Dietary zinc intake and its effects on zinc nutrition in healthy Japanese living in the central area of Japan

In the present study, we first examined the dietary zinc intake from food groups in 109 healthy Japanese (24-82 years old, 45 male and 64 female) by means of the 72-h recall method. We then used the ratio of apo/holo-activities of angiotensin converting enzyme (ACE ratio) that is a more sensitive index of zinc nutrition than zinc concentration in the serum and examined the correlation between their zinc intake and ACE ratio. Dietary zinc intake in healthy Japanese was maximal from rice and rice products. There were significant inverse correlations between the ACE ratio and dietary zinc intake from rice and rice products and shellfish, and a significant positive correlation between ACE ratio and dietary zinc intake from other beans and bean processed foods. On the other hand, there were no significant correlations between serum zinc concentrations and dietary zinc intake from any food group except processed fish. These findings suggested that rice is a major source of dietary zinc intake in healthy Japanese. It is also suggested that shellfish also has a major impact on zinc nutrition, although dietary zinc intake from this source is minimal. Since beans contain phytic acid, which inhibits the absorption of dietary zinc, it is suggested that intake of beans causes impairment of zinc nutrition

Algorithmic Versus Expert Human Interpretation of Instantaneous Wave-Free Ratio Coronary Pressure-Wire Pull Back Data

Objectives The aim of this study was to investigate whether algorithmic interpretation (AI) of instantaneous wave-free ratio (iFR) pressure-wire pull back data would be noninferior to expert human interpretation. Background Interpretation of iFR pressure-wire pull back data can be complex and is subjective. Methods Fifteen human experts interpreted 1,008 iFR pull back traces (691 unique, 317 duplicate). For each trace, experts determined the hemodynamic appropriateness for percutaneous coronary intervention (PCI) and, in such cases, the optimal physiological strategy for PCI. The heart team (HT) interpretation was determined by consensus of the individual expert opinions. The same 1,008 pull back traces were also interpreted algorithmically. The coprimary hypotheses of this study were that AI would be noninferior to the interpretation of the median expert human in determining: 1) the hemodynamic appropriateness for PCI; and 2) the physiological strategy for PCI. Results Regarding the hemodynamic appropriateness for PCI, the median expert human demonstrated 89.3% agreement with the HT in comparison with 89.4% for AI (p < 0.01 for noninferiority). Across the 372 cases judged as hemodynamically appropriate for PCI according to the HT, the median expert human demonstrated 88.8% agreement with the HT in comparison with 89.7% for AI (p < 0.0001 for noninferiority). On reproducibility testing, the HT opinion itself changed 1 in 10 times for both the appropriateness for PCI and the physiological PCI strategy. In contrast, AI showed no change. Conclusions AI of iFR pressure-wire pull back data was noninferior to expert human interpretation in determining both the hemodynamic appropriateness for PCI and the optimal physiological strategy for PCI

NS record History Based Abnormal DNS traffic Detection Considering Adaptive Botnet Communication Blocking

DNS (Domain Name System) based name resolution is one of the most fundamental Internet services for both of the Internet users and Internet service providers. In normal DNS based name resolution process, the corresponding NS (Name Server) records are required prior to sending a DNS query to the authoritative DNS servers. However, in recent years, DNS based botnet communication has been observed in which botnet related network traffic is transferred via DNS queries and responses. In particular, it has been observed that, in some types of malware, DNS queries will be sent to the C&C servers using an IP address directly without obtaining the corresponding NS records in advance. In this paper, we propose a novel mechanism to detect and block abnormal DNS traffic by analyzing the achieved NS record history in intranet. In the proposed mechanism, all DNS traffic of an intranet will be captured and analyzed in order to extract the legitimate NS records and the corresponding glue A records (the IP address(es) of a name server) which will be stored in a white list database. Then all the outgoing DNS queries will be checked and those destined to the IP addresses that are not included in the white list will be blocked as abnormal DNS traffic. We have implemented a prototype system and evaluated the functionality in an SDN-based experimental network. The results showed that the prototype system worked well as we expected and accordingly we consider that the proposed mechanism is capable of detecting and blocking some specific types of abnormal DNS-based botnet communication

Malicious DNS Tunnel Tool Recognition using Persistent DoH Traffic Analysis

DNS over HTTPS (DoH) protocol can mitigate the risk of privacy breaches but makes it difficult to control network security services due to the DNS traffic encryption. However, since malicious DNS tunnel tools for the DoH protocol pose network security threats, network administrators need to recognize malicious communications even after the DNS traffic encryption has become widespread. In this paper, we propose a malicious DNS tunnel tool recognition system using persistent DoH traffic analysis based on machine learning. The proposed system can accomplish continuous knowledge updates for emerging malicious DNS tunnel tools on the machine learning model. The system is based on hierarchical machine learning classification and focuses on DoH traffic analysis. The evaluation results confirm that the proposed system is able to recognize the six malicious DNS tunnel tools in total, not only well-known ones, including dns2tcp, dnscat2, and iodine, but also the emerging ones such as dnstt, tcp-over-dns, and tuns with 98.02% classification accuracy

Detection of DGA-based Malware Communications from DoH Traffic Using Machine Learning Analysis

2023 IEEE 20th Consumer Communications & Networking Conference (CCNC). 08-11 January 2023. Las Vegas, NV, USA