Additional file 3 Visualization of the imputation process. a, c Heatmap of SF and OV lab test data before imputation. b, d Heatmap of SF and OV lab test data after imputation. Black tiles refer to missing entries. Abbreviations: NK, Natural killer cells, Th, T-helper lymphocyte. Ts, T-suppressor lymphocyte. CRP, C reactive protein. PCT, procalcitonin. IFN-γ, interferon-γ. TNF-α, tumor necrosis factor α. IL-1β, interleukin 1β. IL-2R, interleukin 2 receptor. IL-4, interleukin 4. IL-6, interleukin 6. IL-8, interleukin 8. IL-10, interleukin 10. C-IGM, SARS-COV-2 specific antibody IgM. C-IGG, SARS-COV-2 specific antibody IgG. SF, Sino-French New City Campus of Tongji Hospital. OV, Optical Valley Campus of Tongji Hospital.
Abstract Background: Nucleic acid amplification is the main method used to detect infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the false-negative rate of nucleic acid tests cannot be ignored. Methods: Herein, we demonstrated genomic variations at the target sequences for the tests and the geographical distribution of the variations across countries by analyzing the whole-genome sequencing data of SARS-CoV-2 strains from the 2019 Novel Coronavirus Resource (2019nCoVR) database. Results: Among the 21 pairs of primer sequences in regions ORF1ab, S, E, and N, the total length of primer and probe target sequences was 938bp, with 131(13.97%) variant loci in 2415 (38.96%) isolates. Primer targets in the N region contained the most variations that were distributed among the most isolates, and the E region contained the least. Single nucleotide polymorphisms were the most frequent variation, with C to T transitions being detected in the most variant loci. G to A transitions and G to C transversions were the most common and had the highest isolate density. Genomic variations at the three mutation sites N: 28881, N: 28882, and N: 28883 were the most commonly detected, including in 608 SARS-CoV-2 strains from 33 countries, especially in the United Kingdom, Portugal, and Belgium. Conclusions: Our work comprehensively analyzed genomic variations on the target sequences of the nucleic acid amplification tests, offering evidence to optimize primer and probe target sequence selection, thereby improving the performance of the SARS-CoV-2 diagnostic test.
Dear Editor, The dramatic rise in confirmed coronavirus disease 2019 (COVID-19) cases poses a rigorous challenge to the global healthcare system. Previous studies have indicated that the cytokine storm plays a major role in the progression and death of patients diagnosed with COVID-19.1, 2 Therefore, glucocorticoids as an immunomodulatory therapy may be beneficial.3, 4 However, evidence concerning glucocorticoids for patients with COVID-19 is controversial and limited by small sample sizes or flawed study designs.5-9 A recent randomized controlled trial (RCT) showed that 6 mg of dexamethasone once per day for ten days reduced deaths by one-third in ventilated COVID-19 patients.10 However, the practical application of glucocorticoids in clinical treatment has not been clearly stated. Considering the gap between RCT participants and actual clinical users, we believe it is of great value to explore the application of glucocorticoids and their effectiveness on patient prognosis in the real world based on elaborated information from electronic medical records. Herein, we implemented a real-world, multicenter study with comprehensively detailed clinical data of 2044 patients with COVID-19 who had been discharged or died from January 27 to March 21, 2020 in the Sino-French New City campus and the Optical Valley Campus of Tongji Hospital in Wuhan, China. All patients were classified into the noncritical group or critical group based on their most severe condition during the entire course of disease (Supporting Information Methods). The flowchart is shown in Figure S1. We aimed to depict the administration of glucocorticoids in a large population. We employed multivariate logistic regression and Cox regression to explore whether glucocorticoids affect the prognosis of patients with COVID-19. The detailed demographic and clinical characteristics of patients with different severities are shown in Tables S1 and S2. The use of glucocorticoids was heterogeneous in patients in the two groups. Glucocorticoids were especially widely used in critical patients compared with noncritical