Antibody Response to Sars-Cov-2 Vaccination in Adolescents Following Solid Organ Transplantation

Serological responses to Covid-19 vaccines in adults post solid organ transplant are impaired [1]. We sought to measure antibodies to SARS-CoV-2 in adolescents post lung, heart, kidney-liver and kidney transplant following the vaccine rollout to 12-17 year-olds in the UK. We measured anti-spike, receptor binding domain and nucleocapsid IgG in adolescents attending our centre for routine drug monitoring after Pfizer/BioNTech BNT 162b2 was made available in autumn 2021, and collected information on prior infection from electronic records and via patient/parent recall. Samples were obtained from 42 participants, with paired samples pre and post vaccine in 14 patients. 52% had serological evidence of past exposure at baseline (fig 1). Receptor binding domain IgG was positive in all subjects at follow up, with spike IgG positive in 13/14 (p<0.001, fig 2). Individuals who had received vaccine and been infected had higher levels of spike IgG than those who had been infected and unvaccinated (p<0.05). All subjects demonstrated antibody responses to vaccine, and vaccine increases antibody levels in adolescents who have also had a SARS-CoV-2 infection in comparison to the unvaccinated. These data support an age-related relationship to antibody responses in post solid organ transplant recipients and support efforts to increase vaccine uptake in this at-risk group. [ FROM AUTHOR] Copyright of Journal of Heart & Lung Transplantation is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

A Functional Assay to Measure the T Cell Response to Sars-Cov-2 in Primary Immunodeficiency Patients

Background/Aims Effective symptom management may require the use of medications. Medication adherence may be hindered by formulation aspects, such as poor taste. Paediatric studies indicate, that despite concerns of swallowing solid dose forms, children prefer these to liquid forms. They find the solid dose forms more palatable. However, swallowing numerous solid dose forms, may present a significant 'pill' burden to patients and their care-givers. Filling empty gelatine capsules with requisite medications is seen and used as a way to address palatability, decrease pill burden and thereby increase compliance. Yet there is little evidence on the impact this practise may have on the effectiveness of over-encapsulated medicines. This study explored the effect of over-encapsulation on in vitro disintegration and dissolution, of some commonly used medicines in paediatric palliative care. Method Immediate release (Cyclizine Hydrochloride, Gabapentin, Paracetamol) and modified release preparations (Omeprazole, Diclofenac sodium) were over-encapsulated in size 00 gelatin and HPMC capsules (n=6). Dissolution and disintegration were tested according to Pharmacopeia standards. Statistical analyses, using Student's T-test and f1 and f2 tests (respectively) were applied to determine similarities or differences in disintegration or dissolution. Results Disintegration and dissolution was prolonged for all over-encapsulated immediate release preparations, especially when using HPMC capsules. However, percentage of drug dissolved met the acceptance criteria for immediate-release solid oral dosage. Over-encapsulation of modified release preparations did not lead to significant dissolution or disintegration changes. Conclusion Over-encapsulation, may delay medication release, especially for immediate release medicines however, medicine effectiveness may not be. Further studies are required before we can safely recommend use of over-encapsulation as an administration compliance aid.

Rapid Lung Ultrasound COVID-19 Severity Scoring with Resource-Efficient Deep Feature Extraction

Artificial intelligence-based analysis of lung ultrasound imaging has been demonstrated as an effective technique for rapid diagnostic decision support throughout the COVID-19 pandemic. However, such techniques can require days- or weeks-long training processes and hyper-parameter tuning to develop intelligent deep learning image analysis models. This work focuses on leveraging ‘off-the-shelf’ pre-trained models as deep feature extractors for scoring disease severity with minimal training time. We propose using pre-trained initializations of existing methods ahead of simple and compact neural networks to reduce reliance on computational capacity. This reduction of computational capacity is of critical importance in time-limited or resource-constrained circumstances, such as the early stages of a pandemic. On a dataset of 49 patients, comprising over 20,000 images, we demonstrate that the use of existing methods as feature extractors results in the effective classification of COVID-19-related pneumonia severity while requiring only minutes of training time. Our methods can achieve an accuracy of over 0.93 on a 4-level severity score scale and provides comparable per-patient region and global scores compared to expert annotated ground truths. These results demonstrate the capability for rapid deployment and use of such minimally-adapted methods for progress monitoring, patient stratification and management in clinical practice for COVID-19 patients, and potentially in other respiratory diseases. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Lung Ultrasound Segmentation and Adaptation Between COVID-19 and Community-Acquired Pneumonia

