Early Cancer Detection in Blood Vessels Using Mobile Nanosensors

Prediction of outcomes after chemoradiotherapy for cervical cancer by neutrophil-to-lymphocyte ratio and platelet-to-lymphocyte ratio

BACKGROUND

Cervical cancer ranks as the second most fatal tumour globally among females. Neutrophil-to-lymphocyte ratio (NLR) and platelet-to-lymphocyte ratio (PLR) have been widely applied to the diagnosis of cancers.

METHODS

The clinicopathologic data of 180 patients with stage IB2-IIB cervical cancer who underwent radical concurrent chemoradiotherapy from January 2018 to December 2019 were retrospectively analysed. Receiver operating characteristic (ROC) curves were plotted to analyse the optimal cut-off values of NLR and PLR for predicting the therapeutic effects of concurrent chemoradiotherapy. The associations of PLR and other clinicopathological factors with 1-year survival rates were explored through univariate analysis and multivariate Cox regression analysis, respectively.

RESULTS

NLR was significantly associated with the therapeutic effects of neoadjuvant therapy, with the optimal cut-off value of 2.89, area under the ROC curve (AUC) of 0.848 (95% confidence interval [CI]: 0.712-0.896), sensitivity of 0.892 (95% CI: 0.856-0.923) and specificity of 0.564 (95% CI: 0.512-0.592). PLR had a significant association with the therapeutic effects of neoadjuvant therapy, with the optimal cut-off value of 134.27, AUC of 0.766 (95% CI: 0.724-0.861), sensitivity of 0.874 (95% CI: 0.843-0.905) and specificity of 0.534 (95% CI: 0.512-0.556). Lymphatic metastasis ([95% CI: 1.435-5.461], [95% CI: 1.336-4.281], depth of invasion ([95% CI: 1.281-3.546], [95% CI: 1.183-3.359]) and tumour size ([95% CI: 1.129-3.451], [95% CI: 1.129-3.451]) were independent factors influencing the overall survival and disease-free survival (DFS) of patients with cervical cancer. NLR (95%CI: 1.256-4.039) and PLR (95%CI:1.281-3.546) were also independent factors affecting DFS.

CONCLUSION

NLR and PLR in the peripheral blood before treatment may predict DFS of patients with stage IB2-IIB cervical cancer.

Smart Chemical Sensor and Biosensor Networks for Healthcare 4.0

Driven by technological advances from Industry 4.0, Healthcare 4.0 synthesizes medical sensors, artificial intelligence (AI), big data, the Internet of things (IoT), machine learning, and augmented reality (AR) to transform the healthcare sector. Healthcare 4.0 creates a smart health network by connecting patients, medical devices, hospitals, clinics, medical suppliers, and other healthcare-related components. Body chemical sensor and biosensor networks (BSNs) provide the necessary platform for Healthcare 4.0 to collect various medical data from patients. BSN is the foundation of Healthcare 4.0 in raw data detection and information collecting. This paper proposes a BSN architecture with chemical sensors and biosensors to detect and communicate physiological measurements of human bodies. These measurement data help healthcare professionals to monitor patient vital signs and other medical conditions. The collected data facilitates disease diagnosis and injury detection at an early stage. Our work further formulates the problem of sensor deployment in BSNs as a mathematical model. This model includes parameter and constraint sets to describe patient body characteristics, BSN sensor features, as well as biomedical readout requirements. The proposed model's performance is evaluated by multiple sets of simulations on different parts of the human body. Simulations are designed to represent typical BSN applications in Healthcare 4.0. Simulation results demonstrate the impact of various biofactors and measurement time on sensor selections and readout performance.

