Protective effect and pharmacokinetics of dihydromyricetin nanoparticles on oxidative damage of myocardium.

PURPOSE: This study aims to investigate the protective mechanism of dihydromyricetin PLGA nanoparticles (DMY-PLGA NPs) against myocardial ischemia-reperfusion injury (MIRI) in vitro and the improvement of oral bioavailability in vivo. METHODS: DMY-PLGA NPs was prepared and characterized by emulsifying solvent volatilization, and the oxidative stress model of rat H9c2 cardiomyocyte induced by H2O2 was established. After administration, cell survival rate, lactate dehydrogenase (LDH), malondialdehyde (MDA) and superoxide dismutase (SOD) were detected, and the expressions of PGC1α and PPARα were detected by western blot (WB). At the same time, the pharmacokinetics in rats were studied to explore the improvement of bioavailability. RESULTS: DMY-PLGA NPs can significantly increase cell survival rate, decrease LDH and MDA content, increase SOD content and PGC1α、PPARα protein expression. Compared with DMY, the peak time of DMY-PLGA NPs was extended (P<0.1), and the bioavailability was increased by 2.04 times. CONCLUSION: DMY-PLGA NPs has a significant protective effect on H9c2 cardiomyocytes, which promotes the absorption of DMY and effectively improves bioavailability.

Whole-genome resequencing of Chinese indigenous sheep provides insight into the genetic basis underlying climate adaptation.

BACKGROUND: Chinese indigenous sheep are valuable resources with unique features and characteristics. They are distributed across regions with different climates in mainland China; however, few reports have analyzed the environmental adaptability of sheep based on their genome. We examined the variants and signatures of selection involved in adaptation to extreme humidity, altitude, and temperature conditions in 173 sheep genomes from 41 phenotypically and geographically representative Chinese indigenous sheep breeds to characterize the genetic basis underlying environmental adaptation in these populations. RESULTS: Based on the analysis of population structure, we inferred that Chinese indigenous sheep are divided into four groups: Kazakh (KAZ), Mongolian (MON), Tibetan (TIB), and Yunnan (YUN). We also detected a set of candidate genes that are relevant to adaptation to extreme environmental conditions, such as drought-prone regions (TBXT, TG, and HOXA1), high-altitude regions (DYSF, EPAS1, JAZF1, PDGFD, and NF1) and warm-temperature regions (TSHR, ABCD4, and TEX11). Among all these candidate genes, eight ABCD4, CNTN4, DOCK10, LOC105608545, LOC121816479, SEM3A, SVIL, and TSHR overlap between extreme environmental conditions. The TSHR gene shows a strong signature for positive selection in the warm-temperature group and harbors a single nucleotide polymorphism (SNP) missense mutation located between positions 90,600,001 and 90,650,001 on chromosome 7, which leads to a change in the protein structure of TSHR and influences its stability. CONCLUSIONS: Analysis of the signatures of selection uncovered genes that are likely related to environmental adaptation and a SNP missense mutation in the TSHR gene that affects the protein structure and stability. It also provides information on the evolution of the phylogeographic structure of Chinese indigenous sheep populations. These results provide important genetic resources for future breeding studies and new perspectives on how animals can adapt to climate change.

Comparative review of muscle fiber characteristics between porcine skeletal muscles.

Meat derived from skeletal muscles of animals is a highly nutritious type of food, and different meat types differ in nutritional, sensory, and quality properties. This study was conducted to compare the results of previous studies on the muscle fiber characteristics of major porcine skeletal muscles to the end of providing basic data for understanding differences in physicochemical and nutritional properties between different porcine muscle types (or meat cuts). Specifically, the muscle fiber characteristics between 19 major porcine skeletal muscles were compared. The muscle fibers that constitute porcine skeletal muscle can be classified into several types based on their contractile and metabolic characteristics. In addition, the muscle fiber characteristics, including size, composition, and density, of each muscle type were investigated and a technology based on these muscle fiber characteristics for improving meat quality or preventing quality deterioration was briefly discussed. This comparative review revealed that differences in muscle fiber characteristics are primarily responsible for the differences in quality between pork cuts (muscle types) and also suggested that data on muscle fiber characteristics can be used to develop optimal meat storage and packaging technologies for each meat cut (or muscle type).

Unveiling the future: Advancements in MRI imaging for neurodegenerative disorders.

