Displaying publications 1 - 20 of 309 in total

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  1. Zulkipli ZH, Abdul Rahmat AM, Mohd Faudzi SA, Paiman NF, Wong SV, Hassan A
    Accid Anal Prev, 2012 Nov;49:237-44.
    PMID: 23036400 DOI: 10.1016/j.aap.2011.12.011
    This study presents an analysis of crash characteristics of motorcyclists who sustained spinal injuries in motorcycle crashes. The aim of the study is to identify the salient crash characteristics that would help explain spinal injury risks for motorcyclists. Data were retrospectively collected from police case reports that were archived at MIROS from year 2005 to 2007. The data were categorized into two subcategories; the first group was motorcycle crashes with spinal injury (case) and the second group was motorcycle crashes without spinal injury (control). A total of 363 motorcyclists with spinal injury and 873 motorcyclists without spinal injury were identified and analyzed. Descriptive analysis and multivariate analysis were performed in order to determine the odds of each characteristic in contributing to spinal injury. Single vehicle crash, collision with fixed objects and crash configuration were found to have significant influence on motorcyclists in sustaining spinal injury (p<0.05). Although relatively few than other impact configurations, the rear-end impacted motorcyclist shows the highest risk of spinal injury. Helmets have helped to reduce head injury but they did not seem to offer corresponding protection for the spine in the study. With a growing number of young motorcyclists, further efforts are needed to find effective measures to help reduce the crash incidents and severity of spinal injury. In sum, the study provides some insights on some vital crash characteristics associated with spinal injury that can be further investigated to determine the appropriate counter-measures and prevention strategies to reduce spinal injury.
    Matched MeSH terms: Databases, Factual
  2. Zulkhairi Amin FA, Sabri S, Mohammad SM, Ismail M, Chan KW, Ismail N, et al.
    Adv Pharmacol Sci, 2018;2018:6179596.
    PMID: 30687402 DOI: 10.1155/2018/6179596
    Both honeybees (Apis spp.) and stingless bees (Trigona spp.) produce honeys with high nutritional and therapeutics value. Until recently, the information regarding potential health benefits of stingless bee honey (SBH) in medical databases is still scarce as compared to the common European bee honey (EBH) which is well known for their properties as therapeutic agents. Although there have been very few reports on SBH, empirically these products would have similar therapeutic quality as the EBH. In addition, due to the structure of the nest, few studies reported that the antimicrobial activity of SBH is a little bit stronger than EBH. Therefore, the composition of both the types of honey as well as the traditional uses and clinical applications were compared. The results of various studies on EBH and SBH from tissue culture research to randomised control clinical trials were collated in this review. Interestingly, there are many therapeutic properties that are unique to SBH. Therefore, SBH has a great potential to be developed for modern medicinal uses.
    Matched MeSH terms: Databases, Factual
  3. Zuhdi AS, Mariapun J, Mohd Hairi NN, Wan Ahmad WA, Abidin IZ, Undok AW, et al.
    Ann Saudi Med, 2014 1 15;33(6):572-8.
    PMID: 24413861 DOI: 10.5144/0256-4947.2013.572
    BACKGROUND AND OBJECTIVES: Understanding the nature and pattern of young coronary artery disease (CAD) is important due to the tremendous impact on these patients' socio-economic and physical aspect. Data on young CAD in the southeast Asian region is rather patchy and limited. Hence we utilized our National Cardiovascular Disease Database (NCVD)-Percutaneous Coronary Intervention (PCI) Registry to analyze young patients who underwent PCI in the year 2007 to 2009.

    DESIGN AND SETTINGS: This is a retrospective study of all patients who had undergone coronary angioplasty from 2007 to 2009 in 11 hospitals across Malaysia.

    METHODS: Data were obtained from the NCVD-PCI Registry, 2007 to 2009. Patients were categorized into 2 groups-young and old, where young was defined as less than 45 years for men and less than 55 years for women and old was defined as more than or equals to 45 years for men and more than or equals to 55 years for women. Patients' baseline characteristics, risk factor profile, extent of coronary disease and outcome on dis.charge, and 30-day and 1-year follow-up were compared between the 2 groups.

