Alternating Magnetic Field-Promoted Nanoparticle Mixing: The On-Chip Immunocapture of Serum Neuronal Exosomes for Parkinson's Disease Diagnostics

Branch-convergence structure based on double-layer chip: a universal method for enhancing microfluidic mixing

Microfluidic mixing has significant applications in various fields, including materials synthesis and biochemical analysis. In this study, we propose a universal strategy to enhance mixing efficiency in microfluidic chips. This strategy initially divides the liquid into branches, which then converge in an interdigitated manner at the beginning of the mixing segment. This branch-convergence structure reduces the flow width of each liquid, thereby decreasing the diffusion distances required for mixing. Under the conditions of this study, the mixing efficiency could be improved by approximately 10 times. Importantly, this enhancement strategy only requires changing the structure of the liquid inflow channel without changing the structure of the mixing segment. Thus, this strategy has broad applicability, any mixing section with different principles and structures can be connected downstream of the branch-convergence structure. In addition, we applied this universal mixing enhancement strategy to the continuous synthesis of lactic-co-glycolic acid nanoparticles, resulting in a higher uniformity of synthesized nanoparticles compared to unenhanced devices.

Critical review and recent advances of emerging real-time and non-destructive strategies for meat spoilage monitoring

Monitoring and evaluating food quality, especially meat quality, has received a growing interest to ensure human health and decrease waste of raw materials. Standard analytical approaches used for meat spoilage assessment suffer from time consumption, being labor-intensive, operation complexity, and destructiveness. To overcome shortfalls of these traditional methods and monitor spoilage microorganisms or related metabolites of meat products across the supply chain, emerging analysis devices/systems with higher sensitivity, better portability, on-line/in-line, non-destructive and cost-effective property are urgently needed. Herein, we first overview the basic concepts, causes, and critical monitoring indicators associated with meat spoilage. Then, the conventional detection methods for meat spoilage are outlined objectively in their strengths and weaknesses. In addition, we place the focus on the recent research advances of emerging non-destructive devices and systems for assessing meat spoilage. These novel strategies demonstrate their powerful potential in the real-time evaluation of meat spoilage.

Isolation and quantification of L1CAM-positive extracellular vesicles on a chip as a potential biomarker for Parkinson's Disease

Extracellular vesicles (EVs) carry disease-specific molecular profiles, demonstrating massive potential in biomarker discovery. In this study, we developed an integrated biochip platform, termed EVID-biochip (EVs identification and detection biochip), which integrates in situ electrochemical protein detection with on-chip antifouling-immunomagnetic beads modified with CD81 antibodies and zwitterion molecules, enabling efficient isolation and detection of neuronal EVs. The capability of the EVID-biochip to isolate common EVs and detect neuronal EVs associated with Parkinson's disease in human serum is successfully demonstrated, using the transmembrane protein L1-cell adhesion molecule (L1CAM) as a target biomarker. The EVID-biochip exhibited high efficiency and specificity for the detection of L1CAM with a sensitivity of 1 pg/mL. Based on the validation of 76 human serum samples, for the first time, this study discovered that the level of L1CAM/neuronal EV particles in serum could serve as a reliable indicator to distinguish Parkinson's disease from control groups with AUC = 0.973. EVID-biochip represents a reliable and rapid liquid biopsy platform for the analysis of complex biofluids offering EVs isolation and detection in a single chip, requiring a small sample volume (300 µL) and an assay time of 1.5 h. This approach has the potential to advance the diagnosis and biomarker discovery of various neurological disorders and other diseases.

Extracellular vesicles as nanotheranostic platforms for targeted neurological disorder interventions

Central Nervous System (CNS) disorders represent a profound public health challenge that affects millions of people around the world. Diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and traumatic brain injury (TBI) exemplify the complexities and diversities that complicate their early detection and the development of effective treatments. Amid these challenges, the emergence of nanotechnology and extracellular vesicles (EVs) signals a new dawn for treating and diagnosing CNS ailments. EVs are cellularly derived lipid bilayer nanosized particles that are pivotal in intercellular communication within the CNS and have the potential to revolutionize targeted therapeutic delivery and the identification of novel biomarkers. Integrating EVs with nanotechnology amplifies their diagnostic and therapeutic capabilities, opening new avenues for managing CNS diseases. This review focuses on examining the fascinating interplay between EVs and nanotechnology in CNS theranostics. Through highlighting the remarkable advancements and unique methodologies, we aim to offer valuable perspectives on how these approaches can bring about a revolutionary change in disease management. The objective is to harness the distinctive attributes of EVs and nanotechnology to forge personalized, efficient interventions for CNS disorders, thereby providing a beacon of hope for affected individuals. In short, the confluence of EVs and nanotechnology heralds a promising frontier for targeted and impactful treatments against CNS diseases, which continue to pose significant public health challenges. By focusing on personalized and powerful diagnostic and therapeutic methods, we might improve the quality of patients.

