138 research outputs found
Hyperoxia Causes Mitochondrial Fragmentation in Pulmonary Endothelial Cells by Increasing Expression of Pro-Fission Proteins
ObjectiveâWe explored mechanisms that alter mitochondrial structure and function in pulmonary endothelial cells (PEC) function after hyperoxia. Approach and ResultsâMitochondrial structures of PECs exposed to hyperoxia or normoxia were visualized and mitochondrial fragmentation quantified. Expression of pro-fission or fusion proteins or autophagy-related proteins were assessed by Western blot. Mitochondrial oxidative state was determined using mito-roGFP. Tetramethylrhodamine methyl ester estimated mitochondrial polarization in treatment groups. The role of mitochondrially derived reactive oxygen species in mt-fragmentation was investigated with mito-TEMPOL and mitochondrial DNA (mtDNA) damage studied by using ENDO III (mt-tat-endonuclease III), a protein that repairs mDNA damage. Drp-1 (dynamin-related protein 1) was overexpressed or silenced to test the role of this protein in cell survival or transwell resistance. Hyperoxia increased fragmentation of PEC mitochondria in a time-dependent manner through 48 hours of exposure. Hyperoxic PECs exhibited increased phosphorylation of Drp-1 (serine 616), decreases in Mfn1 (mitofusion protein 1), but increases in OPA-1 (optic atrophy 1). Pro-autophagy proteins p62 (LC3 adapterâbinding protein SQSTM1/p62), PINK-1 (PTEN-induced putative kinase 1), and LC3B (microtubule-associated protein 1A/1B-light chain 3) were increased. Returning cells to normoxia for 24 hours reversed the increased mt-fragmentation and changes in expression of pro-fission proteins. Hyperoxia-induced changes in mitochondrial structure or cell survival were mitigated by antioxidants mito-TEMPOL, Drp-1 silencing, or inhibition or protection by the mitochondrial endonuclease ENDO III. Hyperoxia induced oxidation and mitochondrial depolarization and impaired transwell resistance. Decrease in resistance was mitigated by mito-TEMPOL or ENDO III and reproduced by overexpression of Drp-1. ConclusionsâBecause hyperoxia evoked mt-fragmentation, cell survival and transwell resistance are prevented by ENDO III and mito-TEMPOL and Drp-1 silencing, and these data link hyperoxia-induced mt-DNA damage, Drp-1 expression, mt-fragmentation, and PEC dysfunction
Biogenic and biomass burning organic aerosol in a boreal forest at HyytiÀlÀ, Finland, during HUMPPA-COPEC 2010
Submicron aerosol particles were collected during July and August 2010 in HyytiĂ€lĂ€, Finland, to determine the composition and sources of aerosol at that boreal forest site. Submicron particles were collected on Teflon filters and analyzed by Fourier transform infrared (FTIR) spectroscopy for organic functional groups (OFGs). Positive matrix factorization (PMF) was applied to aerosol mass spectrometry (AMS) measurements and FTIR spectra to identify summertime sources of submicron aerosol mass at the sampling site. The two largest sources of organic mass (OM) in particles identified at HyytiĂ€lĂ€ were (1) biogenic aerosol from surrounding local forest and (2) biomass burning aerosol, transported 4â5 days from large wildfires burning near Moscow, Russia, and northern Ukraine. The robustness of this apportionment is supported by the agreement of two independent analytical methods for organic measurements with three statistical techniques. FTIR factor analysis was more sensitive to the chemical differences between biogenic and biomass burning organic components, while AMS factor analysis had a higher time resolution that more clearly linked the temporal behavior of separate OM factors to that of different source tracers even though their fragment mass spectrum were similar. The greater chemical sensitivity of the FTIR is attributed to the nondestructive preparation and the functional group specificity of spectroscopy. The FTIR spectra show strong similarities among biogenic and biomass burning factors from different regions as well as with reference OM (namely olive tree burning organic aerosol and α-pinene chamber secondary organic aerosol (SOA)). The biogenic factor correlated strongly with temperature and oxidation products of biogenic volatile organic compounds (BVOCs), included more than half of the oxygenated OFGs (carbonyl groups at 29% and carboxylic acid groups at 22%), and represented 35% of the submicron OM. Compared to previous studies at HyytiĂ€lĂ€, the summertime biogenic OM is 1.5 to 3 times larger than springtime biogenic OM (0.64 ÎŒg m^â3 and 0.4 ÎŒg m^â3, measured in 2005 and 2007, respectively), even though it contributed only 35% of OM. The biomass burning factor contributed 25% of OM on average and up to 62% of OM during three periods of transported biomass burning emissions: 26â28 July, 29â30 July, and 8â9 August, with OFG consisting mostly of carbonyl (41%) and alcohol (25%) groups. The high summertime terrestrial biogenic OM (1.7 ÎŒg m^â3) and the high biomass burning contributions (1.2 ÎŒg m^â3) were likely due to the abnormally high temperatures that resulted in both stressed boreal forest conditions with high regional BVOC emissions and numerous wildfires in upwind regions