Evaluation of Non-Coherent Signal Detection Techniques for Mobile Molecular Communication

In recent years, there have been more and more research on molecular communication (MC). Because the deployment of mobile nanomachines may be required in some applications of MC, research on mobile MC has become a trend. The signal detection schemes for static MC are no longer applicable due to the time varying channel impulse response (IR), which is caused by the mobile characteristics of nanomachines. In this paper, a low complexity and non-coherent detection scheme is proposed for mobile scenario, which is based on the energy difference between two adjacent symbols. Most of the existing signal detection methods do not consider inter-symbol interference (ISI). Compared with those methods, the proposed scheme can achieve signal detection utilizing ISI without knowing channel state information (CSI). The bit error rate (BER) performance of the proposed method is investigated under different conditions through simulations. Besides, the influence of mobility features of nanomachines on the signal detection accuracy is also investigated in detail. The simulation results demonstrate that the BER performance of the proposed scheme outperforms the latest signal detection scheme for short-distance mobile MC system with high velocity. Consequently, the detection scheme proposed in this paper can reduce the influence of nanomachines' mobility and has the potential to be used in mobile MC systems.

DNA strand displacement reactions to accomplish a two-degree-of-freedom PID controller and its application in subtraction gate

Synthesis control circuits can be used to effectively control biochemical molecule processes. In the controller design based on chemical reaction networks (CRNs), generally only the tracking set-point is considered. However, the influence of disturbances, which are frequently encountered in biochemical systems, is often neglected, thus weakening the control effect of the system. In this article, tracking set-point input and suppressing disturbance input are considered in the control effect. Firstly, CRNs are adopted to construct a two-degree-of-freedom PID controller by combining a one-degree-of-freedom PID controller with a feedforward controller for the first time. Then, CRN expressions of the two input functions (step function and ramp function) used as input signals are defined. Furthermore, the two-degree-of-freedom PID controller is founded by DNA strand displacement (DSD) reaction networks, because DNA is an ideal engineering material to constitute molecular devices based on CRNs. The overshoot of the two-degree-of-freedom PID control system is significantly reduced compared to the one-degree-of-freedom PID control system. Finally, a leak reaction is treated as an extraneous disturbance input to a subtraction gate. The influence of external disturbance is solved by the two-degree-of-freedom PID controller. It is worth noting that the two-degree-of-freedom subtraction gate control system better restrains the impact of a disturbance input (leak reaction).

dMole: A Novel Transreceiver for Mobile Molecular Communication Using Robust Differential Detection Techniques

This paper proposes two differential detection techniques for signal detection in mobile molecular communication (MMC) for targeted drug delivery (TDD) application. In MMC, a nano-transmitter and a nano-receiver are considered to be in Brownian motion in an extracellular fluid medium. Transmitter uses calcium molecules to communicate with the receiver. Detection is performed using concentration difference based detector (CDD) at the receiver which calculates the maximum absolute concentration difference of the received signal within the same bit interval to detect the bit. This improves the bit error rate (BER) performance in MMC. The performance is further enhanced using manchester coded transmission with differential detection (MCD). In MCD, Bit-1 is coded by the symbol [1 0] and Bit-0 is coded by the symbol [0 1] and the difference between peaks of signals received in consecutive bit duration is taken to detect the bit. Simulation results prove that the MCD technique is 3 dB less sensitive to inter symbol interference (ISI) than the CDD technique. The detection threshold is selected using maximum a posteriori probability (MAP) rule. The performance of these detectors is compared with other existing detection techniques. Results reveal that BER performance of the CDD and MCD better by at least 3 dB and 6 dB, respectively. The proposed CDD and MCD techniques perform better in different bit-sequence length, various initial distance and different bit duration than other existing techniques.

Initial Distance Estimation and Signal Detection for Diffusive Mobile Molecular Communication

