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Cramer, Estee, Ray, Evan, Lopez, Velma, Bracher, Johannes, Brennen, Andrea, Castro§Rivadeneira, Alvaro, Gerding, Aaron, Gneiting, Tilmann, House, Katie, Huang, Yuxin, Jayawardena, Dasuni, Kanji, Abdul, Khandelwal, Ayush, Le, Khoa, Mühlemann, Anja, Niemi, Jarad, Shah, Apurv, Stark, Ariane, Wang, Yijin, Wattanachit, Nutcha, Zorn, Martha, Gu, Youyang, Jain, Sansiddh, Bannur, Nayana, Deva, Ayush, Kulkarni, Mihir, Merugu, Srujana, Raval, Alpan, Shingi, Siddhant, Tiwari, Avtansh, White, Jerome, Abernethy, Neil, Woody, Spencer, Dahan, Maytal, Fox, Spencer, Gaither, Kelly, Lachmann, Michael, Meyers, Lauren Ancel, Scott, James, Tec, Mauricio, Srivastava, Ajitesh, George, Glover, Cegan, Jeffrey, Dettwiller, Ian, England, William, Farthing, Matthew, Hunter, Robert, Lafferty, Brandon, Linkov, Igor, Mayo, Michael, Parno, Matthew, Rowland, Michael, Trump, Benjamin, Zhang-James, Yanli, Chen, Samuel, Faraone, Stephen, Hess, Jonathan, Morley, Christopher, Salekin, Asif, Wang, Dongliang, Corsetti, Sabrina, Baer, Thomas, Eisenberg, Marisa, Falb, Karl, Huang, Yitao, Martin, Emily, McCauley, Ella, Myers, Robert, Schwarz, Tom, Sheldon, Daniel, Gibson, Graham Casey, Yu, Rose, Gao, Liyao, Ma, Yian, Wu, Dongxia, Yan, Xifeng, Jin, Xiaoyong, Wang, Yu-Xiang, Chen, YangQuan, Guo, Lihong, Zhao, Yanting, Gu, Quanquan, Chen, Jinghui, Wang, Lingxiao, Xu, Pan, Zhang, Weitong, Zou, Difan, Biegel, Hannah, Lega, Joceline, McConnell, Steve, Nagraj, V. P.; Guertin, Stephanie, Hulme-Lowe, Christopher, Turner, Stephen, Shi, Yunfeng, Ban, Xuegang, Walraven, Robert, Hong, Qi-Jun, Kong, Stanley, van§de§Walle, Axel, Turtle, James, Ben-Nun, Michal, Riley, Steven, Riley, Pete, Koyluoglu, Ugur, DesRoches, David, Forli, Pedro, Hamory, Bruce, Kyriakides, Christina, Leis, Helen, Milliken, John, Moloney, Michael, Morgan, James, Nirgudkar, Ninad, Ozcan, Gokce, Piwonka, Noah, Ravi, Matt, Schrader, Chris, Shakhnovich, Elizabeth, Siegel, Daniel, Spatz, Ryan, Stiefeling, Chris, Wilkinson, Barrie, Wong, Alexander, Cavany, Sean, España, Guido, Moore, Sean, Oidtman, Rachel, Perkins, Alex, Kraus, David, Kraus, Andrea, Gao, Zhifeng, Bian, Jiang, Cao, Wei, Ferres, Juan Lavista, Li, Chaozhuo, Liu, Tie-Yan, Xie, Xing, Zhang, Shun, Zheng, Shun, Vespignani, Alessandro, Chinazzi, Matteo, Davis, Jessica, Mu, Kunpeng, y§Piontti, Ana Pastore, Xiong, Xinyue, Zheng, Andrew, Baek, Jackie, Farias, Vivek, Georgescu, Andreea, Levi, Retsef, Sinha, Deeksha, Wilde, Joshua, Perakis, Georgia, Bennouna, Mohammed Amine, Nze-Ndong, David, Singhvi, Divya, Spantidakis, Ioannis, Thayaparan, Leann, Tsiourvas, Asterios, Sarker, Arnab, Jadbabaie, Ali, Shah, Devavrat, Penna, Nicolas Della, Celi, Leo, Sundar, Saketh, Wolfinger, Russ, Osthus, Dave, Castro, Lauren, Fairchild, Geoffrey, Michaud, Isaac, Karlen, Dean, Kinsey, Matt, Mullany, Luke, Rainwater-Lovett, Kaitlin, Shin, Lauren, Tallaksen, Katharine, Wilson, Shelby, Lee, Elizabeth, Dent, Juan, Grantz, Kyra, Hill, Alison, Kaminsky, Joshua, Kaminsky, Kathryn, Keegan, Lindsay, Lauer, Stephen, Lemaitre, Joseph, Lessler, Justin, Meredith, Hannah, Perez-Saez, Javier, Shah, Sam, Smith, Claire, Truelove, Shaun, Wills, Josh, Marshall, Maximilian, Gardner, Lauren, Nixon, Kristen, Burant, John, Wang, Lily, Gao, Lei, Gu, Zhiling, Kim, Myungjin, Li, Xinyi, Wang, Guannan, Wang, Yueying, Yu, Shan, Reiner, Robert, Barber, Ryan, Gakidou, Emmanuela, Hay, Simon, Lim, Steve, Murray, Chris J. L.; Pigott, David, Gurung, Heidi, Baccam, Prasith, Stage, Steven, Suchoski, Bradley, Prakash, Aditya, Adhikari, Bijaya, Cui, Jiaming, Rodríguez, Alexander, Tabassum, Anika, Xie, Jiajia, Keskinocak, Pinar, Asplund, John, Baxter, Arden, Oruc, Buse Eylul, Serban, Nicoleta, Arik, Sercan, Dusenberry, Mike, Epshteyn, Arkady, Kanal, Elli, Le, Long, Li, Chun-Liang, Pfister, Tomas, Sava, Dario, Sinha, Rajarishi, Tsai, Thomas, Yoder, Nate, Yoon, Jinsung, Zhang, Leyou, Abbott, Sam, Bosse, Nikos, Funk, Sebastian, Hellewell, Joel, Meakin, Sophie, Sherratt, Katharine, Zhou, Mingyuan, Kalantari, Rahi, Yamana, Teresa, Pei, Sen, Shaman, Jeffrey, Li, Michael, Bertsimas, Dimitris, Lami, Omar Skali, Soni, Saksham, Bouardi, Hamza Tazi, Ayer, Turgay, Adee, Madeline.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-329225