Relocation to get venture capital : a resource dependence perspective
This is the author accepted manuscript. The final version is available from SAGE via the DOI in this record.Using a resource dependence perspective, we theorize and show that non-venture-capital-backed ventures founded in U.S. states with a lower availability of venture capital (VC) are more likely to relocate to California (CA) or Massachusetts (MA)âthe two VC richest statesâcompared to ventures founded in states with a greater availability of VC. Moreover, controlling for self-selection, ventures that relocate to CA or MA subsequently have a greater probability of attracting initial VC compared to ventures that stay in their home state. We discuss the implications for theory, future research, and practice
The summertime Boreal forest field measurement intensive (HUMPPA-COPEC-2010): an overview of meteorological and chemical influences
This paper describes the background, instrumentation, goals, and the regional influences on the HUMPPA-COPEC intensive field measurement campaign, conducted at the Boreal forest research station SMEAR II (Station for Measuring Ecosystem-Atmosphere Relation) in HyytiĂ€lĂ€, Finland from 12 Julyâ12 August 2010. The prevailing meteorological conditions during the campaign are examined and contrasted with those of the past six years. Back trajectory analyses show that meteorological conditions at the site in 2010 were characterized by a higher proportion of southerly flow than in the other years studied. As a result the summer of 2010 was anomalously warm and high in ozone making the campaign relevant for the analysis of possible future climates. A comprehensive land use analysis, provided on both 5 and 50 km scales, shows that the main vegetation types surrounding the site on both the regional and local scales are: coniferous forest (Scots pine and/or Norway spruce); mixed forest (Birch and conifers); and woodland scrub (e.g. Willows, Aspen); indicating that the campaign results can be taken as representative of the Boreal forest ecosystem. In addition to the influence of biogenic emissions, the measurement site was occasionally impacted by sources other than vegetation. Specific tracers have been used here to identify the time periods when such sources have impacted the site namely: biomass burning (acetonitrile and CO), urban anthropogenic pollution (pentane and SO<sub>2</sub>) and the nearby Korkeakoski sawmill (enantiomeric ratio of chiral monoterpenes). None of these sources dominated the study period, allowing the Boreal forest summertime emissions to be assessed and contrasted with various other source signatures
A Hybrid Fuzzy Multi-criteria Decision Making Model to Evaluate the Overall Performance of Public Emergency Departments: A Case Study
[EN] Performance evaluation is relevant for supporting managerial decisions related to the improvement of public emergency departments (EDs). As different criteria from ED context and several alternatives need to be considered, selecting a suitable Multicriteria Decision-Making (MCDM) approach has become a crucial step for ED performance evaluation. Although some methodologies have been proposed to address this challenge, a more complete approach is still lacking. This paper bridges this gap by integrating three potent MCDM methods. First, the Fuzzy Analytic Hierarchy Process (FAHP) is used to determine the criteria and sub-criteria weights under uncertainty, followed by the
interdependence evaluation via fuzzy Decision-Making Trial and Evaluation Laboratory(FDEMATEL). The fuzzy logic is merged with AHP and DEMATEL to illustrate vague judgments. Finally, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used for
ranking EDs. This approach is validated in a real 3-ED cluster. The results revealed the critical role of Infrastructure (21.5%) in ED performance and the interactive nature of Patient safety (C+R =12.771).