Potential Exosome Biomarkers for Parkinson's Disease Diagnosis: A Systematic Review and Meta-Analysis

Parkinson's disease (PD) is the second most common neurodegenerative disease worldwide. Given its prevalence, reliable biomarkers for early diagnosis are required. Exosomal proteins within extracellular nanovesicles are promising candidates for diagnostic, screening, prognostic, and disease monitoring purposes in neurological diseases such as PD. This review aims to evaluate the potential of extracellular vesicle proteins or miRNAs as biomarkers for PD. A comprehensive literature search until January 2024 was conducted across multiple databases, including PubMed, EMBASE, Web of Science, and Cochrane Library, to identify relevant studies reporting exosome biomarkers in blood samples from PD patients. Out of 417 articles screened, 47 studies were selected for analysis. Among exosomal protein biomarkers, α-synuclein, tau, Amyloid β 1-42, and C-X-C motif chemokine ligand 12 (CXCL12) were identified as significant markers for PD. Concerning miRNA biomarkers, miRNA-24, miR-23b-3p, miR-195-3p, miR-29c, and mir-331-5p are promising across studies. α-synuclein exhibited increased levels in PD patients compared to control groups in twenty-one studies, while a decrease was observed in three studies. Our meta-analysis revealed a significant difference in total exosomal α-synuclein levels between PD patients and healthy controls (standardized mean difference [SMD] = 1.369, 95% confidence interval [CI] = 0.893 to 1.846, p < 0.001), although these results are limited by data availability. Furthermore, α-synuclein levels significantly differ between PD patients and healthy controls (SMD = 1.471, 95% CI = 0.941 to 2.002, p < 0.001). In conclusion, certain exosomal proteins and multiple miRNAs could serve as potential biomarkers for diagnosis, prognosis prediction, and assessment of disease progression in PD.

A novel viscoelastic microfluidic platform for nanoparticle/small extracellular vesicle separation through viscosity gradient-induced migration

Small extracellular vesicles (sEVs) are extracellular vesicles with diameters ranging from 30 to 150 nm, harboring proteins and nucleic acids that reflect their source cells and act as vital mediators of intercellular communication. The comprehensive analysis of sEVs is hindered by the complex composition of biofluids that contain various extracellular vesicles. Conventional separation methods, such as ultracentrifugation and immunoaffinity capture, face routine challenges in operation complexity, cost, and compromised recovery rates. Microfluidic technologies, particularly viscoelastic microfluidics, offer a promising alternative for sEV separation due to its field-free nature, fast and simple operation procedure, and minimal sample consumption. In this context, we here introduce an innovative viscoelastic approach designed to exploit the viscosity gradient-induced force with size-dependent characteristics, thereby enabling the efficient separation of nano-sized particles and sEVs from larger impurities. We first seek to illustrate the underlying mechanism of the viscosity gradient-induced force, followed by experimental validation with fluorescent nanoparticles demonstrating separation results consistent with qualitative analysis. We believe that this work is the first to report such viscosity gradient-induced phenomenon in the microfluidic context. The presented approach achieves ∼80% for both target purity and recovery rate. We further demonstrate effective sEV separation using our device to showcase its efficacy in the real biological context, highlighting its potential as a versatile, label-free platform for sEV analysis in both fundamental biological research and clinical applications.

Electrochemical detection of extracellular vesicles for early diagnosis: a focus on disease biomarker analysis

This review article presents a detailed examination of the integral role that electrochemical detection of extracellular vesicles (EVs) plays, particularly focusing on the potential application for early disease diagnostics through EVs biomarker analysis. Through an exploration of the benefits and challenges presented by electrochemical detection vetted for protein, lipid, and nucleic acid biomarker analysis, we underscore the significance of these techniques. Evidence from recent studies renders this detection modality imperative in identifying diverse biomarkers from EVs, leading to early diagnosis of diseases such as cancer and neurodegenerative disorders. Recent advancements that have led to enhanced sensitivity, specificity and point-of-care testing (POCT) potential are elucidated, along with equipment deployed for electrochemical detection. The review concludes with a contemplation of future perspectives, recognizing the potential shifts in disease diagnostics and prognosis, necessary advances for broad adoption, and potential areas of ongoing research. The objective is to propel further investigation into this rapidly burgeoning field, thereby facilitating a potential paradigm shift in disease detection, monitoring, and treatment toward human health management.

