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1.
Frontiers in Microbiology ; 13:1062281, 2022.
Article in English | MEDLINE | ID: covidwho-2199021

ABSTRACT

Coronavirus disease 2019 (COVID-19), a disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is currently spreading rapidly around the world. Since SARS-CoV-2 seriously threatens human life and health as well as the development of the world economy, it is very urgent to identify effective drugs against this virus. However, traditional methods to develop new drugs are costly and time-consuming, which makes drug repositioning a promising exploration direction for this purpose. In this study, we collected known antiviral drugs to form five virus-drug association datasets, and then explored drug repositioning for SARS-CoV-2 by Gaussian kernel similarity bilinear matrix factorization (VDA-GKSBMF). By the 5-fold cross-validation, we found that VDA-GKSBMF has an area under curve (AUC) value of 0.8851, 0.8594, 0.8807, 0.8824, and 0.8804, respectively, on the five datasets, which are higher than those of other state-of-art algorithms in four datasets. Based on known virus-drug association data, we used VDA-GKSBMF to prioritize the top-k candidate antiviral drugs that are most likely to be effective against SARS-CoV-2. We confirmed that the top-10 drugs can be molecularly docked with virus spikes protein/human ACE2 by AutoDock on five datasets. Among them, four antiviral drugs ribavirin, remdesivir, oseltamivir, and zidovudine have been under clinical trials or supported in recent literatures. The results suggest that VDA-GKSBMF is an effective algorithm for identifying potential antiviral drugs against SARS-CoV-2.

2.
2022 International Conference on Biomedical and Intelligent Systems, IC-BIS 2022 ; 12458, 2022.
Article in English | Scopus | ID: covidwho-2193339

ABSTRACT

Wearing masks has been generally recommended to reduce the spreading of COVID-19. However, little is known about its effects on metabolic VOC changes in human body. To explore how the duration of wearing masks influences VOC metabolism in the human body, the essay used a self-developed electronic nose to analyse exhaled breath samples from 10 healthy individuals in this study. Firstly, polytetrafluoroethylene sampling bags are used to collect breath samples after volunteers wearing masks for 1h, 2h, 3h, 4h, and 5h. Secondly, data pre-processing, including baseline calibration and normalization are carried out. Thirdly, the study used LDA for dimensionality reduction on the original data to extract 4 features. Fourthly, differences in the length of time of wearing masks are analysed. Then, 4 algorithms were applied for cluster analysis based on extracted features. Moreover, 3 supervised classification algorithms were used to recognize the duration of wearing masks. Finally, multi-dimensional linear regression is used to study the possibility of predicting the duration of wearing masks based on breath signals acquired through electronic noses. As a result, the first feature extracted by LDA significantly differs from each other in the duration of wearing masks (p<0.05). Cluster analysis results show that the optimal internal parameters Adjusted Rand Index, Adjusted Mutual Information, Homogeneity and V-measure reach 80.2%, 81.5%, 83.5% and 83.7% respectively. Using 5-fold cross-validation on the K nearest neighbour classification model, the best accuracy of recognizing durations of wearing a mask reaches 88%. R-square of multi-dimensional linear regression reaches 92.5%, which shows excellent fitting performance. It can be concluded that the VOC metabolism of the human may change with the duration of wearing masks. Further, "breath prints” obtained by electronic nose may have the potential to predict the effective time and even the quality of masks. © 2022 SPIE.

3.
Chemical Engineering Journal ; 456:140930, 2023.
Article in English | MEDLINE | ID: covidwho-2177142

ABSTRACT

Messenger RNA (mRNA) vaccines, while demonstrating great successes in the fight against COVID-19, have been extensively studied in other areas such as personalized cancer immunotherapy based on tumor neoantigens. In addition to the design of mRNA sequences and modifications, the delivery carriers are also critical in the development of mRNA vaccines. In this work, we synthesized fluoroalkane-grafted polyethylenimine (F-PEI) for mRNA delivery. Such F-PEI could promote intracellular delivery of mRNA and activate the Toll-like receptor 4 (TLR4)-mediated signaling pathway. The nanovaccine formed by self-assembly of F-PEI and the tumor antigen-encoding mRNA, without additional adjuvants, could induce the maturation of dendritic cells (DCs) and trigger efficient antigen presentation, thereby eliciting anti-tumor immune responses. Using the mRNA encoding the model antigen ovalbumin (mRNAOVA), our F-PEI-based mRNAOVA cancer vaccine could delay the growth of established B16-OVA melanoma. When combined with immune checkpoint blockade therapy, the F-PEI-based MC38 neoantigen mRNA cancer vaccine was able to suppress established MC38 colon cancer and prevent tumor reoccurrence. Our work presents a new tool for mRNA delivery, promising not only for personalized cancer vaccines but also for other mRNA-based immunotherapies.