Furthermore, this paper evidences the weaknesses to be tackled for upgrading the performance of each ED.Ortiz-Barrios, M.; Alfaro Saiz, JJ. (2020). A Hybrid Fuzzy Multi-criteria Decision Making Model to Evaluate the Overall Performance of Public Emergency Departments: A Case Study. International Journal of Information Technology & Decision Making. 19(6):1485-1548. https://doi.org/10.1142/S0219622020500364S14851548196Lord, K., Parwani, V., Ulrich, A., Finn, E. B., Rothenberg, C., Emerson, B., ⊠Venkatesh, A. K. (2018). Emergency department boarding and adverse hospitalization outcomes among patients admitted to a general medical service. The American Journal of Emergency Medicine, 36(7), 1246-1248. doi:10.1016/j.ajem.2018.03.043SĂžrup, C. M., Jacobsen, P., & Forberg, J. L. (2013). Evaluation of emergency department performance â a systematic review on recommended performance and quality-in-care measures. Scandinavian Journal of Trauma, Resuscitation and Emergency Medicine, 21(1). doi:10.1186/1757-7241-21-62Farokhi, S., & Roghanian, E. (2018). Determining quantitative targets for performance measures in the balanced scorecard method using response surface methodology. Management Decision, 56(9), 2006-2037. doi:10.1108/md-08-2017-0772Ortiz Barrios, M. A., & Felizzola JimĂ©nez, H. (2016). Use of Six Sigma Methodology to Reduce Appointment Lead-Time in Obstetrics Outpatient Department. Journal of Medical Systems, 40(10). doi:10.1007/s10916-016-0577-3Sunder M., V., Ganesh, L. S., & Marathe, R. R. (2018). A morphological analysis of research literature on Lean Six Sigma for services. International Journal of Operations & Production Management, 38(1), 149-182. doi:10.1108/ijopm-05-2016-0273Bergeron, B. P. (2017). Performance Management in Healthcare. doi:10.4324/9781315102214Santos, S. P., Belton, V., Howick, S., & Pilkington, M. (2018). Measuring organisational performance using a mix of OR methods. Technological Forecasting and Social Change, 131, 18-30. doi:10.1016/j.techfore.2017.07.028Ho, W., & Ma, X. (2018). The state-of-the-art integrations and applications of the analytic hierarchy process. European Journal of Operational Research, 267(2), 399-414. doi:10.1016/j.ejor.2017.09.007Dargi, A., Anjomshoae, A., Galankashi, M. R., Memari, A., & Tap, M. B. M. (2014). Supplier Selection: A Fuzzy-ANP Approach. Procedia Computer Science, 31, 691-700. doi:10.1016/j.procs.2014.05.317Jing, M., Jie, Y., Shou-yi, L., & Lu, W. (2015). Application of fuzzy analytic hierarchy process in the risk assessment of dangerous small-sized reservoirs. International Journal of Machine Learning and Cybernetics, 9(1), 113-123. doi:10.1007/s13042-015-0363-4Samanlioglu, F., Taskaya, Y. E., Gulen, U. C., & Cokcan, O. (2018). A Fuzzy AHPâTOPSIS-Based Group Decision-Making Approach to IT Personnel Selection. International Journal of Fuzzy Systems, 20(5), 1576-1591. doi:10.1007/s40815-018-0474-7CHEN, M.-F., TZENG, G.-H., & TANG, T.-I. (2005). FUZZY MCDM APPROACH FOR EVALUATION OF EXPATRIATE ASSIGNMENTS. International Journal of Information Technology & Decision Making, 04(02), 277-296. doi:10.1142/s0219622005001520Gul, M., Celik, E., Gumus, A. T., & Guneri, A. F. (2016). Emergency department performance evaluation by an integrated simulation and interval type-2 fuzzy MCDM-based scenario analysis. European J. of Industrial Engineering, 10(2), 196. doi:10.1504/ejie.2016.075846JovÄiÄ, PrĆŻĆĄa, Dobrodolac, & Ć vadlenka. (2019). A Proposal for a Decision-Making Tool in Third-Party Logistics (3PL) Provider Selection Based on Multi-Criteria Analysis and the Fuzzy Approach. Sustainability, 11(15), 4236. doi:10.3390/su11154236Saaty, T. L., & Vargas, L. G. (2012). Models, Methods, Concepts & Applications of the Analytic Hierarchy Process. International Series in Operations Research & Management Science. doi:10.1007/978-1-4614-3597-6Vargas, L. G. (2016). Voting with Intensity of Preferences. International Journal of Information Technology & Decision Making, 15(04), 839-859. doi:10.1142/s0219622016400058Lee, K.