Displaying publications 1 - 20 of 65 in total

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  1. Soleimani Amiri M, Ramli R
    Sensors (Basel), 2021 May 03;21(9).
    PMID: 34063574 DOI: 10.3390/s21093171
    It is necessary to control the movement of a complex multi-joint structure such as a robotic arm in order to reach a target position accurately in various applications. In this paper, a hybrid optimal Genetic-Swarm solution for the Inverse Kinematic (IK) solution of a robotic arm is presented. Each joint is controlled by Proportional-Integral-Derivative (PID) controller optimized with the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), called Genetic-Swarm Optimization (GSO). GSO solves the IK of each joint while the dynamic model is determined by the Lagrangian. The tuning of the PID is defined as an optimization problem and is solved by PSO for the simulated model in a virtual environment. A Graphical User Interface has been developed as a front-end application. Based on the combination of hybrid optimal GSO and PID control, it is ascertained that the system works efficiently. Finally, we compare the hybrid optimal GSO with conventional optimization methods by statistic analysis.
    Matched MeSH terms: Data Interpretation, Statistical
  2. Chang Y, Yeong KY
    Curr Med Chem, 2021 Mar 29.
    PMID: 33781187 DOI: 10.2174/0929867328666210329124415
    There have been intense research interests in sirtuins since the establishment of their regulatory roles in a myriad of pathological processes. In the last two decades, much research efforts have been dedicated to the development of sirtuin modulators. Although synthetic sirtuin modulators are the focus, natural modulators remain an integral part to be further explored in this area as they are found to possess therapeutic potential in various diseases including cancers, neurodegenerative diseases, and metabolic disorders. Owing to the importance of this cluster of compounds, this review gives a current stand on the naturally occurring sirtuin modulators, , associated molecular mechanisms and their therapeutic benefits.. Furthermore, comprehensive data mining resulted in detailed statistical data analyses pertaining to the development trend of sirtuin modulators from 2010-2020. Lastly, the challenges and future prospect of natural sirtuin modulators in drug discovery will also be discussed.
    Matched MeSH terms: Data Interpretation, Statistical
  3. Balamurugan S, Muthu BA, Peng SL, Wahab MHA
    Big Data, 2020 10;8(5):450-451.
    PMID: 33090023 DOI: 10.1089/big.2020.29038.cfp
    Matched MeSH terms: Data Interpretation, Statistical*
  4. Abdulrauf Sharifai G, Zainol Z
    Genes (Basel), 2020 06 27;11(7).
    PMID: 32605144 DOI: 10.3390/genes11070717
    The training machine learning algorithm from an imbalanced data set is an inherently challenging task. It becomes more demanding with limited samples but with a massive number of features (high dimensionality). The high dimensional and imbalanced data set has posed severe challenges in many real-world applications, such as biomedical data sets. Numerous researchers investigated either imbalanced class or high dimensional data sets and came up with various methods. Nonetheless, few approaches reported in the literature have addressed the intersection of the high dimensional and imbalanced class problem due to their complicated interactions. Lately, feature selection has become a well-known technique that has been used to overcome this problem by selecting discriminative features that represent minority and majority class. This paper proposes a new method called Robust Correlation Based Redundancy and Binary Grasshopper Optimization Algorithm (rCBR-BGOA); rCBR-BGOA has employed an ensemble of multi-filters coupled with the Correlation-Based Redundancy method to select optimal feature subsets. A binary Grasshopper optimisation algorithm (BGOA) is used to construct the feature selection process as an optimisation problem to select the best (near-optimal) combination of features from the majority and minority class. The obtained results, supported by the proper statistical analysis, indicate that rCBR-BGOA can improve the classification performance for high dimensional and imbalanced datasets in terms of G-mean and the Area Under the Curve (AUC) performance metrics.
    Matched MeSH terms: Data Interpretation, Statistical
  5. Lee YL, Lim YMF, Law KB, Sivasampu S
    Trials, 2020 Jun 16;21(1):530.
    PMID: 32546189 DOI: 10.1186/s13063-020-04349-4
    INTRODUCTION: There are few sources of published data on intra-cluster correlation coefficients (ICCs) amongst patients with type 2 diabetes (T2D) and/or hypertension in primary care, particularly in low- and middle-income countries. ICC values are necessary for determining the sample sizes of cluster randomized trials. Hence, we aim to report the ICC values for a range of measures from a cluster-based interventional study conducted in Malaysia.

