To combat antibiotic resistance, it is extremely important to select the right antibiotic by performing rapid diagnosis of pathogens. Traditional techniques require complicated sample preparation and time-consuming processes that are...

Over the past year, the world's attention has focused on combating COVID-19 disease, but the other threat waiting at the door—antimicrobial resistance should not be forgotten. Although making the diagnosis rapidly and accurately is crucial in preventing antibiotic resistance development, bacterial identification techniques include some challenging processes. To address this challenge, we proposed a deep neural network (DNN) that can discriminate antibiotic-resistant bacteria using surface-enhanced Raman spectroscopy (SERS). Stacked autoencoder (SAE)-based DNN was used for the rapid identification of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive S. aureus (MSSA) bacteria using a label-free SERS technique. The performance of the DNN was compared with traditional classifiers. Since the SERS technique provides high signal-to-noise ratio (SNR) data, some subtle differences were found between MRSA and MSSA in relative band intensities. SAE-based DNN can learn features from raw data and classify them with an accuracy of 97.66%. Moreover, the model discriminates bacteria with an area under curve (AUC) of 0.99. Compared to traditional classifiers, SAE-based DNN was found superior in accuracy and AUC values. The obtained results are also supported by statistical analysis. These results demonstrate that deep learning has great potential to characterize and detect antibiotic-resistant bacteria by using SERS spectral data.

Two multivariate calibration-prediction techniques, principal component regression (PCR) and partial least-squares regression (PLSR) were applied to the chromatographic multicomponent analysis of the drug containing lansoprazole (LAN), clarithromycin (CLA) and amoxicillin (AMO). Optimum chromatographic separation of LAN, CLA and AMO with atorvastatin as the internal standard (IS) was obtained by using Xterra® RP18 column 5 μm 4.6 × 250 mm2, and 25 mM ammonium chloride buffer prepared ammonium chloride, acetonitrile and bidistilled water (45:45:10 v/v) as the mobile phase at flow rate 1.0 mL/min. The high pressure liquid chromatography data sets consisting of the ratios of analyte peak areas to the IS peak area were obtained by using diode array detector detection at five wavelengths (205, 210, 215, 220 and 225 nm). LC-chemometric calibration for LAN, CLA and AMO were separately constructed by using the relationship between the peak-area ratio and training sets for each analyte. A series of synthetic solutions containing different concentrations of LAN, CLA and AMO were used to check the prediction ability of the PCR and PLS. Both of the two-chemometric methods in this study can be satisfactorily used for the quantitative analysis and for dissolutions tests of multicomponent commercial drug.

Surface-enhanced Raman scattering (SERS) is an emerging spectroscopy technique for detecting and characterizing chemical or biological structures in the vicinity of plasmonic nanostructures. Colloidal, solid, and flexible nanostructures are widely...

Complementary metal-oxide-semiconductor (CMOS) imaging sensors provide the unique opportunity for combining lensless imaging with new modalities that enable sample handling and chemical characterization. In this study, we present a new CMOS-based sensing platform for trapping, imaging, and chemical characterization of samples via SERS (CMOS-TrICC). The SERS substrate is fabricated directly on a CMOS imaging sensor by depositing a thin metallic layer on top of the CMOS microlenses. SERS activity is based on square unit cell patterned, closely spaced, micrometer-sized microlenses on the surface of the imaging sensor. Morphological analysis of the surface revealed an intracavity depth of approximately 700 nm and height-dependent width ranging from a minimum of just a few nm between two lenses to a maximum of 1400 nm, with a flat valley exhibiting approximately 300 nm width at the bottom between four lenses. These morphological features concentrate electromagnetic fields into SERS hot spots and at the same time help trap nanometer-sized particles in the wells created by the microlenses. The strongest plasmonic effect is expected in the gaps between the microlenses. Simulations were used to map the distribution of the electromagnetic field enhancement on the SERS substrate surface and at a distance above it. The performance of the SERS substrate and its dependence on the silver layer thickness were examined using 4-aminotheophenol and rhodamine 6G with the experimental enhancement factor measured to be 5.0 × 10 4 . We demonstrated the use of this substrate for parallel trapping of 100 nm nanospheres and extracellular vesicles (EVs) in the gaps between the microlenses and SERS characterization of these particles in the hot spots. SERS intensities are 2 orders of magnitude higher in the nanogaps between the microlenses (intracavity area) than on top of the microlenses, and for polystyrene, they exhibited signature peaks centered at 1000 and 1600 cm −1 . SERS spectra of small EVs collected from intracavity areas where EVs were trapped show peaks known to arise from their main biochemical constituents, such as lipids, proteins, and nucleic acids. While the surface of the CMOS imaging sensor became SERS active by the addition of the metallic layer, the imaging capability is maintained and provides the opportunity for direct on-chip lensless imaging with spatial resolution limited by the pixel size, opening new directions for integrated (bio)sensing devices.