Airborne bacterial and PM characterization in intensive care units: correlations with physical control parameters

AlRayess, S.; Sleiman, Ahmad; Alameddine, Ibrahim; Fayad, Antoine Abou; Matar, Ghassan M.; El‐Fadel, Mutasem

doi:10.1007/s11869-022-01222-y

Cited by 2 publications

(2 citation statements)

References 60 publications

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“…Therefore, it is slightly difficult to explain and predict variations in bioaerosol characteristics precisely (21). A literature review stated that indoor air ventilation systems, humidity, and temperature, in hospital buildings influence various potential pathogenic organisms in the air such as fungi, viruses, and bacteria that affect hospitalized patients (22) (18,(23)(24).…”

Section: Discussionmentioning

confidence: 99%

Identification of Airborne Aerobic Bacteria in the Intensive Care Room using MALDI-TOF MS

Palulun,

Rasita,

Massi

et al. 2024

JKL

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Introduction: Indoor air quality can affect the spread of airborne microorganisms which can lead to healthcare-associated infections (HAIs). The quality and quantity of airborne microorganisms are responsible for mortality and morbidity in infection-prone hosts such as patients admitted to the intensive care unit (ICU). The research aims to determine the quality of microorganisms in the air, identify the types of aerobic bacteria, and assess the physical parameters of the air in the ICU. Methods: This study was a cross-sectional study with a descriptive observational method. Air specimens were collected using the MAS-100 NT tool with blood agar plate solid culture media, which then incubated in an aerobic atmosphere for 24 hours at 37±2°C. Observation and measurement of air microbiological quality was by counting the number of microorganisms in CFU/m3 and identification of bacteria using MALDI-TOF MS. Results and Discussion: The maximum concentration of microorganisms in the air exceeds the standard value, and the average value of the concentration of microorganisms in the air is 736 CFU/m3. The most common types of aerobic bacteria in the air were Bacillus sp. (n=12), Coagulase-negative Staphylococci (n=5), and Staphylococcus aureus (n=5). There was an increase in physical parameters in the form of average temperature (26.24°C) and humidity (70%) with a ventilation system and air regulation using mechanical ventilation sourced from a split air conditioner with an exhaust fan without a high-efficiency particulate-absorbing (HEPA) filter. Conclusion: Low indoor air quality has the potential to increase the concentration of microorganisms and bacterial findings in the air

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Section: Discussionmentioning

confidence: 99%

Identification of Airborne Aerobic Bacteria in the Intensive Care Room using MALDI-TOF MS

Palulun,

Rasita,

Massi

et al. 2024

JKL

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“…The OR is a special unit requiring a clean environment with the fewest number of particles in the air [10]. In addition, during surgical procedures, the team and the surrounding environment release dust particles, textile fibers, and respiratory aerosols loaded with vital microorganisms [11]- [13] that can settle on surgical instruments or enter directly into the surgical site causing surgical site infections [14]. The heating, ventilation, and air conditioning systems significantly influence the operating room environment, including temperature, relative humidity, pressurization, particle counts, filtration, and ventilation rate [3].…”

Section: Introductionmentioning

confidence: 99%

Neuro-Fuzzy Decision Support System for Optimization of the Indoor Air Quality in Operation Rooms

Jamali¹,

Gharib²

2023

IJRCS

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In order to minimize surgical site infections, indoor air quality in hospital operating rooms is a major concern. A wide range of literature on the relevant issue has shown that air contamination diminution can be attained by applying a more efficient set of monitoring and controlling systems that improve and optimize the indoor air status level. This paper discusses a fuzzy inference system (FIS) and the integrated model neuro-fuzzy inference system (ANFIS) focusing on the control of contamination via proper airflow distribution in an operating room, which is essential to guarantee the accuracy of the surgical procedure. A deep learning estimation approach is proposed to predict incidence in the presence of airborne contamination. The project's goal is to reduce airborne contamination to improve the surgical environment and reduce the predicted incidence during surgeries. The neuro-fuzzy deep learning model was trained with a neural network structure and tested by considering 3 important parameters that affected the air quality introducing the specialization of the system to control the model’s target. Finally, the proposed approach has been put into practice by making use of data collected by sensors placed within a real operating room in a hospital in Mashhad, Iran. The proposed model attains 97.3% and 95% validation accuracy for estimating the relative humidity and particles, respectively. The efficacy of the proposed neuro-fuzzy indicates that the system significantly lowers risk values and enhances indoor air quality.

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Airborne bacterial and PM characterization in intensive care units: correlations with physical control parameters

Cited by 2 publications

References 60 publications

Identification of Airborne Aerobic Bacteria in the Intensive Care Room using MALDI-TOF MS

Identification of Airborne Aerobic Bacteria in the Intensive Care Room using MALDI-TOF MS

Neuro-Fuzzy Decision Support System for Optimization of the Indoor Air Quality in Operation Rooms

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