Perioperative dynamic alterations in peripheral regulatory T and B cells in patients with hepatocellular carcinoma

<p>Abstract</p> <p>Background</p> <p>Intratumoral and circulating regulatory T cells (Tregs) have been shown to be critical in the pathogenesis of hepatocellular carcinoma (HCC). However there is limited knowledge on the alterations of regulatory B cells (Bregs). We here investigated perioperative dynamic alterations of peripheral circulating Tregs and Bregs in HCC patients to reveal the relationship between regulatory lymphocytes and its clinical implications.</p> <p>Methods</p> <p>36 patients with HCC, 6 with chronic hepatitis B infection and 10 healthy donors were enrolled for this study. Frequencies of peripheral Tregs and Bregs were measured by flow cytometry with antibodies against CD4, CD25, CD127, CD19 and IL-10 before, and after radical surgery. Then, clinical informatics of HCC patients was achieved through Digital Evaluation Score System (DESS) for the assessment of disease severity. Finally, we analysed correlations between digitalized clinical features and kinetics of circulating regulatory lymphocytes.</p> <p>Results</p> <p>Level of circulating CD4⁺CD25⁺CD127^-Tregs in HCC patients was significantly lower than that in healthy donors and patients with chronic hepatitis B infection before surgery, but was increased after surgery. Preoperative level of CD19⁺IL-10⁺Bregs in HCC patients was also significantly lower than the other groups. However it dramatically was elevated right after surgery and remained elevated compared to controls (about 7 days after surgery, <it>P </it>= 0.04). Frequency of circulating Tregs was correlated with circulating leukocytes, ferritin, and clinical features suggesting tumor aggressiveness including portal vein thrombosis, hepatic vein involvement and advanced clinical stages. Frequency of circulating Bregs was associated with Hepatitis B e Antigen (HBeAg) and Hepatitis B virus (HBV) DNA copy number. In addition, DESS was significantly and positively correlated with other staging systems.</p> <p>Conclusion</p> <p>Frequencies of peripheral Tregs and Bregs in HCC patients increased after surgery. These results suggest that a postoperative combination of therapies against Tregs and Bregs may be beneficial for better outcome of HCC patients after resection.</p

How ChatGPT is Solving Vulnerability Management Problem

Recently, ChatGPT has attracted great attention from the code analysis domain. Prior works show that ChatGPT has the capabilities of processing foundational code analysis tasks, such as abstract syntax tree generation, which indicates the potential of using ChatGPT to comprehend code syntax and static behaviors. However, it is unclear whether ChatGPT can complete more complicated real-world vulnerability management tasks, such as the prediction of security relevance and patch correctness, which require an all-encompassing understanding of various aspects, including code syntax, program semantics, and related manual comments. In this paper, we explore ChatGPT's capabilities on 6 tasks involving the complete vulnerability management process with a large-scale dataset containing 78,445 samples. For each task, we compare ChatGPT against SOTA approaches, investigate the impact of different prompts, and explore the difficulties. The results suggest promising potential in leveraging ChatGPT to assist vulnerability management. One notable example is ChatGPT's proficiency in tasks like generating titles for software bug reports. Furthermore, our findings reveal the difficulties encountered by ChatGPT and shed light on promising future directions. For instance, directly providing random demonstration examples in the prompt cannot consistently guarantee good performance in vulnerability management. By contrast, leveraging ChatGPT in a self-heuristic way -- extracting expertise from demonstration examples itself and integrating the extracted expertise in the prompt is a promising research direction. Besides, ChatGPT may misunderstand and misuse the information in the prompt. Consequently, effectively guiding ChatGPT to focus on helpful information rather than the irrelevant content is still an open problem

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals &lt;1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Titanium particles inhibit bone marrow mesenchymal stem cell osteogenic differentiation through the MAPK signaling pathway

