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1.
Brain Sci ; 12(7)2022 Jul 18.
Article in English | MEDLINE | ID: covidwho-1938696

ABSTRACT

According to previous studies, mental status in 1-year COVID-19 survivors might range from 6-43%. Longer-term psychological consequences in recovered COVID-19 subjects are unknown, so we analyzed longer-term quality of life and mental status in recovered COVID-19 subjects at 2 years after infection. Among 144 recovered COVID-19 subjects in the Taizhou region, 73 and 45 completed face-to-face follow-ups at the first year and second year after infection, respectively, with a 61.7% follow-up rate. The questionnaire, which was administered at both follow-ups, included questions about quality of life, psychological health, and post-traumatic stress disorder (PTSD). The Mann-Whitney U test was used to the differences of each scale between the first and second year. Among the 45 people who completed both follow-up visits, the incidence of psychological problems was 4.4% (2/45) in the first year, and no new psychological abnormalities were observed in the second year. Quality of life improved, while the General Health Questionnaire (GHQ-12) and Impact of Event Scale-Revised (IES-R) scores did not improve over time. The incidence of mental disorders was lower than those in previous studies. Multidisciplinary management for COVID-19 in this study hospital may have reduced the frequency to a certain extent. However, among those with mental health problems, such problems may exist for a long time, and long-term attention should be given to the psychological status of recovered COVID-19 subjects.

2.
Int Immunopharmacol ; 110: 109019, 2022 Jul 06.
Article in English | MEDLINE | ID: covidwho-1914514

ABSTRACT

OBJECTIVES: COVID-19 is an immune-related disease caused by novel Coronavirus SARS-COV-2. Lung lesions persist in some recovered patients, making long-term follow-up monitoring of their health necessary. The mechanism of these abnormalities is still unclear. In this study, the immune status was observed to explore the immune mechanism of persistent lung CT abnormalities in one-year COVID-19 recovered subjects. METHODS: One-year follow-up of 73 recovered patients from COVID-19 confirmed in Taizhou City, Zhejiang Province, was conducted to collect laboratory indicators such as blood immune cells, cytokines, complement series, immunoglobulin, and lung imaging; According to the results of lung CT, 60 patients were divided into normal CT group (n = 40) and abnormal CT group (n = 20). We compared the dynamic changes of immune indexes at three timepoints namely onset (T1), discharge (T2), and 1-year follow-up (T3), and studied the relationship between immune indexes and pulmonary sequelae. RESULTS: Compared with the healthy control, there was no significant difference in immune-related indexes, and immune levels had recovered. Patients with elder age, high BMI, severe patients, and those with underlying diseases (hypertension or diabetes) had a higher CT abnormal rate after recovery. Longitudinal observation showed that immunoglobulin increased first and then decreased, immune cell TBNK decreased in the onset period and increased in the recovery period, cytokine level increased significantly in the onset period and decreased to the normal level in the recovery period, and complement series C1q, C3 and C4 increased at the onset and decreased during the one-year follow-up. Complement C3 remained at a high level in the CT abnormal group (CT normal group vs CT abnormal group; P = 0.036). Correlation analysis showed that C3 negatively correlated restrictive ventilation index (TLC-He (ratio) (r = -0.302, P = 0.017). The above results suggest that complement C3 is a negative factor correlating abnormal pulmonary function 1 year after the recovery. CONCLUSION: After one year recovering from COVID-19, the subjects were with stable immune indicators. High levels of complement C3 were associated with persistent lung abnormalities in COVID-19 recovered subjects.

