Novel insights into the intraepithelial spread of extrahepatic cholangiocarcinoma: clinicopathological study of 382 cases on extrahepatic cholangiocarcinoma

BackgroundExtrahepatic cholangiocarcinoma (eCCA) is a rare and aggressive disease and consisted of conventional eCCA and intraductal papillary neoplasm of the bile duct (IPNB). Intraepithelial spread (IES) of cancer cells beyond the invasive area is often observed in IPNBs; however, the prevalence of IES remains to be examined in conventional eCCAs. Here, we evaluated the clinicopathological features of eCCAs according to tumor location, with a focus on the presence of IES. The IES extension was also compared among biliary tract cancers (BTCs).MethodsWe examined the prevalence and clinicopathological significance of IES in eCCAs (n=382) and the IES extension of BTCs, including gallbladder (n=172), cystic duct (n=20), and ampullary cancers (n=102).ResultsAmong the invasive eCCAs, IPNB had a higher rate of IES (89.2%) than conventional eCCAs (57.0%). Among conventional eCCAs, distal eCCAs (75.4%) had a significantly higher prevalence of IES than perihilar eCCAs (41.3%). The presence of IES was associated with a significantly higher survival rate in patients with distal eCCAs (P=0.030). Extension of the IES into the cystic duct (CyD) in distal eCCAs that cancer cells reached the junction of the CyD was a favorable prognostic factor (P<0.001). The association of survival with IES, either on the extrahepatic bile duct or on the CyD, differed depending on the tumor location and type of eCCA. The extension properties of IES were also dependent on different types of tumors among BTCs; usually, the IES incidence became higher than 50% in the tissues that the tumor developed, whereas IES extension to other tissues decreased the incidence.ConclusionThus, eCCAs have different clinicopathological characteristics depending on the tumor location and type

Postmortem Quantitative Analysis of Prion Seeding Activity in the Digestive System

Human prion diseases are neurodegenerative disorders caused by prion protein. Although infectivity was historically detected only in the central nervous system and lymphoreticular tissues of patients with sporadic Creutzfeldt-Jakob disease, recent reports suggest that the seeding activity of Creutzfeldt-Jakob disease prions accumulates in various non-neuronal organs including the liver, kidney, and skin. Therefore, we reanalyzed autopsy samples collected from patients with sporadic and genetic human prion diseases and found that seeding activity exists in almost all digestive organs. Unexpectedly, activity in the esophagus reached a level of prion seeding activity close to that in the central nervous system in some CJD patients, indicating that the safety of endoscopic examinations should be reconsidered

Rapid and Quantitative Assay of Amyloid-Seeding Activity in Human Brains Affected with Prion Diseases

The infectious agents of the transmissible spongiform encephalopathies are composed of amyloidogenic prion protein, PrPSc. Real-time quaking-induced conversion can amplify very small amounts of PrPSc seeds in tissues/body fluids of patients or animals. Using this in vitro PrP-amyloid amplification assay, we quantitated the seeding activity of affected human brains. End-point assay using serially diluted brain homogenates of sporadic Creutzfeldt-Jakob disease patients demonstrated that 50% seeding dose (SD50) is reached approximately 1010/g brain (values varies 108.79-10.63/g). A genetic case (GSS-P102L) yielded a similar level of seeding activity in an autopsy brain sample. The range of PrPSc concentrations in the samples, determined by dot-blot assay, was 0.6-5.4 μg/g brain; therefore, we estimated that 1 SD50 unit was equivalent to 0.06-0.27 fg of PrPSc. The SD50 values of the affected brains dropped more than three orders of magnitude after autoclaving at 121°C. This new method for quantitation of human prion activity provides a new way to reduce the risk of iatrogenic prion transmission

Nationwide surveillance of bacterial respiratory pathogens conducted by the surveillance committee of Japanese Society of Chemotherapy, the Japanese Association for Infectious Diseases, and the Japanese Society for Clinical Microbiology in 2010: General view of the pathogens\u27 antibacterial susceptibility

