Hemoglobin E prevalence in malaria-endemic villages in Myanmar.

The population of Myanmar comprises 8 major indigenous races (Bamar, Kayin, Kachin, Shan, Rakhine, Mon, Chin, and Kayah). The Bamar reside in the 7 central divisions of the country, and the others reside in the 7 peripheral states that border neighboring countries, including China, Laos, and Thailand in the east and India and Bangladesh in the west. Both malaria and HbE are endemic in Myanmar, although the actual prevalence of the latter in the different indigenous races is not yet known. Hemoglobin electrophoresis was performed in 4 malaria-endemic villages, each having a different predominating indigenous race. The overall prevalence of HbE was 11.4% (52/456 villagers), ranging from 2-6% in the Kayin-predominant villages to 13.1-24.4% in the Bamar-predominant villages. Although the overall HbE prevalence in the villages studied was not significantly different from that of the general Myanmar population, this study strongly documented the influence of racial differences on the prevalence of HbE in Myanmar. To prevent and control severe thalassemia syndromes in Myanmar, extensive prevalence studies of the country?s indigenous races are suggested.</p

Artificial Intelligence-assisted automated heart failure detection and classification from electronic health records

AimsElectronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients.Methods and resultsWe used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies.ConclusionsOur study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes

Artificial Intelligence-assisted automated heart failure detection and classification from electronic health records

AimsElectronic health records (EHR) linked to Digital Imaging and Communications in Medicine (DICOM), biological specimens, and deep learning (DL) algorithms could potentially improve patient care through automated case detection and surveillance. We hypothesized that by applying keyword searches to routinely stored EHR, in conjunction with AI-powered automated reading of DICOM echocardiography images and analysing biomarkers from routinely stored plasma samples, we were able to identify heart failure (HF) patients.Methods and resultsWe used EHR data between 1993 and 2021 from Tayside and Fife (~20% of the Scottish population). We implemented a keyword search strategy complemented by filtering based on International Classification of Diseases (ICD) codes and prescription data to EHR data set. We then applied DL for the automated interpretation of echocardiographic DICOM images. These methods were then integrated with the analysis of routinely stored plasma samples to identify and categorize patients into HF with reduced ejection fraction (HFrEF), HF with preserved ejection fraction (HFpEF), and controls without HF. The final diagnosis was verified through a manual review of medical records, measured natriuretic peptides in stored blood samples, and by comparing clinical outcomes among groups. In our study, we selected the patient cohort through an algorithmic workflow. This process started with 60 850 EHR data and resulted in a final cohort of 578 patients, divided into 186 controls, 236 with HFpEF, and 156 with HFrEF, after excluding individuals with mismatched data or significant valvular heart disease. The analysis of baseline characteristics revealed that compared with controls, patients with HFrEF and HFpEF were generally older, had higher BMI, and showed a greater prevalence of co-morbidities such as diabetes, COPD, and CKD. Echocardiographic analysis, enhanced by DL, provided high coverage, and detailed insights into cardiac function, showing significant differences in parameters such as left ventricular diameter, ejection fraction, and myocardial strain among the groups. Clinical outcomes highlighted a higher risk of hospitalization and mortality for HF patients compared with controls, with particularly elevated risk ratios for both HFrEF and HFpEF groups. The concordance between the algorithmic selection of patients and manual validation demonstrated high accuracy, supporting the effectiveness of our approach in identifying and classifying HF subtypes, which could significantly impact future HF diagnosis and management strategies.ConclusionsOur study highlights the feasibility of combining keyword searches in EHR, DL automated echocardiographic interpretation, and biobank resources to identify HF subtypes

Political transition and emergent forest-conservation issues in Myanmar.

Political and economic transitions have had substantial impacts on forest conservation. Where transitions are underway or anticipated, historical precedent and methods for systematically assessing future trends should be used to anticipate likely threats to forest conservation and design appropriate and prescient policy measures to counteract them. Myanmar is transitioning from an authoritarian, centralized state with a highly regulated economy to a more decentralized and economically liberal democracy and is working to end a long-running civil war. With these transitions in mind, we used a horizon-scanning approach to assess the 40 emerging issues most affecting Myanmar's forests, including internal conflict, land-tenure insecurity, large-scale agricultural development, demise of state timber enterprises, shortfalls in government revenue and capacity, and opening of new deforestation frontiers with new roads, mines, and hydroelectric dams. Averting these threats will require, for example, overhauling governance models, building capacity, improving infrastructure- and energy-project planning, and reforming land-tenure and environmental-protection laws. Although challenges to conservation in Myanmar are daunting, the political transition offers an opportunity for conservationists and researchers to help shape a future that enhances Myanmar's social, economic, and environmental potential while learning and applying lessons from other countries. Our approach and results are relevant to other countries undergoing similar transitions

Political transition and emergent forest-conservation issues in Myanmar.