3.
Adv Sci (Weinh) ; 9(18): e2105792, 2022 Jun.
Article in English | MEDLINE | ID: covidwho-1898522

ABSTRACT

Piezoelectric semiconductors have emerged as redox catalysts, and challenges include effective conversion of mechanical energy to piezoelectric polarization and achieving high catalytic activity. The catalytic activity can be enhanced by simultaneous irradiation of ultrasound and light, but the existing piezoelectric semiconductors have trouble absorbing visible light. A piezoelectric catalyst is designed and tested for the generation of hydrogen peroxide (H2 O2 ). It is based on Nb-doped tetragonal BaTiO3 (BaTiO3 :Nb) and is sensitized by carbon quantum dots (CDs). The photosensitizer injects electrons into the conduction band of the semiconductor, while the piezoelectric polarization directed electrons to the semiconductor surface, allowing for a high-rate generation of H2 O2 . The piezoelectric polarization field restricts the recombination of photoinduced electron-hole pairs. A production rate of 1360 µmol gcatalyst -1  h-1  of H2 O2  is achieved under visible light and ultrasound co-irradiation. Individual piezo- and photocatalysis yielded lower production rates. Furthermore, the CDs enhance the piezocatalytic activity of the BaTiO3 :Nb. It is noted that moderating the piezoelectricity of BaTiO3 :Nb via microstructure modulation influences the piezophotocatalytic activity. This work shows a new methodology for synthesizing H2 O2  by using visible light and mechanical energy.

4.
Huan Jing Ke Xue ; 43(6): 2840-2850, 2022 Jun 08.
Article in Chinese | MEDLINE | ID: covidwho-1876195

ABSTRACT

The COVID-19 lockdown was a typical occurrence of extreme emission reduction, which presented an opportunity to study the influence of control measures on particulate matter. Observations were conducted from January 16 to 31, 2020 using online observation instruments to investigate the characteristics of PM2.5 concentration, particle size distribution, chemical composition, source, and transport before (January 16-23, 2020) and during (January 24-31, 2020) the COVID-19 lockdown in Zhengzhou. The results showed that the atmospheric PM2.5 concentration decreased by 4.8% during the control period compared with that before the control in Zhengzhou. The particle size distribution characteristics indicated that there was a significant decrease in the mass concentration and number concentration of particles in the size range of 0.06 to 1.6 µm during the control period. The chemical composition characteristics of PM2.5 showed that secondary inorganic ions (sulfate, nitrate, and ammonium) were the dominant component of PM2.5, and the significant increase in PM2.5 was mainly owing to the decrease in NO3- concentration during the control period. The main sources of PM2.5 identified by the positive matrix factorization (PMF) model were secondary sources, combustion sources, vehicle sources, industrial sources, and dust sources. The emissions from vehicle sources, industrial sources, and dust sources decreased significantly during the control period. The results of analyses using the backward trajectory method and potential source contribution factor method indicated that the effects of transport from surrounding areas on PM2.5 concentration decreased during the control period. In summary, vehicle and industrial sources should be continuously controlled, and regional combined prevention and control should be strengthened in the future in Zhengzhou.


Subject(s)
Air Pollutants , COVID-19 , Air Pollutants/analysis , COVID-19/epidemiology , COVID-19/prevention & control , China , Communicable Disease Control , Dust/analysis , Environmental Monitoring/methods , Humans , Particle Size , Particulate Matter/analysis , Vehicle Emissions/analysis
5.
EuropePMC; 2022.
Preprint in English | EuropePMC | ID: ppcovidwho-334322

ABSTRACT

More than 450 million individuals have recovered from COVID-19, but little is known about the host responses to long COVID. We performed proteomic and metabolomic analyses of 991 blood and urine specimens from 144 COVID-19 patients with comprehensive clinical data and up to 763 days of follow up. Our data showed that the lungs and kidneys are the most vulnerable organs in long COVID patients. Pulmonary and renal long COVID of one-year revisit can be predicted by a machine learning model based on clinical and multi-omics data collected during the first month from the disease onset with an ACC of 87.5%. Serum protein SFTPB and ATR were associated with pulmonary long COVID and might be potential therapeutic targets. Notably, our data show that all the patients with persistent pulmonary ground glass opacity or patchy opacity lesions developed into pulmonary fibrosis at two-year revisit. Together, this study depicts the longitudinal clinical and molecular landscape of COVID-19 with up to two-year follow-up and presents a method to predict pulmonary and renal long COVID.

