搜索结果：IEEE transactions on nanobioscience

With the application of DNA strand displacement (DSD) in the synchronization of chaotic systems within a finite time, the finite time synchronization of a single drive system and a single response system has been realized by DNA. Within this article, the finite time combination synchronization of four-dimensional systems is realized by using DSD technology. First, the four-dimensional system and the combination synchronization controllers are realized by using the designed strand displacement reaction. Second, the dynamic characteristics of the designed four-dimensional system are verified by simulation. Third, by cascading the designed four-dimensional system and the combination synchronization controllers, the combination synchronization of three four-dimensional systems in finite time is realized. Numerical simulation results show that DSD can realize combination synchronization of three four-dimensional systems within a finite time. This study highlights the potential of DSD techniques for achieving complex synchronization tasks in chaotic systems. The research in this article further expands the application prospects in security communication, biological computation and other fields.

The global health burden of cardiovascular diseases, including MI (myocardial infarction), CAD (coronary artery disease), heart arrhythmias, cerebrovascular disease, and HF (heart failure), is substantial. As a primary cause of mortality, there is a pressing need for continuous and real-time heart monitoring to identify and treat irregular heart rhythms. PPMs (permanent pacemakers) constantly monitor the heart's spontaneous electrical activity and only activate when it is either defiant or absent. The PPMs under investigation in our research are special implantable biosensors and biotransducers with nanoscale components. The PPMs do not generally contain biochemical reactants but they interact with physiological fluids to be considered as biosensors, and nanobiosensors if they encompass nanomaterials, as for our case. The objective of this study is to determine the reliability of PPM structures that have been implanted in patients who are suffering from one of the cardiovascular diseases over time. Even though the PPMs have been certified for the above use, however, natural patient conditions such as changes in body posture, temperature, or even changes in metabolic demand, can affect their operating modes. The sidewall roughness surface of PPMs is analyzed using atomic force microscopic 3D structural reconstruction, which is based on the grey images of PPMs from CT scanning for each patient. The angular equivocation (also known as angular entropy) approach is implemented to quantify the uncertainty in the distribution of edge or gradient orientation in PPMs images. Then, in order to address nonlinearities and interactions caused by metallic components in the PPM that introduce harmonic and distortions from biological tissues and device motion, we have conducted a bispectral analysis followed by contour representation plots. Different results obtained are of interest for monitoring the state of implantable devices in activity based on CT cardiac examinations in order to preserve the patient's extended life.

DNA strand displacement (DSD) has enabled significant advances in chaotic secure communication. However, DSD-based drive-response systems often suffer from limited complexity and rigid dynamic control, reducing their adaptability under complex biochemical conditions. To address these limitations, this paper proposes a Dislocated Perturbation Secure Communication (DPSC) scheme based on DSD mechanisms. First, a hyperchaotic system is designed according to the dual-rail representation and law of mass action, integrating emergence, catalysis, fasciation, and annihilation modules. The Chen hyperchaotic system is employed as a parameter perturbation source, and an active perturbation strategy is introduced to emulate nonlinear biochemical fluctuations. Subsequently, a DSD-based Dislocated Perturbation Controller (DPC) is constructed to achieve dislocated projective synchronization, ensuring system stability and accurate dynamic mapping between the drive and response states. Finally, the perturbation-driven hyperchaotic signal is utilized for secondary encryption of the drive signal, significantly enhancing communication security. Numerical simulations confirm that the DPSC-encrypted signal exhibits enhanced complexity and improved resilience to external disturbances as compared to traditional chaotic masking techniques. Furthermore, the receiver is capable of reconstructing the initial signal with high fidelity and without distortion.

