Covid-19 Vaccination Impacts Functional Immune Responses and Plasma Proteome in Plwh

Background: The impact of COVID-19 infection or COVID-19 vaccination on the immune system of people living with HIV (PLWH) is unclear. We therefore studied the effects of COVID-19 infection or vaccination on functional immune responses and systemic inflammation in PLWH. Method(s): Between 2019 and 2021, 1985 virally suppressed, asymptomatic PLWH were included in the Netherlands in the 2000HIV study (NCT039948350): 1514 participants enrolled after the start of the COVID-19 pandemic were separated into a discovery and validation cohort. PBMCs were incubated with different stimuli for 24 hours: cytokine levels were measured in supernatants. ~3000 targeted plasma proteins were measured with Olink Explore panel. Past COVID-19 infection was proven when a positive PCR was reported or when serology on samples from inclusion proved positive. Compared were unvaccinated PLWH with and without past COVID-19 infection, and PLWH with or without anti-COVID-19 vaccination excluding those with past COVID-19 infection. Result(s): 471 out of 1514 participants were vaccinated (median days since vaccination: 33, IQR 16-66) and 242 had a past COVID-19 infection (median days since +PCR: 137, IQR 56-206). Alcohol, smoking, drug use, BMI, age, latest CD4 count and proportion with viral blips were comparable between groups. Systemic inflammation as assessed by targeted proteomics showed 89 upregulated and 43 downregulated proteins in the vaccinated participants. In contrast, individuals with a past COVID-19 infection display lower levels of 138 plasma proteins compared to the uninfected group (see figure). 'Innate immune system' and 'cell death' were upregulated in pathway analysis in vaccinated PLWH, but downregulated in COVID-19 infected participants. The increased systemic inflammation of the COVID-19 vaccinated group was accompanied by lower TNF-alpha and IL-1beta production capacity upon restimulation with a range of microbial stimuli, while production of IL-1Ra was increased. In COVID-19 infected PLWH only a reduced production of TNF-alpha to S. pneumonia was significant. Vaccinated PLWH also showed upregulation of platelet aggregation pathways. Conclusion(s): COVID-19 vaccination in PLWH leads to an increased systemic inflammation, but less effective cytokine production capacity of its immune cells upon microbial stimulation. This pattern is different from that of COVID-19 infection that leads to a decreased inflammatory profile and only minimal effects on cytokine production capacity. (Figure Presented).

Deep learning model for the quantification of lung abnormalities in ILD patients

Lung fibrosis quantification from CT scans is prone to large inter and intra observer variability and its correlation with PFT is essential in the definition of disease progression. There is the need for a reliable and reproducible tool for abnormalities quantification. For this reason, a deep learning abnormalities quantification model was used to explore the correlation with PFT in ILD patients. The abnormalities segmentation model is based on 2D U-Net combined with Res Next as encoder and deep supervision and was trained on axial unenhanced chest CT scans of 199 COVID-19 patients and externally validated on 50 COVID-19 patients. Whole lungs were segmented using RadiomiX toolbox. Validation of the quantification performance was explored in a cohort of 20 ILD patients. The model performed the automatic segmentation of all abnormalities and calculate the ratio on the total lung volume ((abnormalities volume/whole lungs volume) * 100). This value is then correlated with the Forced Vital Capacity (FVC) and Diffusion Lung Capacity for carbon monoxide (DLCO) for each patient with Pearson correlation coefficient (rho). The deep learning segmentation algorithm achieved good performances (mean DSC 0.6 +/- 0.1) on the external test set. The percentage volume of disease region correlated with FVC and DLCO were the rho = -0.70402, -0.58133, respectively (P <. 001 for all). The developed algorithm performed similarly to radiologists for disease-extent contouring, which correlated with pulmonary function to assess CT images from patients with ILD. This automatic quantification tool could help in the prognosis and diagnosis of ILDs, based on the lung abnormalities extent.

Late Breaking Abstract - CNN-RNN network to classify COVID, Influenza and non-infectious cases on CT imaging

COVID-19 associated lung diseases can mimic radiological characteristics of other viral lung diseases such as influenza which may lead to misdiagnosis. In this study, we proposed an Artificial Intelligence framework based on a combination of a Convolutional Neural network architecture and a Recurrent Neural Network architecture to classify CT volumes with COVID-19, Influenza, and no-infection. The model was trained on a dataset of 300 patients (100 patients in each class). Each set of 15 consecutive axial slices with the associated label of the corresponding CT volume was input as a 3 channel input at 5 time points to the CNN-RNN network. Benchmarked against RT-PCR confirmed cases of COVID-19 and Influenza, our model, when evaluated on an independent validation set of 400 CT patients, can accurately classify CT volumes of patients with COVID-19, Influenza, or no-infection with a sensitivity of 96% (COVID-19) and 95% (Influenza) (Tablel). Figurel shows the percentage of correctly classified and misclassified cases in each class. Our model provides rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention.

Late Breaking Abstract - Development and validation of an automated radiomic CT signature for detecting? COVID-i9

The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status and pushed healthcare systems beyond the limits. We aim to develop a fully automatic framework to detect COVID-19 by applying artificial intelligence (Al). A fully automated Al framework was developed to extract radiomics features from chest CT scans to detect COVID-19 patients. We curated and analysed the data from a total of 1381 patients. A cohort of 181 RT-PCR confirmed COVID-19 patients and 1200 control patients was included for model development. An independent dataset of 697 patients was used to validate the model. The datasets were collected from CHU Liège, Belgium. Model performance was assessed by the area under the receiver operating characteristic curve (AUC). Assuming 15% disease prevalence, a comprehensive analysis of classification performance in terms of accuracy, sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) was performed for all possible decision thresholds. The final curated dataset used for model development and testing consisted of chest CT scans of 1224 patients and 641 patients, respectively. The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset. Assuming the cost of false negatives is twice as high as the cost of false positives, the optimal decision threshold resulted in an accuracy of 85.18%, a sensitivity of 69.52, a specificity of 91.63%, an NPV of 94.46% and a PPV of 59.44%. Our Al framework can accurately detect COVID-19. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the implementation of isolation procedures and early intervention.