Search Advanced

AIMS Public Health

2020, Volume 7, Issue 3: 469-477. doi: 10.3934/publichealth.2020038
Previous Article Next Article
Research article

A mobile device reducing airborne particulate can improve air quality

  • 1.
    Department of Molecular and Developmental Medicine, University of Siena, via Aldo Moro 2, Siena, Italy
  • 2.
    Department of Medical Biotechnologies, University of Siena, via Aldo Moro 2, Siena, Italy
  • 3.
    Post Graduate School of Public Health, University of Siena, via Aldo Moro 2, Siena, Italy
  • 4.
    Medical Management, “Le Scotte” Teaching Hospital, viale Mario Bracci 16, Siena, Italy
  • Received: 23 April 2020 Accepted: 23 June 2020 Published: 02 July 2020

  • Surgical site infections are the second major cause of hospital acquired infections, accounting for a large part of overall annual medical costs. Airborne particulate is known to be a potential carrier of pathogenic bacteria. We assessed a mobile air particle filter unit for improvement of air quality in an operating room (OR). A new mobile air decontamination and recirculation unit, equipped with a crystalline ultraviolet C (Illuvia® 500 UV) reactor and a HEPA filter, was tested in an OR. Airborne particulate was monitored in four consecutive phases: I) device OFF and OR at rest; II) device OFF and OR in operation; III) device ON and OR in operation; IV) device OFF and OR in operation. We used a particle counter to measure airborne particles of different sizes: ≥0.3, ≥0.5, ≥1, ≥3, ≥5, >10 µm. Activation of the device (phases III) produced a significant reduction (p < 0.05) in airborne particulate of all sizes. Switching the device OFF (phase IV) led to a statistically significant increase (p < 0.05) in the number of particles of most sizes: ≥0.3, ≥0.5, ≥1, ≥3 µm. The device significantly reduced airborne particulate in the OR, improving air quality and possibly lowering the probability of surgical site infections.

    Citation: Gabriele Messina, Giuseppe Spataro, Laura Catarsi, Maria Francesca De Marco, Anna Grasso, Gabriele Cevenini. A mobile device reducing airborne particulate can improve air quality[J]. AIMS Public Health, 2020, 7(3): 469-477. doi: 10.3934/publichealth.2020038

    Related Papers:

    [1] Zairong Wang, Xuan Tang, Haohuai Liu, Lingxi Peng . Artificial immune intelligence-inspired dynamic real-time computer forensics model. Mathematical Biosciences and Engineering, 2020, 17(6): 7221-7233. doi: 10.3934/mbe.2020370
    [2] Hui Yao, Yuhan Wu, Shuo Liu, Yanhao Liu, Hua Xie . A pavement crack synthesis method based on conditional generative adversarial networks. Mathematical Biosciences and Engineering, 2024, 21(1): 903-923. doi: 10.3934/mbe.2024038
    [3] Jiajia Jiao, Xiao Xiao, Zhiyu Li . dm-GAN: Distributed multi-latent code inversion enhanced GAN for fast and accurate breast X-ray image automatic generation. Mathematical Biosciences and Engineering, 2023, 20(11): 19485-19503. doi: 10.3934/mbe.2023863
    [4] Hao Wang, Guangmin Sun, Kun Zheng, Hui Li, Jie Liu, Yu Bai . Privacy protection generalization with adversarial fusion. Mathematical Biosciences and Engineering, 2022, 19(7): 7314-7336. doi: 10.3934/mbe.2022345
    [5] Jinhua Zeng, Xiulian Qiu, Shaopei Shi . Image processing effects on the deep face recognition system. Mathematical Biosciences and Engineering, 2021, 18(2): 1187-1200. doi: 10.3934/mbe.2021064
    [6] Song Wan, Guozheng Yang, Lanlan Qi, Longlong Li , Xuehu Yan, Yuliang Lu . Multiple security anti-counterfeit applications to QR code payment based on visual secret sharing and QR code. Mathematical Biosciences and Engineering, 2019, 16(6): 6367-6385. doi: 10.3934/mbe.2019318
    [7] Dehua Feng, Xi Chen, Xiaoyu Wang, Xuanqin Mou, Ling Bai, Shu Zhang, Zhiguo Zhou . Predicting effectiveness of anti-VEGF injection through self-supervised learning in OCT images. Mathematical Biosciences and Engineering, 2023, 20(2): 2439-2458. doi: 10.3934/mbe.2023114
    [8] Si Li, Limei Peng, Fenghuan Li, Zengguo Liang . Low-dose sinogram restoration enabled by conditional GAN with cross-domain regularization in SPECT imaging. Mathematical Biosciences and Engineering, 2023, 20(6): 9728-9758. doi: 10.3934/mbe.2023427
    [9] Xiao Wang, Jianbiao Zhang, Ai Zhang, Jinchang Ren . TKRD: Trusted kernel rootkit detection for cybersecurity of VMs based on machine learning and memory forensic analysis. Mathematical Biosciences and Engineering, 2019, 16(4): 2650-2667. doi: 10.3934/mbe.2019132
    [10] Sonam Saluja, Munesh Chandra Trivedi, Ashim Saha . Deep CNNs for glioma grading on conventional MRIs: Performance analysis, challenges, and future directions. Mathematical Biosciences and Engineering, 2024, 21(4): 5250-5282. doi: 10.3934/mbe.2024232

