Perfectionism or a tendency to aim for an unrealistic standard can impair happiness. However, the potential mechanisms of perfectionism to explain the association between trait emotional intelligence (EI) and happiness are still understudied. This study explores the mediating role of perfectionism in the relationship between trait emotional intelligence (EI) and happiness among young adults. A cross-sectional sample of 259 young adults aged between 18 to 35 years old was recruited. All analyses were conducted using SPSS and AMOS Structural Equation Modeling. High trait EI was linked to low perfectionism and high happiness levels. Furthermore, perfectionism mediated the relationship between trait EI and happiness. Although high trait EI lowered maladaptive perfectionism, the negative impact of maladaptive perfectionism remained and subsequently led to decreasing happiness levels of young adults. This study offers an enhanced understanding of the role of perfectionism in explaining the happiness state of young adults. Moreover, it provides practical implications for using trait EI and managing perfectionism tendency to manage the happiness and wellbeing of the young adult population.
Despite several guidelines published by the World Health Organization (WHO) and national authorities, there is a general increase in the number of healthcare workers (HCWs) contracting tuberculosis. This review sought to evaluate the compliance of the HCWs toward tuberculosis preventive measures (TPMs) in their workplace. Both electronic databases and manual searches were conducted to retrieve articles regarding the compliance of HCWs in the workplace published from 2010 onwards. Independent reviewers extracted, reviewed, and analyzed the data using the mixed methods appraisal tool (MMAT) 2018, comprising 15 studies, 1572 HCWs, and 249 health facilities. The results showed there was low compliance toward TPMs in the workplace among HCWs and health facilities from mostly high-burden tuberculosis countries. The failure to comply with control measures against tuberculosis was mainly reported at administrative levels, followed by engineering and personnel protective control measures. In addition, low managerial support and negative attitudes of the HCWs influenced the compliance. Further studies are needed to elucidate how to improve the compliance of HCWs toward the preventive measures against tuberculosis in order to reduce the disease burden among HCWs worldwide.
MeSH terms: Health Facilities*; Health Personnel; Humans; World Health Organization; Workplace
The current academic landscape has overwhelmed faculties and with demands to adopt tech-savvy teaching modes and accelerate scholarly works, administrative duties, and outreach programs. Such demands have deteriorated the health-related quality of life (HRQoL) among university employees. This study aimed to determine the factors associated with HRQoL among university employees in a Malaysian public university. This cross-sectional study was conducted among 397 employees from the Universiti Kebangsaan Malaysia (UKM) between April and June 2019. A self-administered questionnaire that consisted of socio-demographic items, risky health behaviors, health-related information, and validated scales for measuring employees' physical inactivity, psychological states, and HRQoL was utilized. Descriptive and inferential statistics were calculated using SPSS version 23.0. Hierarchical multiple linear regression models were yielded to determine the factors associated with different domains of HRQoL. Mediation analysis was conducted using PROCESS MACRO (Model 4). Statistical significance was set to p < 0.05. Physical HRQoL scored the highest, while environmental HRQoL had the lowest score among the employees. Physical HRQoL was influenced by age, service duration, comorbid conditions, BMI, chronic diseases, and anxiety. Factors associated with psychological HRQoL were age, service duration, depression, and stress. Age, service duration, and chronic diseases affected employees' social relationship HRQoL, while environmental HRQoL was associated with age, occupation type, chronic diseases, and depression. Socio-demographics, risky health behaviors, health profiles, and psychological attributes were significantly associated with employees' HRQoL. Age was the only positively correlated factor across all HRQoL domains, while other factors deteriorated employees' HRQoL.