4.
Clin Chem Lab Med ; 2022.
Article in English | PubMed | ID: covidwho-2154344

ABSTRACT

OBJECTIVES: Various comorbidities associated with COVID-19 add up in severity of the disease and obviously prolonged the time for viral clearance. This study investigated a novel ultrasensitive MAGLUMI(®) SARS-CoV-2 Ag chemiluminescent immunoassay assay (MAG-CLIA) for diagnosis and monitoring the infectivity of COVID-19 patients with comorbid conditions during the pandemic of 2022 Shanghai. METHODS: Analytical performances of the MAG-CLIA were evaluated, including precision, limit of quantitation, linearity and specificity. Nasopharyngeal specimens from 232 hospitalized patients who were SARS-CoV-2 RT-qPCR positive and from 477 healthy donors were included. The longitudinal studies were performed by monitoring antigen concentrations alongside with RT-qPCR results in 14 COVID-19 comorbid participants for up to 22 days. The critical antigen concentration in determining virus infectivity was evaluated at the reference cycle threshold (Ct) of 35. RESULTS: COVID-19 patients were well-identified using an optimal threshold of 0.64 ng/L antigen concentration, with sensitivity and specificity of 95.7% (95% CI: 92.2-97.9%) and 98.3% (95% CI: 96.7-99.3%), respectively, while the Wondfo LFT exhibited those of 34.9% (95% CI: 28.8-41.4%) and 100% (95% CI: 99.23-100%), respectively. The sensitivity of MAG-CLIA remained 91.46% (95% CI: 83.14-95.8%) for the samples with Ct values between 35 and 40. Close dynamic consistence was observed between MAG-CLIA and viral load time series in the longitudinal studies. The critical value of 8.82 ng/L antigen showed adequate sensitivity and specificity in evaluating the infectivity of hospitalized convalescent patients with comorbidities. CONCLUSIONS: The MAG-CLIA SARS-CoV-2 Ag detection is an effective and alternative approach for rapid diagnosis and enables us to evaluate the infectivity of hospitalized convalescent patients with comorbidities.

5.
Quantitative Imaging in Medicine and Surgery ; 0(0):0-0, 2022.
Article in English | Web of Science | ID: covidwho-2124169

ABSTRACT

Background: The coronavirus disease 2019 (COVID-19) led to a dramatic increase in the number of cases of patients with pneumonia worldwide. In this study, we aimed to develop an AI-assisted multistrategy image enhancement technique for chest X-ray (CXR) images to improve the accuracy of COVID-19 classification. Methods: Our new classification strategy consisted of 3 parts. First, the improved U-Net model with a variational encoder segmented the lung region in the CXR images processed by histogram equalization. Second, the residual net (ResNet) model with multidilated-rate convolution layers was used to suppress the bone signals in the 217 lung-only CXR images. A total of 80% of the available data were allocated for training and validation. The other 20% of the remaining data were used for testing. The enhanced CXR images containing only soft tissue information were obtained. Third, the neural network model with a residual cascade was used for the super-resolution reconstruction of low-resolution bone-suppressed CXR images. The training and testing data consisted of 1,200 and 100 CXR images, respectively. To evaluate the new strategy, improved visual geometry group (VGG)-16 and ResNet-18 models were used for the COVID-19 classification task of 2,767 CXR images. The accuracy of the multistrategy enhanced CXR images was verified through comparative experiments with various enhancement images. In terms of quantitative verification, 8-fold cross-validation was performed on the bone suppression model. In terms of evaluating the COVID-19 classification, the CXR images obtained by the improved method were used to train 2 classification models. Results: Compared with other methods, the CXR images obtained based on the proposed model had better performance in the metrics of peak signal-to-noise ratio and root mean square error. The super-resolution CXR images of bone suppression obtained based on the neural network model were also anatomically close to the real CXR images. Compared with the initial CXR images, the classification accuracy rates of the internal and external testing data on the VGG-16 model increased by 5.09% and 12.81%, respectively, while the values increased by 3.51% and 18.20%, respectively, for the ResNet-18 model. The numerical results were better than those of the single-enhancement, double-enhancement, and no-enhancement CXR images. Conclusions: The multistrategy enhanced CXR images can help to classify COVID-19 more accurately than the other existing methods.