  • Surgical site infections are the second major cause of hospital acquired infections, accounting for a large part of overall annual medical costs. Airborne particulate is known to be a potential carrier of pathogenic bacteria. We assessed a mobile air particle filter unit for improvement of air quality in an operating room (OR). A new mobile air decontamination and recirculation unit, equipped with a crystalline ultraviolet C (Illuvia® 500 UV) reactor and a HEPA filter, was tested in an OR. Airborne particulate was monitored in four consecutive phases: I) device OFF and OR at rest; II) device OFF and OR in operation; III) device ON and OR in operation; IV) device OFF and OR in operation. We used a particle counter to measure airborne particles of different sizes: ≥0.3, ≥0.5, ≥1, ≥3, ≥5, >10 µm. Activation of the device (phases III) produced a significant reduction (p < 0.05) in airborne particulate of all sizes. Switching the device OFF (phase IV) led to a statistically significant increase (p < 0.05) in the number of particles of most sizes: ≥0.3, ≥0.5, ≥1, ≥3 µm. The device significantly reduced airborne particulate in the OR, improving air quality and possibly lowering the probability of surgical site infections.




    Acknowledgments


    The authors thank the Teaching Hospital of Siena for permission to conduct the study.

    Author contributions


    Conceptualization, methodology and supervision GM and GC; Investigation GS and LC; Formal analysis GS, LC, GM and GC; Writing-original draft GS, LC; Writing-review & editing GM, GC, MFDM and AG.

    Funding


    The research was funded by Aerobiotix Inc. (Dayton, OH).

    Conflicts of interest


    This study was partly funded by Aerobiotix Inc. (Dayton, OH). An Illuvia® 500UV unit was supplied by Aerobiotix Inc for the study. The sponsor was not involved in study design, data collection, analysis or interpretation, the writing of the paper, or in the decision to publish the results. The authors declare no conflict of interest.

