Classification-based data mining for identification of risk patterns associated with hypertension in Middle Eastern population A 12-year longitudinal study

Hypertension is a critical public health concern worldwide. Identification of risk factors using traditional multivariable models has been a field of active research. The present study was undertaken to identify risk patterns associated with hypertension incidence using data mining methods in a cohort of Iranian adult population. Data on 6205 participants (44 men) age > 20 years, free from hypertension at baseline with no history of cardiovascular disease, were used to develop a series of prediction models by 3 types of decision tree (DT) algorithms. The performances of all classifiers were evaluated on the testing data set. The Quick Unbiased Efficient Statistical Tree algorithm among men and women and Classification and Regression Tree among the total population had the best performance. The C-statistic and sensitivity for the prediction models were (0.70 and 71) in men, (0.79 and 71) in women, and (0.78 and 72) in total population, respectively. In DT models, systolic blood pressure (SBP), diastolic blood pressure, age, and waist circumference significantly contributed to the risk of incident hypertension in both genders and total population, wrist circumference and 2-h postchallenge plasma glucose among women and fasting plasma glucose among men. In men, the highest hypertension risk was seen in those with SBP > 115mm Hg and age > 30 years. In women those with SBP > 114 mmHg and age>33 years had the highest risk for hypertension. For the total population, higher risk was observed in those with SBP > 114mm Hg and age > 38 years. Our study emphasizes the utility of DTs for prediction of hypertension and exploring interaction between predictors. DT models used the easily available variables to identify homogeneous subgroups with different risk pattern for the hypertension. Copyright © 2016 the Author(s). Published by Wolters Kluwer Health, Inc. All

An application of association rule mining to extract risk pattern for type 2 diabetes using tehran lipid and glucose study database

Background: Type 2 diabetes, common and serious global health concern, had an estimated worldwide prevalence of 366 million in 2011, which is expected to rise to 552 million people, by 2030, unless urgent action is taken. Objectives: The aim of this study was to identify risk patterns for type 2 diabetes incidence using association rule mining (ARM). Patients and Methods: A population of 6647 individuals without diabetes, aged � 20 years at inclusion, was followed for 10-12 years, to analyze risk patterns for diabetes occurrence. Study variables included demographic and anthropometric characteristics, smoking status, medical and drug history and laboratory measures. Results: In the case of women, the results showed that impaired fasting glucose (IFG) and impaired glucose tolerance (IGT), in combination with body mass index (BMI) � 30 kg/m2, family history of diabetes, wrist circumference > 16.5 cm and waist to height � 0.5 can increase the risk for developing diabetes. For men, a combination of IGT, IFG, length of stay in the city (> 40 years), central obesity, total cholesterol to high density lipoprotein ratio � 5.3, low physical activity, chronic kidney disease and wrist circumference > 18.5 cm were identified as risk patterns for diabetes occurrence. Conclusions: Our study showed that ARM is a useful approach in determining which combinations of variables or predictors occur together frequently, in people who will develop diabetes. The ARM focuses on joint exposure to different combinations of risk factors, and not the predictors alone. © 2015, Research Institute For Endocrine Sciences and Iran Endocrine Society

High-Energy and High-Power-Density Potassium Ion Batteries Using Dihydrophenazine-Based Polymer as Active Cathode Material

Polymeric aromatic amines were shown to be very promising cathodes for lithium-ion batteries. Surprisingly, these materials are scarcely used for designing post-lithium batteries. In this Letter, we investigate the application of the high-voltage poly(N-phenyl-5,10-dihydrophenazine) (p-DPPZ) cathodes for K-ion batteries. The designed batteries demonstrate an impressive specific capacity of 162 mAh g-1 at the current density of 200 mA g-1, operate efficiently at high current densities of 2-10 A g-1, enabling charge and discharge within ∼1-4 min, and deliver the specific capacity of 125-145 mAh g-1 with a retention of 96 and 79% after 100 and 1000 charge-discharge cycles, respectively. Finally, these K-ion batteries with polymeric p-DPPZ cathodes showed rather outstanding specific power of >3 × 104 W kg-1, thus paving a way to the design of ultrafast and durable high-capacity metal-ion batteries matching the increasing demand for high power and high energy density electrochemical energy storage devices. © 2019 American Chemical Society.Government Council on Grants, Russian Federation: 02.Russian Science Foundation, RSF: 16-13-00111This work was supported by Russian Science Foundation, project 16-13-00111. We acknowledge the support of Dr. A. Mumyatov with FTIR spectroscopy measurements. The XPS measurements were supported by the Government of Russian Federation (Act 211, Agreement No. 02.A03.21.0006) and Theme “Electron” (no. AAAA-A18-118020190098-5)

Decision tree-based modelling for identification of potential interactions between type 2 diabetes risk factors: A decade follow-up in a Middle East prospective cohort study

