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Impact of under-filled k2edta coated blood collection tube on test results of complete blood count, reticulocyte count and white blood cell differential count
No full textHigh blood sample rejection rate is a prominent issue faced by hematology laboratories
these days due to insufficient sample collection especially in pediatric, geriatric and
oncology patients where venous access is difficult. Under-filled blood collection tubes
affect the blood-to-anticoagulant ratio negatively leading to inaccurate results. This
study evaluated the accuracy and reliability of complete blood count, reticulocyte count
and white blood cell differential count parameters from under-filled blood collection
tubes compared to standard volumes tubes. In this study, the aim is to compare
hematological parameters across different collection volumes; 0.5 ml, 1.0 ml, 1.5 ml,
and 2.0 ml in 2.0 ml standard lavender top K2EDTA tubes. The results have shown that
most parameters remain consistent and were within clinically acceptable ranges in
volumes as low as 1.5 ml. Some parameters, such as red blood cell (x1012/l), hematocrit
(%) and lymphocyte (x109/l) remain stable even at 1.0 ml. This excludes hemoglobin,
which has significant differences to all lower volumes. Therefore, under-filled tubes
may be used as an alternative, reducing sample rejections and reducing overall
healthcare costs. Future studies should include diverse populations and different
analysers to support the finding
Evaluation of the effects of kaffir lime and lemon myrtle essential oils on the digestive vacuole of plasmodium falciparum
No full textMalaria is a severe and fatal disease caused by Plasmodium spp. and remains one of the
leading global causes of morbidity and mortality. The emergence of drug- resistance P.
falciparum in various countries has necessitated an effort to discover new antimalarial
drugs targeting different pathways. Medicinal plants have been a fundamental part of
traditional medicine for centuries. Natural compounds extracted from these plants have
shown great promise in serving as lead candidates for drug development. Despite this,
research on the effects of kaffir lime and lemon myrtle essential oils on the digestive
vacuole of P. falciparum remains largely unexplored. Therefore, this study aimed to fill
this knowledge gap by investigating the antimalarial activity of kaffir lime and lemon
myrtle essential oils. The antimalarial potential of the kaffir lime and lemon myrtle
against the chloroquine-sensitive strain (3D7) of P. falciparum was assessed by using in
vitro antimalarial assay. The kaffir lime and lemon myrtle essential oils demonstrated
weak or no antimalarial activity with IC50 values of 150.6 μg/mL and 273.5 μg/mL,
respectively. Additionally, the treatment with different concentrations of kaffir lime and
lemon myrtle essential oil showed no changes on the digestive vacuole pH. This study
revealed that the pH of the digestive vacuole treated with kaffir lime and lemon myrtle
essential oils are stable and comparable to the untreated control. This suggests that these
essential oils do not alters the digestive vacuole pH of P. falciparu
The effect of calibration factor geometries on the accuracy of 99mtc spect activity quantification
No full textQuantitative SPECT imaging has become increasingly important in disease
diagnosis and monitoring. Achieving accurate absolute quantification of radiotracer
distribution is essential for dosimetry-based personalized radionuclide therapy. A key
determinant of SPECT/CT quantification accuracy is the calibration factor (CF). This
study aimed to evaluate the impact of CFs and recovery coefficients (RCs) derived from
different calibration geometries on SPECT/CT quantification accuracy. Three phantom
geometries were assessed: a petri dish filled with 99mTc (Geometry 1), a whole body
NEMA phantom filled with 99mTc (Geometry 2), and a sphere filled with 99mTc attached
to the cylindrical in NEMA phantom (Geometry 3). CFs were calculated for each
geometry, and RCs were obtained for six spheres with varied diameters (1.0 to 3.7 cm).
Quantification errors were analysed both before and after the application of partial
volume correction (PVC).
Geometry 1 yielded the highest CF, primarily due to the use of a different activity
concentration. This resulted in lower RCs and greater quantification errors, largely
attributable to pronounced PVE. Consequently, the results from Geometry 1 are not
directly comparable to those of the other geometries due to the disparity in activity
concentration. Geometry 2 demonstrated the most accurate and consistent RCs reaching
values as high as 1.03 indicating optimal conditions for quantification. Geometry 3
yielded moderate performance, although spill-in effects were observed at smaller sphere
sizes. PVC substantially improved quantification for small spheres across all geometries.