ABSTRACT

Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers. Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies. Starting in April 2020, the US COVID-19 Forecast Hub ( https://covid19forecasthub.org/ ) collected, disseminated, and synthesized tens of millions of specific predictions from more than 90 different academic, industry, and independent research groups. A multi-model ensemble forecast that combined predictions from dozens of different research groups every week provided the most consistently accurate probabilistic forecasts of incident deaths due to COVID-19 at the state and national level from April 2020 through October 2021. The performance of 27 individual models that submitted complete forecasts of COVID-19 deaths consistently throughout this year showed high variability in forecast skill across time, geospatial units, and forecast horizons. Two-thirds of the models evaluated showed better accuracy than a naïve baseline model. Forecast accuracy degraded as models made predictions further into the future, with probabilistic error at a 20-week horizon 3-5 times larger than when predicting at a 1-week horizon. This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks. Significance Statement This paper compares the probabilistic accuracy of short-term forecasts of reported deaths due to COVID-19 during the first year and a half of the pandemic in the US. Results show high variation in accuracy between and within stand-alone models, and more consistent accuracy from an ensemble model that combined forecasts from all eligible models. This demonstrates that an ensemble model provided a reliable and comparatively accurate means of forecasting deaths during the COVID-19 pandemic that exceeded the performance of all of the models that contributed to it. This work strengthens the evidence base for synthesizing multiple models to support public health action.

2.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324508

ABSTRACT

In this work, we developed a deep learning model-based approach to forecast the spreading trend of SARS-CoV-2 in the United States. We implemented the designed model using the United States to confirm cases and state demographic data and achieved promising trend prediction results. The model incorporates demographic information and epidemic time-series data through a Gated Recurrent Unit structure. The identification of dominating demographic factors is delivered in the end.

