Detection of KRAS mutation using plasma samples in non-small-cell lung cancer: a systematic review and meta-analysis

BackgroundThe aim of this study was to investigate the diagnostic accuracy of KRAS mutation detection using plasma sample of patients with non-small cell lung cancer (NSCLC).MethodsDatabases of Pubmed, Embase, Cochrane Library, and Web of Science were searched for studies detecting KRAS mutation in paired tissue and plasma samples of patients with NSCLC. Data were extracted from each eligible study and analyzed using MetaDiSc and STATA.ResultsAfter database searching and screening of the studies with pre-defined criteria, 43 eligible studies were identified and relevant data were extracted. After pooling the accuracy data from 3341 patients, the pooled sensitivity, specificity and diagnostic odds ratio were 71%, 94%, and 59.28, respectively. Area under curve of summary receiver operating characteristic curve was 0.8883. Subgroup analysis revealed that next-generation sequencing outperformed PCR-based techniques in detecting KRAS mutation using plasma sample of patients with NSCLC, with sensitivity, specificity, and diagnostic odds ratio of 73%, 94%, and 82.60, respectively.ConclusionCompared to paired tumor tissue sample, plasma sample showed overall good performance in detecting KRAS mutation in patients with NSCLC, which could serve as good surrogate when tissue samples are not available

Monitoring Reaction Intermediates in Plasma-Driven SO2, NO, and NO2 Remediation Chemistry Using in Situ SERS Spectroscopy

In situ surface-enhanced Raman scattering (SERS) spectroscopy is used to identify the key reaction intermediates during the plasma-based removal of NO and SO2 under dry and wet conditions on Ag nanoparticles. Density functional theory (DFT) calculations are used to confirm the experimental observations by calculating the vibrational modes of the surface-bound intermediate species. Here, we provide spectroscopic evidence that the wet plasma increases the SO2 and the NOx removal through the formation of highly reactive OH radicals, driving the reactions to H2SO4 and HNO3, respectively. We observed the formation of SO3 and SO4 species in the SO2 wet-plasma-driven remediation, while in the dry plasma, we only identified SO3 adsorbed on the Ag surface. During the removal of NO in the dry and wet plasma, both NO2 and NO3 species were observed on the Ag surface; however, the concentration of NO3 species was enhanced under wet-plasma conditions. By closing the loop between the experimental and DFT-calculated spectra, we identified not only the adsorbed species associated with each peak in the SERS spectra but also their orientation and adsorption site, providing a detailed atomistic picture of the chemical reaction pathway and surface interaction chemistry.Fil: Li, Shujin. University of Southern California; Estados UnidosFil: Zhao, Bofan. University of Southern California; Estados UnidosFil: Aguirre, Alejo. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Wang, Yu. University of Southern California; Estados UnidosFil: Li, Ruoxi. University of Southern California; Estados UnidosFil: Yang, Sisi. University of Southern California; Estados UnidosFil: Aravind, Indu. University of Southern California; Estados UnidosFil: Cai, Zhi. University of Southern California; Estados UnidosFil: Chen, Ran. University of Southern California; Estados UnidosFil: Jensen, Lasse. University of Southern California; Estados UnidosFil: Cronin, Stephen B.. University of Southern California; Estados Unido

An Improved Prediction Model Combining Inverse Exponential Smoothing and Markov Chain

On the basis of the triple exponential smoothing prediction model, this paper introduces the reverse prediction idea and establishes the reverse triple exponential smoothing model by setting parameters such as threshold value and iteration times and reasonably correcting its initial value. This method can effectively reduce the error of early prediction value. At the same time, aiming at the problem that the predicting advantage of the reverse triple exponential smoothing model weakens in the later period, Markov theory is introduced to correct its error value, and an improved prediction model combining inverse exponential smoothing and Markov chain is further established. The improved model combines the advantages of index model trend prediction and Markov fluctuation prediction, and the prediction accuracy and stability of the model are significantly improved through case tests

Steamed Multigrain Bread Prepared from Dough Fermented with Lactic Acid Bacteria and Its Effect on Type 2 Diabetes

Multigrain products can prevent the occurrence of chronic noninfectious diseases such as hyperglycemia and hyperlipidemia. In this study, multigrain dough fermented by lactic acid bacteria (LAB) was used for the preparation of good-quality steamed multigrain bread, and its effects on type 2 diabetes were investigated. The results showed that the multigrain dough fermented with LAB significantly enhanced the specific volume, texture, and nutritional value of the steamed bread. The steamed multigrain bread had a low glycemic index and was found to increase liver glycogen and reduce triglyceride and insulin levels, while improving oral glucose tolerance and blood lipid levels in diabetic mice. The steamed multigrain bread made from dough fermented with LAB had comparable effects on type 2 diabetes to steamed multigrain bread prepared from dough fermented without LAB. In conclusion, multigrain dough fermentation with LAB improved the quality of the steamed bread while preserving its original efficacy. These findings provide a novel approach to the production of functional commercial foods

Dispersion-less Kerr solitons in spectrally confined optical cavities

A novel class of solitons, which builds up in spectrally confined optical cavities when dispersion is practically absent, is revealed both theoretically and experimentally, opening new avenues for generating soliton pulses and frequency combs endowed with unprecedented temporal and spectral features

Classification of Mobile-Based Oral Cancer Images Using the Vision Transformer and the Swin Transformer

Oral cancer, a pervasive and rapidly growing malignant disease, poses a significant global health concern. Early and accurate diagnosis is pivotal for improving patient outcomes. Automatic diagnosis methods based on artificial intelligence have shown promising results in the oral cancer field, but the accuracy still needs to be improved for realistic diagnostic scenarios. Vision Transformers (ViT) have outperformed learning CNN models recently in many computer vision benchmark tasks. This study explores the effectiveness of the Vision Transformer and the Swin Transformer, two cutting-edge variants of the transformer architecture, for the mobile-based oral cancer image classification application. The pre-trained Swin transformer model achieved 88.7% accuracy in the binary classification task, outperforming the ViT model by 2.3%, while the conventional convolutional network model VGG19 and ResNet50 achieved 85.2% and 84.5% accuracy. Our experiments demonstrate that these transformer-based architectures outperform traditional convolutional neural networks in terms of oral cancer image classification, and underscore the potential of the ViT and the Swin Transformer in advancing the state of the art in oral cancer image analysis

Phosphorylation-dependent deubiquitinase OTUD3 regulates YY1 stability and promotes colorectal cancer progression

Abstract Yin Yang 1 (YY1) is a key transcription factor that has been implicated in the development of several malignancies. The stability of YY1 is regulated by the ubiquitin-proteasome system. The role of deubiquitinases (DUBs) and their impact on YY1 remain to be fully elucidated. In this study, we screened for ubiquitin-specific proteases that interact with YY1, and identified OTUD3 as a DUB for YY1. Over-expressed OTUD3 inhibited YY1 degradation, thereby increasing YY1 protein levels, whereas OTUD3 knockdown or knockout promoted YY1 degradation, thereby decreasing the proliferation of colorectal cancer (CRC). Furthermore, PLK1 mediates OTUD3 S326 phosphorylation, which further enhances OTUD3 binding and deubiquitination of YY1. In CRC tissues, elevated the expression level of OTUD3 and YY1 were significantly associated with poor prognostic outcomes. These findings suggest that the OTUD3-YY1 pathway has therapeutic potential in CRC, and OTUD3 plays a critical role in regulating YY1