Search

Advancing chimeric antigen receptor (CAR)-engineered T cells for the treatment of solid tumors is a major focus in the field of cellular immunotherapy. Several hurdles have hindered similar CAR T cell clinical responses in solid tumors as seen in hematological malignancies. These challenges include on-target off-tumor toxicities, which have inspired efforts to optimize CARs for improved tumor antigen selectivity and overall safety. We recently developed a CAR T cell therapy targeting prostate stem cell antigen (PSCA) for prostate and pancreatic cancers, showing improved preclinical antitumor activity and T cell persistence by optimizing the intracellular co-stimulatory domain. Similar studies were undertaken to optimize HER2-directed CAR T cells with modifications to the intracellular co-stimulatory domain for selective targeting of breast cancer brain metastasis. In the present study, we evaluate various nonsignaling extracellular spacers in these CARs to further improve tumor antigen selectivity. Our findings suggest that length and structure of the extracellular spacer can dictate the ability of CARs to selectively target tumor cells with high antigen density, while sparing cells with low antigen density. This study contributes to CAR construct design considerations and expands our knowledge of tuning solid tumor CAR T cell therapies for improved safety and efficacy.

In recent years, with the development of modern network, in order to avoid threats caused by cyber attacks, it is important to understand how to implement effective security measures. Malicious traffic detection is an advanced technique, it employs several approaches to distinguish traffic whether it is benign or malicious. Traditional malicious traffic detection methods are usually based on pre-defined signatures. However, limited by the size and timeliness of the signature library, they are usually unable to detect unknown cyber attacks such as zero-day attacks or new malware variants. In order to solve this problem, we propose a machine learning based method to detect realtime malicious traffic. It is divided into two parts: feature extraction on FPGA and abnormal traffic detection on Linux host machine. On the feature extraction part, instead of using conventional network traffic features, we propose a frequency transformation based feature extraction method to extract frequency domain features from network traffic. At the same time, in order to improve the speed of feature extraction and reduce CPU resource usage, we implement the processes required for feature extraction inside the FPGA board. On the abnormal traffic detection part, we use AF-Packet and ring buffer to capture the features, and load a pre-trained model into the CatBoost framework in advance to execute inference process. We evaluate our proposed system on a Xilinx Alveo U50 accelerator card and a Linux host machine. The evaluation results show that we achieve about 0.98 detection accuracy with low resource usage and good realtime detection throughput.

Abstract Chimeric antigen receptor (CAR) T cell therapeutic responses are hampered by limited T cell trafficking, persistence, and durable anti-tumor activity in solid tumors. However, these challenges can be largely overcome by relatively unconstrained synthetic engineering strategies. Here, we describe CAR T cells targeting tumor-associated glycoprotein-72 (TAG72), utilizing the CD28 transmembrane domain upstream of the 4-1BB co-stimulatory domain as a driver of potent anti-tumor activity and IFNγ secretion. CAR T cell-mediated IFNγ production facilitated by IL-12 signaling is required for tumor cell killing, which is recapitulated by engineering an optimized membrane-bound IL-12 (mbIL12) molecule in CAR T cells. These T cells show improved antigen-dependent T cell proliferation and recursive tumor cell killing in vitro, with robust in vivo efficacy in human ovarian cancer xenograft models. Locoregional administration of mbIL12-engineered CAR T cells promotes durable anti-tumor responses against both regional and systemic disease in mice. Safety and efficacy of mbIL12-engineered CAR T cells is demonstrated using an immunocompetent mouse model, with beneficial effects on the immunosuppressive tumor microenvironment. Collectively, our study features a clinically-applicable strategy to improve the efficacy of locoregionally-delivered CAR T cells engineered with antigen-dependent immune-modulating cytokines in targeting regional and systemic disease.

