Transplacental transmission of SARS-CoV-2 immunoglobulin G antibody to infants from maternal COVID-19 vaccine immunization before pregnancy.

The coronavirus disease 2019 (COVID-19) vaccine generates functional antibodies in maternal circulation that are detectable in infants, while the information is restricted to the usage of COVID-19 vaccine during pregnancy. In this study, we aimed to evaluate the effect of maternal COVID-19 vaccines before pregnancy. Infants were included from mothers with no inactivated COVID-19 vaccine, 1-, 2-, and 3-dose before pregnancy, and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) immunoglobulin G (IgG) antibodies were tested. Comparative analysis was done between the groups. A total of 130 infants were enrolled in the study. Significantly higher levels of SARS-CoV-2 IgG antibodies in infants born to mothers with 3-dose COVID-19 vaccine before pregnancy compared with 1- and 2-dose groups (p < 0.0001). The levels of antibodies decreased significantly with age in infants born to mothers with the 3-dose COVID-19 vaccine before pregnancy (r = -0.338, p = 0.035), and it was still higher than that 2-dose COVID-19 vaccine group. The maternal SARS-CoV-2 antibodies produced from the inactivated COVID-19 vaccine before pregnancy can be transferred to newborns via the placenta. Maternal immunization with 3-dose of the COVID-19 vaccine before pregnancy could be more beneficial for both mothers and infants.

WWOX activates autophagy to alleviate lipopolysaccharide-induced acute lung injury by regulating mTOR.

Acute lung injury (ALI) is characterized by acute systemic inflammatory responses that may lead to severe acute respiratory distress syndrome (ARDS). The clinical course of ALI/ARDS is variable; however, it has been reported that lipopolysaccharides (LPS) play a role in its development. The fragile chromosomal site gene WWOX is highly sensitive to genotoxic stress induced by environmental exposure and is an important candidate gene for exposure-related lung disease research. However, the expression of WWOX and its role in LPS-induced ALI still remain unidentified. This study investigated the expression of WWOX in mouse lung and epithelial cells and explored the role of WWOX in LPS-induced ALI model in vitro and in vivo. In addition, we explored one of the possible mechanisms by which WWOX alleviates ALI from the perspective of autophagy. Here, we observed that LPS stimulation reduced the expression of WWOX and the autophagy marker microtubule-associated protein 1 light chain 3ß-II (MAP1LC3B/LC3B) in mouse lung epithelial and human epithelial (H292) cells. Overexpression of WWOX led to the activation of autophagy and inhibited inflammatory responses in LPS-induced ALI cells and mouse model. More importantly, we found that WWOX interacts with mechanistic target of rapamycin [serine/threonine kinase] (mTOR) and regulates mTOR and ULK-1 signaling-mediated autophagy. Thus, reduced WWOX levels were associated with LPS-induced ALI. WWOX can activate autophagy in lung epithelial cells and protect against LPS-induced ALI, which is partly related to the mTOR-ULK1 signaling pathway.

A Deep Learning Approach Considering Image Background for Pneumonia Identification Using Explainable AI (XAI).

Pneumonia mainly refers to lung infections caused by pathogens, such as bacteria and viruses. Currently, deep learning methods have been applied to identify pneumonia. However, the traditional deep learning methods for pneumonia identification take less account of the influence of the lung X-ray image background on the model's testing effect, which limits the improvement of the model's accuracy. In this paper, we propose a deep learning method that considers image background factors and analyzes the proposed method with explainable deep learning for explainability. The essential idea is to remove the image background, improve the pneumonia recognition accuracy, and apply the Grad-CAM method to obtain an explainable deep learning model for pneumonia identification. In the proposed approach, (1) preliminary deep learning models for pneumonia X-ray image identification without considering the background are built; (2) deep learning models for pneumonia X-ray image identification with background consideration are built to improve the accuracy of pneumonia identification; (3) Grad-CAM method is employed to analyze the explainability. The proposed approach improves the accuracy of pneumonia identification, and the highest accuracy of VGG16 reaches 95.6%. The proposed approach can be applied to real pneumonia identification for early detection and treatment.