Analysis of biomarkers in speculative CNS-enriched extracellular vesicles for parkinsonian disorders: a comprehensive systematic review and diagnostic meta-analysis

BACKGROUND AND OBJECTIVE

Parkinsonian disorders, including Parkinson's disease (PD), multiple system atrophy (MSA), dementia with Lewy bodies (DLB), progressive supranuclear palsy (PSP), and corticobasal syndrome (CBS), exhibit overlapping early-stage symptoms, complicating definitive diagnosis despite heterogeneous cellular and regional pathophysiology. Additionally, the progression and the eventual conversion of prodromal conditions such as REM behavior disorder (RBD) to PD, MSA, or DLB remain challenging to predict. Extracellular vesicles (EVs) are small, membrane-enclosed structures released by cells, playing a vital role in communicating cell-state-specific messages. Due to their ability to cross the blood-brain barrier into the peripheral circulation, measuring biomarkers in blood-isolated speculative CNS enriched EVs has become a popular diagnostic approach. However, replication and independent validation remain challenging in this field. Here, we aimed to evaluate the diagnostic accuracy of speculative CNS-enriched EVs for parkinsonian disorders.

METHODS

We conducted a PRISMA-guided systematic review and meta-analysis, covering 18 studies with a total of 1695 patients with PD, 253 with MSA, 21 with DLB, 172 with PSP, 152 with CBS, 189 with RBD, and 1288 HCs, employing either hierarchical bivariate models or univariate models based on study size.

RESULTS

Diagnostic accuracy was moderate for differentiating patients with PD from HCs, but revealed high heterogeneity and significant publication bias, suggesting an inflation of the perceived diagnostic effectiveness. The bias observed indicates that studies with non-significant or lower effect sizes were less likely to be published. Although results for differentiating patients with PD from those with MSA or PSP and CBS appeared promising, their validity is limited due to the small number of involved studies coming from the same research group. Despite initial reports, our analyses suggest that using speculative CNS-enriched EV biomarkers may not reliably differentiate patients with MSA from HCs or patients with RBD from HCs, due to their lesser accuracy and substantial variability among the studies, further complicated by substantial publication bias.

CONCLUSION

Our findings underscore the moderate, yet unreliable diagnostic accuracy of biomarkers in speculative CNS-enriched EVs in differentiating parkinsonian disorders, highlighting the presence of substantial heterogeneity and significant publication bias. These observations reinforce the need for larger, more standardized, and unbiased studies to validate the utility of these biomarkers but also call for the development of better biomarkers for parkinsonian disorders.

Antigen-Mimic Nanoparticles in Ultrasensitive on-Chip Integrated Anti-p53 Antibody Quantification

As a tumor-suppressing protein, p53 plays a crucial role in preventing cancer development. Its utility as an early cancer detection tool is significant, potentially enabling clinicians to forestall disease advancement and improve patient prognosis. In response to the pathological overexpression of this antigen in tumors, the prevalence of anti-p53 antibodies increases in serum, in a manner quantitatively indicative of cancer progression. This spike can be detected through techniques, such as Western blotting, immunohistochemistry, and immunoprecipitation. In this study, we present an electrochemical approach that supports ultrasensitive and highly selective anti-p53 autoantibody quantification without the use of an immuno-modified electrode. We specifically employ antigen-mimicking and antibody-capturing peptide-coated magnetic nanoparticles, along with an AC magnetic field-promoted sample mixing, prior to the presentation of Fab-captured targets to simple lectin-modified sensors. The subfemtomolar assays are highly selective and support quantification from serum-spiked samples within minutes.

Single-Molecule Protein Analysis by Centrifugal Droplet Immuno-PCR with Magnetic Nanoparticles

Detecting proteins in ultralow concentrations in complex media is important for many applications but often relies on complicated techniques. Herein, a single-molecule protein analyzer with the potential for high-throughput applications is reported. Gold-coated magnetic nanoparticles with DNA-labeled antibodies were used for target recognition and separation. The immunocomplex was loaded into microdroplets generated with centrifugation. Immuno-PCR amplification of the DNA enabled the quantification of proteins at the level of single molecules. As an example, ultrasensitive detection of α-synuclein, a biomarker for neurodegenerative diseases, is achieved. The limit of detection was determined to be ∼50 aM in buffer and ∼170 aM in serum. The method exhibited high specificity and could be used to analyze post-translational modifications such as protein phosphorylation. This study will inspire wider studies on single-molecule protein detection, especially in disease diagnostics, biomarker discovery, and drug development.