    RESULTS: We analyzed 10268 patients, and the prevalence of young CAD was 16% (1595 patients). There was a significantly low prevalence of Chinese patients compared to other major ethnic groups. Active smoking (30.2% vs 17.7%) and obesity (20.9% vs 17.3%) were the 2 risk factors more associated with young CAD. There is a preponderance toward single vessel disease in the young CAD group, and they had a favorable clinical outcome in terms of all-cause mortality at discharge (RR 0.49 [CI 0.26-0.94]) and 1-year follow-up (RR 0.47 [CI 0.19-1.15]).

    CONCLUSION: We observed distinctive features of young CAD that would serve as a framework in the primary and secondary prevention of the early onset CAD.

    Matched MeSH terms: Databases, Factual
  4. Zin CS, Taufek NH, Ahmad MM
    Front Pharmacol, 2019;10:1286.
    PMID: 31736760 DOI: 10.3389/fphar.2019.01286
    Limited data are available on the adherence to opioid therapy and the influence of different patient groups on adherence. This study examined the patterns of adherence in opioid naïve and opioid existing patients with varying age and gender. This retrospective cohort study was conducted using the prescription databases in tertiary hospital settings in Malaysia from 2010 to 2016. Adult patients aged ≥18 years, receiving at least two opioid prescriptions, were included and stratified into the opioid naïve and existing patient groups. Adherence to opioid therapy was measured using the proportion of days covered (PDC), which was derived by dividing the total number of days covered with any opioids by the number of days in the follow-up period. Generalized linear modeling was used to assess factors associated with PDC. A total of 10,569 patients with 36,650 prescription episodes were included in the study. Of these, 91.7% (n = 9,696) were opioid naïve patients and 8.3% (n = 873) were opioid existing patients. The median PDC was 35.5% (interquartile range (IQR) 10.3-78.7%) and 26.8% (IQR 8.8-69.5%) for opioid naïve and opioid existing patients, respectively. A higher opioid daily dose (coefficient 0.010, confidence interval (CI) 0.009, 0.012 p < 0.0001) and increasing age (coefficient 0.002, CI 0.001, 0.003 p < 0.0001) were associated with higher levels of PDC, while lower PDC values were associated with male subjects (coefficient -0.0041, CI -0.072, -0.010 p = 0.009) and existing opioid patients (coefficient -0.134, CI -0.191, -0.077 p < 0.0001). The suboptimal adherence to opioid medications was commonly observed among patients with non-cancer pain, and the opioid existing patients were less adherent compared to opioid naïve patients. Increasing age and a higher daily opioid dose were factors associated with higher levels of adherence, while male and opioid existing patients were potential determinants for lower levels of adherence to opioid medications.
    Matched MeSH terms: Databases, Factual
  5. Zhang YY, Vimala R, Chui PL, Hilmi IN
    Surg Endosc, 2023 Apr;37(4):2633-2643.
    PMID: 36369410 DOI: 10.1007/s00464-022-09724-7
    BACKGROUND: Pain is a contributing factor to the low compliance rate for performing a colonoscopy on screening for colorectal cancer.

    PURPOSE: This meta-analysis aimed to evaluate the effect of visual distraction on adults undergoing colonoscopy.

    METHODS: We searched PubMed, EMBASE, Web of Science, and Cochrane Library Database from their inception to February 2022. Randomized controlled trials comparing visual distraction with non-visual distraction were considered for inclusion. The fixed-effects and random-effects models were used to pool the data from individual studies and the Cochrane risk of bias assessment tool was used to determine the methodology quality.

    RESULTS: This meta-analysis included four studies (N = 301) for pain level and total procedure time, three studies (N = 181) for satisfaction score, three studies (N = 196) for anxiety level, and four studie (N = 402) for willingness to repeat the procedure. The pooled analysis shown that significantly lower pain levels (SMD, - 0.25; 95% CI - 0.47 to - 0.02; P = 0.03), higher satisfaction score with the procedure (SMD, 0.63; 95% CI, 0.33 to 0.93; P 

