New insights into immune cells in cancer immunotherapy: from epigenetic modification, metabolic modulation to cell communication.

Cancer is one of the leading causes of death worldwide, and more effective ways of attacking cancer are being sought. Cancer immunotherapy is a new and effective therapeutic method after surgery, radiotherapy, chemotherapy, and targeted therapy. Cancer immunotherapy aims to kill tumor cells by stimulating or rebuilding the body's immune system, with specific efficiency and high safety. However, only few tumor patients respond to immunotherapy and due to the complex and variable characters of cancer immune escape, the behavior and regulatory mechanisms of immune cells need to be deeply explored from more dimensions. Epigenetic modifications, metabolic modulation, and cell-to-cell communication are key factors in immune cell adaptation and response to the complex tumor microenvironment. They collectively determine the state and function of immune cells through modulating gene expression, changing in energy and nutrient demands. In addition, immune cells engage in complex communication networks with other immune components, which are mediated by exosomes, cytokines, and chemokines, and are pivotal in shaping the tumor progression and therapeutic response. Understanding the interactions and combined effects of such multidimensions mechanisms in immune cell modulation is important for revealing the mechanisms of immunotherapy failure and developing new therapeutic targets and strategies.

Modeling and Reliability Analysis of MEMS Gyroscope Rotor Parameters under Vibrational Stress.

Vibrational environments can cause drift or changes in Micro-Electro-Mechanical System (MEMS) gyroscope rotor parameters, potentially impacting their performance. To improve the effective use of MEMS gyroscopes, this study introduced a method for evaluating the reliability of parameter degradation under vibration. We analyzed the working principle of MEMS gyroscope rotors and investigated how vibration affects their parameters. Focusing on zero bias and scale factor as key performance indicators, we developed an accelerated degradation model using the distributional assumption method. We then collected degradation data for these parameters under various vibration conditions. Using the Copula function, we established a reliability assessment approach to evaluate the degradation of the MEMS gyroscope rotor's zero bias and scale factor under vibration, enabling the determination of reliability for these parameters. Experimental findings confirmed that increasing stress levels lead to reduced failure times and increased failure rates for MEMS gyroscope rotors, with significant changes observed in the zero bias parameter. Our evaluation method effectively characterizes changes in the reliability of the MEMS gyroscope rotor's scale factor and zero bias over time, providing valuable information for practical applications of MEMS gyroscopes.

Fasting mimicking diet extends lifespan and improves intestinal and cognitive health.

Caloric restriction is an effective means of extending a healthy lifespan. Fasting mimicking diet (FMD) is a growing pattern of caloric restriction. We found that FMD significantly prolonged the lifespan of prematurely aging mice. In naturally aging mice, FMD improved cognitive and intestinal health. Through a series of behavioral experiments, we found that FMD relieved anxiety and enhanced cognition in aged mice. In the intestine, the FMD cycles enhanced the barrier function, reduced senescence markers, and maintained T cell naïve-memory balance in the lamina propria mucosa. To further explore the causes of immune alterations, we examined changes in the stool microbiota using 16S rRNA sequencing. We found that FMD remodeled gut bacterial composition and significantly expanded the abundance of Lactobacillus johnsonii. Our research revealed that FMD has in-depth investigative value as an anti-aging intervention for extending longevity and improving cognition, intestinal function, and gut microbiota composition.

Precision Phenotypic Profiling and Capture of Circulating Tumor Cells via a Vertical Laminar Flow-Stacked Microfluidic Chip.

The heterogeneity of circulating tumor cells has a significant impact on the diagnosis, treatment, and monitoring of cancer. Research on the subtypes of circulating tumor cells can bring better treatment outcomes for cancer patients. Here, we proposed a microfluidic chip for the magnetic capture of subtypes of circulating tumor cells from the whole blood and phenotypic profiling by stacking laminar flow vertically. Circulating tumor cells were sorted and captured by the three-dimensional regulation of both magnetic fields in the vertical direction and flow fields in the lateral direction. Using EpCAM-magnetic beads, we achieved sorting and sectional capture of target cells in whole blood and analyzed the surface expression levels of the captured cells, confirming the functionality of the microfluidic chip in sorting and capturing subtypes of circulating tumor cells. This microfluidic chip can also aid in the subsequent subtype analysis of other rare cells.