ABSTRACTBackground Periodontitis is caused by a dysbiotic shift in the dental plaque microbiome. Fusobacterium nucleatum is involved in the colonization of Porphyromonas gingivalis, which plays a key role in dysbiosis, via coaggregation and synergy with this microorganism.Aim We investigated the effect of diffusible signaling molecules from P. gingivalis ATCC 33277 on F. nucleatum TDC 100 to elucidate the synergistic mechanisms involved in dysbiosis.Methods The two species were cocultured separated with an 0.4-µm membrane in tryptic soy broth, and F. nucleatum gene expression profiles in coculture with P. gingivalis were compared with those in monoculture.Results RNA sequencing revealed 139 genes differentially expressed between the coculture and monoculture. The expression of 52 genes was upregulated, including the coaggregation ligand-coding gene. Eighty-seven genes were downregulated. Gene Ontology analysis indicated enrichment for the glycogen synthesis pathway and a decrease in de novo synthesis of purine and pyrimidine.Conclusion These results indicate that diffusible signaling molecules from P. gingivalis induce metabolic changes in F. nucleatum, including an increase in polysaccharide synthesis and reduction in de novo synthesis of purine and pyrimidine. The metabolic changes may accelerate biofilm formation by F. nucleatum with P. gingivalis. Further, the alterations may represent potential therapeutic targets for preventing dysbiosis.

Currently, with the increasing popularity of high-speed network, in order to protect the network environment, more and more companies start to explore how to efficiently detect malicious traffic. On the software side, traditional detection systems are usually based on CPU which will consume multi-core processing ability to handle huge network traffic. On the hardware side, current researches focus on using specific hardware to offload some functions such as string matching in malicious traffic detection. However, they cannot detect attack behaviors hidden in TCP out-of-order (OOO) packets well, which are very common in modern complex network environments. To deal with this problem, we present an FPGA-based realtime malicious traffic detection method especially to inspect TCP OOO packets. It employs two core function designs for efficient malicious traffic inspection: TCP OOO reassembly and hierarchical packet match. First, the TCP OOO packets are reassembled to in-order packets to prevent the omission check of malicious traffic. Second, we adapt a hierarchical packet match design which can not only detect the packet header and filter the matching traffic, but also has the ability to inspect the carried payload to further determine whether the traffic is benign or malicious. We use Xilinx Alveo U50 accelerator card as the implementation platform to achieve high speed detection. This paper aims to provide a full detection path and implement all the reassembly and inspection process within an FPGA board. We adapt Cisco TRex as the traffic generator to evaluate the system from detection throughput, resource utilization and power consumption. Compared with the CPU-based approaches, the experiment results show that our system has 485% detection throughput increase and 68% average power decrease.

Abstract One of the complications of esophageal endoscopic submucosal dissection (ESD) is postoperative stricture formation. Stenosis formation is associated with inflammation and fibrosis in the healing process. We hypothesized that the degree of thermal damage caused by the device is related to stricture formation. We aimed to reveal the relationship between thermal damage and setting value of the device. We energized a resected porcine esophagus using the ESD device (Flush Knife 1.5). We performed 10 energization points for 1 s, 3 s, and 5 s at four setting values of the device. We measured the amount of current flowing to the conducted points and the temperature and evaluated the effects of thermal damage pathologically. As results, the mean highest temperatures for 1 s were I (SWIFT Effect3 Wat20): 61.19 °C, II (SWIFT Effect3 Wat30): 77.28 °C, III (SWIFT Effect4 Wat20): 94.50 °C, and IV (SWIFT Effect4 Wat30): 94.29 °C. The mean heat denaturation areas were I: 0.84 mm2, II: 1.00 mm2, III: 1.91 mm2, and IV: 1.54 mm2. The mean highest temperature and mean heat denaturation area were significantly correlated (P

The increased spread of malicious software (malware) through the internet remains a serious threat. Malware authors use obfuscation and deformation techniques to generate new types than can evade traditional detection methods. Hence, it is widely expected that machine learning methods can classify malware and cleanware based on the characteristics of malware samples. This paper investigates malware classification accuracy using static methods for malware detection based on LightGBM by a custom log loss function, which controls learning by installing coefficient $\alpha $ to a loss function of the false-negative side and coefficient $\beta $ to a loss function of the false-positive side. By installing coefficients, we can create a lopsided classifier. We used two malware datasets, non-public and public, to construct a malware baseline model to verify the effectiveness of the proposed method. We extracted the dataset features from PE-file surface analysis and PE-header dumps and customized a binary log loss function to improve all the classification evaluation metrics to a certain extent. We obtained a better result (AUC = 0.979) at $\alpha =430$ and $\beta =339$ than the normal log loss function (AUC = 0.978) on the EMBER dataset. In addition, to maintain malware detection coverage and quick countermeasures to true positive results, we propose a hybrid usage of different custom models to prioritize positive results.