Metallic implants have great application in clinical orthopedics. Implants wear out in vivo due to long‐term mechanical loading. The formation of wear debris is one of the long‐term complications of prosthesis. In the case of artificial joint replacement in particular, aseptic loosening is the most common reason for secondary revision surgery. Previous studies suggested that wear debris caused aseptic loosening mainly by promoting osteolysis around the prosthesis. In this study, titanium particles, the most commonly used particles in clinical practice, were selected to simulate wear debris and explore the influence of titanium particles on osteogenic differentiation of mesenchymal stem cells. Our results show that titanium particles can significantly inhibit osteogenic differentiation in a dose‐dependent manner. While engaged in preliminary exploration of the underlying mechanisms, we found that titanium particles significantly affect phosphorylation of ERK1/2, a key component of MAPK signaling. This suggests that the MAPK signaling pathway is involved in the inhibition of osteogenic differentiation by titanium particles

Dynamic Characteristics Analysis of a 660 MW Ultra-Supercritical Circulating Fluidized Bed Boiler

The 660 MW ultra-supercritical circulating fluidized bed (CFB) boiler, which is the maximum capacity and largest scale boiler in the world has entered construction stage in China. This study established a full-scale dynamic simulation model of the 660 MW ultra-supercritical at 100% boiler maximum continuous rating (BMCR) condition. The model consists of an air-flue gas system, a water-steam system, and an ash circulation system. The “core-annulus” of the gas-solid two-phase flow structure and “six-equation” model of water-steam two-phase flow were applied to simulate the behaviors of the gas-solid phase and water-steam system, respectively. The model was calibrated and verified at 100% BMCR condition, and the steady-state simulation results presented a high accuracy compared with the designed parameters. A dynamic simulation of three typical conditions were carried out as well, including a 5% feed water decrease, 5% air decrease, and 5% coal decrease, respectively. The results showed that the dynamic simulation model established in this study can simulate the dynamic behaviors of the 660 MW ultra-supercritical CFB boiler reasonably

A Multi-channel Multi-classifier Method for Classifying Pancreatic Cystic Neoplasms Based on ResNet

© Springer Nature Switzerland AG 2018. Pancreatic cystic neoplasm (PCN) is one of the most common tumors in the digestive tract. It is still a challenging task for doctors to diagnose the types of pancreatic cystic neoplasms by using Computed Tomography (CT) images. Especially for serous cystic neoplasms (SCNs) and mucinous cystic neoplasms (MCNs), doctors hardly distinguish one from the other by the naked eyes owing to the high similarities between them. In this work, a multi-channel multiple-classifier (MCMC) model is proposed to distinguish the two pancreatic cystic neoplasms in CT images. At first, multi-channel images are used to enhance the image edge of the tumor, then the residual network is adopted to extract features. Finally, the multiple classifiers are applied to classify the results. Experiments show that the proposed method can effectively improve the classification effect, and the results can help doctors to utilize the CT images to achieve reliable non-invasive disease diagnosis.status: publishe

Stealing deep reinforcement learning models for fun and profit

This paper presents the first model extraction attack against Deep Reinforcement Learning (DRL), which enables an external adversary to precisely recover a black-box DRL model only from its interaction with the environment. Model extraction attacks against supervised Deep Learning models have been widely studied. However, those techniques cannot be applied to the reinforcement learning scenario due to DRL models' high complexity, stochasticity and limited observable information. We propose a novel methodology to overcome the above challenges. The key insight of our approach is that the process of DRL model extraction is equivalent to imitation learning, a well-established solution to learn sequential decision-making policies. Based on this observation, our methodology first builds a classifier to reveal the training algorithm family of the targeted black-box DRL model only based on its predicted actions, and then leverages state-of-the-art imitation learning techniques to replicate the model from the identified algorithm family. Experimental results indicate that our methodology can effectively recover the DRL models with high fidelity and accuracy. We also demonstrate two use cases to show that our model extraction attack can (1) significantly improve the success rate of adversarial attacks, and (2) steal DRL models stealthily even they are protected by DNN watermarks. These pose a severe threat to the intellectual property and privacy protection of DRL applications