    Matched MeSH terms: Databases, Factual
  6. Zare MR, Mueen A, Seng WC
    J Digit Imaging, 2014 Feb;27(1):77-89.
    PMID: 24092327 DOI: 10.1007/s10278-013-9637-0
    The demand for automatically classification of medical X-ray images is rising faster than ever. In this paper, an approach is presented to gain high accuracy rate for those classes of medical database with high ratio of intraclass variability and interclass similarities. The classification framework was constructed via annotation using the following three techniques: annotation by binary classification, annotation by probabilistic latent semantic analysis, and annotation using top similar images. Next, final annotation was constructed by applying ranking similarity on annotated keywords made by each technique. The final annotation keywords were then divided into three levels according to the body region, specific bone structure in body region as well as imaging direction. Different weights were given to each level of the keywords; they are then used to calculate the weightage for each category of medical images based on their ground truth annotation. The weightage computed from the generated annotation of query image was compared with the weightage of each category of medical images, and then the query image would be assigned to the category with closest weightage to the query image. The average accuracy rate reported is 87.5 %.
    Matched MeSH terms: Databases, Factual/statistics & numerical data*
  7. Zara J, Nordin SM, Isha ASN
    Front Public Health, 2023;11:1225995.
    PMID: 37614453 DOI: 10.3389/fpubh.2023.1225995
    Health, safety, and environment (HSE) are critical aspects of any industry, particularly in high-risk environments, such as the oil and gas industry. Continuous accident reports indicate the requirement for the effective implementation of safety rules, regulations, and practices. This systematic literature review examines the relationship between safety communication and safety commitment in high-risk workplaces, specifically focusing on the oil and gas industry. The review comprises 1,439 articles from 2004 to 2023, retrieved from the Scopus and Web of Science databases following the PRISMA comprehensive guidelines. This study considers safety communication, communication climate, and communication satisfaction to evaluate their influence on safety commitment under occupational health and safety. This study identifies safety commitment issues and their underlying factors, discussing measures for preventing and reducing accidents and incidents and highlighting preventive measures for future research. It also signifies the variables influencing accident and incident rates. The research underscores the importance of communication dimensions and the need for workers to possess adequate skills, knowledge, and attitudes regarding occupational safety and health procedures. Moreover, the study contributes to the industrial and academic domains by improving organizational safety commitment, promoting a safety culture, and developing effective communication strategies. Furthermore, practitioners may benefit from this comprehensive overview in developing, evaluating, and enhancing occupational safety.
    Matched MeSH terms: Databases, Factual
  8. Zakaria WNA, Wijaya A, Al-Rahbi B, Ahmad AH, Zakaria R, Othman Z
    Psychiatr Genet, 2023 Jun 01;33(3):102-112.
    PMID: 36825833 DOI: 10.1097/YPG.0000000000000338
    This study aims to use a bibliometric technique to evaluate the scientific output of gene and bipolar disorder research. The search query related to gene and bipolar disorder from the Scopus database identified 1848 documents from 1951 to 2020. The growth in the publications increased since early 1990, peaked in 2011, and started to decline thereafter. High occurrence in author keywords suggests that some research topics, such as "polymorphism", "linkage" and "association study" have waned over time, whereas others, such as "DNA methylation," "circadian rhythm," "" and "meta-analysis," are now the emerging trends in gene and bipolar disorder research. The USA was the country with the highest production followed by the UK, Canada, Italy and Germany. The leading institutions were Cardiff University in the UK, the National Institute of Mental Health (NIMH) in the USA, King's College London in the UK and the University of California, San Diego in the USA. The leading journals publishing gene and bipolar literature were the American Journal of Medical Genetics Neuropsychiatric Genetics, Molecular Psychiatry and Psychiatric Genetics. The top authors in the number of publications were Craddock N, Serretti A and Rietschel M. According to the co-authorship network analysis of authors, the majority of the authors in the same clusters were closely linked together and originated from the same or neighbouring country. The findings of this study may be useful in identifying emerging topics for future research and promoting research collaboration in the field of genetic studies related to bipolar disorder.
    Matched MeSH terms: Databases, Factual
  9. Zairina Ibrahim, Md Gapar Md Johar
    MyJurnal
    The process of software development life cycle (SDLC) is an important element of development phases to develop the application. In fact, there are needs to upgrade the sequence of methodology in software development. Thus, the SDLC is very crucial in order for them to ensure the quality of skills is placed accordingly in the workflow. This research contributes to the development of a new approach in system development workflow with the aim to properly manage system development projects. It started by providing some background data related to the previous mode of operation in the teamwork samples as shared by the stakeholders of the transformation projects and the new proposed Analysis System Development Framework (ASDF) method team members. Then, the key findings related to steps of software development such as (1) input for User Requirement Specification (URS) and (2) System Requirement Specification (SRS), (3) process for module, (4) process for database, (5) process for User Acceptance Testing (UAT) (6) output for Final Acceptance Testing (FAT) and empowerment for the whole level based on ASDF method. This paper contribution significantly to support the perception of high quality of skills in a teamwork, results in better performance of software development.
    Matched MeSH terms: Databases, Factual
  10. Zainal N, Rahardja A, Faris Irfan CY, Nasir A, Wan Pauzi WI, Mohamad Ikram I, et al.
    Singapore Med J, 2016 Dec;57(12):690-693.
    PMID: 26805669 DOI: 10.11622/smedj.2016019
    INTRODUCTION: This study aimed to determine the prevalence of asthma-like symptoms among schoolchildren with low birth weight (LBW), and to compare the lung function of these children with that of children with normal birth weight.

