With the improvement of living standards， the incidence rate of metabolic dysfunction-associated fatty liver disease （MAFLD） is gradually increasing with a younger age of onset， and MAFLD has become a global health problem， while its specific pathogenesis remains unclear. Macrophages， as one of the important cells involved in the pathogenesis of MAFLD， have the ability to present antigens， eliminate pathogenic microorganisms， and promote liver inflammatory responses， thereby attracting wide attention for a long time. The latest studies have shown that macrophages may become a new therapeutic target for MAFLD. This article systematically reviews the role of intrahepatic macrophages in liver inflammation caused by MAFLD， including their activation， polarization， recruitment mechanisms， and interactions with other cells， in order to provide new ideas and perspectives for the clinical prevention and treatment of MAFLD.

This paper aims to study the effect of p-cymene on mice with deficiency-cold syndrome induced by hydrocortisone and deficiency-heat syndrome induced by dexamethasone and explore the medicinal properties and mechanism of p-cymene with mild and warm nature based on the dominant characteristics of the two-way applicable conditions of mild drugs. A total of 80 KM mice were randomly divided into blank group, deficiency-cold syndrome model group, deficiency-cold syndrome + ginseng group, and deficiency-cold syndrome + low-dose and high-dose p-cymene groups, as well as blank group, deficiency-heat syndrome model group, deficiency-heat syndrome + American ginseng group, and deficiency-heat syndrome + low-dose and high-dose p-cymene groups. Hydrocortisone and dexamethasone solution were intragastrically administered for 14 consecutive days to prepare deficiency-cold syndrome and deficiency-heat syndrome models. Except for the blank group and the model group intragastrically administered with normal saline, the other groups were intragastrically administrated with drugs for 14 days. The levels of cyclic adenosine monophosphate(cAMP), cyclic guanosine monophosphate(cGMP), triiodothyronine(T3), thyroxine(T4), total cholesterol(TC), triglyceride(TG), immunoglobin G(IgG), and immunoglobin M(IgM) in serum, as well as the activity of Na~+-K~+-ATPase in liver tissue were detected. The expression of transient receptor potential melastatin 8(TRPM8), transient receptor potential vanilloid 1(TRPV1), and uncoupling protein 1(UCP1) in brown adipose tissue of deficiency-cold syndrome model after intervention with p-cymene was studied. The results showed that p-cymene could effectively improve the levels of cAMP, cAMP/cGMP, TC, IgM, and IgG in serum and the activity of Na~+-K~+-ATPase in liver tissue of mice with deficiency-cold syndrome and reduce the content of cGMP. The effects on T3, T4, and TG were not statistically significant. At the same time, p-cymene could reduce the levels of cAMP, cAMP/cGMP, and T4 in serum and the activity of Na~+-K~+-ATPase in liver tissue of mice with deficiency-cold syndrome and increase the levels of cGMP, IgM, and IgG, and it had no effect on T3, TC, and TG. In addition, p-cymene could up-regulate the expression of TRPV1 and UCP1 in brown fat of mice with deficiency-cold syndrome and down-regulate the expression of TRPM8. In summary, p-cymene could significantly regulate the syndrome indexes of mice with deficiency-cold syndrome, and some indexes of mice with deficiency-heat syndrome could be improved, but the effects on lipid metabolism and energy metabolism indexes were not obvious, indicating that the regulation effect of p-cymene on deficiency-cold syndrome model was more prominent and that the medicinal properties of p-cymene were mild and warm. The regulation of TRPV1/TRPM8/UCP1 channel expression may be the molecular biological mechanism of p-cymene with mild and warm nature affecting the energy metabolism of the body.
Animals ; Cymenes ; Mice ; Drugs, Chinese Herbal/administration & dosage* ; Male ; Disease Models, Animal ; Humans ; Cyclic AMP/metabolism* ; Monoterpenes/administration & dosage* ; Liver/metabolism* ; Cyclic GMP/metabolism* ; TRPV Cation Channels/genetics* ; Uncoupling Protein 1/genetics*