    [1] (2013) European Centre for Disease Prevention and ControlSurveillance of surgical site infections in Europe 2010–2011. Stockholm: ECDC.
    [2] Horan TC, Gaynes RP, Martone WJ, et al. (1992) CDC definitions of nosocomial surgical site infections, 1992: a modification of CDC definitions of surgical wound infections. Infect Control Hosp Epidemiol 13: 606-608. doi: 10.2307/30148464
    [3] Roy MC, Perl TM (1997) Basics of surgical-site infection surveillance. Infect Control Hosp Epidemiol 18: 659-668. doi: 10.2307/30141496
    [4] Sadrizadeh S, Pantelic J, Sherman M, et al. (2018) Airborne particle dispersion to an operating room environment during sliding and hinged door opening. J Infect Public Health 11: 631-635. doi: 10.1016/j.jiph.2018.02.007
    [5] Anderson DJ, Kaye KS (2009) Staphylococcal surgical site infections. Infect Dis Clin North Am 23: 53-72. doi: 10.1016/j.idc.2008.10.004
    [6] Zimlichman E, Henderson D, Tamir O, et al. (2013) Health care-associated infections: a meta-analysis of costs and financial impact on the US health care system. JAMA Intern Med 173: 2039-2046. doi: 10.1001/jamainternmed.2013.9763
    [7] O'Keeffe AB, Lawrence T, Bojanic S (2012) Oxford craniotomy infections database: a cost analysis of craniotomy infection. Br J Neurosurg 26: 265-269. doi: 10.3109/02688697.2011.626878
    [8] Badia JM, Casey AL, Petrosillo N, et al. (2017) Impact of surgical site infection on healthcare costs and patient outcomes: a systematic review in six European countries. J Hosp Infect 96: 1-15. doi: 10.1016/j.jhin.2017.03.004
    [9] Pinkney TD, Calvert M, Bartlett DC, et al. (2013) Impact of wound edge protection devices on surgical site infection after laparotomy: multicentre randomised controlled trial (ROSSINI Trial). BMJ 347: f4305. doi: 10.1136/bmj.f4305
    [10] Birgand G, Toupet G, Rukly S, et al. (2015) Air contamination for predicting wound contamination in clean surgery: A large multicenter study. Am J Infect Control 43: 516-521. doi: 10.1016/j.ajic.2015.01.026
    [11] Kowalski W (2007) Air-treatment systems for controlling hospital acquired infections. HPAC Eng 79: 24-48.
    [12] Schaal KP (1991) Medical and microbiological problems arising from airborne infection in hospitals. J Hosp Infect 18: 451-459. doi: 10.1016/0195-6701(91)90056-E
    [13] Edmiston CE, Seabrook GR, Cambria RA, et al. (2005) Molecular epidemiology of microbial contamination in the operating room environment: Is there a risk for infection? Surgery 138: 573-582. doi: 10.1016/j.surg.2005.06.045
    [14] Seal DV, Clark RP (1990) Electronic particle counting for evaluating the quality of air in operating theatres: a potential basis for standards? J Appl Bacteriol 68: 225-230. doi: 10.1111/j.1365-2672.1990.tb02568.x
    [15] Vonci N, De Marco MF, Grasso A, et al. (2019) Association between air changes and airborne microbial contamination in operating rooms. J Infect Public Health 12: 827-830. doi: 10.1016/j.jiph.2019.05.010
    [16] Gormley T, Markel TA, Jones H, et al. (2017) Cost-benefit analysis of different air change rates in an operating room environment. Am J Infect Control 45: 1318-1323. doi: 10.1016/j.ajic.2017.07.024
    [17] Scaltriti S, Cencetti S, Rovesti S, et al. (2007) Risk factors for particulate and microbial contamination of air in operating theatres. J Hosp Infect 66: 320-326. doi: 10.1016/j.jhin.2007.05.019
    [18] Anis HK, Curtis GL, Klika AK, et al. (2020) In-Room Ultraviolet Air Filtration Units Reduce Airborne Particles During Total Joint Arthroplasty. J Orthop Res 38: 431-437. doi: 10.1002/jor.24453
    [19] Curtis GL, Faour M, Jawad M, et al. (2018) Reduction of Particles in the Operating Room Using Ultraviolet Air Disinfection and Recirculation Units. J Arthroplasty 33: S196-S200. doi: 10.1016/j.arth.2017.11.052
    [20] Anderson DJ, Podgorny K, Berrios-Torres SI, et al. (2014) Strategies to prevent surgical site infections in acute care hospitals: 2014 update. Infect Control Hosp Epidemiol 35: S66-S88. doi: 10.1017/S0899823X00193869
    [21] Ban KA, Minei JP, Laronga C, et al. (2017) American College of Surgeons and Surgical Infection Society: Surgical Site Infection Guidelines, 2016 Update. J Am Coll Surg 224: 59-74. doi: 10.1016/j.jamcollsurg.2016.10.029
    [22] Magill SS, Edwards JR, Bamberg W, et al. (2014) Multistate point-prevalence survey of health care-associated infections. N Engl J Med 370: 1198-1208. doi: 10.1056/NEJMoa1306801
    [23] (2018) OECDiLibraryStemming the Superbug Tide: Just A Few Dollars More. Paris: OECD Publishing. Available from: https://www.oecd-ilibrary.org/social-issues-migration-health/stemming-the-superbug-tide_9789264307599-en.

