Hydrophobins are proteins, consisting of approximately 70–130 amino acids and containing eight cysteines, linked by four disulfide bonds, which are characteristic of the entire hydrophobin family. The main advantage of hydrophobins is their ability to form amphiphilic layers on surfaces and thus to change their properties from hydrophilic to hydrophobic and vice versa. It is for this reason that hydrophobins can be widely used in a variety of applications to improve the properties of materials, such as hydrophilicity, activity and stability of immobilized molecules. In this work, the hydrophobin RodA of Aspergillus fumigatus and its properties were investigated. The gene responsible for the synthesis of the RodA protein was identified by molecular biology methods and used to design an expression system. The purified recombinant RodA protein was used to modify the surface of a gold electrode in order to investigate the effect of this hydrophobin as a matrix on the performance of the engineered glucose biosensor. The engineered biosensor with the RodA matrix was compared with a biosensor without the RodA matrix. The data obtained were fitted to Michaelis–Menten and linear models to calculate the K_M and the maximum current generated (I_max). In the case of Au/GOx, the K_M value was 6.99 mM and the I_max was 34.8 μA·cm^-2; in the case of the Au/RodA/GOx biosensor, the K_M value was 2.37 mM and the Imax was 0.432 μA·cm^-2. The lower I_max value for the Au/RodA/GOx biosensor could be explained by the possible formation of an excessively thick monolayer of RodA protein or by possible conformations of the protein that blocked the glucose oxidase molecules. However, the K_M value obtained for Au/RodA/GOx showed that for this biosensor, the immobilized glucose oxidase has a significantly higher affinity for the substrate, indicating that such a protein may be suitable for electrode modifications.

In the realm of healthcare, gas sensors are utilized in medical devices and monitoring systems to measure physiological gases, such as oxygen and carbon dioxide, in patients’ breath and blood. They also find applications in diagnostic equipment for detecting volatile organic compounds associated with various medical conditions, thereby aiding in disease diagnosis and management. As a gas-sensitive layer, carbyne offers unique properties that can significantly enhance sensor performance when volatile organic vapors like ethanol are detected. To ensure the reversibility of the response characteristics, the carbyne gas sensors must be equipped with a microheater. Surface acoustic wave (SAW) gas sensors have emerged as a promising technology for detecting and analyzing trace amounts of various gases. Lithium niobate (LiNbO₃) substrates were used to make the SAW structures. Platinum film with a thickness of 280 nm was vacuum-sputtered, and three topologies were fabricated with photolithography. The geometry of the interdigitated electrodes (IDTs) was as follows: width (in the range 300 - 450 µm for the different topologies), length (1.2 mm for all samples), and number of wires (1, 3 and 5), in order to provide the maximal heating temperature of 80 ºC, taking into account that the adsorbed organic analytes are volatile. The meander microheater likely provides a more uniform temperature distribution across the sensing area, contributing to higher accuracy. The meander microheater exhibits more controlled and predictable heat transfer dynamics, leading to a more consistent and accurate response to changes in gas concentration. Meanwhile, the spot high-density rectangular filled micro-heater probably introduces unpredictable or non-linear heat transfer behavior, impacting accuracy to a greater extent. The small meander-shaped microheater exhibits slower response times and reduced sensitivity to changes in temperature. Its thermal design delays the heating and cooling process, impacting the sensor's ability to detect rapid changes in gas concentration, which can be the reason for its lower sensitivity.

Xanthomonas oryzae pv oryzae (Xoo) causes bacterial leaf blight, a devastating disease in rice crops worldwide. Early diagnosis is critical to limit economic losses. We report a rapid, cost-effective colorimetric biosensor using unmodified gold nanoparticles (AuNPs) and a novel Xoo-specific DNA probe for visual detection of the pathogen. The detection strategy exploits the surface plasmonic property of AuNP solutions that shifts their color to purple/blue upon probe–target binding-induced nanoparticle aggregation. AuNPs were functionalized with probes complementary to a conserved Xoo genomic sequence. Genomic DNA was extracted from pure Xoo cultures and infected rice leaf samples. Aggregation was evaluated after mixing probe-functionalized AuNPs with DNA samples. The color change from red to purple was visually observed within 5 minutes of mixing probe AuNPs with as little as 5 ng/μL of purified Xoo DNA and 0.5 g of infected leaf tissue. No color change occurred using DNA from healthy plants or other pathogens, indicating a high specificity. This assay offers a simple, rapid, and field-deployable colorimetric method for visual detection of Xoo with a high sensitivity and specificity. The unmodified AuNPs and specific probe provide a cost-effective alternative to PCR-based methods. This biosensor has immense potential for on-site diagnosis and management of bacterial leaf blight in rice plants.