-C., Tsai, W.-H., Yang, C.-H., & Lin, Y.-Z. (2018). An MCDM approach for selecting green aviation fleet program management strategies under multi-resource limitations. Journal of Air Transport Management, 68, 76-85. doi:10.1016/j.jairtraman.2017.06.011Labib, A., & Read, M. (2015). A hybrid model for learning from failures: The Hurricane Katrina disaster. Expert Systems with Applications, 42(21), 7869-7881. doi:10.1016/j.eswa.2015.06.020Hosseini, S., & Khaled, A. A. (2016). A hybrid ensemble and AHP approach for resilient supplier selection. Journal of Intelligent Manufacturing, 30(1), 207-228. doi:10.1007/s10845-016-1241-yZavadskas, E. K., Govindan, K., Antucheviciene, J., & Turskis, Z. (2016). Hybrid multiple criteria decision-making methods: a review of applications for sustainability issues. Economic Research-Ekonomska IstraĆŸivanja, 29(1), 857-887. doi:10.1080/1331677x.2016.1237302Lolli, F., Balugani, E., Ishizaka, A., Gamberini, R., Butturi, M. A., Marinello, S., & Rimini, B. (2019). On the elicitation of criteria weights in PROMETHEE-based ranking methods for a mobile application. Expert Systems with Applications, 120, 217-227. doi:10.1016/j.eswa.2018.11.030De Almeida Filho, A. T., Clemente, T. R. N., Morais, D. C., & de Almeida, A. T. (2018). Preference modeling experiments with surrogate weighting procedures for the PROMETHEE method. European Journal of Operational Research, 264(2), 453-461. doi:10.1016/j.ejor.2017.08.006Sun, G., Guan, X., Yi, X., & Zhou, Z. (2018). An innovative TOPSIS approach based on hesitant fuzzy correlation coefficient and its applications. Applied Soft Computing, 68, 249-267. doi:10.1016/j.asoc.2018.04.004FrazĂŁo, T. D. C., Camilo, D. G. G., Cabral, E. L. S., & Souza, R. P. (2018). Multicriteria decision analysis (MCDA) in health care: a systematic review of the main characteristics and methodological steps. BMC Medical Informatics and Decision Making, 18(1). doi:10.1186/s12911-018-0663-1Ortiz-Barrios, M. A., Herrera-Fontalvo, Z., RĂșa-Muñoz, J., Ojeda-GutiĂ©rrez, S., De Felice, F., & Petrillo, A. (2018). An integrated approach to evaluate the risk of adverse events in hospital sector. Management Decision, 56(10), 2187-2224. doi:10.1108/md-09-2017-0917Al Salem, A. A., & Awasthi, A. (2018). Investigating rank reversal in reciprocal fuzzy preference relation based on additive consistency: Causes and solutions. Computers & Industrial Engineering, 115, 573-581. doi:10.1016/j.cie.2017.11.027Aires, R. F. de F., & Ferreira, L. (2019). A new approach to avoid rank reversal cases in the TOPSIS method. Computers & Industrial Engineering, 132, 84-97. doi:10.1016/j.cie.2019.04.023Emrouznejad, A., & Yang, G. (2018). A survey and analysis of the first 40 years of scholarly literature in DEA: 1978â2016. Socio-Economic Planning Sciences, 61, 4-8. doi:10.1016/j.seps.2017.01.008Arya, A., & Yadav, S. P. (2017). Development of FDEA Models to Measure the Performance Efficiencies of DMUs. International Journal of Fuzzy Systems, 20(1), 163-173. doi:10.1007/s40815-017-0325-yMufazzal, S., & Muzakkir, S. M. (2018). A new multi-criterion decision