  • This article has been cited by:

    1. Yangjin Kim, Hyunji Kang, Gibin Powathil, Hyeongi Kim, Dumitru Trucu, Wanho Lee, Sean Lawler, Mark Chaplain, Dominik Wodarz, Role of extracellular matrix and microenvironment in regulation of tumor growth and LAR-mediated invasion in glioblastoma, 2018, 13, 1932-6203, e0204865, 10.1371/journal.pone.0204865
    2. Yangjin Kim, Junho Lee, Donggu Lee, Hans Othmer, Synergistic Effects of Bortezomib-OV Therapy and Anti-Invasive Strategies in Glioblastoma: A Mathematical Model, 2019, 11, 2072-6694, 215, 10.3390/cancers11020215
    3. Christian Engwer, Christian Stinner, Christina Surulescu, On a structured multiscale model for acid-mediated tumor invasion: The effects of adhesion and proliferation, 2017, 27, 0218-2025, 1355, 10.1142/S0218202517400188
    4. Markos Antonopoulos, Dimitra Dionysiou, Georgios Stamatakos, Nikolaos Uzunoglu, Three-dimensional tumor growth in time-varying chemical fields: a modeling framework and theoretical study, 2019, 20, 1471-2105, 10.1186/s12859-019-2997-9
    5. Junho Lee, Donggu Lee, Sean Lawler, Yangjin Kim, Stacey Finley, Role of neutrophil extracellular traps in regulation of lung cancer invasion and metastasis: Structural insights from a computational model, 2021, 17, 1553-7358, e1008257, 10.1371/journal.pcbi.1008257
    6. Stefaan W. Verbruggen, Laoise M. McNamara, 2018, 9780128129524, 157, 10.1016/B978-0-12-812952-4.00006-4
    7. Thomas Hillen, Kevin J. Painter, Magdalena A. Stolarska, Chuan Xue, Multiscale phenomena and patterns in biological systems: special issue in honour of Hans Othmer, 2020, 80, 0303-6812, 275, 10.1007/s00285-020-01473-2
    8. Min-Jhe Lu, Chun Liu, John Lowengrub, Shuwang Li, Complex Far-Field Geometries Determine the Stability of Solid Tumor Growth with Chemotaxis, 2020, 82, 0092-8240, 10.1007/s11538-020-00716-z
    9. Yangjin Kim, Donggu Lee, Junho Lee, Seongwon Lee, Sean Lawler, Eugene Demidenko, Role of tumor-associated neutrophils in regulation of tumor growth in lung cancer development: A mathematical model, 2019, 14, 1932-6203, e0211041, 10.1371/journal.pone.0211041
    10. Raluca Eftimie, Joseph J. Gillard, Doreen A. Cantrell, Mathematical Models for Immunology: Current State of the Art and Future Research Directions, 2016, 78, 0092-8240, 2091, 10.1007/s11538-016-0214-9
    11. S. L. Waters, L. J. Schumacher, A. J. El Haj, Regenerative medicine meets mathematical modelling: developing symbiotic relationships, 2021, 6, 2057-3995, 10.1038/s41536-021-00134-2
    12. Vladimir Simic, Miljan Milosevic, Vladimir Milicevic, Nenad Filipovic, Milos Kojic, A novel composite smeared finite element for mechanics (CSFEM): Some applications, 2022, 09287329, 1, 10.3233/THC-220414
    13. Miloš Kojić, Miljan Milošević, Arturas Ziemys, 2023, 9780323884723, 65, 10.1016/B978-0-323-88472-3.00002-5
