Your browser doesn't support javascript.
Show: 20 | 50 | 100
Results 1 - 14 de 14
Filter
1.
PLoS One ; 17(8): e0270953, 2022.
Article in English | MEDLINE | ID: covidwho-1968864

ABSTRACT

Microblog has become the "first scenario" under which the public learn about the epidemic situation and express their opinions. Public sentiment mining based on microblog data can provide a reference for the government's information disclosure, public sentiment guidance and formulation of epidemic prevention and control policy. In this paper, about 200,000 pieces of text data were collected from Jan. 1 to Feb. 26, 2020 from Sina Weibo, which is the most popular microblog website in China. And a public sentiment analysis framework suitable for Chinese-language scenarios was proposed. In this framework, a sentiment dictionary suitable for Chinese-language scenarios was constructed, and Baidu's Sentiment Analysis API was used to calculate the public sentiment indexes. Then, an analysis on the correlation between the public sentiment indexes and the COVID-19 case indicators was made. It was discovered that there is a high correlation between public sentiments and incidence trends, in which negative sentiment is of statistical significance for the prediction of epidemic development. To further explore the source of public negative sentiment, the topics of the public negative sentiment on Weibo was analyzed, and 20 topics in five categories were got. It is found that there is a strong linkage between the hot spots of public concern and the epidemic prevention and control policies. If the policies cover the hot spots of public concern in a timely and effective manner, the public negative sentiment will be effectively alleviated. The analytical framework proposed in this paper also applies to the public sentiment analysis and policy making for other major public events.


Subject(s)
COVID-19 , Epidemics , Social Media , Attitude , COVID-19/epidemiology , COVID-19/prevention & control , Epidemics/prevention & control , Humans , Policy
2.
Mol Pharm ; 19(6): 1892-1905, 2022 06 06.
Article in English | MEDLINE | ID: covidwho-1860276

ABSTRACT

Lipid nanoparticles (LNPs) are the leading technology for RNA delivery, given the success of the Pfizer/BioNTech and Moderna COVID-19 mRNA (mRNA) vaccines, and small interfering RNA (siRNA) therapies (patisiran). However, optimization of LNP process parameters and compositions for larger RNA payloads such as self-amplifying RNA (saRNA), which can have complex secondary structures, have not been carried out. Furthermore, the interactions between process parameters, critical quality attributes (CQAs), and function, such as protein expression and cellular activation, are not well understood. Here, we used two iterations of design of experiments (DoE) (definitive screening design and Box-Behnken design) to optimize saRNA formulations using the leading, FDA-approved ionizable lipids (MC3, ALC-0315, and SM-102). We observed that PEG is required to preserve the CQAs and that saRNA is more challenging to encapsulate and preserve than mRNA. We identified three formulations to minimize cellular activation, maximize cellular activation, or meet a CQA profile while maximizing protein expression. The significant parameters and design of the response surface modeling and multiple response optimization may be useful for designing formulations for a range of applications, such as vaccines or protein replacement therapies, for larger RNA cargoes.


Subject(s)
COVID-19 , Nanoparticles , Amino Alcohols , COVID-19/therapy , Caprylates , Decanoates , Humans , Liposomes , Nanoparticles/chemistry , RNA, Messenger/metabolism , RNA, Small Interfering
3.
Mol Pharm ; 19(4): 1047-1058, 2022 04 04.
Article in English | MEDLINE | ID: covidwho-1721386

ABSTRACT

The coronavirus disease of 2019 (COVID-19) pandemic launched an unprecedented global effort to rapidly develop vaccines to stem the spread of the novel severe acute respiratory syndrome coronavirus (SARS-CoV-2). Messenger ribonucleic acid (mRNA) vaccines were developed quickly by companies that were actively developing mRNA therapeutics and vaccines for other indications, leading to two mRNA vaccines being not only the first SARS-CoV-2 vaccines to be approved for emergency use but also the first mRNA drugs to gain emergency use authorization and to eventually gain full approval. This was possible partly because mRNA sequences can be altered to encode nearly any protein without significantly altering its chemical properties, allowing the drug substance to be a modular component of the drug product. Lipid nanoparticle (LNP) technology required to protect the ribonucleic acid (RNA) and mediate delivery into the cytoplasm of cells is likewise modular, as are technologies and infrastructure required to encapsulate the RNA into the LNP. This enabled the rapid adaptation of the technology to a new target. Upon the coattails of the clinical success of mRNA vaccines, this modularity will pave the way for future RNA medicines for cancer, gene therapy, and RNA engineered cell therapies. In this review, trends in the publication records and clinical trial registrations are tallied to show the sharp intensification in preclinical and clinical research for RNA medicines. Demand for the manufacturing of both the RNA drug substance (DS) and the LNP drug product (DP) has already been strained, causing shortages of the vaccine, and the rise in development and translation of other mRNA drugs in the coming years will exacerbate this strain. To estimate demand for DP manufacturing, the dosing requirements for the preclinical and clinical studies of the two approved mRNA vaccines were examined. To understand the current state of mRNA-LNP production, current methods and technologies are reviewed, as are current and announced global capacities for commercial manufacturing. Finally, a vision is rationalized for how emerging technologies such as self-amplifying mRNA, microfluidic production, and trends toward integrated and distributed manufacturing will shape the future of RNA manufacturing and unlock the potential for an RNA medicine revolution.