    METHOD: Baseline data from a large study entitled Evaluation of Enhanced Primary Health Care interventions in public health clinics (EnPHC-EVA: Facility) were used in this analysis. Data from 40 public primary care clinics were collected through retrospective chart reviews and a patient exit survey. We calculated the ICCs for processes of care, clinical outcomes and patient experiences in patients with T2D and/or hypertension using the analysis of variance approach.

    RESULTS: Patient experience had the highest ICC values compared to processes of care and clinical outcomes. The ICC values ranged from 0.01 to 0.48 for processes of care. Generally, the ICC values for processes of care for patients with hypertension only are higher than those for T2D patients, with or without hypertension. However, both groups of patients have similar ICCs for antihypertensive medications use. In addition, similar ICC values were observed for clinical outcomes, ranging from 0.01 to 0.09. For patient experience, the ICCs were between 0.03 (proportion of patients who are willing to recommend the clinic to their friends and family) and 0.25 (for Patient Assessment of Chronic Illness Care item 9, Given a copy of my treatment plan).

    CONCLUSION: The reported ICCs and their respective 95% confidence intervals for T2D and hypertension will be useful for estimating sample sizes and improving efficiency of cluster trials conducted in the primary care setting, particularly for low- and middle-income countries.

    Matched MeSH terms: Data Interpretation, Statistical
  6. Acharya UR, Faust O, Ciaccio EJ, Koh JEW, Oh SL, Tan RS, et al.
    Comput Methods Programs Biomed, 2019 Jul;175:163-178.
    PMID: 31104705 DOI: 10.1016/j.cmpb.2019.04.018
    BACKGROUND AND OBJECTIVE: Complex fractionated atrial electrograms (CFAE) may contain information concerning the electrophysiological substrate of atrial fibrillation (AF); therefore they are of interest to guide catheter ablation treatment of AF. Electrogram signals are shaped by activation events, which are dynamical in nature. This makes it difficult to establish those signal properties that can provide insight into the ablation site location. Nonlinear measures may improve information. To test this hypothesis, we used nonlinear measures to analyze CFAE.

    METHODS: CFAE from several atrial sites, recorded for a duration of 16 s, were acquired from 10 patients with persistent and 9 patients with paroxysmal AF. These signals were appraised using non-overlapping windows of 1-, 2- and 4-s durations. The resulting data sets were analyzed with Recurrence Plots (RP) and Recurrence Quantification Analysis (RQA). The data was also quantified via entropy measures.

    RESULTS: RQA exhibited unique plots for persistent versus paroxysmal AF. Similar patterns were observed to be repeated throughout the RPs. Trends were consistent for signal segments of 1 and 2 s as well as 4 s in duration. This was suggestive that the underlying signal generation process is also repetitive, and that repetitiveness can be detected even in 1-s sequences. The results also showed that most entropy metrics exhibited higher measurement values (closer to equilibrium) for persistent AF data. It was also found that Determinism (DET), Trapping Time (TT), and Modified Multiscale Entropy (MMSE), extracted from signals that were acquired from locations at the posterior atrial free wall, are highly discriminative of persistent versus paroxysmal AF data.

    CONCLUSIONS: Short data sequences are sufficient to provide information to discern persistent versus paroxysmal AF data with a significant difference, and can be useful to detect repeating patterns of atrial activation.

