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1.
Pers Individ Dif ; 192: 111589, 2022 Jul.
Article in English | MEDLINE | ID: covidwho-1720711

ABSTRACT

To be or not to be quarantined? That is the question posed by COVID-19 pandemic to almost every resident in the world. Approximately three months after the first application of the COVID-19 lockdown to residents in 17 Asian, African, European, American, and Oceanian countries, we carried out a cross-national survey of 26,266 residents via online platforms such as Sojump and Prolific to investigate their willingness to quarantine and its influencing factors. Findings show that 1) The willingness to quarantine is low in countries with high long-term orientation; 2) Females are more willing to be quarantined than males; 3) Gender difference on willingness to quarantine is large among people with older age and low education. Theoretical and managerial implications are discussed. Understanding how culture and demographics affect people's willingness to quarantine not only provides insight into how to respond to the current pandemic, but also helps the world prepare for future crises.

2.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-325222

ABSTRACT

BACKGROUND: Previous study suggested that Chinese Herbal Medicine (CHM) Formula Huashibaidu granule might shorten disease course of Corona Virus Disease 2019 (COVID-19) patients. Our research aims to investigate the early treatment effect of Huashibaidu granule in mild COVID-19 patients under well clinical management.METHODS: An unblended cluster-randomized clinical trial was conducted at the Dongxihu FangCang hospital. 2 cabins were randomly allocated to CHM or control group, with 204 randomly sampled mild COVID-19 patients in each cabin. All participants received a 7-day conventional treatment, and CHM group cabin used additional Huashibaidu granule 10g twice daily. Participants were followed up until they met clinical endpoint. The primary outcome was patient become worsening before clinical endpoint occurred. The secondary outcomes was discharge with cure before clinical endpoint occurred and relief of composite symptoms after 7 days treatment.FINDINGS: All 408 participants were followed up to meet clinical endpoint and included in statistical analysis. The baseline characteristics were comparable between 2 groups. The number of worsening patients in the CHM group was 5 (2.5%), and that in the control group was 16 (7.8%). There was a significant difference between groups (P=0.014). 8 foreseeable mild adverse events occurred without statistical difference between groups.INTERPRETATION: 7-day early treatment with Huashibaidu granule reduced worsening conversion of mild COVID-19 patients. Our study supports Huashibaidu Granule as an active option for early treatment of mild COVID-19 in similar medical locations with well management.TRIAL REGISTRATION: The Chinese Clinical Trial Registry: ChiCTR2000029763.FUNDING: This study was supported by “National Key R&D Program of China” (No.2020YFC0841500).DECLARATION OF INTERESTS: The authors guaranteed that there existed no competing interest in this paper.ETHICS APPROVAL STATEMENT: Ethics Review Committee of Institute of Basic Research in Clinical Medicine, China Academy of Chinese Medical Sciences Approval of Ethical Review Acceptance Number: S2020-001;Approval Number: P20001/PJ01.

3.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325193

ABSTRACT

Objectives: To investigated the relationship between the neutrophil-to-lymphocyte ratio (NLR) and the severity of lung injury in corona virus disease 2019 (COVID-19) patients.Methods The clinical data, laboratory examination, and chest computed tomography (CT) findings of 167 patients with confirmed COVID-19 admitted to 5 hospitals in Chongqing, China from January 2020 to February 2020 were retrospectively reviewed. According to the diagnostic criteria sixth edition of the “Diagnosis and Treatment of New Coronavirus Pneumonitis” published by the China National Health Commission, the patients were stratified by the severity of their illness to 3 groups: mild (n = 17), moderate (n = 119), or severe (n = 31).Results Differences of the NLR among the three groups and between each of the groups were significant (all p < 0.001). The NLR and CT severity score were positively correlated (r = 0.823, p < 0.001). Receiver operating characteristic (ROC) curve analysis found that NLR had diagnostic and prognostic value in COVID-19 patients with either negative or positive CT results. The area under curve (AUC) was 0.819 (95% CI: 0.729-0.910, p < 0.001), the sensitivity was 61.3%, specificity was 94.1%, and the optimal NLR cutoff value was 3.634.Conclusion NLR reflected the degree of lung injury and predicted the progression of COVID-19. NLR is a low-cost, convenient, bedside alternative to chest CT scanning to indicate the severity of lung injury in patients with COVID-19, especially in relatively underdeveloped areas.