Subject(s)
COVID-19 , COVID-19 Vaccines , Humans , Liposomes , Nanoparticles , RNA, Messenger/metabolism , SARS-CoV-2/genetics
4.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-313403

ABSTRACT

Coronavirus disease 2019 (COVID-19) is a Public Health Emergency of International Concern infecting more than 40 million people across 188 countries and territories. Chest computed tomography (CT) imaging technique benefits from its high diagnostic accuracy and robustness, it has become an indispensable way for COVID-19 mass testing. Recently, deep learning approaches have become an effective tool for automatic screening of medical images, and it is also being considered for COVID-19 diagnosis. However, the high infection risk involved with COVID-19 leads to relative sparseness of collected labeled data limiting the performance of such methodologies. Moreover, accurately labeling CT images require expertise of radiologists making the process expensive and time-consuming. In order to tackle the above issues, we propose a supervised domain adaption based COVID-19 CT diagnostic method which can perform effectively when only a small samples of labeled CT scans are available. To compensate for the sparseness of labeled data, the proposed method utilizes a large amount of synthetic COVID-19 CT images and adjusts the networks from the source domain (synthetic data) to the target domain (real data) with a cross-domain training mechanism. Experimental results show that the proposed method achieves state-of-the-art performance on few-shot COVID-19 CT imaging based diagnostic tasks.

5.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-309717

ABSTRACT

Background: The long-term functional outcome of discharged patients with coronavirus disease 2019 (COVID-19) remains unresolved. We aimed to describe a six-month follow-up of functional status of COVID-19 survivors. Methods: We reviewed the data of COVID-19 patients who had been consecutively admitted to the Tumor Center of Union Hospital (Wuhan, China) between 15 February and 14 March 2020. We quantified a six-month functional outcome reflecting symptoms and disability in COVID-19 survivors using a post-COVID-19 functional status scale ranging from 0 to 5 (PCFS). We examined the risk factors for the incomplete functional status defined as a PCFS > 0 at a six-month follow-up after discharge. Results: We included a total of 95 COVID-19 survivors with a median age of 62 (IQR 53-69) who had a complete functional status (PCFS grade 0) at baseline in this retrospective observational study. At six-month follow-up, 67 (70.5%) patients had a complete functional outcome (grade 0), 9 (9.5%) had a negligible limited function (grade 1), 12 (12.6%) had a mild limited function (grade 2), 7 (7.4%) had moderate limited function (grade 3). Univariable logistic regression analysis showed a significant association between the onset symptoms of muscle or joint pain and an increased risk of incomplete function (unadjusted OR 4.06, 95%CI 1.33 - 12.37). This association remained after adjustment for age and admission delay (adjusted OR 3.39, 95%CI 1.06 - 10.81, p = 0.039). Conclusions: A small proportion of discharged COVID-19 patients may have an incomplete functional outcome at a six-month follow-up;intervention strategies are required.

6.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-305020

ABSTRACT

Background: By the end of August 2020, >23 million cases and 800,000 deaths were attributed to SARS-CoV-2 in >200 countries. The improvement of simple, rapid, and efficient detection methods is of great significance for the early detection, timely isolation, and protection of susceptible populations. This study aimed to provide an alternative method for the rapid detection of viral nucleic acid. Methods: : This study provided a rapid nucleic acid detection method mediated by recombinant enzyme based on the novel coronavirus (SARS-CoV-2). Primers and probes were designed based on the N gene sequence of coronavirus. The method was performed at 39 °C, the detection time was short (<20 min), and the detection limit was up to 10 1 copies/mL. Results: : The primer-probe did not show any cross-reaction with adenovirus, Zika virus, influenza B virus, and chikungunya virus, with good specificity. A total of 106 clinical throat swab samples were compared by reverse transcription recombinase-aided amplification (RT-RAA) and commercial reverse transcription-quantitative real-time polymerase chain reaction (RT-qPCR);the results were identical. Conclusions: : The novel coronavirus RT-RAA method established in this study had high sensitivity, strong specificity, simple operation, and fast detection speed, and hence, is suitable for the rapid detection of novel coronavirus under the current epidemic situation.

