miRNA-34a Gold-Modified Screen-Printed Graphene/MoS₂ Sensor

 

1. Introduction

Breast cancer remains one of the most significant causes of morbidity and mortality among women globally. Traditional diagnostic approaches, such as tissue biopsies, though effective, are often invasive, expensive, and require considerable clinical expertise. This study addresses the growing need for less invasive, rapid, and cost-effective diagnostic alternatives by exploring a liquid biopsy-based strategy using microRNA detection. Specifically, it introduces a novel electrochemical biosensor designed to identify miRNA-34a, a known biomarker of breast cancer, thereby offering a promising solution for early and accurate disease detection.

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2. Development of a Two-Dimensional Nanocomposite-Based Biosensor

The biosensor developed in this research employs a composite of reduced graphene oxide (rGO) and molybdenum disulfide (MoS₂), chosen for their synergistic physicochemical properties. These two-dimensional materials exhibit high surface area and electrical conductivity, which are crucial for improving biosensor sensitivity. Furthermore, the sulfur atoms in MoS₂ facilitate the anchoring of metallic nanoparticles, such as gold, enhancing probe immobilization. This strategic combination forms the basis of a powerful sensing platform suitable for the electrochemical detection of nucleic acid biomarkers.

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3. Functionalization and Enhancement via Gold Nanoparticles

Gold nanoparticles (AuNPs) play a pivotal role in the biosensor’s performance by enhancing conductivity and enabling robust probe immobilization through thiol-gold covalent bonds. The incorporation of AuNPs onto the surface of the SPrGO/MoS₂ composite electrode not only increases the electrochemical activity but also provides high affinity for thiolated DNA probes. This ensures specific and stable hybridization with the target miRNA-34a, enabling precise detection with minimal background interference.

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4. Electrochemical Detection Mechanism

The detection strategy utilizes differential pulse voltammetry (DPV), a highly sensitive electrochemical technique, to monitor hybridization events. The current signal generated by the redox activity of ferrocyanide reflects the presence and concentration of miRNA-34a. The biosensor demonstrates a wide linear detection range from 0.1 nM to 1000 nM and an impressively low detection limit of 66 pM. This sensitivity is crucial for detecting miRNA-34a in clinical samples, where biomarker concentrations are often very low.

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5. Clinical Applicability and Performance Validation

The biosensor was evaluated using serum samples spiked with varying concentrations of miRNA-34a, representing low, medium, and high levels typically seen in patient populations. The results demonstrated high precision, accuracy, and repeatability, highlighting the potential of this platform for clinical diagnostics. The sensor’s stability and ease of use further support its application in point-of-care settings, especially in resource-limited environments where traditional biopsy procedures may not be feasible.

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6. Future Perspectives and Applications

The development of this SPrGO/MoS₂-based biosensor marks a significant step forward in the field of electrochemical diagnostics. Its capability to accurately detect miRNA-34a offers a foundation for expanding the platform to other miRNA biomarkers associated with various cancers or diseases. With further validation, such biosensors could become standard tools for early cancer detection, treatment monitoring, and potentially for personalized medicine approaches, bridging the gap between laboratory research and real-world clinical application.

Application of fluorescence spectroscopy in meat analysis:-

1. Introduction

Meat quality and safety are pivotal concerns in food science and consumer health. Traditional testing methods, while accurate, often involve complex, time-consuming, and sometimes destructive processes. In contrast, fluorescence spectroscopy has emerged as a powerful, non-destructive, and rapid analytical technique for assessing the quality and safety of meat products. This review explores how fluorescence-based technologies can revolutionize the monitoring and evaluation of meat, aligning with industry demands for efficiency and precision.

2. Principles of Fluorescence Spectroscopy in Meat Quality Detection

Fluorescence spectroscopy relies on the interaction between light and matter, where certain compounds in meat absorb light at a specific wavelength and emit it at a longer wavelength. These fluorescence signatures can reveal critical information about the biochemical and structural properties of meat. Understanding the fundamental detection principles enables the development of more targeted, accurate analytical methods for meat quality evaluation.

3. Fluorescence-Based Techniques for Meat Quality Assessment

Several fluorescence-based techniques have been developed to improve meat analysis. These include fluorescence probes for detecting specific chemical markers, fluorescence sensors for real-time monitoring, and surface-enhanced fluorescence to boost signal sensitivity. Advanced methods like excitation-emission matrices (EEMs), synchronous fluorescence spectroscopy, and front-face fluorescence spectroscopy further expand the range of detectable parameters, providing a comprehensive picture of meat quality.

4. Applications of Fluorescence Spectroscopy in Meat Safety

Beyond quality assessment, fluorescence spectroscopy plays a critical role in ensuring meat safety by detecting contamination, spoilage, and adulteration. By targeting key indicators such as microbial load, oxidation products, and chemical residues, fluorescence analysis enables early and accurate identification of potential hazards, thus safeguarding public health.

5. Advantages of Fluorescence Spectroscopy in Meat Analysis

Fluorescence spectroscopy offers numerous advantages, including rapid testing, minimal sample preparation, high sensitivity, and the ability to conduct non-destructive analysis. When combined with data-driven techniques like chemometric analysis and machine learning, fluorescence spectroscopy can achieve even higher precision and reliability, making it ideal for both laboratory and on-site applications.

6. Future Directions and Challenges

Although fluorescence spectroscopy holds immense potential for meat quality and safety analysis, challenges such as standardization of protocols, data interpretation complexity, and adaptation to diverse meat matrices remain. Future research should focus on refining the sensitivity and specificity of fluorescence methods and integrating them with smart technologies for real-time, automated meat inspection systems.

