Electrochemical biosensors for early detection of breast cancer.

Breast cancer continues to be a significant contributor to global cancer deaths, particularly among women. This highlights the critical role of early detection and treatment in boosting survival rates. While conventional diagnostic methods like mammograms, biopsies, ultrasounds, and MRIs are valuable tools, limitations exist in terms of cost, invasiveness, and the requirement for specialized equipment and trained personnel. Recent shifts towards biosensor technologies offer a promising alternative for monitoring biological processes and providing accurate health diagnostics in a cost-effective, non-invasive manner. These biosensors are particularly advantageous for early detection of primary tumors, metastases, and recurrent diseases, contributing to more effective breast cancer management. The integration of biosensor technology into medical devices has led to the development of low-cost, adaptable, and efficient diagnostic tools. In this framework, electrochemical screening platforms have garnered significant attention due to their selectivity, affordability, and ease of result interpretation. The current review discusses various breast cancer biomarkers and the potential of electrochemical biosensors to revolutionize early cancer detection, making provision for new diagnostic platforms and personalized healthcare solutions.

Epidermal secretion-purified biosensing patch with hydrogel sebum filtering membrane and unidirectional flow microfluidic channels.

The development of biosensing electronics for real-time sweat analysis has attracted increasing research interest due to their promising applications for non-invasive health monitoring. However, one of the critical challenges lies in the sebum interference that largely limits the sensing reliability in practical scenarios. Herein, we report a flexible epidermal secretion-purified biosensing patch with a hydrogel filtering membrane that can effectively eliminate the impact of sebum and sebum-soluble substances. The as-prepared sebum filtering membranes feature a dual-layer sebum-resistant structure based on the poly(hydroxyethyl methacrylate) hydrogel functionalized with nano-brush structured poly(sulfobetaine) to eliminate interferences and provide self-cleaning capability. Furthermore, the unidirectional flow microfluidic channels design based on the Tesla valve was incorporated into the biosensing patch to prevent external sebum contamination and allow effective sweat refreshing for reliable sensing. By seamlessly combining these components, the epidermal secretion-purified biosensing patch enables continuous monitoring of sweat uric acid, pH, and sodium ions with significantly improved accuracy of up to 12 %. The proposed strategy for enhanced sweat sensing reliability without sebum interference shows desirable compatibility for different types of biosensors and would inspire the advances of flexible and wearable devices for non-invasive healthcare.

Plasmonic Nanogap-Enhanced Tunable Three-Dimensional Nanoframes in Application to Clinical Diagnosis of Alzheimer's Disease.

Advancements in nanotechnology led to significant improvements in synthesizing plasmon-enhanced nanoarchitectures for biosensor applications, and high-yield productivity at low cost is vital to step further into medical commerce. Metal nanoframes via wet chemistry are gaining attention for their homogeneous structure and outstanding catalytic and optical properties. However, nanoframe morphology should be considered delicately when brought to biosensors to utilize its superior characteristics thoroughly, and the need to prove its clinical applicability still remains. Herein, we controlled the frameworks of double-walled nanoframes (DWFs) precisely via wet chemistry to construct a homogeneous plasmon-enhanced nanotransducer for localized surface plasmon resonance biosensors. By tuning the physical properties considering the finite-difference time-domain simulation results, biomolecular interactions were feasible in the electromagnetic field-enhanced nanospace. As a result, DWF10 exhibited a 10-fold lower detection limit of 2.21 fM compared to DWF14 for tau detection. Further application into blood-based clinical and Alzheimer's disease (AD) diagnostics, notable improvement in classifying mild cognitive impairment patients against healthy controls and AD patients, was demonstrated along with impressive AUC values. Thus, in response to diverse detection methods, optimizing nanoframe dimensions such as nanogap and frame thickness to maximize sensor performance is critical to realize future POCT diagnosis.

Renewable regeneration optic fiber glucose sensor based on succinylaminobenzenoboronic acid modified excessively tilted fiber grating.

