Takotsubo cardiomyopathy vs acute myocardial infarction: diagnostic utility of subtle ECG differences

The clinical findings of Takatsubo Cardiomyopathy and acute myocardial infarction can be very similar. While Takatsubo cardiomyopathy rarely leads to severe complications, acute myocardial infarction can be life threatening. Treatment of both these conditions is different and so it is imperative for clinicians to have a high index of suspicion for either. Several EKG differences between the two entities have been proposed. This article summarizes the EKG changes most likely seen in Takatsubo cardiomyopathy and compares them to those seen in Acute Myocardial infarction

Theory of Behavioral Finance and Its Application to Property Market: A Change in Paradigm

It is considered that behavioral finance is basically the extension of behavioral economics. It is specially related to the arena of thinking of investor and his mind towards the stock market. The main objective of this study is to define the emotions based theories which are used to explain the stock market problems and terms. It is also critically analyzed the issues related property for behavioral research and the theory of behavioral finance. After the analysis of this paper we came to know that investors cannot always motivated by emotions, and it is not necessary that the property market will only be sufficient at the weak form efficient. There is a need of deep analysis for the theory of behavioral finance. The two major concepts of behavioral finance are discussed by this paper. First, investor psychology and limit to arbitrage.  Next, the theories related to psychology used in behavioral finance both are reviewed.  The identification of issues related to property market is made possible by the analysis of behavioral finance theories and development. This analysis is useful to understand these theories by using behavioral model. Investor always want to invest in those projects which having greater profit and the minimum chances of loss or risk. So this psychology of investor is also discussed in behavioral finance. It is also considered the feelings and thinking errors which encourage the investors and process of making decisions.    Next the paper will discuss about the human behavior theories which can motivate or demotivate the human’s mind to make any decision. These theories will be discussed later in detail. These are Prospect theory, judgment under uncertainty (overconfidence, fear of regret, Representativeness heuristic, Availability heuristic, Anchoring and judgment). If we discuss about stock price irregularities related to reaction higher than expectations of investor, under reaction, force strategies, steering Behavior, effect of the size of firm and book value or market value ratio effects. The behavioral model is excellent to explain all these anomalies.  Possessions or real estate research is concentrated on the human behavior because they believe that all the decisions are made in the property or real estate markets are always from the perception of human behavior. Similarly investor is also a human so the investment decisions will always made by the person who is interested in doing some investment. He will always focus the market conditions and then he will decide whether he should invest or not. These human behaviors and psychologies having a great effect on the stock prices. As human behavior might have biases about the property valuation field and also about the market information. This will have to discuss in this research paper that how behavioral finance can help the investor to make wise decisions. The behavioral finance paradigm, claims that investment adoptions are not always made on the basis of full rationality but it is necessary to understand the investment market sensations. The behavioral research issues those will be discussed and analyzed in this paper will lead to the path for developing a joined strategy for the investment in property

Nonlinear Structure based Artificial Neural Computing for Upstream Flow Functional Models

Most of the real world systems are nonlinear and complex and it is challenging to model these types of systems for analyzing and forecasting the hidden behaviour of the systems. In the paradigm of vague complex systems, data-based time series modeling approaches of intelligent systems showed its applicability for coping with the problems of hidden noise and dynamicity which are encapsulated in the data. Getting from nature is one of the humans’ features and they are striving to produce the intellectual schemes by coping rare features of cognitions and intellect of the brain. In this paper, nonlinear autoregressive structure based modeling of the brain (i.e. Artificial Neural Network) is the aim of this study that suggest various Dynamic Neural Network (DNN) models by using time deferred autoregressive configurations, for the stream-flow of Sukkur barrage on lower Indus river basin. The suitability of the models for training, validation and testing stages, are evinced on assessment metrics which demonstrate the accuracy and sufficiency of the models which may be beneficial for water-resource management

Multimodal sensor suite for identification of flow regimes and estimation of phase fractions and velocities – Machine Learning Algorithms in Multiphase flow metering and Control

Multiphase flow metering is a challenging task because of the complexity of multiphase flow. In this paper, nonintrusive multiphase flow metering techniques, including machine learning (ML) / artificial intelligence models for the identification of flow regimes and estimation of flow parameters of a two-phase flow in a horizontal pipe are proposed that use data from Electrical Capacitance Tomography (ECT) and conventional measurements such as differential pressure in the pipe. The flow regimes are classified into five types, namely plug, slug, annular, wavy and stratified. Two-phase air/water flow experimental data from ECT are collected by running extensive experiments using the horizontal section of the multiphase flow rig at the University of South-Eastern Norway (USN). Exploratory data analysis (EDA) is performed on these data to extract features for use in classification and regression algorithms. Time series of normalized capacitance data from ECT sensors are used to classify flow regimes and identify flow parameters. ML techniques of Artificial Neural Network, Support Vector Machine (SVM), K-Nearest Neighbors (KNN) and Decision Tree (DT) are used to classify flow regimes by using features extracted from ECT data. The cross-correlation technique is used to estimate flow velocity using data from a twinplane ECT module. ML regression techniques are used to estimate phase fractions. Fusing data from differential pressure sensors enhances the flow regime classification. An overall system performance is given with suggestions for designing dedicated control algorithms for actuators used in multiphase flow control