4.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325164

ABSTRACT

Background: The novel coronavirus pneumonia (COVID-19) is a highly contagious and highly pathogenic disease caused by a novel coronavirus(SARS-CoV-2)and has become pandemic within a short period of time. The epidemic has brought not only the risk of death from infection but also unbearable psychological pressure. College students as a special group, their mental health status need to be studied during the outbreak of COVID-19.MethodsWe used the Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), and the compulsive behavior part of the Yale-Brown Obsessive-Compulsive Scale (YBOCS), combined with demographic information, using online questionnaires to research, and the study was conducted between February 21 and 24, 2020. A total of 2270 valid questionnaires were collected, the respondents of these questionnaires included 563 medical students and 1707 non-medical students. We separately analyzed the mental health status of medical and non-medical students during the outbreak of COVID-19.ResultsOf the 563 medical students, 20 (3.55%) students had anxiety symptoms, and 57 (10.12%) students had depressive symptoms. Gender, PMH, compulsive behavior, and regularity of daily life during the epidemic outbreak were correlated with their anxiety symptoms and age, PMH, compulsive behavior, and regularity of daily life during the epidemic outbreak were associated with their depressive symptoms. Of the 1707 non-medical students, 66 (3.87%) students had anxiety symptoms, and 180 (10.54%) students had depressive symptoms. Gender, contact history of similar infectious disease, PMH, compulsive behavior, regularity of daily life and exercise during the epidemic outbreak and concern on COVID-19 were correlated with their anxiety symptoms and contact history of similar infectious disease, PMH, compulsive behavior, regularity of daily life and exercise during the epidemic outbreak and concern on COVID-19 were associated with their depressive symptoms.ConclusionsResults indicated that gender, age, contact history of similar infectious disease, past medical history (PMH), compulsive behavior, regularity of daily life, and exercise during the epidemic outbreak are the key factors making college students anxious or depressed. The results provided a theoretical basis for relevant interventions;it is also essential for medical education and public health epidemic prevention.

5.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-306299

ABSTRACT

The COVID-19 is sweeping the world with deadly consequences. Its contagious nature and clinical similarity to other pneumonias make separating subjects contracted with COVID-19 and non-COVID-19 viral pneumonia a priority and a challenge. However, COVID-19 testing has been greatly limited by the availability and cost of existing methods, even in developed countries like the US. Intrigued by the wide availability of routine blood tests, we propose to leverage them for COVID-19 testing using the power of machine learning. Two proven-robust machine learning model families, random forests (RFs) and support vector machines (SVMs), are employed to tackle the challenge. Trained on blood data from 208 moderate COVID-19 subjects and 86 subjects with non-COVID-19 moderate viral pneumonia, the best result is obtained in an SVM-based classifier with an accuracy of 84%, a sensitivity of 88%, a specificity of 80%, and a precision of 92%. The results are found explainable from both machine learning and medical perspectives. A privacy-protected web portal is set up to help medical personnel in their practice and the trained models are released for developers to further build other applications. We hope our results can help the world fight this pandemic and welcome clinical verification of our approach on larger populations.

6.
Transportation Research: Part D ; 103:N.PAG-N.PAG, 2022.
Article in English | Academic Search Complete | ID: covidwho-1683635

ABSTRACT

[Display omitted] • Predict sharing behavior in Chicago's ride-haling trips using ensemble ML methods. • Willingness to share a ride declined over 52% throughout 2019. • Over time, per-mile cost of shared trips increased, shorter trips shifted to solo. • Travel impedance variables have the highest predictive power in sharing behavior. Ride sharing or pooling is important to mitigate negative externalities of ride-hailing such as increased congestion and environmental impacts. However, there lacks empirical evidence on what affect trip-level sharing behavior in ride-hailing. Using a novel dataset from all ride-hailing trips in Chicago in 2019, we show that the willingness of riders to request a shared ride has monotonically decreased from 27.0% to 12.8% throughout the year, while the trip volume and mileage have remained statistically unchanged. We find that the decline in sharing preference is due to an increased per-mile costs of shared trips and shifting shorter trips to solo. Using ensemble machine learning models, we find that the travel impedance variables (trip cost, distance, and duration) collectively contribute to the predictive power by 95% in the propensity to share and 91% in successful matching of a trip. Spatial and temporal attributes, sociodemographic, built environment, and transit supply variables do not entail significant predictive power at the trip level in presence of these travel impedance variables. Our findings shed light on sharing behavior in ride-hailing trips and can help devise strategies that increase shared ride-hailing. [ FROM AUTHOR] Copyright of Transportation Research: Part D is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

