AIM To establish a rapid quantitative analysis model for saponins in Paris polyphylla var.yunnanensis(PPY)by near infrared spectroscopy.METHODS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ and there total content in PPY were determined by HPLC,while spectral data within the range of 10 000 to 4 000 cm-1 were collected.A quantitative analysis model was established by combining these data with partial least squares regression(PLSR).Multivariate scatter correction(MSC)and vector normalization(SNV)were applied prior to further preprocessing the spectra with original,first-order derivative(1stD),or second-order derivative(2ndD)treatments.Lastly,the model was optimized through non-smoothing(NS),Norris Derivative filtering(Nd),and Savitzky-Golay filtering(S-G)method.Model stability was evaluated based on correlation coefficients and variance.The predicted contents of each saponin component in the validation set samples were calculated.RESULTS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ were 0.42-17.98,0.46-10.44,0.23-3.86 mg/g,respectively.The total content ranged from 2.91 to 22.1 mg/g.The optimal parameters of three saponins were achieved when selecting the MSC+2ndD+S-G pretreatment method.The corresponding ratio of line segment length to segment gap was 13∶5,15∶5,11∶5,with correlation coefficients of 0.982,0.930,0.958,respectively.The root mean square errors of calibration(RMSEC)were 0.702,0.797,0.238,and the root mean square errors of prediction(RMSEP)were 1.120,0.835,0.304,respectively.The optimal parameters for the total content were obtained when selecting the MSC+2ndD+NS pretreatment method,with a correlation coefficient of 0.970,a RMSEC of 1.090,and a RMSEP of 1.740.CONCLUSION This accurate and rapid method can be used for detection of saponin contents in P.Polyphylla.

Aim To explore the possible regulatory effect of leptin on acyl-CoA synthetase long chain fami-ly member ACSL5 and their effect on migration and in-vasion of breast cancer cell,and to explore the underly-ing mechanism.Methods The expression of leptin receptor was detected by immunofluorescence assay.The migration and invasion ability of MCF-7 cells were detected by wound healing assay and Transwell assay respectively.The downstream target gene of leptin was analyzed by PCR microarray data.The expression of ACSL5 in breast cancer and its correlation with the staging and prognosis of breast cancer patients were as-sessed uing bioinformatics methods.The expression of ACSL5 in MCF-7 cells treated with different concentra-tions of leptin was detected using real time fluorescence quantitative polymerase chain reaction(RT-qPCR).Overexpressing ACSL5 was constructed by lentiviral transfection;the expressions of EMT related proteins,AMPK-α and p-AMPK-α were detected by Western blot.Results Leptin promoted breast cancer cell mi-gration and invasion and EMT.ACSL5 was significant-ly low expressed in breast cancer and related to progno-sis.Leptin downregulated the expression of ACSL5 through OBR.Leptin activated AMPK pathway to downregulate ACSL5 and promote migration,invasion and EMT of breast cancer cells.Conclusions Leptin may promote the migration,invasion and EMT of breast cancer by downregulating ACSL5 through activating AMPK pathway.

Objective To construct a database for molecular mass spectra of toxins and drugs in order to facilitate the management and retrieval of mass spectra for nerve agents,metabolites and other small molecules.Methods Requirement analysis and functional design were performed using software engineering methods.The Spec2Vec algorithm was used for vector representation of mass spectra,while SMILES molecular structures were vectorized using the extended connectivity fingerprint(ECFP).A data storage model integrating structured information and vector representations was established using the Milvus database.Similarity search of mass spectra and molecular structures was conducted via vector similarity comparison and the FlashEntropySearch algorithm.Results The constructed database of mass spectra encompassed over 400,000 entries from such sources as OCAD,NIST,MASSBANK,metabolic products,and natural products of TCM,which was capable of searching for similarities in mass spectra and molecular structures.On a standard server,similarity search of mass spectra took no more than 5 seconds,while that of molecular structures took no more than 1 second.Conclusion The system enables efficient management of complex mass spectra and provides rapid retrieval and comparison of mass spectra-related information through advanced vector indexing technology,offering robust data support and research tools for toxicology and pharmacology.

