A next‐generation vaccine for broader and long‐lasting COVID‐19 protection

Xi, Jinxiang; April, Xiuhua

doi:10.1002/mco2.138

Cited by 6 publications

(3 citation statements)

References 12 publications

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“…There should be no signs of abnormal densities or infiltrations that are indicative of infection or inflammation, as shown in the upper panels of Figures 8 and 9. COVID-19 X-ray images may show patchy areas of opacity, which indicate fluid buildup in the lungs [58,59]. Additionally, COVID-19 may cause a condition called "ground-glass opacity", which appears as a hazy, cloud-like pattern.…”

Section: Re-training Effects On Model Performance and Results Interpr...mentioning

confidence: 99%

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

Talaat,

Si,

2023

Applied Sciences

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This study aimed to address three questions in AI-assisted COVID-19 diagnostic systems: (1) How does a CNN model trained on one dataset perform on test datasets from disparate medical centers? (2) What accuracy gains can be achieved by enriching the training dataset with new images? (3) How can learned features elucidate classification results, and how do they vary among different models? To achieve these aims, four CNN models—AlexNet, ResNet-50, MobileNet, and VGG-19—were trained in five rounds by incrementally adding new images to a baseline training set comprising 11,538 chest X-ray images. In each round, the models were tested on four datasets with decreasing levels of image similarity. Notably, all models showed performance drops when tested on datasets containing outlier images or sourced from other clinics. In Round 1, 95.2~99.2% accuracy was achieved for the Level 1 testing dataset (i.e., from the same clinic but set apart for testing only), and 94.7~98.3% for Level 2 (i.e., from an external clinic but similar). However, model performance drastically decreased for Level 3 (i.e., outlier images with rotation or deformation), with the mean sensitivity plummeting from 99% to 36%. For the Level 4 testing dataset (i.e., from another clinic), accuracy decreased from 97% to 86%, and sensitivity from 99% to 67%. In Rounds 2 and 3, adding 25% and 50% of the outlier images to the training dataset improved the average Level-3 accuracy by 15% and 23% (i.e., from 56% to 71% to 83%). In Rounds 4 and 5, adding 25% and 50% of the external images increased the average Level-4 accuracy from 81% to 92% and 95%, respectively. Among the models, ResNet-50 demonstrated the most robust performance across the five-round training/testing phases, while VGG-19 persistently underperformed. Heatmaps and intermediate activation features showed visual correlations to COVID-19 and pneumonia X-ray manifestations but were insufficient to explicitly explain the classification. However, heatmaps and activation features at different rounds shed light on the progression of the models’ learning behavior.

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Section: Re-training Effects On Model Performance and Results Interpr...mentioning

confidence: 99%

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

Talaat,

Si,

2023

Applied Sciences

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“…During congestion (N1), elevated nasal resistance confines spray deposition, while decongestion (P1, P2) widens the nasal passage, leading to broader dispersion of spray droplets, especially towards the posterior turbinate section (T3) and nasopharynx. These regions are the target for mucosal immunization against respiratory infectious diseases, as well as the initial site of viral infections such as COVID-19 [ 36 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Discussionmentioning

confidence: 99%

Assessing Nasal Epithelial Dynamics: Impact of the Natural Nasal Cycle on Intranasal Spray Deposition

Seifelnasr,

Si,

2024

Pharmaceuticals

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This study investigated the intricate dynamics of intranasal spray deposition within nasal models, considering variations in head orientation and stages of the nasal cycle. Employing controlled delivery conditions, we compared the deposition patterns of saline nasal sprays in models representing congestion (N1), normal (N0), and decongestion (P1, P2) during one nasal cycle. The results highlighted the impact of the nasal cycle on spray distribution, with congestion leading to confined deposition and decongestion allowing for broader dispersion of spray droplets and increased sedimentation towards the posterior turbinate. In particular, the progressive nasal dilation from N1 to P2 decreased the spray deposition in the middle turbinate. The head angle, in conjunction with the nasal cycle, significantly influenced the nasal spray deposition distribution, affecting targeted drug delivery within the nasal cavity. Despite controlled parameters, a notable variance in deposition was observed, emphasizing the complex interplay of gravity, flow shear, nasal cycle, and nasal morphology. The magnitude of variance increased as the head tilt angle increased backward from upright to 22.5° to 45° due to increasing gravity and liquid film destabilization, especially under decongestion conditions (P1, P2). This study’s findings underscore the importance of considering both natural physiological variations and head orientation in optimizing intranasal drug delivery.

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“…It has been shown that liquid film translocation can substantially enhance the dosing to the olfactory cleft by adopting a head-to-floor position [ 18 ]. To achieve optimal protection against COVID-19 infection, a nasal device that can effectively deliver high doses of nasal sprays to the posterior nose is needed [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ].…”

Section: Introductionmentioning

confidence: 99%

A Supine Position and Dual-Dose Applications Enhance Spray Dosing to the Posterior Nose: Paving the Way for Mucosal Immunization

et al. 2023

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Delivering vaccines to the posterior nose has been proposed to induce mucosal immunization. However, conventional nasal devices often fail to deliver sufficient doses to the posterior nose. This study aimed to develop a new delivery protocol that can effectively deliver sprays to the caudal turbinate and nasopharynx. High-speed imaging was used to characterize the nasal spray plumes. Three-dimensional-printed transparent nasal casts were used to visualize the spray deposition within the nasal airway, as well as the subsequent liquid film formation and translocation. Influencing variables considered included the device type, delivery mode, release angle, flow rate, head position, and dose number. Apparent liquid film translocation was observed in the nasal cavity. To deliver sprays to the posterior nose, the optimal release angle was found to be 40° for unidirectional delivery and 30° for bidirectional delivery. The flow shear was the key factor that mobilized the liquid film. Both the flow shear and the head position were important in determining the translocation distance. A supine position and dual-dose application significantly improved delivery to the nasopharynx, i.e., 31% vs. 0% with an upright position and one-dose application. It is feasible to effectively deliver medications to the posterior nose by leveraging liquid film translocation for mucosal immunization.

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A next‐generation vaccine for broader and long‐lasting COVID‐19 protection

Cited by 6 publications

References 12 publications

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

Multi-Level Training and Testing of CNN Models in Diagnosing Multi-Center COVID-19 and Pneumonia X-ray Images

Assessing Nasal Epithelial Dynamics: Impact of the Natural Nasal Cycle on Intranasal Spray Deposition

A Supine Position and Dual-Dose Applications Enhance Spray Dosing to the Posterior Nose: Paving the Way for Mucosal Immunization

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