IEEE Transactions on NanoBioscience最新文献

Saccharomyces cerevisiae (S. cerevisiae) can be used to treat diarrhea and the diseases associated with malaria, but it may cause invasive infections in individuals with weakened immune systems. While antibiotics are available for treating these infections, they can also produce side effects in the body. Additionally, macrophages in the human immune system can engulf infected S. cerevisiae, but they have some limitations, including a relatively weak active chemotactic response and a prolonged engulfing process. To address this, this paper proposes a novel method to enhance the phagocytosis of S. cerevisiae by macrophages using optically induced dielectrophoresis (ODEP). ODEP is a cellular micromanipulation technique that directs phagocytes towards S. cerevisiae cells, significantly improving phagocytosis efficiency. Furthermore, the optical electrodes created by ODEP expedite the phagocytosis process. To validate this approach, the optimal operating parameters for ODEP were determined through a combination of numerical simulations and experiments, enabling the swift and precise capture of phagocytes targeting S. cerevisiae. A comparative experiment was conducted to assess macrophage phagocytosis of S. cerevisiae with and without the aid of optical electrodes. Results showed that the time required for macrophages to engulf S. cerevisiae was reduced by 50%, highlighting a promising method for the early prevention or accelerated treatment of invasive S. cerevisiae fungal infections.

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.

Droplet-based microfluidics enables miniaturized, high-throughput biochemical assays but faces challenges in selective droplet retrieval, particularly after long-term monitoring. While light-induced bubble generation offers a promising, hardware-simplified strategy for releasing individual droplets from passive traps, current implementations suffer from fabrication complexity or slow-release kinetics. To overcome these limitations, we previously developed a light-responsive fluorosurfactant using fluorinated plasmonic nanoparticles (f-PNPs) that enables millisecond-scale vapor bubble formation and efficient droplet release using a 532 nm laser, without requiring integrated photothermal materials. However, automation of this approach was limited by the need for manual droplet identification and release decisions. In this work, we introduce a fully automated Fluorescence-Activated Droplet Release (FADR) system by integrating the light-triggered release mechanism with a deep-learning-based droplet detector. This AI module autonomously identifies and localizes droplets in real-time, triggering selective release based on fluorescence intensity without human intervention. The closed-loop FADR platform offers a scalable and intelligent solution for precise droplet manipulation, enabling robust, high-throughput screening workflows with minimal hardware complexity.

A number system is a method for representing numbers. Efficient conversion between digital systems is crucial in human-computer interaction and computer information processing. This paper first constructed two SNP systems for the conversions between decimal and binary. Then, a family of SNP systems is constructed, which can convert between two arbitrary number systems. Examples are given to illustrate and verify the feasibility and effectiveness of these SNP systems. Compared with advanced similar systems, the proposed SNP system requires only approximately 1/3 the time slices for decimal-to-binary conversion and approximately 43% for binary-to-decimal conversion.

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 5.12×10^-4 pg. The size distribution peaked at 200 nm and had a mean concentration of 2×10¹⁰ 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 ± 0.93 MPa) was significantly lower than uncoated PFH nanodroplets (PFH-ND, 6.19 ± 1.25 MPa). Au-PFH-ND had a similar pressure threshold to uncoated PFP nanodroplets (PFP-ND, 2.81 ± 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.

Spiking Neural P system (SNP system) is a distributed parallel computing model inspired by the information processing of biological neurons. The SNP system has emerged as a research hotspots in the field of membrane computing, being utilized to tackle NP-hard problems and widely applied in solving practical issues. In this paper, we introduce a SNP system, Fast Division Calculation Spiking Neural P System (FDCSNPS), which is designed to reduce division operation time and minimize the number of spiking neurons. The system flow, input, control, and functional modules of FDCSNPS are discussed in detail. At the same time, the complexity of the system is analyzed, and its feasibility is verified by an example. Compared with the division SNP system based on multiple subtractions, which requires a time slice O(2^k), FDCSNPS only needs O(k²) time slices to complete k-bit binary division.

DNA-based data storage has emerged as a compelling alternative to traditional media due to its ultra-high information density and long-term stability. However, the high read cost caused by the error-prone synthesis, storage, and sequencing processes remains a major bottleneck for practical deployment. To address this challenge, this paper proposes a read-cost-efficient coding framework that enhances reliability without increasing total redundancy. First, a novel two-layer intra-oligo coding scheme based on Bose-Chaudhuri-Hocquenghem (BCH) codes is presented, where index bits and data bits are respectively protected to mitigate base-level errors. Second, a semi-analytical optimization method based on the normal approximation of the finite-length coding rate is developed to allocate redundancy between index and data bits optimally under a fixed total code rate. The inter-oligo protection is further achieved through low-density parity-check (LDPC) codes to combat sequence-level errors.We then present extensive analytical and numerical results to show the effectiveness of the proposed analysis. Finally, we present numerical results to demonstrate that the concatenated code based on the optimized two-layer coding scheme significantly outperforms the concatenated code based on the single-layer coding scheme in terms of frame error rate (FER) under the same sequencing depth and total redundancy. These results underscore the advantages of the two-layer coding scheme and the optimization method for DNA-based data storage systems.

In this study, Hydroxypropyl methyl cellulose-stabilized selenium nanoparticles (HPMC-SeNPs) were successfully synthesized and utilized as a support for pepsin immobilization via glutaraldehyde crosslinking. Characterization through SEM, FTIR, and XRD confirmed their structural integrity, while zeta potential (-12.61 mV) and DLS (PDI: 0.1818) indicated good colloidal stability and uniform size distribution. The immobilized pepsin (HPMC/Se-Pep) demonstrated high immobilization efficiency (81.25%) and yield (78.73%). The enzymatic system exhibited enhanced stability over a wider pH (2-6) and temperature range (20-60 °C), with improved kinetic parameters-lower Km (0.16 mM) and higher Vmax (0.94 μmol/min)-indicating stronger substrate affinity and better catalytic performance than free pepsin. Functional assays revealed significantly enhanced caseinolytic and antimicrobial activities, with inhibition zones of 22 ± 0.05 mm (S. aureus) and 21 ± 0.12 mm (E. coli), outperforming both native NPs and free enzyme. Notably, the immobilized pepsin achieved complete removal of blood stains within 30-40 min and retained 60.2% of its activity after five reuse cycles, confirming excellent operational stability. However, a gradual decline in enzymatic activity was observed after repeated reuse and prolonged storage, indicating the need for further optimisation to enhance long-term stability. These results demonstrate that HPMC-SeNPs are an effective platform for enzyme immobilization, offering improved performance, reusability, and multifunctionality. The developed system holds strong potential for applications in biocatalysis, textile processing, and healthcare industries.