making (MCDM) method based on proximity indexed value for minimizing rank reversals. Computers & Industrial Engineering, 119, 427-438. doi:10.1016/j.cie.2018.03.045Kaliszewski, I., & Podkopaev, D. (2016). Simple additive weightingâA metamodel for multiple criteria decision analysis methods. Expert Systems with Applications, 54, 155-161. doi:10.1016/j.eswa.2016.01.042Mousavi-Nasab, S. H., & Sotoudeh-Anvari, A. (2018). A new multi-criteria decision making approach for sustainable material selection problem: A critical study on rank reversal problem. Journal of Cleaner Production, 182, 466-484. doi:10.1016/j.jclepro.2018.02.062Chen, Z., Ming, X., Zhang, X., Yin, D., & Sun, Z. (2019). A rough-fuzzy DEMATEL-ANP method for evaluating sustainable value requirement of product service system. Journal of Cleaner Production, 228, 485-508. doi:10.1016/j.jclepro.2019.04.145Jumaah, F. M., Zadain, A. A., Zaidan, B. B., Hamzah, A. K., & Bahbibi, R. (2018). Decision-making solution based multi-measurement design parameter for optimization of GPS receiver tracking channels in static and dynamic real-time positioning multipath environment. Measurement, 118, 83-95. doi:10.1016/j.measurement.2018.01.011Singh, A., & Prasher, A. (2017). Measuring healthcare service quality from patientsâ perspective: using Fuzzy AHP application. Total Quality Management & Business Excellence, 30(3-4), 284-300. doi:10.1080/14783363.2017.1302794Otay, Ä°., Oztaysi, B., Cevik Onar, S., & Kahraman, C. (2017). Multi-expert performance evaluation of healthcare institutions using an integrated intuitionistic fuzzy AHP&DEA methodology. Knowledge-Based Systems, 133, 90-106. doi:10.1016/j.knosys.2017.06.028Awasthi, A., Govindan, K., & Gold, S. (2018). Multi-tier sustainable global supplier selection using a fuzzy AHP-VIKOR based approach. International Journal of Production Economics, 195, 106-117. doi:10.1016/j.ijpe.2017.10.013Gul, M., Guneri, A. F., & Nasirli, S. M. (2018). A fuzzy-based model for risk assessment of routes in oil transportation. International Journal of Environmental Science and Technology, 16(8), 4671-4686. doi:10.1007/s13762-018-2078-zKazancoglu, Y., Kazancoglu, I., & Sagnak, M. (2018). Fuzzy DEMATEL-based green supply chain management performance. Industrial Management & Data Systems, 118(2), 412-431. doi:10.1108/imds-03-2017-0121Abdullah, L., & Zulkifli, N. (2015). Integration of fuzzy AHP and interval type-2 fuzzy DEMATEL: An application to human resource management. Expert Systems with Applications, 42(9), 4397-4409. doi:10.1016/j.eswa.2015.01.021Ashtiani, M., & Azgomi, M. A. (2016). A hesitant fuzzy model of computational trust considering hesitancy, vagueness and uncertainty. Applied Soft Computing, 42, 18-37. doi:10.1016/j.asoc.2016.01.023Zyoud, S. H., & Fuchs-Hanusch, D. (2017). A bibliometric-based survey on AHP and TOPSIS techniques. Expert Systems with Applications, 78, 158-181. doi:10.1016/j.eswa.2017.02.016Scholz, S., Ngoli, B., & Flessa, S. (2015). Rapid assessment of infrastructure of primary health care facilities â a relevant instrument for health care systems management. BMC Health Services Research, 15(1). doi:10.1186/s12913-015-0838-8Ivlev, I., Vacek, J., & Kneppo, P. (2015). Multi-criteria decision analysis for supporting the selection of medical devices under uncertainty. European Journal of Operational Research, 247(1), 216-228. doi:10.1016/j.ejor.2015.05.075Kovacs, E., Strobl, R., Phillips, A., Stephan, A.