Anemia during Pregnancy and Its Prevalence

Anemia is a serious health issue throughout the world affecting both sexes of any age group. This nutritional disease is more common among the pregnant women of developing countries, where it is a major cause of maternal death and negative outcome of pregnancy. Among all anemic types, IDA is most prevalent one and is comprises of about 95% of all anemic cases around the world. In many developing countries it is more common in women of low socio-economic background and with no record of antenatal checkup. There is need for further health educational programs to overcome anemia especially for pregnant females

Continuous-time quantum walks for MAX-CUT are hot

By exploiting the link between time-independent Hamiltonians and thermalisation, heuristic predictions on the performance of continuous-time quantum walks for MAX-CUT are made. The resulting predictions depend on the number of triangles in the underlying MAX-CUT graph. We extend these results to the time-dependent setting with multi-stage quantum walks and Floquet systems. The approach followed here provides a novel way of understanding the role of unitary dynamics in tackling combinatorial optimisation problems with continuous-time quantum algorithms.Comment: 25 pages, 29 figure

Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare

Heart disease is one of the lethal diseases causing millions of fatalities every year. The Internet of Medical Things (IoMT) based healthcare effectively enables a reduction in death rate by early diagnosis and detection of disease. The biomedical data collected using IoMT contains personalized information about the patient and this data has serious privacy concerns. To overcome data privacy issues, several data protection laws are proposed internationally. These privacy laws created a huge problem for techniques used in traditional machine learning. We propose a framework based on federated matched averaging with a modified Artificial Bee Colony (M-ABC) optimization algorithm to overcome privacy issues and to improve the diagnosis method for the prediction of heart disease in this paper. The proposed technique improves the prediction accuracy, classification error, and communication efficiency as compared to the state-of-the-art federated learning algorithms on the real-world heart disease dataset

Modified Artificial Bee Colony Based Feature Optimized Federated Learning for Heart Disease Diagnosis in Healthcare

Heart disease is one of the lethal diseases causing millions of fatalities every year. The Internet of Medical Things (IoMT) based healthcare effectively enables a reduction in death rate by early diagnosis and detection of disease. The biomedical data collected using IoMT contains personalized information about the patient and this data has serious privacy concerns. To overcome data privacy issues, several data protection laws are proposed internationally. These privacy laws created a huge problem for techniques used in traditional machine learning. We propose a framework based on federated matched averaging with a modified Artificial Bee Colony (M-ABC) optimization algorithm to overcome privacy issues and to improve the diagnosis method for the prediction of heart disease in this paper. The proposed technique improves the prediction accuracy, classification error, and communication efficiency as compared to the state-of-the-art federated learning algorithms on the real-world heart disease dataset

Complete heart block associated with hepatitis A infection in a female child with fatal outcome

Hepatitis A virus (HAV) infection can cause extra-hepatic manifestations like myocarditis. An 8-year-old female with HAV infection presented with fever, abdominal pain, vomiting, and icterus. She developed viral myocarditis with complete AV dissociation on ECG and was treated with a temporary pacemaker, but her condition worsened, and she died. Hepatitis A viral infection can be associated with viral myocarditis and complete heart block that can lead to cardiogenic shock and death eventually

Influence of sub-specialty surgical care on outcomes for pediatric emergency general surgery patients in a low-middle income country

Background: Whether adult general surgeons should handle pediatric emergencies is controversial. In many resource-limited settings, pediatric surgeons are not available. The study examined differences in surgical outcomes among children/adolescents managed by pediatric and adult general surgery teams for emergency general surgical (EGS) conditions at a university-hospital in South Asia.Methods: Pediatric patients (\u3c18y) admitted with an EGS diagnosis (March 2009–April 2014) were included. Patients were dichotomized by adult vs. pediatric surgical management team. Outcome measures included: length of stay (LOS), mortality, and occurrence of ≥1 complication(s). Descriptive statistics and multivariable regression analyses with propensity scores to account for potential confounding were used to compare outcomes between the two groups. Quasi-experimental counterfactual models further examined hypothetical outcomes, assuming that all patients had been treated by pediatric surgeons.Results: A total of 2323 patients were included. Average age was 7.1y (±5.5 SD); most patients were male (77.7%). 1958 (84.3%) were managed by pediatric surgery. The overall probability of developing a complication was 1.8%; 0.9% died (all adult general surgery). Patients managed by adult general surgery had higher risk-adjusted odds of developing complications (OR [95%CI]: 5.42 [2.10–14.00]) and longer average LOS (7.98 vs. 5.61 days, p \u3c 0.01). 39.8% fewer complications and an 8.2% decrease in LOS would have been expected if all patients had been managed by pediatric surgery. Conclusion: Pediatric patients had better post-operative outcomes under pediatric surgical supervision, suggesting that, where possible in resource-constrained settings, resources should be allocated to promote development and staffing of pediatric surgical specialties parallel to adult general surgical teams