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Background: Lynch syndrome (LS)-associated colorectal cancer (CRC) always ascribes to pathogenic germline mutations in mismatch repair (MMR) genes. However, the penetrance of CRC varies among those with the same MMR gene mutation. Thus, we hypothesized that the gut microbiota is also involved in CRC development in LS families. Methods: This prospective, observational study was performed from December 2020 to March 2023. We enrolled 72 individuals from 9 LS families across six provinces in China and employed 16S rRNA gene amplicon sequencing to analyze the fecal microbiota components among LS-related CRC patients (AS group), their spouses (BS group), mutation carriers without CRC (CS group), and non-mutation carriers (DS group) using alpha and beta diversity indices. Results: There were no apparent differences in age or gender among the four groups. Alpha and beta diversity indices exhibited no significant differences between the AS and BS groups, verifying the role of germline mutations in the occurrence of CRC in LS families. Beta diversity analysis exhibited significant differences between the AS and CS groups, revealing the importance of the gut microbiota for the occurrence of CRC in LS families. A greater difference (both alpha and beta diversity indices) was shown between the AS and DS groups, demonstrating the combined impact of the gut microbiota and genetic germline mutations on the occurrence of CRC in LS families. Compared with those in the CS and DS groups, we identified ten microbial genera enriched in the AS group, and one genus (Bacteroides) decreased in the AS group. Among the elevated genera in the AS group, Agathobacter, Coprococcus and Prevotellaceae_NK3B31_group were butyrate-producing genera. Conclusion: This study found the development of CRC in the LS families can be attributed to the combined effects of gene germline mutations as well as the gut microbiota and provided novel insights into the prevention and treatment of CRC in the LS families. [ABSTRACT FROM AUTHOR]

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]

Under the special geological environment of the buried pipe, the ground is lost at the bottom of the pipe, which is created by various kinds of external factors. The pipe in the suspended state would be greatly deformed due to its own weight, internal pressure, and other factors, resulting in the failure of the pipeline. When a variable wall thickness weld occurs in the suspended section of the pipeline, the change of the pipeline stress will be more complicated and changeable. In this study, ABAQUS software is used to establish a pipe–soil model of variable wall thickness butt welds of suspended pipelines. The axial stress distribution with different affected factors in the pipe, the change of curvature, and Mises stress change of the entire pipe along the axial direction are obtained by analyzing the internal pressure, wall thickness ratio, suspended length, weld position, and cone length. The results show that the stress at the root of the weld changes significantly; therefore, the weld has a greater impact on the stress of the entire pipeline. The change of internal pressure has little effect on the stress at the pipe weld. As the suspended length increases, the change in stress at the weld is more obvious. When the weld seam is close to the soil, the support of the soil will gradually shift the maximum stress position of the pipe from the top of the pipe to the bottom of the pipe. With the increase in cone length, it will reduce the sudden change of pipe section and the change in stress effectively. The places where the curvature greatly changes along the axial direction are at the pipe–soil separation and the middle of the pipeline, while the stress reaches the maximum at the pipe–soil separation, and the place with the largest stress change is the weld in the middle of the pipeline.

Abstract Oesophageal cancer (OC) is associated with high morbidity and mortality, and surgery is the most effective approach to treat it. In order to reduce surgical risks and duration of surgery, we explored a new strategy to determine tumour margins in surgery. In this study, we included 128 cancerous and 128 noncancerous database entries obtained from 32 human patients. Using internal extractive electrospray ionization-MS, in positive ion detection mode, the relative abundances of m/z 104.13, m/z 116.10, m/z 132.13, and m/z 175.13 were higher in cancer tissue while the relative abundances of m/z 82.99, m/z 133.11, m/z 147.08, m/z 154.06, and m/z 188.05 were higher in normal tissue. Using partial least squares analysis, the mass spectra of cancer samples was discriminated from those of normal tissues, and the discriminatory ions were obtained from loading plots. Dimethylglycine(m/z 104), proline(m/z 116), isoleucine(m/z 132), asparagine(m/z 133), glutamine(m/z 147), and arginine(m/z 175) were identified by collision-induced dissociation experiments. Using the ROC curve analysis, we verified the validity of six amino acids for the identification of tumour tissue. Further investigations of tissue amino acids may allow us to better understand the underlying mechanisms involved in OC and develop novel means to identify tumour tissue during operation.