Machine learning (ML) algorithms in molecular simulations have been recently extended to models for machine learning tensorial properties such as molecular dipole moments and polarizability tensors enabling calculations of the IR and Raman spectra. The use of ML methods in DNA/RNA and protein research could enable the automated identification of individual oligomers. The parallel use of ML in surface-enhanced Raman scattering (SERS) sensors and their simulated models can enhance the detection of single oligomers by analyzing spectral variations linked to environmental interactions and conformational changes in the models.

Molecular dynamics (MD) provides vibrational spectra in various interaction environments and molecular conformations reflected in spectral maps of individual bonds. The identification of oligomers relative to environments can be performed by an ML Random Forest (RF) algorithm used for the experimental Raman spectra. In the MD model of the numerical SERS sensor, we applied the RF algorithm for the identification of pyrimidine and purine DNA nucleotides by ring-averaged vibrational spectra obtained during translocation through the nanopore in a graphene sheet with Au nanoparticles (1 to 4 NP) attached to the pore’s edge. The baseline-corrected ring-averaged equal-weight vibrational spectra showed nucleotide recognition by RF on a dataset of 170 points. The vibrational spectral maps of nucleobase bonds were calculated for the ring averages. We demonstrate that the implementation of the bond polarizability model (BPM), which assumes that the overall molecular polarizability is a sum over bond contributions, makes use of bond polarizabilities as weighting coefficients of each bond spectrum possible. The existing literature data for the bond polarizabilities of oligomers were approximated for the weighting coefficients. The calculated spectral maps were baseline-corrected as a whole matrix using the SpectroChemPy (SCPy) framework for processing spectroscopic data with masking of the frequency region below 100 cm^-1. A spectral map weighted by bond polarizabilitieswas added to the averaged spectra in the dataset and used as training test data in the RF algorithm. While for only ring-averaged MD data, the RF algorithm reproduces differences in nucleotide spectra and identifies the methylated forms of cytosine, the accuracy is only qualitative. The use of bond polarizability weights for the cytosine pyrimidine ring spectral map with the ring-averaged spectrum dramatically improved the averaged spectrum reproduction by the RF algorithm. The mode frequencies and intensities were correctly reproduced quantitively by the RF algorithm closely to the calculated data.

Introduction: Bionanohybrids of carbon nanotubes (CNTs) and green fluorescent proteins (GFPs) are promising materials for optical biosensors due to their combination of unique optical and electrical properties inherent to photoactive biological objects and carbon nanomaterials. The tertiary structure of the biopolymer effectively protects the chromophore, while the cylindrical shape of carbon nanotubes minimizes the contact area with proteins, preventing damage to their native structure. This interaction ensures the stability of GFP-CNT conjugates and prevents fluorescence quenching. In this study, we investigated the effect of humidity levels on the photosensitivity of a photodetector based on a field-effect transistor with a carbon nanotube channel modified by green fluorescent protein.

Methods: We used quasi-metallic single-walled carbon nanotubes grown by chemical vapor deposition in a bottom-gate transistor configuration. The device was fabricated on a highly doped (p++) 100 mm silicon substrate with a 300 nm thermally grown SiO₂ dielectric layer and source and drain electrodes consisting of 100 nm Au and 15 nm Ti. Genetically engineered green fluorescent proteins were attached to the carbon nanotubes using a photochemical reaction based on click chemistry.

Results: Reducing the humidity by purging the samples with dry air or an inert gas leads to a decrease in photosensitivity, which can be restored by the reverse process. The likely cause is a conformational change in the GFP, its partial denaturation, and the removal of adsorbates at the GFP/CNT interface. By controlling the humidity, it is possible to regulate the operation of the photodetector, partially or completely "switching it off."

Conclusions: This paper demonstrates that humidity has a significant impact on the photosensitivity of a device based on GFPs and carbon nanotubes, allowing for the regulation of its operation and the adjustment of photodetector characteristics.

