Objective:To detect the serum levels of 25(OH)D,miR-125b-5p,hypoxia-inducible factor-1α(HIF-1α)and vascular endothelial growth factor A(VEGFA)in patients with multiple myeloma(MM),and explore the role of miR-125b-5p/HIF-1α pathway in the involvement of vitamin D deficiency in the pathogenesis of MM.Methods:Fifty three newly diagnosed/relapsed MM patients admitted to the department of hematology of our hospital from October 2021 to December 2023 were included.Meanwhile,25 healthy individuals matched in gender and age from our hospital's Health Management Center were selected as controls.The serum level of 25(OH)D was monitored by mass spectrometry,the serum level of miR-125b-5p was detected by real-time fluorescence quantitative PCR,and serum levels of HIF-1α and VEGFA were measured by enzyme-linked immunosorbent assay.The levels of 25(OH)D,miR-125b-5p,HIF-1α,and VEGFA were compared between the two groups.According to the level of 25(OH)D,the MM patients were divided into vitamin D deficiency group(＜20 ng/ml)and vitamin D non-deficiency group(≥ 20 ng/ml),and the levels of miR-125b-5p,HIF-1α,and VEGFA were compared between the two groups.The correlations between 25(OH)D,miR-125b-5p,HIF-1α and VEGFA were analyzed.The receiver operating characteristic(ROC)curve analysis was used to determine the diagnostic value of25(OH)D combined with miR-125b-5p for newly diagnosed MM.Results:The level of 25(OH)D in MM patients was significantly lower than that in control group(P＜0.01).There was no significant difference in 25(OH)D level between newly diagnosed and relapsed MM patients(P＞0.05).Compared with the control group,the level of miR-125b-5p was significantly reduced in MM patients(P＜0.01),while the levels of HIF-1α and VEGFA were significantly increased(both P＜0.001).In MM patients,the miR-125b-5p level in the vitamin D deficiency group was significantly decreased than that in the non-deficiency group(P＜0.01),while the levels of HIF-1 α and VEGFA were significantly increased(both P＜0.05).In MM patients,25(OH)D was positively correlated with miR-125b-5p,while negatively correlated with HIF-1α and VEGFA(both P＜0.05).Moreover,miR-125b-5p was negatively correlated with HIF-1α and VEGFA(both P＜0.05).The area under the curve(AUC)for diagnosing MM with 25(OH)D,miR-125b-5p,and their combination were 0.699,0.751,and 0.791,respectively.Conclusion:The incidence of vitamin D deficiency is high in MM patients.Vitamin D deficiency may promote angiogenesis and participate in the occurrence and development of MM by downregulating miR-125b-5p and upregulating HIF-1α and VEGFA expression.

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 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 serum metabolomic changes of preterm infants with bronchopulmonary dysplasia(BPD)at postmenstrual age(PMA)36 weeks,screen potential biomarkers and associated metabolic pathways,and assess their relationship with short-term respiratory outcomes.Methods A retrospective case-control study was conducted.Infants with gestational age 28-32 weeks admitted to the Children's Hospital Affiliated to Zhengzhou University from January to December 2024 were included.Twenty infants with BPD and 20 gestational age-,birth weight-,and sex-matched non-BPD preterm infants were included.Serum collected at PMA 36 weeks was subjected to untargeted metabolomics analysis,and associations with short-term respiratory outcomes were analyzed.Results Thirteen potential biomarkers distinguishing BPD were identified(area under the curve>0.75,P<0.05).Eight biomarkers—including terephthalic acid,phosphatidylinositol,fumarate,and lysophosphatidic acid—were significantly upregulated(FC≥1.5),while five biomarkers,such as 7α-hydroxy-3-oxo-4-cholestenoate ester and phosphatidylcholine,were significantly downregulated(FC≤1/1.5).Pathway analysis indicated five pathways associated with BPD,including glycerophospholipid metabolism and phenylalanine metabolism.Dysregulation of glycerophospholipid and bile acid metabolism may affect adverse short-term respiratory outcomes in infants with BPD.Conclusions The 13 significantly different metabolites may serve as biomarkers for the diagnosis of BPD.Glycerophospholipid metabolism is associated with the occurrence of BPD and with adverse short-term respiratory outcomes.