MeSH terms: Anxiety; Cross-Sectional Studies; Humans; Quality of Life*; Surveys and Questionnaires; Universities*
Breastfeeding is the best form of feeding for premature infants. However, mothers with premature delivery are frequently reported to be depressed, and this has been especially the case during the Coronavirus Disease-2019 (COVID-19) pandemic. We aimed to measure the level of breastfeeding attitude and its association with postpartum depression among mothers with premature infants in the Neonatal Intensive Care Unit (NICU) during the COVID-19 pandemic. A total of 248 mothers with a premature infant were observed in this cross-sectional study from the chosen NICUs of government hospitals in Selangor, Malaysia. The Iowa Infant Feeding Attitude Score (IIFAS) and the Edinburgh Postnatal Depression Scale, along with sociodemographic questionnaires, were used to obtain information on the mothers' attitudes towards breastfeeding and the risk of postpartum depression. A higher percentage of mothers had a positive attitude towards breastfeeding (64.9%), with a mean IIFAS score of 66.30 ± 6.92. Meanwhile, about 27% of mothers with premature infants were reported to have high risk of depressive symptoms. Mothers with a high risk of depression were less likely to have a positive attitude towards breastfeeding (OR 0.37, 95% CI 0.199, 0.675) as compared to mothers with a low risk of depression (p < 0.01). We found that there is an association between the risk of depression and the attitude towards breastfeeding. Early identification of maternal mental health problems should be addressed to ensure the willingness of mothers to continue breastfeeding.
MeSH terms: Breast Feeding; Cross-Sectional Studies; Female; Humans; Infant; Infant, Newborn; Infant, Premature; Health Knowledge, Attitudes, Practice; Mothers; Pandemics
The Coronavirus disease 2019 (COVID-19) global pandemic since its onset has had a dramatic and often devastating impact, both physical and psychological, on all healthcare workers. This study aimed to assess the impact of psychological distress that COVID-19 has on nurses, as well as the coping strategies that they employed. This is a cross-sectional national online survey. A total of 859 nurses actively involved in caring for patients with suspected or confirmed COVID-19 in Malaysia participated in the study. More than three-quarters of the nurses experienced stress (77.2%). A total of 88.7% and 7.2% of nurses revealed a moderate and high stress level, respectively. Approximately one in eight (12.1%) nurses reported feeling depressed. Nurses working in the outpatient departments reported significantly higher stress levels than nurses working in inpatient care departments. Nurses having chronic health problems reported significantly higher depression levels than nurses with no chronic health problem. Highly stressed or depressed nurses tend to adopt avoidance coping strategies while religion and emotional support were used regardless of the stress or depression levels experienced. The findings of the study provide insight into the mental health and coping strategies of nurses actively involved in caring for patients with suspected or confirmed COVID-19 in Malaysia. This would be of tremendous help to nursing administrators in implementing mental health services for nurses during and following the COVID-19 global pandemic.
Social media sites, dubbed patient online reviews (POR), have been proposed as new methods for assessing patient satisfaction and monitoring quality of care. However, the unstructured nature of POR data derived from social media creates a number of challenges. The objectives of this research were to identify service quality (SERVQUAL) dimensions automatically from hospital Facebook reviews using a machine learning classifier, and to examine their associations with patient dissatisfaction. From January 2017 to December 2019, empirical research was conducted in which POR were gathered from the official Facebook page of Malaysian public hospitals. To find SERVQUAL dimensions in POR, a machine learning topic classification utilising supervised learning was developed, and this study's objective was established using logistic regression analysis. It was discovered that 73.5% of patients were satisfied with the public hospital service, whereas 26.5% were dissatisfied. SERVQUAL dimensions identified were 13.2% reviews of tangible, 68.9% of reliability, 6.8% of responsiveness, 19.5% of assurance, and 64.3% of empathy. After controlling for hospital variables, all SERVQUAL dimensions except tangible and assurance were shown to be significantly related with patient dissatisfaction (reliability, p < 0.001; responsiveness, p = 0.016; and empathy, p < 0.001). Rural hospitals had a higher probability of patient dissatisfaction (p < 0.001). Therefore, POR, assisted by machine learning technologies, provided a pragmatic and feasible way for capturing patient perceptions of care quality and supplementing conventional patient satisfaction surveys. The findings offer critical information that will assist healthcare authorities in capitalising on POR by monitoring and evaluating the quality of services in real time.
