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.

Electrochemical Nanosensor-Based Emerging Point-Of-Care Tools: Progress and Prospects

Early detection of disease remains a crucial challenge in medicine. Delayed diagnosis often leads to limited treatment options, increased disease progression, and unfortunately, even death in some cases. To address this, the need for rapid, cost-effective, and noninvasive diagnostic tools is paramount. In recent years, electrochemical nanosensor-based point-of-care diagnostic tools have emerged as promising tools for various fields, with significant interest in their biological and chemical applications. These tiny sensors, utilizing nanoparticles and chemical agents, can detect and monitor physical components like disease biomarkers at the nanoscale, offering a unique advantage rarely found in other diagnostic methods. This unprecedented sensitivity has made them highly sought-after tools for biological applications, particularly in disease diagnosis. This review focuses specifically on electrochemical nanosensors and their potential as diagnostic tools in medicine. We will delve into their properties, applications, current advancements, and existing limitations.

Molecular Beamforming for Actuation in Molecular Communication Networks

The actuation accuracy of sensing tasks performed by molecular communication (MC) schemes is a very important metric. Reducing the effect of sensors fallibility can be achieved by improvements and advancements in the sensor and communication networks design. Inspired by the technique of beamforming used extensively in radio frequency communication systems, a novel molecular beamforming design is proposed in this paper. This design can find application in tasks related to actuation of nano machines in MC networks. The main idea behind the proposed scheme is that the utilization of more sensing nano machines in a network can increase the overall accuracy of that network. In other words, the probability of an actuation error reduces as the number of sensors that collectively take the actuation decision increases. In order to achieve this, several design procedures are proposed. Three different scenarios for the observation of the actuation error are investigated. For each case, the analytical background is provided and compared with the results obtained by computer simulations. The improvement in the actuation accuracy by means of molecular beamforming is verified for a uniform linear array as well as for a random topology.

COTiR: Molecular Communication Model for Synthetic Exosome-Based Tissue Regeneration

Mesenchymal stem cell (MSC)-derived exosomes are recognized as an unparalleled therapy for tissue damage rendered by COVID-19 infection and subsequent hyper-inflammatory immune response. However, the natural targeting mechanism of exosomes is challenging to detect the damaged tissue over long diffusion distances efficiently. The coordinated movement of exosomes is desired for successful identification of target sites. In this work, we propose a molecular communication model, CoTiR, with a bio-inspired directional migration strategy (DMS) for guided propagation of exosomes to target the damaged tissues. The model includes directional propagation, reception, and regeneration of tissue. The proposed model has the potential to be used in designing efficient communication systems in the nanodomain. We compare the proposed model to the basic random propagation model and show the efficacy of our model regarding the detection of multiple targets and the detection time required. Simulation results indicate that the proposed model requires a shorter period of time for a similar number of exosomes to detect the targets compared to the basic random propagation model. Furthermore, the results reveal a 99.96% decrease in the collagen concentration in the absence of inflammatory cytokine molecules compared to the collagen concentration in the presence of inflammatory cytokine molecules.

Estimating Time Window to Administer Anti-Cytokine Therapeutic Drugs to COVID-19 Patients

The severe COVID-19 infection often leads to "Cytokine Release Syndrome (CRS)", which is a serious adverse medical condition causing multiple organ failures. Anti-cytokine therapy has shown promising results for the treatment of the CRS. As part of the anti-cytokine therapy, the immuno-suppressants or anti-inflammatory drugs are infused to block the release of cytokine molecules. However, determining the time window to infuse the required dose of drugs is challenging due to the complex processes involving the release of inflammatory markers, such as IL-6 and C-reactive protein (CRP) molecules. In this work, we develop a molecular communication channel to model the transmission, propagation, and reception of cytokine molecules. The proposed analytical model can be used as a framework to estimate the time window to administer anti-cytokine drugs to get successful outcomes. Simulation results show that at a 50 s-1 release rate of IL-6 molecules, the cytokine storm is triggered at ~ 10 hours, and consequently, the CRP molecules reach the severe level of 97 mg/L at ~ 20 hours. Further, the results reveal that with one-half of the release rate of IL-6 molecules, the time to observe the severe level of 97 mg/L CRP molecules increases by 50%.

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.