BACKGROUND: Optical fiber sensors have been used to detect glucose owing to advantages such as low cost, small size, and ease of operation etc. phenylboronic acid is one of the commonly used receptors for glucose detection, however phenylboronic acid based regenerative optical fiber sensors are commonly cumulative regeneration, renewable regeneration sensor has been missing from the literature. RESULTS: In this work, instead of using phenylboronic acid, we synthesized succinylaminobenzenoboronic acid molecule (BPOA) by introducing a short chain containing carboxyl group at the other end of phenylboronic acid then covalently bonded BPOA on the surface of excessively tilted fiber grating (Ex-TFG). This provides a very stable platform for renewable regeneration and the regenerative buffer was also optimized. The proposed renewable regeneration method exhibited higher linearity and sensitivity (R2 = 0.9992, 8 pm/mM) in relative to the conventional cumulative regeneration method (R2 = 0.9718, 4.9 pm/mM). The binding affinity between BPOA and glucose was found to be almost constant over 140 bind/release cycles with a variation of less than 0.3 % relative standard deviation. SIGNIFICANCE: The regenerative and label-free sensing capacity of the proposed device provides a theoretical foundation for label-free saccharide detection and the development of wearable glucose monitoring devices based on fiber optic sensors.

Bioelectrochemical biosensors for water quality assessment and wastewater monitoring.

Bioelectrochemical biosensors offer a promising approach for real-time monitoring of industrial bioprocesses. Many bioelectrochemical biosensors do not require additional labelling reagents for target molecules. This simplifies the monitoring process, reduces costs, and minimizes potential contamination risks. Advancements in materials science and microfabrication technologies are paving the way for smaller, more portable bioelectrochemical biosensors. This opens doors for integration into existing bioprocessing equipment and facilitates on-site, real-time monitoring capabilities. Biosensors can be designed to detect specific heavy metals such as lead, mercury, or chromium in wastewater. Early detection allows for the implementation of appropriate removal techniques before they reach the environment. Despite these challenges, bioelectrochemical biosensors offer a significant leap forward in wastewater monitoring. As research continues to improve their robustness, selectivity, and cost-effectiveness, they have the potential to become a cornerstone of efficient and sustainable wastewater treatment practices.

Electrochemical detection of poly(ADP-ribose) polymerase-1 with silver nanoparticles as signal labels by integrating the advantages of homogeneous reaction with surface-tethered detection.

Poly(ADP-ribose)polymerase-1 (PARP1) could be activated by binding to nucleic acids with specific sequences, thus catalyzing the poly-ADP-ribosylation (PARylation) of target proteins including PARP1 itself. Most of the previously reported electrochemical methods for the determination of PARP1 were relied on the electrostatic interactions, which required the pre-immobilization of DNA on an electrode for the capture of PARP1. Herein, we reported an "immobilization-free" electrochemical strategy for the assays of PARP1 on the basic of avidin-biotin interaction. Once PARP1 was activated by binding with the specific double-stranded DNA (dsDNA) in a homogeneous solution, the biotinylated nicotinamide adenine dinucleotide (biotin-NAD+) was transferred onto PARP1, resulting in the formation of biotinylated PAR polymers. The resulting biotinylated PAR polymers were then captured by a neutravidin (NA)-modified electrode through avidin-biotin interactions. The rich biotin moieties in the PAR polymers allowed for the capture of NA-modified silver nanoparticles (NA-AgNPs) through the avidin-biotin interactions. The surface-tethered AgNPs produced a well-defined electrochemical signal due to the characteristic solid-state Ag/AgCl process. The "immobilization-free", electrostatic interaction-independent electrochemical biosensor exhibited low background current, high sensitivity, and good stability. It has achieved the determination of PARP1 with a detection limit down to 0.7 mU. The biosensor was further applied to determine the inhibition efficiency of potential inhibitors with a satisfactory result. This method shows promising potential applications in PARP1-related clinical diagnosis and drug discovery.

Fiber photometry in neuroscience research: principles, applications, and future directions.

In recent years, fluorescent sensors are enjoying a surge of popularity in the field of neuroscience. Through the development of novel genetically encoded sensors as well as improved methods of detection and analysis, fluorescent sensing has risen as a new major technique in neuroscience alongside molecular, electrophysiological, and imaging methods, opening up new avenues for research. Combined with multiphoton microscopy and fiber photometry, these sensors offer unique advantages in terms of cellular specificity, access to multiple targets - from calcium dynamics to neurotransmitter release to intracellular processes - as well as high capability for in vivo interrogation of neurobiological mechanisms underpinning behavior. Here, we provide a brief overview of the method, present examples of its integration with other tools in recent studies ranging from cellular to systems neuroscience, and discuss some of its principles and limitations, with the aim of introducing new potential users to this rapidly developing and potent technique.

A DNA Aptamer for 2-Aminopurine: Binding-induced Fluorescence Quenching.

