Mechanical Properties of Silicon Nanowires

Nanowires have been taken much attention as a nanoscale building block, which can perform the excellent mechanical function as an electromechanical device. Here, we have performed atomic force microscope (AFM)-based nanoindentation experiments of silicon nanowires in order to investigate the mechanical properties of silicon nanowires. It is shown that stiffness of nanowires is well described by Hertz theory and that elastic modulus of silicon nanowires with various diameters from ~100 to ~600 nm is close to that of bulk silicon. This implies that the elastic modulus of silicon nanowires is independent of their diameters if the diameter is larger than 100 nm. This supports that finite size effect (due to surface effect) does not play a role on elastic behavior of silicon nanowires with diameter of >100 nm

Occipital EEG Activity for the Detection of Nocturnal Hypoglycemia

© 2018 IEEE. Nocturnal hypoglycemia is dangerous that threatens patients because of its unclear symptoms during sleep. This paper is a study of hypoglycemia from 8 patients with type 1 diabetes (T1D) at night. O1 and O2 EEG data of the occipital lobe associated with glycemic episodes were analyzed. Frequency features were computed from Power Spectral Density using Welch's method. Centroid alpha frequency reduced significantly (P < 0.0001) while centroid theta increased considerably (P < 0.01). Spectral entropy of the unified theta-alpha band rose significantly (P < 0.005). These occipital features acted as the input of a Bayesian regularized neural network for detecting hypoglycemic episodes. The classification results were 73% and 60% of sensitivity and specificity, respectively

The Effect of Hypoglycemia on Spectral Moments in EEG Epochs of Different Durations in Type 1 Diabetes Patients.

The potential of using an electroencephalogram (EEG) to detect hypoglycemia in patients with type 1 diabetes (T1D) has been investigated in both time and frequency domains. Under hyperinsulinemic hypoglycemic clamp conditions, we have shown that the brain's response to hypoglycemic episodes could be described by the centroid frequency and spectral gyration radius evaluated from spectral moments of EEG signals. The aim of this paper is to investigate the effect of hypoglycemia on spectral moments in EEG epochs of different durations and to propose the optimal time window for hypoglycemia detection without using clamp protocols. The incidence of hypoglycemic episodes at night time in five T1D adolescents was analyzed from selected data of ten days of observations in this study. We found that hypoglycemia is associated with significant changes (P < 0.05) in spectral moments of EEG segments in different lengths. Specifically, the changes were more pronounced on the occipital lobe. We used effect size as a measure to determine the best EEG epoch duration for the detection of hypoglycemic episodes. Using Bayesian neural networks, this study showed that 30 second segments provide the best detection rate of hypoglycemia. In addition, Clarke's error grid analysis confirms the correlation between hypoglycemia and EEG spectral moments of this optimal time window, with 86% of clinically acceptable estimated blood glucose values. These results confirm the potential of using EEG spectral moments to detect the occurrence of hypoglycemia

Electroencephalogram Reactivity to Hyperglycemia in Patients with Type 1 Diabetes.

This paper is concerned with a study of hyperglycemia on four patients with type 1 diabetes at night time. We investigated the association between hyperglycemic episodes and electroencephalogram (EEG) signals using data from the central and occipital areas. The power spectral density of the brain waves was estimated to compare the difference between hyperglycemia and euglycemia using the hyperglycemic threshold of 8.3 mmol/L. The statistical results showed that alpha and beta bands were more sensitive to hyperglycemic episodes than delta and theta bands. During hyperglycemia, whereas the alpha power increased significantly in the occipital lobe (P<0.005), the power of the beta band increased significantly in all observed channels (P<0.01). Using the Pearson correlation, we assessed the relationship between EEG signals and glycemic episodes. The estimated EEG power levels of the alpha band and the beta band produced a significant correlation against blood glucose levels (P<0.005). These preliminary results show the potential of using EEG signals as a biomarker to detect hyperglycemia

Electroencephalogram Spectral Moments for the Detection of Nocturnal Hypoglycemia.