7.
BMC Infect Dis ; 21(1): 1271, 2021 Dec 20.
Article in English | MEDLINE | ID: covidwho-1633329

ABSTRACT

BACKGROUND: The long-term functional outcome of discharged patients with coronavirus disease 2019 (COVID-19) remains unresolved. We aimed to describe a 6-month follow-up of functional status of COVID-19 survivors. METHODS: We reviewed the data of COVID-19 patients who had been consecutively admitted to the Tumor Center of Union Hospital (Wuhan, China) between 15 February and 14 March 2020. We quantified a 6-month functional outcome reflecting symptoms and disability in COVID-19 survivors using a post-COVID-19 functional status scale ranging from 0 to 4 (PCFS). We examined the risk factors for the incomplete functional status defined as a PCFS > 0 at a 6-month follow-up after discharge. RESULTS: We included a total of 95 COVID-19 survivors with a median age of 62 (IQR 53-69) who had a complete functional status (PCFS grade 0) at baseline in this retrospective observational study. At 6-month follow-up, 67 (70.5%) patients had a complete functional outcome (grade 0), 9 (9.5%) had a negligible limited function (grade 1), 12 (12.6%) had a mild limited function (grade 2), 7 (7.4%) had moderate limited function (grade 3). Univariable logistic regression analysis showed a significant association between the onset symptoms of muscle or joint pain and an increased risk of incomplete function (unadjusted OR 4.06, 95% CI 1.33-12.37). This association remained after adjustment for age and admission delay (adjusted OR 3.39, 95% CI 1.06-10.81, p = 0.039). CONCLUSIONS: A small proportion of discharged COVID-19 patients may have an incomplete functional outcome at a 6-month follow-up; intervention strategies are required.


Subject(s)
COVID-19 , Patient Discharge , Follow-Up Studies , Functional Status , Humans , SARS-CoV-2
8.
Sens Actuators B Chem ; 351: 130897, 2022 Jan 15.
Article in English | MEDLINE | ID: covidwho-1458630

ABSTRACT

The rapid and accurate diagnosis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at the early stage of virus infection can effectively prevent the spread of the virus and control the epidemic. Here, a colorimetric and fluorescent dual-functional lateral flow immunoassay (LFIA) biosensor was developed for the rapid and sensitive detection of spike 1 (S1) protein of SARS-CoV-2. A novel dual-functional immune label was fabricated by coating a single-layer shell formed by mixing 20 nm Au nanoparticles (Au NPs) and quantum dots (QDs) on SiO2 core to produce strong colorimetric and fluorescence signals and ensure good monodispersity and high stability. The colorimetric signal was used for visual detection and rapid screening of suspected SARS-CoV-2 infection on sites. The fluorescence signal was utilized for sensitive and quantitative detection of virus infection at the early stage. The detection limits of detecting S1 protein via colorimetric and fluorescence functions of the biosensor were 1 and 0.033 ng/mL, respectively. Furthermore, we evaluated the performance of the biosensor for analyzing real samples. The novel biosensor developed herein had good repeatability, specificity and accuracy, which showed great potential as a tool for rapidly detecting SARS-CoV-2.

9.
J Natl Cancer Inst ; 113(8): 1111-1112, 2021 08 02.
Article in English | MEDLINE | ID: covidwho-1352217
10.
Medicine (Baltimore) ; 100(12): e25307, 2021 Mar 26.
Article in English | MEDLINE | ID: covidwho-1150011

ABSTRACT

ABSTRACT: In 2020, the new type of coronal pneumonitis became a pandemic in the world, and has firstly been reported in Wuhan, China. Chest CT is a vital component in the diagnostic algorithm for patients with suspected or confirmed COVID-19 infection. Therefore, it is necessary to conduct automatic and accurate detection of COVID-19 by chest CT.The clinical classification of patients with COVID-19 pneumonia was predicted by Radiomics using chest CT.From the COVID-19 cases in our institution, 136 moderate patients and 83 severe patients were screened, and their clinical and laboratory data on admission were collected for statistical analysis. Initial CT Radiomics were modeled by automatic machine learning, and diagnostic performance was evaluated according to AUC, TPR, TNR, PPV and NPV of the subjects. At the same time, the initial CT main features of the two groups were analyzed semi-quantitatively, and the results were statistically analyzed.There was a statistical difference in age between the moderate group and the severe group. The model cohort showed TPR 96.9%, TNR 99.1%, PPV98.4%, NPV98.2%, and AUC 0.98. The test cohort showed TPR 94.4%, TNR100%, PPV100%, NPV96.2%, and AUC 0.97. There was statistical difference between the two groups with grade 1 score (P = .001), the AUC of grade 1 score, grade 2 score, grade 3 score and CT score were 0.619, 0.519, 0.478 and 0.548, respectively.Radiomics' Auto ML model was built by CT image of initial COVID -19 pneumonia, and it proved to be effectively used to predict the clinical classification of COVID-19 pneumonia. CT features have limited ability to predict the clinical typing of Covid-19 pneumonia.


