Objective To explore the relationship between dynamic contrast-enhanced magnetic resonance imaging(DCE-MRI)and clinical pathological features of invasive breast cancer and lymphovascular invasion(LVI).Methods Imaging and clinical pathological data were retrospectively collected from 508 patients with invasive breast cancer who underwent breast DCE-MRI at the First Medical Center of Chinese PLA General Hospital from January 2019 to August 2021.Patients were divided into the LVI-positive(LVI+)group(n=79)and LVI-negative(LVI-)group(n=429)based on postoperative pathological results.Univariate and multivariate logistic regression analyses were used to identify risk factors for LVI.Results Compared with LVI-group,LVI+group had a higher proportion of patients aged<45 years(44.3%vs.27.0%,P=0.002),non-mass-like enhancement(NME)(31.7%vs.17.7%,P=0.004),Ki-67 expression rate(40.0%vs.30.0%,P<0.001),high Ki-67 expression(94.9%vs.78.1%,P=0.001),Luminal B subtype(76.0%vs.60.1%,P=0.008),and positive axillary lymph nodes rate(72.2%vs.31.5%,P<0.001),while the proportion of Luminal A subtype was lower(2.5%vs.21.5%,P<0.001).Univariate and multivariate logistic regression analyses showed that age≥45 years(OR=0.468,95%CI 0.280-0.783,P=0.004)was an independent protective factor for LVI,while NME(OR=1.987,95%CI 1.126-3.444,P=0.016)was an independent risk factor.Compared with Luminal A subtype,patients with Luminal B subtype(OR=10.482,95%CI 3.164-64.923,P=0.001),HER-2 overexpression subtype(OR=11.571,95%CI 2.755-79.341,P=0.003)and triple-negative subtypes(OR=8.433,95%CI 1.985-57.908,P=0.009)had a higher risk of LVI.Conclusions Age≥45 years is an independent protective factor for LVI,while NME is an independent risk factor.Among molecular subtypes,patients with Luminal B,HER-2 overexpression and triple-negative subtypes have a higher risk of LVI compared with the Luminal A subtype.

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.

Objective To investigate the changes of serum levels of soluble scavenger receptor 163 (sCD163),angiopoietin-like protein 3 (ANGPTL3) before and after intravenous thrombolysis in patients with acute cerebral infarction (ACI) and their correlation with prognosis. Methods A total of 60 ACI patients accepted by Cangzhou Central Hospital from June 2021 to June 2022 were collected as the ACI group,and another 60 healthy individuals were regarded as the control group. According to the National Institutes of Health Stroke Scale (NIHSS) score after admission,60 patients were divided into mild group (n=10),moderate group (n=38) and severe group (n=12).According to the scores on the modified Rankin scale 90 days after thrombolysis,patients were separated into a good prognosis group (n=42) and a poor prognosis group (n=18). The serum levels of sCD163 and ANGPTL3 were detected using enzyme linked immunosorbent assay (ELISA),and receiver operating characteristic (ROC) curve was applied to analyze the predictive value of serum sCD163 and ANGPTL3 levels for the prognosis of ACI patients after intravenous thrombolysis therapy. Results Compared with the control group,the levels of serum sCD163 (687.55±86.43 ng/ml vs 411.07±58.24 ng/ml) and ANGPTL3 (60.28±10.55 mg/L vs 25.34±5.93 mg/L) in ACI group were significantly increased,and the differences were significant (t=20.549,22.363,all P<0.05). The levels of serum sCD163 (551.65±69.66 ng/ml,668.92±81.12 ng/ml,859.79±117.24 ng/ml) and ANGPTL3 (44.52±8.12 mg/L,58.67±10.37 mg/L,75.34±13.12 mg/L) in mild,moderate and severe groups were gradually increased,and the differences were significant (F=36.011,23.007,all P<0.05). Compared with the good prognosis group,the proportion of time from onset to thrombolysis≥ 3 h,the proportion of NIHSS score>10 at admission,and the serum sCD163 and ANGPTL3 levels before and after thrombolysis were significantly increased in the poor prognosis group,and the differences were statistically significant (t/x2=5.644,4.775,8.982,10.866,10.293,9.702,all P<0.05). ROC results showed that the area under the curves(95％ confidence intervals)[AUC(95％CI)]of serum sCD163 and ANGPTL3 level alone in predicting the prognosis of ACI patients were 0.830 (0.711～0.915) and 0.783 (0.658～0.879),and their sensitivity and specificity were 72.22％ and 85.71％,77.78％ and 85.71％,respectively. The AUC(95％CI)of combined prediction of serum sCD163 and ANGPTL3 in predicting the prognosis of ACI patients[0.950(0.861～0.990)]was obviously greater than the AUC predicted by sCD163 and ANGPTL3 alone (Z=2.378,2.109,P=0.017,0.035). Conclusion sCD163 and ANGPTL3 levels are elevated in the serum of ACI patients,and are related to their severity and prognosis.

