Causal role of metabolites in non-small cell lung cancer: Mendelian randomization (MR) study.

On a global scale, lung cancer(LC) is the most commonly occurring form of cancer. Nonetheless, the process of screening and detecting it in its early stages presents significant challenges. Earlier research endeavors have recognized metabolites as potentially reliable biomarkers for LC. However, the majority of these studies have been limited in scope, featuring inconsistencies in terms of the relationships and levels of association observed.Moreover, there has been a lack of consistency in the types of biological samples utilized in previous studies. Therefore, the main objective of our research was to explore the correlation between metabolites and Non-small cell lung cancer (NSCLC).Thorough two-sample Mendelian randomization (TSMR) analysis, we investigated potential cause-and-effect relationships between 1400 metabolites and the risk of NSCLC.The analysis of TSMR revealed a significant causal impact of 61 metabolites on NSCLC.To ensure the reliability and validity of our findings, we perform FDR correction for P-values by Benjaminiand Hochberg(BH) method, Our results indicate that Oleate/vaccenate (18:1) levels and Caffeine to paraxanthine ratio may be causally associated with an increased risk of NSCLC [Oleate/vaccenate(18:1)levels: OR = 1.171,95%CI: 1.085-1.265, FDR = 0.036; Caffeine to paraxanthine ratio: OR = 1.386, 95%CI:1.191-1.612,FDR = 0.032].

ETU-Net: efficient Transformer and convolutional U-style connected attention segmentation network applied to endoscopic image of epistaxis.

Epistaxis is a typical presentation in the otolaryngology and emergency department. When compressive therapy fails, directive nasal cautery is necessary, which strongly recommended operating under the nasal endoscope if it is possible. Limited by the operator's clinical experience, complications such as recurrence, nasal ulcer, and septum perforation may occur due to insufficient or excessive cautery. At present, deep learning technology is widely used in the medical field because of its accurate and efficient recognition ability, but it is still blank in the research of epistaxis. In this work, we first gathered and retrieved the Nasal Bleeding dataset, which was annotated and confirmed by many clinical specialists, filling a void in this sector. Second, we created ETU-Net, a deep learning model that smartly integrated the excellent performance of attention convolution with Transformer, overcoming the traditional model's difficulties in capturing contextual feature information and insufficient sequence modeling skills in picture segmentation. On the Nasal Bleeding dataset, our proposed model outperforms all others models that we tested. The segmentation recognition index, Intersection over Union, and F1-Score were 94.57 and 97.15%. Ultimately, we summarized effective ways of combining artificial intelligence with medical treatment and tested it on multiple general datasets to prove its feasibility. The results show that our method has good domain adaptability and has a cutting-edge reference for future medical technology development.

Long-distance random fiber laser point sensing system incorporating active fiber.

In this paper, we investigate the benefits of introducing the active fiber into long-distance point-sensing system based on random fiber laser (RFL). In this scheme the active fiber is placed between two segments of single mode fiber (SMF) and backward RFL pumping scheme is used. Through the numerical analysis, the influence of active fiber location on the spectral and power performance for the RFL is carefully discussed. Compared with the scheme without active fiber, the lasing threshold is much lower and the optical signal to noise ratio (OSNR) of the lasing line can be much higher, and the location of the active fiber has significant flexibility. The RFL experimental results are well coincident with the theoretical analysis; also, the sensing performance of such a system is demonstrated.