Objective To analyze the serum metabolomic changes of preterm infants with bronchopulmonary dysplasia(BPD)at postmenstrual age(PMA)36 weeks,screen potential biomarkers and associated metabolic pathways,and assess their relationship with short-term respiratory outcomes.Methods A retrospective case-control study was conducted.Infants with gestational age 28-32 weeks admitted to the Children's Hospital Affiliated to Zhengzhou University from January to December 2024 were included.Twenty infants with BPD and 20 gestational age-,birth weight-,and sex-matched non-BPD preterm infants were included.Serum collected at PMA 36 weeks was subjected to untargeted metabolomics analysis,and associations with short-term respiratory outcomes were analyzed.Results Thirteen potential biomarkers distinguishing BPD were identified(area under the curve>0.75,P<0.05).Eight biomarkers—including terephthalic acid,phosphatidylinositol,fumarate,and lysophosphatidic acid—were significantly upregulated(FC≥1.5),while five biomarkers,such as 7α-hydroxy-3-oxo-4-cholestenoate ester and phosphatidylcholine,were significantly downregulated(FC≤1/1.5).Pathway analysis indicated five pathways associated with BPD,including glycerophospholipid metabolism and phenylalanine metabolism.Dysregulation of glycerophospholipid and bile acid metabolism may affect adverse short-term respiratory outcomes in infants with BPD.Conclusions The 13 significantly different metabolites may serve as biomarkers for the diagnosis of BPD.Glycerophospholipid metabolism is associated with the occurrence of BPD and with adverse short-term respiratory outcomes.

Objective:To detect the serum levels of 25(OH)D,miR-125b-5p,hypoxia-inducible factor-1α(HIF-1α)and vascular endothelial growth factor A(VEGFA)in patients with multiple myeloma(MM),and explore the role of miR-125b-5p/HIF-1α pathway in the involvement of vitamin D deficiency in the pathogenesis of MM.Methods:Fifty three newly diagnosed/relapsed MM patients admitted to the department of hematology of our hospital from October 2021 to December 2023 were included.Meanwhile,25 healthy individuals matched in gender and age from our hospital's Health Management Center were selected as controls.The serum level of 25(OH)D was monitored by mass spectrometry,the serum level of miR-125b-5p was detected by real-time fluorescence quantitative PCR,and serum levels of HIF-1α and VEGFA were measured by enzyme-linked immunosorbent assay.The levels of 25(OH)D,miR-125b-5p,HIF-1α,and VEGFA were compared between the two groups.According to the level of 25(OH)D,the MM patients were divided into vitamin D deficiency group(＜20 ng/ml)and vitamin D non-deficiency group(≥ 20 ng/ml),and the levels of miR-125b-5p,HIF-1α,and VEGFA were compared between the two groups.The correlations between 25(OH)D,miR-125b-5p,HIF-1α and VEGFA were analyzed.The receiver operating characteristic(ROC)curve analysis was used to determine the diagnostic value of25(OH)D combined with miR-125b-5p for newly diagnosed MM.Results:The level of 25(OH)D in MM patients was significantly lower than that in control group(P＜0.01).There was no significant difference in 25(OH)D level between newly diagnosed and relapsed MM patients(P＞0.05).Compared with the control group,the level of miR-125b-5p was significantly reduced in MM patients(P＜0.01),while the levels of HIF-1α and VEGFA were significantly increased(both P＜0.001).In MM patients,the miR-125b-5p level in the vitamin D deficiency group was significantly decreased than that in the non-deficiency group(P＜0.01),while the levels of HIF-1 α and VEGFA were significantly increased(both P＜0.05).In MM patients,25(OH)D was positively correlated with miR-125b-5p,while negatively correlated with HIF-1α and VEGFA(both P＜0.05).Moreover,miR-125b-5p was negatively correlated with HIF-1α and VEGFA(both P＜0.05).The area under the curve(AUC)for diagnosing MM with 25(OH)D,miR-125b-5p,and their combination were 0.699,0.751,and 0.791,respectively.Conclusion:The incidence of vitamin D deficiency is high in MM patients.Vitamin D deficiency may promote angiogenesis and participate in the occurrence and development of MM by downregulating miR-125b-5p and upregulating HIF-1α and VEGFA expression.