7.
Front Aging ; 22021 Jul.
Article in English | MEDLINE | ID: covidwho-1674417

ABSTRACT

Aging has emerged as the greatest and most prevalent risk factor for the development of severe COVID-19 infection and death following exposure to the SARS-CoV-2 virus. The presence of multiple co-existing chronic diseases and conditions of aging further enhances this risk. Biological aging not only enhances the risk of chronic diseases, but the presence of such conditions further accelerates varied biological processes or "hallmarks" implicated in aging. Given growing evidence that it is possible to slow the rate of many biological aging processes using pharmacological compounds has led to the proposal that such geroscience-guided interventions may help enhance immune resilience and improve outcomes in the face of SARS-CoV-2 infection. Our review of the literature indicates that most, if not all, hallmarks of aging may contribute to the enhanced COVID-19 vulnerability seen in frail older adults. Moreover, varied biological mechanisms implicated in aging do not function in isolation from each other, and exhibit intricate effects on each other. With all of these considerations in mind, we highlight limitations of current strategies mostly focused on individual single mechanisms, and we propose an approach which is far more multidisciplinary and systems-based emphasizing network topology of biological aging and geroscience-guided approaches to COVID-19.

8.
Zhongguo Bingdubing Zazhi = Chinese Journal of Viral Diseases ; - (6):455, 2021.
Article in English | ProQuest Central | ID: covidwho-1675352

ABSTRACT

Objective To analyze the genomics characteristics and nucleic acid detection results of the severe Acute respiratory syndrome coronavirus 2(SARS-CoV-2) in 2 297 clinical samples collected in January and February, 2020 in Laboratory of Microbiology of Changsha Municipal Center for Disease Control and Prevention. Methods Viral RNA of throat swabs or respiratory tract specimens of coronavirus disease 2019(COVID-19) suspected cases from January 19, 2020 to February 29, 2020 was extracted and SARS-CoV-2 nucleic acid was detected by real-time reverse transcription polymerase chain reaction.The full length genome of SARS-CoV-2 in positive samples was enriched by using viral genome capture kit and sequenced on Illumina MiSeq platform.The raw reads were mapped and aligned with SPAdes software v 3.13.0.Reference SARS-CoV-2 sequences were obtained from GISAID(https://www.gisaid.org) andviral genetic evolution and antigen variation were analyzed. Results A total of 215 SARS-Co V2-nucleic acid positive samples were identified from 2 297 clinical samples.Among the SARS-Co V2-positive samples, 110 were males and 105 were from females.The male to female ratio was 1.05∶1.The highest positive rate was among 40-<60 years old people(11.35%) and the lowest positive rate was in children under 6 years old(5.49%).The peak of newly confirmed cases was in the 5 th week(January 26 to February 1, 2020) and then decreased.There was no newly positive case after February 25, 2020.Five SARS-Co V2-whole genome sequences were obtained and there were 4 to 6 nucleotide mutations compared to the Wuhan reference strain, and the homology was more than 99.90%.Most mutations occurred only once except C8782 T and T28144 C, indicating random mutations.Phylogenetic analysis revealed that the 5 sequences belonged to the L/B or S/A lineages and were highly homologous with strains prevalent in other provinces of China at the same time. Conclusions With the quick nucleic acid tests and quarantine measures, the SARS-Co V2-positive cases in Changsha began to decline after a 2-week increasing period, and there was no new confirmed cases 6 weeks later.The genomes of SARS-Co V-2 prevalent in Changsha are highly homology with the Wuhan strains in the early 2020 and no obvious mutation is found in the local pandemic period. Reset

9.
Angew Chem Int Ed Engl ; 61(9): e202112995, 2022 02 21.
Article in English | MEDLINE | ID: covidwho-1633678

ABSTRACT

The transmission of SARS-CoV-2 coronavirus has led to the COVID-19 pandemic. Nucleic acid testing while specific has limitations for mass surveillance. One alternative is the main protease (Mpro ) due to its functional importance in mediating the viral life cycle. Here, we describe a combination of modular substrate and gold colloids to detect Mpro via visual readout. The strategy involves zwitterionic peptide that carries opposite charges at the C-/N-terminus to exploit the specific recognition by Mpro . Autolytic cleavage releases a positively charged moiety that assembles the nanoparticles with rapid color changes (t<10 min). We determine a limit of detection for Mpro in breath condensate matrices <10 nM. We further assayed ten COVID-negative subjects and found no false-positive result. In the light of simplicity, our test for viral protease is not limited to an equipped laboratory, but also is amenable to integrating as portable point-of-care devices including those on face-coverings.


