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1.
Rapid Commun Mass Spectrom ; : e9358, 2022 Jul 26.
Article in English | MEDLINE | ID: covidwho-1955938

ABSTRACT

RATIONALE: Hydroxychloroquine sulfate is effective in the treatment of malaria, autoimmune diseases, and as an antiviral drug. However, unreported impurities are often detected in this drug, which pose a health risk. In this study, the structures of hydroxychloroquine and six unknown impurities were analyzed using ultra-high performance liquid chromatography-quadrupole/time-of-flight tandem mass spectrometry (UHPLC-Q/TOF MS), and the structures were characterized using liquid chromatography-solid-phase extraction-nuclear magnetic resonance spectroscopy (LC-SPE-NMR). METHODS: The column was an Agilent InfinityLad Poroshell HPH-C18 (100 mm × 4.6 mm, 2.7 µm). For the analysis of hydroxychloroquine and six unknown impurities, the mobile phase was 20 mM ammonium formate aqueous solution and methanol/acetonitrile (80:20, v/v), using gradient elution. Full-scan MS and MS2 were performed in order to obtain as much structural information as possible. Additionally, six unknown impurities were separated by semi-preparative liquid chromatography and characterized by LC-SPE-NMR. RESULTS: The MS2 fragmentation patterns of the impurities were investigated, leading to more structural information and an understanding of the fragmentation pathways of the impurities. The unknown impurities' structures were confirmed by NMR. In addition, some possible pathways of the formation of the impurities in the drugs were outlined, and these impurities were found to be process impurities. CONCLUSIONS: Based on the identification and characterization of these impurities, this study also describes the cause of the production of the impurities and provides insights for companies to improve their production processes and a scientific basis for the improvement of the related pharmacopoeias.

2.
Environ Res ; 212(Pt B): 113297, 2022 09.
Article in English | MEDLINE | ID: covidwho-1796872

ABSTRACT

Meteorological factors have been confirmed to affect the COVID-19 transmission, but current studied conclusions varied greatly. The underlying causes of the variance remain unclear. Here, we proposed two scientific questions: (1) whether meteorological factors have a consistent influence on virus transmission after combining all the data from the studies; (2) whether the impact of meteorological factors on the COVID-19 transmission can be influenced by season, geospatial scale and latitude. We employed a meta-analysis to address these two questions using results from 2813 published articles. Our results showed that, the influence of meteorological factors on the newly-confirmed COVID-19 cases varied greatly among existing studies, and no consistent conclusion can be drawn. After grouping outbreak time into cold and warm seasons, we found daily maximum and daily minimum temperatures have significant positive influences on the newly-confirmed COVID-19 cases in cold season, while significant negative influences in warm season. After dividing the scope of the outbreak into national and urban scales, relative humidity significantly inhibited the COVID-19 transmission at the national scale, but no effect on the urban scale. The negative impact of relative humidity, and the positive impacts of maximum temperatures and wind speed on the newly-confirmed COVID-19 cases increased with latitude. The relationship of maximum and minimum temperatures with the newly-confirmed COVID-19 cases were more susceptible to season, while relative humidity's relationship was more affected by latitude and geospatial scale. Our results suggested that relationship between meteorological factors and the COVID-19 transmission can be affected by season, geospatial scale and latitude. A rise in temperature would promote virus transmission in cold seasons. We suggested that the formulation and implementation of epidemic prevention and control should mainly refer to studies at the urban scale. The control measures should be developed according to local meteorological properties for individual city.


Subject(s)
COVID-19 , COVID-19/epidemiology , Humans , Meteorological Concepts , SARS-CoV-2 , Seasons , Temperature
3.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-322234

ABSTRACT

Background: The second wave of the coronavirus disease 2019 (COVID-19) epidemic in India was caused by the COVID-19 Delta variant. However, the epidemiological characteristics and transmission mechanism of the Delta variant remain unclear. To explore whether the epidemic trend will change after effective isolation measures were taken and what is the minimum number of individuals who need to be vaccinated to end the epidemic. Methods: We used actual data from March 5 to April 15, 2021, of daily updates confirmed cases and deaths, to estimate the parameters of the model and predict the severity of possible infection in the coming months. The classical Susceptible-Exposed-Infected-Removed (SEIR) model and extended models [Susceptible-Exposed-Infected-Removed-Quarantine (SERIQ) model and Susceptible-Exposed-Infected-Removed- medicine (SERIM) model] were developed to simulate the development of epidemic under the circumstances of without any measures, after effective isolation measures were taken and after being fully vaccinated. Results: The result demonstrated good accuracy of the classic model. The SEIRQ model showed that after isolation measures were taken, the infections will decrease by 99.61% compared to the actual number of infections by April 15. And the SEIRQ model demonstrated that if the vaccine efficative rate was 90%, when the vaccination rate was 100%, the number of existing cases would reach a peak of 529,723 cases on the 52nd day. Conclusion: Effective quarantine measures and COVID-19 vaccination from official are critical prevention measures to help end the COVID-19 pandemic.

