ABSTRACT
We enrolled seven individuals with recurrent symptoms or antigen test conversion following nirmatrelvir-ritonavir treatment. High viral loads (median 6.1 log10 copies/mL) were detected after rebound for a median of 17 days after initial diagnosis. Three had culturable virus for up to 16 days after initial diagnosis. No known resistance-associated mutations were identified.
ABSTRACT
Protective immunity against SARS-CoV-2 infection after COVID-19 vaccination may differ by variant. We enrolled vaccinated (n = 39) and unvaccinated (n = 11) individuals with acute, symptomatic SARS-CoV-2 Delta or Omicron infection and performed SARS-CoV-2 viral load quantification, whole-genome sequencing, and variant-specific antibody characterization at the time of acute illness and convalescence. Viral load at the time of infection was inversely correlated with antibody binding and neutralizing antibody responses. Across all variants tested, convalescent neutralization titers in unvaccinated individuals were markedly lower than in vaccinated individuals. Increases in antibody titers and neutralizing activity occurred at convalescence in a variant-specific manner. For example, among individuals infected with the Delta variant, neutralizing antibody responses were weakest against BA.2, whereas infection with Omicron BA.1 variant generated a broader response against all tested variants, including BA.2.
Subject(s)
AIDS Vaccines , COVID-19 , Influenza Vaccines , Papillomavirus Vaccines , Respiratory Syncytial Virus Vaccines , SAIDS Vaccines , Antibodies, Neutralizing , Antibodies, Viral , BCG Vaccine , COVID-19/prevention & control , COVID-19 Vaccines , Convalescence , Diphtheria-Tetanus-Pertussis Vaccine , Humans , Measles-Mumps-Rubella Vaccine , Neutralization Tests , SARS-CoV-2ABSTRACT
This research work aims to identify COVID-19 through deep learning models using lung CT-SCAN images. In order to enhance lung CT scan efficiency, a super-residual dense neural network was applied. The experimentation has been carried out using benchmark datasets like SARS-COV-2 CT-Scan and Covid-CT Scan. To mark COVID-19 as positive or negative for the improved CT scan, existing pre-trained models such as XceptionNet, MobileNet, InceptionV3, DenseNet, ResNet50, and VGG (Visual Geometry Group)16 have been used. Taking CT scans with super resolution using a residual dense neural network in the pre-processing step resulted in improving the accuracy, F1 score, precision, and recall of the proposed model. On the dataset Covid-CT Scan and SARS-COV-2 CT-Scan, the MobileNet model provided a precision of 94.12% and 100% respectively.
Subject(s)
COVID-19 , Deep Learning , Lung , COVID-19/diagnostic imaging , Humans , Lung/diagnostic imaging , Tomography, X-Ray ComputedABSTRACT
Background: Fecal microbiota transplantation (FMT) is currently an approved treatment for recurrent and refractory Clostridioides difficile infection. However, its use in ulcerative colitis is at an early stage and significant gaps remain in our understanding of the mechanisms and logistics of its practical application. Methods and results: This article aims to look into specific issues which remain unsettled for use of FMT in ulcerative colitis including donor and recipient selection, route of administration, and duration of therapy. We also discuss optimal ways to assess response to FMT and the current state of FMT regulations. In addition, we postulate the impact of diet on the microbiome profile of the donor and recipient. We also suggest a change in the nomenclature from FMT to fecal microbiome transfer. Conclusion: FMT is an evolving therapy. There are several considerations for its use in UC but its use and role should be directed by further clinical trials.