The Ca2+-dependent phosphatase calcineurin dephosphorylates TBK1 to suppress antiviral innate immunity.

Tumor necrosis factor receptor-associated factor family member-associated NF-κB activator-binding kinase 1 (TBK1) plays a key role in the induction of the type 1 interferon (IFN-I) response, which is an important component of innate antiviral defense. Viruses target calcium (Ca2+) signaling networks, which participate in the regulation of the viral life cycle, as well as mediate the host antiviral response. Although many studies have focused on the role of Ca2+ signaling in the regulation of IFN-I, the relationship between Ca2+ and TBK1 in different infection models requires further elucidation. Here, we examined the effects of the Newcastle disease virus (NDV)-induced increase in intracellular Ca2+ levels on the suppression of host antiviral responses. We demonstrated that intracellular Ca2+ increased significantly during NDV infection, leading to impaired IFN-I production and antiviral immunity through the activation of calcineurin (CaN). Depletion of Ca²+ was found to lead to a significant increase in virus-induced IFN-I production resulting in the inhibition of viral replication. Mechanistically, the accumulation of Ca2+ in response to viral infection increases the phosphatase activity of CaN, which in turn dephosphorylates and inactivates TBK1 in a Ca2+-dependent manner. Furthermore, the inhibition of CaN on viral replication was counteracted in TBK1 knockout cells. Together, our data demonstrate that NDV hijacks Ca2+ signaling networks to negatively regulate innate immunity via the CaN-TBK1 signaling axis. Thus, our findings not only identify the mechanism by which viruses exploit Ca2+ signaling to evade the host antiviral response but also, more importantly, highlight the potential role of Ca2+ homeostasis in the viral innate immune response.IMPORTANCEViral infections disrupt intracellular Ca2+ homeostasis, which affects the regulation of various host processes to create conditions that are conducive for their own proliferation, including the host immune response. The mechanism by which viruses trigger TBK1 activation and IFN-I induction through viral pathogen-associated molecular patterns has been well defined. However, the effects of virus-mediated Ca2+ imbalance on the IFN-I pathway requires further elucidation, especially with respect to TBK1 activation. Herein, we report that NDV infection causes an increase in intracellular free Ca2+ that leads to activation of the serine/threonine phosphatase CaN, which subsequently dephosphorylates TBK1 and negatively regulates IFN-I production. Furthermore, depletion of Ca2+ or inhibition of CaN activity exerts antiviral effects by promoting the production of IFN-I and inhibiting viral replication. Thus, our results reveal the potential role of Ca2+ in the innate immune response to viruses and provide a theoretical reference for the treatment of viral infectious diseases.

Dissipation and Risk Assessment of Propaquizafop in Ginseng under Field Conditions.

Propaquizafop is a highly efficient aryloxy phenoxy propionate chiral herbicide. However, the use of propaquizafop, including its safe use methods, residue patterns, dietary risk assessment, and maximum residue limits, for ginseng, a traditional Chinese medicinal plant, has not been studied. An analytical method was established for the simultaneous determination of propaquizafop and its four metabolites in ginseng soil, fresh ginseng, ginseng plant, and dried ginseng using HPLC-MS/MS. This approach showed good linearity (R2 ranging from 0.9827 to 0.9999) and limit of quantification ranging from 0.01 to 0.05 mg/kg. The intra- and interday recovery rates of this method ranged from 71.6 to 107.1% with relative standard deviation ranging from 1.3 to 23.2%. The method was applied to detect residual samples in the field, and it was found that the degradation of propaquizafop in ginseng plants and soil followed a first-order kinetic equation. R2 was between 0.8913 and 0.9666, and the half-life (t1/2) ranged from 5.04 to 8.05 days, indicating that it was an easily degradable pesticide (T1/2 < 30 days). The final propaquizafop residues in ginseng soil, plants, fresh ginseng, and dried ginseng ranged from 0.017 to 0.691 mg/kg. A dietary risk assessment was conducted on the final propaquizafop residue in fresh and dried ginseng. The results showed that the chronic exposure risk quotient values were less than 100% for fresh and dried ginseng (1.15% for fresh ginseng and 1.13% for dried ginseng). This illustrates that the dietary risk associated with the use of 10% propaquizafop emulsifiable concentrate in ginseng is very low. Thus, applying 750 mL/ha of propaquizafop on ginseng could not pose an unacceptable risk to public health. The results of the present study support the registration of propaquizafop in ginseng.