Mobile molecular communication (MC) attracts much attention in recent years where mobile nanomachines exchange information using molecules. In this paper, we consider a diffusion-based mobile MC system consisting of a pair of diffusive nanomachines. Due to the Brownian motion of nanomachines, the communication distance between the transmitter and the receiver is dynamic. Thus, the channel impulse response (CIR) is a stochastic process. The stochastic CIR brings the difficulty in the detection process. Contrast to the common static MC characterized by the deterministic CIR, in a mobile MC system, the receiver needs to estimate the dynamic distance for CIR reconstruction and detection threshold setting at each bit interval, which achieve high computational complexity. To tackle these difficulties, a new detection technique for mobile MC is proposed in this paper. It is unnecessary to estimate the dynamic communication distance at each bit interval. Instead, the receiver estimates the initial distance between it and the transmitter. The estimated initial distance can be used to reconstruct the statistical characteristics of CIR, setting detection threshold for all the bit intervals in advance. To achieve this goal, a novel two-step scheme based on maximum likelihood (ML) is proposed to estimate the initial distance. In the first step, the transmitter releases some molecules as a pilot signal before the information bits transmission. Then the receiver estimates releasing distance by observations of received signal. In the second step, the estimated value obtained in the previous step is used to estimate the initial distance by ML estimation. The performances of proposed two-step scheme and the detection technique are evaluated via particle-based simulation of the Brownian motion.

Ant-Behavior Inspired Intelligent NanoNet for Targeted Drug Delivery in Cancer Therapy

Targeted drug delivery system is believed as one of the most promising solutions for cancer treatment due to its low-dose requirement and less side effects. However, both passive targeting and active targeting rely on systemic blood circulation and diffusion, which is actually not the real "active" drug delivery. In this paper, an ant-behavior inspired nanonetwork composing of intelligent nanomachines is proposed. A big intelligent nanomachine take small intelligent nanomachines and drugs to the vicinity of of the tumor area. The small intelligent nanomachines can coordinate with each other to find the most effective path to the tumor cell for drug transportation. The framework and mechanism of this cooperative network are proposed. The route finding algorithm is presented. The convergence performance is analytically analyzed where the influence of the factors such as molecule degradation rate, home-destination distance, number of small nanomachines to the convergence is presented. Finally the simulation results validate the effectiveness of the proposed mechanism and analytical analysis.

Current Trends of Nanobiosensors for Point-of-Care Diagnostics

In order to provide better-quality health care, it is very important that high standards of health care management are achieved by making timely decisions based on rapid diagnostics, smart data analysis, and informatics analysis. Point-of-care testing ensures fast detection of analytes near to the patients facilitating a better disease diagnosis, monitoring, and management. It also enables quick medical decisions since the diseases can be diagnosed at an early stage which leads to improved health outcomes for the patients enabling them to start early treatment. In the recent past, various potential point-of-care devices have been developed and they are paving the way to next-generation point-of-care testing. Biosensors are very critical components of point-of-care devices since they are directly responsible for the bioanalytical performance of an essay. As such, they have been explored for their prospective point-of-care applications necessary for personalized health care management since they usually estimate the levels of biological markers or any chemical reaction by producing signals mainly associated with the concentration of an analyte and hence can detect disease causing markers such as body fluids. Their high selectivity and sensitivity have allowed for early diagnosis and management of targeted diseases; hence, facilitating timely therapy decisions and combination with nanotechnology can improve assessment of the disease onset and its progression and help to plan for treatment of many diseases. In this review, we explore how nanotechnology has been utilized in the development of nanosensors and the current trends of these nanosensors for point-of-care diagnosis of various diseases.

Optimal Transmitted Molecules and Decision Threshold for Drift-Induced Diffusive Molecular Channel With Mobile Nanomachines

We study a drift-induced diffusive mobile molecular communication system where source, destination and cooperative nanomachines follow the one-dimensional Brownian motion. For information exchange from source nanomachine to receiver nanomachine, both direct and decode-forward (DF) relay-assisted cooperative paths are considered. The closed-form expressions for the probabilities of detection and false alarm are derived at the cooperative and destination nanomachines considering the multiple-source interference (MSI) and the inter-symbol-interference (ISI). The closed-form expressions for end-to-end average probability of error, and maximum achievable rate are also obtained. Moreover, to achieve minimum expected probability of error the optimum number of molecules to be transmitted from transmitter and optimal detection threshold in receiver nanomachine are found. The analytical expressions are validated through particle-based and Monte-Carlo simulation methods.