New Protections for Healthcare Workers

<p>Supplemental material, Video 1: sj-vid-1-pic-10.1177 0954406218779612 for Self-adaptive grasp analysis of a novel under-actuated cable-truss robotic finger by Shangling Qiao, Hongwei Guo, Rongqiang Liu, Yong Huang and Zongquan Deng in Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science</p

Structure Synthesis and Optimization of Feed Support Mechanisms for a Deployable Parabolic Antenna

ABSTRACT In this paper, a systematic method to synthesize parallel mechanisms for feed support mechanisms based on screw theory is proposed. First, the motion requirement of the feed support mechanism is studied. Then, a class of parallel mechanisms having a translational motion with different degrees of freedom is synthesized based on the constraint-synthesis method. Then, these parallel mechanisms are modified based on the specificities of deployable mechanisms, and two kinds of mechanisms are selected as the unit for the feed support mechanism. Finally, the deployment ratio of two kinds of mechanisms is optimized. The configurations obtained in this paper can enrich the types of feed support mechanisms.</div

Table_1_Prediction Nomogram for Postoperative 30-Day Mortality in Acute Type A Aortic Dissection Patients Receiving Total Aortic Arch Replacement With Frozen Elephant Trunk Technique.docx

ObjectiveTo develop and validate a nomogram model to predict postoperative 30-day mortality in acute type A aortic dissection patients receiving total aortic arch replacement with frozen elephant trunk technique.MethodClinical data on 1,156 consecutive acute type A aortic dissection patients who got total aortic arch replacement using the frozen elephant trunk technique was collected from January 2010 to December 2020. These patients were divided into training and testing cohorts at random with a ratio of 7:3. To predict postoperative 30-day mortality, a nomogram was established in the training set using the logistic regression model. The novel nomogram was then validated in the testing set. The nomogram's calibration and discrimination were evaluated. In addition, we created four machine learning prediction models in the training set. In terms of calibration and discrimination, the nomogram was compared to these machine learning models in testing set.ResultsLeft ventricular end-diastolic diameter 2, persistent abdominal pain, radiological celiac trunk malperfusion, concomitant coronary artery bypass grafting and cardiopulmonary bypass time >4 h were independent predictors of the 30-day mortality. The nomogram based on these 6 predictors manifested satisfying calibration and discrimination. In testing set, the nomogram outperformed the other 4 machine learning models.ConclusionThe novel nomogram is a simple and effective tool to predict 30-day mortality rate for acute type A aortic dissection patients undergoing total aortic arch replacement with frozen elephant trunk technique.</p

Image_1_Prediction Nomogram for Postoperative 30-Day Mortality in Acute Type A Aortic Dissection Patients Receiving Total Aortic Arch Replacement With Frozen Elephant Trunk Technique.TIFF

ObjectiveTo develop and validate a nomogram model to predict postoperative 30-day mortality in acute type A aortic dissection patients receiving total aortic arch replacement with frozen elephant trunk technique.MethodClinical data on 1,156 consecutive acute type A aortic dissection patients who got total aortic arch replacement using the frozen elephant trunk technique was collected from January 2010 to December 2020. These patients were divided into training and testing cohorts at random with a ratio of 7:3. To predict postoperative 30-day mortality, a nomogram was established in the training set using the logistic regression model. The novel nomogram was then validated in the testing set. The nomogram's calibration and discrimination were evaluated. In addition, we created four machine learning prediction models in the training set. In terms of calibration and discrimination, the nomogram was compared to these machine learning models in testing set.ResultsLeft ventricular end-diastolic diameter 2, persistent abdominal pain, radiological celiac trunk malperfusion, concomitant coronary artery bypass grafting and cardiopulmonary bypass time >4 h were independent predictors of the 30-day mortality. The nomogram based on these 6 predictors manifested satisfying calibration and discrimination. In testing set, the nomogram outperformed the other 4 machine learning models.ConclusionThe novel nomogram is a simple and effective tool to predict 30-day mortality rate for acute type A aortic dissection patients undergoing total aortic arch replacement with frozen elephant trunk technique.</p

Table_2_Prediction Nomogram for Postoperative 30-Day Mortality in Acute Type A Aortic Dissection Patients Receiving Total Aortic Arch Replacement With Frozen Elephant Trunk Technique.docx

ObjectiveTo develop and validate a nomogram model to predict postoperative 30-day mortality in acute type A aortic dissection patients receiving total aortic arch replacement with frozen elephant trunk technique.MethodClinical data on 1,156 consecutive acute type A aortic dissection patients who got total aortic arch replacement using the frozen elephant trunk technique was collected from January 2010 to December 2020. These patients were divided into training and testing cohorts at random with a ratio of 7:3. To predict postoperative 30-day mortality, a nomogram was established in the training set using the logistic regression model. The novel nomogram was then validated in the testing set. The nomogram's calibration and discrimination were evaluated. In addition, we created four machine learning prediction models in the training set. In terms of calibration and discrimination, the nomogram was compared to these machine learning models in testing set.ResultsLeft ventricular end-diastolic diameter 2, persistent abdominal pain, radiological celiac trunk malperfusion, concomitant coronary artery bypass grafting and cardiopulmonary bypass time >4 h were independent predictors of the 30-day mortality. The nomogram based on these 6 predictors manifested satisfying calibration and discrimination. In testing set, the nomogram outperformed the other 4 machine learning models.ConclusionThe novel nomogram is a simple and effective tool to predict 30-day mortality rate for acute type A aortic dissection patients undergoing total aortic arch replacement with frozen elephant trunk technique.</p

Table_1_Gut microbiota modulates differential lipid metabolism outcomes associated with FTO gene polymorphisms in response to personalized nutrition intervention.DOCX