Burnout and depression in college students.

Research on burnout has garnered considerable attention since its inception. However, the ongoing debate persists regarding the conceptual model of burnout and its relationship with depression. Thus, we conducted a network analysis to determine the dimensional structure of burnout and the burnout-depression overlap. The Maslach Burnout Inventory-Student Survey and Patient Health Questionnaire-9 were used to measure burnout and depression among 1096 college students. We constructed networks for burnout, depression, and a burnout-depression co-occurrence network. The results showed that cynicism symptom was the most central to the burnout network. In the co-occurrence network, depressive symptoms ("anhedonia", "fatigue") and burnout symptom ("doubting the significance of studies") were the most significant in causing burnout-depression comorbidity. Community detection revealed three communities within burnout symptoms, aligning closely with their three dimensions identified through factor analysis. Additionally, there was no overlap between burnout and depression. In conclusion, our findings support a multidimensional structure of burnout, affirming it as a distinct concept separate from depression. Cynicism, rather than exhaustion, plays the most important role in burnout and the burnout-depression comorbidity.

What is China's efficient power generation portfolio under carbon neutrality target? An empirical analysis considering flexibility and system cost.

Variable renewable energy (VRE) is the most promising form of primary generation under a carbon neutrality target due to its environmental benefits, incentive policy, and technological progress. However, the increasing proportion of VRE generation, such as solar and wind power, has sharply increased integration cost and reduced power grid stability. This study uses portfolio theory to investigate China's optimal power generation portfolio by 2050 considering flexibility constraint and system cost, including technical and integration costs. The results demonstrate that non-fossil-fuel power generation technologies have cost and emission reduction advantages over fossil-fuel-based technologies. VRE generation technologies must be developed in synergy with other forms of power generation when considering flexibility requirement and integration cost. A complete phase-out of fossil-fuel power generation technologies in China appears unlikely in the study period. Gas-fired and coal-fired power generation are the pillar forms of power generation to meet future flexibility needs.

A network analysis bridging the gap between the big five personality traits and burnout among medical staff.

BACKGROUND: Burnout is a common issue among medical professionals, and one of the well-studied predisposing factors is the Big Five personality traits. However, no studies have explored the relationships between these traits and burnout from a trait-to-component perspective. To understand the specific connections between each Big Five trait and burnout components, as well as the bridging effects of each trait on burnout, we employed network analysis. METHODS: A cluster sampling method was used to select a total of 420 Chinese medical personnel. The 15-item Chinese Big Five Personality Inventory-15 (CBF-PI-15) assessed the Big Five personality traits, while the 15-item Maslach Burnout Inventory-General Survey (MBI-GS) assessed burnout components. Network analysis was used to estimate network structure of Big Five personality traits and burnout components and calculate the bridge expected influence. RESULTS: The study revealed distinct and clear relationships between the Big Five personality traits and burnout components. For instance, Neuroticism was positively related to Doubt significance and Worthwhile, while Conscientiousness was negatively related to Accomplish all tasks. Among the Big Five traits, Neuroticism displayed the highest positive bridge expected influence, while Conscientiousness displayed the highest negative bridge expected influence. CONCLUSIONS: The network model provides a means to investigate the connections between the Big Five personality traits and burnout components among medical professionals. This study offers new avenues for thought and potential targets for burnout prevention and treatment in medical personnel, which can be further explored and tested in clinical settings.

An Investigation of the Immune Microenvironment and Genome during Lung Adenocarcinoma Development.

