is divided into two categories that are external and internal exposure. The annual dose limit for the radiation workers is 20 mSv consists of internal and external exposure. Radiation workers in nuclear medicine are not only exposed to ionizing radiation externally but also internally. The widespread use of unsealed radioactive sources in nuclear medicine poses a potential for internal exposure of radiation workers in the field of nuclear medicine. External radiation monitoring using a dosimeter has been developed in Malaysia since 1985. However, assessment of the need for individual internal dose monitoring has not yet been developed in Malaysia. The purpose of this study is to assess occupational risk and to compare determination value of the need individual internal exposure monitoring of radiation workers in nuclear medicine department at Institut Kanser Negara (IKN). This study involves radiation workers at IKN by observation, survey forms and calculation of decision factor of the need for internal exposure monitoring based on IAEA dose criteria. The results show that the highest risk is during the use of radiopharmaceuticals for diagnosis and treatment of disease through inhalation process for lung scan as well as the preparation and oral administration to patient, especially radiopharmaceutical containing I-131 and I-124. In addition, a total of 12 out of 16 workers need internal monitoring involving biochemists, pharmacists, and technologists while physicist do not require internal monitoring. Overall,data obtained from this study is the first step in establishing a comprehensive internal exposure framework and promote to more effective and manageable radiation exposure monitoring.

KIDNEY CANCEROUS TUMOR PREDICTION USING CNN SYSTEM ARCHITECTURE

Fri, 31 May 2024 00:00:00 +0000

The study addresses the challenge of interobserver variability in the treatment decisions for kidney cancers, a concern highlighted by the anticipated 73,750 kidney cancer diagnoses in the United States in 2020. This variability often arises due to the subtle differences in imaging characteristics of tumor subtypes. To address this issue, we propose an endto-end deep learning model leveraging multi-phase CT scans to differentiate between five primary histologic subtypes of kidney cancers, encompassing both benign and malignant tumors. The proposed model demonstrates remarkable precision in identifying kidney cancers, even those of minimal size. In preparing the data for analysis, we divided it into training and validation test sets. The training set was used to employ the random forest method for ranking potential predictors based on their predictive importance. The model’s performance was then validated on the test set using leave-one-out cross-validation. This study utilized convolutional and recurrent neural networks to predict kidney cancer outcomes. We used the models to classify adenoma, adenocarcinoma, and non-neoplastic whole slide images (WSIs). The evaluation of our models was conducted using three distinct test sets. The results showed area under the curve scores of 0.97 and 0.99 for distinguishing between cancerous tumors and adenomas and 0.96 and 0.99 for differentiating between kidney cancer and adenomas, respectively. These findings suggest that our models are not only generalizable but also hold significant potential to integrate and deploy into realistic pathological diagnostic workflows of kidney cancer.

EVALUATION OF ONLINE MEDICAL TECHNOLOGIST TRAINING COURSES CONDUCTED DURING THE COVID-19 PANDEMIC IN 2020 COMPARED WITH FACE-TO-FACE LECTURES IN 2019

Kazuo Goto — Fri, 31 May 2024 00:00:00 +0000

This study compared students enrolled in face-to-face (F/F) lectures in 2019 to those who received online teaching (O/T) during the COVID-19 pandemic in 2020 in terms of class evaluation questionnaires, regular exams scores, and the national medical technologist qualifying examination. A statistical comparison of survey results and grades was conducted with 389 students (first- to fourth-year students) enrolled in the Department of Clinical Laboratory Medicine at Teikyo University’s Faculty of Medical Technology in 2019 who received F/F lectures and 403 first- to fourth-year students enrolled in the same department in 2020 who received O/T entirely. Statistical significance was determined using a t-test with p<0.05 considered statistically significant. The class evaluation questionnaire results showed that students’ self-study time, interest in the subject, and sense of achievement were significantly higher for first-, second-, and third-year students in F/F courses than those who received O/T lectures. However, this trend was reversed for fourth-year students. The fourth-year students scored much higher on the national medical technologist examination than the 2020 class. These results indicate that O/T education encourages students to learn independently, leading to improved performance. Therefore, this study suggests that education quality can be improved by combining O/T education with F/F education.

DOSIMETRIC CHARACTERISATION OF THE NANODOT OPTICALLY STIMULATED LUMINESCENT DOSIMETER FOR USE IN NATIONAL ELECTRON BEAM DOSIMETRY AUDIT SERVICES FOR RADIOTHERAPY FACILITIES

Fri, 31 May 2024 00:00:00 +0000

The Malaysian Nuclear Agency's secondary standard dosimetry laboratory (SSDL) aims to establish a national dosimetry audit service for radiotherapy facilities. For this purpose, a nanoDot optically stimulated luminescent dosimeter (OSLD) was selected as the transfer dosimeter for the audit program. The study aims to establish the basic dosimetric characteristics and associated correction factors of nanoDot OSLD for use in electron beam dosimetry audits. An investigation of the dosimetric characteristics of the nanoDot, comprising the sensitivity correction factor (SCF), dose-response linearity, beam energy dependency, signal depletion per readout, and signal fading when subjected to electron beams, was conducted. A preliminary electron beam dosimetry audit using nanoDot OSLD was performed for two radiotherapy facilities under both reference and non-reference conditions. The measurement uncertainty of the absorbed dose for the nanoDot OSLD was also estimated. The mean SCF of the 91 nanoDot OSLD was 1.001 ± 0.25%. The dose-response curves for the 6 MeV and 9 MeV beams exhibited linear characteristics, with a determination coefficient of 0.9982 for the dose range of 50–300 cGy. However, a high energy dependency was observed at 12 MeV, resulting in a deviation of 4.08% compared to that at 6 MeV. The nanoDot signal decreased by 0.03% after 100 readouts and faded by 3.20% at 70 days post-irradiation. It is noteworthy that all audit results from the six electron beams were in compliance with the tolerance limit of ± 5%, with mean dose deviations of -1.66% ± 0.81% and -1.37% ± 0.65% for the reference and non-reference conditions, respectively. The combined uncertainty was estimated to be ± 1.41% (coverage factor, k = 1). National electron beam dosimetry audits using nanoDot OSLD can now be implemented as a regular service.