Institutional corporate social responsibility (CSR) practices: the influence of leadership styles and their perceived ethics and social responsibility role

This paper investigates leader's perceptions of ethics and social responsibility (PRESOR) on organisation's institutional CSR practices. The results indicate that while the managers in this study perceive that ethics and social responsibility play an important role in determining the organisation's long-term and short-term gains, they do not think that ethics and social responsibility are the only important factors in determining firm's profitability and survival, as indicated by the non-significant results of the PRESOR (social responsibility and profitability) dimension. Another objective was to determine the types of leadership style in influencing the adoption and practices of CSR. As oppose to many previous studies, the results indicate that among the leadership styles, transactional leadership influences institutional CSR practices, while transformational leadership does not. This finding implies that for CSR practices to be implemented, leaders need to use rewards, rules and regulations in a Malaysian context. In other words, in order to institutionalise CSR practices in Malaysia, corporations should start by introducing extrinsic incentives

Associations of staple food consumption and types of cooking oil with waist circumference and body mass index in older Chinese men and women: a panel analysis

Background: The dietary landscape has changed rapidly in China in the past few decades. This research investigates the associations of older adults’ choices and consumption of staple foods and cooking oils with obesity related measurements. Methods: Panel data were extracted from the Chinese Longitudinal Health Longevity Survey from3253 older participants with 6506 observations. Ordinary least squares and ordered logistic regression models were estimated with the outcomes of obesity determined by waist circumference (WC) and body mass index (BMI), respectively. Results: Older men who consumed wheat had wider WCs (β=2.84 [95% confidence interval {CI} 1.55 to 4.13], p\u3c0.01) and higher BMIs (adjusted odds ratio 1.74 [95% CI 1.40 to 2.17], p\u3c0.01) than those who preferred rice. Female participants who used animal-based cooking oil had lower WCs and BMIs than their counterparts who consumed vegetable-based cooking oil. Increased consumption of staple foods was associated with increased rates of obesity in both sexes. Conclusion: Dieticians and nutritionists should design appropriate dietary plans to help reduce obesity and chronic diseases among older Chinese adults. Further clinical trials are needed to continue investigating this topic

Cryptotanshinone inhibits TNF-α-induced early atherogenic events in vitro

Endothelial dysfunction has been implicated in the pathogenesis of atherosclerosis. Salvia miltiorrhiza (danshen) is a traditional Chinese medicine that has been effectively used to treat cardiovascular disease. Cryptotanshinone (CTS), a major lipophilic compound isolated from S. miltiorrhiza, has been reported to possess cardioprotective effects. However, the anti-atherogenic effects of CTS, particularly on tumor necrosis factor-α (TNF-α)-induced endothelial cell activation, are still unclear. This study aimed to determine the effect of CTS on TNF-α-induced increased endothelial permeability, monocyte adhesion, soluble intercellular adhesion molecule 1 (sICAM-1), soluble vascular cell adhesion molecule 1 (sVCAM-1), monocyte chemoattractant protein 1 (MCP-1) and impaired nitric oxide production in human umbilical vein endothelial cells (HUVECs), all of which are early events occurring in atherogenesis. We showed that CTS significantly suppressed TNF-α-induced increased endothelial permeability, monocyte adhesion, sICAM-1, sVCAM-1 and MCP-1, and restored nitric oxide production. These observations suggest that CTS possesses anti-inflammatory properties and could be a promising treatment for the prevention of cytokine-induced early atherogenesis

Machine Learning-Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes : Prediction Model Development Study