Transdermal drug delivery has emerged as a promising alternative to conventional invasive methods, offering advantages such as reduced pain, lower infection risk, and improved patient compliance. However, the influence of age-related skin topography, particularly wrinkle-induced variations, on delivery efficacy in terms of time delay and geometry-dependent total dose remains underexplored. This study presents a computational investigation of iontophoretic drug transport using hollow conical microneedles, focusing on age-variant skin profiles characterized by sinusoidal wrinkle patterns. The transdermal delivery of the ionic dermatological agent Dexamethasone Sodium Phosphate is modeled at initial concentrations of 1-5 mg/L, using microneedle lengths of $100~\mu $ m and $150~\mu $ m. The spatial and temporal concentration profiles of drug diffusion within the dermis are simulated over a 30-minute period. COMSOL Multiphysics is employed to optimize microneedle and electrode design parameters by analyzing applied power, terminal resistance, and the time constant of drug permeation. Skin resistance is modeled across a $1000~\mu $ m surface span under three distinct skin conditions: a) smooth/flat skin, b) increased wrinkle amplitude (deeper crests), and c) increased wrinkle frequency (denser undulations). The results provide quantitative insights into how microneedle geometry and age-related skin surface morphology influence iontophoretic transport efficiency. This study offers design guidelines for age-responsive microneedle systems and informs future regulatory considerations in developing transdermal biomedical devices.

Ultrasound-induced vaporization of perfluorocarbon (PFC) nanodroplets can be used for triggered drug delivery. Nanodroplets of perfluorobutane (PFB) and perfluoropentane (PFP) can vaporize spontaneously at physiological temperature, which can cause off-target effects. Using high-boiling-point PFCs, such as perfluorohexane (PFH), can overcome this limitation. However, PFH requires higher peak negative pressures for vaporization, making its in vivo use challenging. We investigated the feasibility of reducing the vaporization pressure threshold by gold-coating lipid-encapsulated PFH nanodroplets (Au-PFH-ND). We synthesized PFH nanodroplets, and the gold-coating was confirmed by UV-visible spectra. The mass of gold per nanodroplet was ${5}.{12}\times {10}^{-{4}}$ pg. The size distribution peaked at 200 nm and had a mean concentration of ${2}\times {10}^{{10}}$ droplets/ml. Au-PFH-ND demonstrated excellent stability over 8 weeks. Ultrasound imaging in vitro was used to determine the pressure threshold for nanodroplet vaporization upon exposure to 2 MHz ultrasound. The vaporization threshold for Au-PFH-ND ( $3.29~\pm ~0.93$ MPa) was significantly lower than uncoated PFH nanodroplets (PFH-ND, $6.19~\pm ~1.25$ MPa). Au-PFH-ND had a similar pressure threshold to uncoated PFP nanodroplets (PFP-ND, $2.81~\pm ~1.08$ MPa). These findings show that the Au-PFH-ND can be vaporized at a similar ultrasound pressure as PFP-ND. Increasing pulse duration from 2 to 60 cycles enhanced vaporization of Au-PFH-ND, demonstrating the dominant role of a thermal mechanism. Even when accounting for the total ultrasound on-time and effective peak negative pressure, longer bursts (i.e., more cycles per burst) were more effective in inducing vaporization, consistent with the role of localized heating around the gold coating rather than a purely probabilistic effect. Additionally, inertial and stable cavitation emissions were quantified. Au-PFH-ND exhibited a marginally lower inertial cavitation threshold and similar second harmonic emissions than PFH-ND, suggesting that cavitation could also have played a role in reducing the pressure threshold. These findings are a step towards employing gold-coated PFC nanodroplets for multimodal drug delivery.

Operant conditioning is a learning mechanism by which animals adapt to its external environment and past experiences. In the field of artificial intelligence, DNA strand displacement (DSD) technology has performed well in various aspects. Chemical reaction networks (CRNs) are constructed using stochastic DSD technology to study operant conditioning, and the simulation results are verified by Visual DSD software. In this paper, the DSD technology is utilized to construct CRNs to achieve different kinds of learning and forgetting processes and generalization in operant conditioning. A comparative analysis is carried out on the four simulation results, and the peak acquisition values of each experiment are compared. The stochastic DSD technology is used to design stochastic CRNs to construct probabilistic decision making systems. The two-way probabilistic decision making of and the three-way probabilistic decision making of animal behaviors are studied. This paper presents the weight variations for each experiment in tabular form. Finally, a comparative analysis is conducted on the probabilistic outcomes of the two-way and three-way probabilistic decision-making experiments. CRNs can be used to achieve realistic behaviors in engineered bionic systems. It provides a direction for the integration of biology and psychology.

