IEEE Transactions on NanoBioscience最新文献

The advent of evanescent field based fiber optic biosensor and advancements in nanotechnology has create an excellent opportunity in label-free detection of biomarkers which plays vital role in the early, rapid and accurate diagnosis of acute diseases. In this work, we demonstrate a high sensitive Molybdenum Tungsten Disulfide (MoWS2) coated side polished fiber (SPF) biosensor for accurate and early diagnosis of cardio vascular disease (CVD). The Cardiac Troponins I (cTnI) is identified as a biomarker of interest for early and rapid diagnonis of CVD. The proposed SPF biosensor exhibits surface plasmonic resonance (SPR) detection due to the evanescent field interaction between MoWS2 nano coated side polished region and anti-CTnI. The proposed SPF biosensor possess the high sensitivity of 82% to detect the cTnI antibody with a limit of detection (LOD) about 17.5 pg/mL. The peak SPR shift have been calculated as 61 nm for analyte concentrations of 500 pg/mL Moreover, the proposed SPF biosensor possess the high degree of selectivity and environmental stability to CTnI among three analytes such as CTnI, Estrogen and Glucose. The hydrophobic interactions of MoWS2 and cTnI antibody leads to chemical free biofunctionalization of antibody in the sensing region. Hence, the simulation results shows the surface interaction strength calculated as 1.29 KJ mol^-1/nm² in order to evaluate the hydrophobic interactions. Thus, the proposed optical biosensor is a promising candidate for "point-of-care" testing of CVD disorders and preclinical assessments.

Objective: This study aimed to develop doped carbon dots and coat them with carboxyl-polymer to explore their applications in imaging living tissue cells and achieving targeted drug release, particularly for tumor therapy.

Methods: The synthesis of NP-CDs involved a one-pot hydrothermal reaction of seaweed powder, ethylene diamine, and phosphoric acid at atmospheric pressure. Subsequently, the NP-CDs were coated with carboxyl-mounted PEG to create PEG@NP-CDs, serving as a nano carrier for delivering the anti-cancer drug Doxorubicin (DOX). The drug delivery capabilities of PEG@NP-CDs were assessed, and their sensitivity to variations in pH value was studied.

Results: The hydrothermal reaction successfully yielded NP-CDs with distinctive fluorescence properties, exhibiting green fluorescence at 430 nm and varying emission peaks depending on the excitation wavelength used. The subsequent coating of NP-CDs with carboxyl-mounted PEG resulted in PEG@NP-CDs, which demonstrated biocompatibility and potential for drug delivery applications. The MTT assay confirmed the high biocompatibility of PEG@NP-CDs, rendering them suitable for biomedical applications.

Conclusions: The study successfully developed a straightforward method to synthesize CDs doped with nitrogen and phosphorus, which exhibited green fluorescence and sensitivity to excitation wavelengths. These nanomaterials have potential for imaging living tissue cells and achieving slow drug release. Their drug delivery capabilities, especially pH sensitivity, make them promising for targeted therapy, particularly in tumors. The biocompatibility of PEG@NP-CDs further supports their safe biomedical use. Overall, PEG@NP-CDs offer a valuable tool for simultaneous imaging and drug delivery, with promising applications in tumor detection and therapy.

The formation of spatial patterns plays a crucial role in the study of system spatiotemporal dynamics. Previous research has demonstrated that spatial patterns can effectively characterize the macro-scopic spatial structure of the reaction-diffusion system. While specific pattern structures, such as the hexagonal, mixed, and stripe pattern, have been identified, the interconnection between these patterns appears to be isolated and invariant. To facilitate the selection and switching between individual spatial patterns, the hybrid control strategy is applied to the bimolecular model for the first time. For the classical bimolecular model of the chemical reaction-diffusion system, the incorporation of two-dimensional diffusion extends its reaction space to the two-dimensional plane. The Turing instability conditions are obtained for the controlled bimolecular system. Through the weakly nonlinear analysis, the amplitude equations are derived near the Turing bifurcation threshold. Furthermore, we investigate the impact of each control parameter on the Turing bifurcation threshold and determine the distribution of spatial patterns and their stability through the amplitude equations. Simulation results indicate that by selecting appropriate control parameters, we can suppress the occurrence of Turing instability and facilitate transitions between the spatial patterns. The findings of the analysis offer valuable insights into the dynamics and control of pattern formation in reaction-diffusion systems.

