Diagnostic Applications Research Articles

Capillary microfluidics for diagnostic applications: fundamentals, mechanisms, and capillarics

Capillary microfluidics for diagnostic applications: fundamentals, mechanisms, and capillarics

Dual-driven biodegradable nanomotors for enhanced cellular uptake.

Hybrid nano-sized motors with navigation and self-actuation capabilities have emerged as promising nanocarriers for a wide range of delivery, sensing, and diagnostic applications due to their unique ability to achieve controllable locomotion within a complex biological environment such as tissue. However, most current nanomotors typically operate using a single driving mode, whereas propulsion induced by both external and local stimuli could be more beneficial to achieve efficient motility in a biomedical setting. In this work, we present a hybrid nanomotor by functionalizing biodegradable stomatocytes with platinum nanoparticles (Pt NPs). These Pt NPs enable two distinct propulsion mechanisms. First, near-infrared (NIR) laser irradiation causes plasmonic heating, which, due to the asymmetric shape of the stomatocytes, creates a temperature gradient around the nanomotors. Second, the catalytic properties of the Pt NPs allow them to convert hydrogen peroxide into water and oxygen, generating a chemical gradient that serves as an additional driving force. Hydrogen peroxide is thereby locally produced from endogenous glucose by a co-encapsulated enzyme, glucose oxidase. The motile features are employed to achieve enhanced accumulation within tumor cells. This nanomotor design offers a versatile approach for developing dual stimuli-responsive nanomotors that operate more effectively in complex environments.

Characterization of Argonaute Nuclease from Mesophilic Bacterium Chroococcidiopsis

Mesophilic microbial sources of prokaryotic Argonaute (pAgo) programmable nucleases have garnered considerable attention for their potential applications in genome editing and molecular diagnostics. In this study, we characterized a novel pAgo from the mesophilic bacterium Chroococcidiopsis sp. (ChAgo), which can cleave single-stranded DNA (ssDNA) using both 5′-phosphorylated guide DNA (5′P-gDNA) and 5′-hydroxylated guide DNA (5′OH-gDNA). Efficient cleavage occurs using 14–25 nt 5′P-gDNA and 13–20 nt 5′OH-gDNA in the presence of Mn2+ ions at temperatures ranging from 25 to 75 °C, with optimal activity at 55 °C. ChAgo demonstrates low tolerance for single-base mismatches, similar to other pAgo proteins. The cleavage efficiency varies based on the guide/target pair, with mismatches at specific positions significantly reducing activity. For instance, mismatches at positions 4, 5, or 12 in T-gDNA/target pairs and at positions 5 or 8–10 in g38NT-gDNA/target pairs notably decrease efficiency. ChAgo’s sensitivity to mismatches makes it a promising tool for nucleic acid manipulation and detection, requiring initial screening for high cleavage efficiency sites and subsequent identification of mismatch positions.

Natural and Bioengineered Extracellular Vesicles in Diagnosis, Monitoring and Treatment of Cancer.

Extracellular vesicles (EVs) are cell derived nanovesicles which are implicated in both physiological and pathological intercellular communication, including the initiation, progression, and metastasis of cancer. The exchange of biomolecules between stromal cells and cancer cells via EVs can provide a window to monitor cancer development in real time for better diagnostic and interventional strategies. In addition, the process of secretion and internalization of EVs by stromal and cancer cells in the tumor microenvironment (TME) can be exploited for delivering therapeutics. EVs have the potential to provide a targeted, biocompatible, and efficient delivery platform for the treatment of cancer and other diseases. Natural as well as engineered EVs as nanomedicine have immense potential for disease intervention. Here, we provide an overview of current knowledge of EVs' function in cancer progression, diagnostic and therapeutic applications for EVs in the cancer setting, as well as current EV engineering strategies.

Accuracy of ChatGPT 3.5, 4.0, 4o and Gemini in diagnosing oral potentially malignant lesions based on clinical case reports and image recognition.

