Search for: All records

Liquid interfaces facilitate the organization of nanometer‐scale biomaterials with plasmonic properties suitable for molecular diagnostics. Using hierarchical assemblage of 2D hafnium disulfide nanoplatelets and zero‐dimensional spherical gold nanoparticles, the design of a multifunctional material is reported. When the target analyte is present, the nanocomposites’ self‐assembling pattern changes, altering their plasmonic response. Using monkeypox virus (MPXV) as an example, the findings reveal that adding genomic DNA to the nanocomposite surface increases the agglomeration between gold nanoparticles and decreases the π‐stacking distance between hafnium disulfide nanoplatelets. Further, this self‐assembled nanomaterial is found to have minimal cross‐reactivity toward other pathogens and a limit of detection of 7.6 pg µL⁻¹(i.e., 3.57 × 10⁴copies µL⁻¹) toward MPXV. Overall, this study helped to gain a better understanding of the genomic organization of MPXV to chemically design and develop targeted nucleotides. The study has been validated by UV–vis spectroscopy, X‐ray diffraction, scanning transmission electron microscopy, surface‐enhanced Raman microscopy and electromagnetic simulation studies. To the best knowledge, this is the first study in literature reporting selective molecular detection of MPXV within a few minutes and without the use of any high‐end instrumental techniques like polymerase chain reactions.

 

Stem cell‐based therapies carry significant promise for treating human diseases. However, clinical translation of stem cell transplants for effective treatment requires precise non‐destructive evaluation of the purity of stem cells with high sensitivity (<0.001% of the number of cells). Here, a novel methodology using hyperspectral imaging (HSI) combined with spectral angle mapping‐based machine learning analysis is reported to distinguish differentiating human adipose‐derived stem cells (hASCs) from control stem cells. The spectral signature of adipogenesis generated by the HSI method enables identifying differentiated cells at single‐cell resolution. The label‐free HSI method is compared with the standard techniques such as Oil Red O staining, fluorescence microscopy, and qPCR that are routinely used to evaluate adipogenic differentiation of hASCs. HSI is successfully used to assess the abundance of adipocytes derived from transplanted cells in a transgenic mice model. Further, Raman microscopy and multiphoton‐based metabolic imaging is performed to provide complementary information for the functional imaging of the hASCs. Finally, the HSI method is validated using matrix‐assisted laser desorption/ionization‐mass spectrometry imaging of the stem cells. The study presented here demonstrates that multimodal imaging methods enable label‐free identification of stem cell differentiation with high spatial and chemical resolution.

 

A biomarker is a physiological observable marker that acts as a stand-in and, in the best-case scenario, forecasts a clinically significant outcome. Diagnostic biomarkers are more convenient and cost-effective than directly measuring the ultimate clinical outcome. Cancer is among the most prominent global health problems and a major cause of morbidity and death globally. Therefore, cancer biomarker assays that are trustworthy, consistent, precise, and verified are desperately needed. Biomarker-based tumor detection holds a lot of promise for improving disease knowledge at the molecular scale and early detection and surveillance. In contrast to conventional approaches, surface plasmon resonance (SPR) allows for the quick and less invasive screening of a variety of circulating indicators, such as circulating tumor DNA (ctDNA), microRNA (miRNA), circulating tumor cells (CTCs), lipids, and proteins. With several advantages, the SPR technique is a particularly beneficial choice for the point-of-care identification of biomarkers. As a result, it enables the timely detection of tumor markers, which could be used to track cancer development and suppress the relapse of malignant tumors. This review emphasizes advancements in SPR biosensing technologies for cancer detection. 

Rotational dynamics at the molecular level could provide additional data regarding protein diffusion and cytoskeleton formation at the cellular level. Due to the isotropic emission pattern of fluorescence molecules, it is challenging to extract rotational information from them during imaging. Metal nanoparticles show a polarization-dependent response and could be used for sensing rotational motion. Nanoparticles as an orientation sensing probe offer bio-compatibility and robustness against photo-blinking and photo-bleaching compared to conventional fluorescent molecules. Previously, asymmetric geometrical structures such as nanorods have been used for orientational imaging. Here, we show orientational imaging of symmetric geometrical structures such as 100 nm isolated silver nanocubes by coupling a hyperspectral detector and a focused ion beam (FIB)-fabricated correlating substrate. More than 100 nanocubes are analyzed to confirm spectral shifts in the scattering spectra due to variations in the orientation of the nanocubes with respect to the incoming light. Results are further validated using finite-difference time-domain simulations. Our observations suggest a novel strategy for high-throughput orientation imaging of nanoparticles. 