    Matched MeSH terms: Data Interpretation, Statistical
  7. El Hajj MS, Awaisu A, Kheir N, Mohamed MHN, Haddad RS, Saleh RA, et al.
    Trials, 2019 Jan 08;20(1):25.
    PMID: 30621772 DOI: 10.1186/s13063-018-3068-7
    BACKGROUND: Tobacco use is presently responsible for the death of over seven million people across the world. In Qatar, it is one of the main causes of premature deaths and preventable diseases. To reduce tobacco use, Qatar has ratified the World Health Organization (WHO)'s Framework Convention on Tobacco Control (FCTC) and has implemented many tobacco-control initiatives. In spite of these measures, tobacco use is still considered a public health threat in Qatar. Pharmacists practicing in retail/community pharmacy settings are often the first port of call for individuals requiring general health advice. Evidence has proven that they have a pivotal role in health promotion and disease prevention including tobacco cessation. However, pharmacists in Qatar are not actively involved in tobacco control and many have not received any education or training about smoking cessation counseling in the past. In an effort to build the capacity of pharmacists towards tobacco control in Qatar, the aim of the proposed study is to design, implement, and evaluate an intensive education program on tobacco dependence treatment for pharmacists in Qatar.

    METHODS/DESIGN: The study will be a prospective randomized controlled trial comparing an intensive tobacco-related education program versus non-tobacco-related training on pharmacists' tobacco-use-related knowledge, attitudes, self-efficacy, and skills. Community pharmacists practicing in Qatar will be eligible for participation in the study. A random sample of pharmacists will be selected for participation. Consenting participants will be randomly allocated to intervention or control groups. Participants in the intervention group will receive an intensive education program delivered by a multi-disciplinary group of educators, researchers, and clinicians with expertise in tobacco cessation. A short didactic session on a non-tobacco-related topic will be delivered to pharmacists in the control group. The study has two primary outcomes: post-intervention tobacco-related knowledge and post-intervention skills for tobacco cessation assessed using a multiple-choice-based evaluation instrument and an Objective Structured Clinical Examination (OSCE), respectively. The secondary study outcomes are post-intervention attitudes towards tobacco cessation and self-efficacy in tobacco-cessation interventions assessed using a survey instrument. An additional secondary study outcome is the post-intervention performance difference in relation to tobacco-cessation skills in the practice setting assessed using the simulated client approach.

    DISCUSSION: If demonstrated to be effective, this education program will be considered as a model that Qatar and the Middle East region can apply to overcome the burden of tobacco-use disorder.

    TRIAL REGISTRATION: ClinicalTrials.gov, ID: NCT03518476 . Registered on 8 May 2018. Version 1/22 June 2018.

    Matched MeSH terms: Data Interpretation, Statistical
  8. Mahamad Maifiah MH, Velkov T, Creek DJ, Li J
    Methods Mol Biol, 2019;1946:321-328.
    PMID: 30798566 DOI: 10.1007/978-1-4939-9118-1_28
    Acinetobacter baumannii is rapidly emerging as a multidrug-resistant pathogen responsible for nosocomial infections including pneumonia, bacteremia, wound infections, urinary tract infections, and meningitis. Metabolomics provides a powerful tool to gain a system-wide snapshot of cellular biochemical networks under defined conditions and has been increasingly applied to bacterial physiology and drug discovery. Here we describe an optimized sample preparation method for untargeted metabolomics studies in A. baumannii. Our method provides a significant recovery of intracellular metabolites to demonstrate substantial differences in global metabolic profiles among A. baumannii strains.
    Matched MeSH terms: Data Interpretation, Statistical
  9. Ovesen C, Jakobsen JC, Gluud C, Steiner T, Law Z, Flaherty K, et al.
    BMC Res Notes, 2018 Jun 13;11(1):379.
    PMID: 29895329 DOI: 10.1186/s13104-018-3481-8
    OBJECTIVE: We present the statistical analysis plan of a prespecified Tranexamic Acid for Hyperacute Primary Intracerebral Haemorrhage (TICH)-2 sub-study aiming to investigate, if tranexamic acid has a different effect in intracerebral haemorrhage patients with the spot sign on admission compared to spot sign negative patients. The TICH-2 trial recruited above 2000 participants with intracerebral haemorrhage arriving in hospital within 8 h after symptom onset. They were included irrespective of radiological signs of on-going haematoma expansion. Participants were randomised to tranexamic acid versus matching placebo. In this subgroup analysis, we will include all participants in TICH-2 with a computed tomography angiography on admission allowing adjudication of the participants' spot sign status.

