Massive use of antibiotics in veterinary medicine has led to their accumulation in meat and dairy products. Consumption of antibiotic-contaminated food can trigger the development of antibiotic-resistant bacteria, endangering human lives. Among antibiotics, the oxytetracycline (OTC) family of antibiotics is most widely used in veterinary medicine. Strict control of the antibiotics in food necessitates the development of fast and effective methods for OTC detection, for instance, in milk. One of the most promising approaches to OTC detection is based on the use of specially designed DNA aptamers. These DNA aptamers are relatively short, 15-60 bases, nucleotides folded in the solution in a 3D structure, forming a binding site for the target antibiotic. Aptamers can be chemically modified for attachment to sensor electrodes. In this work, we investigated the electrochemical detection of OTC using DNA aptamers specific to OTC that have been covalently immobilized onto the nanocomposite surface of a glassy carbon electrode with electrodeposited reduced graphene oxide and multiwalled carbon nanotubes. Differential pulse voltammetry, DPV, in the presentence of a ferri/ferrocyanide redox couple was used as an internal standard and to monitor the redox current. In the presence of OTC, the amplitude of DPV decreased, evidencing the blocking of charge transfer. After system optimization, we reached the limit of detection of 0,45 ng OTC /ml, which is 200 times lower than the maximum residue limit established by the European Commission of 100 ng OTC /mg.

This work was funded under the European Union’s Horizon 2020 research and innovation program through the Marie Skłodowska-Curie grant agreement No. 101007299 (T.H.), the Science Agency VEGA, project No. 1/0445/23 (T.H.). Part of the project was sponsored by the Center for Nanophase Materials Sciences (CNMS), user proCNMS2022-A-01196, which is a US Department of Energy Office of Science User Facility at Oak Ridge National Laboratory.

A wireless miniature epiretinal stimulator implant is reported. The power and data delivery to the implant is achieved using near-infrared pulses of laser beam operated within the safe illumination intensities. The the implant's 256 electrodes are controlled using an application-specific integrated circuit (ASIC). The ASIC is powered using an advanced photovoltaic (PV) cell and programmed using a single photodiode. High density packaging methods are using to implement the implant circuitry, as well as individual connections between a stimulator chip and 256 electrically conductive nitrogen-doped ultrananocrystalline diamond electrodes with a high charge injection capacity needed for effective simulation of retinal ganglion cells. The device is encapsulated in an optically transparent, mechanically robust and bioinnert diamond capsule. A PV cell with a monochromatic power conversion efficiency of 55% and operated within the safety limits is used to provide 15 mW of power the implant. A photodiode is used to detect the pulse modulate forward data telemetry of stimulation parameters at bandwidth of 3.7 MHz. Laser power delivery enables a high degree of miniaturisation and lower surgical complexity, partially when compared to implants using coils for power and data transfer. This development provides a route to fully wireless miniaturized minimally invasive implants with sophisticated functionalities, in the eye or under the skin.

Serological diagnostics, crucial for disease detection and monitoring, rely on qualitative antibody tests for viral infections (e.g., COVID-19, HIV), bacterial infections, and autoimmune diseases. Immunoenzymatic assays from whole blood often encounter errors due to sample complexity. While pre-separation protocols mitigate these issues, they are costly and time-consuming. Small-scale IgG purification currently involves complex manual processes using microplates coated with bacterial protein, extending analytical time. To address this, cost-effective microfluidic devices using simple technology and materials are being developed, enabling the miniaturization of advanced analytical systems compatible with optical and electrochemical detection. This innovation holds promise for enhancing serological diagnostics by streamlining IgG separation and contributing to the construction of more efficient health monitoring systems.

The presented study investigates the construction of low-cost, flexible and disposable platforms for rapid separation of whole serum antibodies by their reversible capture using protein A/G immobilized on polyester foil substrates. A simple microsystem is fabricated by laser cutting and bonding biomedical films, while the flow is generated by an external pump. The process of capturing and eluting antibodies from the sample is based on a pH-controlled sequence of injections carried out entirely in a single-channel microfluidic system. In addition, a microfluidic mixer module is developed to allow neutralization of the pH of the eluate.

The developed system allows pre-treatment of small-scale samples prior to their further use in bioanalytical assays (e.g., ELISA, lateral flow or homophasic agglutination tests). The developed, simple and versatile IgG separation module can also find application in more advanced, integrated μTAS systems for the analysis of serological profiles or capture of antibody-antigen complexes.

Conductive hydrogels possess promising potential as sensor materials due to their biocompatibility and mechanical flexibility, mimicking the properties of human skin. However, limitations such as lack of stretchability, hardness, and fatigue resistance hinder their sensing capabilities and durability. This study addresses these challenges by developing an extremely flexible, robust, and anti-fatigue conductive nanocomposite hydrogel.

The synthesized nanocomposite hydrogel, denoted as NIPAm_lap_GNP, was obtained through a two-step process involving free-radical polymerization. Initially, Laponite was dispersed in a solution containing gold nanoparticles (GNP) via sonication. Subsequently, NIPA monomer was added, followed by the initiation of polymerization with ammonium persulfate (APS) and N,N,N′,N′-tetramethylethylenediamine (TEMED) at room temperature, yielding a homogeneous solution.