4.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-311705

ABSTRACT

Novel coronavirus (COVID-19) can lead to multiple organ injuries such as acute respiratory distress syndrome (ARDS), acute renal injury (AKI) and so on. ACE2 is an important part of the renin-angiotensin system (RAS) and a key protein needed for COVID-19 to invade cells. First of all, we searched the HPA, GTEx and FANTOM5 Databases and found that the expression of ACE2 in kidney tissue was significantly higher than that in lung tissue. Then, by searching the Nephroseq Database, it is further verified that ACE2 is highly expressed in renal tissue and plays a protective role in renal tissue. However, current studies have found that the incidence of AKI caused by COVID-19 is much lower than that of ARDS. Because of this, we further searched the proteins interacting with ACE2 protein through the STING Database and analyzed the expression of tissue protein mRNA in the HPA Database. It was noted that AGTR2 mRNA was highly expressed in lung tissue, but low in kidney tissue, and hard tissue specificity in lung tissue. Through further research, it is found that AGTR2 plays a major role in the development of pulmonary fibrosis. Therefore, AGTR2 may be a key protein in COVID-19 pneumonia, and AGTR2 may be a potential new therapeutic target for the treatment of COVID-19 patients.

5.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-309031

ABSTRACT

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from Jan 17 to Feb 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920 (95% confidence interval : 0.902-0.938;AUROC=0.915, and its standard deviation of 0.028, as evaluated in 5-fold cross-validation). At a value of whether the predicted score >4.0, the model could detect NCP with a specificity of 98.3%;at a cut-off value of < -0.5, the model could rule out NCP with a sensitivity of 97.9%. The study demonstrated that the rapid screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.

6.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-324324

ABSTRACT

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the two groups were elaborately compared and both traditional machine learning algorithms and deep learning-based methods were used to build the prediction models. Results indicated the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI>25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, leukocyte counting, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, sensitivity, and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helps to optimize the treatment strategy, reduce mortality, and relieve the medical pressure.

7.
Medicine (Baltimore) ; 100(24): e26279, 2021 Jun 18.
Article in English | MEDLINE | ID: covidwho-1269620

ABSTRACT

ABSTRACT: Early determination of coronavirus disease 2019 (COVID-19) pneumonia from numerous suspected cases is critical for the early isolation and treatment of patients.The purpose of the study was to develop and validate a rapid screening model to predict early COVID-19 pneumonia from suspected cases using a random forest algorithm in China.A total of 914 initially suspected COVID-19 pneumonia in multiple centers were prospectively included. The computer-assisted embedding method was used to screen the variables. The random forest algorithm was adopted to build a rapid screening model based on the training set. The screening model was evaluated by the confusion matrix and receiver operating characteristic (ROC) analysis in the validation.The rapid screening model was set up based on 4 epidemiological features, 3 clinical manifestations, decreased white blood cell count and lymphocytes, and imaging changes on chest X-ray or computed tomography. The area under the ROC curve was 0.956, and the model had a sensitivity of 83.82% and a specificity of 89.57%. The confusion matrix revealed that the prospective screening model had an accuracy of 87.0% for predicting early COVID-19 pneumonia.Here, we developed and validated a rapid screening model that could predict early COVID-19 pneumonia with high sensitivity and specificity. The use of this model to screen for COVID-19 pneumonia have epidemiological and clinical significance.


Subject(s)
Algorithms , COVID-19 Testing/methods , COVID-19/diagnosis , Mass Screening/methods , SARS-CoV-2/isolation & purification , Adult , China , Female , Humans , Male , Middle Aged , Prospective Studies , ROC Curve , Sensitivity and Specificity
8.
Front Pharmacol ; 11: 1071, 2020.
Article in English | MEDLINE | ID: covidwho-726004