6.
Infect Drug Resist ; 15: 1857-1870, 2022.
Article in English | MEDLINE | ID: covidwho-1799027

ABSTRACT

Objective: Abnormal liver function and liver injury related to COVID-19 during hospitalization has received widespread attention. However, the long-term observation of patients' liver functions after discharge has not been investigated. This study intends to analyze the abnormal liver function in patients one year after they are discharged. Methods: Serum liver function tests were analyzed for the first time immediately after hospitalization (T1), before discharge (T2), a median of 14.0 (14.0, 15.0) days after discharge (T3) and 1 year (356.0 (347.8, 367.0) days) after discharge (T4). Patients with at least one serum parameter (ALT, AST, ALP, GGT and TB) exceeding the upper limit of reference range were defined as having abnormal liver function. Results: For the 118 COVID-19 patients with a median follow-up time of 376.0 (71.5, 385.3) days from onset to the end of the follow-up after discharge, the proportion with abnormal liver function in T1, T2, T3 and T4 were 32.2%, 45.8%, 54.8% and 28.8%, respectively. The proportion of patients with at least once abnormal liver function detected from T1 to T2, T1 to T3, T1 to T4 was 60.2%, 77.4% and 88.9%, respectively. From T1 to T4, the ALT, AST, GGT and BMI at admission were significantly higher in the patients with persistently abnormal liver function than in the patients with persistently normal liver function. Abnormal liver function was mainly manifested in the elevation of GGT and TB levels. Multivariate logistics regression analysis showed that age and gender-adjusted ALT (odds ratio [OR]=2.041, 95% confidence interval [CI]: 1.170-3.561, P=0.012) at admission was a risk factor for abnormal liver function in the T4 stage. Conclusion: Abnormal liver function in patients with COVID-19 can persist from admission to one year after discharge, and therefore, the long-term dynamic monitoring of liver function in patients with COVID-19 is necessary.

7.
Frontiers in medicine ; 9, 2022.
Article in English | EuropePMC | ID: covidwho-1787285
8.
J Genet Genomics ; 48(9): 792-802, 2021 09 20.
Article in English | MEDLINE | ID: covidwho-1720311

ABSTRACT

Gut microbial dysbiosis has been linked to many noncommunicable diseases. However, little is known about specific gut microbiota composition and its correlated metabolites associated with molecular signatures underlying host response to infection. Here, we describe the construction of a proteomic risk score based on 20 blood proteomic biomarkers, which have recently been identified as molecular signatures predicting the progression of the COVID-19. We demonstrate that in our cohort of 990 healthy individuals without infection, this proteomic risk score is positively associated with proinflammatory cytokines mainly among older, but not younger, individuals. We further discover that a core set of gut microbiota can accurately predict the above proteomic biomarkers among 301 individuals using a machine learning model and that these gut microbiota features are highly correlated with proinflammatory cytokines in another independent set of 366 individuals. Fecal metabolomics analysis suggests potential amino acid-related pathways linking gut microbiota to host metabolism and inflammation. Overall, our multi-omics analyses suggest that gut microbiota composition and function are closely related to inflammation and molecular signatures of host response to infection among healthy individuals. These results may provide novel insights into the cross-talk between gut microbiota and host immune system.


Subject(s)
Gastrointestinal Microbiome/physiology , Inflammation/metabolism , COVID-19/microbiology , Dysbiosis/microbiology , Gastrointestinal Microbiome/genetics , Humans , Inflammation/genetics , Proteomics/methods
9.
Sci Data ; 9(1): 67, 2022 03 02.
Article in English | MEDLINE | ID: covidwho-1721570

ABSTRACT

Most COVID-19 vaccines require temperature control for transportation and storage. Two types of vaccine have been developed by manufacturers (Pfizer and Moderna). Both vaccines are based on mRNA and lipid nanoparticles requiring low temperature storage. The Pfizer vaccine requires ultra-low temperature storage (-80 °C to -60 °C), while the Moderna vaccine requires -30 °C storage. However, the last stage of distribution is quite challenging, especially for rural or suburban areas, where local towns, pharmacy chains and hospitals may not have the infrastructure required to store the vaccine at the required temperature. In addition, there is limited data available to address ancillary challenges of the distribution framework for both transportation and storage stages, including safety concerns due to human exposure to large amounts of CO2 from dry-ice sublimation, issues due to the pressure increase caused by dry-ice sublimation, and the potential issue caused by non-uniform cryogenic temperatures. As such, there is a need for test dataset to assist the development of a quick, effective, secure, and safe solution to mitigate the challenges faced by vaccine distribution logistics.