patients (83.6% vs 24.9%, P < .001). The critical patients received glucocorticoid therapy earlier after illness onset (1.0, IQR [interquartile range]: 1.0-3.0 vs 2.0, IQR: 1.0-4.0, P = .002), and the treatment duration was shorter (5.0, IQR: 3.0-10.0 vs 8.0, IQR: 5.0-12.0, P < .001). The recommended days of glucocorticoid use and the timepoint at which to initiate use remain inconclusive. A further comparison between glucocorticoid users and nonusers is presented in Table 1. In the noncritical patients, the instability of vital signs in users was noticeable, including higher temperature (P < .001), faster respiratory rate (P < .001), lower mean arterial pressure (P = .009), and reduced SpO2 (P < .001). More antibiotics and intravenous immunoglobin were received by users than by nonusers (P < .001; P < .001, respectively). The mortality rates of the users and nonusers were similar (0.7% vs 0.2%, P = .168). However, the incidence of various complications of the users was significantly higher. The median hospital length of stay was significantly prolonged by nearly one week in users (24.0, IQR: 19.0-32.0 vs 18.0, IQR: 12.0-25.0, P < .001), as well as the time from illness onset to discharge or death (36.0, IQR: 29.0-43.0 vs 35.0, IQR: 27.0-43.0, P = .003). In the critical patients, the mortality rates were 84.8% for users and 88.6% for nonusers. Similar to noncritical patients, more users received intravenous immunoglobin treatment (P = .001). This finding suggested that immunomodulatory therapy may be an important method to treat COVID-19. The users among critical patients also experienced a remarkably prolonged hospital length of stay (12.0, IQR: 6.5-21.5 vs 5.5, IQR: 4.0-17.0, P = .001), especially for survivors (34.0, IQR: 28.5-39.5 vs 21.0, IQR: 20.5-25.5, P = .003). Some potential factors were found to influence the effectiveness of glucocorticoids in critical patients. The detailed results are displayed in Table 2. A total of 190 of the 224 glucocorticoid users in the critical patients died, while only 34 recovered. The nonsurvivors presented with older age (70.0, IQR: 64.0-78.0 vs 65.0, IQR: 54.0-73.0, P = .010), lower SpO2 (84.0, IQR: 74.0-91.0 vs 91.5, IQR: 84.5-94.0, P < .001), and higher SOFA score at admission (5.0, IQR: 4.0-7.0 vs 4.0, IQR: 3.0-4.0, P = .002). The lymphocyte and platelet counts were both significantly lower in nonsurvivors than in survivors (0.56, IQR: 0.39-0.80 vs 0.74, IQR: 0.56-1.06, P = .003; 159.0, IQR: 106.3-224.7 vs 223.5, IQR: 148.5-316.5, P = .002). The level of albumin among nonsurvivors was lower (30.8, IQR: 27.9-33.6 vs 33.2, IQR: 29.4-36.8, P = .020), and the levels of blood urea nitrogen, creatinine, prothrombin time, D-dimer, high-sensitivity cardiac troponin I and NT-proBNP were all higher in nonsurvivors (P < .050). This suggested that abnormal metabolism and coagulation function are related to adverse outcomes of glucocorticoid treatment. The initial levels of C reactive protein, ferritin, procalcitonin, interleukin-2R, interleukin-6, interleukin-8, interleukin-10, and tumor necrosis factor-α were remarkably higher in nonsurvivors (P < .050), which revealed that the release of excessive inflammatory factors may also influence the effectiveness of glucocorticoids. More research is needed to explore the underlying mechanism and the interaction between cytokines and glucocorticoids. In summary, highly heterogeneous individuals vary in their response to glucocorticoid treatment. Even for patients with the same disease severity, physicians should fully grasp the auxiliary examination results of COVID-19 patients before the administration of glucocorticoids. We found no association between glucocorticoids and death, the incidence of complications, the incidence of more than one complication, or the use of invasive mechanical ventilation/extracorporeal membrane oxygenation (ECMO) in the multivariate logistic regression analysis (Table S3). In the multivariable Cox regression model, glucocorticoid therapy failed to affect the survival time of patients in the noncritical group (P = .558,Table S4) or critical group (P = .113, Table S4; log-rank P = .15, Figure S2). Incredibly, glucocorticoid treatment prolonged the hospital length of stay of both noncritical patients (HR [hazard ratio]= 0.563, 95% CI [confidence interval]: 0.504-0.628, P < .001, after adjusting for age) and critical patients (HR = 0.080, 95% CI: 0.024-0.262, P < .001). Kaplan-Meier curves with log-rank tests drew consistent conclusions (log-rank P < .0001 for noncritical patients; log-rank P < .0001 for critical patients) (Figure 1A,B). Furthermore, delayed viral shedding time in noncritical patients (HR = 0.892, 95% CI: 0.798-0.997, P = .043) was observed after adjusting for age and time from illness onset to admission (Table S4). However, the Kaplan-Meier curve showed no correlation between glucocorticoids and viral shedding time in either noncritical (log-rank P = .49, Figure 1C) or critical patients (log-rank P = .57, Figure 1D). Our research has several limitations. First, retrospective research has inherent limitations. However, compared with RCT, this study covered a wider population, including all confirmed patients. Second, all patients were located in Wuhan, China. Therefore, national or worldwide experience in treating COVID-19 with glucocorticoids is needed to support our findings. In conclusion, we conducted a real-world study of the early administration of glucocorticoids in patients with COVID-19 in Wuhan, China. Glucocorticoids were used in noncritically ill patients with unstable vital signs and the majority of critically ill patients. The use of glucocorticoids was related to prolonged hospitalization time of patients with different disease severities and prolonged viral shedding time of patients in the noncritical group. Glucocorticoids should be used with caution, especially in noncritical patients with older age and delayed admission. Physicians should prudently prescribe glucocorticoids according to the clinical guidelines and the actual situation of individual patients. We sincerely thank all individuals and communities involved in fighting against COVID-19. The study was supported by the National Science and Technology Major Sub-Project (grant number: 2018ZX10301402-002), the Technical Innovation Special Project of Hubei Province (grant number: 2018ACA138), the National Key Basic Research Program of China (grant number: 2015CB553903), the National Natural Science Foundation of China (grant numbers: 81572570, 81974405, 31822030, 31771458, 81772787, and 81873452), the Fundamental Research Funds for the Central Universities (grant number: 2019kfyXMBZ024), and the Wuhan Municipal Health Commission (grant number: WX18Q16). This study was approved by the Research Ethics Commission of Tongji Hospital of Huazhong University of Science and Technology (TJ-IRB20200406) with written informed consent waived. The trial has been registered in Chinese Clinical Trial Registry (ChiCTR2000032161). Chunrui Li and Qinglei Gao had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. Xiaofei Jiao, Ya Wang, Dan Liu, and Shaoqing Zeng equally contributed to this work. Dan Liu and Qinglei Gao designed the study. Jianhua Chi, Ruyuan Li, Yang Yu, Shaoqing Zeng, Ruidi Yu, Siyuan Wang, Yuan Yuan, Yue Gao, and Sen Xu acquired, analyzed, and interpreted the data. Xiaofei Jiao, Ya Wang, Dan Liu, and Shaoqing Zeng analyzed and interpreted data, and wrote the paper. Chunrui Li and Qinglei Gao provided critical revision of the manuscript for important intellectual content and administrative, technical, or material support. Chunrui Li and Qinglei Gao supervised this work. All authors vouch for the respective data and analysis, approved the final version, and agreed to publish the manuscript. The authors declare that there is no conflict of interest that could be perceived as prejudicing the impartiality of the research reported. Data supporting the findings of this study are available from the corresponding author upon reasonable request. The data containing information that could compromise research participant privacy, and so are not publicly available. