Diabetes Prediction using Machine learning : A Bibliometric Analysis

Diabetes Mellitus is a chronic disease which can be deadly if undetected for longer time. Artificial intelligence is helping in healthcare industry to a great extent by helping professionals to derive useful information and patterns from data available in various formats: Survey data, electronic health records, laboratory data.. Diabetes, if predicted at an early stage can help many people to save lives and cost for healthcare. Decision-making, diagnosing and predicting diabetes have become an increasing trend in recent years. There are numerous publications in diabetes prediction and yet it’s an ongoing research topic with availability of new data and methods. This study aims to provide a global picture on current status of diabetes prediction research field by analyzing trends in publications, authors, countries and institutions from Scopus and Web of Science database for the year 2009-2020. Further, to enhance the analysis, network inspection in terms of co-authorship, collaborative countries, citation analysis and keyword co-occurrences were explored. Tools like VoSviewer and tableau are used for analysis

Diabetes Prediction using Machine learning : A Bibliometric Analysis

Diabetes Mellitus is a chronic disease which can be deadly if undetected for longer time. Artificial intelligence is helping in healthcare industry to a great extent by helping professionals to derive useful information and patterns from data available in various formats: Survey data, electronic health records, laboratory data.. Diabetes, if predicted at an early stage can help many people to save lives and cost for healthcare. Decision-making, diagnosing and predicting diabetes have become an increasing trend in recent years. There are numerous publications in diabetes prediction and yet it’s an ongoing research topic with availability of new data and methods. This study aims to provide a global picture on current status of diabetes prediction research field by analyzing trends in publications, authors, countries and institutions from Scopus and Web of Science database for the year 2009-2020. Further, to enhance the analysis, network inspection in terms of co-authorship, collaborative countries, citation analysis and keyword co-occurrences were explored. Tools like VoSviewer and tableau are used for analysis

Money Laundering and Terrorist Financing Typologies That Reduce Financial Crime Risks

The Covid-19 pandemic has increased concerns over money laundering and terrorist financing and their impacts on societies and the world’s finance and economic systems. Some financial institutions are failing to detect and track new emerging financial crime threats. The purpose of this qualitative descriptive case study was to identify predicate offense typologies that U.S. banking and financial services company compliance managers use to reduce the risks of money laundering and terrorist financing. To understand the concepts of predicate offense and financial crime risks, Gary Becker’s economic theory of criminal behavior was the conceptual framework that grounded this study. The population was comprised of 15 compliance managers and anti-money laundering investigators. Data sources included semistructured interviews, semistructured observations, and document reviews from business and finance academic journals. Coding, thematic analysis, and content analysis revealed eight main themes as predicate offense typologies: structuring, fraud, cybercrime, human trafficking, illicit arms trafficking, illicit drug trafficking, real estate money laundering, and trade-based money laundering. Four subthemes were identified: red flags, key indicators, typology-specific common signs, and 95% or above. The insights drawn from this study may contribute to efforts by compliance managers to increase transparency and close gaps in the anti-money laundering and counter terrorist financing compliance framework, which could enhance business practice. Implications for positive social change include a reduced risk of bank failures, increased employment opportunities, and promotion of public awareness about financial crimes

Money Laundering and Terrorist Financing Typologies That Reduce Financial Crime Risks

The Covid-19 pandemic has increased concerns over money laundering and terrorist financing and their impacts on societies and the world’s finance and economic systems. Some financial institutions are failing to detect and track new emerging financial crime threats. The purpose of this qualitative descriptive case study was to identify predicate offense typologies that U.S. banking and financial services company compliance managers use to reduce the risks of money laundering and terrorist financing. To understand the concepts of predicate offense and financial crime risks, Gary Becker’s economic theory of criminal behavior was the conceptual framework that grounded this study. The population was comprised of 15 compliance managers and anti-money laundering investigators. Data sources included semistructured interviews, semistructured observations, and document reviews from business and finance academic journals. Coding, thematic analysis, and content analysis revealed eight main themes as predicate offense typologies: structuring, fraud, cybercrime, human trafficking, illicit arms trafficking, illicit drug trafficking, real estate money laundering, and trade-based money laundering. Four subthemes were identified: red flags, key indicators, typology-specific common signs, and 95% or above. The insights drawn from this study may contribute to efforts by compliance managers to increase transparency and close gaps in the anti-money laundering and counter terrorist financing compliance framework, which could enhance business practice. Implications for positive social change include a reduced risk of bank failures, increased employment opportunities, and promotion of public awareness about financial crimes

European mtDNA Variants Are Associated With Differential Responses to Cisplatin, an Anticancer Drug: Implications for Drug Resistance and Side Effects.

