The hallmark of Alzheimer’s disease (AD) pathogenesis is believed to be the production and deposition of amyloid-beta (Aβ) peptide into extracellular plaques. Existing research indicates that extracellular vesicles (EVs) can...

Cytokines are small proteins secreted by immune cells in response to pathogens/infections; therefore, these proteins can be used in diagnosing infectious diseases. For example, release of a cytokine interferon (IFN)-γ from T-cells is used for blood-based diagnosis of tuberculosis (TB). Our lab has previously developed an atpamer-based electrochemical biosensor for rapid and sensitive detection of IFN-γ. In this study, we explored the use of silicon nanowires (NWs) as a way to create nanostructured electrodes with enhanced sensitivity for IFN-γ. Si NWs were covered with gold and were further functionalized with thiolated aptamers specific for IFN-γ. Aptamer molecules were designed to form a hairpin and in addition to terminal thiol groups contained redox reporter molecules methylene blue. Binding of analyte to aptamer-modified NWs (termed here nanowire aptasensors) inhibited electron transfer from redox reporters to the electrode and caused electrochemical redox signal to decrease. In a series of experiments we demonstrate that NW aptasensors responded 3× faster and were 2× more sensitive to IFN-γ compared to standard flat electrodes. Most significantly, NW aptasensors allowed detection of IFN-γ from as few as 150 T-cells/mL while ELISA did not pick up signal from the same number of cells. One of the challenges faced by ELISA-based TB diagnostics is poor performance in patients whose T-cell numbers are low, typically HIV patients. Therefore, NW aptasensors developed here may be used in the future for more sensitive monitoring of IFN-γ responses in patients coinfected with HIV/TB.

Using a regular CMOS sensor as a template, we are able to fabricate a simple but highly effective superhydrophobic SERS substrate.

Bovine tuberculosis (TB) is an infectious disease caused byMycobacterium tuberculosis(MTB) in which it is hard to identify the pathological symptoms.

Extracellular vesicles (EVs) are complex biological nanoparticles endogenously secreted by all eukaryotic cells. EVs carry a specific molecular cargo of proteins, lipids, and nucleic acids derived from cells of origin and play a significant role in the physiology and pathology of cells, organs, and organisms. Upon release, they may be found in different body fluids that can be easily accessed via noninvasive methodologies. Due to the unique information encoded in their molecular cargo, they may reflect the state of the parent cell and therefore EVs are recognized as a rich source of biomarkers for early diagnostics involving liquid biopsy. However, body fluids contain a mixture of EVs released by different types of healthy and diseased cells, making the detection of the EVs of interest very challenging. Recent research efforts have been focused on the detection and characterization of diagnostically relevant subpopulations of EVs, with emphasis on label-free methods that simplify sample preparation and are free of interfering signals. Therefore, in this paper, we review the recent progress of the label-free optical methods employed for the detection, counting, and morphological and chemical characterization of EVs. We will first briefly discuss the biology and functions of EVs, and then introduce different optical label-free techniques for rapid, precise, and nondestructive characterization of EVs such as nanoparticle tracking analysis, dynamic light scattering, atomic force microscopy, surface plasmon resonance spectroscopy, Raman spectroscopy, and SERS spectroscopy. In the end, we will discuss their applications in the detection of neurodegenerative diseases and cancer and provide an outlook on the future impact and challenges of these technologies to the field of liquid biopsy via EVs.

We present for the first time a lens-free, oblique illumination imaging platform for on-sensor dark- field microscopy and shadow-based 3D object measurements. It consists of an LED point source that illuminates a 5-megapixel, 1.4 µm pixel size, back-illuminated CMOS sensor at angles between 0° and 90°. Analytes (polystyrene beads, microorganisms, and cells) were placed and imaged directly onto the sensor. The spatial resolution of this imaging system is limited by the pixel size (∼1.4 µm) over the whole area of the sensor (3.6×2.73 mm). We demonstrated two imaging modalities: (i) shadow imaging for estimation of 3D object dimensions (on polystyrene beads and microorganisms) when the illumination angle is between 0° and 85°, and (ii) dark-field imaging, at >85° illumination angles. In dark-field mode, a 3-4 times drop in background intensity and contrast reversal similar to traditional dark-field imaging was observed, due to larger reflection intensities at those angles. With this modality, we were able to detect and analyze morphological features of bacteria and single-celled algae clusters.

The emerging evidence indicates that single nucleotide polymorphisms (SNPs) of the TNF, IL10, TP53, and CD14 genes may determine individual susceptibility to gastric cancer. We aimed to investigate the associations for polymorphisms of the TNF, IL10, TP53, and CD14 genes in a population of Kazakhs, to identify potential risk or protective associations of the SNPs with gastric cancer. A case group of 143 patients hospitalized for gastric cancer was enrolled. Controls were 355 volunteers with no history of any cancer and frequency matched with cases by age. Differences in proportions for categorical variables and the assessment of genotypic frequencies conforming to the Hardy–Weinberg equilibrium law were evaluated by the chi-square test. Associations between genetic polymorphisms and the risk of gastric cancer were estimated by regression analysis. For genetic analysis, three genetic models (additive, dominant, and recessive) were used. Four significant associations were found. The SNPs rs1042522 of TP53 and rs1800896 of IL10 were risk factors for gastric cancer by the additive model. Two polymorphisms of IL10 were protective of gastric cancer, namely, rs1800872 by additive model and rs1800871 by recessive model. No significant associations were observed between TNF and CD14 polymorphisms and gastric cancer. The polymorphisms TP53 rs1042522 and IL10 rs1800896 are associated with gastric cancer risk, while the polymorphisms IL10 rs1800872 and rs1800871 are protective of gastric cancer in the population of Kazakhs.

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