Background: Adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) are two consecutive pathological processes that occur before invasive lung adenocarcinoma (LUAD). However, our understanding of the immune editing patterns during the progression of LUAD remains limited. Furthermore, we know very little about whether alterations in driver genes are involved in forming the tumor microenvironment (TME). Therefore, it is necessary to elucidate the regulatory role of TME in LUAD development from multiple dimensions, including immune cell infiltration, molecular mutation events, and oncogenic signaling pathways. Methods: We collected 145 surgically resected pulmonary nodule specimens, including 28 cases of AIS, 52 cases of MIA, and 65 cases of LUAD. Immunohistochemistry (IHC) was used to detect the expression of immune markers CD3, CD4, CD8, CD68 and programmed death ligand 1 (PD-L1). Genomic data and TMB generated by targeted next-generation sequencing (NGS). Results: LUAD exhibited higher levels of immune cell infiltration, tumor mutation burden (TMB), and activation of oncogenic pathways compared to AIS and MIA. In LUAD, compared to epidermal growth factor receptor (EGFR) single mutation and wild-type (WT) samples, cases with EGFR co-mutations showed a more pronounced rise in the CD4/CD8 ratio and CD68 infiltration. Patients with low-density lipoprotein (LDL) receptor-related protein 1B (LRP1B) mutation have higher TMB and PD-L1 expression. The transition from AIS to LUAD tends to shift the TME towards the PD-L1+CD8+ subtype (adaptive resistance). Progression-associated mutations (PAMs) were enriched in the lymphocyte differentiation pathway and related to exhausted cells' phenotype. Conclusion: Tumor-infiltrating immune cells tend to accumulate as the depth of LUAD invasion increases, but subsequently develop into an immune exhaustion and immune escape state. Mutations in EGFR and LRP1B could potentially establish an immune niche that fosters tumor growth. PAMs in LUAD may accelerate disease progression by promoting T cell differentiation into an exhausted state.

L-arginine metabolism ameliorates age-related cognitive impairment by Amuc_1100-mediated gut homeostasis maintaining.

Aging-induced cognitive impairment is associated with a loss of metabolic homeostasis and plasticity. An emerging idea is that targeting key metabolites is sufficient to impact the function of other organisms. Therefore, more metabolism-targeted therapeutic intervention is needed to improve cognitive impairment. We first conducted untargeted metabolomic analyses and 16S rRNA to identify the aging-associated metabolic adaption and intestinal microbiome change. Untargeted metabolomic analyses of plasma revealed L-arginine metabolic homeostasis was altered during the aging process. Impaired L-arginine metabolic homeostasis was associated with low abundance of intestinal Akkermansia muciniphila (AKK) colonization in mice. Long-term supplementation of AKK outer membranes protein-Amuc_1100, rescued the L-arginine level and restored cognitive impairment in aging mice. Mechanically, Amuc_1100 acted directly as a source of L-arginine and enriched the L-arginine-producing bacteria. In aged brain, Amuc_1100 promoted the superoxide dismutase to alleviated oxidation stress, and increased nitric oxide, derivatives of L-arginine, to improve synaptic plasticity. Meanwhile, L-arginine repaired lipopolysaccharide-induced intestinal barrier damage and promoted growth of colon organoid. Our findings indicated that aging-related cognitive impairment was closely associated with the disorders of L-arginine metabolism. AKK-derived Amuc_1100, as a potential postbiotic, targeting the L-arginine metabolism, might provide a promising therapeutic strategy to maintain the intestinal homeostasis and cognitive function in aging.

Predictive Value of Machine Learning for Platinum Chemotherapy Responses in Ovarian Cancer: Systematic Review and Meta-Analysis.

BACKGROUND: Machine learning is a potentially effective method for predicting the response to platinum-based treatment for ovarian cancer. However, the predictive performance of various machine learning methods and variables is still a matter of controversy and debate. OBJECTIVE: This study aims to systematically review relevant literature on the predictive value of machine learning for platinum-based chemotherapy responses in patients with ovarian cancer. METHODS: Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we systematically searched the PubMed, Embase, Web of Science, and Cochrane databases for relevant studies on predictive models for platinum-based therapies for the treatment of ovarian cancer published before April 26, 2023. The Prediction Model Risk of Bias Assessment tool was used to evaluate the risk of bias in the included articles. Concordance index (C-index), sensitivity, and specificity were used to evaluate the performance of the prediction models to investigate the predictive value of machine learning for platinum chemotherapy responses in patients with ovarian cancer. RESULTS: A total of 1749 articles were examined, and 19 of them involving 39 models were eligible for this study. The most commonly used modeling methods were logistic regression (16/39, 41%), Extreme Gradient Boosting (4/39, 10%), and support vector machine (4/39, 10%). The training cohort reported C-index in 39 predictive models, with a pooled value of 0.806; the validation cohort reported C-index in 12 predictive models, with a pooled value of 0.831. Support vector machine performed well in both the training and validation cohorts, with a C-index of 0.942 and 0.879, respectively. The pooled sensitivity was 0.890, and the pooled specificity was 0.790 in the training cohort. CONCLUSIONS: Machine learning can effectively predict how patients with ovarian cancer respond to platinum-based chemotherapy and may provide a reference for the development or updating of subsequent scoring systems.

