Myocarditis-induced Sudden Death after BNT162b2 mRNA COVID-19 Vaccination in Korea: Case Report Focusing on Histopathological Findings.

We present autopsy findings of a 22-year-old man who developed chest pain 5 days after the first dose of the BNT162b2 mRNA vaccine and died 7 hours later. Histological examination of the heart revealed isolated atrial myocarditis, with neutrophil and histiocyte predominance. Immunohistochemical C4d staining revealed scattered single-cell necrosis of myocytes which was not accompanied by inflammatory infiltrates. Extensive contraction band necrosis was observed in the atria and ventricles. There was no evidence of microthrombosis or infection in the heart and other organs. The primary cause of death was determined to be myocarditis, causally-associated with the BNT162b2 vaccine.

Decreasing Inmate Populations to Mitigate Effects of COVID-19 in State Prisons

With the spread of COVID-19 comes reports of rising outbreaks in prisons throughout the United States. On a national level, the Federal Bureau of Prisons (BOP) has modified operation guidelines for the evaluation and early release of eligible non-violent, at-risk offenders. However, each state government and Department of Corrections have the responsibility and jurisdiction to enforce policies within their own prison systems. To combat the escalation of positive cases and to protect the safety of inmates, some prison systems have accelerated early release procedures for eligible prisoners. Compared to other states, the State of Nevada has started early with the enforcement of quarantine and testing protocols to combat the spread of COVID-19, starting in early March. However, the State of Nevada has not passed orders for the early release or home confinement of eligible inmates. States such as California and Pennsylvania, have already created criteria for inmates eligible for early release and have released prisoners. Other states like Arizona and Indiana have not released guidelines but are looking favorably toward doing so. Growing incarceration rates are rapidly overwhelming facility operational capacities, which only makes the probability of spreading COVID-19 within prisons and outside the prisons inevitable. Data analysis shows an increased probability of having positive cases and community spread of COVID-19 within a correctional facility as prison size, which was indicated by the population capacity, increases. In all states, the number of confirmed cases continues to steadily climb. Before more outbreaks can potentially occur and more prisoners' lives are lost, it is crucial to look at measures to decrease inmate population capacities such as decreasing the intake of new prisoners, expanding space in existing facilities to enforce social distancing, and maximizing storage spaces. Home-confinement is another possible option that prisons can utilize to help decrease prison populations. This study examines the relationship between increasing prison populations and the escalating number of total confirmed positive cases of COVID-19, while proposing for the state Department of Corrections to implement a reduction in prison populations to decrease the transmission of COVID-19.

Deep convolution neural networks to differentiate between COVID-19 and other pulmonary abnormalities on chest radiographs: Evaluation using internal and external datasets.

We aimed to evaluate the performance of convolutional neural networks (CNNs) in the classification of coronavirus disease 2019 (COVID-19) disease using normal, pneumonia, and COVID-19 chest radiographs (CXRs). First, we collected 9194 CXRs from open datasets and 58 from the Korea University Anam Hospital (KUAH). The number of normal, pneumonia, and COVID-19 CXRs were 4580, 3884, and 730, respectively. The CXRs obtained from the open dataset were randomly assigned to the training, tuning, and test sets in a 70:10:20 ratio. For external validation, the KUAH (20 normal, 20 pneumonia, and 18 COVID-19) dataset, verified by radiologists using computed tomography, was used. Subsequently, transfer learning was conducted using DenseNet169, InceptionResNetV2, and Xception to identify COVID-19 using open datasets (internal) and the KUAH dataset (external) with histogram matching. Gradient-weighted class activation mapping was used for the visualization of abnormal patterns in CXRs. The average AUC and accuracy of the multiscale and mixed-COVID-19Net using three CNNs over five folds were (0.99 ± 0.01 and 92.94% ± 0.45%), (0.99 ± 0.01 and 93.12% ± 0.23%), and (0.99 ± 0.01 and 93.57% ± 0.29%), respectively, using the open datasets (internal). Furthermore, these values were (0.75 and 74.14%), (0.72 and 68.97%), and (0.77 and 68.97%), respectively, for the best model among the fivefold cross-validation with the KUAH dataset (external) using domain adaptation. The various state-of-the-art models trained on open datasets show satisfactory performance for clinical interpretation. Furthermore, the domain adaptation for external datasets was found to be important for detecting COVID-19 as well as other diseases.

Heterotrimeric G-protein signalers and RGSs in Aspergillus fumigatus.

The heterotrimeric G-protein (G-protein) signaling pathway is one of the most important signaling pathways that transmit external signals into the inside of the cell, triggering appropriate biological responses. The external signals are sensed by various G-protein-coupled receptors (GPCRs) and transmitted into G-proteins consisting of the α, ß, and γ subunits. Regulators of G-protein signaling (RGSs) are the key controllers of G-protein signaling pathways. GPCRs, G-proteins, and RGSs are the primary upstream components of the G-protein signaling pathway, and they are highly conserved in most filamentous fungi, playing diverse roles in biological processes. Recent studies characterized the G-protein signaling components in the opportunistic pathogenic fungus Aspergillus fumigatus. In this review, we have summarized the characteristics and functions of GPCRs, G-proteins, and RGSs, and their regulatory roles in governing fungal growth, asexual development, germination, stress tolerance, and virulence in A. fumigatus.