Hypoglycemia or low blood glucose is the most feared complication of insulin treatment of diabetes. For people with diabetes, the mismatch between the insulin therapy and the body's physiology could increase the risk of hypoglycemia. Nocturnal hypoglycemia is particularly dangerous for type-1 diabetes patients because its symptoms may obscure during sleep. The early onset detection of hypoglycemia at night time is necessary because it can result in unconsciousness and even death. This paper presents new electroencephalogram spectral features for nocturnal hypoglycemia detection. The system uses high-order spectral moments for feature extraction and Bayesian neural network for classification. From a clinical study of hypoglycemia of eight patients with type-1 diabetes at night, we find that these spectral moments of theta band and alpha band changed significantly. During hypoglycemia episodes, the theta moments increased significantly (P < 0.001) while the features of alpha band reduced significantly (P < 0.001). Using the optimal Bayesian neural network, the classification results were 85% and 52% in sensitivity and specificity, respectively. The significant correlation (P < 0.001) with real blood glucose profiles shows the effectiveness of the proposed features for the detection of nocturnal hypoglycemia

Nocturnal Hypoglycemia Detection using Optimal Bayesian Algorithm in an EEG Spectral Moments Based System.

This paper presents a hypoglycemia detection system using electroencephalogram (EEG) spectral moments from 8 patients with type 1 diabetes (T1D) at night time. Four channels (C3, C4, O1, and O2) associated with glycemic episodes were analyzed. Spectral moments were applied to EEG signal and its corresponding speed and acceleration. During hypoglycemia, theta moments increased significantly (P<; 0.001) and alpha moments decreased significantly (P<; 0.001). The system used an optimal Bayesian neural network for detecting hypoglycemic episodes. Based on the optimal network architecture with the highest log evidence, the final classification results for the test set were 79% and 51% in sensitivity and specificity, respectively. Essentially, the estimated blood glucose profiles correlated significantly to actual values in the test set (P<; 0.0001). Using error grid analysis, 93% of the estimated values were clinically acceptable

Nocturnal Hypoglycemia Detection using EEG Spectral Moments under Natural Occurrence Conditions.

This paper is concerned with a study of hypoglycemia under natural occurrence conditions at night time. Five adolescents with type 1 diabetes (T1D) participated in the experiments. Patients' blood glucose profiles were interpolated to estimate the intermediate values. The proposed system used spectral moments of electroencephalogram (EEG) signals from central and occipital areas as features for detecting hypoglycemia. We found that hypoglycemia could be detected non-invasively using EEG spectral moments. During hypoglycemic episodes, theta moments increased significantly (P<; 0.005) whereas beta moments decreased significantly (P<; 0.001). Based on the optimal network architecture associated with the highest log evidence, the proposed optimal Bayesian neural network resulted in a sensitivity of 82% and a specificity of 52%. In addition, the estimated blood glucose profiles showed a significant correlation (P<; 1e-6) with interpolated blood glucose values in the test set

Improving antibiotic prescribing for community-acquired pneumonia in a provincial hospital in Northern Vietnam

Objectives To test the effectiveness of a quality improvement programme to promote adherence to national quality standards (QS) for patients hospitalized with community-acquired pneumonia (CAP), exploring the factors that hindered improvements in clinical practice. Methods An improvement bundle aligned to the QS was deployed using plan-do-study-act methodology in a 600 bed hospital in northern Vietnam from July 2018 to April 2019. Proposed care improvements included CURB65 score guided hospitalization, timely diagnosis and inpatient antibiotic treatment review to limit the spectrum and duration of IV antibiotic use. Interviews with medical staff were conducted to better understand the barriers for QS implementation. Results The study found that improvements were made in CURB65 score documentation and radiology results available within 4 h (P < 0.05). There were no significant changes in the other elements of the QS studied. We documented institutional barriers relating to the health reimbursement mechanism and staff cultural barriers relating to acceptance and belief as significant impediments to implementation of the standards. Conclusions Interventions led to some process changes, but these were not utilized by clinicians to improve patient management. Institutional and behavioural barriers documented may inhibit wider national uptake of the QS. National system changes with longer term support and investment to address local behavioural barriers are likely to be crucial for future improvements in the management of CAP, and potentially other hospitalized conditions, in Vietnam

