Пандемия коронавируса COVID-19
Влияние вариантов SARS-CoV-2 на контрмеры: WHO отслеживает эволюцию вируса
В июне 2020 года была создана Рабочая группа ВОЗ по эволюции вирусов с целью изучения вариантов SARS-CoV-2, их фенотипа и влияния на контрмеры. Позднее она превратилась в Техническую консультативную группу по эволюции вирусов SARS-CoV-2. В конце 2020 года появление вариантов, увеличивающих риск для мирового здравоохранения, побудило ВОЗ характеризовать некоторые из них как варианты интереса (VOI) и варианты озабоченности (VOC), чтобы приоритизировать мониторинг и исследования, а также информировать и корректировать ответ на COVID-19. С мая 2021 года ВОЗ начала присваивать простые, легко произносимые метки для ключевых вариантов.
Новости
| Pango линии | Nextstrain клады | Генетические особенности | Дата обнаружения |
|—|—|—|—|
| XBB.1.523A | Рекомбинант подлиней? версии BA.2.10.1 и BA.2.75, т.е. BJ.1 и BM.1.1.1, с разделением в S1. XBB.1 + S:F486P (аналогичный генетический профиль Шипа, как и у XBB.1.9.1)XBB.1.5.70 (23G): XBB.1.5 + S:L455F и S:F456L | 21-10-2022 |
| XBB.1.5 Быстроанализ риска, 11 января 2023Обновленный быстроанализ риска XBB.1.5, 25 января 2023Обновленный анализ риска XBB.1.5, 24 февраля 2023Обновленный анализ риска XBB.1.5, 20 июня 2023 |
| XBB.1.1623B | Рекомбинант подлиней? версии BA.2.10.1 и BA.2.75, т.е. BJ.1 и BM.1.1.1XBB.1 + S:E180V, S:K478R и S:F486P | 09-01-2023 | XBB.1.16 Первоначальный анализ риска, 17 апреля 2023Обновленный анализ риска XBB.1.16, 05 июня 2023 |
| EG.5 | Не назначеноXBB.1.9.2 + S:F456LEG.5.1 (23F): EG.5 + S:Q52HHK.3 (23H): EG.5 + S:Q52H, S:L455FHV.1: EG.5 + S:Q52H, S:F157L, S:L452R | 17-02-2023 | EG.5 Первоначальная оценка риска, 09 августа 2023Обновленная оценка риска EG.5, 21 сентября 2023Обновленная оценка риска EG.5, 21 ноября 2023 |
| BA.2.86$23I | Мутации по отношению к BA.2 | 24-07-2023 | BA.2.86 Первоначальная оценка риска, 21 ноября 2023 |
| JN.1 | Первоначальная оценка риска, 18 декабря 2023Обновленная оценка риска JN.1, 9 февраля 2024 |
В настоящее время циркулирующие варианты под наблюдением (VUMs) (на 29 января 2024 года)
BA.2+ S:V83A, S:Y144-, S:H146Q, S:Q183E, S:V213E, S:G252V, S:G339H, S:R346T, S:L368I, S:V445P, S:G446S, S:N460K, S:F486S, S:F490S
- Рекомбинант подлиней? версии BA.2.10.1 и BA.2.75, т.е. BJ.1 и BM.1.1.1XBB.1 + S:F486P (аналогичный генетический профиль Шипа, как и у XBB.1.5)
Рекомбинант подлиней? версии BA.2.10.1 и BA.2.75, т.е. BJ.1 и BM.1.1.1XBB + S:D253G, S:F486P, S:P521S
* Исключает подлиней? XBB, указанные здесь как VOIs и VUMs.
§ Под наблюдение вариант считается неопасным, если его распространенность составляет 1% на глобальном уровне и во всех регионах ВОЗ в течение 8 последовательных недель.
Технические консультативные группы
Оценка производительности PAQ монитора
Предел обнаружения (LoD) монитора pAQ рассчитывается по формуле (1):
[ LoD = 3.3 \times \frac{\sigma}{S} ]где:
- LoD — предел обнаружения
- σ — стандартное отклонение показателей базового уровня (baseline)
- S — наклон калибровочной кривой
Для оценки производительности монитора pAQ был проведен анализ его чувствительности и точности. Кривая калибровки была построена на основе измерений базового уровня (Calibration Solution) и тестовых образцов аэрозолей. Обработка данных выполнена с использованием программного обеспечения Ansys Fluent 2021 R1.