Microfinanças e empoderamento de mulheres: uma análise de regressão de comutação endógena

Women in Pakistan are suffering from a great social and economic deprivation due to gender discrimination and inequitable distribution of resources. This paper examines the determinants and extent of women empowerment by their participation in microfinance programs. Data for this study were collected from different areas of Faisalabad, Pakistan, where most of the households were poor and had borrowed money from different microfinance institutes. Keeping in view the disguised endogeneity, Endogenous Switching Regression Model was employed which accounts for selection bias because of observable and unobservable factors. The analysis revealed that education level, household size, family system, educational expenditures, income level and the ownership of different assets like sewing machines have the statistically significant impact on the women decision to work and hence promote women empowerment. It is concluded that the government in developing countries should introduce income-generating activities, especially for women by providing them access to financial resources.Las mujeres en Pakistán están sufriendo una gran carencia social y económica debido a la discriminación de género y la distribución desigual de los recursos. Este documento examina los determinantes y el alcance del empoderamiento de las mujeres por su participación en los programas de microfinanzas. Los datos para este estudio fueron recolectados de diferentes áreas de Faisalabad, Pakistán, donde la mayoría de los hogares eran pobres y habían tomado dinero prestado de diferentes institutos de microfinanzas. Teniendo en cuenta la endogeneidad disfrazada, se empleó el Modelo de Regresión de Conmutación Endógena que explica el sesgo debido a factores observables y no observables. El análisis reveló que el nivel educativo, el tamaño del hogar, el sistema familiar, los gastos educativos, el nivel de ingresos y la propiedad de diferentes activos, como las máquinas de coser, tienen un impacto estadísticamente significativo en la decisión de las mujeres de trabajar y, por lo tanto, promover el empoderamiento de las mujeres. Se concluye que el gobierno de los países en desarrollo debe introducir actividades generadoras de ingresos, especialmente para las mujeres, proporcionándoles acceso a recursos financierosAs mulheres no Paquistão sofrem de uma grande privação social e econômica devido à discriminação de gênero e à distribuição desigual de recursos. Este artigo examina os determinantes e a extensão do empoderamento das mulheres pela sua participação em programas de microfinanças. Os dados para este estudo foram coletados em diferentes áreas de Faisalabad, Paquistão, onde a maioria dos domicílios era pobre e tinha tomado dinheiro emprestado de diferentes institutos de microfinanças. Tendo em vista a endogeneidade disfarçada, empregou-se o Modelo de Regressão por Comutação Endógena, que considera o viés de seleção por causa de fatores observáveis e inobserváveis. A análise revelou que o nível de escolaridade, tamanho da família, sistema familiar, gastos com educação, nível de renda e posse de diferentes ativos, como máquinas de costura, têm impacto estatisticamente significativo na decisão das mulheres de trabalhar e, portanto, promovem o empoderamento das mulheres. Conclui-se que o governo dos países em desenvolvimento deve introduzir atividades geradoras de renda, especialmente para as mulheres, proporcionando-lhes acesso a recursos financeiro

Role of Short Duration Double Phototherapy in The Treatment of Unconjugated Hyperbilirubinemia

Objective: To determine the role of short duration double phototherapy in the treatment of unconjugated hyperbilirubinemia. Materials and Methods: This prospective cases series study was conducted at pediatric department of CMH hospital at Malir Karachi. All the neonates diagnosed with unconjugated hyperbilirubinemia admitted to the neonatal ward were included. All the cases underwent short duration double phototherapy. Babies were observed for side-effects of phototherapy, like skin reaction and dehydration. Serum bilirubin was checked by bilirubinometre after 6 hourly of the treatment. Al the data was collected via study proforma. Data was analyzed by using SPSS version 20 Results: Total 74 neonates were studied, most of the neonates presented within 48-72 hours after birth. Majority of the term babies as 59.5% had history of 37-40 weeks of gestation and 28.4% had gestational age history >40 weeks. Out of all, males’ babies were 58.1% and female babies were 41.9%. Neonatal bilirubin level was significantly decreased from bassline 18.35+0.97 after 6 hours of double phototherapy as 14.66+1.18 with mean difference of 3.68+1.37 (p-value 0.001). Conclusion: Short duration double phototherapy found to be the effective, reliable and safe for skin reaction in the treatment of unconjugated hyperbilirubinemia. Key words: Hyperbilirubinemia, double phototherapy, six hours

