Aim To systematically investigate the ac-tive components,targets,and regulatory pathways of Po-lygonum capitatum in intervening atherosclerosis(AS)through network pharmacology,molecular docking and animal experiments.Methods Active components of Polygonum capitatum and AS-related targets were screened and identified through database searches.Protein-protein interaction(PPI)network analysis was performed using the STRING database,followed by GO and KEGG enrichment analyses via the David plat-form.Molecular docking validation was conducted with AutoDock.An AS model was established in Syrian golden hamsters fed a high-fat diet.Predicted pathways and targets were validated using qPCR,ELISA,and histopathological assessment of aortic and hepatic tis-sues via HE staining.Results Network pharmacology identified 27 potential active components of Polygonum capitatum(primarily flavonoids such as quercetin and luteolin)and 110 drug-disease intersection targets,in-cluding core targets MMP-9,ALB,and AKT1.GO and KEGG analyses enriched 593 and 125 pathways,re-spectively,with the NF-κB inflammatory pathway,TNF signaling pathway and lipid metabolism/atherosclerosis pathways highlighted as key mechanisms.Animal ex-periments demonstrated that Polygonum capitatum im-proved serum lipid profiles(reduced TC,TG,LDL-C)in AS hamsters,suppressed the MMP-9/NF-κB signa-ling pathway(downregulated MMP-9,p65 phosphoryla-tion,TNF-α,and IL-6),and inhibited VSMC synthetic phenotypic transformation(upregulated α-SMA and myocardin)by downregulating MCPIP1.Additionally,Polygonum capitatum ameliorated aortic lesions and he-patic lipid deposition in AS hamsters.Conclusions Polygonum capitatum alleviates AS by synergistically regulating the MMP-9/NF-κB/MCPIP1 axis through flavonoid components,suppressing vascular inflammato-ry cascades and maintaining VSMC contractile pheno-types.This reflects Polygonum capitatum's multi-com-ponent,multi-pathway,and multi-target characteristics in combating AS.

Cerebral small vessel disease represents a group of common vascular disorders involving pathological changes in arterioles,capillaries and venules,with microvascular investigation remaining a key challenge in stroke.With high signal-to-noise ratio and high contrast enabled by enhanced field strength,7T MRI can surpass the resolution limits of 3T MRI,revealing structural and functional abnormalities in cerebral small vessels below 400 μm,as well as detecting subtle lesions in brain tissue.This paper reviews the research progress of multimodal high-resolution imaging techniques based on 7T MRI,such as time-of-flight angiography,phase contrast imaging and susceptibility imaging,in the study of cerebral small vessel disease.Utilizing these technologies,7T MRI can clearly display the structure of cerebral small vessels,such as the lenticulostriate arteries and deep medullary veins,and measure functional parameters like flow velocity and susceptibility.Additionally,it can sensitively detect cerebral microbleeds and cortical cerebral microinfarct.These imaging data provide valuable information for detecting early features of cerebral small vessel disease and assessing its progression,offering new insights into its pathogenesis.Combined with artificial intelligence-based image analysis methods,7T MRI holds great promise for early diagnosis and progression evaluation in cerebral small vessel disease.

Objective:This study aims to explore the clinical characteristics of patients with multiple myeloma concurrently presenting with a second primary malignancy.By analyzing the causes based on literature reports,this study aims to deepen the understanding of multiple primary cancers in multiple myeloma patients,and provide some assistance for auxiliary examinations and risk assessments of multiple myeloma patients.Methods:A retrospective analysis was conducted on the clinical data of four patients with multiple myeloma who also had a second primary malignancy admitted to The Second Affiliated Hospital,Chongqing Medical University.Each patient was diagnosed with multiple myeloma through bone marrow aspiration and had a second primary malignancy confirmed through pathological biopsy.Results:The onset age of the 4 patients ranged from 42 to 81 years old;three were males and one was female.One patient had smoldering myeloma with a second primary hematological malignancy(follicular lymphoma),while the other 3 patients had concurrent solid malignancies,including bladder cancer,esophageal cancer,and breast cancer,respectively.Conclusion:Multiple myeloma accompanied by a second primary malignancy is rare,and early pathological biopsy is necessary for diagnosis to avoid missed diagnosis.