    RESULTS: Primary outcome will be the ability of tranexamic acid to limit absolute haematoma volume on computed tomography at 24 h (± 12 h) after randomisation among spot sign positive and spot sign negative participants, respectively. Within all outcome measures, the effect of tranexamic acid in spot sign positive/negative participants will be compared using tests of interaction. This sub-study will investigate the important clinical hypothesis that spot sign positive patients might benefit more from administration of tranexamic acid compared to spot sign negative patients. Trial registration ISRCTN93732214 ( http://www.isrctn.com ).

    Matched MeSH terms: Data Interpretation, Statistical*
  10. Kalid N, Zaidan AA, Zaidan BB, Salman OH, Hashim M, Albahri OS, et al.
    J Med Syst, 2018 Mar 02;42(4):69.
    PMID: 29500683 DOI: 10.1007/s10916-018-0916-7
    This paper presents a new approach to prioritize "Large-scale Data" of patients with chronic heart diseases by using body sensors and communication technology during disasters and peak seasons. An evaluation matrix is used for emergency evaluation and large-scale data scoring of patients with chronic heart diseases in telemedicine environment. However, one major problem in the emergency evaluation of these patients is establishing a reasonable threshold for patients with the most and least critical conditions. This threshold can be used to detect the highest and lowest priority levels when all the scores of patients are identical during disasters and peak seasons. A practical study was performed on 500 patients with chronic heart diseases and different symptoms, and their emergency levels were evaluated based on four main measurements: electrocardiogram, oxygen saturation sensor, blood pressure monitoring, and non-sensory measurement tool, namely, text frame. Data alignment was conducted for the raw data and decision-making matrix by converting each extracted feature into an integer. This integer represents their state in the triage level based on medical guidelines to determine the features from different sources in a platform. The patients were then scored based on a decision matrix by using multi-criteria decision-making techniques, namely, integrated multi-layer for analytic hierarchy process (MLAHP) and technique for order performance by similarity to ideal solution (TOPSIS). For subjective validation, cardiologists were consulted to confirm the ranking results. For objective validation, mean ± standard deviation was computed to check the accuracy of the systematic ranking. This study provides scenarios and checklist benchmarking to evaluate the proposed and existing prioritization methods. Experimental results revealed the following. (1) The integration of TOPSIS and MLAHP effectively and systematically solved the patient settings on triage and prioritization problems. (2) In subjective validation, the first five patients assigned to the doctors were the most urgent cases that required the highest priority, whereas the last five patients were the least urgent cases and were given the lowest priority. In objective validation, scores significantly differed between the groups, indicating that the ranking results were identical. (3) For the first, second, and third scenarios, the proposed method exhibited an advantage over the benchmark method with percentages of 40%, 60%, and 100%, respectively. In conclusion, patients with the most and least urgent cases received the highest and lowest priority levels, respectively.
    Matched MeSH terms: Data Interpretation, Statistical*
  11. Zabidin N, Mohamed AM, Zaharim A, Marizan Nor M, Rosli TI
    Int Orthod, 2018 03;16(1):133-143.
    PMID: 29478934 DOI: 10.1016/j.ortho.2018.01.009
    OBJECTIVES: To evaluate the relationship between human evaluation of the dental-arch form, to complete a mathematical analysis via two different methods in quantifying the arch form, and to establish agreement with the fourth-order polynomial equation.

    MATERIALS AND METHODS: This study included 64 sets of digitised maxilla and mandible dental casts obtained from a sample of dental arch with normal occlusion. For human evaluation, a convenient sample of orthodontic practitioners ranked the photo images of dental cast from the most tapered to the less tapered (square). In the mathematical analysis, dental arches were interpolated using the fourth-order polynomial equation with millimetric acetate paper and AutoCAD software. Finally, the relations between human evaluation and mathematical objective analyses were evaluated.

    RESULTS: Human evaluations were found to be generally in agreement, but only at the extremes of tapered and square arch forms; this indicated general human error and observer bias. The two methods used to plot the arch form were comparable.

    CONCLUSION: The use of fourth-order polynomial equation may be facilitative in obtaining a smooth curve, which can produce a template for individual arch that represents all potential tooth positions for the dental arch.