Subject(s)
COVID-19 Vaccines , Refrigeration , Ice , Temperature
10.
EuropePMC;
Preprint in English | EuropePMC | ID: ppcovidwho-328813

ABSTRACT

Background: Classification of disease severity is crucial for the management of COVID-19. Several studies have shown that individual proteins can be used to classify the severity of COVID-19. Here, we aimed to investigate whether integrating the four types of protein context data, namely, protein complexes, stoichiometric ratios, pathways and network degrees will improve the severity classification of COVID-19. Methods: : A SWATH-based proteomic data set of 54 sera samples from 40 COVID-19 patients was employed as the training cohort. Results: : Machine learning prioritized two complexes, one stoichiometric ratio, five pathways, twelve proteins and five network degrees. A model based on these 25 features led to effective classification of severe cases with an AUC of 0.965, outperforming the models with proteins only. Complement component C9, transthyretin (TTR) and TTR-RBP complex, the stoichiometric ratio of SAA2/ YLPM1, and the network extent of SIRT7 and A2M were highlighted in this classifier. This classifier was further validated with a TMT-based proteomic data set from the same cohort and an independent SWATH-based proteomic data set from Germany, reaching an AUC of 0.900 and 0.908, respectively. Machine learning models integrating protein context information achieved higher AUCs than models with only one feature type. Conclusion: Our results show that the integration of protein context including protein complexes, stoichiometric ratios, pathways, network degrees, and proteins improves phenotype prediction.

11.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325244

ABSTRACT

Objective: Based on differences in populations and prevention and control measures, the spread of new coronary pneumonia in different countries and regions also differs. This study aimed to calculate the transmissibility of coronavirus disease 2019 (COVID-19), and to evaluate the effectiveness of countermeasures to control the disease in Jilin Province, China. Methods: : The data of reported COVID-19 cases were collected, including imported and local cases from Jilin Province as of March 14, 2019. A Susceptible–Exposed–Infectious–Asymptomatic–Recovered (SEIAR) model was developed to fit the data, and the effective reproduction number ( R eff ) was calculated at different stages in the province. Finally, the effectiveness of the countermeasures was assessed. Results: : A total of 97 COVID-19 infections were reported in Jilin Province, among which 45 were imported infections (including one asymptomatic infection) and 52 were local infections (including three asymptomatic infections). The model fit well with the reported data ( R 2 = 0.593, P < 0.001). The R eff of COVID-19 before and after February 1, 2020 was 1.64 and 0.05, respectively. Without the intervention taken on February 1, 2020, the predicted cases would reach a peak of 177,011 on October 22, 2020 (284 days from the first case). The projected number of cases until the end of the outbreak (on October 9, 2021) would be 17,129,367, with a total attack rate of 63.66%. Based on the comparison between the predicted incidence of the model and the actual incidence, the comprehensive intervention measures implemented in Jilin Province on February 1 reduced the incidence of cases by 99.99%. Therefore, according to the current measures and implementation efforts, Jilin Province can achieve good control of the virus’s spread. Conclusions: : COVID-19 has a moderate transmissibility in Jilin Province, China. The interventions implemented in the province had proved effective, increasing social distancing and a rapid response by the prevention and control system will help control the spread of the disease.