Folic acid plays a critical role in cellular metabolism, including DNA synthesis and cell division. Understanding its interactions with antibodies is essential for creating diagnostic platforms and therapeutic systems. The effectiveness of these interactions depends on the carrier used for folic acid, which is often a protein like bovine serum albumin, ovalbumin, or keyhole limpet hemocyanin. Polymers such as dextran and gelatin are emerging as promising alternatives. This study compares the interaction of antibodies with folic acid conjugates based on dextran and gelatin using an updated label-free optical biosensor employing spectral interferometry. This method evaluates the influence of carrier structure on interaction kinetics and specificity, identifying optimal materials for various applications.
Conjugates of folic acid with gelatin and dextran were synthesized using carbodiimide chemistry. Gelatin conjugates were prepared by reacting folic acid with 1-ethyl-3-(3-dimethylaminopropyl)carbodiimide and N-hydroxysuccinimide, followed by incubation with gelatin. The product was purified through precipitation and washing cycles. Dextran conjugates were synthesized similarly, replacing gelatin with dextran. A biosensor chip surface was modified with folic acid–gelatin conjugate, and antibodies specific to folic acid were immobilized. Conjugates of folic acid–gelatin or folic acid–dextran were introduced in a competitive binding assay, and interaction dynamics were monitored using spectral interferometry.
The folic acid–gelatin conjugate demonstrated weak interaction at low concentrations and low desorption rates, making it suitable for mimicking complex biological systems where nonspecific interactions and limited accessibility of binding sites are relevant. In contrast, the folic acid–dextran conjugate exhibited higher binding efficiency, specificity, and stability due to enhanced accessibility of folic acid and reduced nonspecific interactions. The biosensor enabled real-time monitoring of these interactions, providing detailed profiles for each conjugate. Folic acid–gelatin is advantageous for systems involving complex biological interactions, while folic acid–dextran offers high specificity and faster binding kinetics.
Folic acid–gelatin and folic acid–dextran conjugates exhibit distinct interaction profiles with antibodies. Gelatin conjugates are suited for systems with nonspecific binding, whereas dextran conjugates provide higher specificity and stability. The experimental results showed that the label-free biosensing with spectral correlation interferometry is a powerful approach for evaluating conjugate interactions and identifying suitable materials for biomedical applications.

The photocatalytic properties of various nanoparticles have been intensively studied recently. Among them, plasmonic and semiconductor nanoparticles show special activity. While it has been found that the mechanism of photocatalytic activity for semiconductor nanoparticles is due to the transition of electrons from the valence band to the conduction band, such a mechanism is still unknown for plasmonic nanoparticles. The photocatalytic effect in the case of plasmonic nanoparticles is possibly associated with the emergence of zones of high electromagnetic intensity between two plasmonic nanoparticles due to the appearance of local plasmonic resonance. There is an urgent need to determine the effective frequency range of electromagnetic radiation at which photocatalytic processes occur.

In the proposed work, a device based on Arduino is developed to establish the spectral dependence of the process of organic structure photodegradation through the example of the dye methylene blue in the presence of nanosized silver under electromagnetic radiation action and to analyze the possible mechanisms of this process. The interval of light radiation frequencies that activate or inhibit the process of organic compound photodegradation is established, and probable mechanisms of interaction of organic structures with light radiation and nanosilver due to the occurrence of local plasmon resonance are also presented.

The rise of antibiotic-resistant bacteria has intensified the need for innovative monitoring and treatment strategies. In this regard, the task of monitoring the state of biofilms formed by bacteria in real time is relevant. In this paper, the adaptive holographic interferometry method is used to construct a biosensor based on microresonance microweighing for monitoring the growth of bacterial biofilm. The sensitive element of the biosensor is a silicon Atomic Force Microscopy cantilever measuring 215×43×7 µm³, with a 100 nm thick gold coating. The cantilever is placed in a glass cuvette with a volume of 100 µl with two tubes for the inflow and outflow of liquid. Using a pulsed Nd:YAG laser (λ=532 nm; τ=5 ns; E_p=100 µJ), the cantilever's natural oscillations were excited and were recorded in an adaptive holographic interferometer using a CdTe:V photorefractive crystal.

Before the experiment, the oscillation frequency of the cantilever in water was 69.5±0.7 kHz. During the experiment, the resonant frequency of the cantilever was measured with a repetition rate of 20 Hz. The oscillations were recorded using an oscilloscope and then processed in MATLAB to obtain an FFT image of the recorded signal. The resonant peak corresponding to the cantilever oscillations was approximated to find the central frequency. In turn, the biofilm mass was calculated using a numerical model [1].