Total Cholesterol (TC) monitoring in blood serum is critically important for assessing cardiovascular risk and metabolic health. In this work, we report a nano-bioscience-enabled cholesterol-sensing portable platform that integrates copper oxide (CuO) nanoparticles and a hybrid microfluidic chip, along with smartphone-based chemiluminescence (CL) imaging and computational analysis. The hybrid microfluidic miniaturized chip, fabricated using PDMS material and Whatman filter paper, enables efficient reagent transport, on chip micromixing, and localized signal generation. CuO nanoparticles act as catalytic enhancers for the luminol-H₂O₂ chemiluminescence reaction initiated by cholesterol oxidase, resulting in strengthened and stable photon emission. Chemiluminescence signals were captured using a smartphone camera placed in a 3D-printed dark box and analyzed through a customized mobile application, Intensity Tracker application, for intensity evaluation proportional to cholesterol concentration. The proposed work gives a linear detection range for the cholesterol over a concentration of 0.06-1.5 mM, with a limit of detection of 0.05 mM and a limit of quantification of 0.191 mM, demonstrating strong analytical performance R² ≈ 0.98. Validation using human serum samples yielded recovery values ranging from 92 to 101%, confirming high accuracy and minimal matrix interference when compared with results from a standard biochemistry analyzer. The developed hybrid microfluidic chemiluminescence analyzer features a compact design, low cost, and user-friendly operation, presenting a promising solution for rapid, accurate, and decentralized cholesterol monitoring in point-of-care and resource-limited settings. Future work will focus on multiplexed biomarker detection, and integration with AI-based analysis for real-time clinical decision support.

Diffusion-based mobile molecular communication (MMC) systems have shown great potential in nanoscale communication, particularly in the scenarios involving anomalous diffusion. Accurately modeling the anomalous diffusion channel of MMC system with multiple receivers is a challenge. However, prior studies have predominantly addressed conventional analytical approaches to characterize the channel impulse response (CIR) of static molecular communication system under normal diffusion channel. However, the deduction method cannot adapt to time-varying and complex channel conditions. In this paper, we study a three dimensional MMC system with one transmitter and multiple receivers under anomalous diffusion channel. We propose a method based on deep neural network (DNN) to predict the parameters of the CIR of this MMC system. Simulation results demonstrate that the prediction ability of DNN-based model outperforms the recurrent neural networks (RNN) based and the long short-term memory (LSTM) based models in terms of prediction ability under different anomalous diffusion conditions. The DNN-based model can effectively improve the accuracy of predicting the CIR for this MMC system, providing a new approach for channel modeling in MMC systems with anomalous diffusion.

Molecular communication (MC) offers a bio-inspired paradigm for information transfer in environments inaccessible to conventional electromagnetic waves. However, translating MC concepts to the microscale has been hampered by a lack of integrated, biocompatible testbeds. Inspired by biological spectral-dependent photothermal transduction of specific light wavelengths into thermal energy, we present the first fully integrated microscopic MC platform utilizing photothermally responsive microrobot swarms. Our platform employs core-shell microrobots that exhibit a strong photothermal response, enabling precise and non-invasive navigation within microfluidic channels via near-infrared (NIR) light. This optofluidic architecture facilitates a symbiotic dual-bit encoding scheme, which concurrently modulates information onto both microrobot arrival and the optical control states. We demonstrate a complete communication workflow, from microrobot emission and laser-guided modulation to real-time optical detection and signal demodulation. The system achieves a data rate of 0.63 bits · min-1 with a low bit error rate of 4%, validated by a multi-sampling detection algorithm and the transmission of the ASCII string "HELLO WORLD". This work provides a robust testbed for validating MC theories in biologically relevant microenvironments and serves as a step toward applications in the Internet of Bio-Nano Things.

