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Cancer is one of the most serious health problems worldwide. Because cancer has no specific symptoms in its early stages, it is often not diagnosed until it is in advanced stages, reducing the likelihood of successful treatment. Therefore, early diagnosis of cancer is a formidable challenge. Mass spectrometry-based proteomics offers a robust technical foundation for cancer diagnosis. However, mass spectrometry data are characterized by high dimensionality, large data volume, and noise interference, which can lead to diagnostic errors in clinical applications. To address this challenge, an improved algorithm combining principal component analysis (PCA) with a convolutional neural network (CNN) algorithm (denoted as PCA-1DSE-ResCNN) was proposed to assist in analyzing high-dimensional mass spectral data. The algorithm initially reduced the dimensionality of the data through the PCA technique. Subsequently, the convolutional neural network algorithm (1DSE-ResCNN) integrating residual blocks and squeeze-and-excitation blocks was used as a classifier. This approach can not only alleviate the issues of overfitting and gradient vanishing caused by deep network layers but also reduce redundant information, enabling the algorithm to effectively learn high-dimensional data features and deal with nonlinear relationships. To validate the effectiveness of the algorithm, the high-dimensional ovarian cancer mass spectrometry dataset was selected as an example to examine its application performance in early diagnosis of ovarian cancer. The experimental results demonstrated that the PCA-1DSE-ResCNN algorithm outperforms other methods in terms of accuracy, specificity, and sensitivity on three high-dimensional ovarian cancer datasets. This study will contribute to the rapid diagnosis and early detection of cancer.

Cardiovascular diseases (CVDs) are recognized as a significant threat to global health. The rapid, sensitive, and precise measurement of relevant biomarkers is essential for the timely diagnosis of CVDs. Molecularly imprinted polymers (MIPs), which act as artificial receptor recognition materials, have been extensively utilized in the detection of CVD biomarkers. Their widespread application is due to their cost-effectiveness, physical and chemical stability, straightforward preparation processes, and excellent compatibility with various sensor types. This review introduces the principles of MIP sensors in combination with electrochemical, optical, thermal transfer, and acoustic detection techniques for detecting CVD-related biomarkers. It then discusses methods developed over the past decade for detecting biomarkers of three major CVDs—coronary artery disease (CAD), acute myocardial infarction (AMI), and heart failure (HF)—using MIP sensors. Finally, the review summarizes the potential of MIP sensors in CVD biomarker detection and provides an outlook on future research directions.

Microfluidic systems incorporating magnetic droplets have emerged as a focal point of significant interest within the biomedical domain. The allure of these systems lies in their capacity to offer precise control, enable contactless operation, and accommodate minimal sample concentration requirements. Such remarkable features serve to mitigate errors arising from human operation and other factors during cell or molecular detection. By providing innovative solutions for molecular diagnostics and immunoassay applications, magnetic droplet microfluidics enhance the accuracy and efficiency of these procedures. This review undertakes a comprehensive examination of the research progress in microfluidic systems centered around magnetic droplets. It adheres to a sequential presentation approach, commencing from the fundamental operation principles, specifically the generation of magnetic droplets on the microfluidic chip, and proceeding to their transmission and mixing within the microchannel via an array of operating techniques. Additionally, the relevant detection technologies associated with magnetic drop microfluidics and their numerous applications within the biomedical field are systematically classified and reviewed. The overarching objective of this review is to spotlight key advancements and offer valuable insights into the future trajectory of this burgeoning field.

Signal Amplification By Reversible Exchange (SABRE) can provide strong signal enhancement (SE) to an array of molecules through reversible exchange of parahydrogen (pH₂) derived hydrides and a suitable substrate coordinated to a transition metal. Among the substrates that can be used as a probe for hyperpolarised NMR and MRI, pyruvate has gained much attention. SABRE can hyperpolarise pyruvate in a low cost, fast, and reversible fashion that does not involve technologically demanding equipment. Most SABRE polarization studies have been done using methanol-d₄ as a solvent, which is not suitable for in vivo application. The main goal of this work was to obtain hyperpolarized pyruvate in a solvent other than methanol which may open the door to further purification steps and enable a method to polarize pyruvate in water in future. This work demonstrates hyperpolarization of the [2-¹³C]pyruvate as well as [1-¹³C]pyruvate by SABRE in an acetone/water solvent system at room temperature as an alternative to methanol, which is commonly used. NMR signals are detected using a 1.1 T benchtop NMR spectrometer. In this work we have primarily focused on the study of [2-¹³C]pyruvate and investigated the effect of catalyst concentration, DMSO presence and water vs. acetone solvent concentration on the signal enhancement. The relaxation times for [2-¹³C]-pyruvate solutions are reported in the hope of informing the development of future purification methods.