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Transcriptional regulator-based biosensors for biomanufacturing in Corynebacterium glutamicum

1. Introduction
Intracellular biosensors based on transcriptional regulators have emerged as critical tools in the realm of biomanufacturing, especially for monitoring intracellular metabolites and aiding in strain optimization. Corynebacterium glutamicum, as a robust industrial microorganism, offers an excellent platform for deploying these biosensors, thereby enhancing the precision of biochemical production processes. This review focuses on the key roles, design principles, and improvements related to transcriptional regulator-based biosensors in C. glutamicum, paving the way for future advancements in microbial engineering.

2. Types and Mechanisms of Transcriptional Regulators in C. glutamicum
Transcriptional regulators, including repressors and activators, serve as the core sensing elements of intracellular biosensors. In C. glutamicum, regulators like LysG, Lrp, and AmtR recognize specific metabolites and trigger responsive genetic circuits. Understanding the interaction between these regulators and their corresponding ligands provides a molecular basis for designing effective biosensors that are both specific and sensitive.

3. Principles of Biosensor Design Based on Transcriptional Regulators
Effective biosensor construction hinges on key principles such as selecting highly specific transcriptional regulators, optimizing promoter-regulator combinations, and tuning reporter gene expression. Rational circuit design and modularity are crucial to maximize the biosensor's responsiveness and minimize noise, ensuring accurate and reliable semi-quantitative intracellular assessments.

4. Applications of Transcriptional Regulator-Based Biosensors in C. glutamicum
These biosensors have revolutionized high-throughput screening processes for production strain improvement, enzyme evolution, and metabolic flux analysis. Applications include enhancing amino acid production (like lysine and glutamate), optimizing pathways for novel chemical synthesis, and enabling dynamic pathway regulation based on real-time metabolite levels.

5. Strategies for Improving Biosensor Performance
Several measures have been developed to enhance biosensor efficacy, including directed evolution of regulators, promoter engineering, increasing dynamic range, improving signal-to-noise ratios, and utilizing synthetic biology tools for fine-tuning regulatory responses. Such strategies ensure biosensors maintain stability, specificity, and adaptability under industrial fermentation conditions.

6. Challenges and Future Perspectives
Despite the impressive progress, challenges like limited regulator availability, cross-reactivity, and metabolic burden remain. Future research will likely focus on expanding the library of transcriptional regulators, integrating AI-guided biosensor optimization, and developing multiplexed biosensing systems. These advancements will further solidify transcriptional regulator-based biosensors as indispensable tools in smart biomanufacturing and synthetic biology.

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A novel conductometric biosensor based on hybrid organic/inorganic recognition element for determination of L-arginine

1. Introduction

L-arginine (L-arg) plays a pivotal role in numerous physiological processes, including protein synthesis and nitric oxide production. Accurate quantification of L-arg in complex real-world samples such as food matrices presents a significant analytical challenge. This study presents the development of a novel conductometric biosensor that addresses these challenges by integrating enzyme specificity and material selectivity for enhanced analytical performance.

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2. Biosensor Design and Component Configuration

The biosensor was constructed using a co-immobilization strategy involving two enzymes—arginase and urease—alongside an ammonium-sensitive zeolite, clinoptilolite (Clt). Various configurations were tested to optimize signal sensitivity, with the most effective design featuring a base layer of Clt on the gold interdigitated electrode surface, followed by the enzyme layer. This arrangement maximized substrate interaction and ion selectivity, crucial for biosensor performance.

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3. Analytical Performance and Optimization

Among the tested designs, the Clt-first configuration exhibited superior analytical characteristics: high sensitivity (9.61 ± 0.01 μS/mM), a low limit of detection (5 μM), and a broad dynamic range (0–15 mM). The biosensor also showed a consistent linear detection range (0–280 μM), making it highly suitable for detecting L-arg at both trace and moderate concentrations in real samples.

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4. Influence of Solution Parameters on Sensor Sensitivity

Environmental conditions such as pH, ionic strength, and buffer capacity significantly influenced biosensor performance. Systematic evaluation revealed optimal operational conditions that minimized signal interference, ensuring accurate and reproducible readings. These findings emphasize the importance of optimizing solution parameters for biosensor application in diverse sample matrices.

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5. Application to Real Sample Analysis

The biosensor was successfully applied to quantify L-arg in various food samples, confirming its effectiveness in real-world complex matrices. When compared with ion chromatography, a standard reference technique, the biosensor results showed high correlation (R = 0.96), validating its accuracy and potential for routine L-arg analysis in food and clinical diagnostics.

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6. Stability and Practical Implications

Long-term stability studies demonstrated the biosensor’s robustness in both operational and storage conditions, with minimal signal degradation over time. Its high precision, coupled with low cost and ease of use, highlights the biosensor’s potential for widespread application in food safety, nutritional science, and biomedical research.

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#BiosensorDevelopment #LArginineDetection #ConductometricBiosensor #EnzymeImmobilization #ZeoliteClinoptilolite #Arginase #Urease #FoodAnalysis #BiochemicalSensors #PointOfCareDiagnostics #BioanalyticalChemistry #SensorTechnology #ElectrochemicalDetection #IonSelectiveSensors #RealSampleAnalysis #AnalyticalPerformance #NutritionalBiochemistry #LabOnChip #BiomedicalApplications #SmartSensors

iPhone LiDAR Meets Conductometric Biosensors! 🔬📱

 

INTRODUCTION

Recent advances in mobile technology have introduced powerful sensing capabilities in consumer devices, including depth sensing based on time-of-flight (ToF) LiDAR integrated into Apple’s iPhone 13 Pro and similar models. This study investigates the feasibility and limitations of such LiDAR systems in capturing structural vibrations for modal analysis, a critical tool in structural health monitoring. By employing a flexible vibrating target and comparing data against a high-precision laser displacement transducer, the study assesses the mobile LiDAR system’s accuracy and utility. The overarching goal is to evaluate whether consumer-grade mobile devices can be effectively employed for non-contact vibration measurement in academic and field-based research settings.

