The role of serum tumor markers (STMs) in the modern management of epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) mutations in lung cancer remains poorly described. In this study, we investigated whether STMs could be a valuable noninvasive tool to predict EGFR mutations and ALK positivity in non-small-cell lung cancer (NSCLC) patients. Experimental design: We retrospectively reviewed and included 1089 NSCLC patients who underwent EGFR or ALK mutation testing and STMs measurement prior to treatment. The differences in several clinical characteristics and STMs between the subgroups were analyzed. Multivariate logistic regression analysis was performed to identify predictors of EGFR mutations and ALK positivity. Results: EGFR mutations were found more frequently in females (63.11%), never-smokers

Background: It remains unclear whether discharged COVID-19 patients have fully recovered from severe complications, including the differences in the post-infection metabolomic profiles of patients with different disease severities. Methods: COVID-19-recovered patients, who had no previous underlying diseases and were discharged from Wuhan Union Hospital for 3 months, and matched healthy controls (HCs) were recruited in this prospective cohort study. We examined the blood biochemical indicators, cytokines, lung computed tomography scans, including 39 HCs, 18 recovered asymptomatic (RAs), 34 recovered moderate (RMs), and 44 recovered severe/ critical patients (RCs). A liquid chromatography-mass spectrometry-based metabolomics approach was employed to profile the global metabolites of fasting plasma of these participants. Results: Clinical data and metabolomic profiles suggested that RAs recovered well, but some clinical indicators and plasma metabolites in RMs and RCs were still abnormal as compared with HCs, such as decreased taurine, succinic acid, hippuric acid, some indoles, and lipid species. The disturbed metabolic pathway mainly involved the tricarboxylic cycle, purine, and glycerophospholipid metabolism. Moreover, metabolite alterations differ between RMs and RCs when compared with HCs. Correlation analysis revealed that many differential metabolites were closely associated with inflammation and the renal, pulmonary, heart, hepatic, and coagulation system functions. Conclusion:We uncovered metabolite clusters pathologically relevant to the recovery state in discharged COVID-19 patients which may provide new insights into the pathogenesis of potential organ damage in recovered patients.

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Objective:The aim of this study was to explore the predictive value of carcinoembryonic antigen (CEA), squamous cell carcinoma antigen (SCCAg), and neuron-specific enolase (NSE) in the prediction of anaplastic lymphoma kinase (ALK) mutations in advance stage non-small cell lung cancer (NSCLC). Subjects and Methods:A total of 482 cases with untreated lung adenocarcinoma were retrospectively reviewed. Finally, 72 patients with stage IV were enrolled because of intact data of the detection of ALK rearrangement and serum tumor markers, as well they have not received any previous anticancer therapy. We used the one-way ANOVA analysis, correlation analysis, and multiple logistic regression analysis to evaluate the relationship between the level of serum tumor markers and ALK mutations.Results: Fifteen cases with ALK mutations and 57 cases without mutations were identified. The result of the one-way ANOVA analysis showed only CEA was significantly associated with ALK mutations (95% CI:39.05-148.88; P = .001). The area under the ROC curve (AUC) of CEA was 0.705 (95%CI:0.567-0.843; P = .015). However, no significant association was observed between CEA and ALK mutations though the result of correlation analysis (P = .069) and multivariate logistic regression analysis (OR = 0.988, 95% CI: 0.972-1.003, P = .111). Conclusions:In our study, we performed on the patients with stage IV lung adenocarcinoma in our region and found preoperative serum levels of SCCAg, CYRF21-1, and NSE not suitable for the detection of ALK mutation. Although we observed a significant association between CEA and ALK mutations; however, it was not strong enough to distinguish ALK status for the patients in our region. K E Y W O R D Sanaplastic lymphoma kinase, carcinoembryonic antigen, CYFRA21-1, neuron-specific enolase, squamous cell carcinoma antigen

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Knowing the residual and future effect of SARS-CoV-2 on recovered COVID-19 patients is critical for optimized long-term patient management. Recent studies focus on the symptoms and clinical indices of recovered patients, but the pathophysiological change is still unclear. To address this question, we examined the metabolomic profiles of recovered asymptomatic (RA), moderate (RM) and severe and critical (RC) patients without previous underlying diseases discharged from the hospital for 3 months, along with laboratory and CT findings. We found that the serum metabolic profiles in recovered COVID-19 patients still conspicuously differed from that in healthy control (HC), especially in the RM, and RC patients. Additionally, these changes bore close relationship with the function of pulmonary, renal, hepatic, microbial and energetic metabolism and inflammation. These findings suggested that RM and RC patients sustained multi-organ and multi-system damage and these patients should be followed up on regular basis for possible organ and system damage.