With greater population density, the likelihood of viral outbreaks achieving pandemic status is increasing. However, current viral screening techniques use specific reagents, and as viruses mutate, test accuracy decreases. Here, we present the first real-time, reagent-free, portable analysis platform for viral detection in liquid saliva, using COVID-19 as a proof-of-concept. We show that vibrational molecular spectroscopy and machine learning (ML) detect biomolecular changes consistent with the presence of viral infection. Saliva samples were collected from 470 individuals, including 65 that were infected with COVID-19 (28 from hospitalized patients and 37 from a walk-in testing clinic) and 251 that had a negative polymerase chain reaction (PCR) test. A further 154 were collected from healthy volunteers. Saliva measurements were achieved in 6 minutes or less and led to machine learning models predicting COVID-19 infection with sensitivity and specificity reaching 90%, depending on volunteer symptoms and disease severity. Machine learning models were based on linear support vector machines (SVM). This platform could be deployed to manage future pandemics using the same hardware but using a tunable machine learning model that could be rapidly updated as new viral strains emerge.

LaNiO₃ perovskite nanoparticles, especially nanospheres (LNNS), show great promise in biomedical assays due to their peroxidase-like catalytic properties. However, LNNS-based diagnostic reagents have not been tested in nanozyme enzyme-linked immunosorbent assay (NLISA) or other enzyme-linked immunosorbent assays, and there is limited data on their synthesis. To fill this gap, it is necessary to develop a method for creating LNNS conjugates with monoclonal antibodies and to investigate the reproducibility, scalability, and applicability of these diagnostic reagents in NLISA. We have successfully developed a method for producing novel diagnostic reagents utilizing LaNiO₃ nanospheres. Our research demonstrates the application of these nanospheres in a NLISA specifically designed for the detection of C-reactive protein (CRP) in real serum samples. This method is both reproducible and scalable, allowing for the efficient production of nanospheres that are functionalized with monoclonal antibodies targeting CRP, with a mean diameter of approximately 270 nm. Based on the promising results obtained from our experiments, we have developed and optimized a sandwich-format NLISA for CRP detection. This assay achieved a lower limit of detection at 0.178 μg L⁻¹, with a dynamic range from 12.5 to 0.195 μg L⁻¹ and a linear detection range extending from 0.195 to 6.25 μg L⁻¹, showcasing its potential for clinical applications. The new NLISA method, utilizing LaNiO₃ nanospheres in a sandwich format for the detection of CRP, significantly enhances sensitivity compared to similar use horseradish peroxidase-based ELISA. In this study for the first time, the functionalization of lanthanum nickelate nanospheres with recognition elements has been demonstrated. This advancement also sheds light on the technological challenges involved in synthesizing diagnostic reagents, identifying areas that need further exploration.

In this article, we present the first demonstration of a distance-based paper analytical device (dPAD) for uric acid quantification in human urine samples with instrument-free readout and user-friendliness for the rapid diagnosis and prognosis of various related diseases. By employing gold nanoparticles (AuNPs) as a peroxidase-like nanozyme, our proposed technique eliminates the utilization of horseradish peroxidase (HRP), making the device cost-effective and stable. In our dPAD, uric acid in the sample is oxidized by the uricase enzyme and subsequently catalysed with AuNPs in the sample zone, generating hydroxyl radicals (˙OH). Then, the produced ˙OH reacts with 3,3′-diaminobenzidine (DAB) to form poly DAB (oxDAB), resulting in a coloured distance signal in the detection zone of the dPAD. The variation of the distance of the observed red-brown colour is directly proportional to the uric acid concentration. Our sensor exhibited a linear range from 0.50 to 6.0 mmol L⁻¹ (R² = 0.9922) with a detection limit (LOD) of 0.25 mmol L⁻¹, covering the clinical range of uric acid in urine. Hence, there is no need for additional sample preparation or dilution. Additionally, this assay is highly selective, with no interferences. We also found that this approach could accurately and precisely determine uric acid in human control samples with the recovery ranging from 99.37 to 100.35 with the highest RSD of 4.05%. Our method is comparable with the use of a commercially available uric acid sensor at a 95% confidence interval. Consequently, the developed dPAD offers numerous advantages such as cost-effectiveness, simplicity, and ease of operation with unskilled individuals. Furthermore, this concept can be applied for extensive biosensing applications in monitoring other biomarkers as an alternative analytical point-of-care (POC) device.

The most common methodology for performing multiple chemical and biological reactions in parallel is to use microtitre plates with either manual or robotic dispensing of reactants and wash solutions. We envision a paradigm shift where acoustically levitated droplets serve as wells of microtitre plates and are acoustically manipulated to perform chemical and biological reactions in a non-contact fashion. This in turn requires that lines of droplets can be levitated and manipulated simultaneously so that the same operations (merge, mix, and detect) can be performed on them in parallel. However, this has not been demonstrated until this work. Because of the nature of acoustic standing waves, a single focus has more than one trap, and can allow levitation of columns of droplets at the focal point and at half a wavelength above and below that point. Using this approach, we increased the number of acoustically levitated and merged droplets to 6 compared to 2 in the state-of-the-art. We showed that droplets in a column can be moved and merged with droplets in another column simultaneously and in a controlled manner to perform repeats and/or parallelisation of chemical and biological reactions. To demonstrate our approach experimentally, we built an acoustic levitator with top and bottom surfaces made of a 16 × 16 grid of 40 kHz phased array transducers and integrated optical detection system, studied two acoustic trap generation and movement algorithms, and performed an exemplar enzyme assay. This work has made significant steps towards acoustic levitation and manipulation of large numbers of droplets to eventually significantly reduce the use of the current state-of-the-art tools, microtitre plates and robots, for performing parallelised chemical and biological reactions.