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The case fatality rate and the risk of complications due to pertussis is very high in infants. Asia has the second highest childhood pertussis burden. The study aimed to assess the prevalence, clinical complications, and mortality rates of pertussis disease requiring hospitalization among young infants in Malaysia.The study was a one-year, hospital-based, multi-site surveillance of infants less than six months of age with symptoms consistent with pertussis and a cross-sectional analysis of their mothers for recent pertussis infection. Information was obtained from medical records and interviews with the parents. Pertussis diagnosis was confirmed for all infants through serum anti-PT titration test or PCR test.441 possible cases of pertussis were included in this study. Of these, 12.7 % had laboratory confirmation of pertussis. Infants with confirmed pertussis had significantly higher rates of cyanosis (37.5 % vs 8.6 %; p 0.0001) and apnea (12.5 % vs 3.9 %; p = 0.027) than test-negative infants. Most infants from both groups were in recovery/recovered at discharge. Those with confirmed pertussis had higher case fatality rate than test-negative cases (5.4 % vs 1.0 %; p = 0.094), but the difference did not reach significance. The majority of confirmed pertussis cases (89.3 %) occurred in infants too young to be fully vaccinated or under-vaccinated for their age. Both test-negative and confirmed pertussis resulted in work-day losses and incurred costs for both parents.A high pertussis disease burden persists in infants less than six months of age, especially among those un- and under-vaccinated. Maternal and complete, on-time infant vaccination is important to reduce disease burden.

Children with epilepsy (CWE) are at risk of vitamin D deficiency. Single nucleotide polymorphisms (SNPs) affecting the vitamin D pathway are potentially important risk factors for serum 25-hydroxyvitamin D [25(OH)D] concentration. The aims of our study were to evaluate the association of vitamin d-related SNPs to serum 25(OH)D concentrations in Malaysian CWE.Cross-sectional study of Malaysian ambulant CWE on antiseizure medication for1 year. Sixteen SNPs in 8 genes (GC, VDR, CYP2R1, CYP24A1, CYP27B1, CYP27A1, CYP3A4, NADSYN1/DHCR7) were genotyped. Linear and logistic regression models and co-variates adjusted analyses were used. SNPs with significant associations were further analysed in a group of ethnically-matched healthy Malaysian children.239 CWE were recruited (52.7% Malay, 24.3% Chinese and 23.0% Indian) with mean serum 25(OH)D of 58.8 nmol/L (SD 25.7). Prevalence of vitamin D deficiency (≤37.5 nmol/L) was 23.0%. Minor allele of GC-rs4588-A was associated with lower serum 25(OH)D in the meta-analysis of both CWE (β -8.11, P = 0.002) and Malaysian healthy children (β -5.08, P0.001), while VDR-rs7975232-A was significantly associated with reduced odds of vitamin D deficiency in Malay subgroup of CWE (OR: 0.16; 95% CI: 0.06-0.49; P = 0.001) and this association was not found in the healthy children group.Our results suggest that GC-rs4588 is associated with lower serum 25(OH)D concentration in both Malaysian CWE and healthy children, while VDR-rs7975232A is associated with lower risk of vitamin D deficiency in Malaysian CWE of Malay ethnicity. Our findings may assist in the genetic risk stratification of low vitamin D status among CWE.