Objective: The current study was undertaken for use of the decision tree (DT) method for development of different prediction models for incidence of type 2 diabetes (T2D) and for exploring interactions between predictor variables in those models. Design: Prospective cohort study. Setting: Tehran Lipid and Glucose Study (TLGS). Methods: A total of 6647 participants (43.4 men) aged >20 years, without T2D at baselines ((1999- 2001) and (2002-2005)), were followed until 2012. 2 series of models (with and without 2-hour postchallenge plasma glucose (2h-PCPG)) were developed using 3 types of DT algorithms. The performances of the models were assessed using sensitivity, specificity, area under the ROC curve (AUC), geometric mean (G-Mean) and F-Measure. Primary outcome measure: T2D was primary outcome which defined if fasting plasma glucose (FPG) was �7 mmol/L or if the 2h-PCPG was �11.1 mmol/L or if the participant was taking antidiabetic medication. Results: During a median follow-up of 9.5 years, 729 new cases of T2D were identified. The Quick Unbiased Efficient Statistical Tree (QUEST) algorithm had the highest sensitivity and G-Mean among all the models for men and women. The models that included 2h-PCPG had sensitivity and G-Mean of (78 and 0.75) and (78 and 0.78) for men and women, respectively. Both models achieved good discrimination power with AUC above 0.78. FPG, 2h-PCPG, waist-toheight ratio (WHtR) and mean arterial blood pressure (MAP) were the most important factors to incidence of T2D in both genders. Among men, those with an FPG�4.9 mmol/L and 2h-PCPG�7.7 mmol/L had the lowest risk, and those with an FPG>5.3 mmol/L and 2h-PCPG>4.4 mmol/L had the highest risk for T2D incidence. In women, those with an FPG�5.2 mmol/L and WHtR�0.55 had the lowest risk, and those with an FPG>5.2 mmol/L and WHtR>0.56 had the highest risk for T2D incidence. Conclusions: Our study emphasises the utility of DT for exploring interactions between predictor variables

Two Decades of Global Progress in Authorized Advanced Therapy Medicinal Products: An Emerging Revolution in Therapeutic Strategies

The introduction of advanced therapy medicinal products (ATMPs) to the global pharma market has been revolutionizing the pharmaceutical industry and has opened new routes for treating various types of cancers and incurable diseases. In the past two decades, a noticeable part of clinical practices has been devoting progressively to these products. The first step to develop such an ATMP product is to be familiar with other approved products to obtain a general view about this industry trend. The present paper depicts an overall perspective of approved ATMPs in different countries, while reflecting the degree of their success in a clinical point of view and highlighting their main safety issues and also related market size as a whole. In this regard, published articles regarding safety, efficacy, and market size of approved ATMPs were reviewed using the search engines PubMed, Scopus, and Google Scholar. For some products which the related papers were not available, data on the relevant company website were referenced. In this descriptive study, we have introduced and classified approved cell, gene, and tissue engineering-based products by different regulatory agencies, along with their characteristics, manufacturer, indication, approval date, related regulatory agency, dosage, product description, price and published data about their safety and efficacy. In addition, to gain insights about the commercial situation of each product, we have gathered accessible sale reports and market size information that pertain to some of these products

Octahydroxytetraazapentacenedione: New Organic Electrode Material for Fast and Stable Potassium Batteries

We report the synthesis and electrochemical characterization of octahydroxytetraazapentacenedione (OHTAPQ). The potassium batteries using OHTAPQ as electrode material delivered the specific capacity of 190 mAh g−1 at the current density of 0.6 A g−1. The use of the concentrated (2.2 M KPF6) diglyme-based electrolyte suppressed significantly the capacity fading of the potassium half-cells with OHTAPQ electrodes thus enabling their stable operation for 1200 charge-discharge cycles. Furthermore, OHTAPQ delivered the specific discharge capacity of 82–103 mAh g−1 at high current densities of 9–21 A g−1, which leads to high power densities approaching 41000 W kg−1. Thus, we demonstrate that the rationally designed organic electrode material enables high-capacity and high-power potassium batteries, which can be considered as a more environment-friendly and scalable alternative to the mainstream lithium-ion battery technology. © 2021 Elsevier B.V.This work was supported by the Russian Ministry of Science and Education (project 0089-2019-0010/AAAA-A19-119071190044-3 ). The XPS measurements were supported by the Ministry of Science and Higher Education of the Russian Federation (FEUZ-2020-0060), and Theme “Electron”, AAAA-A18-118020190098-5 at IPT UrFU and IMP UB RAS . The solid-state NMR spectroscopy experiments were performed at the Center of the Shared Facilities of IPCP RAS and Research Resource Center of the Scientific Center “Chernogolovka” of RAS. PAT acknowledges the support from EU’s Horizon 2020 ERA-Chair project ExCEED, grant agreement No 952008

The role of sources of social support on depression and quality of life for university students.

Prevalence of mental health problems in university students is increasing and attributable to academic, financial and social stressors. Lack of social support is a known determinant of mental health problems. We examined the differential impact of sources of social support on student wellbeing. University students completed an online survey measuring depressive symptoms (Patient Health Questionnaire (PHQ-9)), social support (Multidimensional Perceived Social Support (MPSS)), and quality of life (WHOQOL-BREF). The sample was 461 students (82% female, mean age 20.62 years). The prevalence of depressive symptoms was 33%. Social support from family, and friends was a significant predictor of depressive symptoms (p = 0.000*). Quality of life (psychological) was significantly predicted by social support from family and friends. Quality of life (social relationships) was predicted by social support from significant others and friends. Sources of social support represent a valuable resource for universities in protecting the mental health of students

Mapping 123 million neonatal, infant and child deaths between 2000 and 2017

Since 2000, many countries have achieved considerable success in improving child survival, but localized progress remains unclear. To inform efforts towards United Nations Sustainable Development Goal 3.2—to end preventable child deaths by 2030—we need consistently estimated data at the subnational level regarding child mortality rates and trends. Here we quantified, for the period 2000–2017, the subnational variation in mortality rates and number of deaths of neonates, infants and children under 5 years of age within 99 low- and middle-income countries using a geostatistical survival model. We estimated that 32% of children under 5 in these countries lived in districts that had attained rates of 25 or fewer child deaths per 1,000 live births by 2017, and that 58% of child deaths between 2000 and 2017 in these countries could have been averted in the absence of geographical inequality. This study enables the identification of high-mortality clusters, patterns of progress and geographical inequalities to inform appropriate investments and implementations that will help to improve the health of all populations