-J., MĂŒller, M., Gensichen, J., & Grill, E. (2018). Systematic Review and Meta-analysis of the Effectiveness of Implementation Strategies for Non-communicable Disease Guidelines in Primary Health Care. Journal of General Internal Medicine, 33(7), 1142-1154. doi:10.1007/s11606-018-4435-5Morley, C., Unwin, M., Peterson, G. M., Stankovich, J., & Kinsman, L. (2018). Emergency department crowding: A systematic review of causes, consequences and solutions. PLOS ONE, 13(8), e0203316. doi:10.1371/journal.pone.0203316Hermann, R. M., Long, E., & Trotta, R. L. (2019). Improving Patientsâ Experiences Communicating With Nurses and Providers in the Emergency Department. Journal of Emergency Nursing, 45(5), 523-530. doi:10.1016/j.jen.2018.12.001Hawley, K. L., Mazer-Amirshahi, M., Zocchi, M. S., Fox, E. R., & Pines, J. M. (2015). Longitudinal Trends in U.S. Drug Shortages for Medications Used in Emergency Departments (2001-2014). Academic Emergency Medicine, 23(1), 63-69. doi:10.1111/acem.12838Stang, A. S., Crotts, J., Johnson, D. W., Hartling, L., & Guttmann, A. (2015). Crowding Measures Associated With the Quality of Emergency Department Care: A Systematic Review. Academic Emergency Medicine, 22(6), 643-656. doi:10.1111/acem.12682Chanamool, N., & Naenna, T. (2016). Fuzzy FMEA application to improve decision-making process in an emergency department. Applied Soft Computing, 43, 441-453. doi:10.1016/j.asoc.2016.01.007Farup, P. G. (2015). Are measurements of patient safety culture and adverse events valid and reliable? Results from a cross sectional study. BMC Health Services Research, 15(1). doi:10.1186/s12913-015-0852-xCarter, E. J., Pouch, S. M., & Larson, E. L. (2013). The Relationship Between Emergency Department Crowding and Patient Outcomes: A Systematic Review. Journal of Nursing Scholarship, 46(2), 106-115. doi:10.1111/jnu.12055Ebben, R. H. A., Siqeca, F., Madsen, U. R., Vloet, L. C. M., & van Achterberg, T. (2018). Effectiveness of implementation strategies for the improvement of guideline and protocol adherence in emergency care: a systematic review. BMJ Open, 8(11), e017572. doi:10.1136/bmjopen-2017-017572Innes, G. D., Sivilotti, M. L. A., Ovens, H., McLelland, K., Dukelow, A., Kwok, E., ⊠Chochinov, A. (2018). Emergency overcrowding and access block: A smaller problem than we think. CJEM, 21(2), 177-185. doi:10.1017/cem.2018.446Di Somma, S., Paladino, L., Vaughan, L., Lalle, I., Magrini, L., & Magnanti, M. (2014). Overcrowding in emergency department: an international issue. Internal and Emergency Medicine, 10(2), 171-175. doi:10.1007/s11739-014-1154-8Uthman, O. A., Walker, C., Lahiri, S., Jenkinson, D., Adekanmbi, V., Robertson, W., & Clarke, A. (2018). General practitioners providing non-urgent care in emergency department: a natural experiment. BMJ Open, 8(5), e019736. doi:10.1136/bmjopen-2017-019736Razzak, J. A., Baqir, S. M., Khan, U. R., Heller, D., Bhatti, J., & Hyder, A. A. (2013). Emergency and trauma care in Pakistan: a cross-sectional study of healthcare levels. Emergency Medicine Journal, 32(3), 207-213. doi:10.1136/emermed-2013-202590Dart, R. C., Goldfrank, L. R., Erstad, B. L., Huang, D. T., Todd, K. H., Weitz, J., ⊠Anderson, V. E. (2018). Expert Consensus Guidelines for Stocking of Antidotes in Hospitals That Provide Emergency Care. Annals of Emergency Medicine, 71(3), 314-325.e1. doi:10.1016/j.annemergmed.2017.05.021Mkoka, D. A., Goicolea, I., Kiwara, A., Mwangu, M., & Hurtig, A.