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  1. Haron Z, Sutan R, Zakaria R, Abdullah Mahdy Z
    Belitung Nurs J, 2023;9(1):6-16.
    PMID: 37469635 DOI: 10.33546/bnj.2396
    BACKGROUND: Gestational Diabetes Mellitus (GDM) is a common form of poor carbohydrate intolerance, prevalent among pregnant women and associated with unhealthy lifestyle behaviors. Given the dearth of information on self-empowerment among mothers with GDM, a self-care health education package needs to be developed to prevent related complications.

    OBJECTIVE: This review aimed to identify self-care approaches, domains, and their effectiveness for a proper self-care educational guide package for women with GDM.

    DESIGN: A systematic review using electronic literature databases published between January 2016 and December 2022 was conducted.

    DATA SOURCES: Web of Science, Scopus, and Ovid databases were used.

    REVIEW METHODS: This review utilized the PICO (Population, Intervention, Comparison, and Outcomes) framework to screen the retrieved articles for eligibility in which mothers with GDM, educational materials, standard practice or intervention, and effectiveness were considered the PICO, respectively. The CIPP (Context, Input, Process, Product) model served as a framework for adopting the education development model. Mixed methods appraisal tool was used for quality assessment. Data extraction and synthesis without meta-analysis were presented as evidence tables.

    RESULTS: A total of 19 articles on GDM were included in the final analysis (16 Intervention studies, two qualitative studies, and one mixed-methods study). Four broad domains emerged from the analysis: 1) information or knowledge of GDM, 2) monitoring of blood glucose levels, 3) practice of healthy lifestyles, and 4) other non-specific activities. The majority of the articles employed a face-to-face approach in executing the educational group sessions, and most studies disclosed their positive effects on GDM management. Other methods of evaluating intervention effectiveness were described as improved self-care behavior, increased satisfaction score, enhanced self-efficacy, good glucose control, and better pregnancy outcome.

    CONCLUSION: Knowledge or information about GDM, healthy diet, and exercise or physical activity was found to be the most applied domains of intervention. Framework domains based on the present review can be used in the future development of any interventional program for GDM women in enhancing health information reaching the targeted group in promoting self-efficacy.

    PROSPERO REGISTRATION NUMBER: CRD42021229610.