Subject(s)
COVID-19/diagnosis , Coronavirus 3C Proteases/metabolism , Peptides/metabolism , SARS-CoV-2/metabolism , Biomarkers/metabolism , Breath Tests , COVID-19/virology , Colorimetry/methods , Humans , Limit of Detection , Proteolysis
10.
Front Med (Lausanne) ; 8: 696976, 2021.
Article in English | MEDLINE | ID: covidwho-1450816

ABSTRACT

Background: Previous research suggested that Chinese Medicine (CM) Formula Huashibaidu granule might shorten the disease course in coronavirus disease 2019 (COVID-19) patients. This research aimed to investigate the early treatment effect of Huashibaidu granule in well-managed patients with mild COVID-19. Methods: An unblinded cluster-randomized clinical trial was conducted at the Dongxihu FangCang hospital. Two cabins were randomly allocated to a CM or control group, with 204 mild COVID-19 participants in each cabin. All participants received conventional treatment over a 7 day period, while the ones in CM group were additionally given Huashibaidu granule 10 g twice daily. Participants were followed up to their clinical endpoint. The primary outcome was worsening symptoms before the clinical endpoint. The secondary outcomes were cure and discharge before the clinical endpoint and alleviation of composite symptoms after the 7 days of treatment. Results: All 408 participants were followed up to their clinical endpoint and included in statistical analysis. Baseline characteristics were comparable between the two groups (P > 0.05). The number of worsening patients in the CM group was 5 (2.5%), and that in the control group was 16 (7.8%) with a significant difference between groups (P = 0.014). Eight foreseeable mild adverse events occurred without statistical difference between groups (P = 0.151). Conclusion: Seven days of early treatment with Huashibaidu granule reduced the likelihood of worsening symptoms in patients with mild COVID-19. Our study supports Huashibaidu granule as an active option for early treatment of mild COVID-19 in similar well-managed medical environments. Clinical Trial Registration:www.chictr.org.cn/showproj.aspx?proj=49408, identifier: ChiCTR2000029763.

11.
Phytomedicine ; 91: 153671, 2021 Oct.
Article in English | MEDLINE | ID: covidwho-1313371

ABSTRACT

OBJECTIVE: To evaluate the efficacy and safety of Hua Shi Bai Du Granule (Q-14) plus standard care compared with standard care alone in adults with coronavirus disease (COVID-19). STUDY DESIGN: A single-center, open-label, randomized controlled trial. SETTING: Wuhan Jinyintan Hospital, Wuhan, China, February 27 to March 27, 2020. PARTICIPANTS: A total of 204 patients with laboratory-confirmed COVID-19 were randomized into the treatment group and control group, consisting of 102 patients in each group. INTERVENTIONS: In the treatment group, Q-14 was administered at 10 g (granules) twice daily for 14 days, plus standard care. In the control group, patients were provided standard care alone for 14 days. MAIN OUTCOME MEASURE: The primary outcome was the conversion time for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) viral assay. Adverse events were analyzed in the safety population. RESULTS: Among the 204 patients, 195 were analyzed according to the intention-to-treat principle. A total of 149 patients (71 vs. 78 in the treatment and control groups, respectively) tested negative via the SARS-CoV-2 viral assay. There was no statistical significance in the conversion time between the treatment group and control group (Full analysis set: Median [interquartile range]: 10.00 [9.00-11.00] vs. 10.00 [9.00-11.00]; Mean rank: 67.92 vs. 81.44; P = 0.051). The recovery time for fever was shorter in the treatment group than in the control group. The disappearance rate of symptoms like cough, fatigue, and chest discomfort was significantly higher in the treatment group. In chest computed tomography (CT) examinations, the overall evaluation of chest CT examination after treatment compared with baseline showed that more patients improved in the treatment group. There were no significant differences in the other outcomes. CONCLUSION: The combination of Q-14 and standard care for COVID-19 was useful for the improvement of symptoms (such as fever, cough, fatigue, and chest discomfort), but did not result in a significantly higher probability of negative conversion in the SARS-CoV-2 viral assay. No serious adverse events were observed. TRIAL REGISTRATION: ChiCTR2000030288.