6.
Mobile Information Systems ; 2022, 2022.
Article in English | Scopus | ID: covidwho-2053409

ABSTRACT

COVID-19 is a sudden and highly contagious infectious disease, which has a very bad impact on the psychology of college students in early adulthood. In order to grasp the psychological state of college students in real-time, this work studies the psychological state of college students during COVID-19. First, this study introduces the relevant theories of data mining, and the research object and method are determined. Then, the features of the model are analyzed and constructed from two aspects which are static features and dynamic features, and the characteristics related to the psychological state are excavated. Finally, the GA is selected to build the model and the model is evaluated;the results show that the model can accurately predict the psychological state of students during COVID-19. © 2022 Jian Xiang and Yanjun Zhang.

7.
Annals of the Rheumatic Diseases ; 81:333-334, 2022.
Article in English | EMBASE | ID: covidwho-2008914

ABSTRACT

Background: Published data suggest no increased rate of fare of autoimmune infammatory rheumatic diseases (AIIRD) after COVID-19 mRNA vaccination;however, the studies are limited by small sample size, short follow up or at risk of selection bias (voluntary physician reports or patient surveys). Objectives: To study fares of AIIRD within three months of the frst dose of an anti-SARS-COV2 mRNA vaccine. Methods: A retrospective cohort study of consecutive AIIRD patients ≥ 12 years old, across six public hospitals in Singapore who received at least one dose of an mRNA (Pfzer/BioNTech or Moderna) vaccine. Data were censored at the frst post-vaccine clinic visit when the patient had fared or if ≥ three months had elapsed since the frst dose of the vaccine, whichever came frst. Predictors of fare were determined by Cox proportional hazards analysis and time to fare was examined using a Nelson Aalen cumulative hazard estimate (Figure 1). Results: 2339 patients (74% Chinese, 72% female) of median (IQR) age 64 (53, 71) years were included in the interim analysis (Table 1). 2112 (90%) had the Pfzer/BioNTech vaccine and 195 (8%) had Moderna, with a median (IQR) interval of 21 (21, 23) days between the two doses. The most common AIIRD diagnoses were Rheumatoid arthritis (1063, 45%), Psoriatic arthritis (296, 12.6%) and Systemic lupus erythematosus (SLE) (288, 12.3%). 186 (8%) were treated with biologics/targeted disease modifying agents. 2125 (91%) patients were in low disease activity or remission. Treatment was interrupted for vaccination in only 18 (0.8%) patients. Seven (0.3%) patients had previous COVID-19 infection. 452 (19%) fares were recorded during 9798.8 patient-months [4.6/100 patient-months, median (IQR) follow up duration 4.2 (3.3, 5.3) months], of which 272 (11.6%) patients fared within the 3-month period of interest and 180 (7.7%) fared outside of the 3-month period (Table 1). Median (IQR) time-to-fare was 40.5 (18, 56.6) days. 60 (22.1%) were mild and self-limiting, 170 (62.5%) were mild-moderate and 42 (15.4%) were severe. 190 (69.8%) of those who fared required escalation of treatment and 15 (5.5%) required hospital admission. 239 (10.2%) had improved disease activity after the vaccine. On multivariate Cox regression analysis, patients in the oldest age tertile [median (IQR) 74 (71, 79) years] were less likely to fare [HR 0.80 (95% CI 0.63, 1.00), p = 0.05] Patients with infammatory arthritis (compared with connective tissue disease, vasculitis and others) and patients with baseline active disease were more likely to fare [HR 1.72 (95% CI 1.35, 2.20), p < 0.001 and 1.82 (95% CI 1.39, 2.39), p < 0.001 respectively] Conclusion: There was a moderately high rate of AIIRD fares after mRNA vaccination;however, there was no clustering of fares in the immediate post-vaccine period to suggest causality. Older patients were less likely to fare, while those with infammatory arthritis and active disease at baseline were more likely to fare.