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Although endometrioid carcinoma (EC) and endometrioid ovarian carcinoma (EnOC) display similar pathological features, their molecular characteristics remain to be determined. Somatic mutation data from 2777 EC, 423 EnOC, and 57 endometriosis patients from the Catalogue of Somatic Mutations in Cancer (COSMIC) dataset were analyzed and showed similar profiles with different mutation frequencies among them. By using 275 overlapping mutated genes, EC was clustered into two groups with different disease outcomes and different clinical characteristics. Although BRCA-associated mutation characteristics were identified in both EC and EnOC, the mutation frequencies of BRCA1 (P=0.0146), BRCA2 (P=0.0321), ATR (P=3.25E-11), RAD51 (P=3.95E-08), RAD1 (P=0.0003), TP53 (P=6.11E-33), and BRIP1 (P=2.90E-09) were higher in EnOC. Further analysis showed that EnOC cell lines with BRCA-associated mutation characteristics were more sensitive to poly ADP-ribose polymerase (PARP) inhibitors than EC cell lines, including olaparib, talazoparib, rucaparib, and veliparib. Moreover, based on BRCA-associated mutational and transcriptomic profiles, EC with BRCA-associated mutational burdens shows lower levels of immune cell infiltration, higher expression of immunosuppressive checkpoint molecules and worse prognosis than EC without BRCA mutation. Our study comprehensively analyzed the genome mutation features of EC and EnOC and provide insights into the molecular characteristics of EC and EnOC.
Dear editor, The coronavirus disease 2019 (COVID-19) is characterized by heterogeneous clinical features and multiple organ damage. Many patients with mild symptoms can suddenly develop into critical illness and progress to a refractory state that has significantly increased mortality, indicating the necessity to promptly identify patients at high risk of physiologic deterioration before the occurrence of critical COVID-19. With both innate and adaptive immune compartments contribution, cytokine storm in covid-19 is widely concerned. Hyperinflammatory response induced by immune dysfunction is reported to underpin critical COVID-19.1 Uncontrolled release of cytokines results in tissue damage and further leads to multiple organ failure, which is the major cause of death in patients with COVID-19.2 As expected, the differences of multiple cytokines and immune features between critical ill and noncritical ill patients were observed in clinical practice. Besides, early seroconversion and high antibody titer were linked with less severe clinical symptoms. Using inflammatory/immune factors to predict the risk of developing critical COVID-19 under the assistance of machine learning (ML) is promising to aid management of the disease, but rarely reported. Electronic health records (EHRs) harbor valuable resources generated from routine medical activities and have been widely used. However, medical data are often complex, multidimensional, nonlinear, heterogeneous, and required to be analyzed using more effective statistical methods than traditional logistic regression. ML is a subfield of artificial intelligence that encapsulates statistical and mathematical algorithms, which enables facts interrogation and complex decision making through a given set of data. The combination of EHRs and ML shows potential applications in predicting the risk of atherosclerotic cardiovascular disease and gestational diabetes. In this multicenter study, we developed an online model with four inflammatory factors (C reactive protein [CRP], tumor necrosis factor α [TNF-α], interleukin 2 receptor [IL-2R], and interleukin 6 [IL-6]) that enabled accurate identification of COVID-19 patients prone to critical illness approximately 20 days in advance. The model was validated in an internal validation cohort (SFV cohort) and an external validation cohort (OV cohort). Study design is presented in Figure 2A. The detailed demographic and characteristics of patients are shown in Table S1. A total of 15 raw inflammatory/immune features were collected from COVID-19 patients at admission. After feature filtering (Figure S1) and data imputation (Figure S2),3 eight features were fitted into Least Absolute Shrinkage and Selection Operator (LASSO)4 logistic regression for feature selection (Figure 1A). As illustrated in Figure 1B, we considered features whose coefficients equaled to zero as redundant and less predictive features. As a result, LASSO analysis identified four features (CRP, TNF-α, IL-2R, and IL-6) for the development of critical illness classifier. We conducted the Spearman correlation analysis between the four features and critical illness status. Figure S3A indicates that the positive correlation at varying degrees existed across five features. The top weighted features, IL-6 (R = 0.49), CRP (R = 0.47), IL-2R (R = 0.43), and TNF-α (R = 0.37), were consisted with previously reported risk factors that were highly correlated with poor outcome of