The accurate and timely diagnosis of oral potentially malignant lesions (OPMLs) is crucial for effective management and prevention of oral cancer. Recent advancements in artificial intelligence technologies indicates its potential to assist in clinical decision-making. Hence, this study was carried out with the aim to evaluate and compare the diagnostic accuracy of ChatGPT 3.5, 4.0, 4o and Gemini in identifying OPMLs. The analysis was carried out using 42 case reports from PubMed, Scopus and Google Scholar and images from two datasets, corresponding to different OPMLs. The reports were inputted separately for text description-based diagnosis in GPT 3.5, 4.0, 4o and Gemini, and for image recognition-based diagnosis in GPT 4o and Gemini. Two subject-matter experts independently reviewed the reports and offered their evaluations. For text-based diagnosis, among LLMs, GPT 4o got the maximum number of correct responses (27/42), followed by GPT 4.0 (20/42), GPT 3.5 (18/42) and Gemini (15/42). In identifying OPMLs based on image, GPT 4o demonstrated better performance than Gemini. There was fair to moderate agreement found between Large Language Models (LLMs) and subject experts. None of the LLMs matched the accuracy of the subject experts in identifying the correct number of lesions. The results point towards cautious optimism with respect to commonly used LLMs in diagnosing OPMLs. While their potential in diagnostic applications is undeniable, their integration should be approached judiciously.

Application of Raman Spectroscopy in Non-Invasive Analysis of the Gut Microbiota and Its Impact on Gastrointestinal Health

The gut microbiota, a complex community of microorganisms, plays a crucial role in gastrointestinal (GI) health, influencing digestion, metabolism, immune function, and the gut–brain axis. Dysbiosis, or an imbalance in microbiota composition, is associated with GI disorders, including irritable bowel syndrome (IBS), inflammatory bowel disease (IBD), and colorectal cancer (CRC). Conventional microbiota analysis methods, such as next-generation sequencing (NGS) and nuclear magnetic resonance (NMR), provide valuable insights but are often expensive, time-consuming, and destructive. Raman spectroscopy (RS) is a non-invasive, cost-effective, and highly sensitive alternative. This analytical technique relies on inelastic light scattering to generate molecular “fingerprints,” enabling real-time, marker-free analysis of microbiota composition and metabolic activity. This review explores the principles, sample preparation techniques, and advancements in RS, including surface-enhanced Raman spectroscopy (SERS), for microbiota research. RS facilitates identifying microbial species, analysing key metabolites like short-chain fatty acids (SCFA), and monitoring microbiota responses to dietary and therapeutic interventions. The comparative analysis highlights RS’s advantages over conventional techniques, such as the minimal sample preparation, real-time capabilities, and non-destructive nature. The integration of RS with machine learning enhances its diagnostic potential, enabling biomarker discovery and personalised treatment strategies for GI disorders. Challenges, including weak Raman signals and spectral complexity, are discussed alongside emerging solutions. As RS technology advances, mainly through portable spectrometers and AI integration, its clinical application in microbiota diagnostics and personalised medicine is poised to transform GI healthcare, bridging microbiota research with practical therapeutic strategies.

The Challenge of Developing a Test to Differentiate Actinobacillus pleuropneumoniae Serotypes 9 and 11

