SARS COV-2 virus-laden droplets coughed from deep lungs: Numerical quantification in a single-path whole respiratory tract geometry

April, Xiuhua; Talaat, Mohamed; Xi, Jinxiang

doi:10.1063/5.0040914

Cited by 32 publications

(12 citation statements)

References 96 publications

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“…Regarding the validity of simulation-generated images, previous studies have demonstrated that physiology-based simulations could sufficiently reproduce in vivo conditions [43,44]. Two numerical limitations existed in current simulations: steady flows and rigid walls, which would alter the exhaled aerosol distributions [45][46][47][48][49]. However, the differences caused by other variables could still be captured, which was the basis for classification.…”

Section: Limitationsmentioning

confidence: 99%

Convolutional Neural Network Classification of Exhaled Aerosol Images for Diagnosis of Obstructive Respiratory Diseases

Talaat

Tan

et al. 2023

JNT

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Aerosols exhaled from the lungs have distinctive patterns that can be linked to the abnormalities of the lungs. Yet, due to their intricate nature, it is highly challenging to analyze and distinguish these aerosol patterns. Small airway diseases pose an even greater challenge, as the disturbance signals tend to be weak. The objective of this study was to evaluate the performance of four convolutional neural network (CNN) models (AlexNet, ResNet-50, MobileNet, and EfficientNet) in detecting and staging airway abnormalities in small airways using exhaled aerosol images. Specifically, the model’s capacity to classify images inside and outside the original design space was assessed. In doing so, multi-level testing on images with decreasing similarities was conducted for each model. A total of 2745 images were generated using physiology-based simulations from normal and obstructed lungs of varying stages. Multiple-round training on datasets with increasing images (and new features) was also conducted to evaluate the benefits of continuous learning. Results show reasonably high classification accuracy on inbox images for models but significantly lower accuracy on outbox images (i.e., outside design space). ResNet-50 was the most robust among the four models for both diagnostic (2-class: normal vs. disease) and staging (3-class) purposes, as well as on both inbox and outbox test datasets. Variation in flow rate was observed to play a more important role in classification decisions than particle size and throat variation. Continuous learning/training with appropriate images could substantially enhance classification accuracy, even with a small number (~100) of new images. This study shows that CNN transfer-learning models could detect small airway remodeling (<1 mm) amidst a variety of variants and that ResNet-50 can be a promising model for the future development of obstructive lung diagnostic systems.

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Section: Limitationsmentioning

confidence: 99%

Convolutional Neural Network Classification of Exhaled Aerosol Images for Diagnosis of Obstructive Respiratory Diseases

Talaat

Tan

et al. 2023

JNT

Self Cite

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“…Therefore, this modulation of the innate antiviral response induced by both host and viral factors gives a good head start to the viral replication in the URT and hyper-inflammation in the lungs, resulting in conditions that lead to COVID-19 severity. Additionally, hyper-inflammation in the respiratory bronchioles will increase the probability of airway collapse and opening, further increasing virus-laden aerosol generation [ 98 ]. As supported by recent investigation though lacking perfect experimental data, it is highly likely that the droplets and aerosols exhaled from the alveoli are more infectious than the droplets from the other airway sites and these droplets/aerosols possibly help in the airborne route of SARS-Cov-2 transmission [ 99–101 ].…”

Section: Host Virus Interaction (The Host Immune Response)mentioning

confidence: 99%

An update on host immunity correlates and prospects of re-infection in COVID-19

Negi

Maurya

Singh

et al. 2021

International Reviews of Immunology

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Reinfection with SARS-CoV-2 is not frequent yet the incidence rate of it is increasing globally owing to the slow emergence of drift variants that pose a perpetual threat to vaccination strategies and have a greater propensity for disease reoccurrence. Long-term protection against SARS-CoV-2 reinfection relies on the induction of the innate as well as the adaptive immune response endowed with immune memory. However, a multitude of factors including the selection pressure, the waning immunity against SARS-CoV-2 over the first year after infection possibly favors evolution of more infectious immune escape variants, amplifying the risk of reinfection. Additionally, the correlates of immune protection, the novel SARS-CoV-2 variants of concern (VOC), the durability of the adaptive and mucosal immunity remain major challenges for the development of therapeutic and prophylactic interventions. Interestingly, a recent body of evidence indicated that the gastrointestinal (GI) tract is another important target organ for SARS-CoV-2 besides the respiratory system, potentially increasing the likelihood of reinfection by impacting the microbiome and the immune response via the gut-lung axis. In this review, we summarized the latest development in SARS-CoV-2 reinfection, and explored the untapped potential of trained immunity. We also highlighted the immune memory kinetics of the humoral and cell-mediated immune response, genetic drift of the emerging viral variants, and discussed the current challenges in vaccine development. Understanding the dynamics and the quality of immune response by unlocking the power of the innate, humoral and cell-mediated immunity during SARS-CoV-2 reinfection would open newer avenues for drug discovery and vaccine designs.

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“…Respiratory disease such as coronavirus disease (COVID-19) spreads through the respiratory droplets generated by an infected subject (Modes of transmission of virus causing COVID-19 2022 ; April Si et al. 2021 ; Bhat et al. 2022 ; Yang et al.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of N95 Mask in Preventing COVID-19 Transmission

Narayan

Chatterjee

Agrawal

et al. 2023

Trans Indian Natl. Acad. Eng.

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N95 mask has emerged as a potential measure to mitigate the airborne transmission of respiratory disease such as COVID-19. Herein, we experimentally investigated the impact and interaction of pure water droplets as surrogate to respiratory droplets with the different layers of a commercially available N95 mask to demonstrate the penetration and passage-capability of respiratory fluids through the different layers. The penetration of an impacting droplet through the mask layers was characterized by elucidating the ejection of secondary droplets from the rear-side surface of the target mask material. In addition, the passage of respiratory fluids through the mask layers was characterized by capillary imbibition of the droplet liquid through the pores, as a function of wettability of the mask material. Droplet impact at Weber numbers We = 208 and 416 has been considered in the present study; the chosen We range corresponds to that of cough droplets realized in real respiratory events. Each layer of the N95 mask is hydrophobic that prevents capillary imbibition through the pores: a sessile droplet placed over the surface exhibits classical diffusion-limited evaporation. Droplet impact experiments on N95 mask layer surfaces reveal that a single layer allows liquid penetration at We = 416; while a combination of five layers, as is the case of a commercially available N95 mask, blocks the penetration completely, consistent with the widely known effectiveness of N95 masks. Herein, we devote special attention to compare the so-obtained efficiency of N95 masks to that of a recently designed two-layer cloth mask containing an intermediate High-Efficiency Particulate Air (HEPA) filter layer (Narayan et al. in Phys Fluids 34:061703, 2022). We conclusively show that the performance of the designed cloth mask is identical to that of a commercially available N95 mask. The assessment of mask effectiveness further includes examination of breathability and comfort by means of passage of air through them. A comparative study has been presented herein for a clear demonstration of effectiveness of different masks in preventing air-borne transmission of COVID-19.

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SARS COV-2 virus-laden droplets coughed from deep lungs: Numerical quantification in a single-path whole respiratory tract geometry

Cited by 32 publications

References 96 publications

Convolutional Neural Network Classification of Exhaled Aerosol Images for Diagnosis of Obstructive Respiratory Diseases

Convolutional Neural Network Classification of Exhaled Aerosol Images for Diagnosis of Obstructive Respiratory Diseases

An update on host immunity correlates and prospects of re-infection in COVID-19

Effectiveness of N95 Mask in Preventing COVID-19 Transmission

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