Clinical Severity of SARS-CoV-2 Variants during COVID-19 Vaccination: A Systematic Review and Meta-Analysis.

Due to the variation in the SARS-CoV-2 virus, COVID-19 exhibits significant variability in severity. This presents challenges for governments in managing the allocation of healthcare resources and prioritizing health interventions. Clinical severity is also a critical statistical parameter for researchers to quantify the risks of infectious disease, model the transmission of COVID-19, and provide some targeted measures to control the pandemic. To obtain more accurate severity estimates, including confirmed case-hospitalization risk, confirmed case-fatality risk, hospitalization-fatality risk, and hospitalization-ICU risk, we conducted a systematic review and meta-analysis on the clinical severity (including hospitalization, ICU, and fatality risks) of different variants during the period of COVID-19 mass vaccination and provided pooled estimates for each clinical severity metric. All searches were carried out on 1 February 2022 in PubMed for articles published from 1 January 2020 to 1 February 2022. After identifying a total of 3536 studies and excluding 3523 irrelevant studies, 13 studies were included. The severity results show that the Delta and Omicron variants have the highest (6.56%, 0.46%, 19.63%, and 9.06%) and lowest severities (1.51%, 0.04%, 6.01%, and 3.18%), respectively, according to the four clinical severity metrics. Adults over 65 have higher severity levels for all four clinical severity metrics.

PL-TARMI: A deep learning framework for pixel-level traffic crash risk map inference.

A citywide traffic crash risk map is of great significance for preventing future traffic crashes. However, the fine-grained geographic traffic crash risk inference is still a challenging task, mainly due to the complex road network structure, human behavior and high data requirements. In this work, we propose a deep-learning framework PL-TARMI, which leverages easily accessible data to achieve accurate fine-grained traffic crash risk map inference. Specifically, we integrate the satellite image and road network image, combine with other accessible data (e.g., point of interest distribution, human mobility data, traffic data, etc.) as input, and finally obtain the pixel-level traffic crash risk map, which could provide more reasonable traffic crash prevention guidance with a lower cost. Extensive experiments on real-world datasets demonstrate the effectiveness of PL-TARMI.

Optimizing COVID-19 surveillance using historical electronic health records of influenza infections.

Targeting surveillance resources toward individuals at high risk of early infection can accelerate the detection of emerging outbreaks. However, it is unclear which individuals are at high risk without detailed data on interpersonal and physical contacts. We propose a data-driven COVID-19 surveillance strategy using Electronic Health Record (EHR) data that identifies the most vulnerable individuals who acquired the earliest infections during historical influenza seasons. Our simulations for all three networks demonstrate that the EHR-based strategy performs as well as the most-connected strategy. Compared to the random acquaintance surveillance, our EHR-based strategy detects the early warning signal and peak timing much earlier. On average, the EHR-based strategy has 9.8 days of early warning and 13.5 days of peak timings, respectively, before the whole population. For the urban network, the expected values of our method are better than the random acquaintance strategy (24% for early warning and 14% in-advance for peak time). For a scale-free network, the average performance of the EHR-based method is 75% of the early warning and 109% in-advance when compared with the random acquaintance strategy. If the contact structure is persistent enough, it will be reflected by their history of infection. Our proposed approach suggests that seasonal influenza infection records could be used to monitor new outbreaks of emerging epidemics, including COVID-19. This is a method that exploits the effect of contact structure without considering it explicitly.

The effects of Wuhan highway lockdown measures on the spread of COVID-19 in China.

To verify the effects of Wuhan highway lockdown measures on the spread of COVID-19 across China cities, we extracted the vehicle outflow from Wuhan to 245 cities from the Chinese highway toll system. A dynamic exponential risk model that considered the vehicle outflow, city gross domestic product, city population, and distance between two cities was established to characterize the spread of pandemics and quantify the blocking effects. Results showed that an early highway lockdown measure could indeed reduce the confirmed cases and vehicles with 1-9 seats played a leading role. The confirmed cases in Guangxi, Henan, and Shanxi could be reduced by more than 50%, as well as Hubei by 20% if the highway was closed 3 days in advance. The blocking effects on Fujian, Jiangxi, Guangdong, Hunan, and Shandong were not obvious, where the number of confirmed cases only decreased by a small proportion (below 10%). The findings could be used to help each provincial government to adjust policies properly and improve the effectiveness of epidemic control and prevention. Moreover, the proposed method could also be applied to various countries or regions affected by COVID-19, as well as other similar pandemics.