Actinobacillus pleuropneumoniae is a major swine pathogen, classified into 19 serotypes based on capsular polysaccharide (CPS) loci. This study aimed to improve the diagnostic method to differentiate between serotypes 9 and 11, which are challenging to distinguish using conventional serological and molecular methods. A novel qPCR assay based on locked nucleic acid (LNA) probes was developed and validated using a collection of reference strains representing all known 19 serotypes. The assay demonstrated specificity in detecting the nucleotide variation characteristic of the serotype 9 reference strain. However, the analysis of a clinical isolate collection identified discrepancies between LNA-qPCR and serological results, prompting further investigation of the cps and O-Ag loci. Subsequent nanopore sequencing and whole-genome sequencing of a collection of 31 European clinical isolates, previously identified as serotype 9, 11, or undifferentiated 9/11, revealed significant genetic variations in the cps and O-Ag loci. Ten isolates had a cpsF sequence identical to that of the serotype 11 reference strain, while six isolates had single-nucleotide polymorphisms that were unlikely to cause significant coding changes. In contrast, 15 isolates had interruptions in the cpsF gene, distinct from that found in the serotype 9 reference strain, potentially leading to a serotype 9 CPS structure. In the O-Ag loci, differences between serotypes 9 and 11 were minimal, although some isolates had mutations potentially affecting O-Ag expression. Overall, these findings suggest that multiple genetic events can lead to the formation of a serotype 9 CPS structure, hindering the development of a single qPCR assay capable of detecting all cpsF gene mutations. Our results suggest that, currently, a comprehensive analysis of the cpsF gene is necessary to accurately determine whether the capsule of an isolate corresponds to serotype 9 or 11. Although such analyses are feasible with the advent of third-generation sequencing technologies, their accessibility, cost, and time to result limit their use in routine diagnostic applications. Under these circumstances, the designation of the hybrid serovar 9/11 remains a valid approach.

TNFSF9 Silence Impedes Cerebral Ischemia-Reperfusion Injury via Modulating SLC3A2 Expression in Brain Microvascular Endothelial Cells.

Cerebral ischemia-reperfusion injury (CIRI), which stays unresolved in the clinic, occurs after recanalization of blood vessels serving brain tissues in acute ischemic stroke patients and can result in massive brain cell death, and cell ferroptosis contributes greatly to this process. Our research firstly found that TNFSF9 expression harbored diagnostic value on CIRI patients and intended to further investigate its regulatory mechanism in CIRI, which might facilitate its diagnostic and therapeutic application in the clinic. The level of TNSF9 mRNA was augmented in the plasma of CIR patients, and its silence impeded ferroptosis, apoptosis, and release of inflammatory mediators of BMECs with OGD/R treatment. Besides, SP1 positively regulated TNFSF9 expression as one of its transcription factors, and TNFSF9 overexpression reversed SP1 silence-mediated inhibition on ferroptosis, apoptosis, and release of inflammatory mediators in OGD/R-treated BMECs. In addition, silencing SLC3A2 could neutralize the benefit effects of TNFSF9 downregulation on BMECs under OGD/R context in vitro, and silencing TNFSF9 neutralized necrotic volumes in rat brain induced by CIRI via modulating SLC3A2 expression in vivo. TNFSF9 regulated by SP1 aggravated CIRI via boosting ferroptosis, apoptosis, and release of inflammatory mediators of BMECs under OGD/R situation by suppressing SLC3A2 expression in vitro and in vivo.

Recent advances in biocatalytic and chemoenzymatic synthesis of oligonucleotides.

Access to synthetic oligonucleotides is crucial for applications in diagnostics, therapeutics, synthetic biology, and nanotechnology. Traditional solid phase synthesis is limited by sequence length and complexities, low yields, high costs and poor sustainability. Similarly, polymerase-based approaches such as in vitro transcription and primer extension reactions do not permit any control on the positioning of modifications and display poor substrate tolerance. In response, biocatalytic and chemoenzymatic strategies have emerged as promising alternatives, offering selective and efficient pathways for oligonucleotide synthesis. These methods leverage the precision and efficiency of enzymes to construct oligonucleotides with high fidelity. Recent advancements have focused on optimized systems and/or engineered enzymes enabling the incorporation of chemically modified nucleotides. Biocatalytic approaches, particularly those using DNA/RNA polymerases provide advantages in milder reaction conditions and enhanced sustainability. Chemoenzymatic methods, combining chemical synthesis and enzymes, have proven to be effective in overcoming limitations of traditional solid phase synthesis. This review summarizes recent developments in biocatalytic and chemoenzymatic strategies to construct oligonucleotides, highlighting innovations in enzyme engineering, substrate and reaction condition optimization for various applications. We address crucial details of the methods, their advantages, and limitations as well as important insights for future research directions in oligonucleotide production.

Injectable Fluorescent Bottlebrush Polymers for Interventional Procedures and Biomedical Imaging.

