Bone-on-a-Chip Systems for Hematological Cancers

Biosensors, Journal Year: 2025, Volume and Issue: 15(3), P. 176 - 176

Published: March 9, 2025

Hematological malignancies originating from blood, bone marrow, and lymph nodes include leukemia, lymphoma, myeloma, which necessitate the use of a distinct chemotherapeutic approach. Drug resistance frequently complicates their treatment, highlighting need for predictive tools to guide therapeutic decisions. Conventional 2D/3D cell cultures do not fully encompass in vivo criteria, translating disease models mice humans proves challenging. Organ-on-a-chip technology presents an avenue surmount genetic disparities between species, offering precise design, concurrent manipulation various types, extrapolation data human physiology. The development bone-on-a-chip (BoC) systems is crucial accurately representing microenvironment, predicting drug responses hematological cancers, mitigating resistance, facilitating personalized interventions. BoC modeling cancers research can intricate designs integrated platforms analyzing response simulate scenarios. This review provides comprehensive examination applicable visualizing within context bone. It thoroughly discusses materials pertinent systems, suitable vitro techniques, capabilities clinical settings, potential commercialization.

Language: Английский

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Recent Advances in Artificial Intelligence and Machine Learning Based Biosensing Technologies

IntechOpen eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: March 19, 2025

Advancements in artificial intelligence (AI) and machine learning (ML) have transformed biosensing technologies, enhancing data acquisition, analysis, interpretation biomedical diagnostics. This chapter explores AI integration into biosensing, focusing on natural language processing (NLP), large models (LLMs), augmentation, various paradigms. These technologies improve biosensor sensitivity, precision, real-time adaptability. NLP automates text extraction, while LLMs facilitate complex decision-making using vast datasets. Data augmentation mitigates dataset limitations, strengthening ML model training reducing overfitting. Supervised drives predictive for disease detection, whereas unsupervised uncovers hidden biomarker patterns. Reinforcement optimizes sensor operations, calibration, autonomous control dynamic environments. The discusses case studies, emerging trends, challenges AI-driven biosensing. AI’s convergence with edge computing Internet of Things (IoT)-enabled biosensors enhances processing, latency expanding accessibility resource-limited settings. Ethical concerns, including privacy, interpretability, regulatory compliance, must be addressed responsible applications Future research should focus developing resilient to bias, capable continuous learning, optimized low-power, portable biosensors. Addressing these will enable AI-powered advance precision medicine global healthcare outcomes. Through interdisciplinary approaches, continue drive the evolution next-generation diagnostic solutions.

Language: Английский

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Revolutionary advances in hypertension detection: Gold nanoparticle-enhanced miRNA-based electrochemical biosensors and emerging nanotechnologies

Human Gene, Journal Year: 2025, Volume and Issue: unknown, P. 201401 - 201401

Published: March 1, 2025

Language: Английский

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Advancements of paper-based microfluidics and organ-on-a-chip models in cosmetics hazards

RSC Advances, Journal Year: 2025, Volume and Issue: 15(13), P. 10319 - 10335

Published: Jan. 1, 2025

Different detection approaches for monitoring adulterants/hazards present in cosmetics using paper-based devices and organ-on-a-chip.

Language: Английский

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Innovations in graphene-based electrochemical biosensors in healthcare applications

Microchimica Acta, Journal Year: 2025, Volume and Issue: 192(5)

Published: April 9, 2025

Abstract The isolation of a single atomic layer graphite, known as graphene, marked fundamental moment that transformed the field materials science. Graphene-based nanomaterials are recognized for their superior biocompatibility compared with many other types nanomaterials. Moreover, one main reasons growing interest in graphene is its potential applications emerging technologies. Its key characteristics, including high electrical conductivity, excellent intrinsic charge carrier mobility, optical transparency, substantial specific surface area, and remarkable mechanical flexibility, position it an ideal candidate solar cells touch screens. durability further establishes strong contender developing robust materials. To date, variety methods, such traditional spectroscopic techniques chromatographic approaches, have been developed detecting biomolecules, drugs, heavy metals. Electrochemical portability, selectivity, impressive sensitivity, offer considerable convenience both patients professionals point-of-care diagnostics. Recent advancements significantly improved capacity rapid accurate detection analytes trace amounts, providing benefits biosensor technology. Additionally, integration nanotechnology has markedly enhanced sensitivity selectivity electrochemical sensors, yielding results. Innovations point-of-care, lab-on-a-chip, implantable devices, wearable sensors discussed this review. Graphical abstract

Language: Английский

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Innovative Microfluidic Technologies for Rapid Heavy Metal Ion Detection

Chemosensors, Journal Year: 2025, Volume and Issue: 13(4), P. 149 - 149

Published: April 18, 2025

Heavy metal ion (HMI) contamination poses significant threats to public health and environmental safety, necessitating advanced detection technologies that are rapid, sensitive, field-deployable. While conventional methods like atomic absorption spectroscopy (AAS) inductively coupled plasma mass spectrometry (ICP-MS) remain prevalent, their limitations—including high costs, complex workflows, lack of portability—underscore the urgent need for innovative alternatives. This review consolidates advancements in last five years microfluidic HMI detection, emphasizing transformative potential through miniaturization, integration, automation. We critically evaluate synergy microfluidics with cutting-edge materials (e.g., graphene quantum dots) mechanisms (electrochemical, optical, colorimetric), enabling ultra-trace at parts-per-billion (ppb) levels. highlight novel device architectures, such as polydimethylsiloxane (PDMS)-based labs-on-chip (LOCs), paper-based microfluidics, 3D-printed systems, digital (DMF), which offer unparalleled portability, cost-effectiveness, multiplexing capabilities. Additionally, we address persistent challenges selectivity scalability) propose future directions, including AI integration sustainable fabrication. By bridging gaps between laboratory research practical deployment, this provides a roadmap next-generation solutions, positioning them indispensable tools global monitoring.

Language: Английский

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Integrating Artificial Intelligence and Microfluidics Technology for Psoriasis Therapy: A Comprehensive Review for Research and Clinical Applications

Advanced Intelligent Systems, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 26, 2024

Microfluidics has evolved into a transformative technology with far‐reaching applications in biomedical research. However, designing and optimizing custom microfluidic systems remains challenging because of their inherent complexities. Integrating artificial intelligence (AI) microfluidics promises to overcome these barriers by leveraging AI algorithms automate device design, streamline experimentation, enhance diagnostic therapeutic outcomes. Psoriasis is an incurable dermatological condition that difficult diagnose treat owing its complex pathogenesis. Traditional approaches are often ineffective fail address individual variabilities disease progression treatment responses. AI‐coupled platforms have the potential revolutionize psoriasis research clinical expansive applications. AI‐driven chips embedded biosensors precisely detect biomarkers (BMs), manipulate biological samples, mimic psoriasis‐like vivo vitro models, thereby allowing real‐time monitoring optimized testing. This review examines AI‐powered for advancing It design mechanisms cell screening, diagnosis, drug delivery. highlights recent advances, applications, challenges, future perspectives, ethical considerations personalized care patient

Language: Английский

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