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Bacteriophages, abbreviated as "phages", have been developed as emerging nanoprobes for the detection of a wide variety of biological species, such as biomarker molecules and pathogens. Nanosized phages can display a certain length of exogenous peptides of arbitrary sequence or single-chain variable fragments (scFv) of antibodies that specifically bind to the targets of interest, such as animal cells, bacteria, viruses, and protein molecules. Metal nanoparticles generally have unique plasmon resonance effects. Metal nanoparticles such as gold, silver, and magnetism are widely used in the field of visual detection. A phage can be assembled with metal nanoparticles to form an organic-inorganic hybrid probe due to its nanometer-scale size and excellent modifiability. Due to the unique plasmon resonance effect of this composite probe, this technology can be used to visually detect objects of interest under a dark-field microscope. In summary, this review summarizes the recent advances in the development of phage-based probes for ultra-sensitive detection of various bio-species, outlining the advantages and limitations of detection technology of phage-based assays, and highlighting the commonly used editing technologies of phage genomes such as homologous recombination and clustered regularly interspaced palindromic repeats/CRISPR-associated proteins system (CRISPR-Cas). Finally, we discuss the possible scenarios for clinical application of phage-probe-based detection methods.Enzymes are proteins that control the efficiency and effectiveness of biological reactions and systems, as well as of engineered biomimetic processes. This review highlights current applications of a diverse range of enzymes for biofuel production, plastics, and chemical waste management, as well as for detergent, textile, and food production and preservation industries respectively. Challenges regarding the transposition of enzymes from their natural purpose and environment into synthetic practice are discussed. For example, temperature and pH-induced enzyme fragilities, short shelf life, low-cost efficiency, poor user-controllability, and subsequently insufficient catalytic activity were shown to decrease pertinence and profitability in large-scale production considerations. Enzyme immobilization was shown to improve and expand upon enzyme usage within a profit and impact-oriented commercial world and through enzyme-material and interfaces integration. With particular focus on the growing biomedical market, trends and accountability.Despite its reduced sensitivity, sputum smear microscopy (SSM) remains the main diagnostic test for detecting tuberculosis in many parts of the world. A new diagnostic technique, the magnetic nanoparticle-based colorimetric biosensing assay (NCBA) was optimized by evaluating different concentrations of glycan-functionalized magnetic nanoparticles (GMNP) and Tween 80 to improve the acid-fast bacilli (AFB) count. Comparative analysis was performed on 225 sputum smears 30 with SSM, 107 with NCBA at different GMNP concentrations, and 88 with NCBA-Tween 80 at various concentrations and incubation times. AFB quantification was performed by adding the total number of AFB in all fields per smear and classified according to standard guidelines (scanty, 1+, 2+ and 3+). Smears by NCBA with low GMNP concentrations (≤1.5 mg/mL) showed higher AFB quantification compared to SSM. Cell enrichment of sputum samples by combining NCBA-GMNP, incubated with Tween 80 (5%) for three minutes, improved capture efficiency and increased AFB detection up to 445% over SSM. NCBA with Tween 80 offers the opportunity to improve TB diagnostics, mainly in paucibacillary cases. As this method provides biosafety with a simple and inexpensive methodology that obtains results in a short time, it might be considered as a point-of-care TB diagnostic method in regions where resources are limited.PtSe2 as a novel TMDCs material is used to modify the traditional SPR biosensors to improve the performance. On this basis, this research proposes a metal-Si-metal waveguide structure to further improve the performance of the biosensor. In this study, we not only studied the effects of waveguide structures containing different metals on the performance of biosensor, but also discussed the performance change of the biosensor with the change of PtSe2 thickness. After the final optimization, a BK7-Au-Si-Au-PtSe2 (2 nm) biosensor structure achieved the highest sensitivity of 193.8°/RIU. This work provides a new development idea for the study of SPR biosensors with waveguide structures in the future.The review describes fentanyl and its analogs as new synthetic opioids and the possibilities of their identification and determination using electrochemical methods (e.g., voltammetry, potentiometry, electrochemiluminescence) and electrochemical methods combined with various separation methods. The review also covers the analysis of new synthetic opioids, their parent compounds, and corresponding metabolites in body fluids, such as urine, blood, serum, and plasma, necessary for a fast and accurate diagnosis of intoxication. Identifying and quantifying these addictive and illicit substances and their metabolites is necessary for clinical, toxicological, and forensic purposes. As a reaction to the growing number of new synthetic opioid intoxications and increasing fatalities observed over the past ten years, we provide thorough background for developing new biosensors, screen-printed electrodes, or other point-of-care devices.SERS immunoassay biosensors hold immense potential for clinical diagnostics due to their high sensitivity and growing interest in multi-marker panels. However, their