Field Of Biomedical Analysis Research Articles

Unique biomedical application of fluorescence derivatization based on palladium-catalyzed coupling reactions for HPLC analysis of pharmaceuticals and biomolecules.

Palladium-catalyzed coupling reactions are versatile and powerful tools for the construction of carbon-carbon bonds in organic synthesis. Although these reactions have favorable features that proceed selectively in mild reaction conditions using aqueous organic solvents, no attention has been given to their application in the field of biomedical analysis. Therefore, we focused on these reactions and evaluated the scope and limitations of their analytical performance. In this review, we describe the pros and cons and future trends of fluorescence derivatization of pharmaceuticals and biomolecules based on palladium-catalyzed coupling reactions such as Suzuki-Miyaura coupling, Mizoroki-Heck coupling, and Sonogashira coupling reactions for HPLC analysis.

Nanostructured Substrates as Matrices for Surface Assisted Laser Desorption/Ionization Mass Spectrometry: A Progress Report from Material Research to Biomedical Applications.

Within the past two decades, the escalation of research output in nanotechnology fields has boosted the development of novel nanoparticles and nanostructured substrates for use as matrices in surface assisted laser desorption/ionization mass spectrometry (SALDI-MS). The application of nanomaterials as matrices, rather than organic matrices, offers remarkable characteristics that allow the analysis of small molecules with fewer matrix interfering peaks, and share higher detection sensitivity, specificity, and reproducibility. The technological advancement of SALDI-MS has in turn, propelled the application of the analytical technique in the field of biomedical analysis. In this review, the properties and fabrication methods of nanostructured substrates in SALDI-MS such as metallic-, carbon-, and silicon-based nanostructures, quantum dots, metal-organic frameworks, and covalent-organic frameworks are described. Additionally, the latest progress (most within 5 years) of biomedical applications in small molecule, large biomolecule, and MS imaging analysis including metabolite profiling, drug monitoring, bacteria identification, disease diagnosis, and therapeutic evaluation are demonstrated. Key parameters that govern nanomaterial's SALDI efficiency in biomolecule analysis are also discussed. Finally, perspectives of the future development are given to provide a better advancement and promote practical application in clinical MS.

Molecularly imprinted materials for biomedical sensing

AbstractSignificant advances have been presented in the last decades in the field of biomedical analysis and sensing, especially pertaining to routine procedures based on immunologic and nucleic acids, including enzyme‐linked immunosorbent assay (ELISA) and real‐time polymerase chain reaction (RTq‐PCR). Despite their efficiency, some limitations such as reagent stability and availability, cost and cumbersome analysis procedures still limit further advances in this area. In this context, the development of molecularly imprinted polymers (MIPs) for biomedical purposes has contributed to the improvement of consolidated applications and the development of new approaches in a wide range of application scenarios. This review discusses the contribution of MIPs for advances in biomedical sensing in the past 5 years highlighting their main synthesis strategies, and applications especially for the detection of biomarkers, viruses and bacteria. Last but not least, the utility of MIPs in diagnostic imaging is emphasized.

Progress in biomedical data analysis based on deep learning

Traditional biomedical data analysis technology faces enormous challenges in the context of the big data era. The application of deep learning technology in the field of biomedical analysis has ushered in tremendous development opportunities. In this paper, we reviewed the latest research progress of deep learning in the field of biomedical data analysis. Firstly, we introduced the deep learning method and its common framework. Then, focusing on the proposal of biomedical problems, data preprocessing method, model building method and training algorithm, we summarized the specific application of deep learning in biomedical data analysis in the past five years according to the chronological order, and emphasized the application of deep learning in medical assistant diagnosis. Finally, we gave the possible development direction of deep learning in the field of biomedical data analysis in the future.