CHARACTERIZATION OF LIDAR SENSOR PERFORMANCE

To assess the LiDAR's effectiveness in capturing vibration data, the system was tested on a flexible steel cantilever setup. Noise levels, frequency response, and sensing range were systematically evaluated. One significant finding was that although the device camera operates at 60 Hz, the actual LiDAR depth map updates at only 15 Hz. This discrepancy has implications for frequency-domain analyses and requires downsampling of raw data to avoid aliasing errors. Despite inherent noise and distortion, LiDAR data demonstrated a high degree of correlation with laser displacement transducer results, validating the sensor's potential for modal identification under controlled conditions.

IMPACT OF MEASUREMENT CONDITIONS

The influence of environmental and setup parameters, such as the phone-to-target distance and lighting conditions, was studied to understand their impact on measurement quality. It was found that optimal sensing performance occurs when the device is positioned between 0.30 m and 2.00 m from the target. Lighting conditions had less influence on depth sensing performance due to the infrared nature of ToF LiDAR. These findings highlight the importance of appropriate positioning and environmental awareness in experimental setups using mobile LiDAR for structural analysis.

DATA PROCESSING AND MODAL IDENTIFICATION

Data acquired from the mobile LiDAR were processed using Stochastic Subspace Identification (SSI) in a Monte Carlo framework to extract stochastic modal parameters. This approach helped to account for sensor noise and improve reliability through repeated sampling. The analysis successfully identified natural frequencies with a mean deviation of just 1.9% from reference measurements, showcasing the potential of mobile LiDAR systems for modal analysis. The robustness of the method lies in combining sophisticated data processing techniques with prior structural knowledge to compensate for the lower sampling rate and higher noise.

COMPARATIVE ANALYSIS WITH HIGH-PRECISION SENSORS

Benchmarking against a laser displacement transducer provided crucial validation for the mobile LiDAR approach. Despite limitations in temporal resolution and increased noise, the iPhone-based LiDAR captured mode shapes and natural frequencies that closely matched those from high-precision sensors. While not a replacement for laboratory-grade equipment, the mobile solution offers a highly accessible, scalable alternative, especially suitable for preliminary diagnostics or environments where traditional setups are impractical or impossible to deploy.

APPLICATION POTENTIAL IN STRUCTURAL MONITORING

The study concludes that mobile LiDAR sensors, when correctly utilized, hold significant promise for structural health monitoring and diagnostic applications. Their portability, cost-efficiency, and non-contact operation make them ideal for scenarios such as scaled lab models, inaccessible field structures, or rapid inspections. Future work can explore integrating machine learning for automated diagnostics and expanding use in civil infrastructure systems. With further development, mobile LiDAR may redefine the boundaries of structural monitoring, democratizing access to advanced sensing technologies.

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#LiDAR #iPhone13Pro #StructuralMonitoring #ModalAnalysis #NonContactSensing #TimeOfFlight #VibrationMeasurement #MobileSensors #SSI #MonteCarlo #DepthSensing #CantileverBeam #SensorCharacterization #SmartphoneSensing #FlexibleStructures #FieldDiagnostics #MobileSHM #StructuralHealth #FrequencyAnalysis #InfraredSensing

How Material Properties Impact Laser Cutting Efficiency 🔬

 

INTRODUCTION 🔍

The advancement of laser cutting technology has significantly improved manufacturing processes, particularly in dealing with engineering materials such as mild steel (HA350), aluminium (Al5005), and stainless steel (SS316). Fibre laser cutting, known for its precision and efficiency, is widely adopted across industries, yet its interactions with different materials are complex and require in-depth study. This research investigates the influence of laser cutting parameters on key material responses including surface roughness, hardness, kerf width, and the laser-affected area. The findings aim to provide a clearer understanding of how intrinsic material properties affect cutting performance, surface integrity, and overall quality, laying the foundation for optimizing manufacturing settings for various materials.

INFLUENCE OF MATERIAL PROPERTIES ON LASER RESPONSE 🧪

The behaviour of each material under fibre laser cutting is inherently linked to its physical and thermal properties. For instance, aluminium, with its high thermal conductivity, responded differently from mild and stainless steels. Aluminium exhibited a 46% increase in surface hardness, unlike mild steel and stainless steel, which showed reductions of 20.5% and 22.7%, respectively. These variations suggest that the laser's interaction with the workpiece is material-specific, and parameters must be fine-tuned to the unique properties of each substrate to achieve optimal results.

SURFACE ROUGHNESS AND CUTTING PARAMETERS ✨

Surface quality post-laser cutting is a crucial measure of success in precision manufacturing. This study reveals that surface roughness is highly sensitive to both material type and laser settings, particularly power and speed. Materials with better thermal conductivity, such as aluminium, are more capable of dispersing heat evenly, resulting in smoother cuts. Conversely, inconsistent heat distribution in steels can cause micro-defects and rougher textures. Achieving minimal roughness demands a precise balance between laser power and cutting speed tailored to the material's characteristics.

HARDNESS ALTERATIONS IN MACHINED SURFACES 🔩

One significant outcome of fibre laser cutting is its effect on the hardness of the machined surface. This research documents notable shifts in hardness, emphasizing how laser-induced thermal cycles alter surface properties. Stainless steel and mild steel both exhibited a substantial decrease in hardness after cutting, implying thermal softening. Aluminium, however, demonstrated increased hardness, potentially due to rapid cooling and phase changes. These contrasting outcomes highlight the importance of understanding post-process material behaviour, particularly when mechanical performance is critical.