Comparison of risk prediction models for the progression of pelvic inflammatory disease patients to sepsis: Cox regression model and machine learning model.

Introduction: The present study presents the development and validation of a clinical prediction model using random survival forest (RSF) and stepwise Cox regression, aiming to predict the probability of pelvic inflammatory disease (PID) progressing to sepsis. Methods: A retrospective cohort study was conducted, gathering clinical data of patients diagnosed with PID between 2008 and 2019 from the Medical Information Mart for Intensive Care (MIMIC)-IV database. Patients who met the Sepsis 3.0 diagnostic criteria were selected, with sepsis as the outcome. Univariate Cox regression and stepwise Cox regression were used to screen variables for constructing a nomogram. Moreover, an RSF model was created using machine learning algorithms. To verify the model's performance, a calibration curve, decision curve analysis (DCA), and receiver operating characteristic (ROC) curve were utilized. Furthermore, the capabilities of the two models for estimating the incidence of sepsis in PID patients within 3 and 7 days were compared. Results: A total of 1064 PID patients were included, of whom 54 had progressed to sepsis. The established nomogram highlighted dialysis, reduced platelet (PLT) counts, history of pneumonia, medication of glucocorticoids, and increased leukocyte counts as significant predictive factors. The areas under the curve (AUCs) of the nomogram for prediction of PID progression to sepsis at 3-day and 7-day (3-/7-day) in the training set and the validation set were 0.886/0.863 and 0.824/0.726, respectively, and the C-index of the model was 0.8905. The RSF displayed excellent performance, with AUCs of 0.939/0.919 and 0.712/0.571 for 3-/7-day risk prediction in the training set and validation set, respectively. Conclusion: The nomogram accurately predicted the incidence of sepsis in PID patients, and relevant risk factors were identified. While the RSF model outperformed the Cox regression models in predicting sepsis incidence, its performance exhibited some instability. On the other hand, the Cox regression-based nomogram displayed stable performance and improved interpretability, thereby supporting clinical decision-making in PID treatment.

A secure cross-domain interaction scheme for blockchain-based intelligent transportation systems.

In the intelligent transportation system (ITS), secure and efficient data communication among vehicles, road testing equipment, computing nodes, and transportation agencies is important for building a smart city-integrated transportation system. However, the traditional centralized processing approach may face threats in terms of data leakage and trust. The use of distributed, tamper-proof blockchain technology can improve the decentralized storage and security of data in the ITS network. However, the cross-trust domain devices, terminals, and transportation agencies in the heterogeneous blockchain network of the ITS still face great challenges in trusted data communication and interoperability. In this article, we propose a heterogeneous cross-chain interaction mechanism based on relay nodes and identity encryption to solve the problem of data cross-domain interaction between devices and agencies in the ITS. First, we propose the ITS cross-chain communication framework and improve the cross-chain interaction model. The relay nodes are interconnected through libP2P to form a relay node chain, which is used for cross-chain information verification and transmission. Secondly, we propose a relay node secure access scheme based on identity-based encryption to provide reliable identity authentication for relay nodes. Finally, we build a standard cross-chain communication protocol and cross-chain transaction lifecycle for this mechanism. We use Hyperledger Fabric and FISCO BCOS blockchain to design and implement this solution, and verify the feasibility of this cross-chain interaction mechanism. The experimental results show that the mechanism can achieve a stable data cross-chain read throughput of 2,000 transactions per second, which can meet the requirements of secure and efficient cross-chain communication and interaction among heterogeneous blockchains in the ITS, and has high application value.

Potential Plausible Role of Stem Cell for Treating Depressive Disorder: a Retrospective Review.