-K. (2014). Availability of drugs and medical supplies for emergency obstetric care: experience of health facility managers in a rural District of Tanzania. BMC Pregnancy and Childbirth, 14(1). doi:10.1186/1471-2393-14-108Beck, M. J., Okerblom, D., Kumar, A., Bandyopadhyay, S., & Scalzi, L. V. (2016). Lean intervention improves patient discharge times, improves emergency department throughput and reduces congestion. Hospital Practice, 44(5), 252-259. doi:10.1080/21548331.2016.1254559Morais Oliveira, M., Marti, C., Ramlawi, M., Sarasin, F. P., Grosgurin, O., Poletti, P.-A., ⊠Rutschmann, O. T. (2018). Impact of a patient-flow physician coordinator on waiting times and length of stay in an emergency department: A before-after cohort study. PLOS ONE, 13(12), e0209035. doi:10.1371/journal.pone.0209035Vermeulen, M. J., Stukel, T. A., Boozary, A. S., Guttmann, A., & Schull, M. J. (2016). The Effect of Pay for Performance in the Emergency Department on Patient Waiting Times and Quality of Care in Ontario, Canada: A Difference-in-Differences Analysis. Annals of Emergency Medicine, 67(4), 496-505.e7. doi:10.1016/j.annemergmed.2015.06.028Singh, S., Lin, Y.-L., Nattinger, A. B., Kuo, Y.-F., & Goodwin, J. S. (2015). Variation in readmission rates by emergency departments and emergency department providers caring for patients after discharge. Journal of Hospital Medicine, 10(11), 705-710. doi:10.1002/jhm.2407KĂ€llberg, A.-S., Göransson, K. E., Florin, J., Ăstergren, J., Brixey, J. J., & Ehrenberg, A. (2015). Contributing factors to errors in Swedish emergency departments. International Emergency Nursing, 23(2), 156-161. doi:10.1016/j.ienj.2014.10.002Riga, M., Vozikis, A., Pollalis, Y., & Souliotis, K. (2015). MERIS (Medical Error Reporting Information System) as an innovative patient safety intervention: A health policy perspective. Health Policy, 119(4), 539-548. doi:10.1016/j.healthpol.2014.12.006Norman, G. R., Monteiro, S. D., Sherbino, J., Ilgen, J. S., Schmidt, H. G., & Mamede, S. (2017). The Causes of Errors in Clinical Reasoning. Academic Medicine, 92(1), 23-30. doi:10.1097/acm.0000000000001421Lisbon, D., Allin, D., Cleek, C., Roop, L., Brimacombe, M., Downes, C., & Pingleton, S. K. (2014). Improved Knowledge, Attitudes, and Behaviors After Implementation of TeamSTEPPS Training in an Academic Emergency Department. American Journal of Medical Quality, 31(1), 86-90. doi:10.1177/1062860614545123Li, L., Georgiou, A., Vecellio, E., Eigenstetter, A., Toouli, G., Wilson, R., & Westbrook, J. I. (2015). The Effect of Laboratory Testing on Emergency Department Length of Stay: A Multihospital Longitudinal Study Applying a Crossâclassified Randomâeffect Modeling Approach. Academic Emergency Medicine, 22(1), 38-46. doi:10.1111/acem.12565Telem, D. A., Yang, J., Altieri, M., Patterson, W., Peoples, B., Chen, H., ⊠Pryor, A. D. (2016). Rates and Risk Factors for Unplanned Emergency Department Utilization and Hospital Readmission Following Bariatric Surgery. Annals of Surgery, 263(5), 956-960. doi:10.1097/sla.0000000000001536Rigobello, M. C. G., Carvalho, R. E. F. L. de, Guerreiro, J. M., Motta, A. P. G., Atila, E., & Gimenes, F. R. E. (2017). The perception of the patient safety climate by professionals of the emergency department. International Emergency Nursing, 33, 1-6. doi:10.1016/j.ienj.2017.03.003Farmer, B. (2016). Patient Safety in the Emergency Department. Emergency Medicine, 48(9), 396-404. doi:10.12788/emed.2016.0052Liu, H.-C., You, J.-X., Zhen, L., & Fan, X.