Glycoengineering was carried out in the mammalian cell line CHO for the production of pro-tein-based drugs.Firstly,the genome sequence of the Rosa26 locus of CHO cells was determined,the gRNA sequences were designed,and the landing pad was integrated into the Rosa26 locus of CHO cells by CRISPR/Cas9 technology.Three targeting vectors co-expressed by glycosyltransferases,which are β-1,4 galactosyltransferase(B4GALT1),α-2,6-sialyltransferase 1(ST6GAL1)and N-acetaminoglycosyl-transferase Ⅲ(GnT Ⅲ),were constructed by overlapping PCR and seamless ligation technology,and the three glycosyltransferase genes were integrated into the CHO Rosa26 locus by Cre enzyme-mediated cassette exchange technology.PCR confirmed that three glycosyltransferases had been successfully site-directed integrated into the Rosa26 site.The mRNA expression levels of the three glycosyltransferases were more than 50 000-fold by qRT-PCR,and the protein expression levels of the three glycosyltrans-ferases were more than 4-fold via western blotting(P＜0.001).A CHO-engineered cell line with three glycosyltransferases integrated into Rosa26 site was successfully constructed.

Objective To construct and validate machine learning-based models to predict the risk of acute kidney injury(AKI)following percutaneous coronary intervention(PCI)in patients with acute ST-segment elevation myocardial infarction(STEMI).Methods A total of 2 315 STEMI patients who underwent PCI between January 2020 and June 2023 were included;306(13.2％)of them developed AKI.Baseline variables were screened using LASSO regression,with the optimal λ value selected via 10-fold cross-validation to identify AKI-associated features.Subsequently,eight distinct machine learning models were constructed and evaluated for their predictive performance.SHAP value analysis was employed to assess the impact of key variables on model predictions.Results LASSO regression identified seven variables significantly associated with AKI,including age,multivessel disease,preoperative creatinine,heart failure,white blood cell count,hemoglobin,and albumin levels.Among all the models,the light gradient boosting machine(LGBM)and extreme gradient boosting(XGB)demonstrated the best predictive performance,with training set AUCs being 0.899(95％CI:0.877-0.921)and 0.893(95％CI:0.868-0.918),and validation set AUCs being 0.809(95％CI:0.763-0.856)and 0.871(95％CI:0.833-0.909),respectively.SHAP analysis revealed that albumin,age,preoperative creatinine,and white blood cell count were the primary contributors to AKI risk.Conclusion This study successfully developed and validated machine learning-based predictive models capable of effectively identifying the risk of AKI following PCI in STEMI patients,thus providing valuable support for clinical decision-making.

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.

Aim To explore the possible regulatory effect of leptin on acyl-CoA synthetase long chain fami-ly member ACSL5 and their effect on migration and in-vasion of breast cancer cell,and to explore the underly-ing mechanism.Methods The expression of leptin receptor was detected by immunofluorescence assay.The migration and invasion ability of MCF-7 cells were detected by wound healing assay and Transwell assay respectively.The downstream target gene of leptin was analyzed by PCR microarray data.The expression of ACSL5 in breast cancer and its correlation with the staging and prognosis of breast cancer patients were as-sessed uing bioinformatics methods.The expression of ACSL5 in MCF-7 cells treated with different concentra-tions of leptin was detected using real time fluorescence quantitative polymerase chain reaction(RT-qPCR).Overexpressing ACSL5 was constructed by lentiviral transfection;the expressions of EMT related proteins,AMPK-α and p-AMPK-α were detected by Western blot.Results Leptin promoted breast cancer cell mi-gration and invasion and EMT.ACSL5 was significant-ly low expressed in breast cancer and related to progno-sis.Leptin downregulated the expression of ACSL5 through OBR.Leptin activated AMPK pathway to downregulate ACSL5 and promote migration,invasion and EMT of breast cancer cells.Conclusions Leptin may promote the migration,invasion and EMT of breast cancer by downregulating ACSL5 through activating AMPK pathway.