development has been hindered by difficulties in designing compatible extrinsic Raman labels. Prior studies have largely focused on spectroscopic characteristics in selecting Raman reporter molecules (RRMs) for multiplexing since the presence of well-differentiated spectra is essential for simultaneous detection. However, these candidates often induce aggregation of the gold nanoparticles used as SERS nanotags despite their similarity to other effective RRMs. Thus, an improved understanding of factors affecting the aggregation of RRM-coated gold nanoparticles is needed. Substituent electronic effects on particle stability were investigated using various para-substituted thiophenols. The inductive and resonant effects of functional group modifications were strongly correlated with nanoparticle surface charge and hence their stability. Treatment with thiophenols diminished the negative surface charge of citrate-stabilized gold nanoparticles, but electron-withdrawing substituents limited the magnitude of this diminishment. It is proposed that this phenomenon arises by affecting the interplay of competing sulfur binding modes. This has wide-reaching implications for the design of biosensors using thiol-modified gold surfaces. A proof-of-concept multiplexed SERS biosensor was designed according to these findings using the two thiophenol compounds with the most electron-withdrawing substitutions NO2 and CN.The highly sensitive detection of peanut allergens (PAs) using silicon-based electrolyte-gated transistors (Si-EGTs) was demonstrated. The Si-EGT was made using a top-down technique. The fabricated Si-EGT showed excellent intrinsic electrical characteristics, including a low threshold voltage of 0.7 V, low subthreshold swing of less then 70 mV/dec, and low gate leakage of less then 10 pA. Surface functionalization and immobilization of antibodies were performed for the selective detection of PAs. The voltage-related sensitivity (SV) showed a constant behavior from the subthreshold regime to the linear regime. The current-related sensitivity (SI) was high in the subthreshold regime and then significantly decreased as the drain current increased. The limit of detection (LOD) was calculated to be as low as 25 pg/mL based on SI characteristics, which is the lowest value reported to date in the literature for various sensor methodologies. The Si-EGT showed selective detection of PA through a non-specific control test. These results confirm that Si-EGT is a high-sensitivity and low-power biosensor for PA detection.A liquid biopsy based on circulating small extracellular vesicles (SEVs) has not yet been used in routine clinical practice due to the lack of reliable analytic technologies. Recent studies have demonstrated the great diagnostic potential of nanozyme-based systems for the detection of SEV markers. click here Here, we hypothesize that CD30-positive Hodgkin and Reed-Sternberg (HRS) cells secrete CD30 + SEVs; therefore, the relative amount of circulating CD30 + SEVs might reflect classical forms of Hodgkin lymphoma (cHL) activity and can be measured by using a nanozyme-based technique. A AuNP aptasensor analytics system was created using aurum nanoparticles (AuNPs) with peroxidase activity. Sensing was mediated by competing properties of DNA aptamers to attach onto surface of AuNPs inhibiting their enzymatic activity and to bind specific markers on SEVs surface. An enzymatic activity of AuNPs was evaluated through the color reaction. The study included characterization of the components of the analytic system and its functionality using transmission and scanning electron microscopy, nanoparticle tracking analysis (NTA), dynamic light scattering (DLS), and spectrophotometry. AuNP aptasensor analytics were optimized to quantify plasma CD30 + SEVs. The developed method allowed us to differentiate healthy donors and cHL patients. The results of the CD30 + SEV quantification in the plasma of cHL patients were compared with the results of disease activity assessment by positron emission tomography/computed tomography (PET-CT) scanning, revealing a strong positive correlation. Moreover, two cycles of chemotherapy resulted in a statistically significant decrease in CD30 + SEVs in the plasma of cHL patients. The proposed AuNP aptasensor system presents a promising new approach for monitoring cHL patients and can be modified for the diagnostic testing of other diseases.Automatic high-level feature extraction has become a possibility with the advancement of deep learning, and it has been used to optimize efficiency. Recently, classification methods for Convolutional Neural Network (CNN)-based electroencephalography (EEG) motor imagery have been proposed, and have achieved reasonably high classification accuracy. These approaches, however, use the CNN single convolution scale, whereas the best convolution scale varies from subject to subject. This limits the precision of classification. This paper proposes multibranch CNN models to address this issue by effectively extracting the spatial and temporal features from raw EEG data, where the branches correspond to different filter kernel sizes. The proposed method's promising performance is demonstrated by experimental results on two public datasets, the BCI Competition IV 2a dataset and the High Gamma Dataset (HGD). The results of the technique show a 9.61% improvement in the classification accuracy of multibranch EEGNet (MBEEGNet) from the fixed one-branch EEGNet model, and 2.95% from the variable EEGNet model. In addition, the multibranch ShallowConvNet (MBShallowConvNet) improved the accuracy of a single-scale network by 6.84%. The proposed models outperformed other state-of-the-art EEG motor imagery classification methods.