The nationwide surveillance on antimicrobial susceptibility of bacterial respiratory pathogens from patients in Japan, was conducted by Japanese Society of Chemotherapy, Japanese Association for Infectious Diseases and Japanese Society for Clinical Microbiology in 2010.The isolates were collected from clinical specimens obtained from well-diagnosed adult patients with respiratory tract infections during the period from January and April 2010 by three societies. Antimicrobial susceptibility testing was conducted at the central reference laboratory according to the method recommended by Clinical and Laboratory Standard Institutes using maximum 45 antibacterial agents.Susceptibility testing was evaluable with 954 strains (206 Staphylococcus aureus, 189 Streptococcus pneumoniae, 4 Streptococcus pyogenes, 182 Haemophilus influenzae, 74 Moraxella catarrhalis, 139 Klebsiella pneumoniae and 160 Pseudomonas aeruginosa). Ratio of methicillin-resistant S.aureus was as high as 50.5%, and those of penicillin-intermediate and -resistant S.pneumoniae were 1.1% and 0.0%, respectively. Among H.influenzae, 17.6% of them were found to be β-lactamase-non-producing ampicillin (ABPC)-intermediately resistant, 33.5% to be β-lactamase-non-producing ABPC-resistant and 11.0% to be β-lactamase-producing ABPC-resistant strains. Extended spectrum β-lactamase-producing K.pneumoniae and multi-drug resistant P.aeruginosa with metallo β-lactamase were 2.9% and 0.6%, respectively.Continuous national surveillance of antimicrobial susceptibility of respiratory pathogens is crucial in order to monitor changing patterns of susceptibility and to be able to update treatment recommendations on a regular basis

AI-Assisted Security Alert Data Analysis with Imbalanced Learning Methods

Intrusion analysis is essential for cybersecurity, but oftentimes, the overwhelming number of false alerts issued by security appliances can prove to be a considerable hurdle. Machine learning algorithms can automate a task known as security alert data analysis to facilitate faster alert triage and incident response. This paper presents a bidirectional approach to address severe class imbalance in security alert data analysis. The proposed method utilizes an ensemble of three oversampling techniques to generate an augmented set of high-quality synthetic positive samples and employs a data subsampling algorithm to identify and remove noisy negative samples. Experimental results using an enterprise and a benchmark dataset confirm that this approach yields significantly improved recall and false positive rates compared with conventional oversampling techniques, suggesting its potential for more effective and efficient AI-assisted security operations

Incremental and decremental max-flow for online semi-supervised learning

Max-flow has been adopted for semi-supervised data modelling, yet existing algorithms were derived only for the learning from static data. This paper proposes an online max-flow algorithm for the semi-supervised learning from data streams. Consider a graph learned from labelled and unlabelled data, and the graph being updated dynamically for accommodating online data adding and retiring. In learning from the resulting non stationary graph, we augment and de-augment paths to update max-flow with a theoretical guarantee that the updated max-flow equals to that from batch retraining. For classification, we compute min-cut over current max-flow, so that minimized number of similar sample pairs are classified into distinct classes. Empirical evaluation on real-world data reveals that our algorithm outperforms state-of-the-art stream classification algorithms

Detecting Web-Based Attacks with SHAP and Tree Ensemble Machine Learning Methods

Attacks using Uniform Resource Locators (URLs) and their JavaScript (JS) code content to perpetrate malicious activities on the Internet are rampant and continuously evolving. Methods such as blocklisting, client honeypots, domain reputation inspection, and heuristic and signature-based systems are used to detect these malicious activities. Recently, machine learning approaches have been proposed; however, challenges still exist. First, blocklist systems are easily evaded by new URLs and JS code content, obfuscation, fast-flux, cloaking, and URL shortening. Second, heuristic and signature-based systems do not generalize well to zero-day attacks. Third, the Domain Name System allows cybercriminals to easily migrate their malicious servers to hide their Internet protocol addresses behind domain names. Finally, crafting fully representative features is challenging, even for domain experts. This study proposes a feature selection and classification approach for malicious JS code content using Shapley additive explanations and tree ensemble methods. The JS code features are obtained from the Abstract Syntax Tree form of the JS code, sample JS attack codes, and association rule mining. The malicious and benign JS code datasets obtained from Hynek Petrak and the Majestic Million Service were used for performance evaluation. We compared the performance of the proposed method to those of other feature selection methods in the task of malicious JS code content detection. With a recall of 0.9989, our experimental results show that the proposed approach is a better prediction model