ABSTRACT
Introduction/Objective COVID-19 vaccine-related lymphadenopathy, particularly in the ipsilateral axilla, is a relatively well-known side effect of mRNA vaccines with many reports in radiology, but less is known regarding histopathology and additional sites of lymphadenopathy, as well as other localized potential vaccine-related mass manifestations. In addition to a case of minimal change disease, we report two cases here with associated systemic and local pathologic changes related to COVID-19 vaccination. Methods/Case Report In case #1, a 17-year-old male presented with a 2.4 cm left postauricular mass. He had originally noticed the mass six months prior and thought that it had recently been growing. The mass was soft, nonfluctuant, and nontender to palpation. Given the risk of malignancy, a resection was performed. Histology showed an enlarged lymph node composed of mixed inflammatory cell components consistent with lymphoid hyperplasia and no evidence of malignancy. On further chart review, the patient had received his second COVID-19 vaccination just prior to noticing the mass enlarging. A SARS-CoV-2 Anti-Spike IgG assay was as high as 24,396 AU/ml, suggesting that this benign lymphadenopathy was most likely related to his vaccination. For case #2, a 47-year-old male developed a painless right deltoid mass shortly after receiving his vaccination at the same area that subsequently increased in size over seven months to 6.5 cm. Imaging showed a heterogeneous mass within the deltoid muscle concerning for malignancy and a biopsy was performed. Sections showed wavy, bland spindle cells with nuclei staining diffusely positive for beta-catenin, consistent with fibromatosis at his vaccination site. Results (if a Case Study enter NA) NA. Conclusion In summary, these case reports show potential systemic and local reactive effects in response to COVID-19 vaccination.
ABSTRACT
The novel coronavirus disease (COVID-19) has crushed daily routines and is still rampaging through the world. Existing solution for nonpharmaceutical interventions usually needs to timely and precisely select a subset of residential urban areas for containment or even quarantine, where the spatial distribution of confirmed cases has been considered as a key criterion for the subset selection. While such containment measure has successfully stopped or slowed down the spread of COVID-19 in some countries, it is criticized for being inefficient or ineffective, as the statistics of confirmed cases are usually time-delayed and coarse-grained. To tackle the issues, we propose C-Watcher, a novel data-driven framework that aims at screening every neighborhood in a target city and predicting infection risks, prior to the spread of COVID-19 from epicenters to the city. In terms of design, C-Watcher collects large-scale long-term human mobility data from Baidu Maps, then characterizes every residential neighborhood in the city using a set of features based on urban mobility patterns. Furthermore, to transfer the firsthand knowledge (witted in epicenters) to the target city before local outbreaks, we adopt a novel adversarial encoder framework to learn "city-invariant" representations from the mobility-related features for precise early detection of high-risk neighborhoods, even before any confirmed cases known, in the target city. We carried out extensive experiments on C-Watcher using the real-data records in the early stage of COVID-19 outbreaks, where the results demonstrate the efficiency and effectiveness of C-Watcher for early detection of high-risk neighborhoods from a large number of cities.
ABSTRACT
Objective: To investigate the mental health of clinical first-line medical staff in COVID-19 epidemic and provide theoretical basis for psychological intervention. Methods: The mental health status of the first-line medical staff was investigated by Self-rating Anxiety Scale(SAS) and Post-Traumatic Stress Disorder Self- rating Scale (PTSD-SS). From February 7 to 14, 2020, 246 medical staff participated in the treatment of COVID-19 were investigated using cluster sampling, and received 230 responses, with a recovery rate of 93.5%. Results: The incidence of anxiety in medical staff was 23.04% (53/230) , and the score of SAS was(42.91±10.89). Among them, the incidence of severe anxiety, moderate anxiety and mild anxiety were 2.17%(5/230) , 4.78%(11/230) and 16.09%(37/230) , respectively. The incidence of anxiety in female medical staff was higher than that in male [25.67%(48/187) vs 11.63%(5/43) , Z=-2.008, P=0.045], the score of SAS in female medical staff was higher than that in male [(43.78±11.12) vs (39.14±9.01) , t=-2.548, P=0.012]. The incidence of anxiety in nurses was higher than that in doctors[26.88% (43/160) vs 14.29% (10/70) , Z=-2.066, P=0.039], and the score of SAS in nurses was higher than that in doctors [ (44.84±10.42) vs (38.50±10.72) , t=-4.207, P<0.001]. The incidence of stress disorder in medical staff was 27.39% (63/230) , and the score of PTSD-SS was (42.92±17.88) . The score of PTSD-SS in female medical staff was higher than that in male[ (44.30±18.42) vs (36.91±13.95) , t=-2.472, P=0.014]. Conclusion: In COVID-19 epidemic , the incidence of anxiety and stress disorder is high among medical staff. Medical institutions should strengthen the training of psychological skills of medical staff. Special attention should be paid to the mental health of female nurses.