With society’s increasing reliance on computer systems and network technology, the threat of malicious software grows more and more serious. In the field of information security, malware detection has been a key problem that academia and industry are committed to solving. Machine learning is an effective method for processing large-scale data, such as the Gradient Boosting Decision Tree (GBDT) and deep neural network technology. Although these types of detection methods can deal with cyber threats, most feature extraction methods are based on the statistical information features of portable executable (PE) files and thus lack the decompiled code and execution flow structure of the PE samples. Therefore, we propose a Control-Flow Graph (CFG)- and Graph Isomorphism Network (GIN)-based malware classification system. The feature vectors of CFG basic blocks are generated using the large-scale pre-trained language model MiniLM, which is beneficial for the GIN to further learn and compress the CFG-based representation, and classified with multi-layer perceptron. In addition, we evaluated the effectiveness of the representation under different dimensions and classifiers. To evaluate our method, we set up a CFG-based malware detection graph dataset from a PE file of the Blue Hexagon Open Dataset for Malware Analysis (BODMAS), which we call the Malware Geometric Binary Dataset (MGD-BINARY) and collected the experimental results of CFG representation in different dimensions and classifier settings. The evaluation results show that our proposal has proved an Accuracy metric of 0.99160 and achieved 0.99148 Area Under the Curve (AUC) results.

ObjectivesThis study aimed to characterize the content of frequently asked questions about the treatment of rheumatoid arthritis (RA) on the internet in Japan and to evaluate the quality of websites related to the questions.MethodsWe searched terms on the treatment of RA on Google and extracted frequently asked questions generated by the Google "people also ask" function. The website that answered each question was also obtained. We categorized the questions based on the content. The quality of the websites was evaluated using the brief DISCERN, Journal of American Medical Association benchmark criteria, and Clear Communication Index.ResultsOur search yielded 83 questions and the corresponding websites. The most frequently asked questions were regarding the timeline of treatment (n = 17, 23%) and those on the timeline of the clinical course (n = 13, 16%). The median score of brief DISCERN was 11 points, with only 7 (8%) websites having sufficient quality. Websites having sufficient quality based on the Journal of American Medical Association benchmark criteria and Clear Communication Index were absent.ConclusionsThe questions were most frequently related to the timeline of treatment and clinical course. Physicians should provide such information to patients with RA in the counseling and education materials.

In order to proactively mitigate cyber-security risks, security analysts have to continuously monitor sources of threat information. However, the sheer amount of textual information that needs to be processed is overwhelming, and it requires a great deal of mundane labor to separate the threats from the noise. We propose a novel approach to represent the relevance and significance of the cyber-security text in quantitative numbers. We trained custom Named Entity Recognition (NER) model and constructed a Cyber-security Knowledge Graph (CKG) to infer the subjective relevance of the cyber-security text to the user and to generate correlation features. In addition, the significance of the given text was analyzed in terms of its textual similarity with different repositories of pre-defined “significant” text and the maximum similarities were computed. These analysis results then act as features of the classifier to generate the significance score. The experimental result showed that the overall system could determine the significance and relevance of the text within a controlled environment with 88% accuracy.

BackgroundDelayed identification of infiltration and dysfunction of peripheral intravenous (PIV) access can lead to serious consequences during general anesthesia in children. This preliminary study aimed to describe the application of precordial Doppler ultrasound during general anesthesia in children to detect and confirm the correct PIV access and to evaluate the accuracy of this method.MethodsThis was a single-center, preliminary study that was conducted in children (ResultsThe total incidence of peripheral infiltration and dysfunction of PIV catheter was 7/512 (1.4%). In the S test, the sensitivity, specificity, positive and negative likelihood ratios, and area under the receiver-operating characteristic curves (AUCs) were 5/7 (71.4%; 95% confidence interval [CI], 29.0%-96.3%), 490/505 (97.0%; 95% CI, 95.1%-98.3%), 24.0, 0.29, and 0.84, respectively. The V test showed that the reasonable threshold of blood flow velocity change was 1.0 m/s, with sensitivity, specificity, positive and negative likelihood ratios, and AUC of 4/7 (57.1%; 95% CI, 18.4%-90.1%), 489/505 (96.8%; 95% CI, 94.9%-98.2%), 18.0 and 0.44, and 0.84, respectively.ConclusionsThis preliminary study demonstrated that precordial Doppler ultrasound is a feasible, easy-to-use, and noninvasive technique with good accuracy to confirm the correct PIV access during general anesthesia in children. However, its accuracy requires further evaluation.