COVID-19. Standard box plots presented significant differences (P < 2.2e-16) of the four features between critically ill and noncritically ill COVID-19 patients (Figure S3B). The median (IQR) expression of TNF-α (17.0, 10.5-29.3, pg/mL), CRP (182.7, 103.2-258.6, mg/L), IL-2R (1447.0, 993.0-2327.5, U/mL), and IL-6 (169.4, 58.1-640.9, pg/mL) was significantly higher in critically ill patients compared with TNF-α (8.1, 6.1-10.5, pg/mL), CRP (16.1, 2.5-57.8, mg/L), IL-2R (520.0, 299.0-770.5, U/mL), and IL-6 (5.0, 2.1-18.4, pg/mL) in noncritically ill patients. During model development stage, five models (support vector machine [SVM], logistic regression [LR], gradient boosted decision tree [GBDT], k-nearest neighbor [KNN], and neural network [NN]) were trained for risk prediction. In general, all five models showed varying but promising critical illness risk prediction performance in the internal and external validation cohorts. CIRPMC (critical illness risk prediction model for COVID-19) derived from SVM achieved the highest predictive performance. Relative feature importance rank of SVM is shown in Figure S4. As binary classifier, CIRPMC outputted critical illness risk probability (P) ranged from 0 to 1 for each patient, and stratified patients with P < .5 as low risk, otherwise high risk. For SFV cohort, CIRPMC achieved an AUC (area under the receiver operating characteristics curve) of 0.946 (95% CI 0.923-0.969) to identify patients having high risk of developing critical illness with an accuracy of 92.7% (95% CI 90.4%-94.6%). For OV cohort, CIRPMC demonstrated an AUC of 0.969 (95% CI 0.945-0.992) and an accuracy of 96.6% (95% CI 95.1-97.7%) (Figure 1C, D). The calibration curve of CIRPMC in two validation cohorts is depicted in Figure S5. Intriguingly, CIRPMC also displayed the minimal Brier score of 0.057 for SFV cohort and 0.028 for OV cohort. All other metrics and the performance of other models are listed in Table 1. With critical illness as status and time from admission to critical illness or discharge as the endpoint, Kaplan-Meier analysis further confirmed the risk stratification ability of the model. CIRPMC robustly stratified high-risk patients and low-risk patients with P < .0001 in both internal and external validation cohorts. The univariate Cox analysis also demonstrated the positive correlation between CIRPMC predicted critical illness subgroup and the ground truth critical illness survival for internal (HR: 22.52, 95% CI 14.69-34.53) and external (HR:54.30, 95% CI 32.21-91.52) validation cohorts, respectively (Figure 1E, F). Additionally, we opened up an online calculator based on CIRPMC to input the values of features needed for risk prediction of COVID-19 patients (https://cirpmc.deepomics.org/). After the clinicians fill in the online form with corresponding features, CIRPMC returns a personalized probability and risk group of critical illness. Illustration of an example of the online prediction system is presented in Figure 2B. In this study, CIRPMC was developed to identify COVID-19 patients with high risk of developing critical illness and achieved high predictive performance with an AUC range from 0.946 to 0.969 across the internal and external validation cohorts. The accurate and rapid risk stratification is critical to ensure health systems agile and hopefully will optimize patient outcomes where "time is life." Working flow of the study. A, Study design. B, Illustration of the online prediction model-CIRPMC. Abbreviations: CIRPMC, critical illness risk prediction model for COVID-19; CRP, C reactive protein; IL-2R, interleukin 2 receptor; IL-6, interleukin 6; OV cohort, external validation cohort of Optical Valley Campus of Tongji Hospital; SFT cohort, training cohort of Sino-French New City Campus of Tongji Hospital; SFV cohort, internal validation cohort of Sino-French New City Campus of Tongji Hospital; TNF-α, tumor necrosis factor α Certain interpretability is a strength of CIRPMC. In accord with previous reports, we found that the expression levels of four contributive inflammatory cytokines (CRP, TNF-α, IL-2R, and IL-6) were significantly higher in critically ill patients than those in noncritically ill patients.5 Another strengths of CIRPMC are its stability and generalizability. Four features used for prediction are readily accessible and frequently monitored in routine clinical