Adolescents involved in bullying can be at risk of developing behavioural problems, physical health problems and suicidal ideation. In view of this, a quantitative research design using a cross-sectional study was conducted to determine the prevalence of bullying and associated individual, peer, family and school factors. The study involved 4469 Malaysian public-school students who made up the response rate of 89.4%. The students were selected using a randomized multilevel sampling method. The study found that 79.1% of student respondents were involved in bullying as perpetrators (14.4%), victims (16.3%), or bully–victims (48.4%). In a multivariate analysis, the individual domain showed a significant association between students’ bullying involvement and age (OR = 1.38, 95% CI 1.12–1.70), gender (OR = 1.73, 95% CI 1.47–0.91), ethnicity (OR = 0.66, 95% CI 0.47–0.91), duration of time spent on social media during the weekends (OR = 1.43, 95% CI 1.09–1.87) and psychological distress level (OR = 2.55, 95% CI 1.94–3.34). In the peer domain, the significantly associated factors were the number of peers (OR = 0.69, 95% CI 0.56–0.86) and frequency of quarrels or fights with peers (OR = 2.12, 95% CI 1.24–3.26). Among the items in the school domain, the significantly associated factors were students being mischievous in classrooms (OR = 1.52, 95% CI 1.06–2.06), student’s affection towards their teachers (OR = 1.53, 95% CI 1.06–2.20), frequency of appraisal from teachers (OR = 1.49, 95% CI 1.16–1.94), frequency of friends being helpful in classrooms (OR = 1.92, 95% CI 1.09–3.38) and frequency of deliberately skipping class (OR = 2.91, 95% CI 2.90–1.72). As a conclusion, the study revealed high levels and widespread bullying involvement among students in Malaysia. As such, timely bullying preventions and interventions are essential, especially in terms of enhancing their mental health capacity, which substantially influences the reduction in the prevalence rates of bullying involvement among students in Malaysia.

Automatic infant cry classification is one of the crucial studies under biomedical engineering scope, adopting the medical and engineering techniques for the classification of diverse physical and physiological conditions of the infants by their cry signal. Subsequently, plentiful studies have executed and issued, broadened the potential application of cry analyses. As yet, there is no ultimate literature documentation composed by performing a longitudinal study, emphasizing on the boast trend of automatic classification of infant cry. A review of literature is performed using the key words “infant cry” AND “automatic classification” from different online resources, regardless of the year of published in order to produce a comprehensive review. Review papers were excluded. Results of search reported about more than 300 papers and after some exclusion 101 papers were selected. This review endeavors at reporting an overview about recent advances and developments in the field of automated infant cry classification, specifically focusing on the developed infant cry databases and approaches involved in signal processing and recognition phases. Eventually, this article was accomplished with some possible implications which may lead for development of an advanced automated cry based classification systems for real time applications.

Background and objective Infant cry signal carries several levels of information about the reason for crying (hunger, pain, sleepiness and discomfort) or the pathological status (asphyxia, deaf, jaundice, premature condition and autism, etc.) of an infant and therefore suited for early diagnosis. In this work, combination of wavelet packet based features and Improved Binary Dragonfly Optimization based feature selection method was proposed to classify the different types of infant cry signals. Methods Cry signals from 2 different databases were utilized. First database contains 507 cry samples of normal (N), 340 cry samples of asphyxia (A), 879 cry samples of deaf (D), 350 cry samples of hungry (H) and 192 cry samples of pain (P). Second database contains 513 cry samples of jaundice (J), 531 samples of premature (Prem) and 45 samples of normal (N). Wavelet packet transform based energy and non-linear entropies (496 features), Linear Predictive Coding (LPC) based cepstral features (56 features), Mel-frequency Cepstral Coefficients (MFCCs) were extracted (16 features). The combined feature set consists of 568 features. To overcome the curse of dimensionality issue, improved binary dragonfly optimization algorithm (IBDFO) was proposed to select the most salient attributes or features. Finally, Extreme Learning Machine (ELM) kernel classifier was used to classify the different types of infant cry signals using all the features and highly informative features as well. Results Several experiments of two-class and multi-class classification of cry signals were conducted. In binary or two-class experiments, maximum accuracy of 90.18% for H Vs P, 100% for A Vs N, 100% for D Vs N and 97.61% J Vs Prem was achieved using the features selected (only 204 features out of 568) by IBDFO. For the classification of multiple cry signals (multi-class problem), the selected features could differentiate between three classes (N, A & D) with the accuracy of 100% and seven classes with the accuracy of 97.62%. Conclusion The experimental results indicated that the proposed combination of feature extraction and selection method offers suitable classification accuracy and may be employed to detect the subtle changes in the cry signals.