KERF WIDTH VARIATIONS AMONG MATERIALS 📏

Kerf width—the width of the material removed during cutting—is another critical factor influenced by laser parameters and material type. This study notes considerable variation in kerf dimensions among the three materials, influenced by thermal conductivity, melting points, and absorption rates. Stainless steel and mild steel displayed narrower kerfs compared to aluminium, which showed wider cuts due to its reflective and heat-conductive nature. These observations underline the importance of controlling beam parameters for accurate, material-specific cuts.

DEFECTS AND LASER-AFFECTED ZONES ⚠️

Laser-affected zones (LAZ) often host microstructural changes and surface defects such as splatters, especially when cutting conditions are suboptimal. This research identifies the emergence of such defects across all tested materials, with frequency and severity depending on the interplay between heat input and material characteristics. High laser energy can lead to excess melting and splatter formation, particularly in materials with low melting points. Minimizing LAZ and defect formation is essential for improving part reliability and aesthetic finish in high-precision industries.

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#LaserCutting #FibreLaser #MaterialScience #SurfaceRoughness #KerfWidth #ThermalConductivity #ManufacturingEngineering #MetalCutting #AluminiumCutting #SteelCutting #LaserTechnology #EngineeringResearch #LaserProcessing #CuttingDefects #SurfaceHardness #MaterialBehavior #MachiningScience #SmartManufacturing #Metallurgy #IndustrialInnovation

Vibrational Spectroscopy & AI for Ultra-Sensitive Detection

 

1. Introduction

Cystic echinococcosis (CE) is a neglected tropical disease caused by the larval stage of Echinococcus granulosus, posing a major public health concern worldwide. Characterized by slow progression and often asymptomatic presentation in early stages, CE complicates timely and accurate diagnosis. Traditional imaging and serological methods lack the sensitivity and specificity required for early-stage detection. As a result, research is increasingly focused on the development of innovative diagnostic approaches. This study investigates the use of advanced vibrational spectroscopy techniques, namely surface enhanced Raman spectroscopy (SERS) and Fourier transform infrared spectroscopy (FTIR), in conjunction with machine learning algorithms, to provide a non-invasive and highly accurate method for early diagnosis of CE using mouse models.

2. Spectroscopic Techniques in Disease Diagnostics

Vibrational spectroscopy has emerged as a promising analytical tool for biomedical applications due to its ability to detect subtle biochemical changes in body fluids. SERS enhances Raman signals using metallic nanostructures, offering high sensitivity, while FTIR captures molecular fingerprints through absorption spectra. These techniques provide detailed chemical information from small sample volumes, making them suitable for non-invasive diagnostics. This study applied both SERS and FTIR to serum and urine samples, aiming to evaluate their efficacy in distinguishing early-stage CE from healthy controls. The dual-approach allowed a comparative assessment of their diagnostic potentials.

3. Integration of Machine Learning with Spectroscopic Data

The incorporation of machine learning in analyzing complex spectroscopic data allows for improved classification and pattern recognition in diagnostic applications. In this study, four machine learning algorithms were employed to classify spectroscopic profiles from serum and urine samples. Among them, the support vector machine (SVM) algorithm outperformed the others, achieving diagnostic accuracies of 93.2% for serum SERS and 95.5% for serum FTIR data. The use of machine learning not only enhanced diagnostic accuracy but also enabled the extraction of disease-relevant features from high-dimensional data, marking a significant step toward intelligent diagnostic systems for parasitic diseases.

4. Diagnostic Value of Serum vs. Urine Samples

While both serum and urine are valuable biofluids for non-invasive diagnostics, their diagnostic efficacy can vary based on the technique employed and disease-specific biochemical changes. The current study revealed that serum-based spectroscopy provided significantly better classification results than urine-based approaches, with serum yielding accuracies above 90% while urine remained below 80%. This discrepancy may be due to the lower concentration of disease-specific biomarkers in urine or greater spectral overlap. These findings underscore the importance of sample selection in the development of vibrational spectroscopy-based diagnostic models.

5. Identification of Potential Early Biomarkers

The analysis of spectral features using a linear SVM-based importance map identified specific biochemical signatures associated with early CE. Notable biomarkers included purine metabolites such as uric acid and hypoxanthine, protein-associated bands like amide I and CH3, and lipid-related CH2 vibrations. These components are indicative of metabolic disturbances triggered by early infection and may serve as targets for further biomarker validation. The identification of such markers supports the clinical relevance of vibrational spectroscopy and offers a path toward better understanding of CE pathophysiology at its onset.

6. Future Perspectives and Clinical Implications

The findings from this study highlight the potential of combining vibrational spectroscopy with machine learning for the early diagnosis of CE. With the advantages of high accuracy, non-invasiveness, and rapid analysis, this method could be translated into a point-of-care diagnostic tool, especially valuable in resource-limited and endemic settings. Further research involving larger cohorts and human samples is necessary to validate the method and assess its applicability across different stages of CE. Ultimately, this interdisciplinary approach opens new avenues in parasitic disease diagnostics and may inspire similar strategies for other infectious diseases.

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#CysticEchinococcosis #ParasiticDiseases #VibrationalSpectroscopy #SERS #FTIR #EarlyDiagnosis #MachineLearning #BiomedicalSpectroscopy #NonInvasiveDiagnostics #SupportVectorMachine #SerumBiomarkers #UrineAnalysis #SpectroscopyResearch #AIinHealthcare #ZoonoticDiseases #MedicalDiagnostics #BiofluidAnalysis #InfraredSpectroscopy #RamanSpectroscopy #TranslationalMedicine

Conductometric Biosensor Using MIR & NIR Spectroscopy

 

1. Introduction

Non-enzymatic glycation, the spontaneous reaction between reducing sugars and proteins, plays a critical role in the pathogenesis of chronic diseases such as diabetes mellitus, its vascular complications, and neurodegenerative disorders. Human serum albumin (HSA), the most abundant plasma protein, is especially prone to glycation due to its long half-life and numerous lysine residues. The progression from early glycation to advanced glycation end-products (AGEs) encompasses multiple molecular transitions, making its real-time analysis challenging. Traditional biochemical assays are often insufficient to track the nuanced changes during early and intermediate stages of glycation. This research addresses this gap by applying vibrational spectroscopy techniques to monitor and quantify glycation progression in HSA.