Objective:A model for predicting sentinel lymph node metastasis was established based on positron emission tomography(PET)-related metabolic parameters and clinicopathological characteristics.Methods:A retro-spective analysis was conducted on 211 patients diagnosed with breast cancer through surgical pathology from January 2016 to March 2023 in Meizhou People's Hospital,who underwent whole-body PET/CT examinations prior to surgery.Clinical,pathological,and PET-related metabolic parameters were collected.The study analyzed the association be-tween clinicopathological characteristics of the primary breast cancer lesion,PET metabolic parameters,and sentinel lymph node metastasis.A logistic regression predictive model was constructed using Broussonetia papyrifera.Results:Breast cancer primary lesion PET-suspicious axillary lymph nodes,vascular tumor thrombus,estrogen receptor(ER),and progesterone receptor(PR)showed statistically significant differences between the two groups(all P<0.05),while maxi-mum tumor diameter,SUVmax tumor location,maximum standardized uptake value(SUVmax),metabolic tumor volume(MTV),total lesion glycolysis(TLG),tumor location,number of lesions,pathological type,histological grade,neural inva-sion,Homo sapiens epidermal growth factor receptor 2(HER-2),and nuclear-associated antigen Ki-67(Ki-67)showed no statistically significant differences between the two groups(all P>0.05).Parameters with P<0.05 in the univariate analysis were subjected to multivariate logistic regression analysis,and the logistic regression model was established as Logit(P)=-0.437×vascular tumor thrombus+4.685×suspicious axillary lymph node metastasis.The predictive model AUC was 0.738(P<0.001,95%CI:0.664～0.812),with sensitivity and specificity of 63.4%and 74.4%,respectively.Con-clusion:PET findings of the primary breast cancer lesion regarding suspicious axillary lymph node metastasis,vascular tumor thrombus,ER and PR are associated with sentinel lymph node metastasis.The predictive model established based on PET-detected suspicious axillary lymph node metastasis and vascular tumor thrombus in primary breast cancer lesions has certain value in predicting sentinel lymph node metastasis,potentially providing a non-invasive examination modality for clinical practice.

Objective:This study aims to explore the clinical characteristics of patients with multiple myeloma concurrently presenting with a second primary malignancy.By analyzing the causes based on literature reports,this study aims to deepen the understanding of multiple primary cancers in multiple myeloma patients,and provide some assistance for auxiliary examinations and risk assessments of multiple myeloma patients.Methods:A retrospective analysis was conducted on the clinical data of four patients with multiple myeloma who also had a second primary malignancy admitted to The Second Affiliated Hospital,Chongqing Medical University.Each patient was diagnosed with multiple myeloma through bone marrow aspiration and had a second primary malignancy confirmed through pathological biopsy.Results:The onset age of the 4 patients ranged from 42 to 81 years old;three were males and one was female.One patient had smoldering myeloma with a second primary hematological malignancy(follicular lymphoma),while the other 3 patients had concurrent solid malignancies,including bladder cancer,esophageal cancer,and breast cancer,respectively.Conclusion:Multiple myeloma accompanied by a second primary malignancy is rare,and early pathological biopsy is necessary for diagnosis to avoid missed diagnosis.

Objective:A model for predicting sentinel lymph node metastasis was established based on positron emission tomography(PET)-related metabolic parameters and clinicopathological characteristics.Methods:A retro-spective analysis was conducted on 211 patients diagnosed with breast cancer through surgical pathology from January 2016 to March 2023 in Meizhou People's Hospital,who underwent whole-body PET/CT examinations prior to surgery.Clinical,pathological,and PET-related metabolic parameters were collected.The study analyzed the association be-tween clinicopathological characteristics of the primary breast cancer lesion,PET metabolic parameters,and sentinel lymph node metastasis.A logistic regression predictive model was constructed using Broussonetia papyrifera.Results:Breast cancer primary lesion PET-suspicious axillary lymph nodes,vascular tumor thrombus,estrogen receptor(ER),and progesterone receptor(PR)showed statistically significant differences between the two groups(all P<0.05),while maxi-mum tumor diameter,SUVmax tumor location,maximum standardized uptake value(SUVmax),metabolic tumor volume(MTV),total lesion glycolysis(TLG),tumor location,number of lesions,pathological type,histological grade,neural inva-sion,Homo sapiens epidermal growth factor receptor 2(HER-2),and nuclear-associated antigen Ki-67(Ki-67)showed no statistically significant differences between the two groups(all P>0.05).Parameters with P<0.05 in the univariate analysis were subjected to multivariate logistic regression analysis,and the logistic regression model was established as Logit(P)=-0.437×vascular tumor thrombus+4.685×suspicious axillary lymph node metastasis.The predictive model AUC was 0.738(P<0.001,95%CI:0.664～0.812),with sensitivity and specificity of 63.4%and 74.4%,respectively.Con-clusion:PET findings of the primary breast cancer lesion regarding suspicious axillary lymph node metastasis,vascular tumor thrombus,ER and PR are associated with sentinel lymph node metastasis.The predictive model established based on PET-detected suspicious axillary lymph node metastasis and vascular tumor thrombus in primary breast cancer lesions has certain value in predicting sentinel lymph node metastasis,potentially providing a non-invasive examination modality for clinical practice.

