Browse Articles

Comprehensive analysis of bacteriocins produced by clinical enterococcal isolates and their antibacterial activity against Enterococci including VRE

Examining the multidimensional impact on soft drink packaging preferences through the unified model of aesthetics

Genomic epidemiology and phenotypic characterization of Staphylococcus aureus isolated from atopic dermatitis patients in South China

Non-intrusive load monitoring based on time-enhanced multidimensional feature visualization

99mTc-labeled benzenesulfonamide derivative-entrapped gold citrate nanoparticles as an auspicious tumour targeting

Abstract Sulfonamide derivatives are a significant class of medicinal compounds. Gold nanoparticles (AuNPs) offer precise cancer treatment through targeted delivery, boasting high drug-loading capacity and low toxicity. This study aimed to develop and evaluate 99mTc-labeled benzenesulfonamide derivative-entrapped gold citrate nanoparticles as a tumor-targeting agent. A novel benzenesulfonamide derivative bearing a pyridine moiety was synthesized. Compound 3 (4-((3-cyano-4-(2,4-dichlorophenyl)-6-phenylpyridin-2-yl)amino)-N-(diaminomethylene)benzenesulfonamide) exhibited remarkable anti-cancer activity against MCF-7 cells. The chemical reduction method was employed to create compound 3-citrate-AuNPs. A comprehensive examination of the synthesized nano-platform was conducted, including zeta potential, size analysis, radiochemical yield, and in-vivo biodistribution in tumor-bearing mice. The nano-platform was successfully produced with good stability, optimal particle size (9 nm diameter for AuNPs), and high radiochemical purity for [99mTc]Tc-compound 3 (88.31 ± 2.14%). In-vivo investigations revealed that intravenously administered [99mTc]Tc-compound 3-citrate-AuNPs accumulated in tumors with a high target-to-non-target ratio. The findings validate the efficacy of the novel [99mTc]Tc-compound 3-citrate-AuNPs platform as a tumor-targeting agent.

Buckling suppression of a cantilever structure with a modified fuzzy sliding mode control

Triglyceride-glucose index is associated with microcirculatory resistance in patients with type 2 diabetes and STEMI undergoing primary PCI

Insulin resistance surrogates are associated with all-cause mortality and cardiovascular mortality in population with metabolic syndrome: a retrospective cohort study of NHANES

Identification of the optimal candidates to benefit from surgery and chemotherapy among elderly female breast cancer patients with bone metastases

Enhanced preoperative prediction of breast lesion pathology, prognostic biomarkers, and molecular subtypes using multiple models diffusion-weighted MR imaging

Diagnosis and clinical significance of prostate calcification using computed tomography

Genotype B of deformed wing virus and related recombinant viruses become dominant in European honey bee colonies

Abstract The Varroa destructor mite’s transmission of deformed wing virus (DWV) to honey bees is responsible for most winter mortalities of colonies worldwide. Four DWV genotypes (A, B, C and D) and numerous recombinants have been described. The most recent studies have reported the greater prevalence of DWV-B over DWV-A in several countries, including European ones, while C and D genotypes appear rare or extinct. However, no global evaluation of DWV-A and DWV-B distribution was available at the European level to date. In this study, we quantified both DWV genotypes by real-time PCR from pools or individual honey bees and from V. destructor mites sampled in 15 European countries between 2010 and 2017. These data and the sequencing of the viral RNA provide a first insight into DWV diversity, with a clear dominance of DWV-B and recombinants (A/B) in Europe. Chimeric sequencing reads were used to locate the recombinant junctions along the DWV genome. These were not randomly distributed, but mainly clustered in three genomic areas: the 5’UTR, leader peptide and helicase coding sequences. In our study, the DWV recombinant genomes shared at least the VP1-VP3 coding sequences with the DWV-B. Further studies are needed to explore the apicultural context explaining these differences in DWV genotype dominance.

Experimental study on mechanical properties of S-RM with different rock block proportions in fault zone

Impact analysis of tunnel crossing pile foundation at different angles

Development of highly bioactive long-acting recombinant porcine FSH for batch production management of sows

Catalytic epoxidation of unsaturated fatty acids in palm stearin via in situ peracetic acids mechanism

Copper strontium phosphate glasses with high antimicrobial efficacy

The mediating role of social support in self-management and quality of life in patients with liver cirrhosis

The interaction mechanism of dolomite mineral and phosphoric acid and its impact on the Ti alloy corrosion

Modeling and estimation of physiochemical properties of cancer drugs using entropy measures

Abstract Hyaluronic acid-paclitaxel conjugate is a nanoparticle-based drug delivery system that combines hyaluronic acid with paclitaxel, enhancing its solubility, stability, and targeting specificity. This conjugate shows promise in treating breast, lung, and ovarian cancers with reduced side effects. Entropy measures are used to predict physical and chemical properties of drugs. In this paper, we compute entropy measures for the hyaluronic acid-paclitaxel conjugate using the edge/connectivity partition approach. We establish a quantitative structure-property relationship using reverse entropy measures to predict physical properties of cancer drugs. Multiple linear, Ridge, Lasso, ElasticNet, and Support Vector regression models are employed using Python software. Our results show that reverse entropy measures exhibit high predictive capability for physical properties, based on the highest coefficient of determination and lowest mean squared error. We conclude that physical properties, including boiling point, enthalpy of vaporization, flash point, molar refractivity, molar volume, polarization, molecular weight, monoisotopic mass, topological polar surface area, and complexity, can be predicted using reverse entropy measures. We propose models for each relationship, including only the most significant models for estimating uncalculated physical properties.