To investigate three possible causes of the acute hemolysis in the hemolytic-uremic syndrome, we studied prospectively 207 children and 34 adults with shigellosis in Bangladesh. Nineteen children showed acute hemolytic anemia, a leukemoid reaction, thrombocytopenia and oliguria; nine other had, in addition, a serum urea nitrogen level of over 100 mg per diciliter. Eight of the nine had pseudomembranous colitis, and six of the nine died. The frequency of bacteremia was similar in all grades of shigellosis. Circulating immune complexes were found in 10 of 20 patients with uncomplicated shigellosis and in four of six with severe hemolytic-uremic syndrome. Limulus assay for endotoxemia was positive in nine of 18 patients with hemolysis (50 per cent) and three of 61 with uncomplicated shigellosis (5 per cent) (P less than 0.001). These data support the hypothesis that severe colitis in shigellosis is associated with circulating endotoxin from the colon producing coagulopathy, renal microangiopathy and hemolytic anemia.

Diabetes is a costly and burdensome metabolic disorder that occurs due to the elevation of glucose levels in the bloodstream. If it goes unchecked for an extended period, it can lead to the damage of different body organs and develop life-threatening health complications. Studies show that the progression of diabetes can be stopped or delayed, provided a person follows a healthy lifestyle and takes proper medication. Prevention of diabetes or the delayed onset of diabetes is crucial, and it can be achieved if there exists a screening process that identifies individuals who are at risk of developing diabetes in the future. Although machine learning techniques have been applied for disease diagnosis, there is little work done on long term prediction of disease, type 2 diabetes in particular. Moreover, finding discriminative features or risk-factors responsible for the future development of diabetes plays a significant role. In this study, we propose two novel feature extraction approaches for finding the best risk-factors, followed by applying a machine learning pipeline for the long term prediction of type 2 diabetes. The proposed methods have been evaluated using data from a longitudinal clinical study, known as the San Antonio Heart Study. Our proposed model managed to achieve 95.94% accuracy in predicting whether a person will develop type 2 diabetes within the next 7-8 years or not.

No abstract

Time series econometric methods are frequently used in studies examining how external debt affects economic growth. For the period of 1980-2020, this study creates a panel dataset of five South Asian nations and examines the link between external debt and economic growth. The findings of Cross-sectionally Augmented Panel Unit Root Test by Pesaran's (2007) confirms that all variables are integrated in order I (1). To understand the error correction mechanism that determines the short-run dynamic nature of external debt and economic growth, the study uses the Cross-Sectional Dependence Autoregressive Distributed Lag (CS-ARDL) technique. A significant negative association between external debt and economic growth is found to exist in South Asia both in the long run and in the short run. Since rising foreign debt is associated with slower economic growth, the study recommends that South Asian nations should promote domestic savings and investment to lessen their reliance on external debt.

Background: Coronary Heart Disease (CHD) is the most common category of the heart disease and is found to be the single most important cause that leads to premature death in the developed world. Recognizing a patient with ACS is important because the diagnosis triggers both triage and management. cTnI is 100% tissue-specific for the myocardium and it has shown itself as a very sensitive and specific marker for AMI. Ventricular function is the best predictor of death after an ACS. It serves as a marker of myocardial damage and provides information on systolic function as well as diagnosis and prognosis. The study aimed at investigating the impact of LVEF on elevated troponin-I level in patients with first attack of NSTEMI. Methods: This cross-sectional analytical study was conducted in the department of cardiology in Mymensingh Medical College Hospital from December, 2015 to November, 2016. Total 130 first attack of NSTEMI patients were included considering inclusion and exclusion criteria. The sample population was divided into two groups: Group-I: Patients with first attack of NSTEMI with LVEF: ≥55%. Group-II: Patients with first attack of NSTEMI with LVEF: <55%. Then LVEF and troponin-I levels were correlated using Pearson's correlation coefficient test. Results: In this study mean troponin-I of group-I and group-II were 5.53±7.43 and 16.46±15.79ng/ml respectively. It was statistically significant (p<0.05). The mean LVEF value of groups were 65.31±10.30% and 40.17±4.62% respectively. It was statistically significant (p<0.05). The echocardiography showed that patients with high troponin-I level had low LVEF and patients with low troponin-I level had preserved LVEF. Analysis showed that patients with highest level of troponin-I had severe left ventricular systolic dysfunction (LVEF <35%) and vice versa-the patients with the lowest levels of troponin-I had preserved systolic function (LVEF ≥55%). In our study, it also showed that the levels of troponin-I had negative correlation with LVEF levels with medium strength of association (r= -0.5394, p=0.001). Our study also discovered that Troponin-I level ≥6.6ng/ml is a very sensitive and specific marker for LV systolic dysfunction. Conclusions:The study has enabled the research team to conclude that the higher is the Troponin-I level the lower is the LVEF level and thus more severe is the LV systolic dysfunction in first attack of NSTEMI patients.