Objective: To investigate the value of decreased carbohydrate antigen 19-9(CA19-9) kinetics in predicting short-term outcomes and determining prognosis among advanced biliary or pancreatic cancer patients receiving first-or second-line therapy in the real world. Methods : Eighty-nine patients were retrospectively collected with advanced biliary or pancreatic cancer, especially on the CA19-9 dynamics and decline rates at different time points. This study evaluated the association of CA19-9 changes with clinicopathological features, short-term response to antitumor therapy, and survival outcomes. Results : The enrolled patients recorded baseline CA19-9 levels ranging from 1.20 to 65 706.40 U/ml, with a median of 303.11 U/ml. There was no statistical correlation between baseline CA19-9 levels and gender, age, body mass index, primary tumor site, hepatic metastases, pulmonary metastases, lymph node metastases, peritoneal metastases, performance status, treatment lines, and combinations of drug types. Baseline CA19-9 levels were not associated with systemic immunoinflammatory index, prognostic nutritional index, and total bilirubin. A 25% or 50% decrease in CA19-9 after 2-3 therapy courses indicated short-term efficacy in reaching tumor objective remission or disease control. Both combinations of multiple drug types and a 25% decline in CA19-9 after one course of treatment were independent prognostic factors that affected the longer progression-free survival of patients receiving first or second line of treatment. Conclusion Decreased CA19-9 kinetics has specific values in predicting the efficacy and prognosis of advanced biliary or pancreatic cancer.

With the rapid development of generative artificial intelligence(GAI)technologies,their widespread application in academic research and writing is continuously expanding the boundaries of sci-entific inquiry.However,this trend has also raised a series of ethical and regulatory challenges,inclu-ding issues related to authorship,content authenticity,citation accuracy,and accountability.In light of the growing involvement of AI in generating academic content,establishing an open,controllable,and trustworthy ethical governance framework has become a key task for safeguarding research integrity and maintaining trust within the academic community.This expert consensus outlines ethical requirements across key stages of AI-assisted academic writing-including topic selection,data management,citation practices,and authorship attribution.It aims to clarify the boundaries and ethical obligations surrounding AI use in academic writing,ensuring that technological tools enhance efficiency without compromising in-tegrity.The goal is to provide guidance and institutional support for building a responsible and sustainable research ecosystem.

Objective:To investigate the association between blood selenium levels and sex hormones in Chinese men aged 18-79 years.Methods:Data were derived from the China National Human Biomonitoring survey conducted in 2017-2018, with a final sample size of 5 414 men. General demographic characteristics, behavioral habits, and dietary frequency were collected through questionnaires and physical examinations. Fasting blood samples were collected to measure blood lead, serum testosterone, and estradiol levels. Complex sampling linear regression models were used to analyze the associations between blood selenium levels and testosterone, estradiol, and the testosterone/estradiol ratio, adjusting for confounding factors including age, education level, marital status, smoking status, alcohol consumption, seafood intake, soy product intake, protein supplement intake, BMI, and diabetes status.Results:The mean age of the 5 414 participants was (46.85±27.91) years; 4 774 (91.65%) were of Han ethnicity and 4 505 (86.68%) were married. The median ( Q1, Q3) blood selenium concentration in men was 97.80 (80.64, 116.99) μg/L. After adjusting for confounding factors, the complex sampling linear regression model revealed negative associations between blood selenium levels and both testosterone levels and the testosterone/estradiol ratio, with a significant linear trend ( Ptrend<0.05). Compared with the Q1 group, the β (95% CI) values for testosterone in the Q2, Q3, and Q4 groups were -0.02 (-0.06 to 0.02), -0.03 (-0.08 to 0.01), and -0.06 (-0.09 to -0.02), respectively. Similarly, the β (95% CI) values for the testosterone/estradiol ratio in the Q2, Q3, and Q4 groups were -0.01 (-0.03 to 0.02), -0.01 (-0.04 to 0.04), and -0.03 (-0.06 to -0.01), respectively. Subgroup analysis indicated stronger associations between blood selenium levels and testosterone/estradiol levels in non-smoking and obese men (BMI≥28 kg/m2). Conclusion:Blood selenium levels are negatively associated with testosterone levels and the testosterone/estradiol ratio in Chinese adult males.