Injectable biomaterials play a vital role in modern medicine, offering tailored functionalities for diverse therapeutic and diagnostic applications. In ophthalmology, for instance, viscoelastic materials are crucial for procedures such as cataract surgery but often leave residues, increasing postoperative risks. This study introduces injectable fluorescent viscoelastics (FluoVs) synthesized via one-step controlled radical copolymerization of oligo(ethylene glycol) acrylate and fluorescein acrylate. These bottlebrush-shaped polymers exhibit enhanced fluorescence intensity for improved traceability and facile removal postsurgery. To prevent aggregation, charged terpolymers were synthesized, ensuring intra- and intermolecular electrostatic repulsion. Dynamic light scattering and energy-conserved dissipative particle dynamics simulations revealed how the fluorescein content and monomer sequence affect the hydrodynamic size of these copolymers. Biocompatibility assessments showed that FluoVs maintained cell viability comparable to commercial hydroxypropyl methylcellulose and nonfluorescent poly(oligo(ethylene glycol) acrylate) controls. The FluoVs combine high fluorescence intensity, low viscosity, and excellent biocompatibility, offering intraoperative traceability and significant advancements for ocular and bioimaging applications.

Progress in the role of nanoparticles in the diagnosis and treatment of bone and joint tuberculosis

Bone and joint tuberculosis (BJTB), caused by Mycobacterium tuberculosis (MTB), is a prevalent form of extrapulmonary tuberculosis that poses significant challenges to global public health due to difficulties in early diagnosis, prolonged treatment cycles, and drug resistance. Recent advancements in nanotechnology have introduced novel solutions for the early detection and precise treatment of BJTB, leveraging unique physicochemical properties such as high specific surface area, targeted delivery capabilities, sustained drug release, and excellent biocompatibility. In diagnostic applications, nanomaterials markedly enhance the sensitivity and accuracy of detection methods while reducing testing time. These technologies are adaptable to resource-limited settings, enabling earlier patient intervention and mitigating disease progression risk. In therapeutic applications, nanomaterials prolong drug retention in bone tissue through targeted delivery, thereby decreasing medication frequency and minimizing toxic side effects, which significantly improves treatment efficacy. Despite substantial progress, further research is required to address long-term safety concerns, broaden clinical applicability, and evaluate performance under complex pathological conditions. This review summarizes recent advancements in nanomaterials for diagnosing and treating BJTB and identifies key areas for future research, laying the groundwork for advancing precision medicine and personalized treatments.

A CRISPR-Cas and Argonaute-Driven Two-Factor Authentication Strategy for Information Security.

The escalating growth in computing power and the advent of quantum computing present a critical threat to the security of modern cryptography. Two-factor authentication strategies can effectively resist brute-force attacks to improve the security of access control. Herein, we proposed a two-factor and two-authentication entity strategy based on the trans-cleavage activity of CRISPR-Cas and the "dual-step" sequence-specific cleavage of Pyrococcus furiosus Argonaute. In this strategy, the output of authentication entity 1 acted as a component to operate authentication entity 2, thus enabling a role-based molecular model that implemented access control for the three roles. To further enhance information security, we designed knowledge suppression factors to constitute the command library and possession suppression factors to resist brute-force attacks. This study will promote the development of advanced molecular access control and its applications in biomedical diagnostics and data security.

Ultrasound-Guided Vacuum-Assisted Excision (VAE) in Breast Lesion Management: An Experimental Comparative Study of Two Different VAE Devices Across Various Aspiration Levels and Window Sizes