Nanomaterials are substances with unique properties due to their intrinsic confinement effect and high surface area that enable their use in biology and medicine for sensor applications. The key feature of nanomaterials in such applications is to provide sensitivity enhancement for sensors. On the other hand, nanomaterials possess the ability to change the biological function of cells or tissues; it is from this point of view that nanomaterials can be considered to befunctional. As far as biosensor application is concerned, it is important to optimize the determination of the target molecule in spatial and temporal modes. The purpose of the presented work is to study the effect of functional nanomaterials on the growth (the temporal component) and morphology (the spatial component) of cell culture. The reason was to provide culture conditions where an increase in both the spatial and temporal components of configuration would be achieved for sensor needs to be optimized. Since microalgae have a wide range of possibilities for practical use in medicine, pharmacology and various industries, studying the effect of nanomaterials on their growth and development is very important. It was found that ZnO nanomaterial, which was obtained by volumetric electrospark dispersion, had a concentration-dependent effect on both the growth rate and the color intensity of Chlamydomonas monadina microalgae culture. ZnO functional nanomaterial thus performed the optimization of target molecule formation for biosensor applications. The obtained results will be used in astaxanthin research. Due to its special chemical structure, astaxanthin is an antioxidant of unique strength, which is 10 times more effective than beta carotene and 100 times more effective than vitamin E. This substance can be extracted from Haematococcus pluvialis microalgae culture. ZnO nanomaterial appears to be suitable for use in optimal sensor applications.

Alzheimer's disease is a neurodegenerative condition primarily attributed to environmental factors, abnormal protein deposits, immune system dysregulation, and the consequential death of nerve cells in the brain. On the other hand, Parkinson's disease manifests as a neurological disorder featuring primary motor, secondary motor, and non-motor symptoms, accompanied by the rapid demise of cells in the brain's dopamine-producing region. Utilizing brain images for accurate diagnosis and treatment is integral to addressing both conditions. This study harnessed the power of artificial intelligence for classification processes, employing state-of-the-art transformer models such as Swin Transformer, Vision Transformer (ViT), and Bidirectional Encoder representation from Image Transformers (BEiT). The investigation utilized an open-source dataset comprising 450 images, evenly distributed among healthy, Alzheimer's, and Parkinson's classes. The dataset was meticulously divided, with 80% allocated to the training set (390 images) and 20% to the validation set (90 images). Impressively, the classification accuracy surpassed 80%, showcasing the efficacy of transformer-based models in disease detection. Looking ahead, this study recommends delving into hybrid and ensemble models and leveraging the strengths of multiple transformer-based deep learning architectures. Beyond contributing crucial insights at the intersection of artificial intelligence and neurology, this research emphasizes the transformative potential of advanced models for enhancing diagnostic precision and treatment strategies in Alzheimer's and Parkinson's diseases. It signifies a significant step towards integrating cutting-edge technology into mainstream medical practices for improved patient outcomes.

Red chili, scientifically known as "Capsicum annuum," belongs to the Solanaceae family. It is extensively utilized in various cuisines worldwide to enhance flavor and impart heat to dishes. Moreover, red chili exhibits medicinal properties such as pain relief, anti-inflammatory effects, a metabolism boost, cardiovascular health benefits, and antioxidant properties. The primary objective of this research paper is to identify specific diseases affecting different instances of chili plants. We will analyze this through IoT sensors to determine which soil is optimal for chili cultivation. For this research, we created our dataset by collecting pictures of various specific diseases. The dataset comprises five features: Bacterial Spot, Powdery Mildew, Anthracnose, Phytophthora Root Rot, and Fusarium Wilt. In this study, a machine learning classifier was employed to detect chili plant diseases. Additionally, we identified various types of diseases in chili plants and evaluated their overall health. Experimental observations reveal that a Convolutional Neural Network (CNN) performs well compared to other deep learning classifiers. The training accuracy of CNN is 98.2%, and the testing accuracy is 96.7%. The minimized training and testing errors demonstrate that the model effectively handles new or unseen data. We have compared our proposed model to the state of the art and found that the proposed model performs well.

Introduction: Emotion detection has proven to be valuable in biofeedback for the development of assistive technologies, the enhancement of gaming experiences, and advancements in the treatment of mental health issues, among other applications. The objective of this study was to integrate electroencephalogram (EEG), electrocardiogram (ECG), and galvanic skin response (GSR) sensors using the lazypredict library to return classifier models with the highest accuracy in detecting emotions. Method: OpenVibe software; Brain Products' V-Amp amplifier (sample rate 512Hz); a cap with 16 channels placed in temporal, parietal, frontal, and prefrontal regions for EEG; and a BIP2AUX adapter connected to the AUX ports for acquiring ECG signals with three electrodes on the wrists and leg were utilized. The GSR module adapter, with two electrodes on the index and middle fingers, was used for GSR. Each articipant was exposed to 400 emotional stimuli (100 for each emotion—fear, happiness, anger, and sadness) through PsychoPy software. Data were processed using the Python programming language, involving filtering, epoching, epoch selection, feature extraction using discrete wavelet transform (DWT), and normalization. Subsequently, the data were cleaned and classified using the lazypredict library. Results: The classifier models that exhibited the highest accuracy were the Calibrated Classifier CV, the AdaBoost Classifier, and the Decision Tree Classifier. Conclusion: Our findings contribute to advancements in the field of emotion detection, emphasizing the crucial role played by artificial intelligence in the process.