DNA-based storage has emerged as a promising storage paradigm due to its immense storage potential. However, the error-prone nature of DNA sequencing and synthesis processes limits this potential. Image data is typically compressed before storage, and even a single mismatch can lead to catastrophic error propagation during decompression, rendering the image unrecoverable. To reduce the error rate of DNA storage-based image compression, we have designed a high fault-tolerant DNA image storage system and applied it to image compression for DNA storage. This system achieves significant improvements in both image data compression ratio and resilience through three key innovations: 1) Using a Variational Autoencoder (VAE) to compress the image into uniformly sized latent variable blocks, followed by further compression via Singular Value Decomposition (SVD); 2) Quantizing the floating-point numbers in the latent variable blocks and applying rotational coding to the resulting ternary sequences, effectively ensuring positive constraints on homopolymer run lengths and GC content; 3) Optimizing the error-correction scheme to best recover each type of error by quantizing it back to its original value. Through image scaling, we adjust the compression ratio, and the comparative results of image compression simulations demonstrate the performance of the proposed model, highlighting its superiority in fault tolerance and storage density.

DNA is emerging as a promising medium for storing huge volumes of data in a confined space that remains intact for thousands of years. Although this technique is very efficient, especially for multimedia data like images, there is a lack of efficient searching and retrieval technique. This paper addresses this issue and proposes a novel Content Based Image Retrieval (CBIR) technique to retrieve similar images from the generated DNA-based image feature vectors. The features are obtained by a novel encoding scheme that uses the three Most-Significant Bits from the images and converts them into a string of nucleotides that follow run length and GC constraints to form DNA planes stored in a DNA medium. The nucleotides in these planes are interpreted through three consecutive sequences forming codons. The codon-based features are then utilized to perform instance-based image retrieval. The DNA planes are further adapted and implemented on diverse deep learning architectures, including ResNet-50, VGG-16, VGG-19, and Inception V3, to facilitate classification-based image retrieval tasks. The system's performance has been assessed using a range of datasets, encompassing coral, medical, and multi-label images. Experimental results demonstrate that the proposed approach achieves notable improvements when compared to existing state-of-the-art methods.

This study proposed a miniaturized multi-sensor device prepared using a flexible printed circuit board (FPCB) and applied to detect glucose (Glu) and pH value, where both the readout circuit board and the sensors possess flexible characteristics. Additionally, this work implemented the potentiometric readout circuit. It integrated the die onto the readout circuit board using wire bonding techniques, while the area of the readout circuit board is 5.5 cm × 4.0 cm. The readout circuit board is equipped with a power supply, a readout circuit chip, and a multi-sensor. It is worth mentioning that this study designs the multi-sensor in a double-sided manner. The advantage of this design lies in the fact that both sides of the sensor can be utilized as a working electrode or reference electrode, providing convenience to users during measurement analysis. In addition, the magnesium oxide (MgO) multi-sensor is interconnected with the readout circuit board using slot type. This means the MgO multi-sensor can also be used as a disposable sensor. In this study, the multi-sensor system can measure hydrogen ions and Glu at the same time. The sensitivity of the two is 25.27 mV/pH and 16.78 mV/mM, respectively, and the linearity can reach 99.9 %.

