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BackgroundArtificial intelligence (AI) discrimination models using single radioactive variables in recognition algorithms of lung nodules cannot predict lung cancer accurately. Hence, we developed a clinical model that combines AI with blood test variables to predict lung cancer.MethodsBetween 2018 and 2021, 584 individuals (358 patients with lung cancer and 226 individuals with lung nodules other than cancer as control) were enrolled prospectively. Machine learning algorithms including lasso regression and random forest (RF) were used to select variables from blood test data, Logistic regression analysis was used to reconfirm the features to build the nomogram model. The predictive performance was assessed by performing the receiver operating characteristic (ROC) curve analysis as well as calibration, clinical decision and impact curves. A cohort of 48 patients was used to independently validate the model. The subgroup application was analyzed by pathological diagnosis.FindingsA total of 584 patients were enrolled (358 lung cancers, 61.30%,226 patients for the control group) to establish the model. The integrated model identified eight potential factors including carcinoembryonic antigen (CEA), AI score, Pro-Gastrin Releasing Peptide (ProGRP), cytokeratin 19 fragment antigen21-1(CYFRA211), squamous cell carcinoma antigen(SCC), indirect bilirubin(IBIL), activated partial thromboplastin time(APTT) and age. The area under the curve (AUC) of the nomogram was 0.907 (95% CI, 0.881-0.929). The decision and clinical impact curves showed good predictive accuracy of the model. An AUC of 0.844 (95% CI, 0.710 - 0.932) was obtained for the external validation group.ConclusionThe nomogram model integrating AI and clinical data can accurately predict lung cancer, especially for the squamous cell carcinoma subtype.

Adzuki bean is widely consumed in East Asia. Although the positive effects of its biologically active ingredients on obesity have been confirmed, the role of whole cooked adzuki bean in preventing obesity and the relationship between the effects and gut microbiota remain unclear. Mice were fed either a low-fat diet (LFD) or high-fat diet (HFD) with or without 15% cooked adzuki bean for 12 weeks. Cooked adzuki bean significantly inhibited weight gain and hepatic steatosis, reduced high levels of serum triacylglycerol (TG), alanine aminotransferase (ALT), and aspartate aminotransferase (AST), and alleviated systemic inflammation and metabolic endotoxemia in mice fed a HFD. Importantly, cooked adzuki bean regulated gut microbiota composition, decreased the abundance of lipopolysaccharide (LPS)-producing bacteria (Desulfovibrionaceae,Helicobacter,and Bilophila), and HFD-dependent taxa (Deferribacteraceae, Ruminiclostridium_9, Ruminiclostridium, Mucispirillum, Oscillibacter, Enterorhabdus, Tyzzerella, Anaerotruncus, Intestinimonas, unclassified_f_Ruminococcaceae, Ruminiclostridium_5, and Ruminococcaceae), and enriched Muribaculaceae, norank_f_Muribaculaceae, Anaeroplasma, Lachnospiraceae_NK4A136_group, and Lachnospiraceae to alleviate inflammation and metabolic disorders induced by HFD. These findings provide new evidence for understanding the anti-obesity effect of cooked adzuki bean.