Вычислительная гидродинамика для характеристики производительности влажного циклона
Для оценки производительности влажного циклона было проведено численное моделирование с использованием программного обеспечения для вычислительной гидродинамики (CFD) Ansys Fluent 2021 R1. Отслеживание частиц определенного размера внутри циклона выполнялось с использованием метода дискретной фазы Fluent. Программа моделирования Reynold stress использовалась для симуляции потока жидкости внутри циклона. Анализ предположений и граничных условий, использованных в моделировании, представлен в Дополнительных Методах 1.
Отбор проб воздуха внутри зараженных домов
Сборка влажного циклона (циклон, вакуумный насос и раствор PBS) была отправлена квартирам двух волонтеров, у которых подтвержден диагноз SARS-CoV-2. Волонтеры собирали пробы воздуха в течение 5 минут (n = 3 до 4) из своих спален/квартир, после чего хранили их в центрифужных пробирках на льду. Жидкие образцы затем транспортировали в лабораторию и анализировали методом RT-qPCR для обнаружения наличия SARS-CoV-2. Более подробные сведения о процедуре отбора проб представлены в Дополнительном Методе 6.
Оценка производительности монитора PAQ
Для оценки производительности монитора pAQ была проведена оценка предела обнаружения (LoD) согласно уравнению (1):
[ LoD = 3.3 \times \frac{\sigma}{S} ]где:
- LoD — предел обнаружения
- σ — стандартное отклонение показателей базового уровня
- S — наклон калибровочной кривой
Оценка чувствительности и точности монитора проводилась на основе кривой калибровки, построенной на измерениях базового уровня и тестовых образцах аэрозолей. Программное обеспечение Ansys Fluent 2021 R1 использовалось для обработки данных и анализа производительности устройства.
Расчет предела обнаружения биосенсора MIE и оценка чувствительности монитора pAQ
The coronavirus disease 2019 (COVID-19) pandemic which began in December 2019 still plagues countries worldwide, with the World Health Organization reporting over 1.7 million new confirmed cases globally during the first week of January 20231. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) coronavirus causes this disease and is spread through respiratory droplets expelled from infected people during coughing, sneezing, breathing, and speaking. Airborne transmission is recognized as one of the predominant infection pathways2,3, hence the rapid infectivity rate and virulent nature of the disease. To combat this rapid spread, governments across the globe adopted policies such as mandatory masking in public spaces, quarantining infected individuals, and social distancing to help reduce the risk of airborne transmission. However, such control measures adversely impacted daily life, with consequences such as air travel restrictions, decreased physical activities, restrictions on large social gatherings, and closure of schools and offices. It took many countries almost 2 years to resume normal activities. However, the fear of infection and the periodic rapid resurgence of the disease, for instance, in late December 2022 in China4, highlights the unpreparedness of even the largest nations in combatting the airborne spread of pathogens. The unavailability of quick and affordable community-level infection detection protocols has been a limiting factor for policymakers in implementing prompt COVID-19 transmission mitigation strategies. A real-time noninvasive surveillance device that can detect SARS-CoV-2 aerosols directly in the air is a potential solution for infection management strategies and the resumption of normal activities.
To our knowledge, there are no commercially available automated real-time airborne SARS-CoV-2 detection devices. This is mainly because of two technology gaps: first is the requirement of an efficient high-flow virus aerosol sampler that can be integrated into a real-time virus detector. Second is the need for a virus detection protocol that is fast, accurate, and sensitive enough to measure the low concentration of viruses typically found in ambient air. Past studies have shown that samplers operating at high flow rates can consolidate aerosols from a large air volume and provide a concentrated sample for biological characterization8,14,15. For instance, Ang et al.8 detected SARS-CoV-2 RNA in 72% of samples collected using a 150 lpm dry air sampler compared to no virus detected in samples collected using the same sampler operated at 50 lpm inside a COVID patient isolation ward. They attributed the higher sample detection to better virus recovery at higher sampling flow rates. Furthermore, recent studies have demonstrated the application of high flowrate PILS for directly collecting pathogen-laden aerosols into a liquid solution and quantifying using real-time virus detectors16 or offline8,14,17,18 techniques. While there has been significant progress in developing high-flow PILS devices, very few studies integrate the PILS with real-time sensors for virus detection16.