However, overestimation errors emerged in larger volumes, especially in Geometries 1
and 3. In conclusion, the choice of calibration geometry has a significant influence on the
quantitative accuracy of SPECT/CT imaging. These findings highlight the importance of selecting appropriate calibration strategies and applying geometry-specific corrections to
improve clinical accuracy in radionuclide therapy
Fabrication of angiography quality control phantom for image quality evaluation using machine learning
No full textAngiography's QC suffers from subjective evaluations and a lack of specialised phantoms. This study addresses this by developing an affordable, in-house angiography phantom and evaluating the image quality using a machine learning (ML) approach. Purpose: 1) Design and fabricate an in-house phantom for high contrast and spatial resolution; 2) Assess ML model performance and validation; 3) Validate the best ML for evaluation of phantom image quality. Method: An in-house phantom was 3D-printed using LW-PLA-HT, incorporating tungsten carbide beads for high contrast and a Huttner Type 18-line pair for spatial resolution. 14 angiographic images were acquired from HPUSM and analysed in MATLAB R2024a. Image analysis involved pre-processing, segmentation, feature extraction and augmentation were applied. Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF) classifiers were evaluated using accuracy, precision, recall, F1-score, and AUC, with 10-fold cross-validation and an 80/20 training/testing. Results: Human evaluations showed variability. Among SVM, KNN, and RF, Random Forest demonstrated the best overall performance. For high-contrast image classification, RF achieved exceptional results (100% accuracy, 1.0000 F1 score), followed by KNN (76.11% accuracy, 0.7503 F1 score), and SVM (61.95% accuracy, 0.6095 F1 score). Spatial resolution classification was more challenging, with RF again leading (90.32% accuracy, 0.9050 F1 score), followed by KNN (64.52% accuracy, 0.6650 F1 score), and SVM (32.26% accuracy, 0.3180 F1 score). Conclusion: Random Forest demonstrated the best performance in this research, which highlights the viability of fabricating a cost-effective angiography phantom and utilising ML for image quality assessment
Automated detection and evaluation of ischemic stroke on ct brain imaging using machine learning techniques
No full textThis study investigates the application of machine learning algorithms for the detection of ischemic stroke using CT brain images. Stroke, particularly ischemic stroke, remains a leading cause of death and disability globally. The early detection and diagnosis of ischemic stroke are crucial for minimizing long-term damage and improving patient outcomes. Traditional methods of diagnosis rely on the expertise of radiologists, which can be time-consuming and prone to inter-observer variability. This research aims to develop an automated system for ischemic stroke detection by leveraging machine learning techniques such as Support Vector Machine (SVM), K-Nearest Neighbours (KNN), and Random Forest (RF), applied to CT brain images.
The study uses a dataset consisting of 397 ischemic stroke CT images and 25 normal brain scans. A series of preprocessing steps, including resizing, normalization, and noise reduction, were performed on the CT images to ensure they were suitable for machine learning analysis. Relevant features were extracted from the images, such as intensity, texture, and shape, which were then used to train the machine learning models. The performance of the models was evaluated using metrics such as accuracy, precision, recall, F1-score, and AUC. The Random Forest model achieved the highest accuracy at 92.76%, with an AUC of 0.973, outperforming both the KNN and SVM models. The KNN model achieved an accuracy of 93.93% with an AUC of 0.940, while the SVM model achieved an accuracy of 87.87% with an AUC of 0.984. Additionally, the training time for each model was recorded: SVM took 0.0152 seconds, KNN took 0.0114 seconds, and Random Forest took 0.2083 seconds. The results demonstrate that machine learning models, particularly Random Forest and KNN, can provide accurate and consistent stroke detection, offering potential for rapid and reliable clinical application, with KNN being the fastest in training time
Standardized uptake value (SUV) measurement of normal vertebrae and pelvis using SPECT/CT with 99mTc methylene diphosphonate (99mTc-MDP)
No full textIn Single Photon Emission Computed Tomography/Computerized Tomography (SPECT/CT) imaging, standardised uptake value (SUV) quantification is being used more and more to evaluate bone metabolism. However, consistency in clinical interpretation is limited by differences in SUV values between anatomical sites and normalisation techniques. In order to improve reproducibility, this study will quantify SUV in normal vertebrae and pelvis using SPECT/CT, identify factors that influence variability, and recommend standardized measurement locations. Methods: A retrospective analysis was carried out utilising the SPECT/CT images of 36 adult patients who had 99mTc-MDP imaging at Hospital Pakar Universiti Sains Malaysia (HPUSM) and had normal pelvis and vertebrae. Relevant scan settings and patient data were gathered from logbooks and PACS. Using Q.Metrix software, SUVmax and SUVmean were computed and standardised by body weight (BW), lean body mass (LBM), and body surface area (BSA) from 963 normal sites. The SUV variability between skeletal areas was evaluated using the coefficient of variation (CoV). To evaluate SUV variability and its correlation to patient factors, statistical analysis was carried out using