ABSTRACT

BACKGROUND: Currently, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread globally, causing an unprecedented pandemic. However, there is no specific antiviral therapy for coronavirus disease 2019 (COVID-19). We conducted a clinical trial to compare the effectiveness of three antiviral treatment regimens in patients with mild to moderate COVID-19. METHODS: This was a single-center, randomized, open-labeled, prospective clinical trial. Eligible patients with mild to moderate COVID-19 were randomized into three groups: ribavirin (RBV) plus interferon-α (IFN-α), lopinavir/ritonavir (LPV/r) plus IFN-α, and RBV plus LPV/r plus IFN-α at a 1:1:1 ratio. Each patient was invited to participate in a 28-d follow-up after initiation of an antiviral regimen. The outcomes include the difference in median interval to SARS-CoV-2 nucleic acid negativity, the proportion of patients with SARS-CoV-2 nucleic acid negativity at day 14, the mortality at day 28, the proportion of patients re-classified as severe cases, and adverse events during the study period. RESULTS: In total, we enrolled 101 patients in this study. Baseline clinical and laboratory characteristics of patients were comparable among the three groups. In the analysis of intention-to-treat data, the median interval from baseline to SARS-CoV-2 nucleic acid negativity was 12 d in the LPV/r+IFN-α-treated group, as compared with 13 and 15 d in the RBV+IFN-α-treated group and in the RBV+LPV/r+ IFN-α-treated group, respectively (p=0.23). The proportion of patients with SARS-CoV-2 nucleic acid negativity in the LPV/r+IFN-α-treated group (61.1%) was higher than the RBV+ IFN-α-treated group (51.5%) and the RBV+LPV/r+IFN-α-treated group (46.9%) at day 14; however, the difference between these groups was calculated to be statistically insignificant. The RBV+LPV/r+IFN-α-treated group developed a significantly higher incidence of gastrointestinal adverse events than the LPV/r+ IFN-α-treated group and the RBV+ IFN-α-treated group. CONCLUSIONS: Our results indicate that there are no significant differences among the three regimens in terms of antiviral effectiveness in patients with mild to moderate COVID-19. Furthermore, the combination of RBV and LPV/r is associated with a significant increase in gastrointestinal adverse events, suggesting that RBV and LPV/r should not be co-administered to COVID-19 patients simultaneously. CLINICAL TRIAL REGISTRATION: www.ClinicalTrials.gov, ID: ChiCTR2000029387. Registered on January 28, 2019.

9.
Sci Rep ; 11(1): 3863, 2021 02 16.
Article in English | MEDLINE | ID: covidwho-1087494

ABSTRACT

Novel coronavirus pneumonia (NCP) has been widely spread in China and several other countries. Early finding of this pneumonia from huge numbers of suspects gives clinicians a big challenge. The aim of the study was to develop a rapid screening model for early predicting NCP in a Zhejiang population, as well as its utility in other areas. A total of 880 participants who were initially suspected of NCP from January 17 to February 19 were included. Potential predictors were selected via stepwise logistic regression analysis. The model was established based on epidemiological features, clinical manifestations, white blood cell count, and pulmonary imaging changes, with the area under receiver operating characteristic (AUROC) curve of 0.920. At a cut-off value of 1.0, the model could determine NCP with a sensitivity of 85% and a specificity of 82.3%. We further developed a simplified model by combining the geographical regions and rounding the coefficients, with the AUROC of 0.909, as well as a model without epidemiological factors with the AUROC of 0.859. The study demonstrated that the screening model was a helpful and cost-effective tool for early predicting NCP and had great clinical significance given the high activity of NCP.


Subject(s)
COVID-19/diagnosis , COVID-19/epidemiology , Mass Screening , Models, Biological , Pneumonia/diagnosis , SARS-CoV-2/physiology , Adult , China/epidemiology , Female , Humans , Male , Middle Aged , ROC Curve
10.
Sci Rep ; 11(1): 2933, 2021 02 03.
Article in English | MEDLINE | ID: covidwho-1062775

ABSTRACT

COVID-19 is a newly emerging infectious disease, which is generally susceptible to human beings and has caused huge losses to people's health. Acute respiratory distress syndrome (ARDS) is one of the common clinical manifestations of severe COVID-19 and it is also responsible for the current shortage of ventilators worldwide. This study aims to analyze the clinical characteristics of COVID-19 ARDS patients and establish a diagnostic system based on artificial intelligence (AI) method to predict the probability of ARDS in COVID-19 patients. We collected clinical data of 659 COVID-19 patients from 11 regions in China. The clinical characteristics of the ARDS group and no-ARDS group of COVID-19 patients were elaborately compared and both traditional machine learning algorithms and deep learning-based method were used to build the prediction models. Results indicated that the median age of ARDS patients was 56.5 years old, which was significantly older than those with non-ARDS by 7.5 years. Male and patients with BMI > 25 were more likely to develop ARDS. The clinical features of ARDS patients included cough (80.3%), polypnea (59.2%), lung consolidation (53.9%), secondary bacterial infection (30.3%), and comorbidities such as hypertension (48.7%). Abnormal biochemical indicators such as lymphocyte count, CK, NLR, AST, LDH, and CRP were all strongly related to the aggravation of ARDS. Furthermore, through various AI methods for modeling and prediction effect evaluation based on the above risk factors, decision tree achieved the best AUC, accuracy, sensitivity and specificity in identifying the mild patients who were easy to develop ARDS, which undoubtedly helped to deliver proper care and optimize use of limited resources.


