In Vitro Evaluation of the Antifungal Effect of AgNPs on f. sp. .

The application of nanomaterials in the agri-food industry can lead us to the formulation of new sustainable and effective pesticides for the control of fungi such as f. sp. (Fol).

Qualitative evaluation of ferritin in serum samples by Raman spectroscopy and principal component analysis.

Iron molecule is of great importance in the synthesis of hemoglobin which is essential for oxygen transport. Iron levels are quantified by accurately high sensitivity tests, such as serum ferritin (SF). However, common studies to quantify SF are long and strenuous (~ 5 h), for example enzyme-linked immunosorbent assay (ELISA).

Breast cancer detection based on serum sample surface enhanced Raman spectroscopy.

Raman spectroscopy is a vibrational technique which provides information about the chemical structure. Nevertheless, since many chemicals are present in a sample at very low concentration, the Raman signal observed is extremely weak. In surface enhanced Raman scattering (SERS), Raman signals can be enhanced by many orders of magnitude when nanoparticles are used.

Toxicology of antimicrobial nanoparticles for prosthetic devices.

Advances in nanotechnology are producing an accelerated proliferation of new nanomaterial composites that are likely to become an important source of engineered health-related products. Nanoparticles with antifungal effects are of great interest in the formulation of microbicidal materials. Fungi are found as innocuous commensals and colonize various habitats in and on humans, especially the skin and mucosa.

Monitoring of chemotherapy leukemia treatment using Raman spectroscopy and principal component analysis.

In this research, we used the Raman spectroscopy to distinguish between normal and leukemia blood serum and identify the different types of leukemia based on serum biochemistry. In addition, monitoring of patients under chemotherapy leukemia treatment (CHLT) was studied. Blood samples were obtained from seven patients who were clinically diagnosed with three leukemia types and 21 healthy volunteers.

Cervical cancer detection based on serum sample Raman spectroscopy.

The use of Raman spectroscopy to analyze the biochemical composition of serum samples and hence distinguish between normal and cervical cancer serum samples was investigated. The serum samples were obtained from 19 patients who were clinically diagnosed with cervical cancer, 3 precancer, and 20 healthy volunteer controls. The imprint was put under an Olympus microscope, and around points were chosen for Raman measurement.

Knowledge discovery in spectral data by means of complex networks.

In the last decade, complex networks have widely been applied to the study of many natural and man-made systems, and to the extraction of meaningful information from the interaction structures created by genes and proteins. Nevertheless, less attention has been devoted to metabonomics, due to the lack of a natural network representation of spectral data. Here we define a technique for reconstructing networks from spectral data sets, where nodes represent spectral bins, and pairs of them are connected when their intensities follow a pattern associated with a disease.