2-Aminopurine (2AP) is a fluorescent analog of adenine, and its unique properties make it valuable in various scientific and biotechnological applications. Its fluorescence property probes local dynamics in DNA and RNA because the surrounding bases quench its fluorescence. 2AP-labeled probes that can bind to specific DNA or RNA sequences, enabling the detection of genetic mutations, viral RNA, or other nucleic acid-based markers associated with diseases like cancer and infectious diseases. In this study, we isolated aptamers for 2AP using the library immobilization capture-SELEX technique. Two major aptamer families were isolated after 15 rounds of screening. The Kd values for the 2AP1 aptamer from family 1 are 209 nM in a fluorescence assay and 72 nM in an isothermal titration calorimetry test. The 32 nM 2AP limit of detection was tested. Additionally, we conducted some mutation analysis. Furthermore, we tested the selectivity of our aptamer using various molecules with similar structures and discovered that it can bind adenine and adenosine as well.

Deep Learning-Based Kinetic Analysis in Paper-Based Analytical Cartridges Integrated with Field-Effect Transistors.

This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity challenge observed in paper analytical devices, enabling electrical measurements with kinetic data. The paper-based cartridge eliminates the need for surface chemistry required in FET sensors, ensuring economical operation (cost < $0.15/test). The DL analysis mitigates chronic challenges of FET biosensors such as sample matrix interference, by leveraging kinetic data from target-specific bioreactions. In our proof-of-concept demonstration, our DL-based analyses showcased a coefficient of variation of <6.46% and a decent concentration measurement correlation with an r2 value of >0.976 for cholesterol testing when blindly compared to results obtained from a CLIA-certified clinical laboratory. These integrated technologies have the potential to advance FET-based biosensors, potentially transforming point-of-care diagnostics and at-home testing through enhanced accessibility, ease-of-use, and accuracy.

Sensing Interfaces Engineering for Organic Thin Film Transistors-Based Biosensors: Opportunities and Challenges.

Organic thin film transistors (OTFTs) enable rapid and label-free high-sensitivity detection of target analytes due to their low cost, large-area processing, biocompatibility, and inherent signal amplification. At the same time, the freedom of synthesis, tailorability, and functionalization of organic semiconductor materials and their ability to be combined with flexible substrates make them one of the ideal platforms for biosensing. However, OTFTs-based biosensors still face significant challenges, such as unexpected surface adsorption, disordered conformation, inhomogeneous graft density, and flexibility of probe molecules that biological sensing probes would face during immobilization. In this review, efficient immobilization strategies based on OTFTs biological sensing probes developed in the last 5 years are highlighted. First, the biosensors are classified according to their sensing interface. Second, a comprehensive discussion of the types of biological sensing probes is presented. Third, three commonly used methods for immobilizing biological sensing probes and their challenges are briefly described. Finally, the applications of OTFTs-based biosensors for liquid phase detection are summarized. This review provides a comprehensive and timely review of optimization in sensing interface engineering so that efficient immobilization of biological sensing probes with sensing interfaces will contribute to the development of high-performance OTFTs-based biosensors.

Portable multimodal platform with carbon nano-onions as colorimetric and fluorescent signal output for trypsin detection.

Multimodal biosensors with independent signaling pathways can self-calibrate and improve the reliability of disease biomarker detection. Herein, a colorimetric-fluorescent dual-mode paper-based biosensor with PAN/Fe(III)-CNOs (FPCs) as core components has been developed, which information is recognized by smartphone and naked eye. Using 1-(2-pyridylazo)-2-naphthol (PAN) as a mediator, Fe(III) is enriched on the surface of carbon nano-onions (CNOs), endowing FPCs with excellent mimetic enzyme activity and photothermal conversion ability, which allows it to output amplified colorimetric signals under laser irradiation. In addition, the complexation of PAN with Fe(III) broadens its absorption spectrum, which makes FPCs more suitable to be energy acceptors to quench fluorescence of polymer dots (Pdots), resulting in the changes of output fluorescent signal. Based on the above design, a portable colorimetric-fluorescent dual-mode biosensor is proposed for trypsin detection with Pdots as fluorescence sources and FPCs as fluorescence quenchers and nanoenzymes. This work provides a convenient way for constructing portable visual multimodal biosensors, which is expected to applied in various disease diagnosis.

Emerging sensing, imaging, and computational technologies to scale nano-to macroscale rhizosphere dynamics - Review and research perspectives.