A growing portfolio of research has been reported on the use of machine learning-based architectures and models in the domain of healthcare. The development of data-driven applications and services for the diagnosis and classification of key illness conditions is challenging owing to issues of low volume, low-quality contextual data for the training, and validation of algorithms, which, in turn, compromises the accuracy of the resultant models. Here, a fusion machine learning approach is presented reporting an improvement in the accuracy of the identification of diabetes and the prediction of the onset of critical events for patients with diabetes (PwD). Globally, the cost of treating diabetes, a prevalent chronic illness condition characterized by high levels of sugar in the bloodstream over long periods, is placing severe demands on health providers and the proposed solution has the potential to support an increase in the rates of survival of PwD through informing on the optimum treatment on an individual patient basis. At the core of the proposed architecture is a fusion of machine learning classifiers (Support Vector Machine and Artificial Neural Network). Results indicate a classification accuracy of 94.67%, exceeding the performance of reported machine learning models for diabetes by ~1.8% over the best reported to date.
Automated machine learning (AutoML) has been recognized as a powerful tool to build a system that automates the design and optimizes the model selection machine learning (ML) pipelines. In this study, we present a tree-based pipeline optimization tool (TPOT) as a method for determining ML models with significant performance and less complex breast cancer diagnostic pipelines. Some features of pre-processors and ML models are defined as expression trees and optimal gene programming (GP) pipelines, a stochastic search system. Features of radiomics have been presented as a guide for the ML pipeline selection from the breast cancer data set based on TPOT. Breast cancer data were used in a comparative analysis of the TPOT-generated ML pipelines with the selected ML classifiers, optimized by a grid search approach. The principal component analysis (PCA) random forest (RF) classification was proven to be the most reliable pipeline with the lowest complexity. The TPOT model selection technique exceeded the performance of grid search (GS) optimization. The RF classifier showed an outstanding outcome amongst the models in combination with only two pre-processors, with a precision of 0.83. The grid search optimized for support vector machine (SVM) classifiers generated a difference of 12% in comparison, while the other two classifiers, naïve Bayes (NB) and artificial neural network-multilayer perceptron (ANN-MLP), generated a difference of almost 39%. The method's performance was based on sensitivity, specificity, accuracy, precision, and receiver operating curve (ROC) analysis.
The induction of highly conserved heat shock protein 70 (HSP70) is often related to a cellular response due to harmful stress or adverse life conditions. In this study, we determined the expression of Hsp70 genes in the Antarctic yeast, Glaciozyma antarctica, under different several thermal treatments for several exposure periods. The main aims of the present study were (1) to determine if stress-induced Hsp70 could be used to monitor the exposure of the yeast species G. antarctica to various types of thermal stress; (2) to analyze the structures of the G. antarctica HSP70 proteins using comparative modeling; and (3) to evaluate the relationship between the function and structure of HSP70 in G. antarctica. In this study, we managed to amplify and clone 2 Hsp70 genes from G. antarctica named GaHsp70-1 and GaHsp70-2. The cells of G. antarctica expressed significantly inducible Hsp70 genes after the heat and cold shock treatments. Interestingly, GaHsp70-1 showed 2-6-fold higher expression than GaHsp70-2 after the heat and cold exposure. ATP hydrolysis analysis on both G. antarctica HSP70s proved that these psychrophilic chaperones can perform activities in a wide range of temperatures, such as at 37, 25, 15, and 4 °C. The 3D structures of both HSP70s revealed several interesting findings, such as the substitution of a β-sheet to loop in the N-terminal ATPase binding domain and some modest residue substitutions, which gave the proteins the flexibility to function at low temperatures and retain their functional activity at ambient temperatures. In conclusion, both analyzed HSP70s played important roles in the physiological adaptation of G. antarctica.
To date, various studies have analysed the effects of reinforced ceramic on the properties of AA6061 recycled aluminum alloy chips, such as the tensile strength and fractography. However, a comprehensive analysis of the properties of hybrid composite with the addition of nano-silica oxide and nano-copper oxide reinforcements is still very limited. Therefore, this study aimed to optimise the factors comprising the preheating temperature (PHT), preheating time (PHti), and volume fraction (VF) of reinforcements then determine their impacts on the physical and mechanical properties of the recycled solid-state extruded composite aluminum chips. A total of 45 specimens were fabricated through the hot extrusion technique. The response surface methodology (RSM) was employed to study the optimisation at a PHT range of 450-550 °C with PHti of 1-3 h and VF of 1-3 vol% for both reinforcements (SiO2 and CuO). Moreover, a random forest (RF) model was developed to optimize the model based on a metaheuristic method to improve the model performance. Based on the experimental results the RF model achieve better results than response surface methodology (RSM). The functional quadratic regression is curvature and the tested variable shows stable close data of the mean 0 and α2. Based on the Pareto analysis, the PHT and VF were key variables that significantly affected the UTS, microhardness, and density of the product. The maximum properties were achieved at an optimum PHT, PHti, and VF of 541 °C, 2.25 h, 1 vol% SiO2 and 2.13 vol% CuO, respectively. Furthermore, the morphological results of the tensile fractured surface revealed the homogenous distribution of nano-reinforced CuO and SiO2 particles in the specimens' structure.
