Exploring the impact of under-reported cases on the COVID-19 spatiotemporal distributions using healthcare workers infection data.

A timely understanding of the spatiotemporal pattern and development trend of COVID-19 is critical for timely prevention and control. However, the under-reporting of casesis widespread in fields associated with public health. It is also possible to draw biased inferences and formulate inappropriate prevention and control policies if the phenomenon of under-reporting is not taken into account. Therefore, in this paper, a novel framework was proposed to explore the impact of under-reporting on COVID-19 spatiotemporal distributions, and empirical analysis was carried out using infection data of healthcare workers in Wuhan and Hubei (excluding Wuhan). The results show that (1) the lognormal distribution was the most suitable to describe the evolution of epidemic with time; (2) the estimated peak infection time of the reported cases lagged the peak infection time of the healthcare worker cases, and the estimated infection time interval of the reported cases was smaller than that of the healthcare worker cases. (3) The impact of under-reporting cases on the early stages of the pandemic was greater than that on its later stages, and the impact on the early onset area was greater than that on the late onset area. (4) Although the number of reported cases was lower than the actual number of cases, a high spatial correlation existed between the cumulatively reported cases and healthcare worker cases. The proposed framework of this study is highly extensible, and relevant researchers can use data sources from other counties to carry out similar research.

Detection of Indoor High-Density Crowds via Wi-Fi Tracking Data.

Accurate detection of locations of indoor high-density crowds is crucial for early warning and emergency rescue during indoor safety accidents. The spatial structure of indoor environments is more complicated than outdoor environments. The locations of indoor high-density crowds are more likely to be the sites of security accidents. Existing detection methods for high-density crowd locations mostly focus on outdoor environments, and relatively few detection methods exist for indoor environments. This study proposes a novel detection framework for high-density indoor crowd locations termed IndoorSRC (Simplification-Reconstruction-Cluster). In this paper, a novel indoor spatiotemporal clustering algorithm called Indoor-STAGNES is proposed to detect the indoor trajectory stay points to simplify indoor movement trajectory. Then, we propose use of a Kalman filter algorithm to reconstruct the indoor trajectory and properly align and resample the data. Finally, an indoor spatiotemporal density clustering algorithm called Indoor-STOPTICS is proposed to detect the locations of high-density crowds in the indoor environment from the reconstructed trajectory. Extensive experiments were conducted using indoor Wi-Fi positioning datasets collected from a shopping mall. The results show that the IndoorSRC framework evidently outperforms the existing baseline method in terms of detection performance.

Spatiotemporal Differences of COVID-19 Infection Among Healthcare Workers and Patients in China From January to March 2020.

Studying the spatiotemporal differences in coronavirus disease (COVID-19) between social groups such as healthcare workers (HCWs) and patients can aid in formulating epidemic containment policies. Most previous studies of the spatiotemporal characteristics of COVID-19 were conducted in a single group and did not explore the differences between groups. To fill this research gap, this study assessed the spatiotemporal characteristics and differences among patients and HCWs infection in Wuhan, Hubei (excluding Wuhan), and China (excluding Hubei). The temporal difference was greater in Wuhan than in the rest of Hubei, and was greater in Hubei (excluding Wuhan) than in the rest of China. The incidence was high in healthcare workers in the early stages of the epidemic. Therefore, it is important to strengthen the protective measures for healthcare workers in the early stage of the epidemic. The spatial difference was less in Wuhan than in the rest of Hubei, and less in Hubei (excluding Wuhan) than in the rest of China. The spatial distribution of healthcare worker infections can be used to infer the spatial distribution of the epidemic in the early stage and to formulate control measures accordingly.

CD147 regulates antitumor CD8+ T-cell responses to facilitate tumor-immune escape.