Advancing chimeric antigen receptor (CAR)-engineered adoptive T cells for the treatment of solid cancers is a major focus in the field of immunotherapy, given impressive recent clinical responses in hematological malignancies. Prostate cancer may be amenable to T cell-based immunotherapy since several tumor antigens, including prostate stem-cell antigen (PSCA), are widely over-expressed in metastatic disease. While antigen selectivity of CARs for solid cancers is crucial, it is problematic due to the absence of truly restricted tumor antigen expression and potential safety concerns with “on-target off-tumor” activity. Here, we show that the intracellular co-stimulatory signaling domain can determine a CAR's sensitivity for tumor antigen expression. A 4-1BB intracellular co-stimulatory signaling domain in PSCA-CARs confers improved selectivity for higher tumor antigen density, reduced T cell exhaustion phenotype, and equivalent tumor killing ability compared to PSCA-CARs containing the CD28 co-stimulatory signaling domain. PSCA-CARs exhibit robust in vivo anti-tumor activity in patient-derived bone-metastatic prostate cancer xenograft models, and 4-1BB-containing CARs show superior T cell persistence and control of disease compared with CD28-containing CARs. Our study demonstrates the importance of co-stimulation in defining an optimal CAR T cell, and also highlights the significance of clinically relevant models in developing solid cancer CAR T cell therapies.

In the neutral complex molecule of the title compound, fac-[CrCl3(tpa)] [tpa is tris(pyridin-2-yl)amine; C15H12N4], the CrIII ion is bonded to three N atoms that are constrained to a facial arrangement by the tpa ligand and by three chloride ligands, leading to a distorted octahedral coordination sphere. The average Cr—N and Cr—Cl bond lengths are 2.086 (5) and 2.296 (4) Å, respectively. The complex molecule is located on a mirror plane. In the crystal, a combination of C—H...N and C—H...Cl hydrogen-bonding interactions connect the molecules into a three-dimensional network.

Loss-of-function mutations in ABCC6 can cause chronic or acute forms of dystrophic mineralization described in disease models such as pseudoxanthoma elasticum (OMIM 26480) in human and dystrophic cardiac calcification in mice. The ABCC6 protein is a large membrane-embedded organic anion transporter primarily found in the plasma membrane of hepatocytes. We have established a complex experimental strategy to determine the structural and functional consequences of disease-causing mutations in the human ABCC6. The major aim of our study was to identify mutants with preserved transport activity but failure in intracellular targeting. Five missense mutations were investigated: R1138Q, V1298F, R1314W, G1321S and R1339C. Using in vitro assays, we have identified two variants; R1138Q and R1314W that retained significant transport activity. All mutants were transiently expressed in vivo, in mouse liver via hydrodynamic tail vein injections. The inactive V1298F was the only mutant that showed normal cellular localization in liver hepatocytes while the other mutants showed mostly intracellular accumulation indicating abnormal trafficking. As both R1138Q and R1314W displayed endoplasmic reticulum localization, we tested whether 4-phenylbutyrate (4-PBA), a drug approved for clinical use, could restore their intracellular trafficking to the plasma membrane in MDCKII and mouse liver. The cellular localization of R1314W was significantly improved by 4-PBA treatment, thus potentially rescuing its physiological function. Our work demonstrates the feasibility of the in vivo rescue of cellular maturation of some ABCC6 mutants in physiological conditions very similar to the biology of the fully differentiated human liver and could have future human therapeutic application.

Burosumab, a fully human monoclonal antibody against fibroblast growth factor 23, is mainly administered to patients with severe X-linked hypophosphatemia (XLH). However, there have been few reports on its use in relatively mild cases. In this report, we administered burosumab to two siblings with XLH who had been effectively treated with oral phosphate and active vitamin D. Both patients showed further improvement in radiographic and laboratory findings with burosumab compared with conventional treatment. Upon switching treatment, popliteal pain was reported in case 1 until her phosphorus levels normalized. This emphasizes the importance of monitoring not only rickets and calcium/phosphate metabolism but all symptoms of XLH after initiating burosumab. Notably, in cases 1 and 2, burosumab sustained catch-up growth, especially in case 1, who had not yet reached puberty. Further clinical studies are needed to determine whether burosumab improves growth and proportional abnormalities in patients with mild XLH. [ABSTRACT FROM AUTHOR]