The application of microwaves in axillary hyperhidrosis: Curative effect observation of a pathological examination over 1 year.

Background: In Asia, axillary hyperhidrosis is a frequent problem for many people, and the consequent excessive sweating can seriously affect many aspects of daily life and even lead to mental disorders. Microwave therapy is a new, non-invasive treatment method for axillary hyperhidrosis, whose energy and long-term effectiveness still needs to be clinically validated.

Objective: The aim of this study was to evaluate the clinical efficacy, safety, histological changes, and psychological status of microwave devices in the treatment of axillary hyperhidrosis and osmidrosis.

Enlarged Perivascular Space and Its Correlation with Polysomnography Indicators of Obstructive Sleep Apnea.

Purpose: There is increasing evidence of a causal interaction between obstructive sleep apnea (OSA) and white matter hyperintensity (WMH). WMH and enlarged perivascular space (EPVS) are the neuroimaging markers for cerebral small vessel disease (CSVD). Thus, this study aimed to determine whether a contextual relationship existed between OSA and EPVS.

Structure Learning of Bayesian Network Based on Adaptive Thresholding.

Direct dependencies and conditional dependencies in restricted Bayesian network classifiers (BNCs) are two basic kinds of dependencies. Traditional approaches, such as filter and wrapper, have proved to be beneficial to identify non-significant dependencies one by one, whereas the high computational overheads make them inefficient especially for those BNCs with high structural complexity. Study of the distributions of information-theoretic measures provides a feasible approach to identifying non-significant dependencies in batch that may help increase the structure reliability and avoid overfitting.

Discriminatory Target Learning: Mining Significant Dependence Relationships from Labeled and Unlabeled Data.

Machine learning techniques have shown superior predictive power, among which Bayesian network classifiers (BNCs) have remained of great interest due to its capacity to demonstrate complex dependence relationships. Most traditional BNCs tend to build only one model to fit training instances by analyzing independence between attributes using conditional mutual information. However, for different class labels, the conditional dependence relationships may be different rather than invariant when attributes take different values, which may result in classification bias.