12.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-325190

ABSTRACT

Background: Serum Amyloid A (SAA) is an acute-phase reactant downstream of the pro-inflammatory cytokines released during virus infection. However, the role of this inflammatory marker in SARA-CoV-2 infection is yet to be elucidated. Here, we explored the potential use of SAA in serum as a biomarker for monitoring the clinical course of COVID-19 patients. Methods: The subjects included 95 COVID-19 patients discharged from the hospital with acute and / or convalescent phases data, among them 69 patients had paired data. Mann-Whitney U statistics and Wilcoxon signed-rank test were used to compare SAA level in the acute and convalescent phases. A subgroup of COVID-19 patients (n=9) participated in a follow-up examination with repeated blood collection reach five times during the hospitalization. The correlations of SAA levels with laboratory testing were then analyzed using the Spearman test. Results: The results of the data analysis show that the media SAA levels at acute phases were significantly higher (P < 0.05) compared to that at baseline. Furthermore, ascensional range of SAA were associated with the degree of COVID-19 severity. Media SAA levels at convalescent phases were significantly decreased (P < 0.05) compared to that at acute phases. The same phenomenon was seen in patients with and without comorbidities and with fever patients except without fever patients. Furthermore, The SAA concentration change in 9 COVID-19 patients of longitudinal follow-up along with the CT score and SARS-CoV-2 nucleic acid change. In the course of the disease, SAA changes were greater than CRP, lymphocytes, and neutrophils. Conclusions: The serum SAA levels were found to be significantly correlated with impending course of the COVID-19, and may serve as a useful biomarker to monitor the complicated clinical course of the disease.

13.
EuropePMC; 2021.
Preprint in English | EuropePMC | ID: ppcovidwho-325171

ABSTRACT

The diagnosis and disease course monitoring of COVID-19 are mainly based on RT-PCR analysis of RNAs extracted from pharyngeal or nasopharyngeal swabs with potential live virus, posing a high risk to medical practitioners. Here, we investigated the feasibility of applying serum proteomics to classify COVID-19 patients in the nucleic acid positive (NCP) and negative (NCN) stages. We analyzed the proteome of 320 inactivated serum samples from 144 COVID-19 patients, and 45 controls and shortlisted 42 regulated proteins in the severe group and 12 regulated proteins in the non-severe group. Together with several key clinical indexes including days after symptom onset, platelet counts and magnesium, we developed machine learning models to classify NCP and NCN with an AUC of 0.94 for the severe cases and 0.89 for the non-severe cases. This study suggests the feasibility of utilizing quantitative serum proteomics for NCP-NCN classification.Funding: This work was supported by grants from the National Key R&D Program of China(No. 2020YFE0202200), National Natural Science Foundation of China (81672086), Zhejiang Province Analysis Test Project (2018C37032), the National Natural Science Foundation of China (81972492, 21904107), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Zhejiang Medical and Health Science and Technology Plan (2021KY394), Hangzhou Agriculture andSociety Advancement Program (20190101A04), and Westlake Education Foundation, Tencent Foundation.Conflict of Interest: Tiannan Guo is shareholder of Westlake Omics Inc. W.G. and N.X. are employees of Westlake Omics Inc. The remaining authors declare no competing interests.Ethical Approval: This study has been approved by both the Ethical/Institutional Review Boards of Taizhou Hospital and Westlake University. Informed contents from patients were waived by the boards.

14.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-311429

ABSTRACT

Background: The COVID-19 pandemic is spreading globally with high disparity in the susceptibility of the disease severity. Identification of the key underlying factors for this disparity is highly warranted. Results: : Here we describe constructing a proteomic risk score (PRS) based on 20 blood proteomic biomarkers which related to the progression to severe COVID-19. Among COVID-19 patients, per 10% increment in the PRS was associated with a 57% higher risk of progressing to clinically severe phase (RR=1.57;95% CI, 1.35-1.82). We demonstrate that in our own cohort of 990 individuals without infection, this proteomic risk score is positively associated with proinflammatory cytokines mainly among older, but not younger, individuals. We further discovered that a core set of gut microbiota could accurately predict the blood proteomic biomarkers of COVID-19 using a machine learning model. The core OTU-predicted PRS had a significant correlation with actual PRS both cross-sectionally (n=132, p<0.001) and prospectively (n=169, p<0.05). Most of the core OTUs were highly correlated with proinflammatory cytokines. Fecal metabolomics analysis suggested potential amino acid-related pathways linking the above core gut microbiota to inflammation. Conclusions: : Our study suggests that gut microbiota may underlie the predisposition of healthy individuals to COVID-19-sensitive proteomic biomarkers.