    Matched MeSH terms: Data Interpretation, Statistical*
  12. Mendoza Beltran A, Prado V, Font Vivanco D, Henriksson PJG, Guinée JB, Heijungs R
    Environ Sci Technol, 2018 02 20;52(4):2152-2161.
    PMID: 29406730 DOI: 10.1021/acs.est.7b06365
    Interpretation of comparative Life Cycle Assessment (LCA) results can be challenging in the presence of uncertainty. To aid in interpreting such results under the goal of any comparative LCA, we aim to provide guidance to practitioners by gaining insights into uncertainty-statistics methods (USMs). We review five USMs-discernibility analysis, impact category relevance, overlap area of probability distributions, null hypothesis significance testing (NHST), and modified NHST-and provide a common notation, terminology, and calculation platform. We further cross-compare all USMs by applying them to a case study on electric cars. USMs belong to a confirmatory or an exploratory statistics' branch, each serving different purposes to practitioners. Results highlight that common uncertainties and the magnitude of differences per impact are key in offering reliable insights. Common uncertainties are particularly important as disregarding them can lead to incorrect recommendations. On the basis of these considerations, we recommend the modified NHST as a confirmatory USM. We also recommend discernibility analysis as an exploratory USM along with recommendations for its improvement, as it disregards the magnitude of the differences. While further research is necessary to support our conclusions, the results and supporting material provided can help LCA practitioners in delivering a more robust basis for decision-making.
    Matched MeSH terms: Data Interpretation, Statistical*
  13. Sim SZ, Gupta RC, Ong SH
    Int J Biostat, 2018 Jan 09;14(1).
    PMID: 29306919 DOI: 10.1515/ijb-2016-0070
    In this paper, we study the zero-inflated Conway-Maxwell Poisson (ZICMP) distribution and develop a regression model. Score and likelihood ratio tests are also implemented for testing the inflation/deflation parameter. Simulation studies are carried out to examine the performance of these tests. A data example is presented to illustrate the concepts. In this example, the proposed model is compared to the well-known zero-inflated Poisson (ZIP) and the zero- inflated generalized Poisson (ZIGP) regression models. It is shown that the fit by ZICMP is comparable or better than these models.
    Matched MeSH terms: Data Interpretation, Statistical*
  14. Farhana Sabri, Zahidah Mustafa, Mohd Yahya Mohamed Ariffin
    MyJurnal
    The number of relapsing addicts is increasingly worrying from year to year. This study was conducted
    to examine at the relationship between defense mechanisms and the level of readiness to change in
    relapsing addicts. Although the drug addicts have been treated at the treatment center, however,
    repeated cases of drug addicts still occur. Six research questions were developed to see how far the
    variables consisting of defense mechanisms could correlate with readiness to change among addicts
    who were undergoing treatment. This study was carried out involving 125 addicts in two separate
    treatment centers in Melaka and Selangor. The selected sample were addicts who have undergone a
    relapse phase at least once in drug addiction. Statistical data analysis using Statistical Packages for
    Social Sciences 20 (SPSS-20) were used to analyze the data. Statistical descriptive is used to view the
    results of demographic data constructed. T-test and ANOVA are used to see the relationship between
    the variables. The regression analysis is used to predict the defense mechanisms with the stage of
    readiness to change among relapsing addicts. The results showed that the defense mechanism had a
    significant relationship to the stage of readiness to change among samples. The results of this study
    provide information on treatment services in the drug rehabilitation to improve the treatment method
    appropriately to the drug addicts in preventing relapse.
    Matched MeSH terms: Data Interpretation, Statistical
  15. Flaherty K, Bath PM, Dineen R, Law Z, Scutt P, Pocock S, et al.
    Trials, 2017 Dec 20;18(1):607.
    PMID: 29262841 DOI: 10.1186/s13063-017-2341-5
    RATIONALE: Aside from blood pressure lowering, treatment options for intracerebral haemorrhage remain limited and a proportion of patients will undergo early haematoma expansion with resultant significant morbidity and mortality. Tranexamic acid (TXA), an anti-fibrinolytic drug, has been shown to significantly reduce mortality in patients, who are bleeding following trauma, when given rapidly. TICH-2 is testing whether TXA is effective at improving outcome in spontaneous intracerebral haemorrhage (SICH).

