Hydrogels Based Drug Delivery Synthesis, Characterization and Administration

Hydrogels represent 3D polymeric networks specially designed for various medical applications. Due to their porous structure, they are able to swollen and to entrap large amounts of therapeutic agents and other molecules. In addition, their biocompatibility and biodegradability properties, together with a controlled release profile, make hydrogels a potential drug delivery system. In vivo studies have demonstrated their effectiveness as curing platforms for various diseases and affections. In addition, the results of the clinical trials are very encouraging and promising for the use of hydrogels as future target therapy strategies

New Insights into the Multivariate Analysis of SER Spectra Collected on Blood Samples for Prostate Cancer Detection: Towards a Better Understanding of the Role Played by Different Biomolecules on Cancer Screening: A Preliminary Study

It is possible to obtain diagnostically relevant data on the changes in biochemical elements brought on by cancer via the use of multivariate analysis of vibrational spectra recorded on biological fluids. Prostate cancer and control groups included in this research generated almost similar SERS spectra, which means that the values of peak intensities present in SERS spectra can only give unspecific and limited information for distinguishing between the two groups. Our diagnostic algorithm for prostate cancer (PCa) differentiation was built using principal component analysis and linear discriminant analysis (PCA-LDA) analysis of spectral data, which has been widely used in spectral data management in many studies and has shown promising results so far. In order to fully utilize the entire SERS spectrum and automatically determine the most meaningful spectral features that can be used to differentiate PCa from healthy patients, we perform a multivariate analysis on both the entire and specific spectral intervals. Using the PCA-LDA model, the prostate cancer and control groups are clearly distinguished in our investigation. The separability of the following two data sets is also evaluated using two alternative discrimination techniques: principal least squares discriminant analysis (PLS-DA) and principal component analysis—support vector machine (PCA-SVM)

A Prospective Study on the Progression, Recurrence, and Regression of Cervical Lesions: Assessing Various Screening Approaches

(1) Background: The prediction of cervical lesion evolution is a challenge for clinicians. This prospective study aimed to determine and compare the predictive accuracy of cytology, HPV genotyping, and p16/Ki67 dual staining alone or in combination with personal risk factors in the prediction of progression, regression, or persistence of cervical lesions in human papillomavirus (HPV)-infected patients; (2) Methods: This prospective study included HPV-positive patients with or without cervical lesions who underwent follow-up in a private clinic. We calculated the predictive performance of individual tests (cervical cytology, HPV genotyping, CINtecPlus results, and clinical risk factors) or their combination in the prediction of cervical lesion progression, regression, and persistence; (3) Results: The highest predictive performance for the progression of cervical lesions was achieved by a model comprising a Pap smear suggestive of high-grade squamous intraepithelial lesion (HSIL), the presence of 16/18 HPV strains, a positive p16/Ki67 dual staining result along with the presence of at least three clinical risk factors, which had a sensitivity (Se) of 74.42%, a specificity of 97.92%, an area under the receiver operating curve (AUC) of 0.961, and an accuracy of 90.65%. The prediction of cervical lesion regression or persistence was modest when using individual or combined tests; (4) Conclusions: Multiple testing or new biomarkers should be used to improve HPV-positive patient surveillance, especially for cervical lesion regression or persistence prediction

Minimal Change Disease: Pathogenetic Insights from Glomerular Proteomics

The mechanism underlying podocyte dysfunction in minimal change disease (MCD) remains unknown. This study aimed to shed light on the potential pathophysiology of MCD using glomerular proteomic analysis. Shotgun proteomics using label-free quantitative mass spectrometry was performed on formalin-fixed, paraffin-embedded (FFPE) renal biopsies from two groups of samples: control (CTR) and MCD. Glomeruli were excised from FFPE renal biopsies using laser capture microdissection (LCM), and a single-pot solid-phase-enhanced sample preparation (SP3) digestion method was used to improve yield and protein identifications. Principal component analysis (PCA) revealed a distinct separation between the CTR and MCD groups. Forty-eight proteins with different abundance between the two groups (p-value ≤ 0.05 and |FC| ≥ 1.5) were identified. These may represent differences in podocyte structure, as well as changes in endothelial or mesangial cells and extracellular matrix, and some were indeed found in several of these structures. However, most differentially expressed proteins were linked to the podocyte cytoskeleton and its dynamics. Some of these proteins are known to be involved in focal adhesion (NID1 and ITGA3) or slit diaphragm signaling (ANXA2, TJP1 and MYO1C), while others are structural components of the actin and microtubule cytoskeleton of podocytes (ACTR3 and NES). This study suggests the potential of mass spectrometry-based shotgun proteomic analysis with LCM glomeruli to yield valuable insights into the pathogenesis of podocytopathies like MCD. The most significantly dysregulated proteins in MCD could be attributable to cytoskeleton dysfunction or may be a compensatory response to cytoskeleton malfunction caused by various triggers