Background In critically ill COVID-19 patients, the crucial turning point before critical illness onset (CIO) remain largely unknown, and the combination of baseline risk factors with the turning point during hospitalization was rarely reported.Methods In this retrospective cohort study, 1150 consecutively admitted patients with confirmed COVID-19 were enrolled, including 296 critical and 854 non-critical patients. We compared the differences of all the clinically tested indicators and their dynamic changes between critical and non-critical patients. Three prediction models were established and validated based on the risk factors at admission, and an online baseline predictive tool was developed. Linear mixed model (LMM) was applied for longitudinal data analysis in 296 critical patients throughout the hospitalization, to predict the likelihood and possible time of critical illness in COVID-19 patients. A crucial turning point, where several indicators will experience a greater and significantly continuous change before CIO, was defined as “burning point” in our study. This point indicates the deterioration of patient’s condition before CIO.Results We established a novel two-checkpoint system to predict critical illness for COVID-19 patients in which the first checkpoint happened at patient admission was assessed by a baseline prediction model to project the likelihood of critical illness based on the variables selected from random forest and LASSO regression analysis, including age, SOFA score, neutrophil-to-lymphocyte ratio (NLR), D-dimer, lactate dehydrogenase (LDH), International Normalized Ratio (INR), and pneumonia area derived from CT images, which yields an AUC of 0.960 (95% confidence interval, 0.941-0.972) and 0.958 (0.936-0.980) in the training and testing sets, respectively. This model has been translated into a public web-based risk calculator. Furthermore, the second checkpoint (designated as “burning point” in our study) could be identified as early as 5 days preceding the CIO, and 12 (IQR, 7-17) days after illness onset. Seven most significant and representative “burning point” indicators were SOFA score, NLR, C-reactive protein (CRP), glucose, D-dimer, LDH, and blood urea nitrogen (BUN).Conclusions With this two-checkpoint prediction system, the deterioration of COVID-19 patients could be early identified and more intensive treatments could be started in advance to reduce the incidence of critical illness.

BackgroundWe intended to establish a novel critical illness prediction system combining baseline risk factors with dynamic laboratory tests for patients with coronavirus disease 2019 (COVID-19).MethodsWe evaluated patients with COVID-19 admitted to Wuhan West Union Hospital between 12 January and 25 February 2020. The data of patients were collected, and the illness severity was assessed.ResultsAmong 1,150 enrolled patients, 296 (25.7%) patients developed into critical illness. A baseline nomogram model consists of seven variables including age [odds ratio (OR), 1.028; 95% confidence interval (CI), 1.004–1.052], sequential organ failure assessment (SOFA) score (OR, 4.367; 95% CI, 3.230–5.903), neutrophil-to-lymphocyte ratio (NLR; OR, 1.094; 95% CI, 1.024–1.168), D-dimer (OR, 1.476; 95% CI, 1.107–1.968), lactate dehydrogenase (LDH; OR, 1.004; 95% CI, 1.001–1.006), international normalised ratio (INR; OR, 1.027; 95% CI, 0.999–1.055), and pneumonia area interpreted from computed tomography (CT) images (medium vs. small [OR, 4.358; 95% CI, 2.188–8.678], and large vs. small [OR, 9.567; 95% CI, 3.982–22.986]) were established to predict the risk for critical illness at admission. The differentiating power of this nomogram scoring system was perfect with an area under the curve (AUC) of 0.960 (95% CI, 0.941–0.972) in the training set and an AUC of 0.958 (95% CI, 0.936–0.980) in the testing set. In addition, a linear mixed model (LMM) based on dynamic change of seven variables consisting of SOFA score (value, 2; increase per day [I/d], +0.49), NLR (value, 10.61; I/d, +2.07), C-reactive protein (CRP; value, 46.9 mg/L; I/d, +4.95), glucose (value, 7.83 mmol/L; I/d, +0.2), D-dimer (value, 6.08 μg/L; I/d, +0.28), LDH (value, 461 U/L; I/d, +13.95), and blood urea nitrogen (BUN value, 6.51 mmol/L; I/d, +0.55) were established to assist in predicting occurrence time of critical illness onset during hospitalization.ConclusionThe two-checkpoint system could assist in accurately and dynamically predicting critical illness and timely adjusting the treatment regimen for patients with COVID-19.