Alloplasts are synthetic, inorganic, biocompatible bone substitutes that function as defect fillers to repair skeletal defects. The acceptance of these substitutes by host tissues is determined by the pore diameter and the porosity and inter-connectivity. This narrative review appraises recent developments, characterization, and biological performance of different synthetic materials for bone, periodontal, and dental tissue regeneration. They include calcium phosphate cements and their variants β-tricalcium phosphate (β-TCP) ceramics and biphasic calcium phosphates (hydroxyapatite (HA) and β-TCP ceramics), calcium sulfate, bioactive glasses and polymer-based bone substitutes which include variants of polycaprolactone. In summary, the search for synthetic bone substitutes remains elusive with calcium compounds providing the best synthetic substitute. The combination of calcium sulphate and β-TCP provides improved handling of the materials, dispensing with the need for a traditional membrane in guided bone regeneration. Evidence is supportive of improved angiogenesis at the recipient sites. One such product, (EthOss® Regeneration, Silesden, UK) has won numerous awards internationally as a commercial success. Bioglasses and polymers, which have been used as medical devices, are still in the experimental stage for dental application. Polycaprolactone-TCP, one of the products in this category is currently undergoing further randomized clinical trials as a 3D socket preservation filler. These aforementioned products may have vast potential for substituting human/animal-based bone grafts.
The conventional open ponding system employed for palm oil mill agro-effluent (POME) treatment fails to lower the levels of organic pollutants to the mandatory standard discharge limits. In this work, carbon doped black TiO2 (CB-TiO2) and carbon-nitrogen co-doped black TiO2 (CNB-TiO2) were synthesized via glycerol assisted sol-gel techniques and employed for the remediation of treated palm oil mill effluent (TPOME). Both the samples were anatase phase, with a crystallite size of 11.09-22.18 nm, lower bandgap of 2.06-2.63 eV, superior visible light absorption ability, and a high surface area of 239.99-347.26 m2/g. The performance of CNB-TiO2 was higher (51.48%) compared to only (45.72%) CB-TiO2. Thus, the CNB-TiO2 is employed in sonophotocatalytic reactions. Sonophotocatalytic process based on CNB-TiO2, assisted by hydrogen peroxide (H2O2), and operated at an ultrasonication (US) frequency of 30 kHz and 40 W power under visible light irradiation proved to be the most efficient for chemical oxygen demand (COD) removal. More than 90% of COD was removed within 60 min of sonophotocatalytic reaction, producing the effluent with the COD concentration well below the stipulated permissible limit of 50 mg/L. The electrical energy required per order of magnitude was estimated to be only 177.59 kWh/m3, indicating extreme viability of the proposed process for the remediation of TPOME.
Difficulty in debris removal and the transport of fresh dielectric into discharge gap hinders the process performance of electrical discharge machining (EDM) process. Therefore, in this work, an economical low frequency vibration platform was developed to improve the performance of EDM through vibration assistance. The developed vibratory platform functions on an eccentric weight principle and generates a low frequency vibration in the range of 0-100 Hz. The performance of EDM was evaluated in terms of the average surface roughness (Ra), material removal rate (MRR), and tool wear rate (TWR) whilst varying the input machining parameters viz. the pulse-on-time (Ton), peak current (Ip), vibration frequency (VF), and tool rotational speed (TRS). The peak current was found to be the most significant parameter and contributed by 78.16%, 65.86%, and 59.52% to the Ra, MRR, and TWR, respectively. The low frequency work piece vibration contributed to an enhanced surface finish owing to an improved flushing at the discharge gap and debris removal. However, VF range below 100 Hz was not found to be suitable for the satisfactory improvement of the MRR and reduction of the TWR in an electrical discharge drilling operation at selected machining conditions.