Characterization of the resulting hydrogel composite included assessments of stretchability, toughness, and fatigue resistance using mechanical testing equipment such as a tensile meter, compression meter, and rheometer. Results revealed impressive mechanical properties, with tensile stress ranging from 50 to 60 kPa, a tensile elongation of 1300-1400%, a compressive stress of approximately 25-30 kPa, and a toughness of 27.76 MJ.m^-3. Furthermore, the hydrogel demonstrated remarkable motion sensitivity through electrochemical measurements, exhibiting a linear response to tensile strain (up to 250%) and bending finger angles (15-120°).

In conclusion, the NIPAm_lap_GNP hydrogel exhibited exceptional mechanical properties, including high stretchability and toughness, making it suitable for wearable sensor applications. The incorporation of gold nanoparticles (GNP) significantly enhanced electrical conductivity, while Laponite (Lap) increased crosslinking points and mechanical stability. This innovative nanocomposite hydrogel holds promise for various applications in flexible and conductive wearable sensors. Future research directions may explore its potential in other biomedical or electronic devices.

Heart failure (HF) continues to represent a leading cause of hospitalization and mortality worldwide, with an increasingly high prevalence as a result of population growth and ageing. HF patients are at higher risk of sudden cardiac death (SCD), which is frequently associated withthe cardiac arrythmias that can stem from electrolyte imbalances such as magnesium (Mg²⁺) and potassium (K⁺) deficiencies. Therefore, the regular monitoring of electrolyte levels can enable a timely identification of these depletions, improving patient management and outcomes.

In this work, calmagite (1-(1-hydroxy-4-methyl-2-phenylazo)-2-naphthol-4-sulfonic acid) was employed as a direct dye-complexing method to determine the concentration of Mg²⁺ in a sample. In an alkaline medium, calmagite is blue but, in the presence of Mg²⁺, it forms a metallized reddish complex, whose color is concentration dependent. This method provided a fast response, which was determined using both a UV–Vis spectrophotometer and a smartphone camera for RGB (Red, Green, Blue) analysis. With an increasing Mg²⁺ concentration (0.005-0.3 mM), the ratio between the light absorbance at 620 nm and at 520 nm decreased, while the Red value increased, enabling detection. The specificity of the assay was tested using different ionic solutions that are present in body fluids, namely sodium chloride, calcium chloride and potassium chloride, with EGTA (ethylene glycol-bis(β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid) used to dampen calcium interference.

This research paves the way for this method's application at the point of care to monitor Mg²⁺ levels by demonstrating its rapid response, low limit of detection and simple instrumentation, aiming to improve HF management and prevent SCD.

Bacterial lipopolysaccharides (LPSs) are important indicators of a bacteria presence in any samples. They can therefore be used for the detection of microbiological contamination in food and dairy products. We performed a comparative analysis of different bacterial models by the application of liposomes containing LPS from Salmonella enterica serotype typhimurium on the surface of an 11-mercaptoundecanoic acid (MUA) monolayer chemisorbed on the gold surface of quartz crystal. Using quartz crystal microbalance with dissipation monitoring (QCM-D), we were able to monitor the formation of the lectin, concanavalin A (ConA), layer on the MUA surface. We determined the optimal concentration of the ConA for the layer formation. ConA of 0.3 mg/mL was selected as the most suitable adsorption of liposomes containing LPS. Using the Sauerbrey equation, we calculated that approximately 1.13x10¹² ConA molecules per cm² was adsorbed on the MUA surface, which closely corresponds to the 1.19x10¹² molecules per cm² by theoretical models. Later, mixed LPS liposomes containing dipalmitoyl phosphatidyl choline (DPPC), dipalmitoyl phosphatidyl ethanolamine (DPPE) and cholesterol successfully interacted with the ConA layer, which resulted in a decrease in the resonant frequency and an increase in dissipation. We compared the adsorption of liposomes with different fractions of LPS and containing LPS from different bacteria. Lack of any LPS in liposomes caused weaker adsorption on the ConA layer. Liposomes containing 50 % LPS caused the most prominent adsorption and were suitable for interaction with DNA aptamers specific to certain LPS. The addition of the aptamers to the surface of ConA covered by LPS-containing liposomes resulted in a decrease in resonant frequency and an increase in the dissipation. Using the Kelvin–Voigt viscoelastic model and multiharmonic response of acoustic sensors, we also determined changes in viscoelastic values of the molecular films during interaction with liposomes and the ConA layer.