SPSS. Results: The mean ± SD for SUVmax and SUVmean were 7.082 ± 2.922 and 3.891 ± 1.352 (BW), 5.152 ± 2.135 and 2.843 ± 1.039 (LBM), and 1.803 ± 0.725 and 0.994 ± 0.354 (BSA), respectively. In general, the SUVmean had a lower coefficient of variation than the SUVmax, with the SUVmean BW and BSA having the lowest (0.29). There was no significant correlation between SUVs and patient factors (age, height and weight). The lowest CoV was shown by the T3 vertebral level (BSA SUVmax) and T5 level (BW SUVmean), which were suggested as the standard reference locations. BSA normalization showed superior consistency compared to BW and LBM. Conclusion: As a reference for SPECT/CT studies, BSA-normalized SUVmean offers the most stable measurement in normal bone. T3 and T5 are proposed as the standard reference levels for SUVmax and SUVmean, respectively, in this study. To improve generalisability and clinical value, larger cohorts and standardised cancer types are required for future studies
Discrimination of food wrappers using atr-ftir spectroscopy and chemometrics
No full textFood wrappers are frequently encountered in daily life and at crime scenes but are often overlooked as trace evidence. Their chemical composition and physical characteristics can provide crucial information in forensic investigations. Nonetheless, the potential evidentiary value of food wrappers in forensic applications remains unexplored. Hence, this study evaluated the use of Attenuated Total Reflectance-Fourier Transform Infrared (ATR-FTIR) spectroscopy combined with chemometrics analysis to discriminate food wrappers from 15 different brands across three categories: junk food, chocolates, and candy. ATR-FTIR spectroscopy enabled rapid and non-destructive chemical analysis but revealed that many wrappers had similar polymer compositions within the same category, making differentiation challenging. Principal Component Analysis (PCA) alone was also insufficient for effective brand discrimination, as clustering primarily followed polymer type. Integration of PCA with Linear Discriminant Analysis (PCA-LDA) significantly improved classification accuracy, achieving 93.3% and 98.5% correct classification rates for the outer and inner wrapper layers, respectively. A blind test further validated the model’s reliability where all unknown samples were correctly classified. These findings highlighted the potential of ATR-FTIR spectroscopy combined with chemometrics as a powerful forensic tool for distinguishing food wrappers. By enabling the discrimination between specific brands, this method demonstrated the evidential value of food wrappers, supporting its use as corroborative trace evidence in forensic investigation
Investigation of cutting agent towards the detection of methamphetamine by marquis and simon’s tests
No full textMethamphetamine is a potent stimulant frequently encountered in illicit drug markets, often adulterated with cutting agents to enhance volume or mimic its effects. Presumptive colorimetric tests, such as the Marquis and Simon’s tests, are widely used for preliminary methamphetamine identification. However, the presence of cutting agents may influence test outcomes, leading to potential misinterpretations in forensic analysis. This study aims to investigate the influence of cutting agents on methamphetamine detection using colorimetric tests. In this study, methamphetamine samples with and without various cutting agents including caffeine, starch, fructose, mannitol, PCM, and lactose, were prepared and tested using the Marquis and Simon’s reagents under controlled conditions. The findings revealed that cutting agents such as sugars and paracetamol significantly alter the colour intensity and hue of the colour formed upon the chemical reactions, among are the reaction between hydroxyl groups of the sugars react and the reagents, and the solubility of paracetamol. These chemical reactions could lead to false positives or negatives in forensic investigations. In conclusion, this study highlighted the limitations of presumptive colour tests in the presence of cutting agent and emphasised the need for standardised testing protocols and confirmatory analytical methods to enhance forensic drug analysis accurac
Modelling the determinants of behavioural likelihood to engage in community-based surveillance of infectious diseases among community representatives in Kelantan, Malaysia
No full textCommunity-based surveillance (CBS) is relatively new strategy for early detection of infectious disease. It involves community members actively participate in detecting and reporting changes in health patterns within their communities to health authorities, facilitating disease containment before outbreaks become widespread and difficult to control. However, community participation in CBS remains limited, and factors influencing engagement, such as knowledge, attitudes, and perceptions, are understudied. Additionally, no validated measurement tool exists to assess these factors. This study aimed to develop and validate a Malay language Knowledge, Attitudes, and Perception’s questionnaire, grounded in the Theory of Reasoned Action and the Health Belief Model, to identify determinants of community participation in CBS. The study was conducted in three phases using a cross-sectional design. In Phase I, a questionnaire (KAP-CBS-ID) was developed and translated to Malay. It comprised three main sections—knowledge, attitude, and perception—each with three domains. The knowledge section covered knowledge about infectious diseases, CBS, and community-level case definition; the attitude section initially addressed subjective norms, intention to participate, and