practice. Besides, they are relatively objective, solid, and less susceptible to human memory bias, suggesting that CIRPMC is not susceptible to human interference and has strong generalization to be extended to other medical institutions. During the pandemic, there has emerged many studies on prognosis prediction of COVID-19.6-8 However, the sample size of most studies is small, thus harboring risks of overfitting.6, 7 Moreover, most studies lack independent external validation or the number of patients within external validation is limited,9, 10 which can impair the reproducibility and credibility of models. Our study is with larger sample size, independent external validation, detailed patient description, and relatively long observation time (18-20 days). However, the study has some limitations. First, patients included are primarily locals in Wuhan. Data from multiple provinces or countries could further improve the applicability and robustness of models. Besides, the prognostic implication of CIRPMC has not been evaluated in prospective cohorts due to the retrospective nature of this study. In conclusion, this retrospective, multicenter study showed CIRPMC with readily available features holds great potential in accurately and timely (approximately 20 days in advance) identifying COVID-19 patients prone to develop into critical illness. The model held strong stability, generalizability, universality, and wide prediction horizon to be easily extended to areas with limited medical resources. The proposed model potentially assists clinicians to locate the patients with a higher priority to be early intervened and intensively monitored, and eliminate delays to maximize the number of survivors during the rapidly developing global emergency. Equipped with high predictive performance, the online calculator CIRPMC deserves to be proceeded with. However, these findings warrant further validations in prospective clinical trials. We are grateful to all health-care workers and people nationwide and worldwide, who are involved in the fighting against COVID-19. The opinions expressed reflect the collective views of the coauthors. The study was supported by the National Science and Technology Major Sub-Project (2018ZX10301402-002), the Technical Innovation Special Project of Hubei Province (2018ACA138), the National Natural Science Foundation of China (81572570, 81974405, 81772787, 81873452, 81702572, 81702574, and 82072889), Hubei Natural Science Foundation (2019CFB453), and the Fundamental Research Funds for the Central Universities (2017JYCXJJ025, 2018JYCXJJ001, and 2019kfyXMBZ024). The authors have no conflicts of interest to declare. This study was approved by the Research Ethics Commission of Tongji Hospital of Huazhong University of Science and Technology (TJ-IRB20200406) in view of the retrospective nature of the study and all the procedures performed were part of the routine care. The trial has been registered in the Chinese Clinical Trial Registry (ChiCTR2000032161). The informed consents were waived by the Ethics Commission of Tongji Hospital of Huazhong University of Science and Technology. QG had full access to all data in the study, took responsibility for the integrity of data, and the accuracy of the data analysis. YG designed the study. LC did the analysis. YG, LC, and HL interpreted the data and wrote the paper. SZ, XF, YW, TJ, YY, JC, XJ, DL, XF, SW, RY, YY, SX, XX, PC, QM, XJ, and YW provided patients' samples and clinical data, entered the data into database, and double-checked the data. QG, SL, CL, and DM advised on the conception and design of the study. All authors vouched for the respective data and analysis, approved the final version, and agreed to publish the manuscript. The data contain information that could compromise research participant privacy, and so are not publicly available. Data supporting the findings of this study are available from the corresponding author upon reasonable request. Supplement Methods. Figure S1: Visualization of the denosing and filtering process. Figure S2: Visualization of the imputation process. Figure S3: Statistical analysis of four features selected by Lasso. Figure S4: Relative feature importance of SVM model. Figure S5: Calibration curves of SVM model in cohorts. Table S1: Baseline characteristics of individuals by cohorts. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.