Baicalin ameliorates atherosclerosis by inhibiting NLRP3 inflammasome in apolipoprotein E-deficient mice.

BACKGROUND: NLR family pyrin domain containing 3 (NLRP3) inflammasome has been implicated in the development of atherosclerosis and several studies have suggested that inhibiting NLRP3 inflammasome could be a potential therapeutic approach to treat atherosclerosis. Baicalin is a flavone glycoside with anti-inflammation, anti-oxidative activities. The inhibition of NLRP3 inflammasome activation by baicalin has also been described. Therefore, the effects of baicalin on NLRP3 inflammasome activation and atherosclerosis were evaluated in present study. METHODS: We established the apolipoprotein E-deficient atherosclerosis mice model. After baicalin treatment, the IL-1, IL-18, and reactive oxygen species (ROS) production, and the plaque area was monitored. We also measured the NLRP3, ASC, caspase-1, ICAM-1, and VCAM-1 expression in atherosclerosis mice after baicalin treatment. We silenced NLRP3 by administration of lentivirus expressing NLRP3 shRNA to atherosclerosis mice and monitored the IL-1, IL-18, and ROS production, and NLRP3 inflammasome activation. RESULTS: Baicalin remarkably inhibited the production of IL-1, IL-18, mitochondria ROS, total ROS, ICAM-1, and VCAM-1. Baicalin reduced the expression of NLRP3 inflammasome and suppressed its activation. Baicalin significantly reduced the plaque area. Silencing NLRP3 resulted in decreased production of IL-1, IL-18, mitochondria ROS, total ROS, ICAM-1, and VCAM-1, and inhibition of NLRP3 inflammasome activation. CONCLUSION: Baicalin ameliorated atherosclerosis by inhibiting NLRP3 inflammasome.

Effects of aspirin on hepatocellular carcinoma and its potential molecular mechanism.

PURPOSE: To explore the effects of aspirin (ASP) on the proliferation and apoptosis of HepG2 hepatocellular carcinoma (HCC) cells via the Wnt/ß-catenin signaling pathway. METHODS: Human HCC cells were cultured and treated with ASP at different concentrations. Cell proliferation was determined with cell counting kit-8 (CCK-8) and colony formation, and the rate of apoptosis was measured by flow cytometry. Western blotting (WB) and quantitative polymerase chain reaction (qRT-PCR) assays were used to assess the changes in the expression levels of related proteins. RESULTS: ASP showed a time-and concentration-depented inhibitory effect on HepG2 cell proliferation. The number of colonies formed in ASP-treated HCC cells was significantly lower than in control cells. For HCC cells treated with ASP, the apoptosis rate enhanced with the increase of ASP concentration. The expression levels of TCF4 and LEF1, key molecules of the Wnt/ß-catenin signaling pathway, were lowered in HCC cells treated with 4 mM ASP, and the nuclear translocation of ß-catenin was weakened. The ß-catenin activator exerted a negative influence on the anticancer effect of ASP. CONCLUSIONS: ASP inhibits the proliferation and promotes the apoptosis of HCC cells through the Wnt/ß-catenin signaling pathway.

A pooled analysis of risk factors of surgically treated leiomyosarcoma of the colon in adults.

BACKGROUND: This current systematic review aimed to evaluate the role of surgical management and risk factors by pooled cases from all identified patients with colonic leiomyosarcomas. METHODS: The authors searched the Ovid MEDLINE, Embase, PubMed, and Cochrane databases using the keywords "colonic," "colon," and "leiomyosarcoma." Risk factors of colonic leiomyosarcoma in the pooled cohort were also evaluated. RESULTS: Between 1923 and 2019, 41 cases of colonic leiomyosarcoma were identified in 22 (53.7%) males and 19 (46.3%) females, with a mean and median age of 58.7 ± 2.2 years and 56.0 years. According to univariate analysis, smaller tumor size < 8 cm was significantly associated with longer progression-free survival (HR = 6.957, 95% CI 1.405-34.442; p = 0.017), and younger age < 60 years was trending toward better overall survival (HR = 2.765, 95% CI 0.924-8.272; p = 0.069). CONCLUSIONS: Colonic leiomyosarcomas are rare neoplasms with aggressive clinical behaviors. Age < 60 years and tumor size < 8 cm were favorable factors for patients' better survival.