reusable optical fiber surface plasmon resonance (SPR) sensor for quantitative detection of Pseudomonas aeruginosa is presented. To achieve repeatable measurement, this study used aptamer DNA as the functional monomer and polydopamine as the cross-linking agent to construct a molecularly imprinted nanofilm on the sensing surface. After elution treatment, molecularly imprinted cavities with high matching shape and size to the target molecule are formed in the nanofilm. Experimental results demonstrated that the sensors enhanced by molecular imprinting technology achieved an increase in resonance wavelength response from 10.677 nm to 24.98 nm, a reduction in response time from 22 minutes to 16 minutes, and an improvement in detection limit from 0.00985 OD to 0.00105 OD, compared with sensors only modified with functional monomers. Moreover, to address the problem of temperature interference in practical applications, a Fabry-Perot interferometer (FPI) is innovatively integrated. By taking advantage of the temperature sensitivity but bacterial insensitivity of FPI, the influence of temperature changes on SPR measurement is quantified, effectively providing temperature compensation and further improving the environmental adaptability of the sensor. This study provides a novel, reliable, and cost-effective detection solution for marine environmental microbial monitoring.

A surface plasmon resonance (SPR)-based D-shaped photonic crystal fiber biosensor has been proposed as an effective technique for detecting cancer. Despite several advanced SPR biosensor designs that have been reported to achieve high sensitivity, most exhibit non-uniform responses toward different cancerous cells and lack reconfigurability. Since sensitivity strongly depends on the plasmonic material, distinct sensors are often preferred for specific cancerous cells. However, previous studies on common cancer types have not explicitly addressed the issue of non-uniform sensitivity across different cells, as the widely varying sensitivity has not been systematically analyzed or treated as a key design concern, thereby limiting the general applicability of existing SPR biosensors. In this work, we define and address this gap for the first time by proposing a reconfigurable SPR-based D-shaped PCF biosensor utilizing an Au/Ge2Sb2Te5 (GST) phase change material (PCM) interface. The distinct crystalline and amorphous phases of GST, exhibiting significant optical contrast, provide dual sensing capability and thereby enable different sensitivity responses for the detection of various cancer cells. In the amorphous GST, high sensitivity is observed for skin (4000nm/RIU), cervical (3333.33nm/RIU), and breast II(MCF-7) cancer (2857.14nm/RIU). In contrast, the crystalline phase exhibits high sensitivity in blood (2857.14nm/RIU), adrenal (2857.14nm/RIU), and breast I (MDA-MB-231) cancer (2857.14nm/RIU). Thus, by switching the GST phase, the sensor can be reconfigured to select different cancerous cells. Hence, the reconfigurability of the PCM effectively mitigates the issue of non-uniform sensitivity of conventional SPR-based biosensors, demonstrating the strong potential and versatility for futuristic biosensing technologies.

Intracellular biomolecular circuits often exhibit multimodal stationary distributions due to intrinsic noise, and the dispersion around each peak governs phenotypic robustness and adaptability. However, tuning dispersion by changing reaction parameters typically shifts peak positions or even alters modality. In this paper, we derive conditions that enable peak-shape control without peak shifts using the Chemical Fokker-Planck Equation. First, we formalize sharpness as a peak-local measure via probability ratios and show that the positions of peaks and valleys remain invariant as a control parameter varies. Second, we prove that sharpness varies monotonically as the control parameter increases, while the modality and the positions of extrema remain fixed. We validate these results with Monte Carlo simulations of two univariate networks: the burst-and-trickle gene expression system (unimodal) and the Schlögl system (bimodal), achieving dispersion tuning without peak shifts. Finally, we present preliminary multivariate evidence on the Genetic Toggle Switch, where the marginal distribution of one protein exhibits similar sharpness control. Our results provide structural design rules for engineering stochastic phenotypes while safeguarding the desired modal structure.

