Aim of the study We aimed to explore how weipiling (WPL) decoction WPL alleviates gastric precancerous lesions (GPLs) and uncover its anti-inflammatory roles in GPL treatment. Materials and methods The anti-GPL action mechanisms of WPL were analysed using a network pharmacological method. The WPL extract was prepared in a traditional way and evaluated for its major components using high-performance liquid chromatography with tandem mass spectrometry (HPLC–MS/MS). BALB/c mice were exposed to N-methyl-N-nitro-N-nitrosoguanidine (MNNG) (150 μg/mL) for 6 weeks to induce GPLs. GPL mice were administered WPL (3.75 g/kg/day and 15 g/kg/day) for an additional 8 weeks. Haematoxylin and eosin (H&E) staining was used to investigate histological alterations in gastric tissues. Expression of the T helper 1 (Th1) cell markers CD4+ and interferon-gamma (INF-γ) were tested using immunohistochemistry (IHC). Inflammatory protein and mRNA levels in the nuclear factor kappa B (NF-κB) pathway were detected using western blotting and a quantitative reverse transcription polymerase chain reaction (RT-qPCR), respectively. Results We identified and selected 110 active compounds and 146 targets from public databases and references. Four representative components of WPL were established and quantified by HPLC–MS/MS analysis. WPL attenuated MNNG-induced GPLs, including epithelial shedding, cavity fusion, basement membranes with asymmetrical thickness, intestinal metaplasia, dysplasia, pro-inflammatory Th1-cell infiltration, and INF-γ production, indicating that WPL prevents inflammation in the gastric mucosa. Furthermore, WPL reversed MNNG-induced activation of the IκB/NF-κB signalling pathway and subsequently attenuated the upregulation of inducible nitric oxide synthase (iNOS), cyclooxygenase-2 (COX-2), and nicotinamide adenine dinucleotide phosphate oxidase (NADPH oxidase (NOX)) family members NOX2 and NOX4. Conclusion WPL attenuated GPLs by controlling the generation of pro-inflammatory elements and inhibiting the NF-κB signalling pathway in vivo.

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Background: Artificial intelligence (AI) is used to solve the problem of missed diagnosis of polyps in colonoscopy, which has been proved to improve the detection rate of adenomas. The aim of this review was to evaluate the diagnostic performance of AI-assisted detection and classification of polyps in colonoscopy. Methods:The literature search was undertaken on 4 electronic databases (PubMed, Web of Science, Embase, and Cochrane Library). The inclusion criteria were as follows: studies reporting AI-assisted detection and classification of polyps; studies containing patients, images, or videos receiving AI-assisted diagnosis; studies which included AI-assisted diagnosis and reported classification based on histopathology; and studies providing accurate diagnostic data. Non-English language studies, case-reports, reviews, meeting abstracts and so on were excluded. The Quality Assessment of Diagnostic Accuracy Studies-2 scale was used to evaluate the quality of literature and the Stata 13.0 software was used to perform meta-analysis.Results: Twenty-six articles were included with all of medium quality. Meta-analysis showed none of literature had any obvious publication bias. The application of AI in detection of colorectal polyps achieved a sensitivity of 0.95 [95% confidence interval (CI): 0.89-0.98] and an area under the curve (AUC) of 0.79 (95% CI: 0.79-0.82). In the AI-assisted classification, the sensitivity was 0.92 (95% CI: 0.88-0.95) with a specificity of 0.82 (95% CI: 0.71-0.89) and an AUC of 0.94 (95% CI: 0.92-0.96). For the classification of diminutive polyps, the AI-assisted technique yielded a sensitivity of 0.95 (95% CI: 0.94-0.97), a specificity of 0.88 (95% CI: 0.74-0.95), and an AUC of 0.97 (95% CI: 0.95-0.98). For AI-assisted classification under magnifying endoscopy, the sensitivity was 0.954 (95% CI: 0.92-0.96) with a specificity of 0.95 (95% CI: 0.80-0.99) and an AUC of 0.97 (95% CI: 0.95-0.98).Discussion: The AI-assisted technique demonstrates impressive accuracy for the detection and characterization of colorectal polyps and can be expected to be a novel auxiliary diagnosis method. Our study has inevitable limitations including heterogeneity due to different AI systems and the inability to further analyze the specificity and sensitivity of AI for different types of endoscopes.

An inspiring UTBB SOI MOSFET structure with enhanced immunity to the drain-induced barrier lowering (DIBL) is analyzed. The structure includes the dual-gates in the lateral direction. The voltage difference is applied between the dual-gates, through which the electrostatic potential and the energy band along the channel are modified and the electrical performance is boosted. The electrical characteristics are investigated by measuring the electron concentration, the conduction band energy level, and the potential at the front-surface. The impact of the negative voltage bias applied to the right gate on the performance of the new device is studied, and compared to that of the conventional ultra-thin body ultra-thin box silicon-on-insulator (UTBB SOI) devices. The results reveal that the undesirable DIBL values are lower in this innovative device than that in the conventional UTBB SOI MOSFET.

We verify that one-dimensional (1D) Gaussian expression is an appropriate approximation of the vertical doping profile, which is obtained by combining perpendicular ion implantation and rapid thermal annealing (RTA), for short-channel thin-body (20–30 nm) fully depleted (FD) silicon-on-insulator (SOI) MOSFETs. The two-dimensional (2D) potential distribution of the silicon film is derived by adopting the evanescent mode analysis method, in which the potential function is broken into 1D long-channel and 2D short-channel potentials. The threshold voltage model is represented by the minimum front- and back-surface potentials of the silicon film. The application of the threshold voltage model can be extended to a 12 nm channel length. The results obtained using the models match well with the 2D numerical simulation results obtained using the Synopsys Sentaurus Device™. They provide a feasible way of developing new 2D models for nonuniform nanoscale thin-body FD-SOI devices.

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In this paper, we present an in-built N+ pocket electrically doped tunnel FET (ED-TFET) based on the polarity bias concept that enhances the DC and analog/RF performance. The proposed device begins with a MOSFET like structure (n-p-n) with a control gate (CG) and a polarity gate (PG). The PG is biased at −0.7 V to induce a P+ region at the source side, leaving an N+ pocket between the source and the channel. This technique yields an N+ pocket that is realized in the in-built architecture and removes the need for additional chemical doping. Calibrated 2-D simulations have demonstrated that the introduction of the N+ pocket yields a higher ION and a steeper average subthreshold swing when compared to conventional ED-TFET. Further, a local minimum on the conduction band edge (EC) curve at the tunneling junction is observed, leading to a dramatic reduction in the tunneling width. As a result, the in-built N+ pocket ED-TFET significantly improves the DC and analog/RF figure-of-merits and, hence, can serve as a better candidate for low-power applications.