INTRODUCTION: Although colorectal cancer (CRC) screening has been incorporated into organized programs in many countries, a universally accepted noninvasive and efficient screening method remains unavailable. This study aimed to assess the diagnostic potential of volatile organic compounds in exhaled breath through electronic nose (eNose) for noninvasive CRC detection. METHODS: The Cyranose320 sensor device was used to collect and analyze breath samples. Supervised machine learning was applied to evaluate the diagnostic performance of the eNose in CRC detection, using a randomly assigned training and validation set. Two-thirds of the breath samples were used to train models, which were then validated on the remaining patients (external validation). Three machine learning methods were applied for classification: random forest, extreme gradient boosting (XGBoost), and quadratic discriminant analysis. RESULTS: A total of 105 patients with CRC and 101 healthy controls were included. After adjusting for baseline covariates (age, sex, smoking, and comorbidities), machine learning models based on volatile organic compound profiles could differentiate patients with CRC from healthy controls, achieving areas under the receiver operating characteristic curve (AUC) of at least 0.72 in both the training and validation sets. The final CRC classification models yielded AUCs of 0.93 for random forest, 0.88 for XGBoost, and 0.89 for quadratic discriminant analysis. Furthermore, eNose classified CRC by stage, with an AUC exceeding 0.70 for early and advanced disease. DISCUSSION: Exhaled breath analysis using an eNose may serve as a promising noninvasive method for CRC detection. Further studies with larger populations are needed to confirm its clinical impact.

Background: The role of upper respiratory microbiota in the context of psychological distress (PD), particularly in patients with pulmonary nodules (PNs), remains largely unexplored. The study investigates anxiety and depression prevalence, clinical risk factors, and upper respiratory microbiota composition in patients with PNs, and their associations. Methods: A retrospective nested case-control study (August 2022 to May 2024) surveyed 381 PNs participants using a standardized questionnaire. Univariate analysis and logistic regression examined associations with PD. 16S ribosomal RNA sequencing compared microbiota between groups. Spearman correlation and exploratory mediation analysis explored associations between microbiota, clinical features, and PD. Machine learning algorithms were trained to identify features associated with PD. Results: This study analyzed the sociodemographic and disease characteristics of 381 participants with PNs, revealing that approximately 32% exhibited symptoms of PD. Results showed female patients (OR = 1.77), those with ground-glass nodules (OR = 1.77) and history of nodular disease (OR = 1.94) were associated with a higher prevalence of PD. The main differential bacterial genus between no psychological distress (NPD) and PD groups such as g_Prevotella, g_Neisseria, and g_Haemophilus . The machine learning algorithms revealed that factors such as g_Aggregatibacter, g_Rhodococcus and gender constituted the best predictive group (Area under the curve, AUC = 0.75). Furthermore, exploratory mediation analysis identified associations suggesting that g_Haemophilus (β = 0.738 and β = − 0.523), and g_Dialister (β = 0.452 and β = 0.29) may be involved in the links between BAI and BDI-II scores with clinical factors. Conclusion: Our findings highlight the complex associations among clinical factors, upper respiratory microbiota, and the presence of PD in patients with PNs, laying the groundwork for future research into whether microbiota modulation could be a strategy for improving mental health in PNs patients.

Objective: In recent years, lung cancer prediction models have become popular. However, few bibliometric analyses have been performed in this field. Methods: This study aimed to reveal the scientific output and trends in lung cancer prediction models from a global perspective. In this study, publications were retrieved and extracted from the Web of Science Core Collection (WoSCC) database. CiteSpace 6.1.R3 and VOSviewer 1.6.18 were used to analyze hotspots and theme trends. Results: A marked increase in the number of publications related to lung cancer prediction models was observed. A total of 2711 institutions from in 64 countries/regions published 2139 documents in 566 academic journals. China and the United States were the leading country in the field of lung cancer prediction models. The institutions represented by Fudan University had significant academic influence in the field. Analysis of keywords revealed that lncRNA, tumor microenvironment, immune, cancer statistics, The Cancer Genome Atlas, nomogram, and machine learning were the current focus of research in lung cancer prediction models. Conclusions: Over the last two decades, research on risk prediction models for lung cancer has attracted increasing attention. Prognosis, machine learning, and multi-omics technologies are both current hotspots and future trends in this field. In the future, in-depth explorations using different omics should increase the sensitivity and accuracy of lung cancer prediction models and reduce the global burden of lung cancer. Export