Missing Piece in Colloidal Stability─Morphological Factor of Hydrophobic Nanoparticles.

Hydrophobic nanoparticles (NPs) in water were considered unstable because they lack the repulsive electrostatic interaction and steric effect to prevent aggregation. In this study, porous hydrophobic NPs of two star-shaped giant molecules, , were found to be stable in water and able to retain their kinetic stability in a wide range of temperatures, pH values, and ionic strengths. Unlike the solid hydrophobic NPs that aggregate even with the negative zeta potential (ζ) induced by surface-structured hydrogen-bonded (SHB) water, the porous morphology of NPs reduces the entropically driven hydrophobic effect to prevent aggregation.

Comparison of Clinical Manifestations and Pathology between Kimura Disease and IgG4-Related Disease: A Report of Two Cases and Literature Review.

Kimura disease (KD) is a rare, chronic proliferative condition presenting as a subcutaneous mass predominantly located in the head and neck region; it is characterized by eosinophilia and elevated serum IgE levels. IgG4-related disease (IgG4RD) is a fibroinflammatory condition characterized by swelling in single or multiple organs and the infiltration of IgG4 plasma cells. Herein, we presented two cases.

A fitting machine learning prediction model for short-term mortality following percutaneous catheterization intervention: a nationwide population-based study.

Background: A suitable multivariate predictor for predicting mortality following percutaneous coronary intervention (PCI) remains undetermined. We used a nationwide database to construct mortality prediction models to find the appropriate model.

Methods: Data were analyzed from the Taiwan National Health Insurance Research Database (NHIRD) covering the period from 2004 to 2013.

The Performance of Different Artificial Intelligence Models in Predicting Breast Cancer among Individuals Having Type 2 Diabetes Mellitus.

Early reports indicate that individuals with type 2 diabetes mellitus (T2DM) may have a greater incidence of breast malignancy than patients without T2DM. The aim of this study was to investigate the effectiveness of three different models for predicting risk of breast cancer in patients with T2DM of different characteristics. From 2000 to 2012, data on 636,111 newly diagnosed female T2DM patients were available in the Taiwan's National Health Insurance Research Database.

Predicting weaning difficulty for planned extubation patients with an artificial neural network.

This study aims to construct a neural network to predict weaning difficulty among planned extubation patients in intensive care units.This observational cohort study was conducted in eight adult ICUs in a medical center about adult patients experiencing planned extubation.The data of 3602 patients with planned extubation in ICUs of Chi-Mei Medical Center (from Dec.

Artificial Intelligence Prediction Model for the Cost and Mortality of Renal Replacement Therapy in Aged and Super-Aged Populations in Taiwan.

Background: Prognosis of the aged population requiring maintenance dialysis has been reportedly poor. We aimed to develop prediction models for one-year cost and one-year mortality in aged individuals requiring dialysis to assist decision-making for deciding whether aged people should receive dialysis or not.

Methods: We used data from the National Health Insurance Research Database (NHIRD).

Development of a prediction model for pancreatic cancer in patients with type 2 diabetes using logistic regression and artificial neural network models.

Objectives: Patients with type 2 diabetes (T2DM) are suggested to have a higher risk of developing pancreatic cancer. We used two models to predict pancreatic cancer risk among patients with T2DM.

Methods: The original data used for this investigation were retrieved from the National Health Insurance Research Database of Taiwan.

Comparison of machine learning models for the prediction of mortality of patients with unplanned extubation in intensive care units.

Unplanned extubation (UE) can be associated with fatal outcome; however, an accurate model for predicting the mortality of UE patients in intensive care units (ICU) is lacking. Therefore, we aim to compare the performances of various machine learning models and conventional parameters to predict the mortality of UE patients in the ICU. A total of 341 patients with UE in ICUs of Chi-Mei Medical Center between December 2008 and July 2017 were enrolled and their demographic features, clinical manifestations, and outcomes were collected for analysis.

Development of a Prediction Model for Colorectal Cancer among Patients with Type 2 Diabetes Mellitus Using a Deep Neural Network.

Objectives: Observational studies suggested that patients with type 2 diabetes mellitus (T2DM) presented a higher risk of developing colorectal cancer (CRC). The current study aims to create a deep neural network (DNN) to predict the onset of CRC for patients with T2DM.

Methods: We employed the national health insurance database of Taiwan to create predictive models for detecting an increased risk of subsequent CRC development in T2DM patients in Taiwan.

An Artificial Neural Network Model for Predicting Successful Extubation in Intensive Care Units.

Background: Successful weaning from mechanical ventilation is important for patients in intensive care units (ICUs). The aim was to construct neural networks to predict successful extubation in ventilated patients in ICUs.

Methods: Data from 1/12/2009 through 31/12/2011 of 3602 patients with planned extubation in Chi-Mei Medical Center's ICUs was used to train and test an artificial neural network (ANN).