2. Role of Infrared Spectroscopy in Monitoring Protein Glycation

Infrared spectroscopy, including both near-infrared (NIR) and mid-infrared (MIR) regions, has emerged as a powerful non-destructive tool to probe biomolecular interactions and structural changes in proteins. Its sensitivity to vibrational modes associated with specific chemical bonds allows for detailed monitoring of molecular changes during glycation. In this study, NIR and MIR techniques were effectively employed to investigate HSA glycation over a period of five weeks, providing critical insights into the structural evolution of the protein as glycation progressed.

3. Temporal Dynamics of HSA Glycation and Spectral Signature Identification

Through rigorous NIR analysis, the glycation of HSA was found to peak in fructosamine formation at the three-week mark, indicating the culmination of intermediate glycation stages. Distinctive NIR spectral peaks at 4768 cm⁻¹, 5644 cm⁻¹, 5982 cm⁻¹, 7012 cm⁻¹, and 7143 cm⁻¹ were associated with various molecular vibrations, particularly those influenced by glycation-induced changes in the protein. Complementary MIR spectroscopy revealed additional peaks at 675 cm⁻¹, 1517 cm⁻¹, 1685 cm⁻¹, 1792 cm⁻¹, and 1840 cm⁻¹, shedding light on alterations in protein secondary structure and carbonyl group formation linked to advanced glycation.

4. Development of Quantitative Models for Glycated HSA

The integration of NIR and MIR spectroscopic data enabled the development of robust multivariate models to quantify glycated HSA levels. These models showed exceptional predictive power with high calibration (R²c = 0.9994) and prediction accuracy (R²p = 0.9524), along with a low root mean square error of prediction (RMSEP = 1.59 mmol/L) and a strong ratio of performance to deviation (RPD = 3.35). These metrics validate the reliability and utility of the spectral models for practical applications in glycation monitoring.

5. Significance in Biomedical Research and Diagnostics

This research contributes significantly to the biomedical field by presenting a sensitive, real-time, and non-invasive method for detecting early to intermediate glycation in serum proteins. Monitoring HSA glycation levels has the potential to serve as a biomarker for diabetes progression and risk assessment for complications. Furthermore, the approach can be extended to study other proteins susceptible to glycation, enhancing our understanding of protein aging and disease mechanisms at the molecular level.

6. Future Directions and Applications in Clinical Settings

The successful implementation of NIR and MIR spectroscopy for glycated HSA quantification opens new avenues for clinical diagnostics and monitoring strategies. Future studies may explore miniaturized sensor development, in vivo applications, and the adaptation of this technique for other clinically relevant proteins. With the advancement of spectroscopy-based analytical tools and machine learning for data interpretation, this methodology holds promise for integration into point-of-care diagnostic platforms for chronic disease management.

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#Glycation #HumanSerumAlbumin #InfraredSpectroscopy #NIR #MIR #BiomedicalResearch #DiabetesMonitoring #ProteinModification #SpectroscopyInMedicine #QuantitativeModeling #Fructosamine #AGEs #MolecularDiagnostics #VibrationalSpectroscopy #ChronicDiseaseBiomarkers #GlycatedProteins #ClinicalSpectroscopy #HSAResearch #PredictiveAnalytics #ProteinStructuralChanges

A Novel Conductometric Biosensor Using Quartz Crystal Tuning Fork Spectroscopy

 

1. Introduction

Gas sensing technologies are crucial for environmental monitoring, industrial safety, and medical diagnostics. In this research, a novel gas sensing technique is proposed using a quartz crystal tuning fork (QCTF) enhanced spectroscopy method, integrating self-calibration algorithms that consider both the resonant frequency and quality factor of the QCTF. The aim is to improve sensing accuracy, responsiveness, and adaptability to varying environmental pressures, offering a robust alternative to conventional methods.

2. QCTF-Enhanced Spectroscopy Mechanism

Quartz crystal tuning forks (QCTFs) serve as highly sensitive detectors due to their sharp resonance characteristics. In this technique, the QCTF is used to enhance spectroscopic detection by precisely tracking its resonance behavior. The sensitivity of QCTFs to environmental changes such as pressure and temperature is leveraged by incorporating resonance and quality factor-based calibration to improve the reliability of gas detection.

3. Methane Detection Using Near-Infrared DFB Diode Laser

To validate the proposed sensing technique, methane (CH₄) was selected as the target gas. A distributed feedback (DFB) diode laser near 1653 nm in the near-infrared range was employed for selective excitation of methane. This wavelength corresponds to a strong absorption feature of CH₄, enabling high sensitivity detection. The combination of wavelength modulation spectroscopy (WMS) and second harmonic (2f) detection allows for enhanced signal processing and noise reduction.

4. Real-Time Resonance Tracking and Calibration Algorithms

A hybrid single-frequency modulation algorithm was developed to enable real-time tracking of the QCTF resonance profile. Unlike traditional scanning methods that take up to 30 seconds, this new approach provides a rapid 1-second calibration response. Additionally, a novel quality factor-based algorithm was introduced to calibrate the 2f signal amplitude against dynamic pressure changes, ensuring consistent signal interpretation even under fluctuating conditions.

5. Performance Evaluation and Comparison with Conventional Methods

The proposed gas sensing system demonstrated significantly improved performance over traditional scanning modulation techniques. With a measurement error of less than 1% under dynamic pressure variations up to 320 mbar, the system showcased a 30-fold increase in time resolution. This highlights the potential of the technique for real-time applications in environments where rapid changes in pressure or temperature are expected.