This study presents a novel data-driven modeling approach employing machine learning to develop predictive "soft sensors" for real-time monitoring of ethanol and substrate levels during bioethanol fermentation processes. By utilizing readily measurable parameters such as pH, redox potential, capacitance, and temperature, the model enables continuous prediction of less frequently measured variables including ethanol, substrate, and cell concentrations. Eleven fermentations were conducted, focusing on intensified ethanol production from sugarcane substrate, utilizing cell cycling techniques to augment output. Despite the importance of fermentation data, its acquisition is often constrained by limitations in availability and resources. To address these challenges, this research integrates synthetic time series data generation, thereby enhancing the applicability of machine learning. Through the use of a variational autoencoder (VAE), synthetic time series data was successfully generated, facilitating training and testing of a deep neural network on both original and synthetic datasets. Results demonstrate a significant 30% increase in prediction robustness with the incorporation of generated data, while maintaining comparable accuracy levels. The augmented data effectively enhance the generalization ability of trained models, mitigating overfitting and expanding decision boundaries, thereby overcoming challenges associated with small datasets and inevitable data deviations. This innovative approach offers a promising avenue for enhancing the reliability and scalability of bioethanol fermentation monitoring through AI-based biosensors.

An optical crystalline silicon biosensor was developed for the detection of heavy metals in surface water, deep water, mollusks and sediment in the lagoon of La Paz, Baja California Sur, Mexico, to monitor the presence of heavy metals, the biosensor was built using self-assembled monolayers, the silicon supports were cut with a diameter of 0.5 cm x 0. 5 cm, chemical modifications were made on the surface by adding KOH to obtain Si-OH groups, for the functionalization this was carried out by 3-aminopropyltrimethoxysilane to add NH₂ groups on the surface of the biosensor, the activation was with EDC/NHS as a crosslinking agent, Finally, the urease enzyme was immobilized on the surface of the biosensor in an orbital shaker at 100 rpm in PBS and proceeded to detect each of the concentrations of standard heavy metals methylmercury chloride, cadmium, lead, chromium oxide VI, arsenic oxide III and silver iodide at different concentrations. Subsequently, detection was performed on biological samples by taking the internal part of tissues placed in PBS under refrigeration and water samples were measured without treatment. The self-assembly and detection was characterized by FTIR in the region from 370 to 4000 cm^-1 taking the region from 1000 to 1200 cm^-1 , 1500 to 1700 cm^-1 and from 2800 to 3100 cm^-1 as the most important regions for the principal component analysis, showing that in these regions the characteristic bonds of silicon are present, The functionalization showed the region of the primary and secondary amide and finally the detection was taken as the inhibition of the enzymatic activity, the principal component analysis showed the region where the detection of each heavy metal is performed and corroborates the results obtained in FTIR.

Flexible strain sensors have attracted a lot of interest lately, especially in the field of wearable sensors, electronic skin and soft robotics. They are conformable, making them easy to use, and cause minimum discomfort. There is thus a huge demand for highly sensitive, fast-responding, flexible piezoresistive materials that can be used in wearable sensors and point-of-care diagnostic instruments. The most common approach toward developing such materials is adding conductive fillers to elastomeric polymers, the sensitivity of which depends on the filler concentration and homogeneity of the composite, resulting in variations in properties and making them complex to fabricate. Here, we present the fabrication and application of a flexible, conformable, thin piezoresistive sensor design that can be used for pressure sensing in electronic skin application.

Highly exfoliated graphene oxide (GO) solution in de-ionized water was initially synthesized using the the modified Hummers method. Lint-free tissue paper (Kimberly Clark make) was soaked in the GO solution and dried several times to obtain a good network of GO in the paper. This was then attached to a partially cured PDMS layer for mechanical stability. The GO was further reduced to rGO using a 450 nm laser by optimizing the intensity. This laser-reduced rGO was placed on interdigitated electrodes to make the sensor.

A 40% change in sensitivity was observed for a 60 kPa pressure when tested using a Keysight source meter. The thickness of the sensor was found to be less than 700 micron, hence making it possible to use for electronic skin applications

Surface Plasmon Resonance Imaging (SPRi) is a versatile biosensing platform for protein microarrays. It enables high-throughput, label-free analysis of various analytes in a multiplex format. However, the critical process of protein immobilization poses challenges, particularly in ensuring a reliable and reproducible receptor layer formation, which is essential for the validity of analyte determination. In the SPRi technique, protein immobilization on the chip occurs ex situ, making it challenging to monitor and resulting in an undetermined surface density of immobilized receptors. An optimal protein receptor layer requires reproducible fabrication with controllable density and homogeneous distribution of proteins while preserving their biological activity. A method of protein microarray quality control is yet to be developed, which emphasizes the need for a reliable assessment approach.

This study utilized commercially available protein dyes (Coomassie Brilliant Blue G, Amido Black, and Ponceau S) to map and characterize the quality of protein receptor layers by means of SPR and SPRi studies. Various factors influencing dye–protein binding were investigated using covalently immobilized model proteins (rabbit IgG and transferrin) on mixed -COOH monolayers or carboxylated PEG. Additionally, DNA-directed immobilization, allowing for protein–DNA conjugate immobilization through hybridization with complementary sequences, was explored. This study examined the appropriate pH and ionic strength for efficient protein staining and tested the effect of buffer composition on DNA double helix stability and dye intercalation. The association and dissociation constants of the dyes, along with the correlation between dye–protein and protein–antibody interactions, were determined. The results contribute valuable insights for developing a versatile quality control method for protein receptor layers immobilized using varied procedures. The presented approach seems to be very promising for evaluating the reliability of SPRi biosensors.