    METHODS AND DESIGN: TICH-2 is a pragmatic, phase III, prospective, double-blind, randomised placebo-controlled trial. Two thousand adult (aged ≥ 18 years) patients with an acute SICH, within 8 h of stroke onset, will be randomised to receive TXA or the placebo control. The primary outcome is ordinal shift of modified Rankin Scale score at day 90. Analyses will be performed using intention-to-treat.

    RESULTS: This paper and its attached appendices describe the statistical analysis plan (SAP) for the trial and were developed and published prior to database lock and unblinding to treatment allocation. The SAP includes details of analyses to be undertaken and unpopulated tables which will be reported in the primary and key secondary publications. The database will be locked in early 2018, ready for publication of the results later in the same year.

    DISCUSSION: The SAP details the analyses that will be done to avoid bias arising from prior knowledge of the study findings. The trial will determine whether TXA can improve outcome after SICH, which currently has no definitive therapy.

    TRIAL REGISTRATION: ISRCTN registry, ID: ISRCTN93732214 . Registered on 17 January 2013.

    Matched MeSH terms: Data Interpretation, Statistical
  16. Mehdizadeh S, Sanjari MA
    J Biomech, 2017 11 07;64:236-239.
    PMID: 28958634 DOI: 10.1016/j.jbiomech.2017.09.009
    This study aimed to determine the effect of added noise, filtering and time series length on the largest Lyapunov exponent (LyE) value calculated for time series obtained from a passive dynamic walker. The simplest passive dynamic walker model comprising of two massless legs connected by a frictionless hinge joint at the hip was adopted to generate walking time series. The generated time series was used to construct a state space with the embedding dimension of 3 and time delay of 100 samples. The LyE was calculated as the exponential rate of divergence of neighboring trajectories of the state space using Rosenstein's algorithm. To determine the effect of noise on LyE values, seven levels of Gaussian white noise (SNR=55-25dB with 5dB steps) were added to the time series. In addition, the filtering was performed using a range of cutoff frequencies from 3Hz to 19Hz with 2Hz steps. The LyE was calculated for both noise-free and noisy time series with different lengths of 6, 50, 100 and 150 strides. Results demonstrated a high percent error in the presence of noise for LyE. Therefore, these observations suggest that Rosenstein's algorithm might not perform well in the presence of added experimental noise. Furthermore, findings indicated that at least 50 walking strides are required to calculate LyE to account for the effect of noise. Finally, observations support that a conservative filtering of the time series with a high cutoff frequency might be more appropriate prior to calculating LyE.
    Matched MeSH terms: Data Interpretation, Statistical
  17. Sudarshan VK, Acharya UR, Oh SL, Adam M, Tan JH, Chua CK, et al.
    Comput Biol Med, 2017 04 01;83:48-58.
    PMID: 28231511 DOI: 10.1016/j.compbiomed.2017.01.019
    Identification of alarming features in the electrocardiogram (ECG) signal is extremely significant for the prediction of congestive heart failure (CHF). ECG signal analysis carried out using computer-aided techniques can speed up the diagnosis process and aid in the proper management of CHF patients. Therefore, in this work, dual tree complex wavelets transform (DTCWT)-based methodology is proposed for an automated identification of ECG signals exhibiting CHF from normal. In the experiment, we have performed a DTCWT on ECG segments of 2s duration up to six levels to obtain the coefficients. From these DTCWT coefficients, statistical features are extracted and ranked using Bhattacharyya, entropy, minimum redundancy maximum relevance (mRMR), receiver-operating characteristics (ROC), Wilcoxon, t-test and reliefF methods. Ranked features are subjected to k-nearest neighbor (KNN) and decision tree (DT) classifiers for automated differentiation of CHF and normal ECG signals. We have achieved 99.86% accuracy, 99.78% sensitivity and 99.94% specificity in the identification of CHF affected ECG signals using 45 features. The proposed method is able to detect CHF patients accurately using only 2s of ECG signal length and hence providing sufficient time for the clinicians to further investigate on the severity of CHF and treatments.
    Matched MeSH terms: Data Interpretation, Statistical
  18. Awang, M.S., Abdul Razak, A.H., Che Ahmad, A., Mohd Rus, R.
    MyJurnal
    Introduction: The purpose of this study is to identify the incidence of clavicle fractures in newborn
    associated with fetal, maternal and process of deliveries in Kuantan General Hospital from June 2012 until
    January 2014. This study is to determine epidemiological data of clavicle fractures, maternal and baby risk
    factors associated with clavicle fractures of newborn and its’ outcome.