Background/Objectives: Vacuum-assisted excision (VAE) is a minimally invasive technique for breast tumor treatment, offering precision, comfort, and quick recovery. It is widely used for benign breast lesions and is playing an increasingly important role in the therapeutic management of non-surgical patients or patients who refuse surgery. Optimal outcomes require an understanding of device features to tailor treatment to each lesion. The Mammotome® Elite 10G operates in a fixed mode, while the Mammotome® Revolve EX 8G offers multiple aspiration levels and aperture windows for greater versatility. This study analyzed the specimen features (weight and length), comparing the weight obtained from two different VAE systems to aid the appropriate selection of a device based on the clinical setting. It also determined the number of specimens needed to achieve the 4 g diagnostic threshold. Methods: The Mammotome® Elite 10G and the Mammotome® Revolve EX were evaluated under controlled conditions. For Mammotome® Revolve EX, combinations of five aspiration levels and three aperture lengths (12 mm, 18 mm, and 25 mm) were tested. Twelve samples were collected from a chicken breast phantom for each setting. Specimen weights and the minimum excisions required to reach the 4 g threshold were analyzed. Results: The mean weight per sample for the Mammotome® Elite 10G was 0.16 ± 0.04 g. For the Mammotome® Revolve EX, the weights increased with aperture size and aspiration level, ranging from a minimum of 0.132 ± 0.028 g (a window length of 12 mm and aspiration level 1) to a maximum of 0.407 ± 0.055 g (a window length of 25 mm and aspiration level 5). The 25 mm window at aspiration level 5 achieved the 4 g threshold in as few as 10 samples. By comparison, the Mammotome® Elite required up to 26 samples. Conclusions: Compared to the Mammotome Elite, Mammotome® Revolve EX offers superior versatility and efficiency, reducing patient discomfort by minimizing the required samples. Its technical advantages make it a valuable tool for both diagnostic and therapeutic applications.

Artificial Intelligence in Oral Cancer: A Comprehensive Scoping Review of Diagnostic and Prognostic Applications

Background/Objectives: Oral cancer, the sixth most common cancer worldwide, is linked to smoke, alcohol, and HPV. This scoping analysis summarized early-onset oral cancer diagnosis applications to address a gap. Methods: A scoping review identified, selected, and synthesized AI-based oral cancer diagnosis, screening, and prognosis literature. The review verified study quality and relevance using frameworks and inclusion criteria. A full search included keywords, MeSH phrases, and Pubmed. Oral cancer AI applications were tested through data extraction and synthesis. Results: AI outperforms traditional oral cancer screening, analysis, and prediction approaches. Medical pictures can be used to diagnose oral cancer with convolutional neural networks. Smartphone and AI-enabled telemedicine make screening affordable and accessible in resource-constrained areas. AI methods predict oral cancer risk using patient data. AI can also arrange treatment using histopathology images and address data heterogeneity, restricted longitudinal research, clinical practice inclusion, and ethical and legal difficulties. Future potential includes uniform standards, long-term investigations, ethical and regulatory frameworks, and healthcare professional training. Conclusions: AI may transform oral cancer diagnosis and treatment. It can develop early detection, risk modelling, imaging phenotypic change, and prognosis. AI approaches should be standardized, tested longitudinally, and ethical and practical issues related to real-world deployment should be addressed.

The impact of glycolysis on ischemic stroke: from molecular mechanisms to clinical applications

Ischemic stroke (IS), a leading cause of disability and mortality worldwide, remains a significant challenge due to its complex pathogenesis. Glycolysis, a central metabolic pathway, plays a critical role in bridging the gap between metabolic dysfunction and neurological impairment. During ischemic conditions, glycolysis replaces oxidative phosphorylation as the primary energy source for brain tissue. However, in the ischemia–reperfusion state, neuronal cells show a particular reliance on aerobic glycolysis. Immune cells, such as monocytes, also contribute to atheromatous plaque formation and thrombi through increased aerobic glycolysis. Given glycolysis’s involvement in various pathological stages of IS, it offers the potential for improved diagnosis, treatment, and prevention. This review comprehensively explores the role of glycolysis in different phases of IS, addresses existing controversies, and discusses its diagnostic and therapeutic applications. By elucidating the intricate relationship between glycolysis and IS, this review aims to provide novel insights for future research and clinical advancements.

Advancing DNA Structural Analysis: A SERS Approach Free from Citrate Interference Combined with Machine Learning.