Subject(s)
COVID-19 , Drugs, Chinese Herbal/therapeutic use , COVID-19/therapy , China , Female , Humans , Male , Middle Aged , Treatment Outcome
12.
Clin Breast Cancer ; 22(1): e1-e7, 2022 01.
Article in English | MEDLINE | ID: covidwho-1252588

ABSTRACT

BACKGROUND: The coronavirus disease 2019 pandemic is a global public health event. Wuhan used to be the epicenter of China and finally controlled the outbreak through city lockdown and many other policies. However, the pandemic and the prevention strategies had a huge impact on the medical care procedures for patients with breast cancer, leading to the delay or interruption of anticancer therapies. PATIENTS AND METHODS: To better serve patients with breast cancer under the premise of epidemic control, many strategies have been proposed and optimized in our center. One of the most important parts of these strategies is the promotion of telemedicine, including online consultation, online prescription, and drug mailing services. RESULTS: In keeping with the city and hospital policies, we have also introduced stricter ward management policies and more precise care. CONCLUSION: Here, we collected the diagnosis and treatment process of patients with breast cancer in our center during the coronavirus disease 2019 pandemic, which was found to be correlated to a reduction in chemotherapy-related myelosuppression and hepatic dysfunction, hoping to provide a reference for other cancer centers that may suffer from the similar situation.


Subject(s)
Breast Neoplasms/drug therapy , COVID-19/epidemiology , SARS-CoV-2 , Adult , Aged , Antineoplastic Agents/adverse effects , Bone Marrow/drug effects , Breast Neoplasms/diagnosis , Breast Neoplasms/pathology , Chemical and Drug Induced Liver Injury/etiology , China/epidemiology , Female , Humans , Middle Aged , Telemedicine
13.
ACS Sens ; 6(6): 2356-2365, 2021 06 25.
Article in English | MEDLINE | ID: covidwho-1243274

ABSTRACT

Activatable contrast agents are of ongoing research interest because they offer low background and high specificity to the imaging target. Engineered sensitivity to protease activity is particularly desirable because proteases are critical biomarkers in cancer, infectious disease, inflammatory disorders, and so forth. Herein, we developed and characterized a set of peptide-linked cyanine conjugates for dual-modal detection of protease activity via photoacoustic (PA) and fluorescence imaging. The peptide-dye conjugates were designed to undergo contact quenching via intramolecular dimerization and contained n dyes (n = 2, 3, or 4) with n - 1 cleavable peptide substrates. The absorption peaks of the conjugates were blue-shifted 50 nm relative to the free dye and had quenched fluorescence. This effect was sensitive to solvent polarity and could be reversed by solvent switching from water to dimethyl sulfoxide. Employing trypsin as a model protease, we observed a 2.5-fold recovery of the peak absorbance, 330-4600-fold fluorescent enhancement, and picomolar detection limits following proteolysis. The dimer probe was further characterized for PA activation. Proteolysis released single dye-peptide fragments that produced a 5-fold PA enhancement through the increased absorption at 680 nm with nanomolar sensitivity to trypsin. The peptide substrate could also be tuned for protease selectivity; as a proof-of-concept, we detected the main protease (Mpro) associated with the viral replication in SARS-CoV-2 infection. Last, the activated probe was imaged subcutaneously in mice and signal was linearly correlated to the cleaved probe. Overall, these results demonstrate a tunable scaffold for the PA molecular imaging of protease activity with potential value in areas such as disease monitoring, tumor imaging, intraoperative imaging, in vitro diagnostics, and point-of-care sensing.


