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| 1 | Set up remote workers to thrive显示文摘 | Mulki J Bardhi F Lassk F and Nanavaty-Dahl J | 2009 | MIT SloanManagement Review2009，，1： | 1 |
| 2 | Access - based consumption: The case of car sharing 显示文摘 | BARDHI F ECKHARDT M G | 2012 | Joumal of Consumer Research2012，39，12： | 1 |
| 3 | Understanding how technology paradoxes affect customer satisfaction with self service technol- ogy: the role of performance ambiguity and trust in technology 显示文摘 | Johnson D S Bardhi F | 2008 | Psychology & Marketing2008，25，5： | 1 |
| 4 | Normobaric oxygen therapy attenuates hyperglycolysis in ischemic stroke显示文摘Normobaric oxygen therapy has gained attention as a simple and convenient means of achieving neuroprotection against the pathogenic cascade initiated by acute ischemic stroke.The mechanisms underlying the neuroprotective efficacy of normobaric oxygen therapy,however,have not been fully elucidated.It is hypothesized that cerebral hyperglycolysis is involved in the neuroprotection of normobaric oxygen therapy against ischemic stroke.In this study,Sprague-Dawley rats were subjected to either 2-hour middle cerebral artery occlusion followed by 3-or 24-hour reperfusion or to a permanent middle cerebral artery occlusion event.At 2 hours after the onset of ischemia,all rats received either 95%oxygen normobaric oxygen therapy for 3 hours or room air.Compared with room air,normobaric oxygen therapy significantly reduced the infarct volume,neurological deficits,and reactive oxygen species and increased the production of adenosine triphosphate in ischemic rats.These changes were associated with reduced transcriptional and translational levels of the hyperglycolytic enzymes glucose transporter 1 and 3,phosphofructokinase 1,and lactate dehydrogenase.In addition,normobaric oxygen therapy significantly reduced adenosine monophosphate-activated protein kinase mRNA expression and phosphorylated adenosine monophosphate-activated protein kinase protein expression.These findings suggest that normobaric oxygen therapy can reduce hyperglycolysis through modulating the adenosine monophosphate-activated protein kinase signaling pathway and alleviating oxidative injury,thereby exhibiting neuroprotective effects in ischemic stroke.This study was approved by the Institutional Animal Investigation Committee of Capital Medical University(approval No.AEEI-2018-033)on August 13,2018. | Zhe Cheng Feng-Wu Li Christopher R.Stone Kenneth Elkin Chang-Ya Peng Redina Bardhi Xiao-Kun Geng Yu-Chuan Ding | 2021 | Neural Regeneration Research2021，16，6： | 1 |
| 5 | Machine Learning Techniques Applied to Electronic Healthcare Records to Predict Cancer Patient Survivability显示文摘Breast cancer(BCa)and prostate cancer(PCa)are the two most common types of cancer.Various factors play a role in these cancers,and discovering the most important ones might help patients live longer,better lives.This study aims to determine the variables that most affect patient survivability,and how the use of different machine learning algorithms can assist in such predictions.The AURIA database was used,which contains electronic healthcare records(EHRs)of 20,006 individual patients diagnosed with either breast or prostate cancer in a particular region in Finland.In total,there were 178 features for BCa and 143 for PCa.Six feature selection algorithms were used to obtain the 21 most important variables for BCa,and 19 for PCa.These features were then used to predict patient survivability by employing nine different machine learning algorithms.Seventy-five percent of the dataset was used to train the models and 25%for testing.Cross-validation was carried out using the StratifiedKfold technique to test the effectiveness of the machine learning models.The support vector machine classifier yielded the best ROC with an area under the curve(AUC)=0.83,followed by the KNeighborsClassifier with AUC=0.82 for the BCa dataset.The two algorithms that yielded the best results for PCa are the random forest classifier and KNeighborsClassifier,both with AUC=0.82.This study shows that not all variables are decisive when predicting breast or prostate cancer patient survivability.By narrowing down the input variables,healthcare professionals were able to focus on the issues that most impact patients,and hence devise better,more individualized care plans. | Ornela Bardhi Begonya Garcia Zapirain | 2021 | Computers, Materials & Continua2021，，8： | 0 |
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