Biosensors have recently gained popularity as a promising affordable alternative to RT-qPCR for detecting SARS-CoV-2, as they are low-cost, rapid, sensitive, and highly specific19,20. Immunosensors are affinity ligand-based biosensors in which an immunochemical reaction generates various types of signals (optical, electrochemical, thermometric, or microgravimetric) when they bind to a specific target, allowing them to detect the presence of selected pathogens at low concentrations21,22. Several studies have successfully demonstrated the application of biosensors for detecting SARS-CoV-2 in nasal swabs23,24, saliva20, and exhaled breath condensate samples25, and achieved similar or better results as compared to RT-qPCR. However, no peer-reviewed studies have utilized biosensors for detecting airborne SARS-CoV-2.
Here, we present a pathogen Air Quality (pAQ) monitor that couples a custom high-flow batch-type wet-wall cyclone PILS with a llama-derived nanobody raised against the SARS-CoV-2 spike-protein covalently attached to a micro-immunoelectrode (MIE) biosensor for near-real-time detection of SARS-CoV-2 in air with 5 min time resolution. The MIE technology was adapted from an electrochemical biosensor used to detect amyloid-β in the setting of Alzheimer’s disease26,27,28. Virus-laden aerosols are directly sampled from the air onto a liquid collection medium in the wet cyclone and transferred to the MIE biosensor unit, which detects and reports the presence of virus within 30 s. The wet cyclone performance was compared with other commercially available low-flow PILS. The pAQ monitor performance and sensitivity were validated in the lab using multiple inactivated SARS-CoV-2 virus variants.
Peer review
Nature Communications thanks the anonymous reviewer(s) for their contribution to the peer review of this work. A peer review file is available.
About this article
Puthussery, J.V., Ghumra, D.P., McBrearty, K.R. et al. Real-time environmental surveillance of SARS-CoV-2 aerosols. Nat Commun 14, 3692 (2023). https://doi.org/10.1038/s41467-023-39419-z
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Ethics declarations
The authors declare no competing interests.
Acknowledgements
This work was funded by the National Institutes of Health (NIH) RADx-Rad program under U01 AA029331 and U01 AA029331-S1 (J.R.C., R.K.C., and C.M.Y.), NIH, National Institute of Neurological Disorders and Stroke (NINDS) Intramural Research Program, the Uniformed Services University of the Health Sciences (D.L.B.); NIH SARS-CoV-2 Assessment of Viral Evolution (SAVE) Program (A.C.M.B.), and WashU-IITB Joint Master’s program (J.V.P. and A.G.M.). Y2X Life Sciences has an exclusive option to license this technology for commercialization and were consulted during the design stage. The biosensor used in this study is still in research stage. The corresponding authors can provide the protocol to build and operate the biosensor for interested academic non-commercial groups upon submitting a written request, for non-commercial academic research use. The views expressed in this presentation are those of the authors and do not reflect the official policy or position of the Uniformed Services University, the U.S. Army, the Department of Defense, or the U.S. Government.
Data availability
The source data are provided as a “Source Data” file. Source data are provided with this paper.
Results and discussion
The pAQ monitor comprises a batch-type wet-wall glass cyclone (hereafter referred to as wet cyclone) coupled to an MIE detection unit that houses an automated liquid handling unit and MIE biosensor assembly (Fig. 1). The wet cyclone (Supplementary Fig. 1) is connected to a high-flow vacuum pump (Fein Power Tools, PA, USA) to sample air at ~1,000 (±10%) lpm. Prior to air sampling, the cyclone is filled with a predefined volume (~15 mL) of phosphate-buffered saline (PBS) solution. The pressure drop rapidly draws in ambient air through a tangential inlet creating a vortex, which produces a rotating film of PBS liquid on the inner wall of the cyclone29. Aerosols entering the wet cyclone impact the inner wetted walls and are collected in the liquid media. Aerosols not captured by the wet cyclone exit from the top and are captured by a high-efficiency particulate absorbing (HEPA) filter. Air is sampled for 5 min, after which the concentrated aerosol + PBS solution is transferred to the MIE detection unit.