8.
Data (Basel) ; 7(7)2022 Jul.
Article in English | MEDLINE | ID: covidwho-1963771

ABSTRACT

Developments in deep learning techniques have led to significant advances in automated abnormality detection in radiological images and paved the way for their potential use in computer-aided diagnosis (CAD) systems. However, the development of CAD systems for pulmonary tuberculosis (TB) diagnosis is hampered by the lack of training data that is of good visual and diagnostic quality, of sufficient size, variety, and, where relevant, containing fine region annotations. This study presents a collection of annotations/segmentations of pulmonary radiological manifestations that are consistent with TB in the publicly available and widely used Shenzhen chest X-ray (CXR) dataset made available by the U.S. National Library of Medicine and obtained via a research collaboration with No. 3. People's Hospital Shenzhen, China. The goal of releasing these annotations is to advance the state-of-the-art for image segmentation methods toward improving the performance of fine-grained segmentation of TB-consistent findings in digital Chest X-ray images. The annotation collection comprises the following: 1) annotation files in JSON (JavaScript Object Notation) format that indicate locations and shapes of 19 lung pattern abnormalities for 336 TB patients; 2) mask files saved in PNG format for each abnormality per TB patient; 3) a CSV (comma-separated values) file that summarizes lung abnormality types and numbers per TB patient. To the best of our knowledge, this is the first collection of pixel-level annotations of TB-consistent findings in CXRs. Dataset: https://data.lhncbc.nlm.nih.gov/public/Tuberculosis-Chest-X-ray-Datasets/Shenzhen-Hospital-CXR-Set/Annotations/index.html.

9.
Quant Imaging Med Surg ; 12(7): 3917-3931, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1884868

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19) is a pandemic disease. Fast and accurate diagnosis of COVID-19 from chest radiography may enable more efficient allocation of scarce medical resources and hence improved patient outcomes. Deep learning classification of chest radiographs may be a plausible step towards this. We hypothesize that bone suppression of chest radiographs may improve the performance of deep learning classification of COVID-19 phenomena in chest radiographs. Methods: Two bone suppression methods (Gusarev et al. and Rajaraman et al.) were implemented. The Gusarev and Rajaraman methods were trained on 217 pairs of normal and bone-suppressed chest radiographs from the X-ray Bone Shadow Suppression dataset (https://www.kaggle.com/hmchuong/xray-bone-shadow-supression). Two classifier methods with different network architectures were implemented. Binary classifier models were trained on the public RICORD-1c and RSNA Pneumonia Challenge datasets. An external test dataset was created retrospectively from a set of 320 COVID-19 positive patients from Queen Elizabeth Hospital (Hong Kong, China) and a set of 518 non-COVID-19 patients from Pamela Youde Nethersole Eastern Hospital (Hong Kong, China), and used to evaluate the effect of bone suppression on classifier performance. Classification performance, quantified by sensitivity, specificity, negative predictive value (NPV), accuracy and area under the receiver operating curve (AUC), for non-suppressed radiographs was compared to that for bone suppressed radiographs. Some of the pre-trained models used in this study are published at (https://github.com/danielnflam). Results: Bone suppression of external test data was found to significantly (P<0.05) improve AUC for one classifier architecture [from 0.698 (non-suppressed) to 0.732 (Rajaraman-suppressed)]. For the other classifier architecture, suppression did not significantly (P>0.05) improve or worsen classifier performance. Conclusions: Rajaraman suppression significantly improved classification performance in one classification architecture, and did not significantly worsen classifier performance in the other classifier architecture. This research could be extended to explore the impact of bone suppression on classification of different lung pathologies, and the effect of other image enhancement techniques on classifier performance.

10.
Clinical and Experimental Obstetrics and Gynecology ; 49(3), 2022.
Article in English | EMBASE | ID: covidwho-1780431

ABSTRACT

Background: Ectopic pregnancy is a potential cause of morbidity and mortality among women and is a common diagnosis for women presenting to the emergency room. During the height of the COVID-19 pandemic in New York City (NYC) in the spring of 2020, emergency room visits for all non-COVID related health problems appeared to decrease. We examined visits for ectopic pregnancies and pregnancies of unknown location (PUL) in the emergency department (ED) of three NYC hospitals during the height of the early pandemic and compared them to the same months in the prior year. Methods: Our study is an IRB-approved retrospective chart review of all patients who presented to the ED with a positive pregnancy test during the months of March–June 2020 (pandemic period) and March–June 2019 (pre-pandemic). Demographic data, history, labs, imaging, number of visits and treatment and outcomes were measured. Results: We found that there were 324 ED visits for PUL in 2019 (pre-pandemic) compared to 195 in 2020 (pandemic). Ectopic pregnancies remained somewhat stable and were diagnosed in 59 patients in 2019 and 51 patients in 2020. The percentage of patients diagnosed with ectopic pregnancy increased from 25.1% of all patients with PUL in 2019 to 39% of all patients diagnosed with PUL in 2020. Rates of complications were similar between the two cohorts. Conclusion: Although the number of visits to the ED for PUL fell dramatically from the pre-pandemic to the pandemic time period, the number of patients actually diagnosed with ectopic pregnancy was similar between the two time periods.