Subject(s)
COVID-19 , Photoacoustic Techniques , Animals , Carbocyanines , Fluorescent Dyes , Humans , Mice , Peptide Hydrolases/metabolism , Proteolysis , SARS-CoV-2
14.
Psychol Res Behav Manag ; 14: 563-574, 2021.
Article in English | MEDLINE | ID: covidwho-1232502

ABSTRACT

INTRODUCTION: The COVID-19 pandemic has received broad public attention and has been subject to social media discussion since the beginning of 2020. Previous research has demonstrated that framing could influence perception and behaviors of audience members in the mass media. The question addressed in this paper concerns which information frame is best for reporting negative news (eg, deaths) and positive news (eg, recoveries or cures) related to the outbreak of COVID-19. METHODS: During the Spring Festival holidays of 2020 in China, we investigated a sample of 8170 participants' risk perceptions and emotional responses to the pandemic, and their willingness to forward updates when the information is presented in different frames by using a 2 (domain: living [good news] vs dying [bad news]) × 2 (count: absolute vs relative) × 2 (population base: excluding population base vs including population base) × 2 (content: text-only vs text-plus-graphic) mixed factorial design, with the first factor being a within-subjects factor and the last three being between-subjects factors. RESULTS: Results indicated that (1) participants were more willing to forward good news (eg, cures) than bad news (eg, deaths); (2) when reporting bad news, the inclusion of the "population base" was effective in minimizing negative emotions; (3) when reporting good news, excluding the "population base" was more effective than including it in order to maximize positive emotions; (4) a text-plus-graphic frame worked better than a text-only frame in lowering the level of risk perception and negative emotions. DISCUSSION: This study is relevant to how individuals and organizations communicate information about this viral pandemic and the probable impact of this news on the general public.

15.
Journal of Medical Internet Research ; 23(4), 2021.
Article in English | ProQuest Central | ID: covidwho-1209585

ABSTRACT

Background: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. Objective: In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. Methods: For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. Results: Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. Conclusions: Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.

16.
J Med Internet Res ; 23(4): e23948, 2021 04 07.
Article in English | MEDLINE | ID: covidwho-1133811

ABSTRACT

BACKGROUND: Effectively and efficiently diagnosing patients who have COVID-19 with the accurate clinical type of the disease is essential to achieve optimal outcomes for the patients as well as to reduce the risk of overloading the health care system. Currently, severe and nonsevere COVID-19 types are differentiated by only a few features, which do not comprehensively characterize the complicated pathological, physiological, and immunological responses to SARS-CoV-2 infection in the different disease types. In addition, these type-defining features may not be readily testable at the time of diagnosis. OBJECTIVE: In this study, we aimed to use a machine learning approach to understand COVID-19 more comprehensively, accurately differentiate severe and nonsevere COVID-19 clinical types based on multiple medical features, and provide reliable predictions of the clinical type of the disease. METHODS: For this study, we recruited 214 confirmed patients with nonsevere COVID-19 and 148 patients with severe COVID-19. The clinical characteristics (26 features) and laboratory test results (26 features) upon admission were acquired as two input modalities. Exploratory analyses demonstrated that these features differed substantially between two clinical types. Machine learning random forest models based on all the features in each modality as well as on the top 5 features in each modality combined were developed and validated to differentiate COVID-19 clinical types. RESULTS: Using clinical and laboratory results independently as input, the random forest models achieved >90% and >95% predictive accuracy, respectively. The importance scores of the input features were further evaluated, and the top 5 features from each modality were identified (age, hypertension, cardiovascular disease, gender, and diabetes for the clinical features modality, and dimerized plasmin fragment D, high sensitivity troponin I, absolute neutrophil count, interleukin 6, and lactate dehydrogenase for the laboratory testing modality, in descending order). Using these top 10 multimodal features as the only input instead of all 52 features combined, the random forest model was able to achieve 97% predictive accuracy. CONCLUSIONS: Our findings shed light on how the human body reacts to SARS-CoV-2 infection as a unit and provide insights on effectively evaluating the disease severity of patients with COVID-19 based on more common medical features when gold standard features are not available. We suggest that clinical information can be used as an initial screening tool for self-evaluation and triage, while laboratory test results should be applied when accuracy is the priority.


