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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#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.

Identifying Plastics in Food Packaging Waste.

 

1. Introduction

The growing demand for sustainable recycling solutions has led to significant advancements in plastic waste identification technologies. Accurate classification of post-consumer plastics, particularly from food containers and packaging, is essential for improving recycling efficiency and reducing environmental pollution. However, conventional methods face challenges due to the diversity in plastic types, additives, and physical characteristics. In this study, we explore the integration of near-infrared (NIR) and terahertz (THz) spectroscopies with machine learning (ML) to enhance plastic waste identification.

2. Spectroscopic Techniques for Plastic Identification

NIR and THz spectroscopies offer complementary advantages in distinguishing between plastic materials. NIR spectroscopy is widely used due to its effectiveness in detecting chemical compositions and polymer structures. However, it faces limitations when dealing with black or highly pigmented plastics. On the other hand, THz spectroscopy can penetrate opaque materials and provide additional insights based on transmittance variations. The combination of these two spectroscopic methods enhances the accuracy of plastic classification, enabling a more reliable identification system.

3. Machine Learning in Plastic Waste Identification

Machine learning algorithms play a critical role in analyzing spectroscopic data and improving classification accuracy. In this study, XGBoost and Bayesian optimization were applied to refine the identification of different plastic types. These techniques allow for automated feature selection and optimization, minimizing errors and maximizing precision scores. The use of explainable AI (XAI) further enhances transparency by identifying the most relevant spectral features for classification.

4. Key Findings: THz and NIR Spectroscopy for Plastic Classification

The study demonstrated that different plastic materials exhibit unique transmittance characteristics at specific THz frequencies. Transparent polystyrene (PS) was effectively identified using a frequency of 0.140 THz, while transparent polyethylene terephthalate (PET) was distinguished at 0.075 THz. Additionally, NIR spectroscopy was particularly useful in differentiating black PS from transparent plastics. These findings highlight the importance of selecting the appropriate spectral features for high-precision identification.

5. Advantages and Limitations of Combined Spectroscopic Approaches

While the combination of NIR and THz spectroscopies provides significant improvements in plastic classification, certain challenges remain. Variations in polymer additives, contamination, and physical degradation can impact spectral readings. Additionally, the implementation of THz-based identification systems requires specialized equipment and processing algorithms. However, the ability to enhance classification accuracy and address limitations of conventional recycling methods justifies further investment in this approach.

6. Future Research Directions in Spectroscopy and AI for Recycling

Advancements in spectroscopy and AI-driven analytical techniques continue to shape the future of plastic waste management. Future research could focus on integrating deep learning models to further enhance classification accuracy, optimizing THz frequency selection for broader material differentiation, and developing real-time identification systems for large-scale recycling facilities. Additionally, exploring hybrid approaches that combine spectroscopy with hyperspectral imaging and Raman spectroscopy could further improve the efficiency of plastic sorting and contribute to a more sustainable recycling ecosystem.

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#PlasticIdentification #TerahertzSpectroscopy #MachineLearning #RecyclingTechnology #SustainablePackaging #AIinWasteManagement #SpectralAnalysis #PostConsumerPlastics #NonDestructiveTesting #WasteSorting #SmartRecycling #EcoFriendlyMaterials #CircularEconomy #PlasticWasteManagement #PolymerClassification #EnvironmentalSustainability #ArtificialIntelligence #AdvancedSpectroscopy #FoodPackagingWaste #GreenTechnology #SmartSorting #WasteReduction #SustainabilityInnovation #MaterialRecovery #EcoTech

Quantitative Intra-arterial Fluorescence Angiography for Direct Monitoring of Peripheral Revascularization Effects

1. Introduction

Chronic limb-threatening ischemia (CLTI) is a severe form of peripheral artery disease that leads to reduced blood flow and high risks of limb loss. Accurate intraoperative assessment of tissue perfusion is crucial for optimizing revascularization outcomes. Quantitative fluorescence angiography with intra-arterial dye injection (Q-iaFA) is emerging as a promising technique for real-time evaluation of perfusion changes. This study investigates the feasibility of Q-iaFA in guiding revascularization and its potential to improve intraoperative decision-making in CLTI patients.

2. Quantitative Fluorescence Angiography (Q-iaFA) as a Perfusion Assessment Tool

Q-iaFA employs intra-arterial dye injection to generate intensity-time curves that provide critical insights into blood flow dynamics. Parameters such as time to peak (TTP) and normalized peak slope (PSnorm) help assess tissue perfusion changes before and after revascularization. This technique offers a real-time, quantitative approach to evaluating vascular interventions and has potential advantages over conventional imaging modalities.

3. Methodological Approach in Q-iaFA Evaluation

The study involved fourteen CLTI patients undergoing endovascular revascularization, with Q-iaFA measurements taken before and after intervention. The plantar foot was divided into five regions of interest (ROIs) for perfusion analysis. Changes in TTP and PSnorm were analyzed based on revascularization impact, classified as strong, moderate, or absent. These classifications were derived from intraoperative X-ray imaging and the Trans-Atlantic Inter-Society II standards.

4. Impact of Revascularization on Q-iaFA Parameters

Findings indicate that Q-iaFA parameters are directly influenced by the effectiveness of revascularization. In cases with strong revascularization impact, TTP significantly decreased while PSnorm increased, reflecting improved perfusion. Moderate improvements were observed in some patients but lacked statistical significance. In contrast, no improvements were seen in a patient with absent revascularization impact, highlighting the sensitivity of Q-iaFA in assessing treatment efficacy.

5. Clinical Feasibility and Advantages of Q-iaFA in Vascular Surgery

Q-iaFA was successfully implemented without complications, demonstrating its feasibility as an intraoperative perfusion assessment tool. Compared to conventional imaging methods, Q-iaFA provides a direct and quantifiable measure of tissue perfusion changes. Its ability to detect subtle variations in blood flow may assist surgeons in optimizing treatment strategies, potentially reducing the risk of post-procedural complications and improving patient outcomes.

6. Future Perspectives and Clinical Translation of Q-iaFA

While Q-iaFA shows promise, further refinement is needed to optimize quantification strategies and correlate perfusion metrics with long-term clinical outcomes. Future studies should focus on larger patient cohorts, standardizing measurement protocols, and integrating Q-iaFA with advanced computational models. If validated, Q-iaFA could become a standard tool for intraoperative guidance, enhancing precision in revascularization procedures and ultimately improving limb salvage rates in CLTI patients.