The soil region influenced by plant roots, i.e., the rhizosphere, is one of the most complex biological habitats on Earth and significantly impacts global carbon flow and transformation. Understanding the structure and function of the rhizosphere is critically important for maintaining sustainable plant ecosystem services, designing engineered ecosystems for long-term soil carbon storage, and mitigating the effects of climate change. However, studying the biological and ecological processes and interactions in the rhizosphere requires advanced integrated technologies capable of decoding such a complex system at different scales. Here, we review how emerging approaches in sensing, imaging, and computational modeling can advance our understanding of the complex rhizosphere system. Particularly, we provide our perspectives and discuss future directions in developing in situ rhizosphere sensing technologies that could potentially correlate local-scale interactions to ecosystem scale impacts. We first review integrated multimodal imaging techniques for tracking inorganic elements and organic carbon flow at nano- to microscale in the rhizosphere, followed by a discussion on the use of synthetic soil and plant habitats that bridge laboratory-to-field studies on the rhizosphere processes. We then describe applications of genetically encoded biosensors in monitoring nutrient and chemical exchanges in the rhizosphere, and the novel nanotechnology-mediated delivery approaches for introducing biosensors into the root tissues. Next, we review the recent progress and express our vision on field-deployable sensing technologies such as planar optodes for quantifying the distribution of chemical and analyte gradients in the rhizosphere under field conditions. Moreover, we provide perspectives on the challenges of linking complex rhizosphere interactions to ecosystem sensing for detecting biological traits across scales, which arguably requires using the best-available model predictions including the model-experiment and image-based modeling approaches. Experimental platforms relevant to field conditions like SMART (Sensors at Mesoscales with Advanced Remote Telemetry) soils testbed, coupled with ecosystem sensing and predictive models, can be effective tools to explore coupled ecosystem behavior and responses to environmental perturbations. Finally, we envision that with the advent of novel high-resolution imaging capabilities at nano- to macroscale, and remote biosensing technologies, combined with advanced computational models, future studies will lead to detection and upscaling of rhizosphere processes toward ecosystem and global predictions.

Machine Learning and Wearable Technology: Monitoring Changes in Biomedical Signal Patterns during Pre-Migraine Nights.

Migraine is one of the most common neurological disorders, characterized by moderate-to-severe headache episodes. Autonomic nervous system (ANS) alterations can occur at phases of migraine attack. This study investigates patterns of ANS changes during the pre-ictal night of migraine, utilizing wearable biosensor technology in ten individuals. Various physiological, activity-based, and signal processing metrics were examined to train predictive models and understand the relationship between specific features and migraine occurrences. Data were filtered based on specified criteria for nocturnal sleep, and analysis frames ranging from 5 to 120 min were used to improve the diversity of the training sample and investigate the impact of analysis frame duration on feature significance and migraine prediction. Several models, including XGBoost (Extreme Gradient Boosting), HistGradientBoosting (Histogram-Based Gradient Boosting), Random Forest, SVM, and KNN, were trained on unbalanced data and using cost-sensitive learning with a 5:1 ratio. To evaluate the changes in features during pre-migraine nights and nights before migraine-free days, an analysis of variance (ANOVA) was performed. The results showed that the features of electrodermal activity, skin temperature, and accelerometer exhibited the highest F-statistic values and the most significant p-values in the 5 and 10 min frames, which makes them particularly useful for the early detection of migraines. The generalized prediction model using XGBoost and a 5 min analysis frame achieved 0.806 for accuracy, 0.638 for precision, 0.595 for recall, and 0.607 for F1-score. Despite identifying distinguishing features between pre-migraine and migraine-free nights, the performance of the current model suggests the need for further improvements for clinical application.

Integrating, Validating, and Expanding Information Space in Single-Molecule Surface-Enhanced Raman Spectroscopy for Biomolecules.

Single-molecule surface-enhanced Raman spectroscopy (SM-SERS) is an ultrahigh-resolution spectroscopic method for directly obtaining the complex vibrational mode information on individual molecules. SM-SERS offers a wide range of submolecular information on the hidden heterogeneity in its functional groups and varying structures, dynamics of conformational changes, binding and reaction kinetics, and interactions with the neighboring molecule and environment. Despite the richness in information on individual molecules and potential of SM-SERS in various detection targets, including large and complex biomolecules, several issues and practical considerations remain to be addressed, such as the requirement of long integration time, challenges in forming reliable and controllable interfaces between nanostructures and biomolecules, difficulty in determining hotspot size and shape, and most importantly, insufficient signal reproducibility and stability. Moreover, utilizing and interpreting SERS spectra is challenging, mainly because of the complexity and dynamic nature of molecular fingerprint Raman spectra, and this leads to fragmentary analysis and incomplete understanding of the spectra. In this Perspective, we discuss the current challenges and future opportunities of SM-SERS in views of system approaches by integrating molecules of interest, Raman dyes, plasmonic nanostructures, and artificial intelligence, particularly for detecting and analyzing biomolecules to realize the validation and expansion of information space in SM-SERS.