6. Potential Applications and Future Research Directions

Given its rapid response time, high accuracy, and pressure adaptability, the QCTF-enhanced gas sensing technique holds promise for field applications such as leak detection in pipelines, emission monitoring in industrial plants, and atmospheric trace gas analysis. Future work could focus on multi-gas detection, miniaturization for portable use, and integrating machine learning for adaptive calibration and anomaly detection.

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#GasSensing #QCTF #Spectroscopy #MethaneDetection #InfraredLaser #WavelengthModulation #2fDetection #ResonanceTracking #SelfCalibration #SensorTechnology #RealTimeDetection #EnvironmentalMonitoring #IndustrialSafety #PressureCalibration #TuningForkSensor #DFBLaser #AdvancedSensing #SmartSensors #AnalyticalChemistry #FieldApplications

Biosensor-based dual-color droplet microfluidic platform for precise high-throughput screening of erythromycin hyperproducers

 

1. Introduction

The growing demand for natural products in pharmaceuticals, agriculture, and biotechnology has prompted advancements in microbial cell factory engineering. Among the innovative approaches, biosensor-based droplet microfluidic high-throughput screening has emerged as a powerful technique for detecting and selecting high-yield microbial strains. This method leverages genetically encoded biosensors to produce measurable outputs in response to specific metabolite concentrations, enabling rapid identification of desirable phenotypes from large mutant libraries. However, inherent biological variability among microbial cells poses challenges to the reliability and accuracy of this technique, necessitating refined strategies to enhance screening fidelity.

2. Limitations of Traditional Whole-Cell Biosensors in Droplet Microfluidics

Conventional single-color whole-cell biosensors, while effective under controlled conditions, often fail to maintain accuracy within microfluidic droplets. Environmental fluctuations significantly influence bacterial growth and gene expression, leading to heterogeneous cell populations within each droplet. This variability introduces inconsistencies in biosensor output signals, making it difficult to distinguish true positive signals from background noise. Moreover, the inability to measure or control cell density within individual droplets further exacerbates false-positive rates, creating a substantial burden for downstream validation processes.

3. Engineering Dual-Color Biosensors for Normalized Signal Output

To address the challenge of heterogeneity, this study introduced a novel dual-color biosensor design in Escherichia coli. By integrating a second reporter signal that reflects cell growth or viability, the system provides normalized outputs by comparing the product-indicative fluorescence to a constitutive reference. This internal control corrects for variances in cell density and gene expression, resulting in more accurate and robust detection of desired phenotypes. The dual-color format represents a significant step forward in biosensor design for high-throughput screening applications.

4. Integration with Droplet-Based Microfluidic Platforms

The enhanced dual-color biosensors were successfully implemented in a droplet-based microfluidic screening platform, enabling simultaneous analysis of thousands of individual microbial variants. This integration allowed for high-throughput, parallelized screening with improved signal consistency and droplet uniformity. In proof-of-concept experiments, the dual-color system exhibited a markedly higher enrichment ratio compared to its single-color counterpart, underscoring the effectiveness of this strategy in minimizing heterogeneity-induced noise and improving screening outcomes.

5. Application in Erythromycin-Producing Strain Improvement

To validate the practical benefits of the dual-color approach, the system was employed in screening both wild-type and mutagenized Saccharopolyspora erythraea strains for enhanced erythromycin production. Results showed a 24.2% increase in positive identification rates for the wild-type strain and an 11.9% increase for industrial S0-derived libraries using the dual-color method. Notably, strains exhibiting up to a 19.6% improvement in erythromycin yield were successfully isolated, demonstrating the method's potential for industrial strain development and optimization.

6. A Universal Strategy for Next-Generation Biosensor Applications

The dual-color whole-cell biosensor platform provides a universal framework for improving the accuracy and throughput of microbial screening campaigns. By accounting for biological variability within droplets, this system reduces false positives and minimizes the time and resources required for post-screening verification. Its compatibility with diverse natural product biosynthesis pathways makes it a versatile tool for synthetic biology, metabolic engineering, and bioprocess development. This work lays the foundation for the next generation of biosensor-driven high-throughput screening technologies.

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#Microfluidics #Biosensors #SyntheticBiology #MetabolicEngineering #DualColorBiosensors #HighThroughputScreening #NaturalProductDiscovery #MicrobialCellFactories #StrainEngineering #ErythromycinProduction #DropletMicrofluidics #WholeCellBiosensors #CellHeterogeneity #GeneticallyEncodedSensors #FluorescenceScreening #BiotechnologyInnovation #BioengineeringTools #BioprocessOptimization #BiotechResearch #NextGenScreening

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Hybrid Organic-Inorganic Tech #Biosensor #Research

 

1. Introduction

The development of sensitive, selective, and robust biosensors for detecting biomolecules in complex sample matrices has become a significant focus in analytical chemistry and biotechnology. L-arginine (L-arg), an essential amino acid involved in various physiological processes, demands precise and reliable detection, especially in real samples like food or biological fluids. Conductometric biosensors, which rely on changes in conductivity to indicate analyte concentration, present a promising approach. In the context of L-arg determination, an innovative biosensor combining enzymatic activity with ion-sensitive materials offers a new pathway for enhanced accuracy and stability.

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2. Biosensor Design and Enzyme Immobilization Strategy

The biosensor was fabricated by co-immobilizing two key enzymes—arginase and urease—alongside an ion-selective material, zeolite clinoptilolite (Clt), on gold interdigitated electrodes. The arrangement of these components on the sensor surface was critical to achieving optimal performance. Different configurations were tested to assess their influence on sensitivity, stability, and response characteristics. The most effective design involved the primary deposition of Clt, followed by the secondary co-immobilization of the enzymes. This structure provided a favorable microenvironment for enzymatic reactions and ion exchange, enhancing the biosensor’s responsiveness.