Introduction: Intoxication by marine toxin tetrodotoxin (TTX) through the consumption of contaminated seafood has been consistently reported in Asia, and its recent emergence in Mediterranean Sea waters has been alarming. TTX impairs nervous signals, with the lethal dose for humans being 1-2 mg, and no antidote is known. A simple test for the rapid, cost-effective, and simple analysis of potentially contaminated food is thus required. The accurate detection of TTX relies on costly and time-consuming techniques due to its small size and the potential interferences from seafood components. Biosensors exploiting single-stranded DNA aptamers as bioreceptors have garnered increasing interest recently due to their high affinity and specificity, especially toward small-molecule targets. Our group previously reported the selection of novel TTX-binding aptamers for use in a hybrid sandwich assay in combination with an antibody. Herein, we report the development of a rapid lateral flow device (LFD) for the rapid and facile detection of TTX.

Methods: A TTX aptamer bioreceptor was immobilized on a nitrocellulose membrane, and an anti-TTX IgG antibody-gold nanoparticle conjugate was prepared for detection. Several parameters were optimized for enhanced sensitivity, and the device's stability was evaluated via an accelerated stability study. Finally, the device was used to detect TTX in extracts from contaminated pufferfish.

Results: The developed LFD successfully detected TTX in buffer and in contaminated pufferfish extracts in less than 30 min, exhibiting a limit of detection of ~0.3 ng/mL, suitable for TTX detection in seafood below the 2 mg TTX equivalents/kg limit established in Japan. No interferences were observed from other marine toxins, whereas the stability of the device was more than 1 year when stored at 4°C.

Conclusions: The LFD based on an aptamer–antibody hybrid sandwich assay exhibited high sensitivity, specificity and stability, and its application in the rapid analysis of contaminated seafood was demonstrated.

Alzheimer’s Disease (AD), characterized by a gradual onset and a lack of exact therapeutic interventions, underscores the imperative for the development of uncomplicated and cost-effective biosensors capable of detecting its biomarkers. This necessity arises in anticipation of a projected surge in the incidence of AD. Quantum dots (QDs) represent the promising new generation of luminophores owing to their size, composition and desired optical properties. In this study, an Artificial Intelligence-driven paper-based QD aptasensor for the early detection of AD by targeting amyloid beta (Aβ-42) and p-tau181 proteins using Förster Resonance Energy Transfer (FRET) is developed. The sensor employs a Whatman paper with six sensing wells, integrating hydrophobic and hydrophilic regions, hydrophobic parts created through wax. Blood samples are placed in the inlet, dispersing into six sensing wells containing QD-aptamer-AuNP complexes. Target proteins induce conformational changes in aptamers, leading to fluorescence quenching in CdTe QDs due to FRET. Two wells target p-tau181, two target Aβ-42, and two serve as references. Fluorescence emission spectra from each well are recorded showing a linear correlation between fluorescence quenching and analyte concentration. Values from each pair of wells are then averaged, and the average values from the pairs targeting Aβ-42 and p-tau181 are compared to the average value of the reference wells. Subsequently, the performance of the sensor is enhanced with the Convolutional Neural Network algorithm and a user-friendly app for real-time results which takes the sensor images as input and outputs the biomarker levels of patients is developed. This facilitates monitoring and enabling long-term management of the disease and opens the avenue of personalized medicine for the diagnosis of Alzheimer’s.

The imperative to accurately monitor algae growth and composition stems from their pervasive impact on ecosystem dynamics. Algae serve as early indicators of environmental changes, making their precise assessment crucial for identifying shifts in ecological balance. The information gleaned from such monitoring is pivotal for implementing targeted conservation efforts and sustainable practices, thereby safeguarding the delicate equilibrium of ecosystems. As integral components within the intricate tapestry of ecological processes, algae play indispensable roles in environmental health by facilitating crucial functions such as oxygen production and carbon dioxide absorption, and serving as the cornerstone of aquatic food chains. Positioned as stewards of ecosystem equilibrium, the accurate monitoring of algal growth and composition becomes paramount for comprehending the intricacies of ecosystem dynamics and fostering sustainable environmental practices.

This paper initiates a comprehensive exploration of the revolutionary potential inherent in Raman spectroscopy, with a specific focus on its application to a silica substrate. The distinctive attributes of Raman spectroscopy, marked by its non-invasive nature, rapid data acquisition, and label-free analytical capabilities, position it as a formidable tool poised to unlock intricate molecular insights into the composition of algae. In this context, the research methodology entails a meticulous experimental setup designed to harness the unique capabilities of Raman spectroscopy for the detection and characterization of algae on a silica substrate. By ensuring the uniform deposition of the algal samples, the study seeks to guarantee consistent and reproducible analysis.