BackgroundInterindividual differences in response to personalized nutrition (PN) intervention were affected by multiple factors, including genetic backgrounds and gut microbiota. The fat mass and obesity associated (FTO) gene is an important factor related to hyperlipidemia and occurrence of cardiovascular diseases. However, few studies have explored the differences in response to intervention among subjects with different genotypes of FTO, and the associations between gut microbiota and individual responses.ObjectiveTo explore the differential lipid metabolism outcomes associated with FTO gene polymorphisms in response to PN intervention, the altered taxonomic features of gut microbiota caused by the intervention, and the associations between gut microbiota and lipid metabolism outcomes.MethodsA total of 400 overweight or obese adults were recruited in the study and randomly divided into the PN group and control group, of whom 318 completed the 12-week intervention. The single nucleotide polymorphism (SNP) of rs1121980 in FTO was genotyped. Gut microbiota and blood lipids were determined at baseline and week 12. Functional property of microbiota was predicted using Tax4Fun functional prediction analysis.ResultsSubjects with the risk genotype of FTO had significantly higher weight and waist circumference (WC) at baseline. Generalized linear regression models showed that the reduction in weight, body mass index (BMI), WC, body fat percentage, total cholesterol (TCHO), and low-density lipoprotein (LDL) was greater in subjects with the risk genotype of FTO and in the PN group. Significant interaction effects between genotype and intervention on weight, BMI, WC, TCHO, and LDL were found after stratifying for specific genotype of FTO. All subjects showed significant increasement in α diversity of gut microbiota after intervention except for those with the non-risk genotype in the control group. Gut microbiota, including Blautia and Firmicutes, might be involved in lipid metabolism in response to interventions. The predicted functions of the microbiota in subjects with different genotypes were related to lipid metabolism-related pathways, including fatty acid biosynthesis and degradation.ConclusionSubjects with the risk genotype of FTO had better response to nutrition intervention, and PN intervention showed better amelioration in anthropometric parameters and blood lipids than the control. Gut microbiota might be involved in modulating differential lipid metabolism responses to intervention in subjects with different genotypes.Trial registration[Chictr.org.cn], identifier [ChiCTR1900026226].</p

Image_3_Prediction Nomogram for Postoperative 30-Day Mortality in Acute Type A Aortic Dissection Patients Receiving Total Aortic Arch Replacement With Frozen Elephant Trunk Technique.TIFF

ObjectiveTo develop and validate a nomogram model to predict postoperative 30-day mortality in acute type A aortic dissection patients receiving total aortic arch replacement with frozen elephant trunk technique.MethodClinical data on 1,156 consecutive acute type A aortic dissection patients who got total aortic arch replacement using the frozen elephant trunk technique was collected from January 2010 to December 2020. These patients were divided into training and testing cohorts at random with a ratio of 7:3. To predict postoperative 30-day mortality, a nomogram was established in the training set using the logistic regression model. The novel nomogram was then validated in the testing set. The nomogram's calibration and discrimination were evaluated. In addition, we created four machine learning prediction models in the training set. In terms of calibration and discrimination, the nomogram was compared to these machine learning models in testing set.ResultsLeft ventricular end-diastolic diameter 2, persistent abdominal pain, radiological celiac trunk malperfusion, concomitant coronary artery bypass grafting and cardiopulmonary bypass time >4 h were independent predictors of the 30-day mortality. The nomogram based on these 6 predictors manifested satisfying calibration and discrimination. In testing set, the nomogram outperformed the other 4 machine learning models.ConclusionThe novel nomogram is a simple and effective tool to predict 30-day mortality rate for acute type A aortic dissection patients undergoing total aortic arch replacement with frozen elephant trunk technique.</p

Visualization 1: Stitching interferometry of full cylinder by use of the first-order approximation of cylindrical coordinate transformation

3D video of stitching result Originally published in Optics Express on 20 February 2017 (oe-25-4-3092

Visualization 2: Stitching interferometry of full cylinder by use of the first-order approximation of cylindrical coordinate transformation

Video of cylindricity measured by Taylor Hobson 585 Originally published in Optics Express on 20 February 2017 (oe-25-4-3092

Image_2_Prediction Nomogram for Postoperative 30-Day Mortality in Acute Type A Aortic Dissection Patients Receiving Total Aortic Arch Replacement With Frozen Elephant Trunk Technique.TIFF

ObjectiveTo develop and validate a nomogram model to predict postoperative 30-day mortality in acute type A aortic dissection patients receiving total aortic arch replacement with frozen elephant trunk technique.MethodClinical data on 1,156 consecutive acute type A aortic dissection patients who got total aortic arch replacement using the frozen elephant trunk technique was collected from January 2010 to December 2020. These patients were divided into training and testing cohorts at random with a ratio of 7:3. To predict postoperative 30-day mortality, a nomogram was established in the training set using the logistic regression model. The novel nomogram was then validated in the testing set. The nomogram's calibration and discrimination were evaluated. In addition, we created four machine learning prediction models in the training set. In terms of calibration and discrimination, the nomogram was compared to these machine learning models in testing set.ResultsLeft ventricular end-diastolic diameter 2, persistent abdominal pain, radiological celiac trunk malperfusion, concomitant coronary artery bypass grafting and cardiopulmonary bypass time >4 h were independent predictors of the 30-day mortality. The nomogram based on these 6 predictors manifested satisfying calibration and discrimination. In testing set, the nomogram outperformed the other 4 machine learning models.ConclusionThe novel nomogram is a simple and effective tool to predict 30-day mortality rate for acute type A aortic dissection patients undergoing total aortic arch replacement with frozen elephant trunk technique.</p