Phyto-mediated synthesis can be used for the sustainable fabrication of metallic nanoparticles (NPs). Ethanolic leaf extract of Acacia jacquemontii was used to phyto-fabricate iron oxide nanoparticles (FeO NPs). Synthesized FeO NPs were examined by various analytical techniques for their detailed chemical elemental and morphological features. High-performance thin-layer chromatography (HPTLC) was used for the analysis of the ethanolic extracts of the leaves, which ensured the presence of phenols and terpenoids in the extract. FeO NPs show a peak between 350-400nm when analyzed by UV-Vis spectroscopy, and the typical bands were found in the range of 745 cm^-1 for Fe-O and 1595 cm^-1, 3177 cm^-1 for some other organic molecules by Fourier transform-infrared (FTIR). The spherical shape of FeO NPs was investigated with the help of a Field emission scanning electron microscope (FESEM) analysis which exhibited the size varied from 13.35 to 31.29 nm. Electron diffraction spectroscopy (EDS) confirmed the Fe, O, and C peaks, along with N, Cl, S, and K traces. The adsorption capacity of the FeO NPs for brilliant green (BG) dye was evaluated at different pH, dosages of adsorbent, and contact time. The highest adsorption parentage of 57.2% for 10 ppm BG dye was observed at 9 pH and 10 mg doses of FeO NPs. The highest absorption capacity of FeO NPs is 60 mg/g. The recyclability potential of the FeO NPs continuously decreased with the repletion of the cycle from first to fourth whose value reached 19.33% after the fourth cycle. Such phytofabricated FeO NPs and their application in the removal of organic could prove to be eco-friendly and economical.

Recent experimental studies have shed light on the intriguing possibility that ion channels exhibit cooperative behaviour. However, a comprehensive understanding of such cooperativity remains elusive, primarily due to limitations in measuring separately the response of each channel. Rather, only the superimposed channel response can be observed, challenging existing data analysis methods. To address this gap, we propose IDC (Idealisation, Discretisation, and Cooperativity inference), a robust statistical data analysis methodology that requires only voltage-clamp current recordings of an ensemble of ion channels. The framework of IDC enables us to integrate recent advancements in idealisation techniques and coupled Markov models. Further, in the cooperativity inference phase of IDC, we introduce a minimum distance estimator and establish its statistical guarantee in the form of asymptotic consistency. We demonstrate the effectiveness and robustness of IDC through extensive simulation studies. As an application, we investigate gramicidin D channels. Our findings reveal that these channels act independently, even at varying applied voltages during voltage-clamp experiments. An implementation of IDC is available from GitLab.

Deoxyribonucleic acid (DNA) has become an ideal medium for long-term storage and retrieval due to its extremely high storage density and long-term stability. But access efficiency is an existing bottleneck in DNA storage, especially the lack of high-quality random access address sequences. Therefore, in this paper, we report a series of approaches based on k-weakly mutually uncorrelated (k-WMU) codes to design the address sequence to improve the access efficiency of DNA storage. To address the problem of DNA sequences that are poorly scalable at the base level, we propose a 0-m-ruling coding scheme combined with k-WMU codes that can make address sequences avoid generating secondary structure with stem lengths ranging from 3 to 9. Based on the decoupled structure, We further extend the k-WMU codes with error correction function while satisfying combinatorial biological constraints. In order to investigate the performance of the designed address sequences for real-world applications, we perform simulation experiments based on thermodynamic properties and error correction capability as well as compared the minimum free energy (MFE), melting temperature (TM), and average decoding success rate (ADSR) with previous work. The results show that designed address sequences have a high MFE value and ADSR and a substantial reduction in TM-variance while satisfying the combinatorial biological constraints. As the quality of address sequences improves, this will help to achieve accurate random access as well as enhance the robustness of the DNA storage system.

Diffusive molecular communication (DMC) is an emerging paradigm in nanotechnology, which provides biocompatibility and nanoscale communication for many promising applications, such as targeted drug delivery, environmental monitoring, etc. However, detecting and localizing abnormalities in most of these applications is challenging, such as identifying tumor cells within the body or detecting pollution in air or water. In this paper, we introduce a method for detecting and localizing abnormalities in three dimensional DMC system with multiple sensors, receivers and one fusion center by adopting Transformer-based model with attention mechanism. We make full use of the attention mechanism to capture the inter-symbol interference (ISI) to improve the accuracy of detection and localization. In addition, we simplify the model structure to significantly reduce the complexity of this model. Furthermore, two strategies that different types of molecules (DMT) and same type of molecules (SMT) are released by sensors are considered. The training dataset and testing dataset are generated under these two strategies. Simulation results show that the information about the abnormality detection and localization can be obtained at the same time based on the Transformer-based model under DMT and SMT. Especially, our model outperforms the Informer-based model, deep neural networks (DNN)-based model and log-likelihood ratio (LLR) method.