Patients with malignancy were reportedly more susceptible and vulnerable to Coronavirus Disease 2019 (COVID-19), and witnessed a greater mortality risk in COVID-19 infection than noncancerous patients. But the role of immune dysregulation of malignant patients on poor prognosis of COVID-19 has remained insufficiently investigated. Here we conducted a retrospective cohort study that included 2,052 patients hospitalized with COVID-19 (Cancer, n = 93; Non-cancer, n = 1,959), and compared the immunological characteristics of both cohorts. We used stratification analysis, multivariate regressions, and propensity-score matching to evaluate the effect of immunological indices. In result, COVID-19 patients with cancer had ongoing and significantly elevated inflammatory factors and cytokines (high-sensitivity C-reactive protein, procalcitonin, interleukin (IL)-2 receptor, IL-6, IL-8), as well as decreased immune cells (CD8 + T cells, CD4 + T cells, B cells, NK cells, Th and Ts cells) than those without cancer. The mortality rate was significantly higher in cancer cohort (24.7%) than non-cancer cohort (10.8%). By stratification analysis, COVID-19 patients with immune dysregulation had poorer prognosis than those with the relatively normal immune system both in cancer and non-cancer cohort. By logistic regression, Cox regression, and propensity-score matching, we found that prior to adjustment for immunological indices, cancer history was associated with an increased mortality risk of COVID-19 (p < .05); after adjustment for immunological indices, cancer history was no longer an independent risk factor for poor prognosis of COVID-19 (p > .30). In conclusion, COVID-19 patients with cancer had more severely dysregulated immune responses than noncancerous patients, which might account for their poorer prognosis.
Introduction Nucleic acid amplification is the main method used to detect infections of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). However, the false-negative rate of nucleic acid tests cannot be ignored. Material and methods Herein, we demonstrated genomic variations at the target sequences for the tests and the geographical distribution of the variations across countries by analyzing the whole-genome sequencing data of SARS-CoV-2 strains from the 2019 Novel Coronavirus Resource (2019nCoVR) database. Results Among the 21 pairs of primer sequences in regions ORF1ab, S, E, and N, the total length of primer and probe target sequences was 938 bp, with 131 (13.97%) variant loci in 2415 (38.96%) isolates. Primer targets in the N region contained the most variations that were distributed among the most isolates, and the E region contained the fewest. Single nucleotide polymorphisms were the most frequent variation, with C to T transitions being detected in the most variant loci. G to A transitions and G to C transversions were the most common and had the highest isolate density. Genomic variations at the three mutation sites N: 28881, N: 28882, and N: 28883 were the most commonly detected, including in 608 SARS-CoV-2 strains from 33 countries, especially in the United Kingdom, Portugal, and Belgium. Conclusions Our work comprehensively analyzed genomic variations at the target sequences of the nucleic acid amplification tests, offering evidence to optimize primer and probe target sequence selection, thereby improving the performance of the SARS-CoV-2 diagnostic test.
Integration of human papillomavirus (HPV) DNA into the human genome is a reputed key driver of cervical cancer. However, the effects of HPV integration on chromatin structural organization and gene expression are largely unknown. We studied a cohort of 61 samples and identified an integration hot spot in the CCDC106 gene on chromosome 19. We then selected fresh cancer tissue that contained the unique integration loci at CCDC106 with no HPV episomal DNA and performed whole-genome, RNA, chromatin immunoprecipitation and high-throughput chromosome conformation capture (Hi-C) sequencing to identify the mechanisms of HPV integration in cervical carcinogenesis. Molecular analyses indicated that chromosome 19 exhibited significant genomic variation and differential expression densities, with correlation found between three-dimensional (3D) structural change and gene expression. Importantly, HPV integration divided one topologically associated domain (TAD) into two smaller TADs and hijacked an enhancer from PEG3 to CCDC106, with a decrease in PEG3 expression and an increase in CCDC106 expression. This expression dysregulation was further confirmed using 10 samples from our cohort, which exhibited the same HPV-CCDC106 integration. In summary, we found that HPV-CCDC106 integration altered local chromosome architecture and hijacked an enhancer via 3D genome structure remodeling. Thus, this study provides insight into the 3D structural mechanism underlying HPV integration in cervical carcinogenesis.