VARIABLES IMPACTING GFR ESTIMATION METHOD FOR DRUG DOSING IN CKD: ARTIFICIAL NEURAL NETWORK PREDICTION MODEL

Objective: This study aimed to measure concordance between different renal function estimates in terms of drug doses and determine the potential significant clinical differences. Methods: Around one hundred and eighty patients (≥ 18 y) with chronic kidney disease (CKD) were eligible for inclusion in this study. A paired-proportion cohort design was utilized using an artificial intelligence model. CKD patients refined into those who have drugs adjusted for renal function. For superiority of Cockcroft-Gault (CG) vs. modified diet in renal disease (MDRD) guided with references for concordance or discordance of the two equations and determined the dosing tiers of each drug. Validated artificial neural networks (ANN) was one outcome of interest. Variable impacts and performed reassignments were compared to evaluate the factors that affect the accuracy in estimating the kidney function for a better drug dosing. Results: The best ANN model classified most cases to CG as the best dosing method (79 vs. 72). The probability was 85% and the top performance was slightly above 93%. Creatinine levels and CKD staging were the most important factors in determining the best dosing method of CG versus MDRD. Ideal and actual body weights were second (24%). Whereas drug class or the specific drug was an important third factor (14%). Conclusion: Among many variables that affect the optimal dosing method, the top three are probably CKD staging, weight, and the drug. The contrasting CKD stages from the different methods can be used to recognize patterns, identify and predict the best dosing tactics in CKD patients

Enhancing Interpretability in Anxiety Detection on Reddit: A Machine Learning Approach with LIME and Topic Modeling

In modern society, mental disorders, particularly anxiety, are becoming more and more prevalent concerns. Individuals express their opinions and feelings on social media platforms like Reddit which offers valuable information for understanding mental health. This study applies BERTopic and Local Interpretable Model-agnostic Explanations (LIME) to demonstrate the interpretation of machine learning models in anxiety detection. To analyze and identify the linguistic patterns, a novel dataset has been collected from Reddit communities utilizing multiple subreddits pertaining to anxiety and casual conversations. For topic modeling BERTopic was used to discover key topics in discussions. In addition, TF-IDF features were used to train a Random Forest Classifier, which obtained an accuracy of 88% in classifying the post between anxiety and non-anxiety. Furthermore, to ensure transparency in model decision making process, LIME was used to examine textual features that influence models. This study emphasizes the importance of explainability with regards to AI-assisted mental health solutions while also demonstrating the usefulness of social media data in analyzing how anxiety is articulated, and language is employed differently

Efficacy and safety of sofosbuvir plus ribavirin in treatment-naive chronic hepatitis c genotype 3 patients of South Punjab, Pakistan

Background: To evaluate the efficacy and safety of sofosbuvir (SOF) plus ribavirin (RIB) in naive patients with chronic HCV genotype 3. The study design was open label, quasi experimental study. The study was conducted at Medical Outpatient Department of Medical Unit-1, Bahawal Victoria Hospital, affiliated with Quaid e Azam Medical College (QAMC), Bahawalpur, from April 2016 to June 2019.Methods: A total of 627 treatment-naive patients, aged above 18 years, with chronic Hepatitis C virus (HCV) genotype 3 infection were enrolled. SOF as 400 mg once a day plus weight-based RIB (1000 mg/day 75 kg) was given to all the study participants for 24 weeks. Qualitative polymerase chain reaction (PCR) for HCV ribonucleic acid (RNA) were done at 4 weeks to note the rapid virological response (RVR) whereas end of treatment response (ETR) was recorded at 24 weeks and sustained virological response (SVR) was noted 3 months after completion of treatment.Results: By 4th week, PCR of 524 (83.6%) patients was available, out of which, 492 (93.9%) had undetectable HCV RNA. By the end of treatment (24 weeks), PCR of 401 (64.0%) patients was available, out of which, 393 (98.0%) had undetectable HCV RNA. Data of 291 (46.4%) patients was available for SVR, 274 (94.1%) had undetectable HCV RNA. Weakness and fatigue turned out to be the commonest side effects, observed in 236 (37.6%) patients.Conclusions: Sofosbuvir was found to have good efficacy and safety in the local population of South Punjab having treatment-naïve chronic HCV genotype 3 infection