  2. Ramli Z, Farizan A, Tamchek N, Haron Z, Abdul Karim MK
    Cureus, 2024 Jan;16(1):e52132.
    PMID: 38347995 DOI: 10.7759/cureus.52132
    The diffusion-weighted imaging (DWI) technique is known for its capability to differentiate the diffusion of water molecules between cancerous and non-cancerous cervix tissues, which enhances the accuracy of detection. Despite the potential of DWI-MRI, its accuracy is limited by technical factors influencing in vivo data acquisition, thus impacting the quantification of radiomics features. This study aimed to measure the radiomics stability of manual and semi-automated segmentation on contrast limited adaptive histogram equalization (CLAHE)-enhanced DWI-MRI cervical images. Eighty diffusion-weighted MRI images were obtained from patients diagnosed with cervical cancer, and an active contour model was used to analyze the data. Radiomics analysis was conducted to extract the first statistical order, shape, and textural features with intraclass correlation coefficient (ICC) measurement. The results of the CLAHE segmentation approach showed a marked improvement when compared to the manual and semi-automated segmentation methods, with an ICC value of 0.990 ± 0.005 (p<0.05), compared to 0.864 ± 0.033 (p<0.05) and 0.554 ± 0.185 (p>0.05), respectively. The CLAHE segmentation displayed a higher level of robustness than the manual groups in terms of the features present in both categories. Thus, CLAHE segmentation is owing to its potential to generate radiomics features that are more durable and consistent.
  3. Han LM, Haron Z, Yahya K, Bakar SA, Dimon MN
    PLoS One, 2015;10(4):e0120667.
    PMID: 25875019 DOI: 10.1371/journal.pone.0120667
    Strategic noise mapping provides important information for noise impact assessment and noise abatement. However, producing reliable strategic noise mapping in a dynamic, complex working environment is difficult. This study proposes the implementation of the random walk approach as a new stochastic technique to simulate noise mapping and to predict the noise exposure level in a workplace. A stochastic simulation framework and software, namely RW-eNMS, were developed to facilitate the random walk approach in noise mapping prediction. This framework considers the randomness and complexity of machinery operation and noise emission levels. Also, it assesses the impact of noise on the workers and the surrounding environment. For data validation, three case studies were conducted to check the accuracy of the prediction data and to determine the efficiency and effectiveness of this approach. The results showed high accuracy of prediction results together with a majority of absolute differences of less than 2 dBA; also, the predicted noise doses were mostly in the range of measurement. Therefore, the random walk approach was effective in dealing with environmental noises. It could predict strategic noise mapping to facilitate noise monitoring and noise control in the workplaces.
  4. Yap ZS, Khalid NHA, Haron Z, Mohamed A, Tahir MM, Hasyim S, et al.
    Materials (Basel), 2021 Oct 02;14(19).
    PMID: 34640174 DOI: 10.3390/ma14195777
    Massive waste rock wool was generated globally and it caused substantial environmental issues such as landfill and leaching. However, reviews on the recyclability of waste rock wool are scarce. Therefore, this study presents an in-depth review of the characterization and potential usability of waste rock wool. Waste rock wool can be characterized based on its physical properties, chemical composition, and types of contaminants. The review showed that waste rock wool from the manufacturing process is more workable to be recycled for further application than the post-consumer due to its high purity. It also revealed that the pre-treatment method-comminution is vital for achieving mixture homogeneity and enhancing the properties of recycled products. The potential application of waste rock wool is reviewed with key results emphasized to demonstrate the practicality and commercial viability of each option. With a high content of chemically inert compounds such as silicon dioxide (SiO2), calcium oxide (CaO), and aluminum oxide (Al2O3) that improve fire resistance properties, waste rock wool is mainly repurposed as fillers in composite material for construction and building materials. Furthermore, waste rock wool is potentially utilized as an oil, water pollutant, and gas absorbent. To sum up, waste rock wool could be feasibly recycled as a composite material enhancer and utilized as an absorbent for a greener environment.
  5. Isaksson LJ, Pepa M, Summers P, Zaffaroni M, Vincini MG, Corrao G, et al.
    BMC Med Imaging, 2023 Feb 11;23(1):32.
    PMID: 36774463 DOI: 10.1186/s12880-023-00974-y
    BACKGROUND: Contouring of anatomical regions is a crucial step in the medical workflow and is both time-consuming and prone to intra- and inter-observer variability. This study compares different strategies for automatic segmentation of the prostate in T2-weighted MRIs.

    METHODS: This study included 100 patients diagnosed with prostate adenocarcinoma who had undergone multi-parametric MRI and prostatectomy. From the T2-weighted MR images, ground truth segmentation masks were established by consensus from two expert radiologists. The prostate was then automatically contoured with six different methods: (1) a multi-atlas algorithm, (2) a proprietary algorithm in the Syngo.Via medical imaging software, and four deep learning models: (3) a V-net trained from scratch, (4) a pre-trained 2D U-net, (5) a GAN extension of the 2D U-net, and (6) a segmentation-adapted EfficientDet architecture. The resulting segmentations were compared and scored against the ground truth masks with one 70/30 and one 50/50 train/test data split. We also analyzed the association between segmentation performance and clinical variables.

    RESULTS: The best performing method was the adapted EfficientDet (model 6), achieving a mean Dice coefficient of 0.914, a mean absolute volume difference of 5.9%, a mean surface distance (MSD) of 1.93 pixels, and a mean 95th percentile Hausdorff distance of 3.77 pixels. The deep learning models were less prone to serious errors (0.854 minimum Dice and 4.02 maximum MSD), and no significant relationship was found between segmentation performance and clinical variables.

    CONCLUSIONS: Deep learning-based segmentation techniques can consistently achieve Dice coefficients of 0.9 or above with as few as 50 training patients, regardless of architectural archetype. The atlas-based and Syngo.via methods found in commercial clinical software performed significantly worse (0.855[Formula: see text]0.887 Dice).

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