Poly-adenine regulated DNA density on AuNPs to construct efficient DNA walker for microRNA-21 detection

DNA-modified gold nanoparticles (AuNPs) are useful nanomaterials for detecting multiple molecules. However, their performance is greatly dependent on the density of probe DNA on the surface of AuNPs. Here, we used Poly-adenine (PolyA) to regulate the surface density of probe DNA to achieve a highly efficient DNA walking biosensor system to detection miRNA-21. The movement track of the biosensor system consists of PolyA-DNA probe was connected to AuNPs, and exonuclease III (Exo III) acted as a motor driving the walker movement to achieve signal amplification. By optimizing the length of PolyA, the surface density of probe DNA was changed, thereby affecting the target binding and enzymatic processing of the bound probes, which ultimately enhanced the sensitivity and reduced timeliness of the DNA walker. Furthermore, the designed PolyA-DNA probe exhibits an outstanding sensitivity, due to the effect of density regulation, which is 7.9 times and 11.1 times lower than those of the SH-DNA and the free-DNA, respectively. In addition, the hairpin structure of DNA probe locates fluorophore at a zone adjacent to AuNPs surface, which reduces the background signal by 1.1 times compared with traditional straight probe. In this work, the biosensor system shows a high selectivity towards miRNA-21. Moreover, the biosensor system has been demonstrated to be potentially useful for the miRNA-21 detection in human serum with the recoveries of 93.2%–110.0% and has high repeatability. Considering these advantages, this PolyA-regulated DNA walking biosensor system has great potential as a routine tool for miRNA detection and has wide applications in the field of biomedical analysis.

Surface-textured polymer microspheres generated through interfacial instabilities of microfluidic droplets for cell capture

Polymer microparticles with various compositions and morphologies have recently received much attention. Their surface-roughness significantly affects the physical and chemical properties, which especially counts in regulating the interaction between biological materials and living systems. In this paper, we design a polystyrene microsphere with controllable surface textures. At first, a microfluidic device is used to generate droplets with uniform size containing the hydrophobic polymer and a co-surfactant. During the volatilization of the organic solvent, the shrinking droplets appear to be unstable at the interface. Thus, the surface area increases spontaneously, and microspheres with wrinkles on the surface are obtained after being solidified. The results show that tuning the concentration of the co-surfactant and the rate of solvent evaporation can effectively regulate the surface roughness of the microspheres. Circulating tumor cell capture experiments reveal that this textured structure can facilitate the cell adhesion and increase the number of the captured cells. These features indicate that the coarse microspheres possess a promising application prospect in the field of biomedical analysis.

A nonenzymatic DNA nanomachine for biomolecular detection by target recycling of hairpin DNA cascade amplification

Synthetic enzyme-free DNA nanomachine performs quasi-mechanical movements in response to external intervention, suggesting the promise of constructing sensitive and specific biosensors. Herein, a smart DNA nanomachine biosensor for biomolecule (such as nucleic acid, thrombin and adenosine) detection is developed by target-assisted enzyme-free hairpin DNA cascade amplifier. The whole DNA nanomachine system is constructed on gold nanoparticle which decorated with hundreds of locked hairpin substrate strands serving as DNA tracks, and the DNA nanomachine could be activated by target molecule toehold-mediated exchange on gold nanoparticle surface, resulted in the fluorescence recovery of fluorophore. The process is repeated so that each copy of the target can open multiplex fluorophore-labeled hairpin substrate strands, resulted in amplification of the fluorescence signal. Compared with the conventional biosensors of catalytic hairpin assembly (CHA) without substrate in solution, the DNA nanomachine could generate 2–3 orders of magnitude higher fluorescence signal. Furthermore, the DNA nanomachine could be used for nucleic acid, thrombin and adenosine highly sensitive specific detection based on isothermal, and homogeneous hairpin DNA cascade signal amplification in both buffer and a complicated biomatrix, and this kind of DNA nanomachine could be efficiently applied in the field of biomedical analysis.

Cascaded Electrochemiluminescence Signal Amplifier for the Detection of Telomerase Activity from Tumor Cells and Tissues.

Telomerase is closely linked to the physiological transformation of tumor cells and is commonly overexpressed in most types of tumor cells. Therefore, telomerase has become a potential biomarker for the process of tumorigenesis, progression, prognosis and metastasis. Thus, it is important to develop a simple, accurate and reliable method for detecting telomerase activity. As a high signal-to-noise ratio mode, electrochemiluminescence (ECL) has been widely applied in the field of biomedical analysis. Here, our objective was to construct an improved ECL signal amplifier for the detection of telomerase activity.Methods: A cascaded ECL signal amplifier was constructed to detect telomerase activity with high selectivity via controllable construction of a lysine-based dendric Ru(bpy)32+ polymer (DRP). The sensitivity, specificity and performance index were simultaneously evaluated by standard substance and cell and tissue samples.Results: With this cascaded ECL signal amplifier, high sensitivities of 100, 50, and 100 cells for three tumor cell lines (A549, MCF7 and HepG2 cell lines) were simultaneously achieved, and desirable specificity was also obtained. Furthermore, the excellent performance of this platform was also demonstrated in the detection of telomerase in tumor cells and tissues.Conclusion: This cascaded ECL signal amplifier has the potential to be a technological innovation in the field of telomerase activity detection.