15.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-310860

ABSTRACT

Background: Coronavirus disease 2019 (COVID-19) is a current global pandemic. However, impact of recent influenza A virus infection on the clinical course and outcomes of severe COVID-19 adult inpatients needs to be further explored. Methods: : In this retrospective cohort study, severe, laboratory confirmed COVID-19 adult patients from Wuhan Tongji Hospital were included. Data were obtained from electronic medical records and compared between patients with and without recent influenza A virus infection. Results: : 200 patients were included, 51.5% with recent influenza A virus infection. Recent influenza A virus infection group presented with longer persistence of cough and sputum from illness onset (35.0 vs. 27.0 days, P = 0.018) and (33.0 vs. 26.0 days, P = 0.015), respectively. Median time of progression to critical illness from illness onset was shorter (day 11.5 vs. day 16.0, P = 0.034). Time to clinical improvement and length of hospital stay were longer in recent infection group (23.0 vs. 19.0 days, P = 0.044) and (22.0 vs. 18.0 days, P = 0.030), respectively. Conclusions: : Patients with recent influenza A virus infection showed a delay in time to clinical improvement and increased length of hospital stay. There is a high clinical need to improve the detection of common respiratory pathogens to identify co-infection during the epidemic of COVID-19.

16.
International Communications in Heat & Mass Transfer ; 130:N.PAG-N.PAG, 2022.
Article in English | Academic Search Complete | ID: covidwho-1608889

ABSTRACT

A key issue with the distribution of vaccines to prevent COVID-19 is the temperature level required during transport, storage, and distribution. Typical refrigerated transport containers can provide a temperature-controlled environment down to −30 °C. However, the Pfizer vaccine must be carefully transported and stored under a lower temperature between −80 °C and − 60 °C. One way to provide the required temperature is to pack the vaccine vials into small packages containing dry ice. Dry ice sublimates from a solid to a gas, which limits the allowable transport duration. This can be mitigated by transporting in a − 30 °C refrigerated container. Moreover, because the dry ice will sublimate and thereby release CO 2 gas into the transport container, monitoring the CO 2 concentration within the refrigerated container is also essential. In the present work, a 3D computational fluid dynamics model was developed based on a commercially available refrigerated container and validated with experimental data. The airflow, temperature distribution, and CO 2 concentration within the container were obtained from the simulations. The modeling results can provide guidance on preparing experimental setups, thus saving time and lowering cost, and also provide insight into safety precautions needed to avoid hazardous conditions associated with the release of CO 2 during vaccine distribution. [ FROM AUTHOR] Copyright of International Communications in Heat & Mass Transfer 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.)

17.
Cell Rep ; 38(3): 110271, 2022 01 18.
Article in English | MEDLINE | ID: covidwho-1588135

ABSTRACT

The utility of the urinary proteome in infectious diseases remains unclear. Here, we analyzed the proteome and metabolome of urine and serum samples from patients with COVID-19 and healthy controls. Our data show that urinary proteins effectively classify COVID-19 by severity. We detect 197 cytokines and their receptors in urine, but only 124 in serum using TMT-based proteomics. The decrease in urinary ESCRT complex proteins correlates with active SARS-CoV-2 replication. The downregulation of urinary CXCL14 in severe COVID-19 cases positively correlates with blood lymphocyte counts. Integrative multiomics analysis suggests that innate immune activation and inflammation triggered renal injuries in patients with COVID-19. COVID-19-associated modulation of the urinary proteome offers unique insights into the pathogenesis of this disease. This study demonstrates the added value of including the urinary proteome in a suite of multiomics analytes in evaluating the immune pathobiology and clinical course of COVID-19 and, potentially, other infectious diseases.