11.
Forest Chemicals Review ; 2021(September-October):849-877, 2021.
Article in English | Scopus | ID: covidwho-1717610

ABSTRACT

In the context of the continuing outbreak of COVID-19, the Chinese people, together with the people of the world, are suffering from the problems brought about by COVID-19. Besides the goal of adequacy, how to improve affordability, sustainability and robustness of pension system is also the key issue as limited individual contribution capacity and limited financial affordability of governments at all levels, and the need for sustainable level of basic living needs. Therefore, this article focuses on limited supply constraints, analyzes individual contribution capacity, and discusses the relationship between individual contribution capacity and government financial subsidies, then, based on the analysis of limited supply constraints above, this article discusses the needed individual contributions of rural residents in China based on the condition that the multi-level annual basic living needs are satisfied to achieve the goal of adequacy. At last, some suggestions for optimizing the incentive payment policy of rural social pension system in China are put forward. The aims are to promote rural residents to continue to pay their contributions, raise the payment level and extend the payment period within the scope of their abilities, then to achieve the goals of adequacy, affordability, sustainability and robustness. © 2021 Kriedt Enterprises Ltd. All right reserved.

12.
Blood ; 138:3018, 2021.
Article in English | EMBASE | ID: covidwho-1582322

ABSTRACT

Background: Patients with relapsed/refractory acute myeloid leukemia (AML) have poor outcomes and high levels of healthcare utilization at end of life. Palliative care remains underused in this population despite the high symptom burden. Questions remain regarding how best to integrate palliative care for high risk hematology patients. Prior implementation of standardized palliative care consultation triggers on an inpatient solid tumor service led to increased palliative care consultations and decreased healthcare utilization (Adelson et al, JOP 2017). We conducted a prospective cohort study evaluating standardized palliative care consultation triggers for patients admitted to a tertiary academic center with advanced AML. Method: Trigger criteria were developed for hospitalized patients with hematologic malignancies on the inpatient hematology floors at Smilow Cancer Hospital and included: 1) persistent disease after ≥ 2 lines of therapy, 2) length of stay (LOS) >7 days for symptom management, 3) Eastern Cooperative Oncology Group (ECOG) performance status > 2, and 4) refractory graft versus host disease (GVHD) after ≥ 3 lines of therapy. Patients with relapsed/refractory AML were included if they met criteria #1. A palliative care nurse coordinator performed chart review of admitted patients 1-2 times per week from June to December 2020 and contacted the primary team when a patient met eligibility. Patient characteristics and healthcare outcomes were compared with patients with AML meeting criteria #1 admitted pre-intervention (June to December 2019) using Fisher t-tests. Results: A total of 110 admitted patients with advanced AML met eligibility criteria #1 (64 pre-intervention and 46 post-intervention). Baseline patient and disease characteristics were similar, including mean age at admission (60.4 vs 60.9 years, p=0.848), gender (64% vs 59% male, p=0.691), prior transplant (56% vs 52%, p=0.702), and AML risk stratification (67% vs 78% adverse risk, p=0.283). In the post-intervention group, 61% of eligible patients were screened. Of the screened patients, 54% received a palliative care consult, 18% were declined by the primary team, 14% were marked as not eligible, and 14% did not have a palliative care consult with reason unspecified. Within the same admission, there was a significant increase in advance care planning and/or advanced directive documentation post-intervention (13% vs 28%, p=0.049). There was no differences in pre- and post-intervention groups in time to palliative care consult from admission (7.2 vs 4.9 days, p=0.245), LOS (12.13 vs 12.33 days, p=0.941), 30-day readmissions (52% vs 39%, p=0.246), critical/intermediate care escalation (22% vs 13%, p=0.318) during the same admission. By July 2021, 92% of the pre-intervention patients and 57% of the post-intervention patients were deceased. Of the deceased patients, there was no differences in pre- and post-intervention groups in blood transfusions (100% vs 96%, p=0.306) or hospice enrollment (46% vs 62%, p=0.157) within 14 days of death. There was also no significant differences in pre- and post-intervention groups in non-palliative anti-neoplastic therapy use (37% vs 38%, p=0.999), hospital admissions (95% vs 88% p=0.364), or critical/intermediate care escalation (51% vs 38%, p=0.350) within 30 days of death. Conclusion: A trigger-based palliative care referral intervention is feasible and doubled palliative care use in patients with relapsed/refractory AML. Our intervention was associated with increased advance care planning documentation during the admission. There were directional changes in other healthcare measures, including decreased time to palliative care consult and escalation of care, as well as increased hospice enrollment. These differences, however, were not statistically significant due to the small sample size. The significant healthcare use likely reflected high symptom burden at end of life, associated with transfusions and admissions for infection and symptom management. More research is needed to determine how best to sup ort these patients at end of life. Of note, our intervention period occurred during the COVID-19 pandemic, which may have impacted threshold for inpatient admissions and the inpatient census. Disclosures: Adelson: Carrum Health: Other: Stock;Abbvie: Consultancy;Roche/Genentech: Consultancy, Honoraria, Patents & Royalties, Research Funding;Heron: Consultancy;Celgene: Consultancy. Prebet: BMS: Research Funding;BMS, Curios, Daichi: Consultancy.