4.
Nanomicro Lett ; 13: 109, 2021 12.
Article in English | MEDLINE | ID: covidwho-1182358

ABSTRACT

The current COVID-19 pandemic urges the extremely sensitive and prompt detection of SARS-CoV-2 virus. Here, we present a Human Angiotensin-converting-enzyme 2 (ACE2)-functionalized gold "virus traps" nanostructure as an extremely sensitive SERS biosensor, to selectively capture and rapidly detect S-protein expressed coronavirus, such as the current SARS-CoV-2 in the contaminated water, down to the single-virus level. Such a SERS sensor features extraordinary 106-fold virus enrichment originating from high-affinity of ACE2 with S protein as well as "virus-traps" composed of oblique gold nanoneedles, and 109-fold enhancement of Raman signals originating from multi-component SERS effects. Furthermore, the identification standard of virus signals is established by machine-learning and identification techniques, resulting in an especially low detection limit of 80 copies mL-1 for the simulated contaminated water by SARS-CoV-2 virus with complex circumstance as short as 5 min, which is of great significance for achieving real-time monitoring and early warning of coronavirus. Moreover, here-developed method can be used to establish the identification standard for future unknown coronavirus, and immediately enable extremely sensitive and rapid detection of novel virus. Supplementary Information: The online version contains supplementary material available at 10.1007/s40820-021-00620-8.

5.
Preprint in English | bioRxiv | ID: ppbiorxiv-432853

ABSTRACT

SARS-Cov-2 infected cells fused with the ACE2-positive neighboring cells forming syncytia. However, the effect of syncytia in disease development is largely unknown. We established an in vitro cell-cell fusion system and used it to mimic the fusion of SARS-CoV-2 infected cells with ACE2-expressing cells to form syncytia. We found that Caspase-9 was activated after syncytia formation, and Caspase-3/7 was activated downstream of Caspase-9, but it triggered GSDME-dependent pyroptosis rather than apoptosis. What is more, single cell RNA-sequencing data showed that both ACE2 and GSDME were expression in alveolar type 2 cells in human lung. We propose that pyroptosis is the fate of syncytia formed by SARS-CoV-2 infected host cells and ACE2-positive cells, which indicated that lytic death of syncytia may contribute to the excessive inflammatory responses in severe COVID-19 patients.

6.
Nanomicro Lett ; 13: 52, 2021 01.
Article in English | MEDLINE | ID: covidwho-1059908

ABSTRACT

The outbreak of coronavirus disease 2019 has seriously threatened human health. Rapidly and sensitively detecting SARS-CoV-2 viruses can help control the spread of viruses. However, it is an arduous challenge to apply semiconductor-based substrates for virus SERS detection due to their poor sensitivity. Therefore, it is worthwhile to search novel semiconductor-based substrates with excellent SERS sensitivity. Herein we report, for the first time, Nb2C and Ta2C MXenes exhibit a remarkable SERS enhancement, which is synergistically enabled by the charge transfer resonance enhancement and electromagnetic enhancement. Their SERS sensitivity is optimized to 3.0 × 106 and 1.4 × 106 under the optimal resonance excitation wavelength of 532 nm. Additionally, remarkable SERS sensitivity endows Ta2C MXenes with capability to sensitively detect and accurately identify the SARS-CoV-2 spike protein. Moreover, its detection limit is as low as 5 × 10-9 M, which is beneficial to achieve real-time monitoring and early warning of novel coronavirus. This research not only provides helpful theoretical guidance for exploring other novel SERS-active semiconductor-based materials but also provides a potential candidate for the practical applications of SERS technology.

7.
SciFinder; 2020.
Preprint | SciFinder | ID: ppcovidwho-5228

ABSTRACT

A review on orthopedic diagnosis and treatment during epidemic period of New Coronavirus Pneumonia: suggestions for standardized processes

8.
Tianjin Medical Journal ; 48(8):753-756, 2020.
Article in Chinese | GIM | ID: covidwho-976575

ABSTRACT

Objective: To observe the changes of serum cystatin C (CysC) and inflammatory factors in patients with coronavirus disease 2019 (COVID-19), and to explore their relationship.