Plug-and-play nucleic acid-mediated multimerization of biparatopic nanobodies for molecular imaging.

In cancer molecular imaging, selecting binders with high specificity and affinity for biomarkers is paramount for achieving high-contrast imaging within clinical time frames. Nanobodies have emerged as potent candidates, surpassing antibodies in pre-clinical imaging due to their convenient production, rapid renal clearance, and deeper tissue penetration. Multimerization of nanobodies is a popular strategy to enhance their affinity and pharmacokinetics; however, traditional methods are laborious and may yield heterogeneous products. In this study, we employ a Holliday junction (HJ)-like nucleic acid-based scaffold to create homogeneous nanostructures with precise multivalent and multiparatopic nanobody displays. The plug-and-play assembly allowed the screening of several nanobody multimer configurations for the detection of the breast cancer biomarker, human epidermal growth factor receptor 2 (HER2). In vitro studies demonstrated significant improvements in binding avidity, particularly with the biparatopic construct exhibiting high sensitivity, surpassing that of traditional antibody-based cell binding. Furthermore, our HJ platform allowed for adaptation from fluorescence-based to nuclear imaging, as demonstrated in xenografted mice, thereby allowing for future in vivo applications. This work highlights the potential of nucleic acid-mediated multimerization to markedly enhance nanobody binding, by exploring synergistic combinations and offering versatility for both in vitro diagnostics and cancer molecular imaging with prospects for future theranostic applications.

Innovative Biosensor Technologies in the Diagnosis of Urinary Tract Infections: A Comprehensive Literature Review.

Urinary tract infections (UTIs) are prevalent bacterial infections globally, posing significant challenges due to their frequency, recurrence, and antibiotic resistance. This review delves into the advancements in UTI diagnostics over the past decade, particularly focusing on the development of biosensor technologies. The emergence of biosensors, including microfluidic, optical, electrochemical, immunosensors, and nanotechnology-based sensors, offers enhanced diagnostic accuracy, reduced healthcare costs. Despite these advancements, challenges such as technical limitations, the need for cross-population validation, and economic barriers for widespread implementation persist. The integration of artificial intelligence and smart devices in UTI diagnostics, highlighting the innovative approaches and their implications for patient care. The article envisions a future where multidisciplinary research and innovation overcome current obstacles, fully leveraging the potential of biosensor technologies to transform biosensor-based UTIs diagnosis. The ultimate goal is to achieve rapid, accurate, and non-invasive diagnostics, making healthcare more accessible and effective.

Identification of novel genetic factors that regulate c-di-AMP production in Staphylococcus aureus using a riboswitch-based biosensor.

Nucleotide secondary messengers regulate various processes in bacteria allowing them to rapidly respond to changes in environmental conditions. c-di-AMP is an essential second messenger required for the growth of the human pathogen Staphylococcus aureus, regulating potassium, osmolyte uptake, and beta-lactam resistance. Cellular concentrations of c-di-AMP are regulated by the activities of two enzymes, DacA and GdpP, which synthesize and hydrolyze c-di-AMP, respectively. Besides these, only a limited number of other factors are known to regulate c-di-AMP levels. Using a c-di-AMP biosensor consisting of the Bacillus subtilis c-di-AMP-binding kimA riboswitch and yfp, we were able to efficiently detect differences in cellular c-di-AMP levels in S. aureus. To identify novel factors that regulate c-di-AMP levels, we introduced the biosensor into a library of S. aureus transposon mutants. In this manner, we obtained mutants with increased c-di-AMP levels that contained insertions in gdpP coding for the c-di-AMP hydrolase and ybbR (cdaR) coding for a c-di-AMP cyclase regulator, thus validating our screen. We also identified two high c-di-AMP mutants with insertions upstream of the nrdIEF operon coding for the ribonucleotide reductase enzyme. Further analysis revealed that the insertion down-regulated nrdIEF expression, indicating that the enzyme is a negative regulator of c-di-AMP production. This negative regulation was dependent on rsh, encoding for the synthase of the endogenous GdpP inhibitor (p)ppGpp. The methods established in this work can be readily adapted for use in other bacteria to uncover genetic or environmental factors regulating c-di-AMP levels.IMPORTANCEc-di-AMP is an important secondary messenger, produced by many bacterial species including the opportunistic pathogen Staphylococcus aureus. In this bacterium, c-di-AMP controls cell wall homeostasis, cell size, and osmotic balance. In addition, it has been shown that strains with high c-di-AMP levels exhibit increased resistance to beta-lactam antibiotics. Here, we developed a biosensor-based method for the rapid detection of c-di-AMP levels in S. aureus. We utilized the biosensor in a genetic screen for the identification of novel factors that impact cellular c-di-AMP. In this manner, we identified the ribonucleotide reductase as a novel factor altering cellular c-di-AMP levels and showed that reducing its expression leads to increased cellular c-di-AMP levels. As methicillin-resistant S. aureus strains are considered as a global health threat, it is important to study processes that dictate cellular c-di-AMP levels, which are associated with antibiotic resistance.

