Selective suppression of de novo SARS-CoV-2 vaccine antibody responses in patients with cancer on B cell-targeted therapy.

We assessed vaccine-induced antibody responses to the SARS-CoV-2 ancestral virus and Omicron variant before and after booster immunization in 57 patients with B cell malignancies. Over one-third of vaccinated patients at the pre-booster time point were seronegative, and these patients were predominantly on active cancer therapies such as anti-CD20 monoclonal antibody. While booster immunization was able to induce detectable antibodies in a small fraction of seronegative patients, the overall booster benefit was disproportionately evident in patients already seropositive and not receiving active therapy. While ancestral virus- and Omicron variant-reactive antibody levels among individual patients were largely concordant, neutralizing antibodies against Omicron tended to be reduced. Interestingly, in all patients, including those unable to generate detectable antibodies against SARS-CoV-2 spike, we observed comparable levels of EBV- and influenza-reactive antibodies, demonstrating that B cell-targeting therapies primarily impair de novo but not preexisting antibody levels. These findings support rationale for vaccination before cancer treatment.

A Super-resolution-based Approach for the Detection of Covid—19 Infection From Chest X-ray Images

X-ray is the most accessible imaging modality for detecting Covid-19 infection. However, X-ray image resolution depends on the amount of radiation dose. The Lesser the dosage, the lower the resolution, the higher the noise and patient safety. Detecting Covid-19 infection would be more precise with high-resolution chest X-ray images. The current article explores an edge-preserving single-scale residual learning-based super-resolution method to enhance low-resolution chest X-ray images. We used unsharp masking to preserve small, medium, and high-scale details while super-resolving the given image. The method produces a clear view of the pulmonary opacities in chest X-ray images after super-resolution reconstruction. Statistical feature metrics of first and second-order showed superior quality reconstruction by the proposed method for the given Covid-19 chest X-ray images. Further, to measure the effectiveness of super-resolution, we used an Inception v3 based deep learning model to classify chest X-ray images of Covid-19, pneumonia, and normal class. The performance of the classification model with super-resolved chest X-ray images was tested against 400 images belonging to two different classes at a time. We obtained increased precision of 94% and 96% accuracy in detecting Covid-19 infection in chest X-ray images after super-resolution compared to 64% precision and 68% accuracy before super-resolution.

Hairy cell leukemia and COVID-19 adaptation of treatment guidelines.

Standard treatment options in classic HCL (cHCL) result in high response rates and near normal life expectancy. However, the disease itself and the recommended standard treatment are associated with profound and prolonged immunosuppression, increasing susceptibility to infections and the risk for a severe course of COVID-19. The Hairy Cell Leukemia Foundation (HCLF) has recently convened experts and discussed different clinical strategies for the management of these patients. The new recommendations adapt the 2017 consensus for the diagnosis and management with cHCL to the current COVID-19 pandemic. They underline the option of active surveillance in patients with low but stable blood counts, consider the use of targeted and non-immunosuppressive agents as first-line treatment for cHCL, and give recommendations on preventive measures against COVID-19.