Inorganic lead-halide perovskite, cesium lead bromide (CsPbBr3), shows outstanding optoelectronic properties. Both solution- and melt-based methods have been proposed for CsPbBr3 crystal growth. The solution-based growth was done at low-temperature, whereas the melt-based growth was done at high-temperature. However, the comparison of optical, physical, and defect states using these two different growth conditions has been scarcely studied. Here, we have compared the thermal and optical properties of solution-grown and melt-grown single crystals of CsPbBr3. Positron Annihilation Lifetime Spectroscopy (PALS) analysis showed that melt-grown crystal has a relatively smaller number of defects than the chemical synthesis method. In addition, crystals grown using the chemical method showed a higher fluorescence lifetime than melt-grown CsPbBr3. 

In this study, we explored machine learning approaches for predictive diagnosis using surface-enhanced Raman scattering (SERS), applied to the detection of COVID-19 infection in biological samples. To do this, we utilized SERS data collected from 20 patients at the University of Maryland Baltimore School of Medicine. As a preprocessing step, the positive-negative labels are obtained using Polymerase Chain Reaction (PCR) testing. First, we compared the performance of linear and nonlinear dimensionality techniques for projecting the high-dimensional Raman spectra to a low-dimensional space where a smaller number of variables defines each sample. The appropriate number of reduced features used was obtained by comparing the mean accuracy from a 10-fold cross-validation. Finally, we employed Gaussian process (GP) classification, a probabilistic machine learning approach, to correctly predict the occurrence of a negative or positive sample as a function of the low-dimensional space variables. As opposed to providing rigid class labels, the GP classifier provides a probability (ranging from zero to one) that a given sample is positive or negative. In practice, the proposed framework can be used to provide high-throughput rapid testing, and a follow-up PCR can be used for confirmation in cases where the model’s uncertainty is unacceptably high. 

Abstract Approximately 7% of pregnant women in the United States use electronic-cigarette (e-cig) devices during pregnancy. There is, however, no scientific evidence to support e-cig use as being ‘safe’ during pregnancy. Little is known about the effects of fetal exposures to e-cig aerosols on lung alveologenesis. In the present study, we tested the hypothesis that in utero exposure to e-cig aerosol impairs lung alveologenesis and pulmonary function in neonates. Pregnant BALB/c mice were exposed 2 h a day for 20 consecutive days during gestation to either filtered air or cinnamon-flavored e-cig aerosol (36 mg/mL of nicotine). Lung tissue was collected in offspring during lung alveologenesis on postnatal day (PND) 5 and PND11. Lung function was measured at PND11. Exposure to e-cig aerosol in utero led to a significant decrease in body weights at birth which was sustained through PND5. At PND5, in utero e-cig exposures dysregulated genes related to Wnt signaling and epigenetic modifications in both females (~ 120 genes) and males (40 genes). These alterations were accompanied by reduced lung fibrillar collagen content at PND5—a time point when collagen content is close to its peak to support alveoli formation. In utero exposure to e-cig aerosol also increased the Newtonian resistance of offspring at PND11, suggesting a narrowing of the conducting airways. At PND11, in females, transcriptomic dysregulation associated with epigenetic alterations was sustained (17 genes), while WNT signaling dysregulation was largely resolved (10 genes). In males, at PND11, the expression of only 4 genes associated with epigenetics was dysregulated, while 16 Wnt related-genes were altered. These data demonstrate that in utero exposures to cinnamon-flavored e-cig aerosols alter lung structure and function and induce sex-specific molecular signatures during lung alveologenesis in neonatal mice. This may reflect epigenetic programming affecting lung disease development later in life.