Negative regulation of antitumor T-cell-immune responses facilitates tumor-immune escape. Here, we show that deletion of CD147, a type I transmembrane molecule, in T cells, strongly limits in vivo tumor growth of mouse melanoma and lung cancer in a CD8+ T-cell-dependent manner. In mouse tumor models, CD147 expression was upregulated on CD8+ tumor-infiltrating lymphocytes (TILs), and CD147 was coexpressed with two immune-checkpoint molecules, Tim-3 and PD-1. Mining publicly available gene-profiling data for CD8+ TILs in tumor biopsies from metastatic melanoma patients showed a higher level of CD147 expression in exhausted CD8+ TILs than in other subsets of CD8+ TILs, along with expression of PD-1 and TIM-3. Additionally, CD147 deletion increased the abundance of TILs, cytotoxic effector function of CD8+ T cells, and frequency of PD-1+ CD8+ TILs, and partly reversed the dysfunctional status of PD-1+Tim-3+CD8+ TILs. The cytotoxic transcription factors Runx3 and T-bet mediation enhanced antitumor responses by CD147-/- CD8+ T cells. Moreover, CD147 deletion in T cells increased the frequency of TRM-like cells and the expression of the T-cell chemokines CXCL9 and CXCL10 in the tumor microenvironment. Analysis of tumor tissue samples from patients with non-small-cell lung cancer showed negative correlations between CD147 expression on CD8+ TILs and the abundance of CD8+ TILs, histological grade of the tumor tissue samples, and survival of patients with advanced tumors. Altogether, we found a novel function of CD147 as a negative regulator of antitumor responses mediated by CD8+ TILs and identified CD147 as a potential target for cancer immunotherapy.

Metallophosphoesterase 1, a novel candidate gene in hepatocellular carcinoma malignancy and recurrence.

BACKGROUND: There is an unmet need to identify novel mechanism-based prognostic genes associated with hepatocellular carcinoma (HCC) recurrence that can predict patient outcomes and provide therapeutic targets. This study aims to identify potential novel driver genes and mutations in HCC. METHODS: Single nucleotide variations (SNVs) contributing to HCC recurrence were identified using whole-exome sequencing of 5 DNA samples extracted from a single HCC patient with HBV-induced cirrhosis. SNVs were verified in primary HCC (n = 87), recurrent HCC (n = 34), and benign liver disease with cirrhosis tissues (n = 43). A candidate gene was identified, and its association and function in HCC development and recurrence were examined. RESULTS: 177 SNVs were identified and 70 SNVs were verified. A MPPE1 missense mutation on chr18_11897016 was the most frequent mutation (16.5%) in primary and recurrent HCC tissues, occurring with a higher frequency in recurrent HCC than primary HCC or benign liver tumor tissues. The MPPE1 mutation was significantly associated with HCC recurrence (P = .003), TNM stage (P = .002), and Child-Pugh classification (P = .039), and was an independent risk factor for HCC recurrence (HR = 1.969; 95%CI = 1.043-3.714, P = .037). Analysis of publically available data deposited in the GEO and TCGA showed MPPE1 expression levels were significantly increased in HCC tumor samples compared to adjacent nontumor tissues. The knockdown of MPPE1 in HCC cell lines significantly inhibited cell proliferation, migration and invasion, induced cell cycle arrest and apoptosis in vitro, and inhibited xenograft tumor growth in nude mice in vivo (P < .05). CONCLUSIONS: MPPE1 is a novel gene associated with HCC malignancy and recurrence.

Building an Open Resources Repository for COVID-19 Research.

The COVID-19 outbreak is a global pandemic declared by the World Health Organization, with rapidly increasing cases in most countries. A wide range of research is urgently needed for understanding the COVID-19 pandemic, such as transmissibility, geographic spreading, risk factors for infections, and economic impacts. Reliable data archive and sharing are essential to jump-start innovative research to combat COVID-19. This research is a collaborative and innovative effort in building such an archive, including the collection of various data resources relevant to COVID-19 research, such as daily cases, social media, population mobility, health facilities, climate, socioeconomic data, research articles, policy and regulation, and global news. Due to the heterogeneity between data sources, our effort also includes processing and integrating different datasets based on GIS (Geographic Information System) base maps to make them relatable and comparable. To keep the data files permanent, we published all open data to the Harvard Dataverse (https://dataverse.harvard.edu/dataverse/2019ncov), an online data management and sharing platform with a permanent Digital Object Identifier number for each dataset. Finally, preliminary studies are conducted based on the shared COVID-19 datasets and revealed different spatial transmission patterns among mainland China, Italy, and the United States.