Pneumonia Recognition by Deep Learning: A Comparative Investigation

Pneumonia is a common infectious disease. Currently, the most common method of pneumonia identification is manual diagnosis by professional doctors, but the accuracy and identification efficiency of this method is not satisfactory, and computer-aided diagnosis technology has emerged. With the development of artificial intelligence, deep learning has also been applied to pneumonia diagnosis and can achieve high accuracy. In this paper, we compare five deep learning models in different situations for pneumonia recognition. The objective was to employ five deep learning models to identify pneumonia X-ray images and to compare and analyze them in different cases, thus screening out the optimal model for each type of case to improve the efficiency of pneumonia recognition and further apply it to the computer-aided diagnosis of pneumonia species. In the proposed framework: (1) datasets are collected and processed, (2) five deep learning models for pneumonia recognition are built, (3) the five models are compared, and the optimal model for each case is selected. The results show that the LeNet5 and AlexNet models achieved better pneumonia recognition for small datasets, while the MobileNet and ResNet18 models were more suitable for pneumonia recognition for large datasets. The comparative analysis of each model under different situations can provide a deeper understanding of the efficiency of each model in identifying pneumonia, thus making the practical application and selection of deep learning models for pneumonia recognition more convenient.

Epidemics in Pre-industrial Europe: Impacts of Climate Change, Economic Well-Being, and Population

Academic attention is increasing to examine historical epidemics from the perspective of human ecology. Studies are still inadequate, however, from a macro-scale perspective (quantitative studies in particular) focusing on long-term dynamics of epidemics in pre-industrial Europe. In this study, two pathways in Europe during AD1350–1850, namely, “climate plus economy on epidemics” and “climate plus population on epidemics”, were empirically investigated via correlation, multivariate regression analysis, and autoregressive exogenous (ARX) analysis under the framework of human ecology. The statistical findings show that climate change, particularly cooling, affected epidemics significantly. Economic well-being was the important factor that influenced the dynamics of epidemics alongside climate change. Furthermore, if considering climatic impacts, population was also significant, but its effects had limited importance on epidemics compared with economic well-being. This study not only supplements current understanding of epidemic mechanisms within the context of human ecology, but also examines the economy–epidemic link from a pre-industrial perspective to consider the role of economy in epidemic outbreaks in the modern time. Lessons from macro-history will provide historical references to current societies when facing to unprecedented pandemic globally.

Internet-of-Things-Enabled Data Fusion Method for Sleep Healthcare Applications.

The Internet of Medical Things (IoMT) aims to exploit the Internet-of-Things (IoT) techniques to provide better medical treatment scheme for patients with smart, automatic, timely, and emotion-aware clinical services. One of the IoMT instances is applying IoT techniques to sleep-aware smartphones or wearable devices' applications to provide better sleep healthcare services. As we all know, sleep is vital to our daily health. What is more, studies have shown a strong relationship between sleep difficulties and various diseases such as COVID-19. Therefore, leveraging IoT techniques to develop a longer lifetime sleep healthcare IoMT system, with a tradeoff between data transferring/processing speed and battery energy efficiency, to provide longer time services for bad sleep condition persons, especially the COVID-19 patients or survivors, is a meaningful research topic. In this study, we propose an IoT-enabled sleep data fusion networks (SDFN) module with a star topology Bluetooth network to fuse data of sleep-aware applications. A machine learning model is built to detect sleep events through an audio signal. We design two data reprocessing mechanisms running on our IoT devices to alleviate the data jam problem and save the IoT devices' battery energy. The experiments manifest that the presented module and mechanisms can save the energy of the system and alleviate the data jam problem of the device.

Chronological Changes of Viral Shedding in Adult Inpatients With COVID-19 in Wuhan, China.