GPR126, also known as ADGRG6, is one of the most deeply studied aGPCRs. Initially, GPR126 was thought to be a receptor associated with muscle development and was primarily expressed in the muscular and skeletal systems. With the deepening of research, it was found that GPR126 is expressed in multiple mammalian tissues and organs, and is involved in many biological processes such as embryonic development, nervous system development, and extracellular matrix interactions. Compared with other aGPCRs proteins, GPR126 has a longer N-terminal domain, which can bind to ligands one-to-one and one-to-many. Its N-terminus contains five domains, a CUB (complement C1r/C1s, Uegf, Bmp1) domain, a PTX (Pentraxin) domain, a SEA (Sperm protein, Enterokinase, and Agrin) domain, a hormone binding (HormR) domain, and a conserved GAIN domain. The GAIN domain has a self-shearing function, which is essential for the maturation, stability, transport and function of aGPCRs. Different SEA domains constitute different GPR126 isomers, which can regulate the activation and closure of downstream signaling pathways through conformational changes. GPR126 has a typical aGPCRs seven-transmembrane helical structure, which can be coupled to Gs and Gi, causing cAMP to up- or down-regulation, mediating transmembrane signaling and participating in the regulation of cell proliferation, differentiation and migration. GPR126 is activated in a tethered-stalk peptide agonism or orthosteric agonism, which is mainly manifested by self-proteolysis or conformational changes in the GAIN domain, which mediates the rapid activation or closure of downstream pathways by tethered agonists. In addition to the tethered short stem peptide activation mode, GPR126 also has another allosteric agonism or tunable agonism mode, which is specifically expressed as the GAIN domain does not have self-shearing function in the physiological state, NTF and CTF always maintain the binding state, and the NTF binds to the ligand to cause conformational changes of the receptor, which somehow transmits signals to the GAIN domain in a spatial structure. The GAIN domain can cause the 7TM domain to produce an activated or inhibited signal for signal transduction, For example, type IV collagen interacts with the CUB and PTX domains of GPR126 to activate GPR126 downstream signal transduction. GPR126 has homology of 51.6%-86.9% among different species, with 10 conserved regions between different species, which can be traced back to the oldest metazoans as well as unicellular animals.In terms of diseases, GPR126 dysfunction involves the pathological process of bone, myelin, embryo and other related diseases, and is also closely related to the occurrence and development of malignant tumors such as breast cancer and colon cancer. However, the biological function of GPR126 in various diseases and its potential as a therapeutic target still needs further research. This paper focuses on the structure, interspecies differences and conservatism, signal transduction and biological functions of GPR126, which provides ideas and references for future research on GPR126.

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

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 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.

Objective:To analyze the intercomparision result of personal dose monitoring and gross alpha/beta detection capability between the disease control and prevention institutions at provincial, municpal and county levels during 2022-2024, in order to provide a reference for radiation detection capability and policy making of these institutions in China.Methods:Data on personal dose monitoring and gross alpha/beta detection, obtanied from the 2022-2024 radiation detection capability intercomparison between the disease control and prevention institutions at different levels, were recorded. Both the qualification rates (including excellence) and excellence rates of all the institutions at all evels from 2022 to 2024 were analyzed using SPSS 22.0.Results:From 2022 to 2024, the number of participating institutions at all levels showed an overall upward trend, increasing from 262 to 430, with particularly notable growth at the municipal level. In the 2024 gross α/β detection capability intercomparison, the non-qualification rate in municipal-level institutions was significantly higher than in county-level institutions ( χ2=10.53, P < 0.05). The qualification rate (including excellence) of the municipal-level gross alpha/beta detection in 2022 and 2024 were lower than in 2023( χ2=29.93, P < 0.01). Among all regions, the 2024 personal dose monitoring intercomparison result showed that the qualification rate (including excellence) in East China was higher than in Southwest and Northwest China ( χ2=15.62, P <0.01). East China also outperformed Northwest China ( χ2=16.21, P <0.01). In the 2022 gross alpha/beta detection capability intercomparison, the qualification rate (including excellent performance) in East China and South China was higher than in Northwest China ( χ2=23.76, 11.22, P<0.01). Overall, East China showed relatively higher qualification rate in intercomparison result of both personal dose monitoring and radiation detection (including excellence), while Northwest China showed lower qualification rates (including excellence). Conclusions:Although the radiation detection capability of disease control and prevention institutions at all levels has improved in recent years, there are still insufficience of detection capabilities at some municipal- and county-level laboratories and even unbalance between different regionas. Policy support should prioritize underdeveloped areas, such as municipal- and county-level institutions and the Northwest, and enhance the technical proficiency and quality management of laboratory personnel.