    METHODS: This was a comparative cross-sectional study. We recruited children aged 8-11 years from eight primary schools in Kota Bharu, Kelantan, Malaysia. The children were divided into two groups: those with LBW (< 2,500 g) and those with normal birth weight (≥ 2,500 g). Parents of the enrolled children were asked to complete a translated version of the International Study of Asthma and Allergies in Childhood questionnaire. Lung function tests, done using a MicroLoop Spirometer, were performed for the children in both groups by a single investigator who was blinded to the children's birth weight.

    RESULTS: The prevalence of 'ever wheezed' among the children with LBW was 12.9%. This value was significantly higher than that of the children with normal birth weight (7.8%). Forced vital capacity (FVC), forced expiratory volume in one second, and forced expiratory flow when 50% and 75% of the FVC had been exhaled were significantly lower among the children with LBW as compared to the children with normal birth weight.

    CONCLUSION: LBW is associated with an increased prevalence of asthma-like symptoms and impaired lung function indices later in life. Children born with LBW may need additional follow-up so that future respiratory problems can be detected early.

    Matched MeSH terms: Databases, Factual
  11. Zaharan NL, Williams D, Bennett K
    Ir J Med Sci, 2014 Jun;183(2):311-8.
    PMID: 24013870 DOI: 10.1007/s11845-013-1011-1
    BACKGROUND: Over the last decade there have been significant changes in the prescribing of antidiabetic therapies. It is of interest to know about these trends and variations in the Irish population so that future prescribing patterns can be estimated.

    AIMS: To examine the trends in prescribed antidiabetic treatments, including variations across age, gender, socioeconomic status and regions in the Irish population over the last 10 years.

    METHODS: The Irish national pharmacy claims database was used to identify patients ≥ 16 years dispensed antidiabetic agents (oral or insulin) from January 2003 to December 2012 through the two main community drug schemes for diabetes. The rate of prescribing per 1,000 population was calculated. Logistic regression was used to examine variations in prescribing in patients with diabetes.

    RESULTS: There was a significant increase in the prescribing of fast and long-acting insulin analogues with a rapid decline in the prescribing of human insulin (p < 0.0001). Increased prescribing of metformin, incretin modulators and fixed oral combination agents was observed (p < 0.0001). Females and older aged patients were more likely to be prescribed human insulin than other insulins. Metformin was less likely while sulphonylureas were more likely to be prescribed in older than younger aged patients. Socioeconomic differences were observed in increased prescribing of the newer and more expensive antidiabetic agents in the non-means tested scheme. Regional variations were observed in the prescribing of both insulin and oral antidiabetic agents.

    CONCLUSION: There has been an increase over time in the prescribing of both insulin and oral antidiabetic agents in the Irish population with increasing uptake of newer antidiabetic agents. This has implications for projecting future uptake and expenditure of these agents given the rising level of diabetes in the population.

    Matched MeSH terms: Databases, Factual
  12. Za'im NAN, Al-Dhief FT, Azman M, Alsemawi MRM, Abdul Latiff NMA, Mat Baki M
    J Otolaryngol Head Neck Surg, 2023 Sep 20;52(1):62.
    PMID: 37730624 DOI: 10.1186/s40463-023-00661-6
    BACKGROUND: A multidimensional voice quality assessment is recommended for all patients with dysphonia, which requires a patient visit to the otolaryngology clinic. The aim of this study was to determine the accuracy of an online artificial intelligence classifier, the Online Sequential Extreme Learning Machine (OSELM), in detecting voice pathology. In this study, a Malaysian Voice Pathology Database (MVPD), which is the first Malaysian voice database, was created and tested.

