Diatomite-Based, Flexible SERS Immunosensor Platform for Rapid, Specific, and Sensitive Detection of Circulating Cancer-Specific Protein Biomarkers in Serum Using Raman Probes.

Cancer is one of the most actively researched diseases having a high mortality rate when not detected at an early stage. Thus, rapid, simultaneous, and sensitive quantification of cancer biomarkers plays an important role in early diagnosis, with patient impact to disability adjusted life years. Herein, a diatomite-based SERS flexible platform for the rapid and sensitive detection of circulating cancer-specific protein biomarkers in serum is presented.

Exposure to phagolysosomal simulated fluid altered the cytotoxicity of PET micro(nano)plastics to human lung epithelial cells.

The occurrence of micro(nano)plastics into various environmental and biological settings influences their physicochemical and toxic behavior. Simulated body fluids are appropriate media for understanding the degradation, stability, and interaction with other substances of any material in the human body. When the particles enter the human body via inhalation, which is one of the avenues for micro(nano)plastics, they first come into contact with the lung lining fluid under neutral conditions and then are phagocytosed under acidic conditions to be removed.

Layer-by-layer coating of natural diatomite with silver nanoparticles for identification of circulating cancer protein biomarkers using SERS.

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 used in SERS experiments to enhance the Raman intensity. The nanostructure used in SERS is one of the main influencing parameters and a growing research area.

Characterizing the interaction between micro(nano)plastics and simulated body fluids and their impact on human lung epithelial cells.

Micro(nano)plastics are considered an emerging threat to human health because they can interact with biological systems. In fact, these materials have already been found in the human body, such as in the lungs. However, limited data are available on the behavior of these materials under biological conditions and their impact on human cells, specifically on alveolar epithelial cells.

Drug-resistant Staphylococcus aureus bacteria detection by combining surface-enhanced Raman spectroscopy (SERS) and deep learning techniques.

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).

Identification of methicillin-resistant bacteria using surface-enhanced Raman spectroscopy and machine learning techniques.

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 which are not suitable for rapid diagnosis. To address this problem, we used surface-enhanced Raman spectroscopy combined with machine learning techniques for rapid identification of methicillin-resistant and methicillin-sensitive Gram-positive Staphylococcus aureus strains and Gram-negative Legionella pneumophila (control group).

Liquid Chromatographic-Chemometric Techniques for the Simultaneous HPLC Determination of Lansoprazole, Amoxicillin and Clarithromycin in Commercial Preparation.

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.