Targeting alternative splicing in cancer immunotherapy

Tumor immunotherapy has made great progress in cancer treatment but still faces several challenges, such as a limited number of targetable antigens and varying responses among patients. Alternative splicing (AS) is an essential process for the maturation of nearly all mammalian mRNAs. Recent studies show that AS contributes to expanding cancer-specific antigens and modulating immunogenicity, making it a promising solution to the above challenges. The organoid technology preserves the individual immune microenvironment and reduces the time/economic costs of the experiment model, facilitating the development of splicing-based immunotherapy. Here, we summarize three critical roles of AS in immunotherapy: resources for generating neoantigens, targets for immune-therapeutic modulation, and biomarkers to guide immunotherapy options. Subsequently, we highlight the benefits of adopting organoids to develop AS-based immunotherapies. Finally, we discuss the current challenges in studying AS-based immunotherapy in terms of existing bioinformatics algorithms and biological technologies

Estimate of Saturation Pressures of Crude Oil by Using Ensemble-Smoother-Assisted Equation of State

The equation of state (EOS) has been extensively used to evaluate the saturation pressures of petroleum fluids. However, the accurate determination of empirical parameters in the EOS is challenging and time-consuming, especially when multiple measurements are involved in the regression process. In this work, an ensemble smoother (ES) -assisted EOS method has been proposed to compute the saturation pressure by intelligently optimizing the to-be-tuned parameters. To be specific, the to-be-tuned parameters for the Peng–Robinson EOS (PR EOS) are integrated into a model input matrix and the measured saturation pressures are collected into a model output matrix. The model input matrix is then integrally and iteratively updated with respect to the model output matrix by using the iterative ES algorithm. For convenience, an in-house module is compiled to implement the ES-assisted EOS for determining the saturation pressures of crude oils. Subsequently, the experimentally measured saturation pressures of 45 mixtures of heavy oil and solvents are used to validate the performance of the in-house module. In addition, 130 measured saturation pressures of worldwide light oil samples are collected to verify the applicability of the developed ES-assisted EOS method. The in-house module is found to be competent by not only matching 45 measured saturation pressures with a better agreement than a commercial simulator but also providing a quantitative means to analyze the uncertainties associated with the estimated model parameters and the saturation pressure. Moreover, the application of the ES-assisted EOS to 130 light oil samples distinctly demonstrates that the new method greatly improves the accuracy and reliability of the EOS regression. Consequently, the in-house module representing the ES-assisted EOS is proven as an efficient and flexible tool to determine the saturation pressure under various conditions and implement uncertain analyses associated with the saturation pressure

Photo-Otto engine with quantum correlations

We theoretically prose and investigate a photo-Otto engine that is working with a single-mode radiation field inside an optical cavity and alternatively driven by a hot and a cold reservoir, where the hot reservoir is realized by sending one of a pair of correlated two-level atoms to pass through the optical cavity, and the cold one is made of a collection of noninteracting boson modes. In terms of the quantum discord of the pair of atoms, we derive the analytical expressions for the performance parameters (power and efficiency) and stability measure (coefficient of variation for power). We show that quantum discord boosts the performance and efficiency of the quantum engine, and even may change the operation mode. We also demonstrate that quantum discord improves the stability of machine by decreasing the coefficient of variation for power which satisfies the generalized thermodynamic uncertainty relation. Finally, we find that these results can be transferred to another photo-Otto engine model, where the optical cavity is alternatively coupled to a hot thermal bosonic bath and to a beam of pairs of the two correlated atoms that play the role of a cold reservoir

Regional-based strategies for municipality carbon mitigation: a case study of Chongqing in China

Different CO carbon mitigation strategies are required due to the uneven development of regions. In China, the western region is rich in natural resources, but its industrial technology is not as advanced as other regions. In addition, a few studies have attempted to explore the CO carbon mitigation strategies for a municipality of this region. In terms of modeling, the current studies often focus on the low-carbon potentials at the country, province, city and sector levels, while the carbon flows and their integration in neighboring regions are not well studied. In this paper, to explore the impact of regional-difference factors on CO reduction, we propose regional-based CO mitigation for a municipality and use Chongqing as a case study. In our methodology, the hierarchical structure analysis is conducted to identify the primary contradictions of regional CO emissions. Then, using system dynamics, CO emission systems of major industries, including cement, power and transportation, are modeled. Through simulations of baseline and low-carbon scenarios, key influencing factors in each industry are analyzed. They are then generalized to identify the important aspects of CO emission reduction for this region. Finally, the low-carbon development strategy covering three sub-pathways, i.e., the industrial system, energy structure and socio development is discussed to help the local government for policy-making