As global greenhouse gases continue rising, the urgency of more ambitious action is clearer than ever before. China is the world's biggest emitter of greenhouse gases and one of the countries affected most by climate change. The evidence about the impacts of climate change on the environment and human health may encourage China to take more decisive action to mitigate greenhouse gas emissions and adapt to climate impacts. This article aimed to review the evidence of environmental damages and health risks posed by climate change and to provide a new science-based perspective for the delivery of sustainable development goals. Over recent decades, China has experienced a strong warming pattern with a growing frequency of extreme weather events, and the impacts of climate change on China's environment and human health have been consistently observed, with increasing O ₃ air pollution, decreases in water resources and availability, land degradation, and increased risks for both communicable and non-communicable diseases. Therefore, China's climate policy should target the key factors driving climate change and scale up strategic measures to curb carbon emissions and adapt to inevitable increasing climate impacts. It provides new insights for not only China but also other countries, particularly developing and emerging economies, to ensure climate and environmental sustainability whilst pursuing economic growth.
Climate Change ; China ; Humans ; Greenhouse Gases ; Air Pollution ; Sustainable Development ; Environment

Objective:To explore the interrelationship between brain functional networks and features in functional magnetic resonance imaging(fMRI)of patients with Alzheimer's disease(AD),and to construct mixed-function networks(MFN),and apply them in machine learning classification models to improve the accuracy of AD classification.Methods:102 AD patients and 227 healthy subjects in the Alzheimer's Neuroimaging Initiative(ADNI)dataset were retrospectively analyzed.The partial correlation brain network of the blood oxygen level dependent(BOLD)signal was calculated and fused with low-frequency wave amplitude(ALFF),fractional low-frequency wave amplitude(fALFF)and local consistency(ReHo)features to construct MFN.Network topology parameters were extracted,and a variety of machine learning classification models were constructed based on MFN topological parameters,accuracy,precision,recall and area under the curve(AUC)were used to evaluate the predictive efficiency of the models.Results:By constructed MFN and calculated intra group to inter group ratio(IIGR),35 features could be obtained from ALFF,fALFF and ReHo feature topological parameter analysis,after rank sum test and FDR correction,there were statistical differences among 28 features(P＜0.05).The classification results show that,all the five classifiers have high classification performance on the test data set.The accuracy,precision and recall rates of random forest(RF),adaptive lifting algorithm(AdaBoost),guided aggregation algorithm(Bagging)and support vector machine(SVM)were all 99.7％,and the AUC values were up to 100％,99.5％,99.1％and 99.5％,respectively.The accuracy(98.5％),precision(98.5％),recall(98.5％),and AUC(99.1％)of the multi-layer perceptron(MLP)were slightly lower than other models,but remained excellent.It was worth noting that RF has the highest AUC value of all models at 100.0％,while Bagging has the lowest AUC value(99.1％)in the integrated approach.The results of performance comparison show that,MFN classification model can significantly improve the recognition and classification of AD disease,and greatly improve the performance of various indicators of the classifier.The results showed that,MFN classification model was superior to intelligent classification based fusion,DBN-based multitask learning,PVT-TSVM,unsupervised learning and clustering,SVM and SVM of degree 3 polynomial kernel function in key indicators such as accuracy(99.13％),AUC(99.42％),recall rate(99.46％)and specificity(99.42％)with plasma proteins,machine learning algorithms.It was further proved that MFN classification model has good generalization ability and robustness in AD disease classification.Conclusion:The AD classification model constructed based on brain mixed function network topology parameters and machine learning can improve the accuracy of AD classification.