Neurodegenerative disorders represent a significant and growing global health challenge, necessitating continuous advancements in diagnostic tools for accurate and early detection. This work explores the recent progress in Magnetic Resonance Imaging (MRI) techniques and their application in the realm of neurodegenerative disorders. The introductory section provides a comprehensive overview of the study's background, significance, and objectives. Recognizing the current challenges associated with conventional MRI, the manuscript delves into advanced imaging techniques such as high-resolution structural imaging (HR-MRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and positron emission tomography-MRI (PET-MRI) fusion. Each technique is critically examined regarding its potential to address theranostic limitations and contribute to a more nuanced understanding of the underlying pathology. A substantial portion of the work is dedicated to exploring the applications of advanced MRI in specific neurodegenerative disorders, including Parkinson's disease, Alzheimer's disease, Huntington's disease, and Amyotrophic Lateral Sclerosis (ALS). In addressing the future landscape, the manuscript examines technological advances, including the integration of machine learning and artificial intelligence in neuroimaging. The conclusion summarizes key findings, outlines implications for future research, and underscores the importance of these advancements in reshaping our understanding and approach to neurodegenerative disorders.

Prediction and analysis of quality markers for Chuantieling gel patches based on HPLC fingerprinting and network pharmacology.

The Chuantieling gel patch (CGP), a traditional Chinese medicine compound, is an external treatment for asthma. It has shown remarkable effectiveness in alleviating asthma-related airway hyperresponsiveness and inflammation. Nevertheless, there is currently no information available regarding the analysis of quality markers for CGP, and there is a need for further improvement in quality control research. In this study, we developed an HPLC fingerprinting method for CGP and conducted a comprehensive methodological investigation. We assessed the similarity among 10 batches of CGP, identified common peaks, and quantified the content of seven major quality markers. Furthermore, we built a network pharmacology-based 'active ingredients-targets-pathways-diseases' network to forecast the potential mechanisms of action for the primary active components in asthma treatment. Our findings demonstrated that the developed CGP fingerprinting and content determination methods were consistent and trustworthy. We verified the existence of 25 shared peaks and successfully identified 7 chromatographic peaks, including sinigrin thiocyanate, ephedrine hydrochloride, methyleugenol, imperatorin, cinnamaldehyde, emodin, and 6-gingerol, using reference standards. The network pharmacology analysis suggested that these seven active components may target proteins such as STAT3 (signal transducer and activator of transcription 3), MAPK3 (mitogen-activated protein kinase 3), and TP53 (tumor protein P53) and influence various diseases through pathways including cancer pathways, hepatitis B, and PI3K-Akt (phosphoinositide 3-kinase-protein kinase B) signaling. This study provides insight into the complex multicomponent composition of CGP, and the predictive analysis through network pharmacology sets the stage for uncovering the mechanisms responsible for the therapeutic effects of CGP.

Therapeutic Potential of Ginsenoside Rb1-PLGA Nanoparticles for Heart Failure Treatment via the ROS/PPARα/PGC1α Pathway.

(1) Background: Ginsenoside Rb1-PLGA nanoparticles (GRb1@PLGA@NPs) represent a novel nanotherapeutic system, yet their therapeutic efficacy and underlying mechanisms for treating heart failure (HF) remain unexplored. This study aims to investigate the potential mechanisms underlying the therapeutic effects of GRb1@PLGA@NPs in HF treatment; (2) Methods: The left anterior descending coronary artery ligation was employed to establish a HF model in Sprague-Dawley rats, along with an in vitro oxidative stress model using H9c2 myocardial cells. Following treatment with GRb1@PLGA@NPs, cardiac tissue pathological changes and cell proliferation were observed. Additionally, the serum levels of biomarkers such as NT-proBNP, TNF-α, and IL-1ß were measured, along with the expression of the ROS/PPARα/PGC1α pathway; (3) Results: GRb1@PLGA@NPs effectively ameliorated the pathological status of cardiac tissues in HF rats, mitigated oxidative stress-induced myocardial cell damage, elevated SOD and MMP levels, and reduced LDH, MDA, ROS, NT-proBNP, TNF-α, and IL-1ß levels. Furthermore, the expression of PPARα and PGC1α proteins was upregulated; (4) Conclusions: GRb1@PLGA@NPs may attenuate myocardial cell injury and treat HF through the ROS/PPARα/PGC1α pathway.

Preparation, characterization and in vivo pharmacokinetic study of ginsenoside Rb1-PLGA nanoparticles.

This study aimed to construct a Ginsenoside Rb1-PLGA nano drug delivery system, optimize its preparation process, characterize and evaluate the resulting Ginsenoside Rb1-PLGA Nanoparticles (GRb1@PLGA@NPs). GRb1@PLGA@NPs were prepared using the emulsion solvent evaporation method. The optimal preparation process was determined using Plackett-Burman design combined with Box-Behnken experiments. Physical characterization and in vitro release studies were conducted. LC-MS/MS technique was employed to investigate the pharmacokinetic characteristics of GRb1 and GRb1@PLGA@NPs in rat plasma. The optimal preparation process yielded GRb1@PLGA@NPs with a particle size of 120.63 nm, polydispersity index (PDI) of 0.172, zeta potential of - 22.67 mV, encapsulation efficiency of 75%, and drug loading of 11%. In vitro release demonstrated sustained drug release. Compared to GRb1, GRb1@PLGA@NPs exhibited a shortened time to peak concentration by approximately 0.72-fold. The area under the plasma concentration-time curve significantly increased to 4.58-fold of GRb1. GRb1@PLGA@NPs formulated using the optimal process exhibited uniform distribution and stable quality, its relative oral bioavailability was significantly improved compared to free GRb1.

