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Although plasma complement factor B (CFB, NX_P00751), both alone and in combination with CA19-9 (i.e., the ComB-CAN), previously exhibited a reliable diagnostic ability for pancreatic cancer (PC), its detectability of the early stages and the cancer detection mechanism remained elusive. We first evaluated the diagnostic accuracy of ComB-CAN using plasma samples from healthy donors (HDs), patients with chronic pancreatitis (CP), and patients with different PC stages (I/II vs III/IV). An analysis of the area under the curve (AUC) by PanelComposer using logistic regression revealed that ComB-CAN has a superior diagnostic ability for early-stage PC (97.1.% [95% confidence interval (CI): (97.1-97.2)]) compared with CFB (94.3% [95% CI: 94.2-94.4]) or CA19-9 alone (34.3% [95% CI: 34.1-34.4]). In the comparisons of all stages of patients with PC vs CP and HDs, the AUC values of ComB-CAN, CFB, and CA19-9 were 0.983 (95% CI: 0.983-0.983), 0.950 (95% CI: 0.950-0.951), and 0.873 (95% CI: 0.873-0.874), respectively. We then investigated the molecular mechanism underlying the detection of early-stage PC by using stable cell lines of CFB knockdown and CFB overexpression. A global transcriptomic analysis coupled to cell invasion assays of both CFB-modulated cell lines suggested that CFB plays a tumor-promoting role in PC, which likely initiates the PI3K-AKT cancer signaling pathway. Thus our study establishes ComB-CAN as a reliable early diagnostic marker for PC that can be clinically applied for early PC screening in the general public.

In tactical networks, traffic should be delivered in a timely manner satisfying the quality of service (QoS) requirements for survivability and mission success. In this paper, we propose a centralized TDMA slot scheduling based on deep reinforcement learning (DRL) to guarantee the QoS requirements by minimizing end-to-end delay. We consider situations in which mission criticality of tactical traffic is dynamically changing. We introduce a DRL actor-critic algorithm to find a TDMA scheduling policy to minimize the weighted end-to-end delay which is a new metric reflecting the mission criticality of tactical traffic. The simulation results verify that the proposed scheduling policy can guarantee QoS requirements in tactical networks.

In this paper, we propose a deadline-aware routing algorithm that considers a probabilistic delay constraint with a pre-specified deadline for cyber-physical systems (CPS). Most routing algorithms minimize a certain average performance metric such as mean delay. Whereas, our proposed routing algorithm can route traffic to maximize the probability reached within a given deadline by considering the delay distribution rather than the mean delay only. Therefore, our algorithm can enhance the quality of control (QoC) of networked control in CPS. We analyze the proposed routing algorithm by using the queueing delay distribution when the single hop delay follows exponential distribution. We then construct a network topology and carry out simulation to evaluate the performance of the proposed scheme. Our simulation results show that the proposed routing algorithm can effectively increase the probability of delay reached within the deadline and can improve the performance of networked control in CPS.

In this paper, we deal with the problem of assuring medical-grade quality of service (QoS) for real-time medical applications in wireless healthcare systems based on IEEE 802.11e. Firstly, we show that the differentiated channel access of IEEE 802.11e cannot effectively assure medical-grade QoS because of priority inversion. To resolve this problem, we propose an efficient channel access algorithm. The proposed algorithm adjusts arbitrary inter-frame space (AIFS) in the IEEE 802.11e protocol depending on the QoS measurement of medical traffic, to provide differentiated near-absolute priority for medical traffic. In addition, based on rigorous capacity analysis, we propose an admission control scheme that can avoid performance degradation due to network overload. Via extensive simulations, we show that the proposed mechanism strictly assures the medical-grade QoS and improves the throughput of low-priority traffic by more than several times compared to the conventional IEEE 802.11e.

In this paper, we propose an adaptive tuning scheme of IEEE 802.11 WLAN for healthcare applications. Our proposed method can significantly improve medical-grade quality of service (QoS) and network performance at the same time. Though the conventional IEEE 802.11e protocol supports a certain level of QoS, it does not provide medical-grade QoS due to its relative priority among different traffic classes. Our proposed scheme adaptively tunes the arbitrary inter frame space number (AIFSN) of the IEEE 802.11e protocol for enhancing the overall network performance while providing the required medical-grade QoS. In our healthcare scenario, we consider the following three medical traffic categories: medical alarm, real-time electrocardiogram (ECG) transmission, and TCP connection. Our simulation results show that the proposed scheme improves the performance of low-priority TCP traffic while protecting high-priority medical alarms from lower priority traffic.

In this paper, we propose an adaptive tuning algorithm for wireless medical applications to in order to meet medical-grade quality of service (QoS). The conventional IEEE 802.11e protocol is designed to satisfy a certain level of QoS, which is insufficient for medical-grade QoS. In the proposed algorithm, we adaptively tune the AIFS value of the IEEE 802.11e protocol for enhancing the overall network performance while providing the required medical-grade QoS. In our scenario, as representative applications, we consider medical alarms, ECG transmission, and TCP file transfer. We categorize these three applications according to their urgency. Then, we compare the performance of the proposed scheme with the conventional IEEE 802.11e protocol.