Artificial intelligence (AI) is an epoch-making technology, among which the 2 most advanced parts are machine learning and deep learning algorithms that have been further developed by machine learning, and it has been partially applied to assist EUS diagnosis. AI-assisted EUS diagnosis has been reported to have great value in the diagnosis of pancreatic tumors and chronic pancreatitis, gastrointestinal stromal tumors, esophageal early cancer, biliary tract, and liver lesions. The application of AI in EUS diagnosis still has some urgent problems to be solved. First, the development of sensitive AI diagnostic tools requires a large amount of high-quality training data. Second, there is overfitting and bias in the current AI algorithms, leading to poor diagnostic reliability. Third, the value of AI still needs to be determined in prospective studies. Fourth, the ethical risks of AI need to be considered and avoided.

Background. The epithelial‐mesenchymal transition (EMT) is a pivotal process for fibrotic disease, embryonic development, and wound healing. Moreover, some evidence has proven that the disorder of EMT also plays an important role in carcinogenesis, especially invasion and metastasis of various tumors (Ritchie et al., 2015). Additionally, gastric adenocarcinoma (GAC) is a common gastrointestinal malignancy which is the fourth most commonly diagnosed tumor. Our study is aimed at identifying the prognostic value of EMT‐related genes in gastric adenocarcinoma. Methods. Firstly, high‐throughput and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. 99 differentially expressed EMT‐related genes (ERGs) were obtained in these gastric adenocarcinoma data. Secondly, GO and KEGG enrichment analyses show that EMT may promote gastric carcinogenesis. Next, 10 ERGs associated with prognosis of gastric adenocarcinoma patients are screened out by univariate Cox regression, and 6 pivotal prognostic ERGs (MMP8, MMP11, TFDP3, MYB, F2, and CNTN1) are identified through multivariate Cox regression. These 6 genes are confirmed with significant prognostic value in gastric adenocarcinoma through overall survival (OS) analysis. Finally, a risk score formula is constructed and tested in another gastric adenocarcinoma cohort from GEO. Results. 99 differentially expressed EMT‐related genes (ERGs) and their enriched pathways are identified. 10 ERGs are strongly related to the prognosis of GAC patients. A risk score formula of 6 prognosis‐related ERGs used to predict the prognosis of gastric adenocarcinoma patients is identified and tested (risk score = 0.448115∗expression value of MMP8 + 0.378892∗expression value of MMP11 − 0.3226∗expression value of MYB + 1.322812∗expression value of TFDP3 + 0.325063∗expression value of F2 + 0.334197∗expression value of CNTN1). Conclusion. This study provides a potential prognostic signature for predicting prognosis of gastric adenocarcinoma patients and molecular insights of EMT in gastric adenocarcinoma, and the formula focusing on the prognosis of gastric adenocarcinoma can be effective.

Enteropeptidase (EP) initiates intestinal digestion by proteolytically processing trypsinogen, generating catalytically active trypsin. EP dysfunction causes a series of pancreatic diseases including acute necrotizing pancreatitis. However, the molecular mechanisms of EP activation and substrate recognition remain elusive, due to the lack of structural information on the EP heavy chain. Here, we report cryo-EM structures ofhumanEP in inactive, active, and substrate-bound states at resolutions from 2.7 to 4.9 Å. The EP heavy chain was observed to clamp the light chain with CUB2 domain for substrate recognition. The EP light chain N-terminus induced a rearrangement of surface-loops from inactive to active conformations, resulting in activated EP. The heavy chain then served as a hinge for light-chain conformational changes to recruit and subsequently cleave substrate. Our study provides structural insights into rearrangements of EP surface-loops and heavy chain dynamics in the EP catalytic cycle, advancing our understanding of EP-associated pancreatitis.

Background: The progression rate from CIN1 to CIN3 is 9.0% and that for invasive cancer is 1.0%. The large majority of CIN1 lesions regress spontaneously, and the treatment of CIN1 is still controversial.Aims: The aim of this study is to investigate the responsible HPV genotype in the low‐grade SILs, then to predict the presence of high‐grade SILs, and determine whether further treatment is needed.Methods: We use the methods of manual microdissection with FFPE tissue specimens and the E6/E7 uniplex polymerase chain reaction (PCR) to detect HPV in the lesions.Results: The HPV test was performed on 72 biopsy tissue specimens, and 55 (76.4%, 55/72) of them were HPV positive. Nine (16.4%, 9/55) of them escalated to CIN2 after LEEP or cervical conization, and 46 (83.6%, 46/55) were still CIN1. There were 17 (23.6%, 17/72) cases with HPV‐negative results in cervical biopsy tissues.HPV test of cervical biopsy diagnosed with CIN1 has a positive predictive value of 16.4% in the presence of CIN2 or higher lesions, a negative predictive value of 94.1%, a specificity of 25.8%, and a sensitivity of 90.0%. HPV test of cervical biopsy tissues for the prediction of HPV infection in LEEP or cone surgery tissues had a positive predictive value of 80.0%, a negative predictive value of 82.3%, a specificity of 56.0%, and a sensitivity of 93.6%.Conclusions: It is the first time that we have detected HPV genotype in the low‐grade SILs by the methods of manual microdissection with FFPE tissue specimens and the E6/E7 uniplex PCR. Patients with cervical biopsy tissue diagnosed with CIN1 and with a negative or only low‐risk HPV type result can be considered for follow‐up. Conversely, in cases of cervical biopsy tissue diagnosed with CIN1 positive for high‐risk HPV, surgery or a close follow‐up program can be selected.

An abstract is unavailable. This article is available as a PDF only.

Let f f be a self-dual Hecke-Maass eigenform for the group S L 3 ( Z ) S{L}_{3}\left({\mathbb{Z}}) . For 1 2 < σ < 1 \frac{1}{2}\lt \sigma \lt 1 fixed we define m ( σ ) m\left(\sigma ) ( ≥ 2 \ge 2 ) as the supremum of all numbers m m such that ∫ 1 T ∣ L ( s , f ) ∣ m d t ≪ f , ε T 1 + ε , \underset{1}{\overset{T}{\int }}| L\left(s,f){| }^{m}{\rm{d}}t{\ll }_{f,\varepsilon }{T}^{1+\varepsilon }, where L ( s , f ) L\left(s,f) is the Godement-Jacquet L -function related to f f . In this paper, we first show the lower bound of m ( σ ) m\left(\sigma ) for 2 3 < σ < 1 \frac{2}{3}\lt \sigma \lt 1 . Then we establish asymptotic formulas for the second, fourth and sixth powers of L ( s , f ) L\left(s,f) as applications.