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15篇 您的检索式：作者名="Sweeney MD"

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		题名	作者	年代	出处	被引量
	1	A comparison of biological coatings for the promotion of corneal epithelialization of synthetic surface in vivo显示文摘	Sweeney DF Xie RZ Evans MD Vannas A Tout SD Griesser HJ	2003	Invest Ophthalmol Vis Sci2003，44，8：	1
	2	Blueberry flavonoids inhibit matrix metalloproteinase activity in DU145 human prostate cancer cells显示文摘	Matchett MD Mac Kinnon SL Sweeney MI	2005	Biochemistry and Cell Biology2005，83，5：	1
	3	Risk Factors for Chest Infection in Acute Stroke A Prospective Cohort Study显示文摘	Cameron Sellars Lynsey Bowie Jeremy Bagg Petrina Sweeney DDS Hazel Miller MB ChB Jennifer Tilston MB ChB Peter Langhorne PhD David J Scott MD		0，，：	1
	4	Cerebrospinal flu- id biomarkers of neurovascular dysfunction in mild de- mentia and Alzheimer's disease 显示文摘	Sweeney MD Sagare AP Zlokovic BV	2015	J Cereb Blood Flow Metab2015，，：	1
	5	Blood-brain barrier breakdown in the aging human hippocampus显示文摘	Montagne A Barnes SR Sweeney MD	2015	Neuron2015，85，2：	1
	6	Blueberryflavonoids inhibit matrix metalloproteinase activity in DU145human prostate cancer cells显示文摘	Matchett MD MacKinnon SL Sweeney MI	2005	Biochemistry and Cell Biology-Biochimie Et Biologie Cellulaire2005，83，5：	1
	7	Insights into the oxidative degradation of cellulose by a copper metalloenzyme that exploits biomass components显示文摘	Quinlan R J Sweeney MD Leggio LL	2011	Proceedings of the National Academy of Sciences of the United Seates of America2011，108，15：	1
	8	Blood-brainbarrier breakdown in the aging human hippocampus 显示文摘	Montagne A Barnes SR Sweeney MD	2015	Neuron2015，85，：	1
	9	Blueberry flavonoids inhibit matrix metalloproteinase activity in DU145 human prostate cancer cells 显示文摘	Matchett MD MacKinnon SL Sweeney MI	2005	Biochem Cell Biol2005，83，5：	1
	10	Blueberryflavonoids inhibit matrix metalloproteinase activity in DU 145 humanprostate cancer cells 显示文摘	Matchett MD MacKinnon SL Sweeney MI	2005	Biochemistry and Cell Biology2005，83，5：	1
	11	Ventricular pacing or dual-chamber pacing for sinus-node dysfunction显示文摘	Lamas GA Lee KL Sweeney MD	2002	N Engl J Med2002，346，24：	1
	12	Abnormalities in MRI-measured signal intensity in the corpus callosum in schizophrenia显示文摘	Diwaclkar VA DeBellis MD Sweeney JA	2004	Schizophr Res2004，67，23：	1
	13	A comparison of biological coatings for the promotion of cotneal epithelialization of synthetic surface in vivo 显示文摘	Sweeney DE Xie RZ Evans MD Vannas A Tout SD Griesser HJ	2003	Invest Ophthalmol Vis Sci2003，44，8：	1
	14	Abnormalities in MRI-measured signal intensity in the corpus callosum in schizo- phrenia显示文摘	Diwadkar VA DeBellis MD Sweeney JA	2004	Schizophrenia Research2004，67，23：	1
	15	Prediction of permanent pacemaker implantation after transcatheter aortic valve replacement:The role of machine learning显示文摘BACKGROUND Atrioventricular block requiring permanent pacemaker(PPM)implantation is an important complication of transcatheter aortic valve replacement(TAVR).Application of machine learning could potentially be used to predict preprocedural risk for PPM.AIM To apply machine learning to be used to predict pre-procedural risk for PPM.METHODS A retrospective study of 1200 patients who underwent TAVR(January 2014-December 2017)was performed.964 patients without prior PPM were included for a 30-d analysis and 657 patients without PPM requirement through 30 d were included for a 1-year analysis.After the exclusion of variables with near-zero variance or≥50%missing data,167 variables were included in the random forest gradient boosting algorithm(GBM)optimized using 5-fold cross-validations repeated 10 times.The receiver operator curve(ROC)for the GBM model and PPM risk score models were calculated to predict the risk of PPM at 30 d and 1 year.RESULTS Of 964 patients included in the 30-d analysis without prior PPM,19.6%required PPM post-TAVR.The mean age of patients was 80.9±8.7 years.42.1%were female.Of 657 patients included in the 1-year analysis,the mean age of the patients was 80.7±8.2.Of those,42.6%of patients were female and 26.7%required PPM at 1-year post-TAVR.The area under ROC to predict 30-d and 1-year risk of PPM for the GBM model(0.66 and 0.72)was superior to that of the PPM risk score(0.55 and 0.54)with a P value<0.001.CONCLUSION The GBM model has good discrimination and calibration in identifying patients at high risk of PPM post-TAVR.	Pradyumna Agasthi Hasan Ashraf Sai Harika Pujari Marlene Girardo Andrew Tseng Farouk Mookadam Nithin Venepally Matthew R Buras Bishoy Abraham Banveet K Khetarpal Mohamed Allam Siva K Mulpuru MD Mackram F Eleid Kevin L Greason Nirat Beohar John Sweeney David Fortuin David R Jr Holmes Reza Arsanjani	2023	World Journal of Cardiology2023，15，3：	0

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