Publisher Copyright: © Mukkesh Kumar, Li Ting Ang, Cindy Ho, Shu E Soh, Kok Hian Tan, Jerry Kok Yen Chan, Keith M Godfrey, Shiao-Yng Chan, Yap Seng Chong, Johan G Eriksson, Mengling Feng, Neerja KarnaniBackground: The increasing prevalence of gestational diabetes mellitus (GDM) is concerning as women with GDM are at high risk of type 2 diabetes (T2D) later in life. The magnitude of this risk highlights the importance of early intervention to prevent the progression of GDM to T2D. Rates of postpartum screening are suboptimal, often as low as 13% in Asian countries. The lack of preventive care through structured postpartum screening in several health care systems and low public awareness are key barriers to postpartum diabetes screening. Objective: In this study, we developed a machine learning model for early prediction of postpartum T2D following routine antenatal GDM screening. The early prediction of postpartum T2D during prenatal care would enable the implementation of effective strategies for diabetes prevention interventions. To our best knowledge, this is the first study that uses machine learning for postpartum T2D risk assessment in antenatal populations of Asian origin. Methods: Prospective multiethnic data (Chinese, Malay, and Indian ethnicities) from 561 pregnancies in Singapore's most deeply phenotyped mother-offspring cohort study-Growing Up in Singapore Towards healthy Outcomes-were used for predictive modeling. The feature variables included were demographics, medical or obstetric history, physical measures, lifestyle information, and GDM diagnosis. Shapley values were combined with CatBoost tree ensembles to perform feature selection. Our game theoretical approach for predictive analytics enables population subtyping and pattern discovery for data-driven precision care. The predictive models were trained using 4 machine learning algorithms: logistic regression, support vector machine, CatBoost gradient boosting, and artificial neural network. We used 5-fold stratified cross-validation to preserve the same proportion of T2D cases in each fold. Grid search pipelines were built to evaluate the best performing hyperparameters. Results: A high performance prediction model for postpartum T2D comprising of 2 midgestation features-midpregnancy BMI after gestational weight gain and diagnosis of GDM-was developed (BMI_GDM CatBoost model: AUC=0.86, 95% CI 0.72-0.99). Prepregnancy BMI alone was inadequate in predicting postpartum T2D risk (ppBMI CatBoost model: AUC=0.62, 95% CI 0.39-0.86). A 2-hour postprandial glucose test (BMI_2hour CatBoost model: AUC=0.86, 95% CI 0.76-0.96) showed a stronger postpartum T2D risk prediction effect compared to fasting glucose test (BMI_Fasting CatBoost model: AUC=0.76, 95% CI 0.61-0.91). The BMI_GDM model was also robust when using a modified 2-point International Association of the Diabetes and Pregnancy Study Groups (IADPSG) 2018 criteria for GDM diagnosis (BMI_GDM2 CatBoost model: AUC=0.84, 95% CI 0.72-0.97). Total gestational weight gain was inversely associated with postpartum T2D outcome, independent of prepregnancy BMI and diagnosis of GDM (P = .02; OR 0.88, 95% CI 0.79-0.98). Conclusions: Midgestation weight gain effects, combined with the metabolic derangements underlying GDM during pregnancy, signal future T2D risk in Singaporean women. Further studies will be required to examine the influence of metabolic adaptations in pregnancy on postpartum maternal metabolic health outcomes. The state-of-the-art machine learning model can be leveraged as a rapid risk stratification tool during prenatal care.Peer reviewe

Ultra-Violet Treatment for Fermenting Low-Salt Soya Sauce

Low-salt soya sauce has become a market trend due to consumers' demand for a low sodium diet life. In tradition, a low-salt soya sauce (with salt concentration below 14.4%) is made from a high-salt one (18% salt concentration) through diluting or reducing the sodium content. The post processing deteriorates the quality of the soya sauce produce as some specific, beneficial chemical components are inevitably removed. In production of a native-born low-salt soya sauce, a key problem encountered is possible microbial contamination that easily develops in a low salt environment. In this study, we evaluated the effect of ultra-violet (UVC 254nm) irradiation on soya mash of 12% salt concentration fermented at 35°C. The ultra-violet treatment could effectively prevent the soya mash from microbial contamination

Automated Machine Learning (AutoML)-Derived Preconception Predictive Risk Model to Guide Early Intervention for Gestational Diabetes Mellitus

The increasing prevalence of gestational diabetes mellitus (GDM) is contributing to the rising global burden of type 2 diabetes (T2D) and intergenerational cycle of chronic metabolic disorders. Primary lifestyle interventions to manage GDM, including second trimester dietary and exercise guidance, have met with limited success due to late implementation, poor adherence and generic guidelines. In this study, we aimed to build a preconception-based GDM predictor to enable early intervention. We also assessed the associations of top predictors with GDM and adverse birth outcomes. Our evolutionary algorithm-based automated machine learning (AutoML) model was implemented with data from 222 Asian multi-ethnic women in a preconception cohort study, Singapore Preconception Study of Long-Term Maternal and Child Outcomes (S-PRESTO). A stacked ensemble model with a gradient boosting classifier and linear support vector machine classifier (stochastic gradient descent training) was derived using genetic programming, achieving an excellent AUC of 0.93 based on four features (glycated hemoglobin A(1c) (HbA(1c)), mean arterial blood pressure, fasting insulin, triglycerides/HDL ratio). The results of multivariate logistic regression model showed that each 1 mmol/mol increase in preconception HbA(1c) was positively associated with increased risks of GDM (p = 0.001, odds ratio (95% CI) 1.34 (1.13-1.60)) and preterm birth (p = 0.011, odds ratio 1.63 (1.12-2.38)). Optimal control of preconception HbA(1c) may aid in preventing GDM and reducing the incidence of preterm birth. Our trained predictor has been deployed as a web application that can be easily employed in GDM intervention programs, prior to conception.Peer reviewe

Effectiveness and response differences of a multidisciplinary workplace health promotion program for healthcare workers