13.
Journal of Clinical Oncology ; 39(28 SUPPL), 2021.
Article in English | EMBASE | ID: covidwho-1496265

ABSTRACT

Background: Patient-trial matching is a critical step in clinical research recruitment that requires extensive review of clinical data and trial requirements. Prescreening, defined as identifying potentially eligible patients using select eligibility criteria, may streamline the process and increase study enrollment. We describe the real-world experience of implementing a standardized, universal clinical research prescreening protocol within a VA cancer center and its impact on research enrollment. Methods: An IRB approved prescreening protocol was implemented at the VACT Cancer Center in March 2017. All patients with a suspected or confirmed diagnosis of cancer are identified through tumor boards, oncology consults, and clinic lists. Research coordinators perform chart review and manually enter patient demographics, cancer type and stage, and treatment history into a REDCap (Research Electronic Data Capture) database. All clinical trials and their eligibility criteria are also entered into REDCap and updated regularly. REDCap generates real time lists of potential research studies for each patient based on his/her recorded data. The primary oncologist is alerted to a patient's potential eligibility prior to upcoming clinic visits and thus can plan to discuss clinical research enrollment as appropriate. Results: From March 2017 to December 2020, a total of 2548 unique patients were prescreened into REDCAP. The mean age was 71.5 years, 97.5% were male, and 15.5% were African American. 32.57 % patients had genitourinary cancer, 17.15% had lung cancer, and 46.15% were undergoing malignancy workup. 1412 patients were potentially eligible after prescreening and 556 patients were ultimately enrolled in studies. The number of patients enrolled on therapeutic clinical trials increased after the implementation of the prescreening protocol (35 in 2017, 64 in 2018, 78 in 2019, and 55 in 2020 despite the COVID19 pandemic). Biorepository study enrollment increased from 8 in 2019 to 15 in 2020. The prescreening protocol also enabled 200 patients to be enrolled onto a lung nodule liquid biopsy study from 2017 to 2019. Our prescreening process captured 98.57% of lung cancer patients entered into the cancer registry during the same time period. Conclusions: Universal prescreening streamlined research recruitment operations and was associated with yearly increases in clinical research enrollment at a VA cancer center. Our protocol identified most new lung cancer patients, suggesting that, at least for this malignancy, potential study patients were not missed. The protocol was integral in our program becoming the top accruing VA site for NCI's National Clinical Trial Network (NCTN) studies since 2019.