Protein phosphatase 2 regulatory subunit B''Alpha silencing inhibits tumor cell proliferation in liver cancer.

AIM: To explore the effects of protein phosphatase 2 regulatory subunit B''Alpha (PPP2R3A) on the proliferation and migration of liver cancer cells. METHODS: Expression of PPP2R3A in tumor tissues of hepatocellular carcinoma (HCC) patients was detected by immunohistochemistry and western blotting. In two liver cancer cell lines (HepG2 and HuH7), PPP2R3A expression was silenced and then overexpression with PPP2R3A lentiviral vectors, and the effects of PPP2R3A knockdown or overexpression on the proliferation, cell cycle, migration, and invasion of HCC cells were determined in vitro. In a xenograft cancer model in nude mice, the in vivo effects of PPP2R3A knockdown on tumor growth and cancer cell proliferation were evaluated. RESULTS: PPP2R3A expression was found in tumor foci in six of eight HCC samples, at a level higher than that in the adjacent para-tumor tissues. PPP2R3A expression was observed primarily in the cytoplasm of the cancer cells. Knockdown of PPP2R3A resulted in significant inhibition of hepatoma cell proliferation (P < .05), migration (P < .01), and invasion (P < .01) as well as a significant delay in the G1/S transition in both liver cancer lines (P < .05) and increased p53 expression. Conversely, overexpression of PPP2R3A promoted the proliferation (P < .05) and altered cell cycle progression (P < .05) of both liver cancer cell lines. In vivo, PPP2R3A knockdown in liver cancer cells led to significant reductions in the tumor volume (P < .001) and the expression of Ki-67 in tumor tissues (P < .05). CONCLUSION: PPP2R3A may play a role in liver cancer via the regulation of tumor cell proliferation and invasion.

Diabetes mellitus may affect the long-term survival of hepatitis B virus-related hepatocellular carcinoma patients after liver transplantation.

AIM: To determine whether diabetes mellitus (DM) affects prognosis/recurrence after liver transplantation (LT) for hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). METHODS: A retrospective study was conducted between January 2000 and August 2013 on 1631 patients with HBV-related HCC who underwent LT with antiviral prophylaxis. Patient data were obtained from the China Liver Transplant Registry (https://www.cltr.org/). To compare the outcomes and tumor recurrence in the HBV-related HCC patients with or without DM, statistical analyses were conducted using χ2 tests, Mann-Whitney tests, the Kaplan-Meier method, log-rank tests and multivariate step-wise Cox regression analysis. RESULTS: Univariate analysis of 1631 patients who underwent LT found overall 1-, 3- and 5-year survival rates of 79%, 73% and 71% respectively in the DM patients, and 84%, 78% and 76% in the non-DM patients respectively. Overall survival rate differences after LT between the two groups were significant (P = 0.041), but recurrence-free survival rates were not (P = 0.096). By stratified analysis, the overall survival rates in DM patients for age > 50 years (P = 0.002), the presence of vascular invasion (P = 0.096), tumors ≤ 3 cm (P = 0.047), two to three tumor nodules (P = 0.007), Child-Pugh grade B (P = 0.018), and pre-LT alanine aminotransferase levels between 40 and 80 IU/L (P = 0.017) were significantly lower than in non-DM patients. Additionally, serum α-fetoprotein level > 2000 ng/mL (P = 0.052) was associated with a significant survival difference trend between DM and non-DM patients. Multivariate analysis showed that the presence of DM (P < 0.001, HR = 1.591; 95%CI: 1.239-2.041) was an independent predictor associated with poor survival after LT. CONCLUSION: HBV-related HCC patients with DM have decreased long-term overall survival and poor LT outcomes. Prevention strategies for HCC patients with DM are recommended.