- Comment
- Open access
- Published:
Real-time monitoring of air pathogens in the ICU with biosensors and robots
Critical Care volume 26, Article number: 353 (2022)
The management of nosocomial infections in the intensive care unit (ICU) became a tough challenge during the outbreak of COVID-19 [1]. Hospital infection caused by drug-resistant bacteria, viruses, and other microorganisms emerged as a serious problem, especially in the ICU due to its relatively closed space. The airborne pathogenic microorganisms from patients and equipment would spread in the air and contaminate the environment of the ICU [2]. Unfortunately, the prevalence of emerging infectious diseases, such as SARS coronavirus, influenza virus H7N9, and monkeypox, increased the complexity of ICU air microbial communities and further deteriorated this situation [3, 4]. Thus, monitoring airborne pathogens was an essential part of ICU management strategies in preventing hospital-acquired infections. Detecting these airborne pathogens in real-time could help to prevent nosocomial infections and reduce the burden of ICU management in the future.
Several measures were widely adopted in ICU for detecting and monitoring airborne microorganisms. ICU staff used passive collection dishes or active air samplers to collect air samples and then analyze the microbes periodically [5, 6]. But there were obvious limitations to these methods, including cost-effectiveness and inability to achieve real-time detection and warning.
Biosensor technology has been used for decades to test pathogens. Once the target pathogens were detected, the biosensors would change colors or emit fluorescence accordingly [7]. A robot equipped with biosensors could crawl around the ICU and test the airborne pathogens in real-time. The combination of biosensors and robots enabled us to better understand the dynamics of airborne microorganisms and improve infection control measures in the future.
Herein, we introduced a real-time airborne microbes monitoring system to improve the management of nosocomial infection in the ICU (Fig. 1A). The monitoring system was designed as a highly autonomous robot that consists of an automated guided vehicle (AGV), an air sample collector, and an airborne pathogenic microorganisms detecting system (Fig. 1B). Under the control of autopilot and route planning, the robot navigated around the ICU wards and collected air samples. A Mecanum wheel chassis enabled the robot to crawl freely even in narrow spaces. The airborne microbial particles went through the air passage and were kept in the buffer of the sampling tubes. The liquid containing the sampled particles entered the reaction wells through the portal when detection started. The microbes detecting system was designed to have a one-pot reaction procedure, which means the lysis of the pathogenic microorganism, target sequence amplification and detection are all taking place in the same reaction well (Fig. 1C). The target sequences of the pathogenic microorganism were amplified by recombinase polymerase amplification (RPA) and were then detected by a highly sensitive CRISPR-Cas system (CRISPR-12a for detecting DNA sequence and CRISPR-13a for detecting RNA sequence, respectively) [8]. CRISPR-Cas is a rapid, inexpensive, and sensitive nucleic acid detection system with high sensitivity and single base-pair resolution [9]. When aligned with the target sequence, the CRISPR-Cas system will cut the report RNA adjacent to the target sequence and release fluorescence. These biosensors were kept in the reaction wells as freeze-dried. When the fluid containing the sampled microbes enters the wells, the biosensors were rehydrated and the detecting procedure started. Target sequences were released by lysis of the pathogenic microbial particles, amplified by RPA, and were then detected by the CRISPR-Cas system which cuts the reporter RNA and emits fluorescence under the presence of the target sequence (Fig. 1D). The fluorescence event was monitored by a CMOS sensor, and the staff was alarmed about the potential threat of hospital infection through the wireless when the system found the pathogenic microorganisms.
In our preliminary experiments, we verified the real-time airborne microbes monitoring system with Escherichia coli. A fragment of the ybbW gene, which was regarded as relatively conservative and specific, was used to identify Escherichia coli in our test. RPA primers (forward: 5`-TGCATGATACTGATCGGCAAACTCTGGTCG-3`, reverse: 5`-GCCTTATCGCCGATAACGATAGATGCACGGCTT-3`) and CRISPR RNA (crRNA, 5`-CUUAAUGCAGACAGUUCGGAUCACAUGC-3`) that specific to the ybbW gene were designed with online software. Reagents used in the test mainly included Tris–HCl(pH 7.5), EDTA(pH 9) and Triton X-100 (Sigma Chemical) for lysis reaction, RPA primers and TwistAmp Basic (TwistDx) for RPA reaction, and crRNA, Cas12a (New England Biolabs) and ssDNA fluorescent reporter (Integrated DNA Technologies) for target DNA detection. Our data showed the air sample in ICU wards was positive for Escherichia coli and the limit of detection (LoD) was 5 copies per reaction. The result was further confirmed by 16 s rRNA sequencing with air sample collected by an Andersen sampler.