Political and economic transitions have had substantial impacts on forest conservation. Where transitions are underway or anticipated, historical precedent and methods for systematically assessing future trends should be used to anticipate likely threats to forest conservation and design appropriate and prescient policy measures to counteract them. Myanmar is transitioning from an authoritarian, centralized state with a highly regulated economy to a more decentralized and economically liberal democracy and is working to end a long-running civil war. With these transitions in mind, we used a horizon-scanning approach to assess the 40 emerging issues most affecting Myanmar's forests, including internal conflict, land-tenure insecurity, large-scale agricultural development, demise of state timber enterprises, shortfalls in government revenue and capacity, and opening of new deforestation frontiers with new roads, mines, and hydroelectric dams. Averting these threats will require, for example, overhauling governance models, building capacity, improving infrastructure- and energy-project planning, and reforming land-tenure and environmental-protection laws. Although challenges to conservation in Myanmar are daunting, the political transition offers an opportunity for conservationists and researchers to help shape a future that enhances Myanmar's social, economic, and environmental potential while learning and applying lessons from other countries. Our approach and results are relevant to other countries undergoing similar transitions

A Philosophical Analysis of the Concept of Alienation

The term 'alienation' · has many different meanings in everyday life, science, and philosophy. Alienation is the act or result of the act through which something, or somebody, becomes strange to something, or somebody, else. In our days, since science and technology have developed, human beings may face the very actual problem of alienation . Alienation is not a problem of what a man can have but a problem of what a man is. It is a problem of man's attitude towards hims.elf and towards the society in which he lives. This paper will discuss alienation as a philosophical problem and will present the way that can cure alienation and it is provided by the Buddhist tradition in Myanmar society

A basic Principle of the Myanmar Way of Thinking Reflected in Myanmar Proverbs

The word 'proverb' is derived from the Latin word 'proverbium', which means a brief popular saying. A proverb expresses a truth based on common fense or the practical experience of mankind. Every society has its own prove..-bs that can display thoughts of its people. Myanmar proverbs through the Ages have exhibited the Myanmar way of thinking and Myanmar way of living that identifies the national characteristics of the Myanmar people. This paper tries to reveal one of the basic principles of the Myanmar way of thinking reflected in Myanmar proverbs

The Seven Virtues in Myanmar Ethical Philosophy

The aim of this paper is to investigate the issue on whether the traditional idea of virtue in Myanmar is still living or not in this Age of Knowledge. The research question of this paper is why the seven virtues are still important in Myanmar society. This question may be best answered through the descriptive, evaluative and reflective methods. It will contribute that the traditional seven virtues as a guiding principle may be a positive and supporting factor to build our modern democracy society

The two Basic Laws: the Law of Kamma and the law of Impermanence in Myanmar Way of Thinking

The term 'Way of Thinking' refers to any individual's thinking in which the characteristic features of the thinking habits of the culture to which he belongs are revealed. The way of thinking of a society is conditioned by its cultural habits and attitudes. It can be said that in the Myanmar way of thinking, there are two basic laws, namely, the law of Kamma and the law of Impermanence. This paper points out these two basic laws in the general tendency of the way of thinking of Myanmar people with reference to Myanmar literature

Performance Analysis of a Scalable Naïve Bayes Classifier on MapReduce and Beyond MapReduce

Many real world areas from different sourcesgenerate the big data with large volume of highvelocity, complex and variable data. Big databecomes a challenge when they are difficult toprocess and extract knowledge using traditionalanalysis tools. Therefore the scalable machinelearning algorithms are needed for processing suchbig data. Recently Hadoop MapReduce frameworkhas been adapted for parallel computing. MapReducemay not fit for most of the real world dataapplications. For large scale machine learning ondistributed system, Spark has finally become muchmore viable beyond MapReduce. Although both ofthese frameworks are Apache-hosted data analyticframework, their performance varies significantlybased on the use case under their implementation.This paper aims to analyze the performance ofscalable Naïve Bayes classifier (SNB) which isimplemented on MapReduce and Beyond MapReduceover different real world datasets. The comparisonresults show that SNB on Beyond MapReduceprovides minimal processing time than SNB onMapReduce for efficiently big data classification