BACKGROUND: Chronic liver disease (CLD), mainly non-alcoholic fatty liver disease (NAFLD), is a significant public health concern worldwide. This study aims to quantify the burden of NAFLD in CLD globally and within China, using data from the Global Burden of Disease (GBD) Study 2021, providing crucial insights for global and local health policies. METHODS: The study used comprehensive data from the GBD study 2021. It included estimates of prevalence, incidence, mortality, and disability-adjusted life years (DALYs). Age-standardized rates and average annual percent change (AAPC) from 2011 to 2021 were reported. A meticulous decomposition analysis was conducted. RESULTS: In 2021, there were 1582.5 million prevalent cases, 47.6 million incident cases, 1.4 million deaths, and 44.4 million DALYs attributable to CLD, globally. Among these, NAFLD has emerged as the predominant cause, accounting for 78.0% of all prevalent CLD cases (1234.7 million) and 87.2% of incident cases (41.5 million). Correspondingly, NAFLD had the highest age-standardized prevalence (15,017.5 per 100,000 population) and incidence (876.5 per 100,000 population) rates among CLDs. In addition, China's CLD age-standardized prevalence rate was 21,659.5 per 100,000 population, and the age-standardized incidence rate was 752.6 per 100,000 population, higher than the global average. From 2011 to 2021, the global prevalence rate of CLD increased slowly (AAPC = 0.17), consistent with the trend in China (AAPC = 0.23). Furthermore, the prevalence rate of NAFLD rose significantly in China (AAPC = 1.30) compared with the global average (AAPC = 0.91). Decomposition analysis also showed the worldwide increase in deaths and DALYs for NAFLD, which were primarily attributable to population growth and aging. CONCLUSIONS The burden of CLD and NAFLD remains substantial globally and within China in terms of high prevalence and incidence. As such, this underscores the need for targeted prevention and treatment strategies. These findings emphasize the importance of continued surveillance and research to mitigate the growing impact of liver diseases on global and Chinese health systems.
Humans ; Non-alcoholic Fatty Liver Disease/mortality* ; Global Burden of Disease ; China/epidemiology* ; Prevalence ; Male ; Disability-Adjusted Life Years ; Female ; Incidence ; Middle Aged ; Chronic Disease ; Adult ; Quality-Adjusted Life Years ; Liver Diseases/epidemiology* ; Aged

Hypoxic-ischemic brain damage (HIBD) is one of the main causes of disability in middle-aged and elderly people, as well as high mortality rates and long-term physical impairments in newborns. The pathological manifestations of HIBD include neuronal damage and loss of myelin sheaths. Tau protein is an important microtubule-associated protein in brain, exists in neurons and oligodendrocytes, and regulates various cellular activities such as cell differentiation and maturation, axonal transport, and maintenance of cellular cytoskeleton structure. Phosphorylation is a common chemical modification of Tau. In physiological condition, it maintains normal cell cytoskeleton and biological functions by regulating Tau structure and function. In pathological conditions, it leads to abnormal Tau phosphorylation and influences its structure and functions, resulting in Tauopathies. Studies have shown that brain hypoxia-ischemia could cause abnormal alteration in Tau phosphorylation, then participating in the pathological process of HIBD. Meanwhile, brain hypoxia-ischemia can induce oxidative stress and inflammation, and multiple Tau protein kinases are activated and involved in Tau abnormal phosphorylation. Therefore, exploring specific molecular mechanisms by which HIBD activates Tau protein kinases, and elucidating their relationship with abnormal Tau phosphorylation are crucial for future researches on HIBD related treatments. This review aims to focus on the mechanisms of the role of Tau phosphorylation in HIBD, and the potential relationships between Tau protein kinases and Tau phosphorylation, providing a basis for intervention and treatment of HIBD.
Humans ; tau Proteins/physiology* ; Phosphorylation ; Hypoxia-Ischemia, Brain/physiopathology* ; Animals ; Oxidative Stress

AIM To establish a rapid quantitative analysis model for saponins in Paris polyphylla var.yunnanensis(PPY)by near infrared spectroscopy.METHODS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ and there total content in PPY were determined by HPLC,while spectral data within the range of 10 000 to 4 000 cm-1 were collected.A quantitative analysis model was established by combining these data with partial least squares regression(PLSR).Multivariate scatter correction(MSC)and vector normalization(SNV)were applied prior to further preprocessing the spectra with original,first-order derivative(1stD),or second-order derivative(2ndD)treatments.Lastly,the model was optimized through non-smoothing(NS),Norris Derivative filtering(Nd),and Savitzky-Golay filtering(S-G)method.Model stability was evaluated based on correlation coefficients and variance.The predicted contents of each saponin component in the validation set samples were calculated.RESULTS The contents of polyphyllins Ⅰ,Ⅱ,Ⅶ were 0.42-17.98,0.46-10.44,0.23-3.86 mg/g,respectively.The total content ranged from 2.91 to 22.1 mg/g.The optimal parameters of three saponins were achieved when selecting the MSC+2ndD+S-G pretreatment method.The corresponding ratio of line segment length to segment gap was 13∶5,15∶5,11∶5,with correlation coefficients of 0.982,0.930,0.958,respectively.The root mean square errors of calibration(RMSEC)were 0.702,0.797,0.238,and the root mean square errors of prediction(RMSEP)were 1.120,0.835,0.304,respectively.The optimal parameters for the total content were obtained when selecting the MSC+2ndD+NS pretreatment method,with a correlation coefficient of 0.970,a RMSEC of 1.090,and a RMSEP of 1.740.CONCLUSION This accurate and rapid method can be used for detection of saponin contents in P.Polyphylla.