behavioural likelihood; and the perception section included perceived susceptibility, perceived benefits, and self-efficacy. Content validation was performed by public health and epidemiology experts, and face validation and pretesting were conducted with community representatives. In Phase II, the questionnaire was tested with 152 participants using 2-parameter logistic Item Response Theory (2-PL IRT) for knowledge section, and Exploratory Factor Analysis (EFA) for attitude and perceptions. The 2-PL IRT of for knowledge retained 31 of 45 items after removing poorly performing ones. The EFA confirmed a multidimensional structure for attitudes and perceptions, revealing additional factors: negative attitudes and perceived barriers under attitude and perception respectively. Reliability analysis showed good internal consistency (Cronbach’s alpha: 0.71–0.91), with the attitude and perception sections explaining 50.8% and 58.7% of the variance, respectively. Phase III involved Confirmatory Factor Analysis (CFA) and Structural Equation Modelling (SEM) with 470 participants. CFA supported the measurement models with satisfactory fit indices. The SEM, integrating components of TRA and HBM. SEM analysis revealed significant positive associations between knowledge of infectious diseases, subjective norms, and intention to participate, while negative attitudes had an inverse relationship. Behavioural likelihood was positively influenced by intention, perceived susceptibility, and benefits but negatively affected by perceived barriers. Self-efficacy strongly influenced perceived benefits and susceptibility. The model explained 46.1% of behavioural likelihood variance, 57.0% of intention, 70.4% of perceived susceptibility, and 61.7% of perceived benefits. In conclusion, the study successfully developed and validated the Malay KAP-CBS-ID questionnaire, demonstrating good reliability and validity. Key findings highlighted the importance of addressing knowledge gaps, fostering positive attitudes, and reducing barriers to enhance community participation in CBS. This tool provides a foundation for future research and interventions to strengthen CBS systems, improving early disease detection and outbreak contro
Lived experience, coping skill, and quality of life among women with breast cancer in Iraq: a mixed methods study
No full textBreast cancer (BC) is not only a leading cause of mortality among women worldwide but also has profound effects on their quality of life (QOL), particularly in the context of physical, emotional, and social well-being. Gaining insight into the lived experiences of women is crucial for enhancing healthcare delivery, particularly in Iraq, where religious and cultural factors deeply influence health outcomes. Understanding the lived experiences of women with BC becomes vital for improving healthcare delivery. This study aimed to investigate the QOL, coping skills, their associated factors, and life experience among women with BC in Iraq. A mixed methods approach was employed in two phases among women with BC at The Medical City – Teaching Oncology Hospital, Iraq. The phase I study was a cross-sectional study among 244 Women with BC. The instruments used were the European Organization for Research and Treatment of Cancer Quality of Life Questionnaire (EORTC QLQ-C30) to assess QOL, the Brief COPE-28 Questionnaire to assess coping skills and the Hospital Anxiety and Depression Scale-21 (HADs-14) to assess psychological status. The data was analyzed using Multiple linear regression analysis. The Phase II study was a qualitative study using a phenomenological approach. The participants were selected through purposive sampling from the Phase I study, and in-depth interview was conducted with 18 Women with BC. The interviews were recorded, transcribed, and analyzed using NVivo 12 software. In the phase 1 study, the QOL scores for global, functional, and symptom domains were (53.24 ± 16.80, 46.23 ± 25.16, and 53.82 ± 22.10) respectively. The significant factors influencing the global QOL domain were age, marital status, disease duration, hormonal therapy, passive coping, and anxiety. Age, disease duration, passive coping domain, and anxiety were significant factors for the functional domain of the QOL. The symptom domain of the QOL was notably affected by menstrual status, mastectomy, passive coping, and depression. The mean (SD) coping skills for active coping, passive coping, and seeking support domains were 5.96 (1.31), 4.00 (1.10), and 7.58 (0.84), respectively. Hormonal therapy, anxiety, and depression were significant factors influencing the active coping domain. The passive coping domain-associated factors were the type of surgery and anxiety. The seeking support domain was notably affected by the stage of disease, duration since surgery, breast reconstruction, and anxiety. Five themes were identified for the QOL challenges. The themes were body image, psychological distress, disease and treatment-related events, personal reflection, and financial challenges. To cope with these challenges, women with BC rely on various strategies such as network and financial support, self-distraction, acceptance, surrendering to God, seeking information, and navigating cultural beliefs and stigmas. This mixed-methods study provided an in-depth understanding of the QOL and coping mechanisms among women with BC. The findings indicated that participants had a moderate overall QOL and a high level of coping skills. This study revealed a comprehensive perspective that emphasizes the need for interventions addressing psychological and social needs, promoting active coping and supportive networks to improve QO