The bacterial cell suspension of E. coli K-12 strain XL1-Blue at an optical density of OD₆₀₀ = 1 was fed into a cuvette housing a microcantilever sensor for 1 h at a flow rate of 1 ml/min. Following the initial incubation, fresh LB medium was continuously supplied into the cuvette at a rate of 0.2 ml/min. Over 6 hours, the change in cantilever frequency due to bacterial attachment was 10.3±0.7 kHz, which corresponds to a bacterial mass of 5.6±0.4 ng.

The proposed monitoring method can be used to test the effect of various agents on the process of biofilm formation. Due to the adaptive signal processing, there are no requirements for the precise alignment of light beams, and the sensitive element can have a complex shape and a low-reflective surface.

1 Efimov, T.A.; Rassolov, E.A.; Andryukov, B.G.; Zaporozhets, T.S.; Romashko, R.V. Calculation of resonant frequencies of silicon AFM cantilevers. J. Phys. Conf. Ser. 2020, 1439, 012017.

Diabetes stands as a widespread and critical health concern on a global scale, presenting formidable obstacles to healthcare systems around the world. The increasing prevalence of this condition, coupled with its numerous complications, poses significant challenges for effective medical intervention and resource allocation. It is of paramount importance that diabetes is detected early and accurately to facilitate proper management strategies and prevent severe health consequences that can arise from uncontrolled diabetes. Unfortunately, current diagnostic methods often fall short in terms of precision and sensitivity when it comes to conducting large-scale screenings. This inadequacy is particularly evident in their ability to detect specific biomarkers associated with diabetes at low concentrations, which are crucial for accurate diagnosis.

Moreover, existing techniques tend to not take into consideration the intricate interrelations among various biomarkers, thereby further diminishing their effectiveness for reliable diabetes diagnostics. In response to these pressing limitations, the present study proposes an innovative solution in the form of an octagon-shaped terahertz (THz) photonic crystal fiber (PCF) biosensor specifically engineered for enhanced coordination in diabetes detection. This cutting-edge biosensor has been meticulously optimized through the application of a novel approach known as the Simplicial Causal Graph Dilated Botox Quaternion Convolutional Attention Network (SCG-DBQCAN). This advanced framework seamlessly integrates methodologies such as simplicial causal graph modeling and dilated quaternion convolutions along with attention mechanisms inspired by Botox technology—all aimed at achieving robust feature extraction and highly efficient classification of biomarkers.

The design of this biosensor significantly boosts light–matter interactions within its structure, resulting in exceptional sensitivity towards detecting diabetes-related biomarkers while simultaneously minimizing potential signal loss during measurement processes. Impressively, this state-of-the-art device boasts a remarkable detection accuracy rate of 99.9%, showcasing its high sensitivity even towards those low-concentration biomarkers that are often missed by traditional methods. Furthermore, its adaptability makes it suitable for various diagnostic contexts, presenting a game-changing solution for early detection of diabetes—a critical step toward mitigating long-term health risks associated with this chronic disease.

In the last twenty years, optical fiber sensing technology has significantly advanced in sensor architecture, manufacturing process refinement, and system integration, establishing itself as a cornerstone of cutting-edge sensing systems. A diverse array of optical phenomena, including interference, scattering, total internal reflection, and surface plasmon resonance, are harnessed in the design of optical fiber (OF) sensors. As a novel OF sensor modality, nanostructured plasmonic OF sensors have garnered considerable interest owing to their exceptional performance and distinctive characteristics, enabling the realization of the Lab-on-Fiber concept. Since nanostructured plasmonic OF sensors embody the attributes of both conventional OF sensors and plasmonic sensors, they exhibit unique benefits and can serve as potent biochemical sensing instruments or integrated photonic components. One of the primary domains within the "Lab on Fiber" field is the use of the optical fiber in order to realize probes able to detect biomolecules at very low concentrations. A straightforward approach involves employing metal nanopatterns or nanoparticles to modify the lateral displacement or the tip of the optical fiber in order to realize an innovative metasurface or Surface-Enhanced Raman Spectroscopy (SERS) substrate on the optical fiber tip. This amalgamation aspires to create robust, adaptable, and miniaturized spectroscopic instruments for the remote and highly sensitive detection of low-concentration molecular analytes across a wide range of environments, including challenging conditions.