Fig. 1: The layout of the pAQ monitor.
a pAQ monitor schematic showing the wet cyclone PILS coupled with the MIE detection unit comprising a submerged MIE biosensor connected to a potentiostat and automated liquid handling accessories, and b 3D rendering of the proposed pAQ monitor.
Full size image
Virus aerosol sampling performance in laboratory
Fig. 2: Wet cyclone performance testing.
Virus aerosol sampling performance in infected households
We shipped the pAQ monitor assembly to the apartments of two SARS-CoV-2-positive patients for indoor air sampling (Supplementary Method 6). All seven air samples collected using the wet cyclone in the two apartments occupied by SARS-CoV-2 patients tested positive based on RT-qPCR (Fig. 2c). The RT-qPCR results of bedroom samples were compared with air samples collected from a virus-free control room. The Ct values of the air samples from the infected households ranged from 32.7–34.9. In contrast, SARS-CoV-2 RNA was not detected in the control air samples. The significantly lower Ct values observed in the apartment air samples compared to the control air indicate the presence of SARS-CoV-2 RNA in the apartment air. Note, the high Ct value (32.7–34.9) measured suggests that the samples collected were weakly SARS-CoV-2 positive and had very low RNA concentration (see Supplementary Method 8), suggesting low virus aerosol shedding by both volunteers, who self-reported as being asymptomatic during the sampling period. These results are consistent with other studies that have also reported low but statistically significant presence of SARS-CoV-2 in COVID patient isolation rooms and highlight the importance of controlling the air transmission of the virus5, 11.
Limit of detection (LoD) and sensitivity
Fig. 3: Laboratory characterization of the pAQ monitor.
a SARS-CoV-2 variant-specific LoD. The data are presented as mean ± 2 SD of n = 3 independent samples, b Proof of concept box plot data showing the pAQ monitor oxidation current while sampling aerosolized inactivated WA-1 (Washington, n = 13) and BA-1 (Omicron, n = 6). The box contains 25–75th percentile of the measurements, the center line of the box denotes the mean, and the whiskers denote the minimum and maximum oxidation current measured from ‘n’ independent experiments.
Toward real-world deployment
This study demonstrates a proof-of-concept pAQ monitor built by coupling a wet cyclone-based PILS with an ultrasensitive MIE biosensor. The chamber experiments and indoor air sampling inside the apartments of two SARS-CoV-2-positive patients demonstrate the high virus capture efficiency of the wet cyclone even in low virus concentration environments. The high sensitivity (77–83%), high time resolution (5 min), low LoD (7-35 RNA copies/m3), and automation capability of the pAQ monitor make it an ideal choice for affordable (see Supplementary Discussion 1) real-time detection of viruses in different indoor environments such as schools, residences, offices, conference halls, and crowded public places, where real-time virus monitoring (longitudinal or grab sampling) would enable the occupants to take immediate action to prevent or limit the air transmission of SARS-CoV-2.
A limitation of the proposed pAQ monitor is the high noise level (75–80 dB) during device operation, which can have an adverse effect on the health and comfort of the occupants of a building. Current efforts are underway to find economically feasible solutions to reduce the noise levels to <65 dB, such as using a low-noise motor and soundproofing the device exterior using an acoustic liner. Additionally, we are working on simultaneously detecting other airborne pathogens using the pAQ monitor via multiplexing of MIE biosensors with different target-specific nanobodies. While the findings of this study demonstrate the suitability of the pAQ monitor for real-world applications, the system still requires further testing to verify the robustness of the results in various environments with different aerosol compositions. Future work will focus on comprehensively investigating potential interfering agents in the air that could influence biosensor performance.
Author information
J.R.C., R.K.C., C.M.Y., J.V.P., B.J.S. conceptualized research; J.V.P., D.P.G., K.R.M., W.D.G., B.M.D., J.P.M., A.S., A.G.M. performed research and data analysis. D.L.B., T.J.E., T.L.B., and A.C.M.B. provided expertize and guidance, during experiment design and sample acquisition. J.V.P., D.P.G., R.K.C. wrote the paper with input from J.R.C., C.M.Y., B.J.S., and N.J.S.
Corresponding authors
Correspondence to Carla M. Yuede, John R. Cirrito or Rajan K. Chakrabarty.