14.
Acta Geoscientica Sinica ; 42(2):159-166, 2021.
Article in Chinese | Scopus | ID: covidwho-1210196

ABSTRACT

At present, the COVID-19 epidemic is still spreading around the world, which greatly affects the global supply of mineral resources. Due to numerous resources supply nodes and complex and changeable risk factors, it is urgent to carry out comprehensive research on the supply base, supply node and supply chain of mineral resources from the perspective of production and transportation supply chain. Based on the global perspective and long-term research on risk assessment and countermeasures of global mineral resources, this paper puts forward the concept, connotation and evaluation method of mineral resources supply chain for the first time and traces the whole chain of mineral products from production, transportation and smelting process in space. With the mineral resources supply base as the entry point, the method of determination and comprehensive evaluation of mineral resources supply base based on space technology is established to realize the transformation of the delineation of mineral resources supply base from the traditional planarization to the spatialization. The collation and stipulation of the technical system through the establishment of supply key nodes and supply chain of mineral resources can collate and stipulate supply chain and key knots by systematically taking into consideration the mineral resources supply of the target country, and can utilize the risk evaluation method of the mineral resources supply chain to carry out risk evaluation directed against the supply chain. These measures help the target country wholly grasp hidden risk of the mineral resources supply chain and, according to different forewarning grades, draw up safety contingency plans in accord with different grades and kinds, thus guaranteeing continued and stable supply of mineral resources. © 2021, Science Press. All right reserved.

15.
Quant Imaging Med Surg ; 10(11): 2191-2207, 2020 Nov.
Article in English | MEDLINE | ID: covidwho-854853

ABSTRACT

Started during December 2019, following the emergence of several COVID-19 cases in Wuhan City, Hubei Province, there was a rapid surge and spread of new COVID-19 cases throughout China. The disease has since been included in the Class B infectious diseases category, as stipulated in the Law of the People's Republic of China on the Prevention and Treatment of Infectious Diseases and shall be managed according to Class A infectious diseases. During the early phases of COVID-19 infection, no specific pulmonary imaging features may be evident, or features overlapping with other pneumonia may be observed. Although CT is not the gold standard for the diagnosis of COVID-19, it nonetheless is a convenient and fast method, and its application can be deployed in community hospitals. Furthermore, CT can be used to render a suggestive diagnosis and evaluate the severity as well as the effects of therapeutic interventions for typical cases of COVID-19. The mobile emergency special CT device described in this document (also known as Emergency Mobile Cabin CT) has several unique characteristics, including its mobility, flexibility, and networking capabilities. Furthermore, it adopts a fully independent isolation design to avoid cross-infection between patients and medical staff. It can play an important role in screening suspected cases presenting with imaging features of COVID-19 in hospitals of various levels that provide care to suspected or confirmed COVID-19 patients as part of the first line procedures of epidemic prevention and control.

17.
Quant Imaging Med Surg ; 10(6): 1307-1317, 2020 Jun.
Article in English | MEDLINE | ID: covidwho-604098

ABSTRACT

BACKGROUND: Many studies have described lung lesion computed tomography (CT) features of coronavirus disease 2019 (COVID-19) patients at the early and progressive stages. In this study, we aim to evaluate lung lesion CT radiological features along with quantitative analysis for the COVID-19 patients ready for discharge. METHODS: From February 10 to March 10, 2020, 125 COVID-19 patients (age: 16-67 years, 63 males) ready for discharge, with two consecutive negative reverse transcription-polymerase chain reaction (RT-PCR) and no clinical symptoms for more than 3 days, were included. The pre-discharge CT was performed on all patients 1-3 days after the second negative RT-PCR test, and the follow-up CTs were performed on 44 patients 2-13 days later. The imaging features and quantitative analysis were evaluated on both the pre-discharge and the follow-up CTs, by both radiologists and an artificial intelligence (AI) software. RESULTS: On the pre-discharge CT, the most common CT findings included ground-glass opacity (GGO) (99/125, 79.2%) with bilateral mixed distribution, and fibrosis (56/125, 44.8%) with bilateral subpleural distribution. Enlarged mediastinal lymph nodes were also commonly observed (45/125, 36.0%). AI enabled quantitative analysis showed the right lower lobe was mostly involved, and lesions most commonly had CT value of -570 to -470 HU consistent with GGO. Follow-up CT showed GGO decrease in size and density (40/40, 100%) and fibrosis reduction (17/26, 65.4%). Compared with the pre-discharge CT results, quantitative analysis shows the lung lesion volume regressed significantly at follow-up. CONCLUSIONS: For COVID-19 patients ready for discharge, GGO and fibrosis are the main CT features and they further regress at follow-up.

18.
19.
Ann Transl Med ; 8(4): 145, 2020 Feb.
Article in English | MEDLINE | ID: covidwho-8656
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