The development of self-compacting alkali-activated concrete (SCAAC) has become a hot topic in the scientific community; however, most of the existing literature focuses on the utilization of fly ash (FA), ground blast furnace slag (GBFS), silica fume (SF), and rice husk ash (RHA) as the binder. In this study, both the experimental and theoretical assessments using response surface methodology (RSM) were taken into account to optimize and predict the optimal content of ceramic waste powder (CWP) in GBFS-based self-compacting alkali-activated concrete, thus promoting the utilization of ceramic waste in construction engineering. Based on the suggested design array from the RSM model, experimental tests were first carried out to determine the optimum CWP content to achieve reasonable compressive, tensile, and flexural strengths in the SCAAC when exposed to ambient conditions, as well as to minimize its strength loss, weight loss, and UPVL upon exposure to acid attack. Based on the results, the optimum content of CWP that satisfied both the strength and durability aspects was 31%. In particular, a reasonable reduction in the compressive strength of 16% was recorded compared to that of the control specimen (without ceramic). Meanwhile, the compressive strength loss of SCAAC when exposed to acid attack minimized to 59.17%, which was lower than that of the control specimen (74.2%). Furthermore, the developed RSM models were found to be reliable and accurate, with minimum errors (RMSE < 1.337). In addition, a strong correlation (R > 0.99, R2 < 0.99, adj. R2 < 0.98) was observed between the predicted and actual data. Moreover, the significance of the models was also proven via ANOVA, in which p-values of less than 0.001 and high F-values were recorded for all equations.
Norfloxacin (NOR), widely employed as an anti-bacterial drug, has poor oral bioavailability. Nano based drug delivery systems are widely used to overcome the existing oral bioavailability challenges. Lipid-Polymer Hybrid Nanoparticles (LPHNs) exhibit the distinctive advantages of both polymeric and liposomes nanoparticles, while excluding some of their disadvantages. In the current study, NOR loaded LPHNs were prepared, and were solid amorphous in nature, followed by in vitro and in vivo evaluation. The optimized process conditions resulted in LPHNs with the acceptable particle size 121.27 nm, Polydispersity Index (PDI) of 0.214 and zeta potential of -32 mv. The addition of a helper lipid, oleic acid, and polymers, ethyl cellulose, substantially increased the encapsulation efficiency (EE%) (65% to 97%). In vitro study showed a sustained drug release profile (75% within 12 h) for NOR LPHNs. The optimized NOR LPHNs showed a significant increase (p < 0.05) in bioavailability compared to the commercial product. From the acute toxicity study, the LD50 value was found to be greater than 1600 mg/kg. The molecular modelling studies substantiated the experimental results with the best combination of polymers and surfactants that produced highly stable LPHNs. Therefore, LPHNs proved to be a promising system for the delivery of NOR, as well as for other antibiotics and hydrophobic drugs.
Wound dressings have become a crucial treatment for wound healing due to their convenience, low cost, and prolonged wound management. As cutting-edge biomaterials, marine polysaccharides are divided from most marine organisms. It possesses various bioactivities, which allowing them to be processed into various forms of wound dressings. Therefore, a comprehensive understanding of the application of marine polysaccharides in wound dressings is particularly important for the studies of wound therapy. In this review, we first introduce the wound healing process and describe the characteristics of modern commonly used dressings. Then, the properties of various marine polysaccharides and their application in wound dressing development are outlined. Finally, strategies for developing and enhancing marine polysaccharide wound dressings are described, and an outlook of these dressings is given. The diverse bioactivities of marine polysaccharides including antibacterial, anti-inflammatory, haemostatic properties, etc., providing excellent wound management and accelerate wound healing. Meanwhile, these biomaterials have higher biocompatibility and biodegradability compared to synthetic ones. On the other hand, marine polysaccharides can be combined with copolymers and active substances to prepare various forms of dressings. Among them, emerging types of dressings such as nanofibers, smart hydrogels and injectable hydrogels are at the research frontier of their development. Therefore, marine polysaccharides are essential materials in wound dressings fabrication and have a promising future.
