Persistence of inflammatory and vascular damage mediators 5 months after acute COVID-19

Background: In the acute phase, patients with severe COVID-19 exhibit pulmonary inflammation and vascular injury, as well as an exaggerated cytokine response. Aim(s): To describe the inflammatory cytokine and vascular injury mediator profiles in patients previously hospitalised with COVID-19, and to compare these profiles with those in healthy controls and in patients recovering from severe sepsis of other aetiology. Method(s): Plasma levels of 28 different cytokine, chemokine, angiogenic and vascular injury markers were measured by MSD V-PLEX multiplex assays in 49 post-COVID patients 5.0+/-1.9 (mean+/-SD) months after hospitalisation with COVID-19 pneumonia, 11 post-sepsis patients (5.4+/-2.9 months after hospitalised non-COVID sepsis) and 18 healthy controls. Kruskal-Wallis or ANOVA were used to compare groups;false discovery rate correction (Benjamini Hochberg) allowed for multiple testing. Result(s): In the post-COVID group, IL-6, TNFalpha, SAA, MCP1, Tie-2, Flt1, PIGF and CRP were significantly elevated, whereas IL-7 and bFGF were significantly depressed. The differences in TNFalpha, SAA, MCP1, Tie-2, Flt1, IL-7 and bFGF appeared unique to the post-COVID group, but increased IL-6, PIGF and CRP levels were also seen in postsepsis patients compared with controls. In post-COVID patients we found strong negative spearman correlations between each of IL-6 (r=-0.51) and CRP (r=-0.57) with TLCO %predicted (p<0.001) and positive correlations with post-recovery CT abnormality scores: IL-6 (r=0.28) and CRP (r=0.46), p<0.05. Conclusion(s): A unique signature of inflammatory and vascular damage markers is seen months after acute COVID19 infection. Further research is needed to determine their pathophysiological significance.

Inflammation profiling and recovery clusters five months post-hospitalisation for COVID-19

Background Understanding the underlying mechanisms of post-COVID sequelae (Long COVID) is urgently needed to guide interventions. Aim To compare the inflammation profiles of four recovery clusters post-hospitalisation. Methods Post-Hospitalisation COVID-19 (PHOSP-COVID) is a prospective, multi-centre study across UK. Four recovery clusters previously identified using clinical data (symptoms, mental health, cognitive impairment, and physical function) at 5 months post-discharge were used based on severity of on-going health impairments: very severe, severe, moderate (cognitive), and mild. Inflammatory profiling performed from plasma samples using the Olink Explore 384 inflammation panel. Multinomial logistic regression for each protein was undertaken comparing the mild cluster with each of the remaining clusters with FDR of 0.1 to adjust p values. Results 626 participants (clusters: very severe n=111, severe n=173, moderate/cognitive n=73 and mild n=269). Proteomic results from 296 proteins were included. After adjustment for age, BMI, and comorbidity count, 13 proteins were significantly elevated in the very severe cluster, and 2 proteins in the moderate/cognitive cluster, compared to the mild cluster (Figure 1). Conclusion Inflammatory mediators consistent with persistent lung and systemic inflammation were associated with the severity of ongoing health impairments highlighting potential therapeutic pathways to be tested.

Analysis of the Effect of Black Box Adversarial Attacks on Medical Image Classification Models

In the field of medical science, the reliability of the results produced by deep learning classifiers on disease diagnosis plays a crucial role. The reliability of the classifier substantially reduces by the presence of adversarial examples. The adversarial examples mislead the classifiers to give wrong prediction with equal or more confidence than the actual prediction. The adversarial attacks in the black box type is done by creating a pseudo model that resembles the target model. From the pseudo model, the attack is created and is transferred to the target model. In this work, the Fast Gradient Sign Method and its variants Momentum Iterative Fast Gradient Sign Method, Projected Gradient Descent and Basic Iterative Method are used to create adversarial examples on a target VGG-16 model. The datasets used are Diabetic Retinopathy 2015 Data Colored Resized and SARS-CoV-2 CT Scan Dataset. The experimentation revealed that the transferability of attack is true for the above described attack methods on a VGG-16 model. Also, the Projected Gradient Descent attack provides a higher success in attack in comparison with the other methods experimented in this work. © 2022 IEEE.