BACKGROUND: In December 2019, the coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) broke out in Wuhan. Epidemiological and clinical characteristics of patients with COVID-19 have been reported, but the relationships between laboratory features and viral load has not been comprehensively described. METHODS: Adult inpatients (≥18 years old) with COVID-19 who underwent multiple (≥5 times) nucleic acid tests with nasal and pharyngeal swabs were recruited from Renmin Hospital of Wuhan University, including general patients (n = 70), severe patients (n = 195), and critical patients (n = 43). Laboratory data, demographic data, and clinical data were extracted from electronic medical records. The fitted polynomial curve was used to explore the association between serial viral loads and illness severity. RESULTS: Viral load of SARS-CoV-2 peaked within the first few days (2-4 days) after admission, then decreased rapidly along with virus rebound under treatment. Critical patients had the highest viral loads, in contrast to the general patients showing the lowest viral loads. The viral loads were higher in sputum compared with nasal and pharyngeal swab (P = .026). The positive rate of respiratory tract samples was significantly higher than that of gastrointestinal tract samples (P < .001). The SARS-CoV-2 viral load was negatively correlated with portion parameters of blood routine and lymphocyte subsets and was positively associated with laboratory features of cardiovascular system. CONCLUSIONS: The serial viral loads of patients revealed whole viral shedding during hospitalization and the resurgence of virus during the treatment, which could be used for early warning of illness severity, thus improve antiviral interventions.

Public safety under imperfect taxation

Standard benefit-cost analysis often ignores distortions caused by taxation and the heterogeneity of taxpayers. In this paper, we theoretically and numerically explore the effect of imperfect taxation on the public provision of mortality risk reductions (or public safety). We show that this effect critically depends on the source of imperfection as well as on the individual utility and survival probability functions. Our simulations based on the calibration of distributional weights and applied to the COVID-19 example suggest that the value per statistical life, and in turn the optimal level of public safety, should be adjusted downwards because of imperfect taxation. However, we also identify circumstances under which this result is reversed, so that imperfect taxation cannot generically justify less public safety.

Impact of Transmission Control Measures on the Epidemiology of Maxillofacial Injuries in Wuhan City During the COVID-19 Epidemic.

ABSTRACT: In December 2019, a novel coronavirus (severe acute respiratory syndrome coronavirus 2) emerged in Wuhan City. The present study aimed to assess the demographic variables, causes, and patterns of maxillofacial injuries managed at a teaching hospital in Wuhan City during the transmission control measures in the coronavirus disease 2019 (COVID-19) epidemic. In this retrospective study, all patients treated for maxillofacial injuries in the hospital between January 23 and April 7 (2019 and 2020) were involved. Epidemiologic information, including the number of patients, gender, age, etiology, time since injury to the clinic visit, and type of maxillofacial injuries, was recorded. Data of the 2 periods (2019 and 2020) were compared and analyzed. A total of 337 patients had maxillofacial injuries at the 2-time intervals: 74 in 2020 and 263 in 2019. The characteristics of maxillofacial injuries had changes during the transmission control measures in the COVID-19 epidemic, which included the number of patients, gender, age, etiology, time since injury to the clinic visit, and type of maxillofacial injuries. The transmission control measures during the COVID-19 epidemic had a significant impact on the epidemiology of maxillofacial injuries in Wuhan City.

Characteristic changes of traumatic dental injuries in a teaching hospital of Wuhan under transmission control measures during the COVID-19 epidemic.

BACKGROUND/AIMS: In December 2019, a novel coronavirus emerged in Wuhan City, and a retrospective analysis is necessary to provide clinicians with the characteristics of traumatic dental injuries (TDIs) during the epidemic. The aim of this study was to evaluate the changes in the characteristics of TDIs under the transmission control measures in Wuhan City utilizing an epidemiologic investigation. MATERIALS AND METHOD: In this retrospective study, epidemiologic information, including the number of patients, gender, age, and TDI parameters such as time since injury to the clinic visit, etiology, tooth location, and the type of injury was extracted from the records of patients in the hospital from two periods: period 1 (between January 23, 2020, and April 7, 2020) and period 2 (between January 23, 2019, and April 7, 2019). The data from the two periods were compared and analyzed. RESULT: A total of 158 patients were treated for TDIs (120 in 2019 and 38 in 2020). Males were more likely to suffer from TDIs than females with a ratio of 1.5:1, both in 2020 and 2019. Other than that, there were characteristic changes in TDIs during the transmission control measures in the COVID-19 epidemic, which included the number of patients, age, time since injury to the clinic visit, etiology, tooth location and the type of TDI. CONCLUSION: The transmission control measures during the COVID-19 epidemic had a significant impact on the epidemiology and etiology of TDIs in Wuhan City.