Association between high-density lipoprotein cholesterol and type 2 diabetes mellitus: dual evidence from NHANES database and Mendelian randomization analysis

BackgroundLow levels of high-density lipoprotein cholesterol (HDL-C) are commonly seen in patients with type 2 diabetes mellitus (T2DM). However, it is unclear whether there is an independent or causal link between HDL-C levels and T2DM. This study aims to address this gap by using the The National Health and Nutrition Examination Survey (NHANES) database and Mendelian randomization (MR) analysis.Materials and methodsData from the NHANES survey (2007-2018) with 9,420 participants were analyzed using specialized software. Logistic regression models and restricted cubic splines (RCS) were used to assess the relationship between HDL-C and T2DM incidence, while considering covariates. Genetic variants associated with HDL-C and T2DM were obtained from genome-wide association studies (GWAS), and Mendelian randomization (MR) was used to evaluate the causal relationship between HDL-C and T2DM. Various tests were conducted to assess pleiotropy and outliers.ResultsIn the NHANES study, all groups, except the lowest quartile (Q1: 0.28-1.09 mmol/L], showed a significant association between HDL-C levels and reduced T2DM risk (all P < 0.001). After adjusting for covariates, the Q2 [odds ratio (OR) = 0.67, 95% confidence interval (CI): (0.57, 0.79)], Q3 [OR = 0.51, 95% CI: (0.40, 0.65)], and Q4 [OR = 0.29, 95% CI: (0.23, 0.36)] groups exhibited average reductions in T2DM risk of 23%, 49%, and 71%, respectively. In the sensitivity analysis incorporating other lipid levels, the Q4 group still demonstrates a 57% reduction in the risk of T2DM. The impact of HDL-C levels on T2DM varied with age (P for interaction = 0.006). RCS analysis showed a nonlinear decreasing trend in T2DM risk with increasing HDL-C levels (P = 0.003). In the MR analysis, HDL-C levels were also associated with reduced T2DM risk (OR = 0.69, 95% CI = 0.52-0.82; P = 1.41 × 10-13), and there was no evidence of pleiotropy or outliers.ConclusionThis study provides evidence supporting a causal relationship between higher HDL-C levels and reduced T2DM risk. Further research is needed to explore interventions targeting HDL-C levels for reducing T2DM risk

The association between systemic immune-inflammation index and chronic obstructive pulmonary disease in adults aged 40 years and above in the United States: a cross-sectional study based on the NHANES 2013–2020

BackgroundInflammation is the core of Chronic obstructive pulmonary disease (COPD) development. The systemic immune-inflammation index (SII) is a new biomarker of inflammation. However, it is currently unclear what impact SII has on COPD. This study aims to explore the relationship between SII and COPD.MethodsThis study analyzed patients with COPD aged ≥40 years from the National Health and Nutrition Examination Survey (NHANES) in the United States from 2013 to 2020. Restricted Cubic Spline (RCS) models were employed to investigate the association between Systemic immune-inflammation index (SII) and other inflammatory markers with COPD, including Neutrophil-to-Lymphocyte Ratio (NLR) and Platelet-to-Lymphocyte Ratio (PLR). Additionally, a multivariable weighted logistic regression model was utilized to assess the relationship between SII, NLR and PLR with COPD. To assess the predictive values of SII, NLR, and PLR for COPD prevalence, receiver operating characteristic (ROC) curve analysis was conducted. The area under the ROC curve (AUC) was used to represent their predictive values.ResultsA total of 10,364 participants were included in the cross-sectional analysis, of whom 863 were diagnosed with COPD. RCS models observed non-linear relationships between SII, NLR, and PLR levels with COPD risk. As covariates were systematically adjusted, it was found that only SII, whether treated as a continuous variable or a categorical variable, consistently remained positively associated with COPD risk. Additionally, SII (AUC = 0.589) slightly outperformed NLR (AUC = 0.581) and PLR (AUC = 0.539) in predicting COPD prevalence. Subgroup analyses revealed that the association between SII and COPD risk was stable, with no evidence of interaction.ConclusionSII, as a novel inflammatory biomarker, can be utilized to predict the risk of COPD among adults aged 40 and above in the United States, and it demonstrates superiority compared to NLR and PLR. Furthermore, a non-linear association exists between SII and the increased risk of COPD