The development of reliable point-of-source devices for soil nutrient profiling holds the key to unlocking maximum agricultural output while promoting sustainable practices with minimal environmental impact. The dynamic nature of the soil, its testing protocols, and multistep pre-processing of samples results in time-dependent responses from the sensors increasing the testing time and cost requires additional peripheral equipment. Thus, portability along with precision gets affected simultaneously. Moreover, signal processing, data generation, and acquisition also compromise the soil nutrient assessment. In this work, a standalone device was developed with an alternate soil nutrient quantification protocol for nitrate and potassium, leveraging the capillary forces in the cellulose substrate owed to porous architecture and inter-cellulose fiber voids to eliminate conventional protocols like extraction, centrifugation, and filtration (to eliminate matrix effects) to achieve single-step soil nutrient quantification. Additionally, the use of external 24-bit analog-to-digital conversion (ADC), a quick 2-point calibration smartphone was employed to increase the resolution of the measurements and accuracy of the nutrient measurements. Compared to traditional soil testing methods, the proposed system demonstrated a detection limit and quantization limit of 0.1 mM, with a linear response range of 0.5-21 mM for potassium and 0.2-1.4 mM for nitrate. Precision tests across 15 reuse cycles showed average variability below ±5%, confirming the reliability and repeatability of the sensor. The proposed approach can have broader implications such as the development of portable, low-cost, processing-free, and reliable soil nutrient sensors for in-field applications.

As a non-contact physical intervention technique, pulsed magnetic field (PMF) has been shown to regulate cell membrane permeability. However, the underlying mechanism remains unclear, and their permeabilization efficiency is relatively low. Building on the advantages of magneto-mechanical regulation with magnetic nanoparticles, this study proposes combining PMF with magnetic nanoparticles. By leveraging magneto-mechanical force (MMF) as the central mechanism, the aim is to enhance cell permeabilization rate through optimization of the applied force magnitude. First, a theoretical analysis of the forces acting on magnetic nanoparticles was performed to guide particle parameter selection. Next, the effects of PMF alone and its combination with magnetic nanoparticles on cell membrane permeability were examined through in vitro experiments. Finally, fluorescence probes were used to investigate the biochemical mechanisms underlying cell permeabilization induced by both treatments. The permeabilization experiment results showed that the combined treatment significantly enhanced cell permeabilization. Compared to PMF treatment alone, the half-maximal effective dose decreased by 27.85%, and the rate of change in permeabilization rate increased by 49.7%. Fluorescence staining further revealed that, unlike the biochemical pathways activated by PMF treatment alone, the combined treatment caused multiple disruptions in cytoskeletal microfilaments, confirming that it induced cell permeabilization through a physical mechanism involving mechanical stress. This study leveraged the MMF generated by magnetic nanoparticles under PMF to regulate cell membrane permeability, providing a novel approach for precise control of cell membrane permeability based on physical parameters.

With the rapid development of science and technology, various intelligent devices continuously generate large amounts of real-time location data. Efficiently utilizing this data for accurate location prediction has become a critical issue in fields such as intelligent transportation and smart logistics. To realize low-power location prediction, this paper constructs a molecular recurrent neural network (RNN) model based on DNA strand displacement (DSD) technology. The RNN model can process sequence data and accurately predict the next position. Firstly, multiple modules are designed based on DSD circuits, including dual-channel weighted summation module, dual-domain data module and Tanh activation function module. Secondly, a RNN model for processing sequence data is constructed using the above modules. Finally, the constructed RNN model successfully achieves position prediction for multiple inputs and a single output. The robustness and accuracy of the neural network are verified through data experiment. It has been demonstrated that DNA molecules can effectively process complex sequence data. This method holds significant potential in the field of path planning. This method holds significant potential in the field of path planning. This paper uses MAE and RMSE to evaluate the experimental data. The results prove that the RNN model constructed in this paper demonstrates strong accuracy and stability.

Data-driven channel models often require extensive simulation data and capture only limited statistics of the underlying physical process. To address these limitations in diffusion-based particle signaling, we build upon physics-informed machine learning to develop a framework that fuses sparse channel measurements with governing diffusion-reaction laws. This work explores physics-informed operator learning for particle-based communication channels, aiming to bridge mechanistic PDE modeling and data-driven surrogates in this domain. Our method employs a Physics-Informed Neural Operator (PINO) to predict the spatiotemporal particle concentration field and generalizes across channel configurations with reduced dependence on explicit geometric parameterization. We further extend the framework to model quorum sensing between bacterial colonies and capture autoinducer dynamics. Compared with Physics-Informed Neural Networks (PINNs) and Deep Operator Networks (DeepONets), PINO achieves high accuracy and significant computational efficiency: on the nanomachine channel, PINO reduces the relative ℓ2 error from 99.3% to 9.2%; on the quorum sensing model, PINO improves R2 from 0.808 (DeepONet) to 0.999, while multi-resolution inference yields 4-5× speed-ups on coarse grids. These results highlight physics-informed operator learning as a promising method for particle-based communication networks.

