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MF59-based lipid nanocarriers for paclitaxel delivery: optimization and anticancer evaluation
Abstract Breast cancer is the most common invasive cancer in women worldwide, necessitating innovative therapeutic strategies to enhance treatment efficacy and safety. This study focuses on the development and optimization of novel paclitaxel (PTX)-loaded nanostructured lipid carriers (NLCs) that incorporate components of MF59, an oil-in-water emulsion adjuvant approved for use in influenza vaccines and known for its safety in humans. The formulation of these NLCs is designed to overcome significant challenges in PTX delivery, particularly its poor solubility and the side effects associated with traditional formulations containing Cremophor EL. We prepared two sets of NLC formulations using different liquid-to-solid lipid ratios through hot melt ultrasonication. Characterization of the selected formulations, NLCPre and NLCLec, was conducted using dynamic light scattering (DLS), scanning electron microscopy (SEM), Fourier-transform infrared (FT-IR) spectroscopy, and ultraviolet-visible (UV-Vis) spectroscopy. The mean diameters were 120.6 ± 36.4 nm and 112 ± 41.7 nm, with encapsulation efficiencies (EE) of 85% and 82%, and drug loading (DL) of 4.25% and 4.1%, respectively for NLCPre and NLCLec. In vitro cytotoxicity assays demonstrated that these MF59-based NLCs effectively target MCF-7 (Michigan Cancer Foundation) breast cancer cells while minimizing toxicity to normal HDF (human dermal fibroblasts) cells, thus enhancing the therapeutic index of PTX and offering promising clinical implications for breast cancer treatment.
Endosomal trafficking participates in lipid droplet catabolism to maintain lipid homeostasis
Deep structured learning with vision intelligence for oral carcinoma lesion segmentation and classification using medical imaging
Abstract Oral carcinoma (OC) is a toxic illness among the most general malignant cancers globally, and it has developed a gradually significant public health concern in emerging and low-to-middle-income states. Late diagnosis, high incidence, and inadequate treatment strategies remain substantial challenges. Analysis at an initial phase is significant for good treatment, prediction, and existence. Despite the current growth in the perception of molecular devices, late analysis and methods near precision medicine for OC patients remain a challenge. A machine learning (ML) model was employed to improve early detection in medicine, aiming to reduce cancer-specific mortality and disease progression. Recent advancements in this approach have significantly enhanced the extraction and diagnosis of critical information from medical images. This paper presents a Deep Structured Learning with Vision Intelligence for Oral Carcinoma Lesion Segmentation and Classification (DSLVI-OCLSC) model for medical imaging. Using medical imaging, the DSLVI-OCLSC model aims to enhance OC’s classification and recognition outcomes. To accomplish this, the DSLVI-OCLSC model utilizes wiener filtering (WF) as a pre-processing technique to eliminate the noise. In addition, the ShuffleNetV2 method is used for the group of higher-level deep features from an input image. The convolutional bidirectional long short-term memory network with a multi-head attention mechanism (MA-CNN‐BiLSTM) approach is utilized for oral carcinoma recognition and identification. Moreover, the Unet3 + is employed to segment abnormal regions from the classified images. Finally, the sine cosine algorithm (SCA) approach is utilized to hyperparameter-tune the DL model. A wide range of simulations is implemented to ensure the enhanced performance of the DSLVI-OCLSC method under the OC images dataset. The experimental analysis of the DSLVI-OCLSC method portrayed a superior accuracy value of 98.47% over recent approaches.
Structural basis of siderophore export and drug efflux by Mycobacterium tuberculosis
Machine learning selection of basement membrane-associated genes and development of a predictive model for kidney fibrosis
Structural insights into transmembrane helix S0 facilitated RyR1 channel gating by Ca2+/ATP
Sex differences in the relationship between short sleep duration and obesity among koreans
RNF167 mediates atypical ubiquitylation and degradation of RLRs via two distinct proteolytic pathways
Construction organoid model of ovarian endometriosis and the function of estrogen and progesterone in the model
Abstract Endometriosis is a refractory estrogen-dependent gynecological disease in which ovarian endometriosis(OE) is the most common, and the main cell components are endometrial epithelial cells and stromal cells. However, constructing ectopic endometrial epithelial cell models in basic studies is still challenging. In this study, we explored the feasibility and influencing factors of constructing and validating eutopic and ectopic endometrial organoid models of OE as in-vitro models. Eutopic and ectopic endometrial tissues of OE patients were selected to establish organoids. Morphologically, the organoids showed a three-dimensional glandular structure with vacuoles or cystic irregularities, and the histological features of the epithelial organoids in endometriosis were well preserved. Immunofluorescence showed positive expression of epithelial markers and estrogen/progesterone receptors. Genetic identification revealed a 100% match between endometriosis epithelial organoids and endometrial tissue, indicating a common origin. The effects of estrogen and progesterone on the proliferation and secretion of organoids differed with the change in concentration. The successful construction of ectopic endometrial organoids provides a new in vitro model for drug intervention and mechanism study of ovarian endometriosis.