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3. Analytical Performance of the Developed Biosensor

Among all tested configurations, the biosensor with Clt as the base layer and enzyme mixture on top exhibited superior analytical performance. It achieved a high sensitivity of 9.61 ± 0.01 μS/mM and a low detection limit of 5 μM, making it suitable for detecting trace levels of L-arg. The linear detection range of 0–280 μM and a broad dynamic range of up to 15 mM further demonstrate its applicability across various sample concentrations. These attributes underscore its potential for accurate L-arg quantification in complex samples.

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4. Stability and Reproducibility of the Biosensor

Long-term usability is vital for practical applications of biosensors. The developed L-arg biosensor showed excellent operational and storage stability, retaining consistent performance over extended periods. Stability tests confirmed minimal signal degradation and high reproducibility of results across multiple uses. This robustness makes the biosensor suitable for routine analysis in laboratories or on-site food testing environments, reducing the need for frequent recalibration or replacement.

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5. Influence of Solution Parameters on Sensor Response

The effect of environmental and solution parameters—such as pH, ionic strength, and buffer capacity—on biosensor sensitivity was systematically investigated. These factors significantly influence enzyme activity and ion exchange dynamics at the biosensor surface. The sensor's design effectively minimized fluctuations in performance under varied conditions, indicating its adaptability to real-world samples with differing chemical compositions. This adaptability is a crucial feature for sensors used in diverse applications, including food quality control and clinical diagnostics.

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6. Application in Real Sample Analysis and Method Validation

To validate its real-world applicability, the biosensor was employed to quantify L-arg in food samples with complex matrices. The results were benchmarked against a reference method—ion chromatography. The close agreement between the biosensor and ion chromatography data (correlation coefficient R = 0.96) confirmed the accuracy and reliability of the biosensor. This high correlation proves its potential as a fast, user-friendly alternative to conventional laboratory techniques for L-arg analysis in food and possibly biological fluids.

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#biosensor, #LArginine, #conductometricsensor, #hybridbiosensor, #organicinorganic, #biosensortechnology, #biomarker, #biosensing, #analyticalchemistry, #electrochemistry, #biosensorresearch, #biosensordesign, #healthtech, #clinicaldiagnostics, #realtimesensor, #metabolicsensor, #bioengineering, #labtech, #sensorinnovation, #biophotonics, #nanobiosensor, #sensorplatform, #chemicaldetection, #biosensorstudy, #researchbreakthrough

The triangle of biomedicine framework to analyze the impact of citations on the dissemination of categories in the PubMed database.

 

1. Introduction

Scientific literature classification is essential for organizing biomedical knowledge and evaluating research trends. The Triangle of Biomedicine (TB) offers a geometric representation of how publications are distributed across human, animal, and molecular-cellular research domains. This framework supports translational medicine by visually mapping the focus and trajectory of biomedical studies. Yet, the integration of citation-based analysis with TB classification presents a novel opportunity to enhance understanding of research dynamics.

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2. Methodology for Citation Vector Generation

To determine the evolving position of biomedical articles in the TB, this study introduces a method for generating citation vectors based on MeSH (Medical Subject Headings) term distributions. These vectors are calculated using the metadata of directly cited articles in PubMed, quantifying the proportion of citations within human, animal, and molecular-cellular domains. This approach enables researchers to track the translational movement of an article's influence through its citations.

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3. Mapping Citation Dynamics in the Triangle of Biomedicine

The TB is used not only to map the initial categorization of a biomedical paper but also to assess the shift in its disciplinary influence over time through citations. By analyzing the citation vectors, researchers can determine if a paper originally focused on molecular research, for instance, later impacts human studies. This dynamic positioning offers deeper insights into the translational value and interdisciplinary nature of biomedical publications.

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4. Translational Distance and the Human-Animal-Molecular Continuum

Citation vector analysis also enables the measurement of translational distance—a conceptual metric reflecting how far an article travels from its original research domain toward others. This is particularly useful in evaluating the extent to which molecular or animal studies contribute to human-centered biomedical advancements, thereby providing evidence for the real-world impact of foundational research.

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5. Information Entropy as a Measure of Citation Diversity

To complement the citation vector analysis, information entropy is applied to quantify the diversity and spread of MeSH terms in the citation networks of different article sets. High entropy indicates broader interdisciplinary influence, while low entropy suggests a concentrated impact within a specific domain. Studying entropy dynamics offers a novel metric for understanding the translational consistency or evolution of research contributions.

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6. Implications for Research Evaluation and Policy

This multidimensional approach to analyzing biomedical literature has practical implications for science policy, funding allocation, and translational medicine. By identifying articles with wide-ranging citation vectors and high entropy, stakeholders can better assess the real-world applicability of research. Furthermore, this method provides a framework for evaluating how different fields contribute to human health outcomes, aiding strategic decision-making in research development.


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Hashtags

#BiomedicalResearch #TriangleOfBiomedicine #CitationAnalysis #MeSHterms #ScientificMapping #TranslationalMedicine #ResearchClassification #PubMedAnalysis #Biomedicine #ResearchDynamics #AIinScience #InformationEntropy #MedicalResearch #HumanAnimalMolecular #CitationNetworks #Bioinformatics #ResearchPolicy #ScholarlyCommunication #BiomedicalDataScience #ScientificImpact

Integrating Raman spectroscopy and RT-qPCR for enhanced diagnosis of thyroid lesions: A comparative study of biochemical and molecular markers:

1. Introduction

Thyroid lesions encompass a range of benign and malignant disorders, with accurate early diagnosis being critical for effective clinical management. Conventional diagnostic approaches, including fine-needle aspiration cytology (FNAC), often yield indeterminate results, prompting the need for supplementary techniques. This study explores the integration of Raman spectroscopy and reverse transcription quantitative PCR (RT-qPCR) to enhance diagnostic accuracy. By combining biochemical and molecular data, this dual approach holds promise for a more comprehensive, sensitive, and specific analysis of thyroid lesions.