This review intends to integrate the relevant information that is related to pesticide applications in food commodities and will cover three main sections. The first section encompasses some of the guidelines that have been implemented on management of pesticide application worldwide, such as the establishment of a value called Maximum Residue Level (MRL) through the application of Good Agricultural Practices (GAPs) into daily agricultural activities. A brief overview of the methods adopted in quantification of these trace residues in different food samples will also be covered. Briefly, pesticide analysis is usually performed in two stages: sample preparation and analytical instrumentation. Some of the preparation methods such as QuEChERs still remain as the technique of choice for most of the analytical scientists. In terms of the instrumentation such as the gas chromatography-mass spectrophotometry (GC-MS) and high performance-liquid chromatography (HPLC), these are still widely used, in spite of new inventions that are more sustainable and efficient such as the capillary electrophoresis (CE). Finally, the third section emphasizes on how pesticides can affect our health significantly whereby different types of pesticides result in different adverse health implications, despite its application benefits in agriculture in controlling pests. To date, there are limited reviews on pesticide usage in many agricultural-based nations; for the purpose of this review, Malaysia is selected to better illustrate pesticide regulations and implementation of policies. Finally, the review aims to provide an insight on how implementation of GAP and food safety assurance are inter-related and with this established correlation, to identify further measures for improvement to enable reinforcement of optimised agricultural practices specifically in these countries.

Wavelet theory is emerging as one of the prevalent tool in signal and image processing applications. However, the most suitable mother wavelet for these applications is still a relative question mark amongst researchers. Selection of best mother wavelet through parameterization leads to better findings for the analysis in comparison to random selection. The objective of this article is to compare the performance of the existing members of mother wavelets and to select the most suitable mother wavelet for accurate infant cry classification. Optimal wavelet is found using three different criteria namely the degree of similarity of mother wavelets, regularity of mother wavelets and accuracy of correct recognition during classification processes. Recorded normal and pathological infant cry signals are decomposed into five levels using wavelet packet transform. Energy and entropy features are extracted at different sub bands of cry signals and their effectiveness are tested with four supervised neural network architectures. Findings of this study expound that, the Finite impulse response based approximation of Meyer is the best wavelet candidate for accurate infant cry classification analysis.

SummaryObjective Long-term use of antiepileptic drugs (AEDs) is a significant risk factor for vitamin D deficiency in children with epilepsy. The aims of our study were to evaluate the prevalence and risk factors for vitamin D deficiency among Malaysian children with epilepsy. Methods Cross-sectional study of ambulant children with epilepsy on long-term AEDs for >1 year seen in three tertiary hospitals in Malaysia from April 2014 to April 2015. Detailed assessment of pubertal status, skin pigmentation, sunshine exposure behavior, physical activity, dietary vitamin D and calcium intake, anthropometric measurements and bone health blood tests (vitamin D, alkaline phosphatase, calcium, phosphate, and parathyroid hormone levels) were obtained on all patients. Vitamin D deficiency was defined as 25-hydroxy vitamin D [25(OH)D] levels ≤35 nmol/L and insufficiency as 25(OH)D levels of 36–50 nmol/L. Results A total of 244 children (146 male) participated in the study. Ages ranged between 3.7 and 18.8 years (mean 12.3 years). 25(OH)D levels ranged between 7.5 and 140.9 nmol/L (mean 53.9 nmol/L). Vitamin D deficiency was identified in 55 patients (22.5%), and a further 48 (19.7%) had vitamin D insufficiency. Multivariate logistic regression analysis identified polytherapy >1 AED (odds ratio [OR] 2.16, 95% confidence interval [CI] 1.07–4.36), age >12 years (OR 4.16, 95% CI 1.13–15.30), Indian ethnicity (OR 6.97, 95% CI 2.48–19.55), sun exposure time 30–60 min/day (OR 2.44, 95% CI 1.05–5.67), sun exposure time

A new method has been implemented based on Dual Tree Complex Wavelet Packet Transform (DT-CWPT) feature extraction for infant cry signal classification. The infant cry signals were decomposed into five levels using DT-CWPT. A total of 124 energy features and 124 Shannon entropy features were extracted from each sub-band. Two classifiers Extreme Learning Machine (ELM) and Support Vector Machine (SVM) were used to classify the infant cry signal based on the extracted features. Three category of two-class experiments were conducted in this paper (asphyxia versus normal, hunger versus pain, and deaf versus normal). The results demonstrate that the DT-CWPT feature extraction and classification methods give a high accuracy of 97.87%, 87.26%, 100.00% for asphyxia versus normal, hunger versus pain, and deaf versus normal respectively.