Lung ultrasound imaging has been shown effective in detecting typical patterns for interstitial pneumonia, as a point-of-care tool for both patients with COVID-19 and other community-acquired pneumonia (CAP). In this work, we focus on the hyperechoic B-line segmentation task. Using deep neural networks, we automatically outline the regions that are indicative of pathology-sensitive artifacts and their associated sonographic patterns. With a real-world data-scarce scenario, we investigate approaches to utilize both COVID-19 and CAP lung ultrasound data to train the networks;comparing fine-tuning and unsupervised domain adaptation. Segmenting either type of lung condition at inference may support a range of clinical applications during evolving epidemic stages, but also demonstrates value in resource-constrained clinical scenarios. Adapting real clinical data acquired from COVID-19 patients to those from CAP patients significantly improved Dice scores from 0.60 to 0.87 (p <0.001) and from 0.43 to 0.71 (p <0.001), on independent COVID-19 and CAP test cases, respectively. It is of practical value that the improvement was demonstrated with only a small amount of data in both training and adaptation data sets, a common constraint for deploying machine learning models in clinical practice. Interestingly, we also report that the inverse adaptation, from labelled CAP data to unlabeled COVID-19 data, did not demonstrate an improvement when tested on either condition. Furthermore, we offer a possible explanation that correlates the segmentation performance to label consistency and data domain diversity in this point-of-care lung ultrasound application. © 2021, Springer Nature Switzerland AG.

Cross-sectional prevalence of SARS-CoV-2 antibodies in healthcare workers in paediatric facilities in eight countries.

BACKGROUND: Healthcare workers (HCWs) have been disproportionately affected by coronavirus disease 2019 (COVID-19), which may be driven, in part, by nosocomial exposure. If HCW exposure is predominantly nosocomial, HCWs in paediatric facilities, where few patients are admitted with COVID-19, may lack antibodies to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) and be at increased risk during the current resurgence. AIM: To compare the seroprevalence of SARS-CoV-2 amongst HCWs in paediatric facilities in seven European countries and South Africa (N=8). METHODS: All categories of paediatric HCWs were invited to participate in the study, irrespective of previous symptoms. A single blood sample was taken and data about previous symptoms were documented. Serum was shipped to a central laboratory in London where SARS-CoV-2 immunoglobulin G was measured. FINDINGS: In total, 4114 HCWs were recruited between 1st May and mid-July 2020. The range of seroprevalence was 0-16.93%. The highest seroprevalence was found in London (16.93%), followed by Cape Town, South Africa (10.36%). There were no positive HCWs in the Austrian, Estonian and Latvian cohorts; 2/300 [0.66%, 95% confidence interval (CI) 0.18-2.4] HCWs tested positive in Lithuania; 1/124 (0.81%, 95% CI 0.14-4.3) HCWs tested positive in Romania; and 1/76 (1.3%, 95% CI 0.23-7.0) HCWs tested positive in Greece. CONCLUSION: Overall seroprevalence amongst paediatric HCWs is similar to their national populations and linked to the national COVID-19 burden. Staff working in paediatric facilities in low-burden countries have very low seroprevalence rates and thus are likely to be susceptible to COVID-19. Their susceptibility to infection may affect their ability to provide care in the face of increasing cases of COVID-19, and this highlights the need for appropriate preventative strategies in paediatric healthcare settings.