Given the current outbreak of coronavirus disease 2019 and the development and implementation of mass vaccination, data are being obtained by analyzing vaccination campaigns. in the present study, 69 healthcare workers who were exposed to patients with severe acute respiratory syndrome coronavirus-2 were monitored for specific immunoglobulin (Ig)G and IgA levels at different time periods. Prior to vaccination, after the first round of vaccination at 21 days (when the second dose of vaccine was administrated) and 24 days after the second round of vaccination, with an mrna-based vaccine. The basal igG and iga levels in previously infected subjects and non-infected subjects notably differed. Vaccination increased the igG and iga levels after the first dose in most subjects from both groups, the levels of which further increased following the second round of vaccination. The associations between igG and iga levels following the first and second rounds of vaccination demonstrated that in the entire vaccination group, regardless of prior exposure to the infectious agent, the increment and levels of igG and iga were similar. Thus, the levels upon vaccination were statistically similar irrespective of the starting base line prior to vaccination. in the present study, seroconversion was achieved in all subjects following the second round of vaccination, with similar antibodies levels.

Cutaneous melanoma is a significant immunogenic tumoral model, the most frequently described immune phenomenon being tumor regression, as a result of the interaction of tumoral antigens and stromal microenvironment. We present a retrospective cohort study including 52 cases of melanoma with regression. There were evaluated correlations of the most important prognostic factors (Breslow depth and mitotic index) with FOXP3 expression in tumor cells and with the presence of regulatory T cells and dendritic cells in the tumoral stroma. FOXP3 expression in tumor cells seems an independent factor of poor prognosis in melanoma, while regression areas are characterized by a high number of dendritic cells and a low number of regulatory T cells. FOXP3 is probably a useful therapeutical target in melanoma, since inhibition of FOXP3-positive tumor clones and of regulatory T cells could eliminate the ability of tumor cells to escape the immune defense of the host.

Mycobacteria identification is crucial to diagnose tuberculosis. Since the bacillus is very small, finding it in Ziehl–Neelsen (ZN)-stained slides is a long task requiring significant pathologist’s effort. We developed an automated (AI-based) method of identification of mycobacteria. We prepared a training dataset of over 260,000 positive and over 700,000,000 negative patches annotated on scans of 510 whole slide images (WSI) of ZN-stained slides (110 positive and 400 negative). Several image augmentation techniques coupled with different custom computer vision architectures were used. WSIs automatic analysis was followed by a report indicating areas more likely to present mycobacteria. Our model performs AI-based diagnosis (the final decision of the diagnosis of WSI belongs to the pathologist). The results were validated internally on a dataset of 286,000 patches and tested in pathology laboratory settings on 60 ZN slides (23 positive and 37 negative). We compared the pathologists’ results obtained by separately evaluating slides and WSIs with the results given by a pathologist aided by automatic analysis of WSIs. Our architecture showed 0.977 area under the receiver operating characteristic curve. The clinical test presented 98.33% accuracy, 95.65% sensitivity, and 100% specificity for the AI-assisted method, outperforming any other AI-based proposed methods for AFB detection.