We believed that real-time monitoring of air pathogens in the ICU will provide new insight into the management of nosocomial infections. Combining the biosensors and robot enabled us to monitor the airborne pathogens in real-time, which helped to prevent hospital-acquired infection and ease the burden of ICU management in the future.
Availability of data and materials
Not applicable.
References
Rhanbaz SE, Kettani AE, Zerouali K and Soussi-Abdallaoui M. Nosocomial bacterial infections in Covid-19 ICU at Ibn rochd university hospital Casablanca Morocco. Clin Lab 2022; 68:
Kollef MH, Torres A, Shorr AF, Martin-Loeches I, Micek ST. Nosocomial infection. Crit Care Med. 2021;49:169–87.
Kotfis K, Williams Roberson S, Wilson JE, Dabrowski W, Pun BT, Ely EW. COVID-19: ICU delirium management during SARS-CoV-2 pandemic. Crit Care. 2020;24:176.
Beumer MC, Koch RM, van Beuningen D, OudeLashof AM, van de Veerdonk FL, Kolwijck E, van der Hoeven JG, Bergmans DC, Hoedemaekers CWE. Influenza virus and factors that are associated with ICU admission, pulmonary co-infections and ICU mortality. J Crit Care. 2019;50:59–65.
Mainelis G. Bioaerosol sampling: classical approaches, advances, and perspectives. Aerosol Sci Technol. 2020;54:496–519.
Passos RG, Silveira MB, Abrahão JS. Exploratory assessment of the occurrence of SARS-CoV-2 in aerosols in hospital facilities and public spaces of a metropolitan center in Brazil. Environ Res. 2021;195: 110808.
Shah PS. Wireless monitoring in the ICU on the horizon. Nat Med. 2020;26:316–7.
Nguyen PQ, Soenksen LR, Donghia NM, Angenent-Mari NM, de Puig H, Huang A, Lee R, Slomovic S, Galbersanini T, Lansberry G, Sallum HM, Zhao EM, Niemi JB, Collins JJ. Wearable materials with embedded synthetic biology sensors for biomolecule detection. Nat Biotechnol. 2021;39:1366–74.
Gootenberg JS, Abudayyeh OO, Lee JW, Essletzbichler P, Dy AJ, Joung J, Verdine V, Donghia N, Daringer NM, Freije CA, Myhrvold C, Bhattacharyya RP, Livny J, Regev A, Koonin EV, Hung DT, Sabeti PC, Collins JJ, Zhang F. Nucleic acid detection with CRISPR-Cas13a/C2c2. Science. 2017;356:438–42.
Funding
Funding was supported by grants from the Medical health Project of Zhejiang Province (2022KY1238), Key project of the Affiliated Hospital of Jiaxing University(2020YJZD005, 2022-ZD-003), Key medical discipline in Jiaxing –Gastroenterology(2019-ZC-08), and the First Hospital of Jiaxing /Affiliated Hospital of Jiaxing University Joint Research Fund (2021LHJJ004).
Author information
Authors and Affiliations
Contributions
LY participated in the design, and interpretation, and drafted the manuscript. Figure 1 is originally created by LY. The author reviewed the manuscript. The author read and approved the final manuscript.
Corresponding author
Ethics declarations
Ethics approval and consent to participate
Not applicable.
Competing interests
The authors declare no competing interests.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
About this article
Cite this article
Yu, L. Real-time monitoring of air pathogens in the ICU with biosensors and robots. Crit Care 26, 353 (2022). https://doi.org/10.1186/s13054-022-04222-7
Received:
Accepted:
Published:
DOI: https://doi.org/10.1186/s13054-022-04222-7