Surface-enhanced Raman spectroscopy (SERS) has become an indispensable tool for biomolecular analysis, yet the detection of DNA signals remains hindered by spectral interference from citrate ions, which overlap with key DNA features. This study introduces an innovative, ultrasensitive SERS platform utilizing thiol-modified silver nanoparticles (Ag@SDCNPs) that overcomes this challenge by eliminating citrate interference. This platform enables direct, interference-free detection and structural characterization of a wide range of DNA conformations, including single-stranded DNA (ssDNA), double-stranded DNA (dsDNA), i-motif, hairpin, G-quadruplex, and triple-stranded DNA (tsDNA). Employing calcium ions as aggregating agents and deuterated methanol as an internal standard, the system achieved high spectral quality and reproducibility. Machine learning (ML) techniques, such as linear discriminant analysis (LDA) and t-distributed stochastic neighbor embedding (t-SNE), were utilized for spectral classification, alongside support vector machines (SVM) for predictive modeling, yielding accuracies above 99%. These findings establish a robust and versatile platform for DNA structural analysis, offering transformative potential for applications in clinical diagnostics and biomedical research.

Three-dimensional complementary breast ultrasound (3D CBUS): Improving 3D spatial resolution uniformity with orthogonal images.

With increasing evidence supporting three-dimensional (3D) automated breast (AB) ultrasound (US) for supplemental screening of breast cancer in increased-risk populations, including those with dense breasts and in limited-resource settings, there is an interest in developing more robust, cost-effective, and high-resolution 3DUS imaging techniques. Compared with specialized ABUS systems, our previously developed point-of-care 3D ABUS system addresses these needs and is compatible with any conventional US transducer, which offers a cost-effective solution and improved availability in clinical practice. While conventional US transducers have high in-plane resolution (axial and lateral), their out-of-plane resolution is constrained by the poor intrinsic elevational US resolution. Consequently, any oblique view plane in an acquired 3DUS image will contain high in-plane and poor out-of-plane resolution components, diminishing spatial resolution uniformity and overall diagnostic utility. To develop and validate a novel 3D complementary breast ultrasound (CBUS) approach to improve 3DUS spatial resolution uniformity using a conventional US transducer by acquiring and generating orthogonal 3DUS images. We previously developed a cost-effective, portable, dedicated 3D ABUS system consisting of a wearable base, a compression assembly, and a mechanically driven scanner for automated 3DUS image acquisition, compatible with any commercial linear US transducer. For this system, we have proposed 3D CBUS approach which involves acquiring and registering orthogonal 3DUS images ( and ) with an aim of overcoming the poor resolution uniformity in the scanning direction in 3D US images. The voxel intensity values in the 3D CBUS image are computed with a spherical-weighted algorithm from the original orthogonal 3DUS images. Experimental validation was performed with 2DUS frame densities of 2, 4, 6 frames mm-1 using an agar-based phantom with a speed of sound of 1540 ms-1 and an embedded nylon bead. Lateral and axial full-width at half-maximum (FWHMLAT and FWHMAX) values were calculated from cross-sections taken at polar view planes ranging from 0° to 90° for 3DUS and 3D CBUS images of a bead phantom in focal zone and far field regions. Kendall's Tau-b correlation coefficients were calculated between FWHM measurements and cross-section angle for all frame density settings at a significance level of . Volumetric 3D segmentations were performed for 3DUS and 3D CBUS images of an inclusion phantom to confirm volumetric reconstruction accuracy. For statistical analysis, a repeated measures ANOVA with the Greenhouse-Geisser correction was performed at a significance level of . Experimental validation of the orthogonal 3DUS images show complementary trends of increasing and decreasing FWHMLAT from in-plane to out-of-plane (0° and 90° and vice versa) views. This is exemplified with the scan taken at 4 frames mm-1 in the focal zone, where FWHMLAT ranges from 3.51 to 1.10 mm for and 1.02-3.02 mm for , spanning 0°-90°, respectively. When combined in the 3D CBUS image, the FWHMLAT exhibits greater uniformity across view angles by mitigating poor out-of-plane resolution using its complementary in-plane component, with corresponding FWHMLAT values of 1.27 and 1.46 mm. While visual enhancements were seen in the 3D CBUS image, no statistically significant differences were found in volumetric measurements of the spherical inclusions in the 3DUS and 3D CBUS images. The out-of-plane resolution in the orthogonal 3DUS images is improved upon their combination into a single 3D CBUS image. These results demonstrate that the proposed 3D CBUS generation approach can improve 3D spatial resolution uniformity, while employing a commercial US transducer. The proposed 3D CBUS method shows potential utility for improving image resolution uniformity in 3D ABUS images, with the goal of improving point-of-care breast cancer supplemental screening and diagnostic applications, particularly in women with dense breasts and limited resource settings.

