Pachymic acid alleviates metabolic dysfunction-associated steatotic liver disease by inhibiting ferroptosis through PPARα

IntroductionMetabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by excessive lipid deposition in hepatocytes without a history of significant alcohol consumption. Pachymic acid (Pac), a bioactive triterpenoid from Poria cocos, has shown promise in treating MASLD due to its antioxidant and anti-inflammatory capabilities. This study aimed to elucidate the molecular impact and mechanisms of Pac in MASLD.MethodsMale C57BL/6J mice were subjected to a high-fat diet (HFD) for 8 weeks, followed by a 4 weeks treatment with the Pac. Comprehensive assessments including physiological, biochemical, and histomorphological evaluations were performed post-treatment. Transcriptomic analysis of liver samples from normal control (NC), HFD, and HFD + high-dose Pac (Pac-H) groups was conducted, with validation through Western blot, and immunofluorescence.ResultsHFD induced biochemical abnormalities and liver injury, which were significantly reversed by Pac, as evidenced by decreased plasma levels of AST (Aspartate aminotransferase), ALT (Alanine aminotransferase), TG (Triglyceride), and TC (Total cholesterol), and reduced hepatic TG and TC levels. Pac also mitigated lipid accumulation, peroxidation, and ferroptosis. Pac modulated the expression of PPARα (Peroxisome proliferator-activated receptor α), MAPKs (Mitogen-activated protein kinases), and ferroptosis pathways, thereby ameliorating MASLD.DiscussionThe study demonstrated that Pac inhibited the MAPKs signaling pathway and reduced hepatic ferroptosis, alleviated steatosis through PPARα regulation, offering a potential therapeutic strategy for MASLD

CrowdBC: A blockchain-based decentralized framework for crowdsourcing

Crowdsourcing systems which utilize the human intelligence to solve complex tasks have gained considerable interest and adoption in recent years. However, the majority of existing crowdsourcing systems rely on central servers, which are subject to the weaknesses of traditional trust-based model, such as single point of failure. They are also vulnerable to distributed denial of service (DDoS) and Sybil attacks due to malicious users involvement. In addition, high service fees from the crowdsourcing platform may hinder the development of crowdsourcing. How to address these potential issues has both research and substantial value. In this paper, we conceptualize a blockchain-based decentralized framework for crowdsourcing named CrowdBC, in which a requester’s task can be solved by a crowd of workers without relying on any third trusted institution, users’ privacy can be guaranteed and only low transaction fees are required. In particular, we introduce the architecture of our proposed framework, based on which we give a concrete scheme. We further implement a software prototype on Ethereum public test network with real-world dataset. Experiment results show the feasibility, usability and scalability of our proposed crowdsourcing system

Study on the degradation of T-2 toxin in beer by glow discharge plasma

Objective: Exploring the optimal process for the degradation of T-2 toxin in beer by glow discharge plasma（GDP） and its impact on the physicochemical indicators of beer. Methods: Based on single-factor experiments, a response surface optimization experiment with four factors and three levels was conducted using the Box Behnken method to determine the optimal degradation conditions for T-2 toxin in beer. Results: When the discharge voltage was 570 V, the action time was 18 minutes, the discharge current was 99 mA, and initial concentration of T-2 toxin was 8.5 μg/mL. Under the control of these conditions, the degradation efficiency of the T-2 toxin was the highest （89.21%）. After GDP treatment, the physical and chemical indicators of beer were tested, and the results showed a significant decrease in beer foam retention （P<0.05）, while other indicators remained unchanged. Conclusion: The optimal degradation conditions of GDP obtained by the response surface optimization model are accurate and reliable, which can be used for the degradation of T-2 toxin in beer. GDP can affect the brewing ability of beer, but it will not have a significant impact on other indicators

Load frequency optimal control of the hydropower-photovoltaic hybrid microgrid system based on the off-policy integral reinforcement learning algorithm

With the promotion and development of clean energy, it is challenging to ensure the optimization of control performance in frequency control of the hydropower-photovoltaic hybrid microgrid system caused by the output power fluctuation of photovoltaic power generation. In this study, an optimal load frequency controller (LFC) for a hydropower-photovoltaic hybrid microgrid system was designed to improve the dynamic response when the load and photovoltaic output power are perturbed based on the off-policy integral reinforcement learning algorithm. First, a mechanism model of the hydropower-photovoltaic hybrid microgrid system was established. Next, the LFC problem was transformed into a zero-sum game control problem based on the characteristics of the power system. Subsequently, three neural networks were employed to approximate the Nash equilibrium solution of the zero-sum game with historical input and output data when the system dynamics are completely unknown. Finally, simulation experiments were conducted to verify the effectiveness and optimality of the proposed method. The introduction of this method provides a new perspective for frequency control for the hydropower-photovoltaic hybrid microgrid system

Plasma Mutagenesis of Haematococcus lacustris and Optimization of Culture Conditions for High-yield Astaxanthin Algae Strains