Subject(s)
COVID-19/diagnostic imaging , Image Processing, Computer-Assisted/methods , Machine Learning , Tomography, X-Ray Computed/methods , Adult , Age Factors , Aged , COVID-19/pathology , Female , Humans , Lung/diagnostic imaging , Lung/pathology , Male , Middle Aged , Predictive Value of Tests , SARS-CoV-2 , Severity of Illness Index
11.
J Natl Cancer Inst ; 113(3): 344-345, 2021 03 01.
Article in English | MEDLINE | ID: covidwho-1123319
12.
J Natl Cancer Inst ; 113(4): 371-380, 2021 04 06.
Article in English | MEDLINE | ID: covidwho-1066370

ABSTRACT

BACKGROUND: Previous studies have indicated coronavirus disease 2019 (COVID-19) patients with cancer have a high fatality rate. METHODS: We conducted a systematic review of studies that reported fatalities in COVID-19 patients with cancer. A comprehensive meta-analysis that assessed the overall case fatality rate and associated risk factors was performed. Using individual patient data, univariate and multivariable logistic regression analyses were used to estimate odds ratios (OR) for each variable with outcomes. RESULTS: We included 15 studies with 3019 patients, of which 1628 were men; 41.0% were from the United Kingdom and Europe, followed by the United States and Canada (35.7%), and Asia (China, 23.3%). The overall case fatality rate of COVID-19 patients with cancer measured 22.4% (95% confidence interval [CI] = 17.3% to 28.0%). Univariate analysis revealed age (OR = 3.57, 95% CI = 1.80 to 7.06), male sex (OR = 2.10, 95% CI = 1.07 to 4.13), and comorbidity (OR = 2.00, 95% CI = 1.04 to 3.85) were associated with increased risk of severe events (defined as the individuals being admitted to the intensive care unit, or requiring invasive ventilation, or death). In multivariable analysis, only age greater than 65 years (OR = 3.16, 95% CI = 1.45 to 6.88) and being male (OR = 2.29, 95% CI = 1.07 to 4.87) were associated with increased risk of severe events. CONCLUSIONS: Our analysis demonstrated that COVID-19 patients with cancer have a higher fatality rate compared with that of COVID-19 patients without cancer. Age and sex appear to be risk factors associated with a poorer prognosis.


Subject(s)
COVID-19/epidemiology , Neoplasms/epidemiology , Asia/epidemiology , COVID-19/mortality , COVID-19/virology , Canada/epidemiology , Comorbidity , Europe/epidemiology , Female , Humans , Male , Neoplasms/mortality , Risk Factors , SARS-CoV-2/physiology , Survival Rate , United Kingdom/epidemiology , United States/epidemiology
14.
Sci Rep ; 10(1): 18926, 2020 11 03.
Article in English | MEDLINE | ID: covidwho-910231

ABSTRACT

To explore the possibility of predicting the clinical types of Corona-Virus-Disease-2019 (COVID-19) pneumonia by analyzing the non-focus area of the lung in the first chest CT image of patients with COVID-19 by using automatic machine learning (Auto-ML). 136 moderate and 83 severe patients were selected from the patients with COVID-19 pneumonia. The clinical and laboratory data were collected for statistical analysis. The texture features of the Non-focus area of the first chest CT of patients with COVID-19 pneumonia were extracted, and then the classification model of the first chest CT of COVID-19 pneumonia was constructed by using these texture features based on the Auto-ML method of radiomics, The area under curve(AUC), true positive rate(TPR), true negative rate (TNR), positive predictive value(PPV) and negative predictive value (NPV) of the operating characteristic curve (ROC) were used to evaluate the accuracy of the first chest CT image classification model in patients with COVID-19 pneumonia. The TPR, TNR, PPV, NPV and AUC of the training cohort and test cohort of the moderate group and the control group, the severe group and the control group, the moderate group and the severe group were all greater than 95% and 0.95 respectively. The non-focus area of the first CT image of COVID-19 pneumonia has obvious difference in different clinical types. The AUTO-ML classification model of Radiomics based on this difference can be used to predict the clinical types of COVID-19 pneumonia.


Subject(s)
Coronavirus Infections/diagnostic imaging , Lung/diagnostic imaging , Machine Learning , Pneumonia, Viral/diagnostic imaging , Tomography, X-Ray Computed/methods , Adult , Aged , COVID-19 , Coronavirus Infections/pathology , Female , Humans , Lung/pathology , Male , Middle Aged , Pandemics , Pneumonia, Viral/pathology
SELECTION OF CITATIONS
SEARCH DETAIL