    METHODS: The study included 382 participants (252 normal voices and 130 dysphonic voices) in the proposed database MVPD. Complete data were obtained for both groups, including voice samples, laryngostroboscopy videos, and acoustic analysis. The diagnoses of patients with dysphonia were obtained. Each voice sample was anonymized using a code that was specific to each individual and stored in the MVPD. These voice samples were used to train and test the proposed OSELM algorithm. The performance of OSELM was evaluated and compared with other classifiers in terms of the accuracy, sensitivity, and specificity of detecting and differentiating dysphonic voices.

    RESULTS: The accuracy, sensitivity, and specificity of OSELM in detecting normal and dysphonic voices were 90%, 98%, and 73%, respectively. The classifier differentiated between structural and non-structural vocal fold pathology with accuracy, sensitivity, and specificity of 84%, 89%, and 88%, respectively, while it differentiated between malignant and benign lesions with an accuracy, sensitivity, and specificity of 92%, 100%, and 58%, respectively. Compared to other classifiers, OSELM showed superior accuracy and sensitivity in detecting dysphonic voices, differentiating structural versus non-structural vocal fold pathology, and between malignant and benign voice pathology.

    CONCLUSION: The OSELM algorithm exhibited the highest accuracy and sensitivity compared to other classifiers in detecting voice pathology, classifying between malignant and benign lesions, and differentiating between structural and non-structural vocal pathology. Hence, it is a promising artificial intelligence that supports an online application to be used as a screening tool to encourage people to seek medical consultation early for a definitive diagnosis of voice pathology.