An electrochemical aptasensor based on bimetallic carbon nanocomposites AuPt@rGO for ultrasensitive detection of adenosine on portable potentiostat.

Adenosine plays a crucial role in the cardiovascular and nervous systems of living organisms. Excessive adenosine can lead to arrhythmias or heart failure, making the accurate detection of adenosine highly valuable. Given the widespread use of sensors for detecting small molecules, we propose a sensitive electrochemical aptasensor for adenosine detection in this study. The aptasensor utilized reduced graphene oxide (rGO) as the base material, and its performance was further enhanced by loading gold-platinum nanoparticles (AuPt@rGO) to improve the current response. The AuPt@rGO composite was synthesized using a simple process and was modified onto screen-printed electrodes (SPE) fabricated in the laboratory. This modification increased the effective area of the working electrode, providing more contact sites and enhancing sensitivity for adenosine detection. As a result, the sensor exhibited excellent performance in selectivity, sensitivity, and stability, with a linear response range for adenosine from 10nM to 10μM and a detection limit of 1.28nM (n=3). Furthermore, successful detection of adenosine levels in real samples was achieved using a portable potentiostat, demonstrating excellent recovery rates (95.99%-103.4%). These results indicate the aptasensor's significant potential for future applications in field analysis and medical diagnostics.

MicroRNA Gets a Mighty Award.

Recent advancements in microRNAs (miRNAs) research have revealed their key roles in both normal physiological processes and pathological conditions, leading to potential applications in diagnostics and therapeutics. However, the path forward is fraught with several scientific and technical challenges. This review article briefly explores the milestones of the discovery, biogenesis, functions, and application for clinical diagnostic and therapeutic strategies of miRNAs. The potential challenges and future directions are also discussed to fully harness their capabilities.

Ultrasensitive Isomer Discrimination: A Joint Surface‐Enhanced Raman Scattering (SERS) Spectroscopy and Machine Learning Strategy

AbstractIsomer discrimination is of paramount importance across various sectors, including pharmaceuticals, agriculture, and the food industry, owing to their unique physicochemical characteristics. Because of their extremely similar characteristics, traditional analytical methods fail or encounter severe limitations in isomer discrimination. To overcome this grand challenge, a novel sensing strategy is proposed based on surface‐enhanced Raman scattering (SERS) substrates (i.e., plasmonic platforms) combined with machine learning algorithms. These plasmonic platforms exhibit exceptional signal uniformity across wide regions and sensitivity, enabling the discrimination of structural isomers (hydroquinone, resorcinol, pyrocatechol), geometric isomers ((Z/E)‐stilbene, (Z/E)‐resveratrol), and optical isomers (R/S‐ibuprofen). Notably, for the analysis of optical isomers, 1‐naphthalenethiol is employed as a probe to facilitate specific isomer orientation on the surface of the plasmonic platform through, for the first time, π–π interactions. The integration of machine learning methodologies, such as Partial Least Squares Regression and Artificial Neural Networks, significantly enhances both quantitative analysis and classification accuracy, achieving detection limits as low as 2 × 10⁻⁸ m. Validation with commercially available ibuprofen samples shows excellent agreement with traditional circular dichroism results, highlighting the method's robustness and precision. The strategy provides a versatile, ultrasensitive, and reliable solution for isomer discrimination, with broad applications in pharmaceuticals, environmental monitoring, and clinical diagnostics.