Background: Cisplatin, a powerful antitumor agent, causes formation of DNA adducts, and activation of apoptotic pathways. Presently, cisplatin resistance develops in up to 70% of patients but the underlying molecular mechanism(s) are unclear and there are no markers to determine which patients will become resistant. Mitochondria play a significant role not only in energy metabolism but also retrograde signaling (mitochondria to nucleus) that modulates inflammation, complement, and apoptosis pathways. Maternally inherited mitochondrial (mt) DNA can be classified into haplogroups representing different ethnic populations that have diverse susceptibilities to diseases and medications. Methods: Transmitochondrial cybrids, where all cell lines possess identical nuclear genomes but either the H (Southern European) or J (Northern European) mtDNA haplogroups, were treated with cisplatin and analyzed for differential responses related to viability, oxidative stress, and expression levels of genes associated with cancer, cisplatin-induced nephrotoxicity and resistance, apoptosis and signaling pathways. Results: The cisplatin-treated-J cybrids showed greater loss of cell viability along with lower levels of reactive oxygen species and mitochondrial membrane potential compared to cisplatin-treated-H cybrids. After cisplatin treatment, J cybrids showed increased gene expression of BAX, CASP3, and CYP51A, but lower levels of SFRP1 compared to untreated-J cybrids. The cisplatin-treated-H cybrids had elevated expression of CDKN1A/P21, which has a role in cisplatin toxicity, compared to untreated-H cybrids. The cisplatin-treated H had higher transcription levels of ABCC1, DHRS2/HEP27, and EFEMP1 compared to cisplatin-treated-J cybrids. Conclusions: Cybrid cell lines that contain identical nuclei but either H mtDNA mitochondria or J mtDNA mitochondria respond differently to cisplatin treatments suggesting involvement of the retrograde signaling (from mitochondria to nucleus) in the drug-induced cell death. Varying toxicities and transcription levels of the H vs. J cybrids after cisplatin treatment support the hypothesis that mtDNA variants play a role in the expression of genes affecting resistance and side effects of cisplatin

Racial/Ethnic Disparities and Determinants of Sufficient Physical Activity Levels

Introduction. It is important to identify racial/ethnic groups that may be less likely to achieve sufficient physical activity levels, and to address barriers to meeting physical activity requirements. Methods. Cross-sectional data from the 2006-2015 National Health Interview Survey (NHIS) was used to compare self-reported sufficient physical activity among different racial/ethnic groups: non-Hispanic (NH) Whites, NH Blacks, NH Asians, and Hispanics. Sufficient physical activity was defined as ≥150 minutes per week of moderate-intensity physical activity, ≥75 minutes per week of vigorous-intensity physical activity, or ≥150 minutes per week of moderate and vigorous physical activity. Results. The study sample consisted of 296,802 individuals, mean age ± standard error age 46.4 ± 0.10 years, 52% women, 70% NH White, 12% NH Black, 5% NH Asian, 14% Hispanic. The prevalence of sufficient physical activity in the overall population was 46%, while it was 48% among NH Whites, 39% among NH Blacks, 45% among NH Asians, and 40% among Hispanics. In multivariable-adjusted models (odds ratio; 95% confidence interval), NH Blacks (0.79; 0.64,0.97), NH Asians (0.72; 0.62,0.85) and Hispanics (0.71; 0.61,0.82) were significantly less likely to engage in sufficient physical activity compared with NH Whites. Older age, women, and low income were inversely associated with sufficient physical activity, while a college education or higher was directly associated with it. Conclusions. NH Black, NH Asian, and Hispanic adults are less likely to engage in sufficient physical activity levels compared with NH Whites. It is important to address barriers to meeting physical activity thresholds to help achieve optimal cardiovascular health

Healthcare Access Among Individuals of Asian Descent in the U.S.

