Cost of myopia correction: a systematic review

Myopia is one of the leading causes of visual impairment globally. Despite increasing prevalence and incidence, the associated cost of treatment remains unclear. Health care spending is a major concern in many countries and understanding the cost of myopia correction is the first step eluding to the overall cost of myopia treatment. As the cost of treatment will reduce the burden of the cost of illness, this will aid in future cost-benefit analysis and the allocation of healthcare resources, including considerations in integrating eye care (refractive correction with spectacles) into universal health coverage (UHC). We performed a systematic review to determine the economic costs of myopia correction. However, there were few studies for direct comparison. Costs related to myopia correction were mainly direct with few indirect costs. Annual prevalence-based direct costs for myopia ranged from $14-26 (USA),$56 (Iran), and $199 (Singapore) per capita, respectively (population: 274.63 million, 75.15 million, and 3.79 million, respectively). Annually, the direct costs of contact lens were$198.30-$378.10 while spectacles and refractive surgeries were$342.50 and $19.10, respectively. This review provides an insight into the cost of myopia correction. Myopia costs are high from nationwide perspectives because of the high prevalence of myopia, with contact lenses being the more expensive option. Without further interventions, the burden of illness of myopia will increase substantially with the projected increase in prevalence worldwide. Future studies will be necessary to generate more homogenous cost data and provide a complete picture of the global economic cost of myopia.info:eu-repo/semantics/publishedVersio

CYLD Proteolysis Protects Macrophages from TNF-Mediated Auto-necroptosis Induced by LPS and Licensed by Type I IFN

SummaryTumor necrosis factor (TNF) induces necroptosis, a RIPK3/MLKL-dependent form of inflammatory cell death. In response to infection by Gram-negative bacteria, multiple receptors on macrophages, including TLR4, TNF, and type I IFN receptors, are concurrently activated, but it is unclear how they crosstalk to regulate necroptosis. We report that TLR4 activates CASPASE-8 to cleave and remove the deubiquitinase cylindromatosis (CYLD) in a TRIF- and RIPK1-dependent manner to disable necroptosis in macrophages. Inhibiting CASPASE-8 leads to CYLD-dependent necroptosis caused by the TNF produced in response to TLR4 ligation. While lipopolysaccharides (LPS)-induced necroptosis was abrogated in Tnf−/− macrophages, a soluble TNF antagonist was not able to do so in Tnf+/+ macrophages, indicating that necroptosis occurs in a cell-autonomous manner. Surprisingly, TNF-mediated auto-necroptosis of macrophages requires type I IFN, which primes the expression of key necroptosis-signaling molecules, including TNFR2 and MLKL. Thus, the TNF necroptosis pathway is regulated by both negative and positive crosstalk

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

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

Deep learning system to predict the 5-year risk of high myopia using fundus imaging in children

Our study aims to identify children at risk of developing high myopia for timely assessment and intervention, preventing myopia progression and complications in adulthood through the development of a deep learning system (DLS). Using a school-based cohort in Singapore comprising 998 children (aged 6-12 years old), we train and perform primary validation of the DLS using 7456 baseline fundus images of 1878 eyes; with external validation using an independent test dataset of 821 baseline fundus images of 189 eyes together with clinical data (age, gender, race, parental myopia, and baseline spherical equivalent (SE)). We derive three distinct algorithms - image, clinical, and mix (image + clinical) models to predict high myopia development (SE ≤ -6.00 diopter) during teenage years (5 years later, age 11-17). Model performance is evaluated using the area under the receiver operating curve (AUC). Our image models (Primary dataset AUC 0.93-0.95; Test dataset 0.91-0.93), clinical models (Primary dataset AUC 0.90-0.97; Test dataset 0.93-0.94) and mixed (image + clinical) models (Primary dataset AUC 0.97; Test dataset 0.97-0.98) achieve clinically acceptable performance. The addition of 1 year SE progression variable has minimal impact on the DLS performance (clinical model AUC 0.98 versus 0.97 in the primary dataset, 0.97 versus 0.94 in the test dataset; mixed model AUC 0.99 versus 0.97 in the primary dataset, 0.95 versus 0.98 in test dataset). Thus, our DLS allows prediction of the development of high myopia by teenage years amongst school-going children. This has potential utility as a clinical decision support tool to identify "at-risk" children for early intervention.info:eu-repo/semantics/publishedVersio

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

Longitudinal seroepidemiologic study of the 2009 pandemic influenza A (H1N1) infection among health care workers in a children's hospital

<p>Abstract</p> <p>Background</p> <p>To probe seroepidemiology of the 2009 pandemic influenza A (H1N1) among health care workers (HCWs) in a children's hospital.</p> <p>Methods</p> <p>From August 2009 to March 2010, serum samples were drawn from 150 HCWs in a children's hospital in Taipei before the 2009 influenza A (H1N1) pandemic, before H1N1 vaccination, and after the pandemic. HCWs who had come into direct contact with 2009 influenza A (H1N1) patients or their clinical respiratory samples during their daily work were designated as a high-risk group. Antibody levels were determined by hemagglutination inhibition (HAI) assay. A four-fold or greater increase in HAI titers between any successive paired sera was defined as seroconversion, and factors associated with seroconversion were analyzed.</p> <p>Results</p> <p>Among the 150 HCWs, 18 (12.0%) showed either virological or serological evidence of 2009 pandemic influenza A (H1N1) infection. Of the 90 unvaccinated HCWs, baseline and post-pandemic seroprotective rates were 5.6% and 20.0%. Seroconversion rates among unvaccinated HCWs were 14.4% (13/90), 22.5% (9/40), and 8.0% (4/50) for total, high-risk group, and low-risk group, respectively. Multivariate analysis revealed being in the high-risk group is an independent risk factor associated with seroconversion.</p> <p>Conclusion</p> <p>The infection rate of 2009 pandemic influenza A (H1N1) in HCWs was moderate and not higher than that for the general population. The majority of unvaccinated HCWs remained susceptible. Direct contact of influenza patients and their respiratory samples increased the risk of infection.</p