Assessment of the Molecular Expression and Structure of Gangliosides in Brain Metastasis of Lung Adenocarcinoma by an Advanced Approach Based on Fully Automated Chip-Nanoelectrospray Mass Spectrometry

Gangliosides (GGs), sialic acid-containing glycosphingolipids, are known to be involved in the invasive/metastatic behavior of brain tumor cells. Development of modern methods for determination of the variations in GG expression and structure during neoplastic cell transformation is a priority in the field of biomedical analysis. In this context, we report here on the first optimization and application of chip-based nanoelectrospray (NanoMate robot) mass spectrometry (MS) for the investigation of gangliosides in a secondary brain tumor. In our work a native GG mixture extracted and purified from brain metastasis of lung adenocarcinoma was screened by NanoMate robot coupled to a quadrupole time-of-flight MS. A native GG mixture from an age-matched healthy brain tissue, sampled and analyzed under identical conditions, served as a control. Comparative MS analysis demonstrated an evident dissimilarity in GG expression in the two tissue types. Brain metastasis is characterized by many species having a reduced N-acetylneuraminic acid (Neu5Ac) content, however, modified by fucosylation or O-acetylation such as Fuc-GM4, Fuc-GM3, di-O-Ac-GM1, O-Ac-GM3. In contrast, healthy brain tissue is dominated by longer structures exhibiting from mono- to hexasialylated sugar chains. Also, significant differences in ceramide composition were discovered. By tandem MS using collision-induced dissociation at low energies, brain metastasis-associated GD3 (d18:1/18:0) species as well as an uncommon Fuc-GM1 (d18:1/18:0) detected in the normal brain tissue could be structurally characterized. The novel protocol was able to provide a reliable compositional and structural characterization with high analysis pace and at a sensitivity situated in the fmol range.

Gold nanoparticle film grown on quartz fiber and its application as a microsensor of nitric oxide

A simple electroless plating method for growing a gold nanoparticle (GNP) film on a quartz fiber by chemical liquid deposition was reported. The gold film coated quartz fiber possessed good conductivity and mechanical property, and can be used as a novel gold fiber microelectrode (GFME). Being electropolymerized with niacinamide (NA) and further coated by Nafion for GFME, a disposable nitric oxide (NO) electrochemical microsensor was fabricated. This microsensor exhibited high sensitivity based on the catalytic effect of GNPs and polyniacinamide (PNA) toward the oxidation of NO. A good linear relationship between the amperometric response of the microsensor and the concentration of NO in the range of 7.2 × 10 −9–1.8 × 10 −5 mol L −1 was achieved, along with a low detection limit of 3.6 nmol L −1 (S/N = 3). In vivo detection of NO release from mice liver indicated that this microsensor had a promising application in the field of biomedical analysis.

A review of multivariate calibration methods applied to biomedical analysis

The determination of the contents of therapeutic drugs, metabolites and other important biomedical analytes in biological samples is usually performed by using high-performance liquid chromatography (HPLC). Modern multivariate calibration methods constitute an attractive alternative, even when they are applied to intrinsically unselective spectroscopic or electrochemical signals. First-order (i.e., vectorized) data are conveniently analyzed with classical chemometric tools such as partial least-squares (PLS). Certain analytical problems require more sophisticated models, such as artificial neural networks (ANNs), which are especially able to cope with non-linearities in the data structure. Finally, models based on the acquisition and processing of second- or higher-order data (i.e., matrices or higher dimensional data arrays) present the phenomenon known as “second-order advantage”, which permits quantitation of calibrated analytes in the presence of interferents. The latter models show immense potentialities in the field of biomedical analysis. Pertinent literature examples are reviewed.