    Methods: This is a prospective
    study. 13 patients were identified to fulfill the inclusion criteria of the study. The data of
    sociodemographic, associated fetal and maternal risk factors and the outcomes were recorded using
    proforma. The statistical data analysis was done using SPSS 12.0.

    Results: Out of 20,257 live births at our
    centre during the study period, 13 infants were diagnosed to have clavicle fractures, giving an incidence of
    0.64 per 1000 live births. There were 5 (38.5%) left, 7 (53.8%) right and one (7.7%) bilateral fracture. All
    fractures located at the mid shaft of the clavicle and none have associated brachial plexus injuries. All
    infants were delivered through vaginal delivery (61.5%); five through assisted delivery (instrumental); 2
    (15.4%) forcep and 3 (23.1%) vacuum. Two of the babies developed shoulder dystocia. The average birth
    weight was 3371 grams (SD 0.269) and mean gestational age was 38.7 weeks (SD 1.16). Five of the mothers
    (38.5%) were primigravida and eight (61.5%) were multigravida in which,7 (53.8%)were healthy without
    other co-morbidty, 5 (38.5%) having gestational diabetis and one (7.7%) hypertension. The average maternal
    weight was 62.0 kg and height 1.58 metres with average BMI of 24.16 (3.29SD). All eventually had a
    complete recovery at 6 weeks with clinical and radiological evident of fracture union.

    Conclusions: In
    conclusion, all patients with clavicle fractures were found following vaginal delivery. There were no
    associations between neonatal clavicle fractures with maternal or baby risk factors. All fractures healed
    without any complications.
    Matched MeSH terms: Data Interpretation, Statistical
  19. Noh, C.H.C., Azmin, N.F.M., Amid, A., Asnawi, A.L.
    MyJurnal
    Bioactive compounds are one of the natural products used especially for medicinal, pharmaceutical and food application. Increasing research performed on the extraction, isolation and identification of bioactive compounds, however non to date has explored on the identification of flavonoids classes. Therefore, this study was focused on the development of algorithm for rapid identification of flavonoids classes which are flavanone, flavone and flavonol and also their derivatives. Fourier Transform Infrared (FTIR) spectroscopy coupled with multivariate statistical data analysis, which is Principal Component Analysis (PCA) was utilized. The results exhibited that few significant wavenumber range provides the identification and characterization of the flavonoids classes based on PCA algorithm. The study concluded that FTIR coupled with PCA analysis can be used as a molecular fingerprint for rapid identification of flavonoids.
    Matched MeSH terms: Data Interpretation, Statistical
  20. Khairani AZ, Ahmad NS, Khairani MZ
    J Appl Meas, 2017;18(4):449-458.
    PMID: 29252212
    Adolescences is an important transitional phase in human development where they experience physiological as well as psychological changes. Nevertheless, these changes are often understood by teachers, parents, and even the adolescents themselves. Thus, conflicts exist and adolescents are affected from the conflict physically and emotionally. An important state of emotions that result from this conflict is anger. This article describes the development and validation of the 34-item Adolescent Anger Inventory (AAI) to measure types of anger among Malaysian adolescents. A sample of 2,834 adolescents in secondary school who provide responses that were analyzed using Rasch model measurement framework. The 4 response category worked satisfactorily for the scale developed. A total of 11 items did not fit to the model's expectations, and thus dropped from the final scale. The scale also demonstrated satisfactory reliability and separation evidence. Also, items in the AAI depicted no evidence of DIF between 14- and 16-year-old adolescents. Nevertheless, the AAI did not have sufficient items to target adolescents with a high level of physical aggressive anger.
    Matched MeSH terms: Data Interpretation, Statistical*
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