Influence of the application of inelastic taping on shoulder subluxation and pain changes in acute stroke patients

[Purpose] The purpose of this study was to investigate the impact on the shoulder joints of performing inelastic taping and bed physical therapy for acute stroke. [Subjects and Methods] The intervention was conducted for eight weeks with an experimental group of 18 stroke patients who received bed physical therapy and inelastic taping and a control group of 18 stroke patients who received only bed physical therapy. [Results] After the intervention, the subluxation degree of the experimental group, which received bed physical therapy and inelastic taping, was found to be significantly different from that of the control group, which received only bed physical therapy. [Conclusion] In conclusion, the application of inelastic taping for acute stroke patients was confirmed to be effective at reducing shoulder subluxation and pain, and was confirmed to be a good physical therapy intervention, based on its efficacy.Key words: Inelastic taping, Shoulder subluxation, Stroke     

Retinoic acid receptor alpha: One of plasma biomarkers associated with exacerbation of chronic obstructive pulmonary disease

Background: Exacerbation of COPD is a major risk factor for bad prognosis of COPD. A few plasma proteins have been discovered to associate with hospital admission due to exacerbation up to date. We tried to find new plasma biomarkers to predict the exacerbation of COPD. Methods: We examined the plasma of normal control (n = 8) and COPD stable (n = 8) and exacerbation (n = 8) using 2-Dimentional Electrophoresis. The differentially expressed protein spots were identified by MALDI-TOF. ELISA were performed for quantitative measurement of RARα in plasma from normal control (n = 37) and COPD (n = 35). Results: 17 proteins were differentially expressed in plasma between stable and exacerbation state in the subjects with COPD. Identification using MALDI-TOF showed that retinoic acid receptor alpha, ninein, isoform CRA_a, alpha-1 antitrypsin, fibrinogen gamma, tyrosyl-DNA phosphodiesterase 2, and T cell receptor delta chain were increased in exacerbation of COPD, while fibrin beta, Crystal Structure Of An Autoimmune Complex Between A Human Igm RF* And Igg1 Fc, transferrin, serpin peptidase inhibitor member 6, complement factor B preproprotein, Chain B, Crig Bound To C3c, and WD repeat-containing protein 1 isoform 1 were decreased. Quantitative measurement showed that RARα plasma levels significantly increased in exacerbation state compared to stable state of COPD (n = 14). In the plasma of stable state, the COPD subjects (n = 14) having more than 0.4 time/yr of admission had very high levels of RAR alpha protein and those (n = 11) having less than 0.4 times/yr of admission had the intermediate levels compared to those having no exacerbation (n = 10). ROC analysis of RAR alpha levels to frequency of admission showed an area under the curve of 0.844. A cut-off of 0.154 ng/ml of RAR alpha predicted hospital admission with a sensitivity of 71.4% and a specificity of 92.8%. Conclusion: The proteomic analysis of plasma indicates that alteration of several proteins may be associated with admission of COPD. Among them, plasma RAR alpha level may predict hospital admission with a sensitivity of 71.4% and a specificity of 92.8%.     

Risk prediction platform for pancreatic fistula after pancreatoduodenectomy using artificial intelligence

BACKGROUND Despite advancements in operative technique and improvements in postoperative managements,postoperative pancreatic fistula(POPF) is a life-threatening complication following pancreatoduodenectomy(PD).There are some reports to predict POPF preoperatively or intraoperatively,but the accuracy of those is questionable.Artificial intelligence(AI) technology is being actively used in the medical field,but few studies have reported applying it to outcomes after PD.AIM To develop a risk prediction platform for POPF using an AI model.METHODS Medical records were reviewed from 1769 patients at Samsung Medical Center who underwent PD from 2007 to 2016.A total of 38 variables were inserted into AI-driven algorithms.The algorithms tested to make the risk prediction platform were random forest(RF) and a neural network(NN) with or without recursive feature elimination(RFE).The median imputation method was used for missing values.The area under the curve(AUC) was calculated to examine the discriminative power of algorithm for POPF prediction.RESULTS The number of POPFs was 221(12.5%) according to the International Study Group of Pancreatic Fistula definition 2016.After median imputation,AUCs using 38 variables were 0.68 ± 0.02 with RF and 0.71 ± 0.02 with NN.The maximal AUC using NN with RFE was 0.74.Sixteen risk factors for POPF were identified by AI algorithm:Pancreatic duct diameter,body mass index,preoperative serum albumin,lipase level,amount of intraoperative fluid infusion,age,platelet count,extrapancreatic location of tumor,combined venous resection,co-existing pancreatitis,neoadjuvant radiotherapy,American Society of Anesthesiologists' score,sex,soft texture of the pancreas,underlying heart disease,and preoperative endoscopic biliary decompression.We developed a web-based POPF prediction platform,and this application is freely available at http://popfrisk.smchbp.org.CONCLUSION This study is the first to predict POPF with multiple risk factors using AI.This platform is reliable(AUC 0.74),so it could be used to select patients who need especially intense therapy and to preoperatively establish an effective treatment strategy.