The ferocious global assault of COVID-19 continues. Critically ill patients witnessed significantly higher mortality than severe and moderate ones. Herein, we aim to comprehensively delineate clinical features of COVID-19 and explore risk factors of developing critical disease.This is a Mini-national multicenter, retrospective, cohort study involving 2,387 consecutive COVID-19 inpatients that underwent discharge or death between January 27 and March 21, 2020. After quality control, 2,044 COVID-19 inpatients were enrolled. Electronic medical records were collected to identify the risk factors of developing critical COVID-19.The severity of COVID-19 climbed up straightly with age. Critical group was characterized by higher proportion of dyspnea, systemic organ damage, and long-lasting inflammatory storm. All-cause mortality of critical group was 85•45%, by contrast with 0•58% for severe group and 0•18% for moderate group. Logistic regression revealed that sex was an effect modifier for hypertension and coronary heart disease (CHD), where hypertension and CHD were risk factors solely in males. Multivariable regression showed increasing odds of critical illness associated with hypertension, CHD, tumor, and age ≥ 60 years for male, and chronic obstructive pulmonary disease (COPD), chronic kidney disease (CKD), tumor, and age ≥ 60 years for female.We provide comprehensive front-line information about different severity of COVID-19 and insights into different risk factors associated with critical COVID-19 between sexes. These results highlight the significance of dividing risk factors between sexes in clinical and epidemiologic works of COVID-19, and perhaps other coronavirus appearing in future.10.13039/100000001 National Science Foundation of China.
Abstract Background Paclitaxel dose-dense regimen has been controversial in clinical trials in recent years. This systematic review and meta-analysis tried to evaluate the efficacy and safety of paclitaxel dose-dense chemotherapy in primary epithelial ovarian cancer. Methods An electronic search following PRISMA guidelines was conducted (Prospero registration number: CRD42020187622), and then a systematic review and meta-analysis of included literature were initiated to determine which regimen was better. Results Four randomized controlled trials were included in the qualitative evaluation, and 3699 ovarian cancer patients were included in the meta-analysis. The meta-analysis revealed that the dose-dense regimen could prolong PFS (HR0.88, 95%CI 0.81–0.96; p = 0.002) and OS (HR0.90, 95%CI 0.81–1.02; p = 0.09), but it also increased the overall toxicity (OR = 1.102, 95%CI 0.864–1.405; p = 0.433), especially toxicity of anemia (OR = 1.924, 95%CI 1.548–2.391; p < 0.001), neutropenia (OR = 2.372, 95%CI 1.674–3.361; p < 0.001). Subgroup analysis indicated that the dose-dense regimen could significantly prolong not only PFS (HR0.76, 95%CI 0.63–0.92; p = 0.005 VS HR0.91, 95%CI 0.83–1.00; p = 0.046) but also OS (HR0.75, 95%CI 0.557–0.98; p = 0.037 VS HR0.94, 95%CI 0.83–1.07; p = 0.371) in Asian, and overall toxicity was significantly increased in Asians (OR = 1.28, 95%CI: 0.877–1.858, p = 0.202) compared to non-Asians (OR = 1.02, 95%CI 0.737–1.396, p = 0.929). Conclusion Paclitaxel dose-dense regimen could prolong PFS and OS, but it also increased the overall toxicity. Therapeutic benefits and toxicity of dose-dense are more obvious in Asians compared to non-Asians, which need to be further confirmed in clinical trials.
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