Depression poses a significant threat to global physical and mental health, impacting around 3.8% of the population with a rising incidence. Current treatment options primarily involve medication and psychological support, yet their effectiveness remains limited, contributing to high relapse rates. There is an urgent need for innovative and more efficacious treatment modalities. Stem cell therapy, a promising avenue in regenerative medicine for a spectrum of neurodegenerative conditions, has recently garnered attention for its potential application in depression. While much of this work remains preclinical, it has demonstrated considerable promise. Identified mechanisms underlying the antidepressant effects of stem cell therapy encompass the stimulation of neurotrophic factors, immune function modulation, and augmented monoamine levels. Nonetheless, these pathways and other undiscovered mechanisms necessitate further investigation. Depression fundamentally manifests as a neurodegenerative disorder. Given stem cell therapy's success in addressing a range of neurodegenerative pathologies, it opens the door to explore its application in depression treatment. This exploration may include repairing damaged nerves directly or indirectly and inhibiting neurotoxicity. Nevertheless, significant challenges must be overcome before stem cell therapies can be applied clinically. Successful resolution of these issues will ultimately determine the feasibility of incorporating stem cell therapies into the clinical landscape. This narrative review provides insights into the progress of research, potential avenues for exploration, and the prevailing challenges in the implementation of stem cell therapy for treatment of depression.

Lactobacillus Intestinalis Primes Epithelial Cells to Suppress Colitis-Related Th17 Response by Host-Microbe Retinoic Acid Biosynthesis.

Gut microbiome is integral to the pathogenesis of ulcerative colitis. A novel probiotic Lactobacillus intestinalis (L. intestinalis) exerts a protective effect against dextran sodium sulfate-induced colitis in mice. Based on flow cytometry, colitis-associated Th17 cells are the target of L. intestinalis, which is supported by the lack of protective effects of L. intestinalis in T cell-null Rag1-/- mice or upon anti-IL-17-A antibody-treated mice. Although L. intestinalis exerts no direct effect on T cell differentiation, it decreases C/EBPA-driven gut epithelial SAA1 and SAA2 production, which in turn impairs Th17 cell differentiation. Cometabolism of L. intestinalis ALDH and host ALDH1A2 contributed to elevated biosynthesis of retinoic acid (RA), which accounts for the anti-colitis effect in RAR-α -mediated way. In a cohort of ulcerative colitis patients, it is observed that fecal abundance of L. intestinalis is negatively associated with the C/EBPA-SAA1/2-Th17 axis. Finally, L. intestinalis has a synergistic effect with mesalazine in alleviating murine colitis. In conclusion, L. intestinalis and associated metabolites, RA, have potential therapeutic effects for suppressing colonic inflammation by modulating the crosstalk between intestinal epithelia and immunity.

Anti-stigma narratives and emotional comfort against health crisis: a context analysis of UGC short videos from patients with COVID-19 infections.

Patients narratives are being recorded increasingly frequently and spontaneously in short user produced content (UGC) films, which may have an impact on the vlogger's health as well as the public's comprehension of the relevant health concerns. This paper addressed three research questions regarding the population characteristics of UGC video publishers, the narrative theme of the videos, and the emotional orientation of the commenters. This study aimed to deepen our understanding of COVID-19 patients' narrative intentions and emotional needs through the theoretical frameworks of theory of planned behavior (TPB) and negative dominance theory (NDT). We collected 335 videos from 28 COVID-19 patients and 572,052 comments as samples on Douyin platform, the largest short-video website in China. Using Latent Semantic Analysis, we analyzed the descriptive information of the video blogs, the narrative textual information of the videos, and the emotional orientation of the comments. Our findings revealled seven categories of narrative themes, with 52.1% of video comments exhibiting a positive emotional orientation. Within a framework integrating TPB and NDT theories, we analyzed the behavioral intentions of vloggers and viewers during COVID-19 epidemic, and interpreted the persistent posting of videos and the active posting of comments as positive actions that counteracted the multiple effects of negative messages. This study contributes to the understanding of individual narratives in macro-risk communication, both theoretically and empirically, and offers policy recommendations in relevant fields.

Machine learning approaches reveal highly heterogeneous air quality co-benefits of the energy transition.

Estimating health benefits of reducing fossil fuel use from improved air quality provides important rationales for carbon emissions abatement. Simulating pollution concentration is a crucial step of the estimation, but traditional approaches often rely on complicated chemical transport models that require extensive expertise and computational resources. In this study, we develop a machine learning framework that is able to provide precise and robust annual average fine particle (PM2.5) concentration estimations directly from a high-resolution fossil energy use dataset. Applications of the framework with Chinese data reveal highly heterogeneous health benefits of avoiding premature mortality by reducing fossil fuel use in different sectors and regions in China with a mean of $19/tCO2 and a standard deviation of $38/tCO2. Reducing rural and residential coal use offers the highest co-benefits with a mean of $151/tCO2. Our findings prompt careful policy designs to maximize cost-effectiveness in the transition toward a carbon-neutral energy system.