Subject(s)
COVID-19/urine , Immunity , Metabolome , Proteome/analysis , SARS-CoV-2/immunology , Adolescent , Adult , Aged , Aged, 80 and over , COVID-19/blood , COVID-19/immunology , COVID-19/pathology , Case-Control Studies , Child , Child, Preschool , China , Cohort Studies , Female , Humans , Immunity/physiology , Male , Metabolome/immunology , Metabolomics , Middle Aged , Patient Acuity , Proteome/immunology , Proteome/metabolism , Proteomics , Urinalysis/methods , Young Adult
18.
EuropePMC; 2020.
Preprint in English | EuropePMC | ID: ppcovidwho-293976

ABSTRACT

Severe COVID-19 patients account for most of the mortality of this disease. Early detection and effective treatment of severe patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model correctly classified severe patients with an accuracy of 93.5%, and was further validated using ten independent patients, seven of which were correctly classified. We identified molecular changes in the sera of COVID-19 patients implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study shows that it is possible to predict progression to severe COVID-19 disease using serum protein and metabolite biomarkers. Our data also uncovered molecular pathophysiology of COVID-19 with potential for developing anti-viral therapies.<br><br>Funding: This work is supported by grants from Westlake Special Program for COVID19 (2020), and Tencent foundation (2020), National Natural Science Foundation of China (81972492, 21904107, 81672086), Zhejiang Provincial Natural Science Foundation for Distinguished Young Scholars (LR19C050001), Hangzhou Agriculture and Society Advancement Program (20190101A04). <br><br>Conflict of Interest: The research group of T.G. is partly supported by Tencent, Thermo Fisher Scientific, SCIEX and Pressure Biosciences Inc. C.Z., Z.K., Z.K. and S.Q. are employees of DIAN Diagnostics.

19.
Int J Refrig ; 133: 313-325, 2022 Jan.
Article in English | MEDLINE | ID: covidwho-1549838

ABSTRACT

Most COVID-19 vaccines require ambient temperature control for transportation and storage. Both Pfizer and Moderna vaccines are based on mRNA and lipid nanoparticles requiring low temperature storage. The Pfizer vaccine requires ultra-low temperature storage (between -80 °C and -60 °C), while the Moderna vaccine requires -30 °C storage. Pfizer has designed a reusable package for transportation and storage that can keep the vaccine at the target temperature for 10 days. However, the last stage of distribution is quite challenging, especially for rural or suburban areas, where local towns, pharmacy chains and hospitals may not have the infrastructure required to store the vaccine. Also, the need for a large amount of ultra-low temperature refrigeration equipment in a short time period creates tremendous pressure on the equipment suppliers. In addition, there is limited data available to address ancillary challenges of the distribution framework for both transportation and storage stages. As such, there is a need for a quick, effective, secure, and safe solution to mitigate the challenges faced by vaccine distribution logistics. The study proposes an effective, secure, and safe ultra-low temperature refrigeration solution to resolve the vaccine distribution last mile challenge. The approach is to utilize commercially available products, such as refrigeration container units, and retrofit them to meet the vaccine storage temperature requirement. Both experimental and simulation studies are conducted to evaluate the technical merits of this solution with the ability to control temperature at -30 °C or -70 °C as part of the last mile supply chain for vaccine candidates.

20.
J Proteome Res ; 21(1): 90-100, 2022 01 07.
Article in English | MEDLINE | ID: covidwho-1531980

ABSTRACT

RT-PCR is the primary method to diagnose COVID-19 and is also used to monitor the disease course. This approach, however, suffers from false negatives due to RNA instability and poses a high risk to medical practitioners. Here, we investigated the potential of using serum proteomics to predict viral nucleic acid positivity during COVID-19. We analyzed the proteome of 275 inactivated serum samples from 54 out of 144 COVID-19 patients and shortlisted 42 regulated proteins in the severe group and 12 in the non-severe group. Using these regulated proteins and several key clinical indexes, including days after symptoms onset, platelet counts, and magnesium, we developed two machine learning models to predict nucleic acid positivity, with an AUC of 0.94 in severe cases and 0.89 in non-severe cases, respectively. Our data suggest the potential of using a serum protein-based machine learning model to monitor COVID-19 progression, thus complementing swab RT-PCR tests. More efforts are required to promote this approach into clinical practice since mass spectrometry-based protein measurement is not currently widely accessible in clinic.


Subject(s)
COVID-19 , Humans , Proteomics , Reverse Transcriptase Polymerase Chain Reaction , SARS-CoV-2 , Specimen Handling
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