Highly reproducible and cost-effective one-pot organoid differentiation using a novel platform based on PF-127 triggered spheroid assembly.

Organoid technology offers sophisticatedin vitrohuman models for basic research and drug development. However, low batch-to-batch reproducibility and high cost due to laborious procedures and materials prevent organoid culture standardization for automation and high-throughput applications. Here, using a novel platform based on the findings that Pluronic F-127 (PF-127) could trigger highly uniform spheroid assembly through a mechanism different from plate coating, we develop a one-pot organoid differentiation strategy. Using our strategy, we successfully generate cortical, nephron, hepatic, and lung organoids with improved reproducibility compared to previous methods while reducing the original costs by 80%-95%. In addition, we adapt our platform to microfluidic chips allowing automated culture. We showcase that our platform can be applied to tissue-specific screening, such as drug toxicity and transfection reagents testing. Finally, we generateNEAT1knockout tissue-specific organoids and showNEAT1modulates multiple signaling pathways fine-tuning the differentiation of nephron and hepatic organoids and suppresses immune responses in cortical organoids. In summary, our strategy provides a powerful platform for advancing organoid research and studying human development and diseases.

Imaging-based assessment of body composition in patients with Crohn's disease: a systematic review.

BACKGROUND AND AIMS: Body composition changes in patients with Crohn's disease (CD) have received increasing attention in recent years. This review aims to describe the changes in body composition in patients with CD on imaging and to analyze and summarize the prognostic value of body composition. METHODS: We systematically searched Web of Science, PubMed, Embase, Cochrane Library, and Medline via OVID for literature published before November 2022, and two researchers independently evaluated the quality of the retrieved literature. RESULTS: A total of 39 publications (32 cohort studies and 7 cross-sectional studies) involving 4219 patients with CD were retrieved. Imaging methods for body composition assessment, including dual-energy X-ray absorptiometry (DXA), computed tomography (CT) and magnetic resonance imaging (MRI), were included in this review. The study found that patients with CD typically have more visceral adipose tissue and less skeletal muscle mass, and the prevalence of sarcopenia and visceral obesity was significantly different in different studies (sarcopenia: 16-100%; visceral obesity: 5.3-30.5%). Available studies suggest that changes in the body composition of CD patients are significantly related to inflammatory status, disease behavior, poor outcomes, and drug efficacy. CONCLUSION: Altered body composition can be a significant predictor of poor outcomes for CD patients. Therefore, the body composition of CD patients may serve as a potential therapeutic target to help optimize disease management strategies in clinical practice.

Assessing the linkage among green finance, technology, and education expenditure: evidence from G7 economies.

The growth of green finance is a multifaceted system, including the interaction of three spheres: the economy, the environment, and the finance sector. Spending on education is a singular intellectual contribution to a society's attempts to achieve sustainability through the application of skills, the provision of consultancies, the delivery of training, and the dissemination of knowledge. University scientists sound the first warnings about environmental problems and help lead the charge toward transdisciplinary technological solutions. Researchers are compelled to look into the environmental crisis because it has become a worldwide concern that needs regular examination. In this research, we examine how the GDP per capita, green financing, health expenditure, educational expenditure, and technology in the G7 economies affect the growth of renewable energy (Canada, Japan, Germany, France, Italy, UK, and the USA). The research makes use of panel data from the year 2000 through the year 2020. Long-term correlations between the variables are estimated using the CC-EMG in this study. The study's trustworthy results were determined using a combination of AMG and MG regression calculations. The research shows that the growth of renewable energy is positively affected by green finance, educational spending, and technology but negatively affected by GDP per capita and health expenditure. The growth of renewable energy is also positively affected by the influence of the term "green financing" on such variables as GDP per capita, health expenditure, educational expenditure, and technological advancement. The estimated outcomes also provide significant policy implications for the chosen and other developing economies in scheming a suitable route to a sustainable environment.

A novel Monte Carlo simulation procedure for modelling COVID-19 spread over time.

The coronavirus disease 2019 (COVID-19) has now spread throughout most countries in the world causing heavy life losses and damaging social-economic impacts. Following a stochastic point process modelling approach, a Monte Carlo simulation model was developed to represent the COVID-19 spread dynamics. First, we examined various expected performances (theoretical properties) of the simulation model assuming a number of arbitrarily defined scenarios. Simulation studies were then performed on the real COVID-19 data reported (over the period of 1 March to 1 May) for Australia and United Kingdom (UK). Given the initial number of COVID-19 infection active cases were around 10 for both countries, the model estimated that the number of active cases would peak around 29 March in Australia (≈ 1,700 cases) and around 22 April in UK (≈ 22,860 cases); ultimately the total confirmed cases could sum to 6,790 for Australia in about 75 days and 206,480 for UK in about 105 days. The results of the estimated COVID-19 reproduction numbers were consistent with what was reported in the literature. This simulation model was considered an effective and adaptable decision making/what-if analysis tool in battling COVID-19 in the immediate need, and for modelling any other infectious diseases in the future.

Assessment of lake eutrophication using a novel multidimensional similarity cloud model.

Lake eutrophication is characterized by a variety of indicators, including nitrogen and phosphorus concentrations, chemical oxygen demand, chlorophyll levels, and water transparency. In this study, a multidimensional similarity cloud model (MSCM) is combined with a random weighting method to reduce the impacts of random errors in eutrophication monitoring data and the fuzziness of lake eutrophication definitions on the consistency and reliability of lake eutrophication evaluations. Measured samples are assigned to lake eutrophication levels based on the cosine of the angle between the cloud digital characteristics vectors of each sample and those of each eutrophication grade. To field test this method, the eutrophication level of Nansi Lake in Shandong Province was evaluated based on monitoring data collected in 2009-2016. Results demonstrate that, in 2009 and in 2011-2015, the upper lake of Nansi Lake exhibited moderate eutrophication while the lower lake exhibited mild eutrophication. In 2010, 2016, elevated concentrations of total nitrogen and total phosphorus led to an increase in the eutrophication level of the lower lake, matching that of the upper lake. Based on the results of these field tests, we conclude that the MSCM presented in this study provides a more flexible and effective method for evaluating lake eutrophication data than the existing multidimensional normal cloud model.