To further enhance the industrial utilization value of Haematococcus lacustris, the plasma mutagenesis of Haematococcus lacustris was carried out by an atmospheric and room temperature plasma (ARTP) mutagenesis equipment. The optimum input power and mutagenesis time for plasma mutagenesis were determined with lethal rate of algal cells as the index. After mutagenesis, high-yield astaxanthin mutant algae strains were obtained through primary screening of solid plate culture and secondary screening of liquid culture. Then, the culture conditions of high yield algal plants at vegetative growth stage were optimized by single-factor and orthogonal experiment with algae cell density as the index, and the suitable high light conditions for astaxanthin accumulation during astaxanthin induction stage were selected. The genetic stability of the high yielding mutant algae strains was observed after multiple subcultures under the optimized culture conditions. The results showed that the optimum conditions for plasma mutagenesis of Haematococcus lacustris were 240 W for 150 s or 400 W for 120 s. 11 Mutant alga strains with fast growth and high astaxanthin yield were obtained through primary screening and rescreening, wherein the strain HP3 grew fastest and had the highest astaxanthin yield. After culture, its cell density and astaxanthin yield were increased by 25.5% and 61.6% respectively compared with the original strain. After two-stage optimization, the cell density and astaxanthin yield of HP3 increased by 14.3% and 19.3% respectively, reaching 7.2×105 cell/mL and 31.264 mg/L. HP3 showed good growth and stable heredity. Its cell density and astaxanthin yield were similar to those of primary culture. The results have practical significance for the breeding of industrial algal strains producing astaxanthin from Haematococcus lacustris

Structure-based virtual screening and characterization of a novel IL-6 antagonistic compound from synthetic compound database

According to the three-dimensional (3D) complex structure of (hIL-6⋅hIL-6R⋅gp 130)(2) and the binding orientation of hIL-6, three compounds with high affinity to hIL-6R and bioactivity to block hIL-6 in vitro were screened theoretically from the chemical databases, including 3D-Available Chemicals Directory (ACD) and MDL Drug Data Report (MDDR), by means of the computer-guided virtual screening method. Using distance geometry, molecular modeling and molecular dynamics trajectory analysis methods, the binding mode and binding energy of the three compounds were evaluated theoretically. Enzyme-linked immunosorbent assay analysis demonstrated that all the three compounds could block IL-6 binding to IL-6R specifically. However, only compound 1 could effectively antagonize the function of hIL-6 and inhibit the proliferation of XG-7 cells in a dose-dependent manner, whereas it showed no cytotoxicity to SP2/0 or L929 cells. These data demonstrated that the compound 1 could be a promising candidate of hIL-6 antagonist

Deciphering the role of apoptosis signature on the immune dynamics and therapeutic prognosis in breast cancer: Implication for immunotherapy

Background: In breast cancer oncogenesis, the precise role of cell apoptosis holds untapped potential for prognostic and therapeutic insights. Thus, it is important to develop a model predicated for breast cancer patients’ prognosis and immunotherapy response based on apoptosis-related signature.Methods: Our approach involved leveraging a training dataset from The Cancer Genome Atlas (TCGA) to construct an apoptosis-related gene prognostic model. The model’s validity was then tested across several cohorts, including METABRIC, Sun Yat-sen Memorial Hospital Sun Yat-sen University (SYSMH), and IMvigor210, to ensure its applicability and robustness across different patient demographics and treatment scenarios. Furthermore, we utilized Quantitative Polymerase Chain Reaction (qPCR) analysis to explore the expression patterns of these model genes in breast cancer cell lines compared to immortalized mammary epithelial cell lines, aiming to confirm their differential expression and underline their significance in the context of breast cancer.Results: Through the development and validation of our prognostic model based on seven apoptosis-related genes, we have demonstrated its substantial predictive power for the survival outcomes of breast cancer patients. The model effectively stratified patients into high and low-risk categories, with high-risk patients showing significantly poorer overall survival in the training cohort and across all validation cohorts. Importantly, qPCR analysis confirmed that the genes constituting our model indeed exhibit differential expression in breast cancer cell lines when contrasted with immortalized mammary epithelial cell lines.Conclusion: Our study establishes a groundbreaking prognostic model using apoptosis-related genes to enhance the precision of breast cancer prognosis and treatment, particularly in predicting immunotherapy response

Anti-IGF-1R monoclonal antibody inhibits the carcinogenicity activity of acquired trastuzumab-resistant SKOV3

BACKGROUND: Antibody resistance, not only de novo but also acquired cases, usually exists and is related with lower survival rate and high risk of recurrence. Reversing the resistance often results in better clinical therapeutic effect. Previously, we established a trastuzumab-resistant ovarian cancer cell line, named as SKOV3-T, with lower HER2 and induced higher IGF-1R expression level to keep cell survival. METHODS: IGF-1R was identified important for SKOV3-T growth. Then, a novel anti-IGF-1R monoclonal antibody, named as LMAb1, was used to inhibit SKOV3-T in cell growth/proliferation, migration, clone formation and in vivo carcinogenicity. RESULTS: In both in vitro and in vivo assays, LMAb1 showed effective anti-tumor function, especially when being used in combination with trastuzumab, which was beneficial to longer survival time of mice as well as smaller tumor. It was also confirmed preliminarily that the mechanism of antibody might be to inhibit the activation of IGF-1R and downstream MAPK, AKT pathway transduction. CONCLUSION: We achieved satisfactory anti-tumor activity using trastuzumab plus LMAb1 in trastuzumab-resistant ovarian cancer model. In similar cases, not only acquired but also de novo, good curative effect might be achieved using combined antibody therapy strategies