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 Ovarian cancer (OC) patients benefited little from systematic pelvic/para-aortic lymph node dissection during surgery, which may attribute to the difficulties in identifying the patients with pelvic/para-aortic lymph node metastases (LNM) preoperatively. Unfortunately, risk factors predicting the pelvic/para-aortic LNM in OC patients are lacking now. The purpose of this study was to investigate preoperative risk factors of predicting OC patients at high risk of pelvic/para-aortic LNM and preventing OC patients at low risk of LNM from receiving unnecessary lymphadenectomy. Methods Patients diagnosed with OC between January 2012 and May 2020 from Tongji Hospital, Chongqing Cancer Hospital, and Tumor Hospital of Henan, were retrospectively reviewed. Demographics, pathology, and preoperative laboratory features were extracted from Electronic-Medical Records. The correlation between factors and LNM was assessed by chi-square test and multivariate logistic regression analysis. Results A total of 827 patients were included in this study. Univariate analysis indicated 23 preoperative features were significantly associated with LNM. Multivariate analysis showed that BMI ≥ 23.23 kg/m 2 (odds ratio [OR], 2.082; 95% confidence interval [CI], 1.448–2.995), ascites (OR, 3.022, 95% CI, 2.058–4.438), CA125 ≥ 432.15 U/ml (OR, 4.665, 95% CI, 3.158–6.891), neutrophil count ≥ 2.965*10 9 /L (OR, 2.882, 95% CI, 1.606–5.172), lymphocyte count < 1.30*10 9 /L (OR, 1.554, 95% CI, 1.086–2.223), and monocyte count ≥ 0.415*10 9 /L (OR, 1.506, 95% CI, 1.047–2.166) were independent risk factors in predicting LNM. The area under the curve (AUC) of predicting LNM by combining these factors was 0.836 (95% CI 0.808–0.864). The predicting performance of this model was also promising in OC patients with early-stage (stage I-II) (AUC, 0.809, 95% CI, 0.619–1.000) and advanced-stage (stage III-IV) (AUC, 0.764, 95% CI, 0.723–0.805). Furthermore, patients with 0–3 risk factors had significantly lower LNM rates than those of patients with 4–6 risk factors (15.40% vs 58.92%, p < 0.001). Conclusions Preoperative BMI, ascites, CA125 level, neutrophil count, lymphocyte count, and monocyte count can predict the risk of LNM and facilitate decision-making of systematic lymphadenectomy in OC patients, which could avoid unnecessary lymphadenectomy.
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.
ABSTRACT Introduction Macrophages are essential in maintaining homeostasis, combating infections, and influencing the process of various diseases, including cancer. Macrophages originate from diverse lineages: Notably, tissue‐resident macrophages (TRMs) differ from hematopoietic stem cells and circulating monocyte‐derived macrophages based on genetics, development, and function. Therefore, understanding the recruited and TRM populations is crucial for investigating disease processes. Methods By searching literature databses, we summarized recent relevant studies. Research has shown that tumor‐associated macrophages (TAMs) of distinct origins accumulate in tumor microenvironment (TME), with TRM‐derived TAMs closely resembling gene signatures of normal TRMs. Results Recent studies have revealed that TRMs play a crucial role in cancer progression. However, organ‐specific effects complicate TRM investigations. Nonetheless, the precise involvement of TRMs in tumors is unclear. This review explores the multifaceted roles of TRMs in cancer, presenting insights into their origins, proliferation, the latest research methodologies, their impact across various tumor sites, their potential and strategies as therapeutic targets, interactions with other cells within the TME, and the internal heterogeneity of TRMs. Conclusions We believe that a comprehensive understanding of the multifaceted roles of TRMs will pave the way for targeted TRM therapies in the treatment of cancer.
Abstract Objective To explore the impact of the primary treatment sequence (primary debulking surgery, PDS, versus neoadjuvant chemotherapy and interval debulking surgery, NACT‐IDS) on post‐relapse survival (PRS) and recurrence characteristics of recurrent epithelial ovarian cancer (REOC). Design Real‐world retrospective study. Setting Tertiary hospitals in China. Population A total of 853 patients with REOC at International Federation of Gynaecology and Obstetrics stages IIIC–IV from September 2007 to June 2020. Overall, 377 and 476 patients received NACT‐IDS and PDS, respectively. Methods Propensity score‐based inverse probability of treatment weighting (IPTW) was performed to balance the between‐group differences. Main Outcome Measures Clinicopathological factors related to PRS. Results The overall median PRS was 29.3 months (95% CI 27.0–31.5 months). Multivariate analysis before and after IPTW adjustment showed that NACT‐IDS and residual R1/R2 disease were independent risk factors for PRS ( p < 0.05). Patients with diffuse carcinomatosis and platinum‐free interval (PFI) ≤ 12 months had a significantly worse PRS ( p < 0.001). Logistic regression analysis revealed that NACT‐IDS was an independent risk factor for diffuse carcinomatosis (OR 1.36, 95% CI 1.01–1.82, p = 0.040) and PFI ≤ 12 months (OR 1.59, 95% CI 1.08–2.35, p = 0.019). In IPTW analysis, NACT‐IDS was still significantly associated with diffuse carcinomatosis (OR 1.29, 95% CI 1.05–1.58, p = 0.015) and PFI ≤ 12 months (OR 1.90, 95% CI 1.52–2.38, p < 0.001). Conclusions The primary treatment sequence may affect the PRS of patients with REOC by altering the recurrence pattern and PFI duration.
Objectives Advancements in big data technology are reshaping the healthcare system in China. This study aims to explore the role of medical big data in promoting digital competencies and professionalism among Chinese medical students. Design, setting and participants This study was conducted among 274 medical students who attended a workshop on medical big data conducted on 8 July 2021 in Tongji Hospital. The workshop was based on the first nationwide multifunction gynecologic oncology medical big data platform in China, at the National Union of Real-World Gynecologic Oncology Research & Patient Management Platform (NUWA platform). Outcome measures Data on knowledge, attitudes towards big data technology and professionalism were collected before and after the workshop. We have measured the four skill categories: doctor‒patient relationship skills, reflective skills, time management and interprofessional relationship skills using the Professionalism Mini-Evaluation Exercise (P-MEX) as a reflection for professionalism. Results A total of 274 students participated in this workshop and completed all the surveys. Before the workshop, only 27% of them knew the detailed content of medical big data platforms, and 64% knew the potential application of medical big data. The majority of the students believed that big data technology is practical in their clinical practice (77%), medical education (85%) and scientific research (82%). Over 80% of the participants showed positive attitudes toward big data platforms. They also exhibited sufficient professionalism before the workshop. Meanwhile, the workshop significantly promoted students’ knowledge of medical big data (p< 0.05 ), and led to more positive attitudes towards big data platforms and higher levels of professionalism. Conclusions Chinese medical students have primitive acquaintance and positive attitudes toward big data technology. The NUWA platform-based workshop may potentially promote their understanding of big data and enhance professionalism, according to the self-measured P-MEX scale.
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.
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