Nanotechnology improves the detection of bacteria: Recent advances and future perspectives.

Nanotechnology has advanced significantly, particularly in biomedicine, showing promise for nanomaterial applications. Bacterial infections pose persistent public health challenges due to the lack of rapid pathogen detection methods, resulting in antibiotic overuse and bacterial resistance, threatening the human microbiome. Nanotechnology offers a solution through nanoparticle-based materials facilitating early bacterial detection and combating resistance.

Nanoparticles-based therapeutics for the management of bacterial infections: A special emphasis on FDA approved products and clinical trials.

Microbial resistance has increased in recent decades as a result of the extensive and indiscriminate use of antibiotics. The World Health Organization listed antimicrobial resistance as one of ten major global public health threats in 2021. In particular, six major bacterial pathogens, including third-generation cephalosporin-resistant Escherichia coli, methicillin-resistant Staphylococcus aureus, carbapenem-resistant Acinetobacter baumannii, Klebsiella pneumoniae, Streptococcus pneumoniae, and Pseudomonas aeruginosa, were found to have the highest resistance-related death rates in 2019.

Cardiac failure detection in 30 minutes: new approach based on gold nanoparticles.

Cardiac failure occurs when heart is unable to pump sufficiently to maintain blood flow to meet the body's needs. The aim of this work is to detect highly expressed genes: follistatin-related protein 1 (FSTL1) in heart failure within 30 minutes, using gold nanoparticles. Gold nanoparticles were prepared by citrate reduction of HAuCl 3HO; probe sequence was designed based on the FSTL1 gene region.

Fundamentals of protein and cell interactions in biomaterials.

The extracellular matrix (ECM) is an active and complex microenvironment with outstanding biomechanical, biophysical, and biochemical characteristics, which can indirectly or directly controls cell adhesion, migration, proliferation, and differentiation, as well as partaking in regeneration and homeostasis of organs and tissues. The ECM has captivated a great deal of attention with the rapid progress of tissue engineering (TE) in the field of regenerative medicine (RM). Approaches to TE, RM and cancer therapy center on the necessity to deliver cell signals to direct cell proliferation and differentiation.

Role of protease and protease inhibitors in cancer pathogenesis and treatment.

Cancer is the second cause of death in 2015, and it has been estimated to surpass heart diseases as the leading cause of death in the next few years. Several mechanisms are involved in cancer pathogenesis. Studies have indicated that proteases are also implicated in tumor growth and progression which is highly dependent on nutrient and oxygen supply.

Synthesis and Characterization of Chrysin-loaded PCL-PEG-PCL nanoparticle and its effect on breast cancer cell line.

Background: Nano-therapy exhibit the potential of revolutionizing cancer therapy. This field introduces nanovectors/nanocarriers for anticancer drugs targeted delivery, and also finds application in imaging. Chrysin, a natural flavonoid, was recently studied as having important biological roles in chemical defenses, nitrogen fixation, anti-inflammatory, and anti-oxidant properties.

Accurate classification of brain gliomas by discriminate dictionary learning based on projective dictionary pair learning of proton magnetic resonance spectra.

Proton magnetic resonance spectroscopy is a powerful noninvasive technique that complements the structural images of cMRI, which aids biomedical and clinical researches, by identifying and visualizing the compositions of various metabolites within the tissues of interest. However, accurate classification of proton magnetic resonance spectroscopy is still a challenging issue in clinics due to low signal-to-noise ratio, overlapping peaks of metabolites, and the presence of background macromolecules. This paper evaluates the performance of a discriminate dictionary learning classifiers based on projective dictionary pair learning method for brain gliomas proton magnetic resonance spectroscopy spectra classification task, and the result were compared with the sub-dictionary learning methods.