    Matched MeSH terms: Databases, Factual
  13. Yusoff N, Alias M, Ismail N
    F1000Res, 2023;12:1286.
    PMID: 38196406 DOI: 10.12688/f1000research.140765.1
    Background: Green purchasing is an important aspect of sustainable consumption, which decreases society's environmental effect. Although numerous research has been conducted to investigate the determinants of green buying behaviour, there has been a lack of effort in comprehensively analysing these findings. The purpose of this study is to examine the available literature on the factors that influence green purchasing behaviour. Methods: The review focused on empirical research published in peer-reviewed English-language publications between 2017 and 2021 in Web of Science and Scopus. The research took place from May to June 2021. Mixed Methods Appraisal Tool (MMAT) is used to assess the risk of bias in systematic literature reviews. Results: 41 articles were included, with significant focus on the retailing sector. Most of these studies were centred in Asian countries, primarily China and India. The Theory of Planned Behaviour was the most prominent, appearing 15 times, followed by the Theory of Reasoned Action (seven times). Analysis identified five main themes and 15 sub-themes related to green purchase behaviour drivers. These themes were categorized by occurrence: People (34 papers), marketing (13), knowledge (12), environment (12), and influence (nine). The dominant driver was people (34 studies), encompassing sub-themes including motivation (three), perception (eight), behavioural (13), and psychographic characteristics (10). Conclusions: This study has given an overview of the present status of green purchasing behaviour, which serves as a foundation for future studies and guidance for policymakers and practitioners. However, it does not include unpublished materials and non-English papers. Secondly, it focuses on articles from two databases within the last five years which doesn't encompass all article types, prompting the need for future exploration. Thirdly, extending the review's time frame could unveil more pronounced GPB patterns. Lastly, although all eligible papers were assessed based on criteria, the chance of overlooking some papers is acknowledged.
    Matched MeSH terms: Databases, Factual
  14. Yu K, Feng L, Chen Y, Wu M, Zhang Y, Zhu P, et al.
    Comput Biol Med, 2024 Feb;169:107835.
    PMID: 38096762 DOI: 10.1016/j.compbiomed.2023.107835
    Current wavelet thresholding methods for cardiogram signals captured by flexible wearable sensors face a challenge in achieving both accurate thresholding and real-time signal denoising. This paper proposes a real-time accurate thresholding method based on signal estimation, specifically the normalized ACF, as an alternative to traditional noise estimation without the need for parameter fine-tuning and extensive data training. This method is experimentally validated using a variety of electrocardiogram (ECG) signals from different databases, each containing specific types of noise such as additive white Gaussian (AWG) noise, baseline wander noise, electrode motion noise, and muscle artifact noise. Although this method only slightly outperforms other methods in removing AWG noise in ECG signals, it far outperforms conventional methods in removing other real noise. This is attributed to the method's ability to accurately distinguish not only AWG noise that is significantly different spectrum of the ECG signal, but also real noise with similar spectra. In contrast, the conventional methods are effective only for AWG noise. In additional, this method improves the denoising visualization of the measured ECG signals and can be used to optimize other parameters of other wavelet methods to enhancing the denoised periodic signals, thereby improving diagnostic accuracy.
    Matched MeSH terms: Databases, Factual
  15. Yildirim O, Talo M, Ay B, Baloglu UB, Aydin G, Acharya UR
    Comput Biol Med, 2019 10;113:103387.
    PMID: 31421276 DOI: 10.1016/j.compbiomed.2019.103387
    In this study, a deep-transfer learning approach is proposed for the automated diagnosis of diabetes mellitus (DM), using heart rate (HR) signals obtained from electrocardiogram (ECG) data. Recent progress in deep learning has contributed significantly to improvement in the quality of healthcare. In order for deep learning models to perform well, large datasets are required for training. However, a difficulty in the biomedical field is the lack of clinical data with expert annotation. A recent, commonly implemented technique to train deep learning models using small datasets is to transfer the weighting, developed from a large dataset, to the current model. This deep learning transfer strategy is generally employed for two-dimensional signals. Herein, the weighting of models pre-trained using two-dimensional large image data was applied to one-dimensional HR signals. The one-dimensional HR signals were then converted into frequency spectrum images, which were utilized for application to well-known pre-trained models, specifically: AlexNet, VggNet, ResNet, and DenseNet. The DenseNet pre-trained model yielded the highest classification average accuracy of 97.62%, and sensitivity of 100%, to detect DM subjects via HR signal recordings. In the future, we intend to further test this developed model by utilizing additional data along with cloud-based storage to diagnose DM via heart signal analysis.
    Matched MeSH terms: Databases, Factual*
  16. Yildirim O, Baloglu UB, Tan RS, Ciaccio EJ, Acharya UR
    Comput Methods Programs Biomed, 2019 Jul;176:121-133.
    PMID: 31200900 DOI: 10.1016/j.cmpb.2019.05.004
    BACKGROUND AND OBJECTIVE: For diagnosis of arrhythmic heart problems, electrocardiogram (ECG) signals should be recorded and monitored. The long-term signal records obtained are analyzed by expert cardiologists. Devices such as the Holter monitor have limited hardware capabilities. For improved diagnostic capacity, it would be helpful to detect arrhythmic signals automatically. In this study, a novel approach is presented as a candidate solution for these issues.

    METHODS: A convolutional auto-encoder (CAE) based nonlinear compression structure is implemented to reduce the signal size of arrhythmic beats. Long-short term memory (LSTM) classifiers are employed to automatically recognize arrhythmias using ECG features, which are deeply coded with the CAE network.

    RESULTS: Based upon the coded ECG signals, both storage requirement and classification time were considerably reduced. In experimental studies conducted with the MIT-BIH arrhythmia database, ECG signals were compressed by an average 0.70% percentage root mean square difference (PRD) rate, and an accuracy of over 99.0% was observed.

    CONCLUSIONS: One of the significant contributions of this study is that the proposed approach can significantly reduce time duration when using LSTM networks for data analysis. Thus, a novel and effective approach was proposed for both ECG signal compression, and their high-performance automatic recognition, with very low computational cost.