Introduction. Some groups of Asian Americans, especially Asian Indians, experience higher rates of atherosclerotic cardiovascular disease (ASCVD) compared with other groups in the U.S. Barriers in accessing medical care partly may explain this higher risk as a result of delayed screening for cardiovascular risk factors and timely initiation of preventive treatment. Methods. Cross-sectional data were utilized from the 2006 to 2015 National Health Interview Survey (NHIS). Barriers to accessing medical care included no place to seek medical care when needed, no healthcare coverage, no care due to cost, delayed care due to cost, inability to afford medication, or not seeing a doctor in the past 12 months. Results. The study sample consisted of 18,150 Asian individuals, of whom 20.5% were Asian Indian, 20.5% were Chinese, 23.4% were Filipino, and 35.6% were classified as “Other Asians”. The mean (standard error) age was 43.8 (0.21) years and 53% were women. Among participants with history of hypertension, diabetes mellitus, or ASCVD (prevalence = 25%), Asian Indians were more likely to report delayed care due to cost (2.58 (1.14,5.85)), while Other Asians were more likely to report no care due to cost (2.43 (1.09,5.44)) or delayed care due to cost (2.35 (1.14,4.86)), compared with Chinese. Results among Filipinos were not statistically significant. Conclusions. Among Asians living in the U.S. with cardiovascular risk factors or ASCVD, Asian Indians and Other Asians are more likely to report delayed care or no care due to cost compared with Chinese

Estimation of Gestational Age via Image Analysis of Anterior Lens Capsule Vascularity in Preterm Infants: A Pilot Study

Introduction: Anterior lens capsule vascularity (ALCV) is resorbed in the developing fetus from 27 to 35 weeks gestation. In this pilot study, we evaluated the feasibility and validity of combining smartphone ophthalmoscope videos of ALCV and image analysis for gestational age estimation.Methods: ALCV videos were captured longitudinally in preterm neonates from delivery using a PanOptic® Ophthalmoscope with an iExaminer® adapter (Welch-Allyn). ALCV video frames were manually selected and quantified using semi-automatic image analysis. A predictive model based on ALCV features was compared to gold-standard ultrasound gestational age estimates.Results: A total of 64 image-capture sessions were carried out in 24 neonates. Ultrasound-estimated gestational age and ALCV-predicted gestational age estimates indicate that the two methods are similar (r = 0.78, p < 0.0001). ALCV estimates of gestational age were within 0.11 ± 1.3 weeks of ultrasound estimates. In the final model, gestational age was predicted within ± 1 week for 54% and within ± 2 weeks for 86% of the measures.Conclusions: This novel application of smartphone ophthalmoscopy and ALCV image analysis may provide a safe, accurate and non-invasive technology to estimate postnatal gestational age, especially in low income countries where gestational age may not be known at birth

Mathematical modelling and numerical simulation of CO2/CH4 separation in a polymeric membrane

YesCO2 capture from natural gas was experimentally and theoretically studied using a dead-end polymeric permeation cell. A numerical model was proposed for the separation of CO2/CH4 using Polytetrafluoroethylene (PTFE) in a flat sheet membrane module and developed based upon the continuity, momentum and mass transfer equations. The slip velocity condition was considered to show the reflection of gas flow in contact with the membrane surface. The solution method was based on the well-known SIMPLE algorithm and implemented using MATLAB to determine the velocity and concentration profiles. Due to change in velocity direction in the membrane module, the hybrid differencing scheme was used to solve the diffusion-convection equation. The results of the model were compared with the experimental data obtained as part of this work and good agreement was observed. The distribution of CO2 concentration inside the feed and permeate chambers was shown and the velocity profile at the membrane surface was also determined using reflection factor for polymericmembrane. The modelling result revealed that increasing the amount of CO2 in gas feed resulted in an increase in the CO2 in the permeate stream while the gas feed pressure increased. By changing the permeability, the model developed by use of the solution-diffusion concept could be used for all polymeric membranes with flat sheet modules