CRISPR-Driven Biosensors: A New Frontier in Rapid and Accurate Disease Detection.

This comprehensive review delves into the advancements and challenges in biosensing, with a strong emphasis on the transformative potential of CRISPR technology for early and rapid detection of infectious diseases. It underscores the versatility of CRISPR/Cas systems, highlighting their ability to detect both nucleic acids and non-nucleic acid targets, and their seamless integration with isothermal amplification techniques. The review provides a thorough examination of the latest developments in CRISPR-based biosensors, detailing the unique properties of CRISPR systems, such as their high specificity and programmability, which make them particularly effective for detecting disease-associated nucleic acids. While the review focuses on nucleic acid detection due to its critical role in diagnosing infectious diseases, it also explores the broader applications of CRISPR technology in detecting non-nucleic acid targets, thereby acknowledging the technology's broader potential. Additionally, the review identifies existing challenges, such as the need for improved signal amplification and real-world applicability, and offers future perspectives aimed at overcoming these hurdles. The ultimate goal is to advance the development of highly sensitive and specific CRISPR-based biosensors that can be used widely for improving human health, particularly in point-of-care settings and resource-limited environments.

Progress of machine learning-based biosensors for the monitoring of food safety: A review.

Rapid urbanization and growing food demand caused people to be concerned about food safety. Biosensors have gained considerable attention for assessing food safety due to selectivity, and sensitivity but poor stability inherently limits their application. The emergence of machine learning (ML) has enhanced the efficiency of different sensors for food safety assessment. The ML combined with various noninvasive biosensors has been implemented efficiently to monitor food safety by considering the stability of bio-recognition molecules. This review comprehensively summarizes the application of ML-powered biosensors to investigate food safety. Initially, different detector-based biosensors using biological molecules with their advantages and disadvantages and biosensor-related various ML algorithms for food safety monitoring have been discussed. Next, the application of ML-powered biosensors to detect antibiotics, foodborne microorganisms, mycotoxins, pesticides, heavy metals, anions, and persistent organic pollutants has been highlighted for the last five years. The challenges and prospects have also been deliberated. This review provides a new prospect in developing various biosensors for multi-food contaminants powered by suitable ML algorithms to monitor in-situ food safety.

Electrochemical Sensors for Antibiotic Detection: A Focused Review with a Brief Overview of Commercial Technologies.

Antimicrobial resistance (AMR) poses a significant threat to global health, powered by pathogens that become increasingly proficient at withstanding antibiotic treatments. This review introduces the factors contributing to antimicrobial resistance (AMR), highlighting the presence of antibiotics in different environmental and biological matrices as a significant contributor to the resistance. It emphasizes the urgent need for robust and effective detection methods to identify these substances and mitigate their impact on AMR. Traditional techniques, such as liquid chromatography-mass spectrometry (LC-MS) and immunoassays, are discussed alongside their limitations. The review underscores the emerging role of biosensors as promising alternatives for antibiotic detection, with a particular focus on electrochemical biosensors. Therefore, the manuscript extensively explores the principles and various types of electrochemical biosensors, elucidating their advantages, including high sensitivity, rapid response, and potential for point-of-care applications. Moreover, the manuscript investigates recent advances in materials used to fabricate electrochemical platforms for antibiotic detection, such as aptamers and molecularly imprinted polymers, highlighting their role in enhancing sensor performance and selectivity. This review culminates with an evaluation and summary of commercially available and spin-off sensors for antibiotic detection, emphasizing their versatility and portability. By explaining the landscape, role, and future outlook of electrochemical biosensors in antibiotic detection, this review provides insights into the ongoing efforts to combat the escalating threat of AMR effectively.