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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]

AbstractDental Fluorosis (DF) is one of the negative outcomes of excessive fluoride (F) intake through food sources. This systematic review aimed to compare F content in two important food sources for infants, Mother’s Milk (MoM) and Infant Formula (IF), and then evaluate the risk of DF related to F in those two types of food. For this purpose, 181 studies were initially found by searching the relevant keywords in widely recognized databases, including Google Scholar, Scopus, Science Direct, and PubMed. Then, 29 final studies were selected considering the inclusion and exclusion criteria. The results showed that the F level of MoM in five out of nine (55.5%) studies and its overall average worldwide does not comply with the European Food Safety Authority (EFSA) guideline (100 µg/L). Therefore, MoM’s value of F must be continuously monitored, and risk mitigation solutions must be used to reduce F. Accordingly, tracking F in drinking water, tea, and food consumed by lactating mothers (LMs) was the essential measure that could lead to the reduction of F of MoM. The level of F of IF was much lower than its value in MoM. The estimated hazard quotient (HQ) of DF for both types of food was lower than the acceptable level (HQ = 1), but, due to other sources of F intake, it is necessary to use control solutions to reduce the level of F in MoM and IF consumed by infants. [ABSTRACT FROM AUTHOR]

Background Methods Results Conclusions The use of nomograms in predicting the prognosis of early‐stage non‐small cell lung cancer (NSCLC), particularly in elderly patients, is not widespread. A validated prognostic model specifically for NSCLC patients over 80 years old holds promising potential for clinical application in forecasting patient outcomes.The prognostic value of various factors for NSCLC patients aged 80 and above was evaluated using data from the Surveillance, Epidemiology, and End Results (SEER) database (2010–2017). Kaplan–Meier (KM) curves, Cox proportional hazards regression models, and nomogram were utilized to evaluate the impact of each factor on cancer‐specific survival (CSS).A cohort comprising 7045 individuals was selected for inclusion in the analysis. Through rigorous statistical analysis, 10 independent prognostic factors were identified and incorporated into the nomogram. The nomogram's receiver operating characteristic (ROC) curve area under the curve (AUC) was higher than that of the AJCC 7th edition TNM staging system's predicted CSS (0.744 versus 0.602), establishing the superior prognostic value of the nomogram.We have successfully created a highly accurate and discriminative nomogram that enables oncologists to predict the survival outcome of each individual patient with I/II NSCLC who is 80 years or older. [ABSTRACT FROM AUTHOR]