9.
Med. J. Chin. Peoples Liberation Army ; 5(45): 481-485, 20200528.
Article in Chinese | WHO COVID, ELSEVIER | ID: covidwho-701008

ABSTRACT

Objective To investigate the clinical features of 13 fatal cases of corona virus disease 2019 (COVID-19). Methods The clinical data of 13 patients who died of COVID-19 in Central Theater General Hospital, China, between January 4, 2020, and February 24, 2020, were analyzed retrospectively. The data reviewed included clinical manifestations, laboratory test results, radiographic features and dinical treatment plan. The cellular immune function, the expression of inflammatory factors, and lactate level in deceased patients at different stages of the disease were analyzed. Results Of those who died, the patients consisted of 10 men and 3 women. The age of those who died was (74±19) years, and 10(76.9%) patients were over 70 years old. For the patients who died, 9 presented with underlying diseases, 6(46.2%) of whom had more than 2 diseases. On admission, the chest computed tomography (CT) for 8 patients (61.5%) mainly showed multiple patchy ground-glass opacities. When the disease progressed, the ground-glass opacities rapidly developed into diffuse lesions in both lungs. The lymphocyte and CD3+, CD4+, and CD8+ T lymphocyte counts in the peripheral blood of 13 patients were significantly lower than normal levels and decreased more substantially during the disease course based on the levels when admitted (P<0.01). Additionally, the interleukin (IL)-6, D-dimer, C-reactive protein (CRP), lactic acid levels gradually increased, and most peaked before death. The cause of death for most patients was acute respiratory distress syndrome (ARDS) with type I respiratory failure. Three patients eventually developed multiorgan deficiency syndrome (MODS). Conclusions The risk factors of death for COVID-19 patients included older men, more underlying diseases, poor cellular immune function and over-expression of inflammatory factors. The main cause of death in patients with COVID-19 was ARDS, which led to respiratory failure and MODS.

11.
J Infect Public Health ; 13(9): 1229-1236, 2020 Sep.
Article in English | MEDLINE | ID: covidwho-611439

ABSTRACT

BACKGROUND: Since December 2019, when it first occurred in Wuhan, China, coronavirus disease 2019 (COVID-19) has spread rapidly worldwide via human-to-human transmission. We aimed to describe the epidemiological and demographic features of COVID-19 outside Wuhan. METHODS: A single-center case series of 136 consecutive (from January 16 to February 17, 2020) patients with confirmed COVID-19 hospitalized in The First People's Hospital of Jingzhou, China, was retrospectively analyzed. Outcomes were followed up until February 19, 2020. RESULTS: Of the 136 patients (median age, 49 years; interquartile range [IQR], 33-63 years; range, 0.3-83 years), 91 (67%) had been to Wuhan or contacted persons from Wuhan. Forty-five (33.1%) were familial clusters. The median incubation period was 6 days (IQR: 4-11 days). All children had an exact exposure history, family members with COVID-19, and "Mild/Moderate" symptoms at admission. Among the 64 elderly patients, 14 (21.9%) had no exposure history, and 43 (67.2%) had a chronic illness. All 11 (8.1%) "Severe/very severe" illness at onset cases and 5 (3.7%) fatal cases were elderly patients. The duration from symptom onset to admission was positively correlated with the duration from symptom onset to endpoint. Overall, patients with a longer incubation period had more severe outcomes. CONCLUSION: As high-risk susceptible groups, strong protection should be implemented for children and the elderly. Universal screening should be performed for people with a clear exposure history, even lacking apparent symptoms. Given the rapid progression of COVID-19, people should be admitted quickly following symptom onset.


Subject(s)
Coronavirus Infections/epidemiology , Coronavirus Infections/prevention & control , Infectious Disease Incubation Period , Pandemics/prevention & control , Pneumonia, Viral/epidemiology , Pneumonia, Viral/prevention & control , Adolescent , Adult , Age Factors , Aged , Aged, 80 and over , Betacoronavirus , COVID-19 , Child , Child, Preschool , China/epidemiology , Chronic Disease/epidemiology , Cluster Analysis , Comorbidity , Coronavirus Infections/mortality , Coronavirus Infections/transmission , Disease Susceptibility , Family Health , Female , Humans , Infant , Male , Middle Aged , Patient Acuity , Patient Admission/statistics & numerical data , Patient Discharge/statistics & numerical data , Pneumonia, Viral/mortality , Pneumonia, Viral/transmission , Retrospective Studies , Risk Factors , SARS-CoV-2 , Time Factors , Young Adult
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