Capsular contracture is a prevalent and severe complication that affects the post-operative outcomes of patients who receive silicone breast *** present,prosthesis replacement is the major treatment for capsular contr...
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Capsular contracture is a prevalent and severe complication that affects the post-operative outcomes of patients who receive silicone breast *** present,prosthesis replacement is the major treatment for capsular contracture after both breast augmentation procedures and breast reconstruction following breast cancer ***,the mecha-nism(s)underlying breast capsular contracture remains *** study aimed to identify the biological features of breast capsular contracture and reveal the potential underlying mechanism using RNA *** tissues from 12 female patients(15 breast capsules)were divided into low capsular contracture(LCC)and high capsular contracture(HCC)groups based on the Baker ***,41 lipid metabolism-related genes were identified through enrichment analysis,and three of these genes were identified as candidate genes by SVM-RFE and LASSO *** then compared the proportions of the 22 types of im-mune cells between the LCC and HCC groups using a CIBERSORT analysis and explored the Cor-relation between the candidate hub features and immune ***,PRKAR2B was positively correlated with the differentially clustered immune cells,which were M1 macro-phages and follicular helper T cells(area under the ROC=0.786).In addition,the expression of PRKAR2B at the mRNA or protein level was lower in the HCC group than in the LCC *** molecular mechanisms were identified based on the expression levels in the high and low PRKAR2B *** findings indicate that PRKAR2B is a novel diagnostic biomarker for breast capsular contracture and might also influence the grade and progression of capsularcontracture.
Background: The exponential growth in computing power and the increasing digitization of information have substantially advanced the machine learning (ML) research field. However, ML algorithms are often considered &q...
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Background: The exponential growth in computing power and the increasing digitization of information have substantially advanced the machine learning (ML) research field. However, ML algorithms are often considered "black boxes," and this fosters distrust. In medical domains, in which mistakes can result in fatal outcomes, practitioners may be especially reluctant to trust ML algorithms. Objective: The aim of this study is to explore the effect of user-interface design features on intensivists' trust in an ML-based clinical decision support system. Methods: A total of 47 physicians from critical care specialties were presented with 3 patient cases of bacteremia in the setting of an ML-based simulation system. Three conditions of the simulation were tested according to combinations of information relevancy and interactivity. Participants' trust in the system was assessed by their agreement with the system's prediction and a postexperiment questionnaire. Linear regression models were applied to measure the effects. Results: Participants' agreement with the system's prediction did not differ according to the experimental conditions. However, in the postexperiment questionnaire, higher information relevancy ratings and interactivity ratings were associated with higher perceived trust in the system (P<.001 for both). The explicit visual presentation of the features of the ML algorithm on the user interface resulted in lower trust among the participants (P=.05). Conclusions: Information relevancy and interactivity features should be considered in the design of the user interface of ML-based clinical decision support systems to enhance intensivists' trust. This study sheds light on the connection between information relevancy, interactivity, and trust in human-ML interaction, specifically in the intensive care unit environment.
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