    Matched MeSH terms: Databases, Factual
  17. Yeow PH, Yuen YY, Loo WH
    Appl Ergon, 2013 Sep;44(5):719-29.
    PMID: 22841592 DOI: 10.1016/j.apergo.2012.04.017
    Ever since the 9/11 terrorist attack, many countries are considering the use of smart national identity card (SNIC) which has the ability to identify terrorists due to its biometric verification function. However, there are many ergonomics issues in the use of SNIC, e.g. card credibility. This research presents a case study survey of Malaysian users. Although most citizens (>96%) own MyKad (Malaysia SNIC), many do not carry it around and use its applications. This defeats one of its main purposes, i.e. combating terrorism. Thus, the research investigates ergonomics issues affecting the citizens' Intention to Use (ITU) MyKad for homeland security by using an extended technology acceptance model. Five hundred questionnaires were collected and analysed using structural equation modelling. Results show that perceived credibility and performance expectancy are the key issues. The findings provide many countries with insights into methods of addressing ergonomics issues and increasing adoption of SNIC for homeland security.
    Matched MeSH terms: Databases, Factual
  18. Yenyuwadee S, Achavanuntakul P, Phisalprapa P, Levin M, Saokaew S, Kanchanasurakit S, et al.
    Acta Derm Venereol, 2024 Jan 08;104:adv18477.
    PMID: 38189223 DOI: 10.2340/actadv.v104.18477
    Utilization of lasers and energy-based devices for surgical scar minimization has been substantially evaluated in placebo-controlled trials. The aim of this study was to compare reported measures of efficacy of lasers and energy-based devices in clinical trials in preventing surgical scar formation in a systematic review and network meta-analyses. Five electronic databases, PubMed, Scopus, Embase, ClinicalTrials.gov, and the Cochrane Library, were searched to retrieve relevant articles. The search was limited to randomized controlled trials that reported on clinical outcomes of surgical scars with treatment initiation no later than 6 months after surgery and a follow-up period of at least 3 months. A total of 18 randomized controlled trials involving 482 participants and 671 postsurgical wounds were included in the network meta-analyses. The results showed that the most efficacious treatments were achieved using low-level laser therapy) (weighted mean difference -3.78; 95% confidence interval (95% CI) -6.32, -1.24) and pulsed dye laser (weighted mean difference -2.46; 95% CI -4.53, -0.38). Nevertheless, low-level laser therapy and pulsed dye laser demonstrated comparable outcomes in surgical scar minimization (weighted mean difference -1.32, 95% CI -3.53, 0.89). The findings of this network meta-analyses suggest that low-level laser therapy and pulsed dye laser are both effective treatments for minimization of scar formation following primary closure of surgical wounds with comparable treatment outcomes.
    Matched MeSH terms: Databases, Factual
  19. Yeap EJ, Rao J, Pan CH, Soelar SA, Younger ASE
    Foot Ankle Surg, 2016 Sep;22(3):164-169.
    PMID: 27502224 DOI: 10.1016/j.fas.2015.06.008
    BACKGROUND: This study compares the outcomes of calcaneal fracture surgery after open reduction internal fixation and plating (ORIF) versus arthroscopic assisted percutaneous screw fixation (APSF).

    METHODS: Group I (N=12) underwent ORIF. Group II (N=15) underwent APSF. Anthropometric data, pre and post-operative stay, complications and duration off work were recorded in this retrospective case cohort study. Radiographs were analyzed for Bohler's, Gissane's angle and Sanders' classification. AOFAS Hindfoot and SF 36 scores were collected at final follow-up.

    RESULTS: Anthropometric data, Bohler's and Gissane's angles, AOFAS and SF 36 scores were not significantly different. Pre-operative duration was 12.3 days in ORIF and 6.9 days in APSF. Post-operative duration was 7.3 days vs 3.8 days. Duration off work was 6.2 months vs 2.9 months.

    CONCLUSION: The APSF group was able to have surgery earlier, go home faster, and return to work earlier. This study was not powered to demonstrate a difference in wound complication rates.

    Matched MeSH terms: Databases, Factual
  20. Yavar, A.R., S. Sarmani, Tan, C.Y., N.N. Rafie, Lim, S.W. Edwin, Khoo, K.S.
    MyJurnal
    An electronic database has been developed and implemented for ko-INAA method in Malaysia. Databases are often developed according to national requirements. This database contains constant nuclear data for ko-INAA method; Hogdahl-convention and Westcott-formalism as 3 separate command user interfaces. It has been created using Microsoft Access 2007 under a Windows operating system. This database saves time and the quality of results can be assured when the calculation of neutron flux parameters and concentration of elements by ko-INAA method are utilised. An evaluation of the database was conducted by IAEA Soil7 where the results published which showed a high level of consistency.
    Matched MeSH terms: Databases, Factual
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