Traditional Chinese Medicine (TCM), as a treasure of the Chinese nation, plays a significant role in maintaining public health. In 2019, the Central Committee of the Communist Party of China and the State Council proposed for the first time the establishment of a TCM registration and evaluation evidence system that integrates TCM theory, "personal experience" and clinical trials (referred to as the "Three-in-One" System) to promote the inheritance and innovation of TCM. Subsequently, the National Medical Products Administration issued several guiding principles to advance the improvement and implementation of this system. Owing to the complexity of its implementation, there are still differing understandings within the TCM industry regarding the positioning of the "Three-in-One" Registration and Evaluation Evidence System, as well as the connotation and value orientation of the "personal experience." To address this, Academician WANG Qi, President of the TCM Association, China International Exchange and Promotion Association for Medical and Healthcare and TCM master, led a group of academicians, TCM masters, TCM pharmacology experts and clinical TCM experts to convene a "Seminar on Promoting the Implementation of the ′Three-in-One′ Registration and Evaluation Evidence System for Chinese Medicinals." Through extensive discussions, an expert consensus was formed, clarifying the different roles of the TCM theory, "personal experience" and clinical trials within the system. It was further emphasized that the "personal experience" is the core of this system, and its data should be derived from clinical practice scenarios. In the future, the improvement of this system will require collaborative efforts across multiple fields to promote the high-quality development of the Chinese medicinal industry.

This study investigated the effects of Rehmanniae Radix Praeparata on striatal neuronal apoptosis in rats with attention deficit hyperactivity disorder(ADHD) based on the B-cell lymphoma-2(Bcl-2)/Bcl-2-associated X protein(Bax)/caspase-3 signaling pathway. Twenty-four 3-week-old male spontaneously hypertensive rats(SHR) were randomly divided into a model group, a methylphenidate group(2 mg·kg~(-1)·d~(-1)), and a Rehmanniae Radix Praeparata group(2.4 mg·kg~(-1)·d~(-1)). Age-matched male Wistar Kyoto(WKY) rats were used as the normal control group, with 8 rats in each group. The rats were administered by gavage for 28 days. Body weight and food intake were recorded for each group. The open field test and elevated plus maze test were used to assess hyperactivity and impulsive behaviors. Nissl staining was used to detect changes in striatal neurons and Nissl bodies. Terminal deoxynucleotidyl transferase dUTP nick end labeling(TUNEL) fluorescence staining was used to detect striatal cell apoptosis. Western blot was employed to detect the expression levels of Bcl-2, Bax, and caspase-3 proteins in the striatum. The results showed that compared with the model group, Rehmanniae Radix Praeparata significantly reduced the total movement distance, average movement speed, and central area residence time in the open field test, and significantly reduced the ratio of open arm entries, open arm stay time, and head dipping in the elevated plus maze test. Furthermore, it increased the number of Nissl bodies in striatal neurons, significantly downregulated the apoptosis index, significantly increased Bcl-2 protein expression and the Bcl-2/Bax ratio, and reduced Bax and caspase-3 protein expression. In conclusion, Rehmanniae Radix Praeparata can reduce hyperactivity and impulsive behaviors in ADHD rats. Its mechanism may be related to the regulation of the Bcl-2/Bax/caspase-3 signaling pathway in the striatum, enhancing the anti-apoptotic capacity of striatal neurons.
Animals ; Male ; Apoptosis/drug effects* ; Rats ; Drugs, Chinese Herbal/administration & dosage* ; Caspase 3/genetics* ; Proto-Oncogene Proteins c-bcl-2/genetics* ; bcl-2-Associated X Protein/genetics* ; Rehmannia/chemistry* ; Attention Deficit Disorder with Hyperactivity/physiopathology* ; Signal Transduction/drug effects* ; Neurons/cytology* ; Rats, Inbred SHR ; Rats, Inbred WKY ; Humans ; Corpus Striatum/cytology* ; Plant Extracts