Dietary pattern (DP) and its relationship with disease biomarkers have received recognition in nutritional epidemiology investigations. However, DP relationships with adipokines (i.e., adiponectin and leptin) among breast cancer survivors remain unclear. Therefore, we assessed relationships between DP and high-molecular weight (HMW) adiponectin and leptin concentration among breast cancer survivors. This cross-sectional study involved 128 breast cancer survivors who attended the oncology outpatient clinic at two main government hospitals in the East Coast of Peninsular Malaysia. The serum concentration of HMW adiponectin and leptin were measured using enzyme-linked immunosorbent assay (ELISA) kits. A reduced rank regression method was used to analyze DP. Relationships between DP with HMW adiponectin and leptin were examined using regression models. The findings show that with every 1-unit increase in the 'energy-dense, high-SFA, low-fiber' DP z-score, there was a reduction by 0.41 μg/mL in HMW adiponectin which was independent of age, BMI, education level, occupation status, cancer stage, and duration since diagnosis. A similar relationship with leptin concentration was not observed. In conclusion, the 'energy-dense, high-saturated fat and low-fiber' DP, which is characterized by high intake levels of sugar-sweetened drinks and fat-based spreads but low intake of fruits and vegetables, is an unhealthy dietary pattern and unfavorable for HMW adiponectin concentration, but not for leptin. These findings could serve as a basis in developing specific preventive strategies that are tailored to the growing population of breast cancer survivors.
Rheumatoid arthritis (RA) is a progressive inflammatory disorder characterized by swollen joints, discomfort, tightness, bone degeneration and frailty. Genetic, agamogenetic and sex-specific variables, Prevotella, diet, oral health and gut microbiota imbalance are all likely causes of the onset or development of RA, perhaps the specific pathways remain unknown. Lactobacillus spp. probiotics are often utilized as relief or dietary supplements to treat bowel diseases, build a strong immune system and sustain the immune system. At present, the action mechanism of Lactobacillus spp. towards RA remains unknown. Therefore, researchers conclude the latest analysis to effectively comprehend the ultimate pathogenicity of rheumatoid arthritis, as well as the functions of probiotics, specifically Lactobacillus casei or Lactobacillus acidophilus, in the treatment of RA in therapeutic and diagnostic reports. RA is a chronic inflammation immunological illness wherein the gut microbiota is affected. Probiotics are organisms that can regulate gut microbiota, which may assist to relieve RA manifestations. Over the last two decades, there has been a surge in the use of probiotics. However, just a few research have considered the effect of probiotic administration on the treatment and prevention of arthritis. Randomized regulated experimental trials have shown that particular probiotics supplement has anti-inflammatory benefits, helps people with RA enhance daily activities and alleviates symptoms. As a result, utilizing probiotic microorganisms as therapeutics could be a potential possibility for arthritis treatment. This review highlights the known data on the therapeutic and preventative effects of probiotics in RA, as well as their interactions.
Healthy Eating Index (HEI) is a diet quality measure that assesses the population's compliance towards dietary guidelines. In Malaysia, diet quality measure, though existing, has some limitations in terms of application and relevance. This study aims to develop a new standardized Malaysian Healthy Eating Index (S-MHEI) that can measure the diet quality of all Malaysians regardless of their energy requirement level. The Malaysian Dietary Guidelines (MDG) 2010 and MDG for Children and Adolescents (MDGCA) 2013 were used as main references in developing the index components. In addition, the latest Malaysian Adults Nutrition Survey (MANS) and Adolescent Nutrition Survey (ANS) were also referred to ensure the relevance of the components selected. For adequacy components, the least restrictive method was used in setting the standard for the scoring system. Meanwhile, the scoring system for moderation components was built based on the Recommended Nutrient Intake (RNI) 2017. The new S-MHEI comprises of 11 components with a maximum total score of 100. The least restrictive method allowed the index to be used across energy requirement levels. However, the index will not be sensitive towards adhering to the specific recommended amount of intake-which in effect, made the index focus on measuring diet quality rather than diet quantity.