Subject(s)
COVID-19/pathology , Machine Learning , Respiratory Distress Syndrome/diagnosis , Adult , Area Under Curve , Body Mass Index , COVID-19/complications , COVID-19/virology , Comorbidity , Female , Humans , Lymphocyte Count , Male , Middle Aged , ROC Curve , Respiratory Distress Syndrome/etiology , Risk Factors , SARS-CoV-2/isolation & purification , Severity of Illness Index , Sex Factors
11.
Front Pharmacol ; 11: 1066, 2020.
Article in English | MEDLINE | ID: covidwho-698305

ABSTRACT

BACKGROUND: Coronavirus Disease 2019 (COVID-19) is an emerging and rapidly evolving disease, with no recommended effective anti-coronavirus drug treatment. Traditional Chinese Patent Medicines (CPMs) have, however, been widely used to treat COVID-19 in China, and a number of clinical practice results have shown them to have a significant role in its treatment. Consequently, numerous guidelines and expert consensus have recommended the use of CPMs to treat COVID-19. AIM OF THE STUDY: The objectives of this review are to provide up-to-date information on the pharmacology and clinical research on CPMs in the treatment of COVID-19, discuss the research findings, and to better guide clinical application and scientific research on CPMs in the treatment of COVID-19. METHODS: The frequencies of CPM recommendations by guidelines and expert consensus for treatment of COVID-19 in China were ranked. This report identifies the top 10 CPMs, which include Huoxiang Zhengqi capsule (HXZQC), Lianhua Qingwen capsule (LHQWC), Jinhua Qinggan granule (JHQGG), Shufeng Jiedu capsule (SFJDC), Tanreqing injection (TRQI), Xiyanping injection (XYPI), Xuebijing injection (XBJI), Shenfu injection (SFI), Shengmai injection (SMI), and Angong Niuhuang pill (AGNHP). Relevant studies from 2000 to 2020 on these top 10 CPMs, covering usage, dosage, mechanism, curative effect, and precautions, were collected from pharmacopoeia, reports, and theses via library and digital databases (including PubMed, CNKI, Google Scholar, Web of Science, and Elsevier). RESULTS: The properties of the top 10 CPMs included antiviral, antibacterial, anti-inflammatory, antipyretic and analgesic, anti-acute lung injury, anti-shock, immune regulation, and enhancement of pulmonary function. In addition, clinical research results and Chinese treatment data showed that the CPMs had good therapeutic efficacy in the treatment of COVID-19, and adverse reactions were minimal. CONCLUSIONS: Knowledge of the characteristics of the top 10 CPMs and precautions that should be taken may help clinicians to rationally improve therapeutic efficacy, and promote the role of Chinese Medicine in the control of the COVID-19 global epidemic.

12.
Preprint in English | medRxiv | ID: ppmedrxiv-20120881

ABSTRACT

With the dramatically fast spread of COVID-9, real-time reverse transcription polymerase chain reaction (RT-PCR) test has become the gold standard method for confirmation of COVID-19 infection. However, RT-PCR tests are complicated in operation andIt usually takes 5-6 hours or even longer to get the result. Additionally, due to the low virus loads in early COVID-19 patients, RT-PCR tests display false negative results in a number of cases. Analyzing complex medical datasets based on machine learning provides health care workers excellent opportunities for developing a simple and efficient COVID-19 diagnostic system. This paper aims at extracting risk factors from clinical data of early COVID-19 infected patients and utilizing four types of traditional machine learning approaches including logistic regression(LR), support vector machine(SVM), decision tree(DT), random forest(RF) and a deep learning-based method for diagnosis of early COVID-19. The results show that the LR predictive model presents a higher specificity rate of 0.95, an area under the receiver operating curve (AUC) of 0.971 and an improved sensitivity rate of 0.82, which makes it optimal for the screening of early COVID-19 infection. We also perform the verification for generality of the best model (LR predictive model) among Zhejiang population, and analyze the contribution of the factors to the predictive models. Our manuscript describes and highlights the ability of machine learning methods for improving the accuracy and timeliness of early COVID-19 infection diagnosis. The higher AUC of our LR-base predictive model makes it a more conducive method for assisting COVID-19 diagnosis. The optimal model has been encapsulated as a mobile application (APP) and implemented in some hospitals in Zhejiang Province.

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