Magnetic Particle Spectroscopy (MPS) is a highly sensitive, label-free technique for detecting biomolecular interactions through the nonlinear magnetization of magnetic nanoparticles (MNPs). This study presents a comparative performance evaluation of four commercial carboxyl-functionalized MNPs as: Synomag®, Perimag®, Resovist®, and SHP-30 (Ocean NanoTech), to assess their biosensing suitability using MPS. Measurements at 5, 15, and 25 kHz in water, glycerol, and agarose characterized medium- and frequency-dependent relaxation: SHP-30 exhibited predominantly Brownian relaxation with the highest sensitivity to hydrodynamic size changes; Perimag showed slower Brownian behavior with reduced sensitivity; Resovist was predominantly Néel-dominated; and Synomag displayed mixed relaxation. For biosensing efficiency, all four MNPs were conjugated with H1N1 hemagglutinin protein via EDC-NHS chemistry, and bio-conjugation was confirmed by FT-IR (amide I/II) and DLS (increased hydrodynamic size). ICP-MS quantified the retained iron content after conjugation and washing, and all samples were normalized to the same iron mass for MPS measurement. Frequency-tuned MPS measurements identified that SHP-30 exhibited significantly greater signal suppression at low frequencies (~7.74 kHz) upon protein binding, enabling protein detection limit down to 10 nM. Collectively, these findings establish SHP-30 as a highly sensitive and efficient candidate for biomarker-conjugated MPS diagnostics, with potential utility in infectious disease detection and point-of-care applications.

In this work, we consider a three-dimensional slow diffusive heterogeneous media-based mobile molecular communication (MC) system, with the communicating devices as point transmitters and passive spherical-shaped receiver nanomachines (NMs). For the considered slow diffusive MC system, we propose a time-varying stochastic diffusivity-based model for communicating devices and information-carrying molecules, and we characterize the mobile MC channel by the channel impulse response (CIR) and derive its mean. For the considered slow and stochastic diffusivity-based mobile MC system, we propose a novel silence-based multi-type hybrid transmission scheme, which combines communication through silence (CtS) with molecular shift keying (MoSK) and concentration shift keying (CSK) and we derive the closed-form expression for the average probability of error. For the slow diffusive environment, we compare the proposed transmission scheme with the position and concentration-based run-length aware, MoSK, and CSK transmission schemes. For the proposed silence-based multi-type hybrid and considered position and concentration-based run-length aware transmission schemes, we design their respective maximum likelihood (ML) threshold detectors. The proposed scheme outperforms and shows robust behavior in the presence of inter-symbol interference.

Molecular Communication (MC) utilizes chemical molecules to transmit information, introducing innovative strategies for pharmaceutical interventions and enhanced immune system monitoring. This paper explores Molecular communication-based approach to disrupt Quorum Sensing (QS) pathways to bolster immune defenses against antimicrobial-resistant bacteria. Quorum Sensing enables bacteria to coordinate critical behaviors, including virulence and antibiotic resistance, by exchanging chemical signals, known as autoinducers. By interfering with this bacterial communication, we can disrupt the synchronization of activities that promote infection and resistance. One of the key points is a discussion of the RNAIII-inhibitor (RIP) that blocks RNAII and RNAIII synthesis in the Accessory Gene Regulator (AGR) system, being important transcripts determining the production of toxins and immune evasion in Staphylococcus aureus. This interference in effect cripples the bacterial defensive mechanisms against immune responses hence promoting the host capability to recognize and kill the pathogen. In addition, QS inhibitors such as RIP can be combined with established antimicrobials to synergistically lower the necessary dose of the latter agent to alleviate the resistance selective pressure. Overall, this MC-based method does not only focus on taking care of bacterial virulence on a communication level but also allows to create an environment that promotes a more effective and stronger immune response, which seems a highly encouraging trend in managing resistant bacterial infections.