Triclocarban (TCC) serves as a highly effective antimicrobial agent with a broad range of applications, often found in daily chemical cleaning and disinfection products. Its accumulation in organisms poses ecological risks and potential health hazards. Therefore, it is essential to detect TCC quickly and with high sensitivity. A multi-taper optical fiber biosensor (OFB) based on local surface plasmon resonance (LSPR) was developed for the immediate and label-free identification of TCC in everyday chemical products. The sensor utilizes multi-mode fiber (MMF) along with a specialized type of nano-doped fiber (NDF). Nano-doped optical fibers feature intensified evanescent field and high sensing sensitivity with favorable environmental stability. Gold nanoparticles (AuNPs) were fixed to the taper section of the sensing fiber probe for generating LSPR. Potassium manganate nanoflowers (KMO-NFs) and nickel oxide nanoparticles (NiO-NPs) were also utilized to alter the fiber probe's surface, enhancing the sensor's specific surface area and accelerating its electronic response, thereby improving its performance. The sensing surface was coated with a TCC antibody in this work. Experimental results show that the biosensor maintained a high degree of linear correlation in the 0-100 μg/L range and a limit of identification is 9.87 μg/L. Examinations confirmed that this biosensor possesses outstanding stability, reproducibility, reusability, selectivity, and adaptability to different pH levels. Real sample tests also yielded satisfactory recoveries, proving the accuracy and reliability of the method, which has good prospects for TCC detection.

DNA binding proteins interact with DNA in a sequence-specific manner or indiscriminately without sequence specificity. While these properties make them highly attractive for assay development, their use as DNA biorecognition elements has remained limited. In this work, we describe a new application using the non-sequence specific Escherichia coli histone-like HU protein for general DNA detection. We engineered a single-chain HU heterodimer (scHupBA) that exhibits high binding affinity for double-stranded DNA (≥50 bp) and considerably lower affinity for single-stranded DNA. By combining this protein with a scHupBA-horseradish peroxidase conjugate, we developed an assay capable of detecting PCR amplicons, either post-purification or directly within a PCR reaction, using microtiter plates and magnetic beads. This system efficiently detected DNA fragments >100 bp regardless of sequence, with detection signals increasing non-linearly with fragment length. To the best of our knowledge, this is the first report of the use of a non-sequence-specific double-stranded DNA binding protein for general DNA detection. The approach described herein is target-independent, it does not require any primer modification or labeling nor the use of expensive reagents. It is an overall cost-effective and efficient method for universal dsDNA amplicon detection, especially for rapid analysis of amplification reactions.

To enable random access in artificial DNA-based storage systems, it is essential to develop a balanced and stable address sequence with minimal cross-correlation, ensuring address independence. The goal of our paper is to design primers suitable for use as addresses within these systems. We extend the construction of secondary structure-avoiding DNA codes from [1] and modify these codes to create primers that meet the necessary specifications. To achieve the desired functionality, the paper presented three methods of primer construction. The first method generates the set of 13[8] primer sequences of length 64 which are mutually uncorrelated, while the other constructions generate a much higher number of primer sequences with weak uncorrelatedness. Finally, the paper concludes with the analysis and the examples for each construction. We also stimulated the primers under insertion, deletion, and substitutuion channels and also provided a further direction for the scheme.

Multiple myeloma (MM) is the second most prevalent hematologic malignancy worldwide. Proteasome inhibitors (PIs) are currently first-line clinical therapies, yet drug resistance has become a major challenge in clinical diagnosis and treatment. The invasiveness of bone marrow biopsies and the low sensitivity of serum monoclonal protein assays impede the early and precise assessment of proteasome inhibitors (PIs) resistance. Circulating MM cells (CMMCs) enable minimally invasive, dynamic monitoring of PI efficacy but are limited by insufficient sensitivity and specificity of current detection methods. In this study, an engineered and robust immunomagnetic nanoprobe (Fe3O4@NH2-MIL88B@CD138/CD38) was fabricated, by effectively loading magnetic Fe3O4 nanoparticles onto metal-organic framework nanorods (NH2-MIL88B) functionalized with dual CD138 and CD38 antibodies. This nanoprobe created an efficient bio-interface to achieve high-efficiency CMMC capture within the range of 8~250 cells/300 μL. Using 1.0 mL peripheral blood from each patient, we captured CMMCs with the Fe3O4@NH2-MIL88B@CD138/CD38 nanoprobe and systematically evaluated the correlations between CMMC counts and key clinicopathologic parameters and clinical therapeutic response to PIs in MM patients. CMMC counts were associated with MM clinical stages. Moreover, dynamic changes in CMMC counts measured by this nanoprobe can sensitively reflect responses to PIs therapy. Specifically, CMMC counts were elevated in nearly all patients with progressive disease and decreased in patients with a favorable treatment response. This work provides a potential index for real-time assessing MM disease progression and PIs treatment outcomes.

