Plasma fatty acids and the risk of metabolic syndrome in ethnic Chinese adults in Taiwan

<p>Abstract</p> <p>Background</p> <p>Evidence of predictive power of various fatty acids on the risk of metabolic syndrome was scanty. We evaluated the role of various fatty acids, including saturated fat, monounsaturated fat, transfat, n-6 fatty acid, eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), for the risk of the metabolic syndrome in Taiwan.</p> <p>Results</p> <p>A nested case-control study based on 1000 cases of metabolic syndrome and 1:1 matched control subjects. For saturated fat, monounsaturated fat and transfat, the higher the concentration the higher the risk for metabolic syndrome: participants in the highest quintile had a 2.22-fold (95% confidence interval [CI], 1.66 to 2.97) higher risk of metabolic syndrome. In addition, the participants in higher EPA quintiles were less likely to have the risk of metabolic syndrome (adjusted risk, 0.46 [0.34 to 0.61] for the fifth quintile). Participants in the highest risk group (low EPA and high transfat) had a 2.36-fold higher risk of metabolic syndrome (95% CI, 1.38 to 4.03), compared with those in the lowest risk group (high EPA and low transfat). For prediction power, the area under ROC curves increased from 0.926 in the baseline model to 0.928 after adding fatty acids. The net reclassification improvement for metabolic syndrome risk was substantial for saturated fat (2.1%, <it>P </it>= 0.05).</p> <p>Conclusions</p> <p>Plasma fatty acid components improved the prediction of the metabolic syndrome risk in Taiwan.</p

Implement of a 6-DOF manipulator with machine vision and machine learning algorithms

This paper explores the application of a Machine Vision and Machine Learning Algorithm to a Manipulator with six degrees of freedom (6-DOF). A Kinect sensor were used to extract images from a screen and obtain the relevant target information. Image processing was accomplished using a Scale-invariant feature transform (SIFT) Algorithm to capture image of the target object. The processed visual is rendered on the computer controller and Manipulator Learning is accomplished using a Reinforcement Learning Algorithm. Markov Decision Processes (MDP) were used to train the Manipulator to move to the location of the target object. Experimental results showed the Reinforcement Learning Algorithm proposed in this paper is effective and can be utilized on a 6-DOF Manipulator with reproducible results

A Simple Algorithm Using Ventilator Parameters to Predict Successfully Rapid Weaning Program in Cardiac Intensive Care Unit Patients

Background: Ventilator weaning is one of the most significant challenges in the intensive care unit (ICU). Approximately 30% of patients fail to wean, resulting in prolonged use of ventilators and increased mortality. There are numerous high-performance prediction models available today, but they require a large number of parameters to predict and are thus impractical in clinical practice. Objectives: This study aims to create an artificial intelligence (AI) model for predicting weaning time and to identify the most simplified key predictors that will allow the model to achieve adequate accuracy with as few parameters as possible. Methods: This is a retrospective study of to-be-weaned patients (n = 1439) hospitalized in the cardiac ICU of Cheng Hsin General Hospital&rsquo;s Department of Cardiac Surgery from November 2018 to August 2020. The patients were divided into two groups based on whether they could be weaned within 24 h (i.e., &ldquo;patients weaned within 24 h&rdquo; (n = 1042) and &ldquo;patients not weaned within 24 h&rdquo; (n = 397)). Twenty-eight variables were collected including demographic characteristics, arterial blood gas readings, and ventilation set parameters. We created a prediction model using logistic regression and compared it to other machine learning techniques such as decision tree, random forest, support vector machine (SVM), extreme gradient boosting, and artificial neural network. Forward, backward, and stepwise selection methods were used to identify significant variables, and the receiver operating characteristic curve was used to assess the accuracy of each AI model. Results: The SVM [receiver operating characteristic curve (ROC-AUC) = 88%], logistic regression (ROC-AUC = 86%), and XGBoost (ROC-AUC = 85%) models outperformed the other five machine learning models in predicting weaning time. The accuracies in predicting patient weaning within 24 h using seven variables (i.e., expiratory minute ventilation, expiratory tidal volume, ventilation rate set, heart rate, peak pressure, pH, and age) were close to those using 28 variables. Conclusions: The model developed in this research successfully predicted the weaning success of ICU patients using a few and easily accessible parameters such as age. Therefore, it can be used in clinical practice to identify difficult-to-wean patients to improve their treatment