Hypercholesterolemia is pathologically characterized by abnormal accumulation of low-density lipoprotein cholesterol,which is closely associated with metabolic dysfunction-associated fatty liver disease and increased cardiovascular risks.Hepatocytes maintain cholesterol homeostasis through LDL receptor-mediated uptake and esterification storage mechanisms.However,chronic cholesterol overload induces mitochondrial dysfunction,reactive oxygen species accumulation,and endoplasmic reticulum stress,leading to hepatocyte injury.Moreover,systemic hypercholesterolemia disrupts gut microbiota balance and impairs short-chain fatty acid and ketone metabolism,exacerbating metabolic disturbances and aggravating hepatic injury through enhanced metabolic stress.In this article,we review the advance of studies on hypercholesterolemia in recent years and summary its association with hepatic injury,which can provide theoretical support for further research.

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 The emergence of evolving variants of Coronavirus disease 2019(COVID-19)has fostered the need for change of newer and adaptive treatments for these infections.During the COVID-19 pandemic and persists,traditional Chinese medicine(TCM)herbs exhibit significant bioactivity and therapeutic effect.This study is aimed to evaluate the efficacy of four TCM preparations on 28-day mortality risk of patients and changes of the laboratory indicators.METHODS The retrospective cohort study included patients with COVID-19 who were admitted to the Jiangsu Province Hospital of Chinese Medicine from December 15,2022 to January 15,2023,and those died within 48 hours of admission or cannot be tracked for outcomes were excluded.The pri-mary outcome was survival status in 28 days(death or survival)starting from the day of admission.The second outcomes were labora-tory indicators,including absolute lymphocyte count,lactate dehydrogenase,creatinine,and blood urea nitrogen.Binary logistic re-gressions were used to estimate the effect of TCM preparations on the primary and secondary outcomes in main analysis.Meanwhile,heterogeneity and robustness of results from main analysis were assessed by subgroup analyses and multiple sensitivity analyses.RESULTS 1 816 eligible patients were included in analysis dataset,including 573 patients received standard care(control group)and 1 243 patients received TCM preparations(hospital preparation group).The 28-day mortality rate of hospital preparation group was lower than that of control group(4.75%vs.14.83%),and the difference was statistically significant(χ2=54.666,P<0.001).The risk of 28-day mortality was 0.535 times lower in the hospital preparation group as compared with the control group(OR=0.46,95%CI:0.305-0.708,P<0.001)showed by multivariable binary logistic regressions.Subgroup analyses showed that taking TCM preparations reduced the 28-day mortality risk.Sensitivity analyses demonstrated that the results of the main analysis for primary outcomes were robust.For secondary outcomes,the risk of abnormal absolute lymphocyte counts at discharge in the hospital prepara-tion group decreased by 0.284 times(OR=0.703,95%CI:0.515-0.961,P=0.027).CONCLUSION Compared with standard of care,taking four hospital preparations including Kanggan Heji,Feining Heji,Qishen Gubiao Keli,and Qianghuo Qushi Qingwen Heji decreased risk of 28-day mortality among hospitalized COVID-19 patients.TCM therapy achieves adequate therapeutic effects in COVID-19.

Hypercholesterolemia is pathologically characterized by abnormal accumulation of low-density lipoprotein cholesterol,which is closely associated with metabolic dysfunction-associated fatty liver disease and increased cardiovascular risks.Hepatocytes maintain cholesterol homeostasis through LDL receptor-mediated uptake and esterification storage mechanisms.However,chronic cholesterol overload induces mitochondrial dysfunction,reactive oxygen species accumulation,and endoplasmic reticulum stress,leading to hepatocyte injury.Moreover,systemic hypercholesterolemia disrupts gut microbiota balance and impairs short-chain fatty acid and ketone metabolism,exacerbating metabolic disturbances and aggravating hepatic injury through enhanced metabolic stress.In this article,we review the advance of studies on hypercholesterolemia in recent years and summary its association with hepatic injury,which can provide theoretical support for further research.