Subject(s)
COVID-19 , Machine Learning , SARS-CoV-2 , Severity of Illness Index , Triage , China , Female , Humans , Male , Middle Aged , Models, Theoretical , Reproducibility of Results
17.
J Med Internet Res ; 23(1): e25535, 2021 01 06.
Article in English | MEDLINE | ID: covidwho-1011363

ABSTRACT

BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical features and appropriate analytical approaches for enabling the early detection and effective diagnosis of patients with COVID-19. OBJECTIVE: We aimed to combine low-dimensional clinical and lab testing data, as well as high-dimensional computed tomography (CT) imaging data, to accurately differentiate between healthy individuals, patients with COVID-19, and patients with non-COVID viral pneumonia, especially at the early stage of infection. METHODS: In this study, we recruited 214 patients with nonsevere COVID-19, 148 patients with severe COVID-19, 198 noninfected healthy participants, and 129 patients with non-COVID viral pneumonia. The participants' clinical information (ie, 23 features), lab testing results (ie, 10 features), and CT scans upon admission were acquired and used as 3 input feature modalities. To enable the late fusion of multimodal features, we constructed a deep learning model to extract a 10-feature high-level representation of CT scans. We then developed 3 machine learning models (ie, k-nearest neighbor, random forest, and support vector machine models) based on the combined 43 features from all 3 modalities to differentiate between the following 4 classes: nonsevere, severe, healthy, and viral pneumonia. RESULTS: Multimodal features provided substantial performance gain from the use of any single feature modality. All 3 machine learning models had high overall prediction accuracy (95.4%-97.7%) and high class-specific prediction accuracy (90.6%-99.9%). CONCLUSIONS: Compared to the existing binary classification benchmarks that are often focused on single-feature modality, this study's hybrid deep learning-machine learning framework provided a novel and effective breakthrough for clinical applications. Our findings, which come from a relatively large sample size, and analytical workflow will supplement and assist with clinical decision support for current COVID-19 diagnostic methods and other clinical applications with high-dimensional multimodal biomedical features.


Subject(s)
COVID-19/diagnosis , Decision Support Systems, Clinical , Health , Machine Learning , Pneumonia, Viral/diagnosis , COVID-19/diagnostic imaging , Diagnosis, Differential , Humans , Middle Aged , Pneumonia, Viral/diagnostic imaging , SARS-CoV-2 , Support Vector Machine , Tomography, X-Ray Computed
18.
World J Diabetes ; 11(12): 644-653, 2020 Dec 15.
Article in English | MEDLINE | ID: covidwho-1004904

ABSTRACT

BACKGROUND: Coronavirus disease 2019 (COVID-19) is a pandemic disease spreading all over the world and has aroused global concerns. The increasing mortality has revealed its severity. It is important to distinguish severe patients and provide appropriate treatment and care to prevent damages. Diabetes is reported to be a common comorbidity in COVID-19 patients and associated with higher mortality. We attempted to clarify the relationship between diabetes and COVID-19 patients' severity. AIM: To determine the role of type 2 diabetes in COVID-19 patients. METHODS: To study the relationship between diabetes and COVID-19, we retrospectively collected 61 patients' data from a tertiary medical center in Wuhan. All the patients were diagnosed with laboratory-confirmed COVID-19 and admitted to the center from February 13 to March 1, 2020. Patients' age, sex, laboratory tests, chest computed tomography findings, capillary blood glucose (BG), and treatments were collected and analyzed. Fisher exact test was used for categorical data. Univariate and multivariate logistic regressions were used to explore the relationship between clinical characteristics and patients' severity. RESULTS: In the 61 patients, the comorbidity of type 2 diabetes, hypertension, and heart diseases were 24.6% (15 out of 61), 37.7% (23 out of 61), and 11.5% (7 out of 61), respectively. The diabetic group was related to more invasive treatments (P = 0.02) and severe status (P = 0.003). In univariate logistic regression, histories of diabetes (OR = 7.13, P = 0.003), hypertension (OR = 3.41, P = 0.039), and hepatic dysfunction (OR = 7.69, P = 0.002) were predictors of patients' severity while heart disease (OR = 4.21, P = 0.083) and large lung involvement (OR = 2.70, P = 0.093) also slightly exacerbated patients' conditions. In the multivariate analysis, diabetes (OR = 6.29, P = 0.016) and hepatic dysfunction (OR = 5.88, P = 0.018) were risk factors for severe patients. Diabetic patients showed elevated BG in 61.7% of preprandial tests and 33.3% of postprandial tests, revealing the limited control of glycemia in COVID-19 patients. CONCLUSION: A history of type 2 diabetes is correlated with invasive treatments and severe status. Suboptimal glycemic control and hepatic dysfunction have negative effects on severity status and may lead to the exacerbation of COVID-19 patients.