BackgroundWorkplace health promotion (WHP) in the healthcare industry is an important yet challenging issue to address, given the high workload, heterogeneity of work activities, and long work hours of healthcare workers (HCWs). This study aimed to investigate the effectiveness and response differences of a multidisciplinary WHP program conducted in HCWs.MethodsThis retrospective cohort study included HCWs participating in a multidisciplinary WHP program in five healthcare facilities. The 20-week intervention included multiple easy-to-access 90-min exercise classes, one 15-min nutrition consultation, and behavioral education. Pre- and post-interventional anthropometrics, body composition, and physical fitness (PF) were compared with paired sample t-tests. Response differences across sex, age, weight status, and shiftwork status were analyzed with a generalized estimating equation.ResultsA total of 302 HCWs were analyzed. The intervention effectively improved all anthropometric (body mass index, waist circumference, waist-hip ratio, and waist-to-height ratio), body composition (body fat percentage, muscle weight, visceral fat area), and PF (grip strength, high jump, sit-up, sit-and-reach, step test) parameters in all participants (all p < 0.05). Subgroup analyses revealed shift workers had a more significant mean reduction in body mass index than non-shift workers (adjusted p = 0.045). However, there was no significant response difference across sex, age, and weight subgroups.ConclusionThis study suggested that a multidisciplinary WHP program can improve anthropometric and PF profiles regardless of sex, age, and weight status for HCWs, and shifter workers might benefit more from the intervention

Comparison assessment between SIM and MRM mode in the analysis of 3-MCPD ester, 2-MCPD ester and glycidyl ester

The detection of 3- and 2-MCPD ester and glycidyl ester was transformed from selected ion monitoring (SIM) mode to multiple reaction monitoring (MRM) mode by gas chromatography triple quadrupole spectrometry. The derivatization process was adapted from AOCS method Cd 29a-13. The results showed that the coefficient of determination (R2) of all detected compounds obtained from both detection mode was comparable, which falls between 0.997 and 0.999. The limit of detection and quantification (LOD and LOQ) were improved in MRM mode as compared to SIM mode. In MRM mode, the LOD of 3- and 2-MCPD ester was achieved 0.01 mg/kg while the LOQ was 0.05 mg/kg. Besides, LOD and LOQ of glycidyl ester were 0.024 and 0.06 mg/kg respectively. A blank spiked with MCPD esters (0.03, 0.10 and 0.50 mg/kg) and GE (0.06, 0.24 and 1.20 mg/kg) were chosen for repeatability and recovery tests. MRM mode showed better repeatability in area ratio and recovery with relative standard deviation (RSD %) < 5% for 2-, 3-MCPD ester at 0.5 mg/kg and GE at 1.2 mg/kg. Quantification of 22 food samples from different category were performed by repeated injections in both detection modes. Briefly, the contaminants from crude palm oil, mustard and olive oil were present in minute amount which below the LOD or LOQ in both detection modes. Sample from chocolate and infant formula products showed certain level of MCPD esters and GE, and their detection was more precisely quantitated based on MRM mode. Besides, margarine products showed a higher level of contaminations due to the high fat content in these products. MRM mode detection was proven to provide precise data with low RSD % in different food matrices. MRM mode detection was robust and selective for MCPD esters and GE analyses, it should be applied to determine the concentration of MCPD esters and GE contaminations in food

Rapid assessment of total MCPD esters in palm-based cooking oil using ATR-FTIR application and chemometric analysis

The technique of Fourier transform infrared spectroscopy is widely used to generate spectral data for use in the detection of food contaminants. Monochloropropanediol (MCPD) is a refining process-induced contaminant that is found in palm-based fats and oils. In this study, a chemometric approach was used to evaluate the relationship between the FTIR spectra and the total MCPD content of a palm-based cooking oil. A total of 156 samples were used to develop partial least squares regression (PLSR), artificial neural network (nnet), average artificial neural network (avNNET), random forest (RF) and cubist models. In addition, a consensus approach was used to generate fusion result consisted from all the model mentioned above. All the models were evaluated based on validation performed using training and testing datasets. In addition, the box plot of coefficient of determination (R²), root mean square error (RMSE), slopes and intercepts by 100 times randomization was also compared. Evaluation of performance based on the testing R² and RMSE suggested that the cubist model predicted total MCPD content with the highest accuracy, followed by the RF, avNNET, nnet and PLSR models. The overfitting tendency was assessed based on differences in R² and RMSE in the training and testing calibrations. The observations showed that the cubist and avNNET models possessed a certain degree of overfitting. However, the accuracy of these models in predicting the total MCPD content was high. Results of the consensus model showed that it slightly improved the accuracy of prediction as well as significantly reduced its uncertainty. The important variables derived from the cubist and RF models suggested that the wavenumbers corresponding to the MCPDs originated from the –CH=CH₂ or CH=CH (990–900 cm⁻¹) and C-Cl stretch (800–700 cm⁻¹) regions of the FTIR spectrum data. In short, chemometrics in combination with FTIR analysis especially for the consensus model represent a potential and flexible technique for estimating the total MCPD content of refined vegetable oils