-J. (2014). A novel hybrid multiple criteria decision making model for material selection with target-based criteria. Materials & Design, 60, 380-390. doi:10.1016/j.matdes.2014.03.071Kou, G., Ergu, D., & Shang, J. (2014). Enhancing data consistency in decision matrix: Adapting Hadamard model to mitigate judgment contradiction. European Journal of Operational Research, 236(1), 261-271. doi:10.1016/j.ejor.2013.11.035Keshavarz Ghorabaee, M., Amiri, M., Zavadskas, E. K., & Antucheviciene, J. (2017). Supplier evaluation and selection in fuzzy environments: a review of MADM approaches. Economic Research-Ekonomska IstraĆŸivanja, 30(1), 1073-1118. doi:10.1080/1331677x.2017.1314828Barrios, M. A. O., De Felice, F., Negrete, K. P., Romero, B. A., Arenas, A. Y., & Petrillo, A. (2016). An AHP-Topsis Integrated Model for Selecting the Most Appropriate Tomography Equipment. International Journal of Information Technology & Decision Making, 15(04), 861-885. doi:10.1142/s021962201640006xYeh, D.-Y., & Cheng, C.-H. (2016). Performance Management of Taiwanâs National Hospitals. International Journal of Information Technology & Decision Making, 15(01), 187-213. doi:10.1142/s0219622014500199Chen, T.-Y. (2014). An Interactive Signed Distance Approach for Multiple Criteria Group Decision-Making Based on Simple Additive Weighting Method with Incomplete Preference Information Defined by Interval Type-2 Fuzzy Sets. International Journal of Information Technology & Decision Making, 13(05), 979-1012. doi:10.1142/s0219622014500229Gou, X., Xu, Z., & Liao, H. (2019). Hesitant Fuzzy Linguistic Possibility Degree-Based Linear Assignment Method for Multiple Criteria Decision-Making. International Journal of Information Technology & Decision Making, 18(01), 35-63. doi:10.1142/s0219622017500377Saksrisathaporn, K., Bouras, A., Reeveerakul, N., & Charles, A. (2016). Application of a Decision Model by Using an Integration of AHP and TOPSIS Approaches within Humanitarian Operation Life Cycle. International Journal of Information Technology & Decision Making, 15(04), 887-918. doi:10.1142/s0219622015500261Hsiao, B., & Chen, L.-H. (2019). Performance Evaluation for Taiwanese Hospitals by Multi-Activity Network Data Envelopment Analysis. International Journal of Information Technology & Decision Making, 18(03), 1009-1043. doi:10.1142/s0219622018500165Saaty, T. L., & Ergu, D. (2015). When is a Decision-Making Method Trustworthy? Criteria for Evaluating Multi-Criteria Decision-Making Methods. International Journal of Information Technology & Decision Making, 14(06), 1171-1187. doi:10.1142/s021962201550025xChang, K.-H., Chang, Y.-C., & Lee, Y.-T. (2014). Integrating TOPSIS and DEMATEL Methods to Rank the Risk of Failure of FMEA. International Journal of Information Technology & Decision Making, 13(06), 1229-1257. doi:10.1142/s0219622014500758Yeh, T.-M., & Huang, Y.-L. (2014). Factors in determining wind farm location: Integrating GQM, fuzzy DEMATEL, and ANP. Renewable Energy, 66, 159-169. doi:10.1016/j.renene.2013.12.003OrtĂz, M. A., Felizzola, H. A., & Isaza, S. N. (2015). A contrast between DEMATEL-ANP an
Efeito agudo de exercĂcios de alongamento estĂĄtico e dinĂąmico na impulsĂŁo vertical de jogadores de futebol
The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study
AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4âweeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4âweeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, PÂ =Â 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, Pâ<â0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, PÂ =Â 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, PÂ =Â 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease
Organic Constituents on the Surfaces of Aerosol Particles from Southern Finland, Amazonia, and California Studied by Vibrational Sum Frequency Generation
- âŠ