19.
J Thorac Dis ; 12(10): 5336-5346, 2020 Oct.
Article in English | MEDLINE | ID: covidwho-934699

ABSTRACT

BACKGROUND: The study is designed to explore the chest CT features of different clinical types of coronavirus disease 2019 (COVID-19) pneumonia based on a Chinese multicenter dataset using an artificial intelligence (AI) system. METHODS: A total of 164 patients confirmed COVID-19 were retrospectively enrolled from 6 hospitals. All patients were divided into the mild type (136 cases) and the severe type (28 cases) according to their clinical manifestations. The total CT severity score and quantitative CT features were calculated by AI pneumonia detection and evaluation system with correction by radiologists. The clinical and CT imaging features of different types were analyzed. RESULTS: It was observed that patients in the severe type group were older than the mild type group. Round lesions, Fan-shaped lesions, crazy-paving pattern, fibrosis, "white lung", pleural thickening, pleural indentation, mediastinal lymphadenectasis were more common in the CT images of severe patients than in the mild ones. A higher total lung severity score and scores of each lobe were observed in the severe group, with higher scores in bilateral lower lobes of both groups. Further analysis showed that the volume and number of pneumonia lesions and consolidation lesions in overall lung were higher in the severe group, and showed a wider distribution in the lower lobes of bilateral lung in both groups. CONCLUSIONS: Chest CT of patients with severe COVID-19 pneumonia showed more consolidative and progressive lesions. With the assistance of AI, CT could evaluate the clinical severity of COVID-19 pneumonia more precisely and help the early diagnosis and surveillance of the patients.

20.
Phytomedicine ; 81: 153367, 2021 Jan.
Article in English | MEDLINE | ID: covidwho-837550

ABSTRACT

BACKGROUND: Treatments for coronavirus disease 2019 (COVID-19) are limited by suboptimal efficacy. METHODS: From January 30, 2020 to March 23, 2020, we conducted a non-randomised controlled trial, in which all adult patients with laboratory-confirmed COVID-19 were assigned to three groups non-randomly and given supportive treatments: Group A, Lopinavir-Ritonavir; Group B, Huashi Baidu Formula (a Chinese medicineformula made by the China Academy of Chinese Medical Sciences to treat COVID-19, which is now in the clinical trial period) and Lopinavir-Ritonavir; and Group C, Huashi Baidu Formula. The use of antibiotics, antiviruses, and corticosteroids was permitted in Group A and B. Traditional Chinese medicine injections were permitted in Group C. The primary outcomes were clinical remission time (interval from admission to the first time the patient tested negatively for novel coronavirus or an obvious improvement was observed from chest CT) and clinical remission rate (number of patients whose clinical time was within 16 days/total number of patients). RESULTS: A total of 60 adult patients with COVID-19 were enrolled at sites in Wuhan, China, and the sample size of each group was 20. In Groups A, B and C, the clinical remission rates were 95.0%%(19/20), 100.0%%(20/20) and 100.0%%(20/20), respectively. Compared with Groups A and B, the clinical remission time of Group C was significantly shorter (5.9 days vs. 10.8 days, p < 0.05; 5.9 days vs. 9.7 days, p < 0.05). There was no significant difference among Groups A, B, and C in terms of the time taken to be released from quarantine. The clinical biochemical indicators and safety indexes showed no significant differences among the three groups. CONCLUSIONS: Our findings suggest that Lopinavir-Ritonavir has some efficacy in the treatment of COVID-19, and the Huashi Baidu Formula might enhance this effect to an extent. In addition, superiority was displayed in the treatment of COVID-19 through a combination of the Huashi Baidu Formula and traditional Chinese medicine injection. In future, well-designed prospective double-blinded randomised control trials are required to confirm our findings.


Subject(s)
Antiviral Agents/therapeutic use , COVID-19/drug therapy , Drugs, Chinese Herbal/therapeutic use , Lopinavir/therapeutic use , Ritonavir/therapeutic use , Adult , Aged , Aged, 80 and over , Antiviral Agents/adverse effects , COVID-19/diagnostic imaging , Drug Combinations , Drug Therapy, Combination , Drugs, Chinese Herbal/adverse effects , Female , Humans , Lopinavir/adverse effects , Male , Medicine, Chinese Traditional , Middle Aged , Patient Safety , Prospective Studies , Ritonavir/adverse effects , Thorax/diagnostic imaging , Tomography, X-Ray Computed , Treatment Outcome
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