Spatiotemporal Transmission Patterns and Determinants of Dengue Fever: A Case Study of Guangzhou, China.

Dengue fever is one of the most common vector-borne diseases in the world and is mainly affected by the interaction of meteorological, human and land-use factors. This study aims to identify the impact of meteorological, human and land-use factors on dengue fever cases, involving the interplay between multiple factors. The analyses identified the statistically significant determinants affecting the transmission of dengue fever, employing cross-correlation analysis and the geo-detector model. This study was conducted in Guangzhou, China, using the data of confirmed cases of dengue fever, daily meteorological records, population density distribution and land-use distribution. The findings highlighted that the dengue fever hotspots were mainly distributed in the old city center of Guangzhou and were significantly shaped by meteorological, land-use and human factors. Meteorological factors including minimum temperature, maximum temperature, atmospheric pressure and relative humidity were correlated with the transmission of dengue fever. Minimum temperature, maximum temperature and relative humidity presented a statistically significant positive correlation with dengue fever cases, while atmospheric pressure presented statistically significant negative correlation. Minimum temperature, maximum temperature, atmospheric pressure and humidity have lag effects on the transmission of dengue fever. The population, community age, subway network density, road network density and ponds presented a statistically significant positive correlation with the number of dengue fever cases, and the interaction among land-use and human factors could enhance dengue fever transmission. The ponds were the most important interaction factors, which might strengthen the influence of other factors on dengue fever transmission. Our findings have implications for pre-emptive dengue fever control.

The temporal geographically-explicit network of public transport in Changchun City, Northeast China.

The vehicle trajectory data is a feasible way for us to understand and reveal urban traffic conditions and human mobility. Therefore, it is extremely valuable to have a fine-grained picture of large-scale vehicle trajectory data, particularly in two different modes, taxis and buses, over the same period at an urban scale. This paper integrates the trajectory data of approximately 7,000 taxis and 1,500 buses in Changchun City, China and accesses the temporal geographically-explicit network of public transport via sequential snapshots of vehicle trajectory data every 30 seconds of the first week in March 2018. In order to reveal urban traffic conditions and human mobility, we construct two-layer urban traffic network (UTN) between these two different transport modes, take crossings as nodes and roads as edges weighted by the volume or average speed of vehicles in each hour. We released this temporal geographically-explicit network of public transport and the dynamics, weighted and directed UTN in simple formats for easy access.

Fast and Automatic Reconstruction of Semantically Rich 3D Indoor Maps from Low-quality RGB-D Sequences.

Semantically rich indoor models are increasingly used throughout a facility's life cycle for different applications. With the decreasing price of 3D sensors, it is convenient to acquire point cloud data from consumer-level scanners. However, most existing methods in 3D indoor reconstruction from point clouds involve a tedious manual or interactive process due to line-of-sight occlusions and complex space structures. Using the multiple types of data obtained by RGB-D devices, this paper proposes a fast and automatic method for reconstructing semantically rich indoor 3D building models from low-quality RGB-D sequences. Our method is capable of identifying and modelling the main structural components of indoor environments such as space, wall, floor, ceilings, windows, and doors from the RGB-D datasets. The method includes space division and extraction, opening extraction, and global optimization. For space division and extraction, rather than distinguishing room spaces based on the detected wall planes, we interactively define the start-stop position for each functional space (e.g., room, corridor, kitchen) during scanning. Then, an interior elements filtering algorithm is proposed for wall component extraction and a boundary generation algorithm is used for space layout determination. For opening extraction, we propose a new noise robustness method based on the properties of convex hull, octrees structure, Euclidean clusters and the camera trajectory for opening generation, which is inapplicable to the data collected in the indoor environments due to inevitable occlusion. A global optimization approach for planes is designed to eliminate the inconsistency of planes sharing the same global plane, and maintain plausible connectivity between the walls and the relationships between the walls and openings. The final model is stored according to the CityGML3.0 standard. Our approach allows for the robust generation of semantically rich 3D indoor models and has strong applicability and reconstruction power for complex real-world datasets.