Application of Atorvastatin Combined with Ezetimibe in Elderly Patients with Hypertension Combined with T2DM and Analysis of Significance of Changes in Serum Bilirubin Levels During Treatment.

Objective: To investigate the application of atorvastatin (AT) combined with ezetimibe (EZ) in elderly patients with hypertension (HY) combined with type 2 diabetes mellitus (T2DM) and the significance analysis of changes in serum bilirubin levels during treatment. Methods: One hundred and twelve elderly patients with HY combined with T2DM admitted to our hospital from September 2019 to March 2022 were selected and divided into a control group (AT) and a combined group (AT + EZ) according to the random number table method, with 56 cases in each group. It revealed that blood glucose, lipid function, inflammatory factors, and serum bilirubin [(total bilirubin, direct bilirubin (DBIL), indirect bilirubin (IBIL))] were also compared in both groups. The combined group was divided into high and low expression groups according to the mean total bilirubin value, and the incidence of adverse reactions was compared between the two groups. Results: Glucose, lipid function, and inflammatory factors were lower in the combined group than in the control group (P < .05). Total bilirubin, DBIL, and IBIL were higher in the combined group than in the control group (P < .05). The total incidence of adverse reactions in the high expression group was significantly lower than that in the low expression group (12.50% vs. 28.57%, P < .05). Conclusion: AT combined with EZ can effectively improve glucose, lipids, inflammation and upregulate serum bilirubin in patients with HY combined with T2DM.

Associations between obesity, smoking behaviors, reproductive traits and spontaneous abortion: a univariable and multivariable Mendelian randomization study.

Background: The correlation between potential risk factors such as obesity (leg fat percentage (left), arm fat percentage (left), waist circumference, body fat percentage, trunk fat percentage), smoking behaviors (past tobacco smoking, smoking initiation, smoking/smokers in household, current tobacco smoking) and reproductive traits (age first had sexual intercourse (AFS), age at menarche (AAM), and age at first birth (AFB)) have been linked to the occurrence of spontaneous abortion (SA). However, the causal associations between these factors and SA remain unclear. Methods: We conducted univariable and multivariable Mendelian randomization (MR) analyses to evaluate the associations of obesity, smoking behavior and reproductive traits with SA. To select appropriate genetic instruments, we considered those that had reached the genome-wide significance level (P < 5 × 10-8) in their corresponding genome-wide association studies (GWAS) involving a large number of individuals (ranging from 29,346 to 1,232,091). SA was obtained from the FinnGen consortium, which provided summary-level data for 15,073 SA cases and 135,962 non-cases. Results: Assessed individually using MR, the odds ratios (ORs) of SA were 0.728 (P = 4.3608×10-8), 1.063 (P = 0.0321), 0.926 (P = 9.4205×10-4), 1.141 (P = 7.9882×10-3), 5.154 (P = 0.0420), 1.220 (P = 0.0350), 1.228 (P = 0.0117), 0.795 (P = 0.0056), 1.126 (P = 0.0318), for one standard deviation (SD) increase in AFS, AAM, AFB, smoking initiation, smoking/smokers in household, arm fat percentage (left), leg fat percentage (left), waist circumference and body fat percentage, 0.925 (P = 0.4158) and 1.075 (P = 0.1479) for one SD increase in past tobacco smoking, trunk fat percentage for one SD increase in SA. In multivariable MR (MVMR), only AFS (OR = 0.802; P = 0.0250), smoking initiation (OR = 1.472, P = 0.0258), waist circumference (OR = 0.813, P = 0.0220) and leg fat percentage (left) (OR = 4.446, P = 0.043) retained a robust effect. Conclusion: Smoking behaviors, reproductive traits and obesity-related anthropometric indicators are potential causal factors for SA. Higher leg fat percentage; smoking initiation; and lower waist circumference and AFS may increase the risk of SA. Understanding the causal relationship for SA may provide more information for SA intervention and prevention strategies.