In real-world clinical settings, the diverse types and unknown causes of missing data in tabular medical datasets pose significant challenges for accurate imputation. In particular, non-random missingness-where missing values are related to unobserved variables-limits the effectiveness of many existing imputation models. To address these challenges, we propose a robust and generalized imputation method: Multiple Imputation based on Neighborhood Perturbation Denoising Autoencoder (MI_NPDAE). MI_NPDAE identifies optimal donor records by leveraging neighborhood information, which is used as input to the autoencoder. The model reconstructs perturbed inputs to learn robust feature representations around missing regions, while the introduction of additive noise exposes the model to a variety of missingness patterns, enhancing its adaptability. We evaluate MI_NPDAE on two datasets: a publicly available Breast dataset and a lung cancer nutrition dataset from the Chinese Anti-Cancer Society. Experimental results demonstrate that MI_NPDAE consistently outperforms baseline methods across various missing mechanisms and ratios, maintaining lower imputation errors. Moreover, the imputed data significantly improves performance in downstream predictive tasks, highlighting the practical value of our approach in clinical data analysis.

Studying neurotoxicological responses in a physiologically relevant and translatable manner remains a major challenge in biomedical research. Consequently, there has been a major push toward establishing human tissue-native approaches in research and diagnostic pipelines. Here, we present a transparent microfluidic lab-on-a-chip platform integrating an embedded array of enzymatic electrochemical glutamate sensors for real-time monitoring of extracellular neurotransmitter dynamics in human induced pluripotent stem cell-derived (hiPSC) neuronal cell cultures. The system enables continuous, multimodal-compatible interrogation of cellular responses under controlled microenvironmental conditions. We validated the platform by measuring glutamate dynamics in under baseline conditions and following exposure to the environmental neurotoxins methylmercury (MeHg) and manganese (Mn); both known to alter glutamate dynamics. The sensors exhibited stable operation over more than one week in culture and reliably detected glutamate transients with concentrations up to 120 μM glutamate. MeHg exposure resulted in significant alterations in extracellular glutamate relative to control conditions, indicating disrupted glutamate homeostasis. Similarly, neuronal cultures exposed to 500 μM Mn for 24 h demonstrated significantly altered glutamate uptake dynamics. These results validate the proposed platform as a robust tool for investigating neurotoxin-induced perturbations in glutamatergic signaling and demonstrate the feasibility of integrating electrochemical enzymatic sensing into microfluidic systems for neurotoxicity research and discovery.

This study investigates the impact of microchannel geometrical parameters on the separation of glomerular ultrafiltrate in a non-cell-based microfluidic device. The analysis focuses on the device's ability to selectively separate plasma from other blood components, considering parameters such as the radius of curvature at the channel junction, the side channel angle, and the membrane pore shape. Among the configurations tested, a channel junction with radii of curvature Rc1=5 μm and Rc2=0 μm achieved the highest separation efficiency. A straight, perpendicular side channel outperformed other angular variations, while cylindrical membrane pores promoted laminar flow with minimal turbulence and shear stress, enhancing ultrafiltrate separation. The glomerular filtration fraction was observed to be around 20%, closely matching values reported for the human glomerulus under whole blood conditions. The proposed device functions as a glomerular ultrafiltration unit and can potentially be integrated with other modules to develop a complete artificial kidney. This numerical study provides insights into key microchannel design parameters that influence separation efficiency. Moreover, as the device does not rely on cultured cells, it is expected to have a longer operational lifespan and reduced maintenance costs by eliminating the need for cell cultivation.

Herein, a multifunctional nanocomposite was developed through a green synthesis route using Dolomiaea costus extract to produce ZnFe2O4 nanoparticles, which were subsequently integrated with multi-walled carbon nanotubes (MWCNTs) and cellulose acetate (CA) to form a stable ZnFe2O4-MWCNT-CA matrix for horseradish peroxidase (HRP) immobilization. The eco-friendly synthesis avoided toxic precursors by employing plant-derived biomolecules as natural reducing and stabilizing agents. Structural and morphological analyses revealed characteristic XRD reflections of cubic spinel ZnFe2O4 at 2θ ≈ 30.4°, 35.4°, 54.1°, 56.6°, and 62.2°, along with graphitic carbon peaks at 24.1° and 42.8°, confirming the successful integration of ZnFe2O4 and MWCNTs within the cellulose acetate matrix. FTIR spectra showed metal-oxygen vibrations at 565 cm[-1], cellulose acetate bands at 1735 and 1040-1240 cm[-1], and amide bands after immobilization, while SEM-EDX confirmed a porous interconnected morphology and the presence of C, O, Fe, and Zn as the main elements. The immobilized HRP exhibited an immobilization efficiency of 82%, a 0.5-unit alkaline shift in optimum pH (7.0 to 7.5), and an enhanced optimum temperature (50 to 60 °C) compared with the free enzyme. Kinetic analysis revealed an increased apparent Km (4.6 to 10.6 mM) and Vmax (20 to 30 μmol min[-1]), indicating reduced substrate affinity but improved catalytic turnover after immobilization. The immobilized enzyme maintained 81% activity after ten reuse cycles and retained 71% activity after 57 days of storage, outperforming comparable magnetic or carbon-based supports. These results demonstrate that the green-synthesized ZnFe2O4-MWCNT-CA hybrid is a robust, recyclable, and eco-compatible platform for enzyme immobilization, offering significant promise for biocatalysis, pollutant degradation, and industrial wastewater treatment applications.