MRI-based radiomics model for distinguishing Stage I endometrial carcinoma from endometrial polyp: a multicenter study.

BACKGROUND: Patients with early endometrial carcinoma (EC) have a good prognosis, but it is difficult to distinguish from endometrial polyps (EPs). PURPOSE: To develop and assess magnetic resonance imaging (MRI)-based radiomics models for discriminating Stage I EC from EP in a multicenter setting. MATERIAL AND METHODS: Patients with Stage I EC (n = 202) and EP (n = 99) who underwent preoperative MRI scans were collected in three centers (seven devices). The images from devices 1-3 were utilized for training and validation, and the images from devices 4-7 were utilized for testing, leading to three models. They were evaluated by the area under the receiver operating characteristic curve (AUC) and metrics including accuracy, sensitivity, and specificity. Two radiologists evaluated the endometrial lesions and compared them with the three models. RESULTS: The AUCs of device 1, 2_ada, device 1, 3_ada, and device 2, 3_ada for discriminating Stage I EC from EP were 0.951, 0.912, and 0.896 for the training set, 0.755, 0.928, and 1.000 for the validation set, and 0.883, 0.956, and 0.878 for the external validation set, respectively. The specificity of the three models was higher, but the accuracy and sensitivity were lower than those of radiologists. CONCLUSION: Our MRI-based models showed good potential in differentiating Stage I EC from EP and had been validated in multiple centers. Their specificity was higher than that of radiologists and may be used for computer-aided diagnosis in the future to assist clinical diagnosis.

Glutathione and Esterase Dual-Responsive Smart Nano-drug Delivery System Capable of Breaking the Redox Balance for Enhanced Tumor Therapy.

Conventional chemotherapy usually fails to achieve its intended effect because of the poor water solubility, poor tumor selectivity, and low tumor accumulation of chemotherapy drugs. The systemic toxicity of chemotherapy agents is also a problem that cannot be ignored. It is expected that smart nano-drug delivery systems that are able to respond to tumor microenvironments will provide better therapeutic outcomes with decreased side effects of chemotherapeutics. Nano-drug delivery systems capable of breaking the redox balance can also increase the sensitivity of tumor cells to chemotherapeutics. In this study, using polymer-containing disulfide bonds, ester bonds, and d-α-tocopherol polyethylene glycol succinate (TPGS), which can amplify reactive oxygen species (ROS) in tumor cells, we have successfully prepared a smart glutathione (GSH) and esterase dual-responsive nano-drug delivery system (DTX@PAMBE-SS-TPGS NPs) with the ability to deplete GSH as well as amplify ROS and effectively release an encapsulated chemotherapy drug (DTX) in tumor cells. The potential of DTX@PAMBE-SS-TPGS NPs for enhanced antitumor effects was thoroughly evaluated using in vitro as well as in vivo experiments. Our research offers a promising strategy for maximizing the efficacy of tumor therapy.

Dictionary learning LASSO for feature selection with application to hepatocellular carcinoma grading using contrast enhanced magnetic resonance imaging.

Introduction: The successful use of machine learning (ML) for medical diagnostic purposes has prompted myriad applications in cancer image analysis. Particularly for hepatocellular carcinoma (HCC) grading, there has been a surge of interest in ML-based selection of the discriminative features from high-dimensional magnetic resonance imaging (MRI) radiomics data. As one of the most commonly used ML-based selection methods, the least absolute shrinkage and selection operator (LASSO) has high discriminative power of the essential feature based on linear representation between input features and output labels. However, most LASSO methods directly explore the original training data rather than effectively exploiting the most informative features of radiomics data for HCC grading. To overcome this limitation, this study marks the first attempt to propose a feature selection method based on LASSO with dictionary learning, where a dictionary is learned from the training features, using the Fisher ratio to maximize the discriminative information in the feature. Methods: This study proposes a LASSO method with dictionary learning to ensure the accuracy and discrimination of feature selection. Specifically, based on the Fisher ratio score, each radiomic feature is classified into two groups: the high-information and the low-information group. Then, a dictionary is learned through an optimal mapping matrix to enhance the high-information part and suppress the low discriminative information for the task of HCC grading. Finally, we select the most discrimination features according to the LASSO coefficients based on the learned dictionary. Results and discussion: The experimental results based on two classifiers (KNN and SVM) showed that the proposed method yielded accuracy gains, compared favorably with another 5 state-of-the-practice feature selection methods.