Longitudinal trajectory of cardiac magnetic resonance and cardiopulmonary exercise testing findings in moderate to severe COVID-19 and association with symptoms

Background: Cardiac magnetic resonance (CMR) and cardiopulmonary exercise testing (CPET) have provided important insights into the prevalence of early cardiopulmonary abnormalities in COVID-19 patients. It is currently unknown whether such abnormalities persist over time and relate to ongoing symptoms. Purpose: To describe the longitudinal trajectory of cardiopulmonary abnormalities on CMR and CPET in moderate to severe COVID-19 patients and assess their relationship with ongoing symptoms. Methods: Fifty-eight previously hospitalised COVID-19 patients and 30 age, sex, body mass index, comorbidity-matched controls underwent CMR, CPET and a symptom-based questionnaire at 2-3 months (2-3m). Repeat assessments (including gas transfer) were performed in 46 patients at 6 months (6m). Results: During admission, 1/3rd of patients needed ventilation or intensive care (Table 1) and three (5%) had a raised troponin. On CMR, patients had preserved left (LV) and right ventricular (RV) volumes and function at 2-3m from infection. By 6m, LV function did not change but RV end diastolic volume decreased (mean difference -4.3 mls/m2, p=0.005) and RV function increased (mean difference +3.2%, p<0.001, Fig. 1A). Patients had higher native T1 (a marker of fibroinflammation) at 2-3m compared to controls (Table 1, Fig. 1B), which normalised by 6m. Extracellular volume was normal and improved by 6m. Native T2, a marker of myocardial oedema, did not differ between patients and controls on serial CMR. At 2- 3m, late gadolinium enhancement (LGE) was higher in patients (p=0.023) but became comparable to controls by 6m (p=0.62). Six (12%) patients had LGE in a myocarditis pattern and one (2%) had myocardial infarction. None had active myocarditis using the Modified Lake Louise Criteria. Lung imaging (T2-weighted) revealed parenchymal abnormalities in 2/3rds of patients at 2-3 and 6 months. The extent of abnormalities improved on serial imaging (Table 1). Gas transfer (DLco) was worse in those with lung abnormalities (77% vs 91% of predicted, p=0.009). CPET revealed reduced peak oxygen consumption (pVO2) in patients at 2-3m, which normalised by 6m (80.5% to 93.3% of predicted, p=0.001) (Table 1, Fig. 1C). At 2-3m, 49% of patients had submaximal tests (respiratory exchange ratio <1.1), reducing to 25% by 6m (p=0.057). VE/VCO2 slope, a marker of lung efficiency, was abnormal in patients but improved on serial CPET (Table 1, Fig. 1D). Cardiac symptoms (chest pain, dyspnoea, palpitations, dizziness or syncope) were present in 83% of patients at 2-3m, reducing to 52% by 6m (p<0.001). There was no significant association between CMR or CPET parameters and persistent cardiac symptoms at 6m (Fig. 1E). Conclusions: Cardiopulmonary parameters (on CMR and CPET) improved in moderate-severe COVID-19 patients from 2-3 to 6 months post infection. Despite this, patients continued to experience cardiac symptoms which had no relationship with measured parameters. (Figure Presented).

Z-Score-Based Secure Biomedical Model for Effective Skin Lesion Segmentation over eHealth Cloud

This study aims to process the private medical data over eHealth cloud platform. The current pandemic situation, caused by Covid19 has made us to realize the importance of automatic remotely operated independent services, such as cloud. However, the cloud servers are developed and maintained by third parties, and may access user's data for certain benefits. Considering these problems, we propose a specialized method such that the patient's rights and changes in medical treatment can be preserved. The problem arising due to Melanoma skin cancer is carefully considered and a privacy-preserving cloud-based approach is proposed to achieve effective skin lesion segmentation. The work is accomplished by the development of a Z-score-based local color correction method to differentiate image pixels from ambiguity, resulting the segmentation quality to be highly improved. On the other hand, the privacy is assured by partially order homomorphic Permutation Ordered Binary (POB) number system and image permutation. Experiments are performed over publicly available images from the ISIC 2016 and 2017 challenges, as well as PH dataset, where the proposed approach is found to achieve significant results over the encrypted images (known as encrypted domain), as compared to the existing schemes in the plain domain (unencrypted images). We also compare the results with the winners of the ISBI 2016 and 2017 challenges, and show that the proposed approach achieves a very close result with them, even after processing test images in the encrypted domain. Security of the proposed approach is analyzed using a challenge-response game model. © 2021 Association for Computing Machinery.