Reduced Gray Matter Volume and Risk of Falls in Parkinson’s Disease with Dementia Patients: A Voxel-Based Morphometry Study

Purpose: Risk of falls is a common sequela affecting patients with Parkinson&rsquo;s disease (PD). Although motor impairment and dementia are correlated with falls, associations of brain structure and cognition deficits with falls remain unclear. Material and Methods: Thirty-five PD patients with dementia (PDD), and 37 age- and sex-matched healthy subjects were recruited for this study. All participants received structural magnetic resonance imaging (MRI) scans, and disease severity and cognitive evaluations. Additionally, patient fall history was recorded. Regional structural differences between PDD with and without fall groups were performed using voxel-based morphometry processing. Stepwise logistic regression analysis was used to predict the fall risk in PDD patients. Results: The results revealed that 48% of PDD patients experienced falls. Significantly lower gray matter volume (GMV) in the left calcarine and right inferior frontal gyrus in PDD patients with fall compared to PDD patients without fall were noted. The PDD patients with fall exhibited worse UPDRS-II scores compared to PDD patients without fall and were negatively correlated with lower GMV in the left calcarine (p/r = 0.004/&minus;0.492). Furthermore, lower GMV in the left calcarine and right inferior frontal gyrus correlated with poor attention and executive functional test scores. Multiple logistic regression analysis showed that the left calcarine was the only variable (p = 0.004, 95% CI = 0.00&ndash;0.00) negatively associated with the fall event. Conclusions: PDD patients exhibiting impaired motor function, lower GMV in the left calcarine and right inferior frontal gyrus, and notable cognitive deficits may have increased risk of falls

Regulated Expressions of MMP-2, -9 by Diterpenoids from Euphorbia formosana Hayata

Two new abietane type diterpenoids, namely seco-helioscopinolide (1) and 3b,7b-dihydroxy-ent-abieta-8,13-diene-12,16-olide (2) were isolated from the aerial parts of Euphorbia formosana Hayata together with helioscopinolide A (3), helioscopinolide B (4), helioscopinolide C (5) and ent-(5b,8a,9b,10a,12a)-12-hydroxyatis-16-ene-3,14-dione (6). The structures of compounds 1−6 were elucidated by analyzing their spectroscopic data and comparison with the literature. Further biological tests by gelatin zymographic analysis revealed that 3−5 significantly up-regulated the expressions and activation of MMP-2 and -9 in human fibrosarcoma cell line HT1080

Ectopic expression of specific GA2 oxidase mutants promotes yield and stress tolerance in rice

A major challenge of modern agricultural biotechnology is the optimization of plant architecture for enhanced productivity, stress tolerance and water use efficiency (WUE). To optimize plant height and tillering that directly link to grain yield in cereals and are known to be tightly regulated by gibberellins (GAs), we attenuated the endogenous levels of GAs in rice via its degradation. GA 2-oxidase (GA2ox) is a key enzyme that inactivates endogenous GAs and their precursors. We identified three conserved domains in a unique class of C20 GA2ox, GA2ox6, which is known to regulate the architecture and function of rice plants. We mutated nine specific amino acids in these conserved domains and observed a gradient of effects on plant height. Ectopic expression of some of these GA2ox6 mutants moderately lowered GA levels and reprogrammed transcriptional networks, leading to reduced plant height, more productive tillers, expanded root system, higher WUE and photosynthesis rate, and elevated abiotic and biotic stress tolerance in transgenic rice. Combinations of these beneficial traits conferred not only drought and disease tolerance but also increased grain yield by 10-30% in field trials. Our studies hold the promise of manipulating GA levels to substantially improve plant architecture, stress tolerance and grain yield in rice and possibly in other major crops