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Background: The incidence and mortality rates of cancer are the highest globally. Developing novel methodologies that precisely, safely, and economically differentiate between benign and malignant lung conditions holds immense clinical importance. This research seeks to construct a predictive model utilizing a combination of diverse biomarkers to effectively discriminate between benign and malignant lung diseases. Methods: This retrospective study included patients admitted to the two general hospitals in Shanghai from 2014 to 2015. This study was developed using five tumor markers: carcinoembryonic antigen (CEA), carbohydrate antigen 199 (CA199), cytokeratin fragment 21-1 (CA211), squamous cell carcinoma antigen (SCC), and neuron specific enolase (NSE). The entire sample was divided into two groups according to the hospital: 1033 cases were included in the development cohort and 300 cases in the validation cohort. Logistic regression analysis was used for univariate analysis to explore individual correlations between each selected clinical variable and lung cancer diagnostic outcome. Diagnostic prediction models were constructed and validated based on independent prognostic factors identified using multifactorial analysis. A nomogram was created using these tumor markers (age and sex were additionally included) and validated using the concordance index and calibration curves. Clinical prediction models were evaluated using decision curve analysis. Results: Fully adjusted multivariate analysis showed that the risk of lung cancer was 2.38 times higher in men than in women. CEA positivity was associated with an 13.41-fold increased risk in lung cancer. The area under the curve (AUC) values for the development cohort and validation cohort models were 0.907 and 0.954, respectively. In the established nomogram, the AUC for the receiver operating characteristic curve was 0.907 (95% CI, 0.889–0.925). The validation model confirmed the strong discriminative power of the nomogram (AUC = 0.954). The described calibration curves demonstrated good fit predictions and observation probabilities. In addition, decision curve analysis concluded that the newly established nomogram has important implications for clinical decision making. Conclusions: Combined prediction models based on CEA, CA199, CA211, SCC, and NSE biomarkers could significantly the differentiation between benign and malignant lung diseases, thus facilitating better clinical decision making. [ABSTRACT FROM AUTHOR]

Background: Surgical aortic valve replacement (SAVR) currently stands as a primary surgical intervention for addressing aortic valve disease in patients. This retrospective study focused on the role of the red blood cell distribution width (RDW) in predicting adverse outcomes among SAVR patients. Methods: The subjects for this study were exclusively derived from the Medical Information Mart for Intensive Care database (MIMIC IV 2.0). Kaplan‒Meier (K-M) curves and Cox proportional hazards regression models were employed to assess the correlation between RDW, one-year mortality, and postoperative atrial fibrillation (POAF). The smooth-fitting curves were used to observe the relative risk (RR) of RDW in one-year mortality and POAF. Furthermore, time-dependent receiver operating characteristic (ROC) curves, the continuous-net reclassification index (NRI), and integrated discrimination improvement (IDI) were employed for comprehensive assessment of the prognostic value of RDW. Results: Analysis of RDW revealed a distinctive inverted U-shaped relationship with one-year mortality, while its association with POAF appeared nearly linear. Cox multiple regression models showed that RDW > 14.35%, along with preoperative potassium concentration and perioperative red blood cell transfusion, were significantly linked to one-year mortality (K-M curves, log-rank P < 0.01). Additionally, RDW was associated with both POAF and prolonged hospital stays (P < 0.05). There was no significant difference in length of stay in ICU. Notably, the inclusion of RDW in the predictive models substantially enhanced its performance. This was evidenced by the time-dependent ROC curve (AUC = 0.829), NRI (P< 0.05), IDI (P< 0.05), and K-M curves (log-rank P< 0.01). Conclusions: RDW serves as a robust prognostic indicator for SAVR patients, offering a novel means of anticipating adverse postoperative events. [ABSTRACT FROM AUTHOR]

There are numerous causes of abdominal aortic calcification (AAC), among which the relationship between serum uric acid and AAC still needs to be investigated further. The aim of this research was to ascertain whether serum uric acid is correlated with AAC. Our study included 3007 participants. We described the study population characteristics and utilized univariate analysis, stratified analysis, multiple equation regression analysis, smoothed curve fitting, and threshold effects analysis. AAC Total 24 score is used to reflect the range of aortic calcification at each vertebral level. As serum uric acid increased, the AAC Total 24 score first decreased and then increased. The fold point is located when serum uric is at 3.5 mg/dL. After adjusting for 16 covariates, the beta values for the groups with moderate and high serum uric acid levels were 0.34 and 0.53, respectively, compared with the low serum uric acid tertile group (P < .05). Our research indicates a negative correlation between serum acid level and AAC when serum uric acid <3.5 mg/dl, but it is positively correlated with the formation of AAC when serum uric acid >3.5 mg/dl. [ABSTRACT FROM AUTHOR]

Background: Spontaneous pneumothorax (SP) is a common pleural disease in adolescents and adults. However, the role of immunological characteristics in the pathogenesis of SP remains unclear. This study aims to clarify the causal associations between circulating immune cells, lymphocyte subgroups, and SP susceptibility., Methods: Employing Mendelian randomization (MR), the causal association between circulating immune blood cells and lymphocyte subgroups on SP susceptibility have been assessed. Reverse MR analysis was used to further explore the causal relationship. The MR analysis ensured the reliability of the study results through the deletion of confounding single nucleotide polymorphisms (SNPs), heterogeneity testing, sensitivity analysis., Results: Seven immune cells and SP risk under stringent and lenient threshold conditions were identified. Eosinophils absolute count (AC) [odds ratio (OR) =1.0014, 95% confidence interval (CI): 1.0001-1.0014, P=0.02], memory B cell %B cell ratio (OR =1.008, 95% CI: 1.0002-1.0015, P=0.01), CD4 + T cell AC (OR =1.0014, 95% CI: 1.0003-1.0025, P=0.009), effector memory CD4 + T cell %T cell ratio (OR =1.0028, 95% CI: 1.0010-1.0046, P=0.003), and HLA-DR + CD8 + T cell %T cell ratio (OR =1.0019, 95% CI: 1.0004-1.0035, P=0.01) were identified as risk factors for increased susceptibility to SP. Conversely, CD8dim T cell AC (OR =0.9983, 95% CI: 0.9967-0.9999, P=0.03) and CD8dim natural killer T (NKT) %T cell ratio (OR =0.9982, 95% CI: 0.9965-0.9999, P=0.04) exhibited protective effects on SP. In natural killer (NK) cell subgroups and reverse MR analysis, no significance was found., Conclusions: This study establishes a close causal relationship between immune cells and SP through genetic methods, providing a new perspective for understanding the pathophysiological mechanisms of SP., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-24-798/coif). The authors have no conflicts of interest to declare., (2024 AME Publishing Company. All rights reserved.)