    14. Gabriella Bretti, Adele De Ninno, Roberto Natalini, Daniele Peri, Nicole Roselli, Estimation Algorithm for a Hybrid PDE–ODE Model Inspired by Immunocompetent Cancer-on-Chip Experiment, 2021, 10, 2075-1680, 243, 10.3390/axioms10040243
    15. Dimitrios G. Patsatzis, Algorithmic asymptotic analysis: Extending the arsenal of cancer immunology modeling, 2022, 534, 00225193, 110975, 10.1016/j.jtbi.2021.110975
    16. Joseph D. Butner, Prashant Dogra, Vittorio Cristini, Thomas S. Deisboeck, Zhihui Wang, 2023, 9780128216248, 251, 10.1016/B978-0-12-821618-7.00244-3
    17. Jonggul Lee, Donggu Lee, Yangjin Kim, Mathematical model of STAT signalling pathways in cancer development and optimal control approaches, 2021, 8, 2054-5703, 10.1098/rsos.210594
    18. Junho Lee, Jin Su Kim, Yangjin Kim, Stacey Finley, Atorvastatin-mediated rescue of cancer-related cognitive changes in combined anticancer therapies, 2021, 17, 1553-7358, e1009457, 10.1371/journal.pcbi.1009457
    19. Aurelio A. de los Reyes, Yangjin Kim, Optimal regulation of tumour-associated neutrophils in cancer progression, 2022, 9, 2054-5703, 10.1098/rsos.210705
    20. Min-Jhe Lu, Wenrui Hao, Chun Liu, John Lowengrub, Shuwang Li, Nonlinear simulation of vascular tumor growth with chemotaxis and the control of necrosis, 2022, 459, 00219991, 111153, 10.1016/j.jcp.2022.111153
    21. Tingzhe Sun, Multi-scale modeling of hippo signaling identifies homeostatic control by YAP-LATS negative feedback, 2021, 208, 03032647, 104475, 10.1016/j.biosystems.2021.104475
    22. Yangjin Kim, Junho Lee, Chaeyoung Lee, Sean Lawler, Role of senescent tumor cells in building a cytokine shield in the tumor microenvironment: mathematical modeling, 2023, 86, 0303-6812, 10.1007/s00285-022-01850-z
    23. Rebecca M. Crossley, Philip K. Maini, Tommaso Lorenzi, Ruth E. Baker, Traveling waves in a coarse‐grained model of volume‐filling cell invasion: Simulations and comparisons, 2023, 0022-2526, 10.1111/sapm.12635
    24. Anneke S.K. Verbruggen, Elan C. McCarthy, Roisin M. Dwyer, Laoise M. McNamara, Mechanobiological cues to bone cells during early metastasis drive later osteolysis: a computational mechanoregulation framework prediction, 2024, 29499070, 100100, 10.1016/j.mbm.2024.100100
  • Reader Comments
  • © 2020 the Author(s), licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0)
通讯作者: 陈斌, bchen63@163.com
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

  1. 本站搜索
  2. 百度学术搜索
  3. 万方数据库搜索
  4. CNKI搜索
AIMS Public Health
3.1 5.3

Metrics

Article views(4696) PDF downloads(210) Cited by(8)

Preview PDF
Download XML
Export Citation
Article outline
Abstract
References

Other Articles By Authors

Related pages

Tools

Copyright © AIMS Press

/

DownLoad:  Full-Size Img  PowerPoint
Return
Return

Catalog