Catastrophic health expenditure of Vietnamese patients with gallstone diseases &ndash; a case for health insurance policy revaluation

Bach Xuan Tran,1,2,* Tho Dinh Tran,3,* Nila Nathan,4 Chau Quy Ngo,5 Loi Thi Nguyen,6 Long Hoang Nguyen,7 Huong Lan Thi Nguyen,8 Cuong Tat Nguyen,8 Huyen Phuc Do,7 Trang Huyen Thi Nguyen,9 Tung Thanh Tran,9 Thao Phuong Thi Thai,10,11 Anh Kim Dang,8 Nam Ba Nguyen,7 Carl A Latkin,2 Cyrus SH Ho,12 Roger CM Ho7,13 1Department of Health Economics, Institute for Preventive Medicine and Public Health, Hanoi Medical University, Hanoi, Vietnam; 2Department of Health, Behavior and Society, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, USA; 3Department of Hepatobiliary Surgery, Vietnam-Germany Hospital, Hanoi, Vietnam; 4University of California, Santa Barbara, Santa Barbara, CA, USA; 5Department of Internal Medicine, Hanoi Medical University, Hanoi, Vietnam; 6Woolcock Institute of Medical Research Vietnam, Hanoi, Vietnam; 7Center of Excellence in Behavioral Medicine, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam; 8Institute for Global Health Innovations, Duy Tan University, Danang, Vietnam; 9Center of Excellence in Evidence-based Medicine, Nguyen Tat Thanh University, Ho Chi Minh City, Vietnam; 10Department of General Planning, Friendship Hospital, Hanoi, Vietnam; 11Department of Cardiology, Friendship Hospital, Hanoi, Vietnam; 12Department of Psychological Medicine, National University Hospital, Singapore; 13Department of Psychological Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore *These authors contributed equally to this work Purpose: Despite gallstone diseases (GSDs) being a major public health concern with both acute and chronic episodes, none of the studies in Vietnam has been conducted to investigate the household expenditure for the GSD treatment. The objective of this study was to estimate the costs of managing GSD and to explore the prevalence and determinants of catastrophic health expenditure (CHE) among Vietnamese patients.Materials and methods: A cross-sectional study was conducted from June 2016 to March 2017 in the Department of Hepatobiliary and Pancreatic Surgery, Viet Duc Hospital in Hanoi, Vietnam. A total of 206 patients were enrolled. Demographic and socioeconomic data, household income, and direct and indirect medical costs of patients seeking treatment for GSD were collected through face-to-face interview. Multivariate logistic regression was used to explore factors associated with CHE.Results: The prevalence of CHE in patients suffering from GSD was 35%. The percentage of patients who were covered by health insurance and at risk for CHE was 41.2%, significantly higher than that of those noninsured (15.8%). Proportions of patients with and without health insurance who sought outpatient treatment were 30.6% and 81.6%, respectively. Patients who were divorced or widowed and had intrahepatic gallstones were significantly more likely to experience CHE. Those who were outpatients, were women, had history of pharmacological treatment to parasitic infection, and belong to middle and highest monthly household income quantile were significantly less likely to experience CHE.Conclusion: The findings suggested that efforts to re-evaluate health insurance reimbursement capacity, especially for acute diseases and taking into account the varying preferences of people with different disease severity, should be conducted by health authority. Further studies concerning CHE of GSD in the context of ongoing health policy reform should consider utilizing WHO-recommended measures like the fairness in financial contribution index, as well as taking into consideration the behavioral aspects of health care spending. Keywords: catastrophic health expenditure, gallstone, health insurance, out-of-pocket payments, Vietnam &nbsp