Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
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Journal of chromatography. A
Microfluidics in separation science: Fabrication, applications and intelligent systems
The emergence of advanced, innovative microfluidic devices has reshaped analytical separation methodologies and biomolecular diagnostics by introducing highly integrated, miniaturized systems that enable next-generation fluid manipulation, enhanced automation, precision, cost efficiency, and high-throughput capabilities. Advances in fabrication methods, such as soft lithography, photolithography, and 3D printing, have enabled the production of customizable microfluidic platforms from materials such as PDMS, glass, and thermoplastics. These microfluidic systems support a range of analytical functionalities of the technologies, including gas chromatography (GC), high-performance liquid chromatography (HPLC), and electrophoresis, delivering rapid, high-resolution analyses with minimal sample and reagent requirements, quicker analysis, and enhanced uniformity and stability, facilitating high productivity screening, improved sensitivity and portability. Integration of micro-GC systems enables rapid, portable detection of volatile compounds, while microfluidic HPLC and electrophoresis provide precise biomolecular separations and real-time diagnostics. Recent advancements in artificial intelligence (AI) have now brought significant innovations in microfluidic devices, resulting in a new era of intelligent analytical separations, where machine learning algorithms and neural networks are applicable not only for data processing and pattern recognition but also for real-time optimization, anomaly detection, innovative read-out systems, and autonomous system control. The fusion of AI and microfluidics has enabled new approaches to dynamic experimentation, reshaping traditional microfluidics and personalized diagnostics and health care systems, where rapid, reliable analysis is paramount. In this review article, we comprehensively explain the progressive convergence of microfluidics and AI across various electrophoretic and chromatographic analytical separation techniques, elaborating on the latest technological innovations, fundamental fabrication strategies, and emergent applications. The article also presents a forward-looking perspective on how AI-enabled microfluidics enhances automation, scalability, and application-specific customization, which could shape the future of intelligent analytical separation systems.
12 May 2026
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Machine learning-driven molecular engineering of nucleic acids
Molecular engineering has played a pivotal role in biomedical fields, driving significant advancements in gene therapy, disease diagnosis, and biosensing. However, nucleic acid molecular engineering faces various challenges including vast design spaces, complex structure-function relationships, lengthy application validation cycles, and inefficient optimization processes. Machine learning (ML), with its superior pattern recognition, multidimensional data integration, and automated optimization capabilities, offers a unique opportunity to construct predictive models of sequence-structure-function relationships, thereby enabling a paradigm shift from empirically driven to data-driven approaches. This review systematically surveys recent progress in ML applications across three major domains: nucleic acid structure construction, performance modulation, and application expansion. It also explores core challenges such as data quality, model interpretability, and experimental validation efficiency, along with potential resolution strategies. These insights are poised to propel nucleic acid molecular engineering from static structure prediction toward dynamic behavior simulation, and from single-molecule design to complex system engineering, guiding future directions in hybrid ML-quantum models and expanded applications to non-canonical nucleic acids for transformative innovation in biomedicine, environmental monitoring, and information technology.
20 Apr 2026
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