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2. Raman Spectroscopy as a Biochemical Fingerprinting Tool

Raman spectroscopy offers a rapid, non-destructive method for biochemical analysis by detecting vibrational energy changes in molecular bonds. In the context of thyroid lesion diagnosis, Raman spectroscopy can identify changes in proteins, nucleic acids, and lipid content that correlate with malignancy. This technique provides immediate insight into the biochemical landscape of thyroid tissues, helping differentiate between benign and malignant states with high spectral resolution and minimal sample preparation.

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3. RT-qPCR for Quantitative Molecular Marker Assessment

RT-qPCR remains a gold standard for assessing gene expression levels, particularly in cancer diagnostics. In this study, RT-qPCR was used to quantify the expression of known thyroid cancer-associated genes such as BRAF, RAS, and RET/PTC rearrangements. These molecular markers serve as critical indicators of malignancy and, when interpreted alongside biochemical data from Raman spectroscopy, offer a multidimensional view of the lesion's biological status.

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4. Comparative Analysis of Diagnostic Performance

This research systematically compares the diagnostic performance of Raman spectroscopy and RT-qPCR, individually and in combination. While RT-qPCR provides high specificity through genetic data, Raman spectroscopy adds complementary biochemical insights. The combined approach showed improved sensitivity and accuracy in differentiating various types of thyroid lesions, including follicular neoplasms and papillary thyroid carcinomas, suggesting a synergistic diagnostic value.

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5. Integration Strategy and Multivariate Data Analysis

To harmonize the outputs from both techniques, multivariate statistical analysis, such as principal component analysis (PCA) and partial least squares discriminant analysis (PLS-DA), was employed. These tools enabled the integration of spectral and gene expression datasets, revealing distinct clusters for benign versus malignant lesions. This integration strategy enhances interpretability and provides a robust diagnostic model for clinical applications.

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6. Clinical Implications and Future Perspectives

The integration of Raman spectroscopy and RT-qPCR represents a promising step toward personalized and precision diagnostics in thyroid pathology. This dual-modality approach not only improves diagnostic confidence but also has potential for intraoperative assessments and real-time decision-making. Future research will focus on expanding sample sizes, automating the analysis process, and validating the model in multi-center clinical trials to facilitate adoption in routine pathology labs.

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#RamanSpectroscopy #RTqPCR #ThyroidCancer #MolecularDiagnostics #BiochemicalMarkers #PrecisionMedicine #ThyroidLesions #CancerDiagnostics #SpectroscopyInMedicine #MultiOmics #GeneExpression #OncologyResearch #BiomedicalSpectroscopy #RTqPCRAnalysis #ThyroidPathology #NonInvasiveDiagnostics #MachineLearningInHealthcare #VibrationalSpectroscopy #TranslationalMedicine #Biophotonics

Artificial intelligence guided Raman spectroscopy in biomedicine: Applications and prospects

 

1. Introduction

Raman spectroscopy has emerged as a critical tool in biopharmaceutical analysis due to its non-destructive, label-free, and highly sensitive nature. Its ability to provide detailed molecular fingerprints of compounds makes it invaluable in analyzing drug composition, structures, and interactions. However, as the complexity and volume of data generated by Raman spectroscopy increase, there is a growing need for intelligent data analysis methods to enhance its diagnostic and research applications.

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2. Integration of Artificial Intelligence in Raman Spectroscopy

Artificial Intelligence (AI), particularly deep learning, has significantly revolutionized Raman spectroscopy by automating complex data processing tasks, extracting hidden features, and optimizing analytical models. These advancements enable higher detection accuracy, faster analysis, and improved reproducibility. The synergy between AI and Raman spectroscopy is transforming how large-scale spectral data are handled, opening new possibilities in real-time monitoring and high-throughput screening.

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3. Applications in Drug Characterization and Quality Control

AI-enhanced Raman spectroscopy is increasingly being utilized in pharmaceutical research to characterize drug structures, differentiate polymorphic forms, and analyze molecular interactions. It supports quality control processes by ensuring the consistency and authenticity of pharmaceutical formulations. These capabilities are vital for ensuring drug safety, efficacy, and regulatory compliance, especially in the production and distribution of biopharmaceuticals.

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4. AI-Guided Detection of Drug-Biomolecule Interactions

Understanding how drugs interact with biological molecules is essential for designing effective therapeutics. AI-guided Raman spectroscopy enables precise monitoring of these interactions at the molecular level. Through pattern recognition and spectral mapping, it provides insight into binding mechanisms, conformational changes, and the biological effects of drug candidates—supporting the development of targeted therapies.

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5. Clinical Diagnostics and Early Disease Detection

In clinical diagnostics, AI-integrated Raman spectroscopy offers remarkable potential for non-invasive disease detection, including cancer, neurological disorders, and infections. Its high sensitivity allows for the identification of subtle biochemical changes associated with disease progression. AI algorithms enhance diagnostic capabilities by classifying spectra with high precision, facilitating early intervention and treatment planning.

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6. Future Prospects in Biopharmaceutical and Biomedical Research

The fusion of AI and Raman spectroscopy marks a new era in biomedicine. As algorithmic techniques continue to evolve, this integration will support advanced research into disease mechanisms, personalized medicine, and pharmaceutical process control. Future developments may include portable AI-driven Raman devices for point-of-care testing, automated drug screening platforms, and real-time therapeutic monitoring systems, solidifying its role in next-generation medical technologies.