Abstract: Many metal oxide nanozymes have been demonstrated to exhibit a peroxidase-like activity much higher than an oxidase-like one in triggering the oxidation of colorless TMB to blue oxTMB. Unlike the conventional phenomenon, here, we observe that amorphous CoO_x can show strong oxidase-like activity, but its activity decreases after the addition of H₂O₂. Further experimental investigation revealed the underlying mechanism: H₂O₂ decomposes CoO_x into inactive cobalt ions. Through redox potential calculations, the feasibility of their reaction was theoretically confirmed. Moreover, the unusual phenomenon mainly occurs in amorphous CoO_x and does not exist in cobalt oxides with distinct crystallinity. Based on the finding, a biocatalytic enzyme–nanozyme cascade system can be constructed using CoO_x and glucose oxidase (GOx) for detecting glucose in human serum. In the presence of oxygen, glucose is catalyzed by GOx to generate H₂O₂, which decomposes the CoO_x nanozyme, reducing its oxidase-like activity and thereby inhibiting the TMB coloration system. Therefore, with the increase in glucose concentration, the absorbance of the color-developing system also gradually decreases, thus achieving the quantitative detection of glucose. The developed method has high sensitivity and also shows good anti-interference ability in real samples, providing a new strategy for blood glucose detection based on nanozymes.

Abstract: Biothiols play an essential role in antioxidant defense and the maintenance of normal cell function, and they are also biomarkers of many diseases. It is of great significance to establish an effective, reliable and simple method to accurately distinguish and quantify biothiols. Here, we design a quasi - amorphous material Mn_x(DTPMP) which exhibits excellent oxidase-like activity to catalyze the oxidation of colorless 3, 3', 5, 5' - tetramethylbenzidine (TMB) to blue oxTMB for the machine learning-assisted nanozyme-coded recognition and quantification of biothiols. The addition of biothiols inhibits the conversion process of TMB, leading to time-resolved reduction in the color signal of oxTMB. Due to their different abilities to inhibit oxidation over time, specific fingerprints can be drawn for each target. On this basis, a unified stepwise prediction model is established using pattern recognition and classification and regression algorithms in support vector machine (SVM), enabling qualitative identification as well as the precise determination of biothiols simultaneously. The time-resolved nanozyme-coded pattern recognition can not only differentiate cancer cells from normal ones according to intracellular glutathione (GSH), but also evaluate the severity of disease according to serum homocysteine (Hcy), showing a promising application prospect in disease diagnosis.

Optical biosensors have emerged as a transformative technology for food safety monitoring. These devices combine bio-recognition molecules with advanced optical transducers, enabling the detection of a wide array of food contaminants, including pathogens, toxins, pesticides, and antibiotic residues. This review comprehensively explores the principles, advancements, applications, and future trends of optical biosensors in ensuring food safety. The key advantages of optical biosensors, such as high sensitivity to trace contaminants, fast response times, and portability, make them an attractive alternative to traditional analytical methods. Types of optical biosensors discussed include surface plasmon resonance (SPR), interferometric, fluorescence and chemiluminescence, and colorimetric biosensors. SPR biosensors stand out for their real-time, label-free analysis of foodborne pathogens and contaminants, while fluorescence and chemiluminescence biosensors offer exceptional sensitivity for detecting low levels of toxins. Interferometric and colorimetric biosensors, characterized by their portability and visual signal output, are well suited for field-based applications. Biosensors have proven invaluable in monitoring heavy metals, pesticide residues, and antibiotic contaminants, ensuring compliance with stringent food safety standards. The integration of nanotechnology has further enhanced the performance of optical biosensors, with nanomaterials such as quantum dots and nanoparticles enabling ultra-sensitive detection and signal amplification. Optical biosensors represent a vital advancement in the field of food safety, addressing critical public health concerns through their rapid and reliable detection capabilities. Continued interdisciplinary efforts in nanotechnology, material science, and device engineering are poised to further expand their applications, making them indispensable tools for safeguarding global food supply chains.