This research presents the development and optimization of a Tin Oxide (SnO2)-based resistive humidity sensor for accurate monitoring in incubator systems. SnO2 nanomaterials were synthesized using hydrothermal synthesis technique, followed by comprehensive characterization through X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), current-voltage (J-V) analysis, ultravioletvisible (UV-Vis) spectroscopy, and electrochemical impedance spectroscopy (EIS). The best-performing sensor exhibited a flower-like nanostructure with a response time of 18 s, a recovery time of 14 s, and a sensitivity of 72.4%. The sensor functioned effectively within the required incubator humidity range of 40-60% relative humidity (RH) and demonstrated superior performance compared to the commercial DHT11 sensor. To ensure accuracy and reliability, machine learning algorithms were applied to estimate error and validate the correctness of the sensed humidity data. The optimized SnO2 sensor was integrated into an incubator system, with real-time data transmission enabled through an Internet of Things (IoT) platform. These findings highlight the potential of SnO2-based sensors for high-precision humidity monitoring in biomedical applications, particularly for neonatal care where stable humidity levels are critical.

Antimicrobial resistance has been recognized as a global public health problem, contributing to high rates of morbidity, mortality and costs associated with infectious diseases. In 2024, the World Health Organization identified carbapenem-resistant Klebsiella pneumoniae (K. pneumoniae) as the pathogen of greatest importance for research and development of new drugs. In this context, the work analyzes the association of silver nanoparticles with the carbapenem antibiotic Imipenem (IPM) in a multidrug-resistant (MDR) clinical isolate of K. pneumoniae, in comparison to susceptible isolate. The silver nanoparticles (AgNPs) were synthesized using the bottom-up method and characterized through UV-Vis spectroscopy, dynamic light scattering, infrared spectroscopy and X-ray diffraction. Antimicrobial susceptibility tests were carried out on planktonic cells and biofilms. The pH, zeta potential and electrophoretic mobility analysis of the bacteria and antimicrobials was carried out at concentrations identical to those used in the antimicrobial susceptibility assay, aiming to understand the mechanism of internalization and synergistic potentiation. The combination of imipenem (IPM) and AgNPs exhibited an inhibitory effect on planktonic cells and biofilms of the MDR clinical isolate of K. pneumoniae. The surfaces of the bacterial isolates and antimicrobials displayed a negative surface charge. From these results and insights into bacterial electrokinetics, an anionic influx of the antimicrobials through the porin channels can be proposed, leading to increased permeability. The association of silver nanoparticles with IPM was shown to restore the efficacy of the carbapenem antibiotic against MDR K. pneumoniae.

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.

This paper presents a potentiometric enzyme biosensor for acetylcholine based on a zinc oxide (ZnO) thin film. The sensing film is modified with acetylcholinesterase (AChE) and choline oxidase (ChOx) to enhance sensing performance. Furthermore, the sensing film utilizes the surface chemistry of metal oxides, functionalizing the ZnO thin film with (3-aminopropyl) triethoxysilane (APTES). AChE-ChOx is then immobilized on the working electrode via glutaraldehyde (GA) cross-linking. Potentiometric measurements using the time-voltage (V-T) method demonstrate a linear detection range of 10 nM to 100 μM, with a detection limit as low as 6.33 nM, reaching physiological concentrations. This sensor exhibits an average sensitivity of 48.20 mV/decade, a linearity of 0.990, a response time of 17 seconds, and excellent anti-interference performance.

DNA data storage is an emerging data storage method with an extremely long storage duration and high density, yet it currently lacks adequate consideration for data security. Existing encryption methods for DNA storage are based on bit-level encryption, which makes them incompatible with DNA encoding methods based on data characteristics. In this study, we propose a method for directly encrypting DNA sequences via automata cryptography for secure DNA storage (DNA-AC) to solve the above challenges. In DNA-AC, sequence-level encryption is achieved through diffusion and rotation of bases, making the base distribution more uniform and random, improving the security of DNA storage data. Security analysis demonstrates that DNA-AC exhibits a high key space, an information entropy close to 2, a Number of Bases Change Rate (NBCR) of 75% and Bases Average Changing Intensity (BACI) of 42%, showing strong resistance to malicious attacks. Compared to representative works, DNA-AC supports parallel processing to accommodate high-throughput parallel DNA synthesis and sequencing. In general, DNA-AC offers a secure, high-performance, and scalable solution for DNA storage encryption, with the potential to be an ideal data encryption approach for DNA storage devices.

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.

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 R[2] 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.