Cerebral small vessel disease represents a group of common vascular disorders involving pathological changes in arterioles,capillaries and venules,with microvascular investigation remaining a key challenge in stroke.With high signal-to-noise ratio and high contrast enabled by enhanced field strength,7T MRI can surpass the resolution limits of 3T MRI,revealing structural and functional abnormalities in cerebral small vessels below 400 μm,as well as detecting subtle lesions in brain tissue.This paper reviews the research progress of multimodal high-resolution imaging techniques based on 7T MRI,such as time-of-flight angiography,phase contrast imaging and susceptibility imaging,in the study of cerebral small vessel disease.Utilizing these technologies,7T MRI can clearly display the structure of cerebral small vessels,such as the lenticulostriate arteries and deep medullary veins,and measure functional parameters like flow velocity and susceptibility.Additionally,it can sensitively detect cerebral microbleeds and cortical cerebral microinfarct.These imaging data provide valuable information for detecting early features of cerebral small vessel disease and assessing its progression,offering new insights into its pathogenesis.Combined with artificial intelligence-based image analysis methods,7T MRI holds great promise for early diagnosis and progression evaluation in cerebral small vessel disease.

Objective To construct a predictive model for high-grade pathological components of early invasive lung adenocarcinoma(ILAC)based on radiomics.Methods Collecting information on total 495 patients who underwent radical operation and were pathologically diagnosed as stage Ⅰ in the cardiothoracic surgery of Zhoushan Hospital from January 2015 to December 2019,including gender,age,pathological findings,tumor markers and preoperative chest CT images.The micropapillary and solid components in postoperative pathology were defined as"high-grade pathological components",while those without high-grade pathological components were classified into the low-grade group and those with high-grade pathological components were classified into the high-grade group.And patients were randomly divided into the training set(343 cases)and the validation set(152 cases)with a ratio of 7∶3 using the simple randomization grouping method.The region of interest of nodules on CT images were delineated layer by layer by scientific research platform and 1950 radiomics features were extracted.And then those features were filtrated by F-test,Pearson correlation coefficient,and L1 based feature selection.A model was built by using Logistic regression machine learning classifier,named mod 2,and radscore was also obtained.Differences between general information and CT features were analyzed.Binary Logistic regression analysis was used to construct a model for statistically significant variables,named mod 1.At the same time,Radscore was added to build the mod and named comb mod.The area under the curve(AUC),sensitivity and specificity of the three models were calculated.A nomogram was also drawn.Results A total of 495 patients were divided into the training set(n=343)and the validation set(n=152).Gender,carcinoma embryonic antigen(CEA),nodule,and maximum diameter were screened out in clinical features and involved in constructing the mod 1.Twelve features were selected from the radiomics features to build mod 2.Comb mod performed best,training set AUC:0.887,validation set AUC:0.875,and had good clinical practicability.Conclusion The model composed of general feature,CT feature and radiomics features could accurately predict high-grade pathological components in early ILAC,and provide references for clinicians to choose surgical method for patients.

Aim To systematically investigate the ac-tive components,targets,and regulatory pathways of Po-lygonum capitatum in intervening atherosclerosis(AS)through network pharmacology,molecular docking and animal experiments.Methods Active components of Polygonum capitatum and AS-related targets were screened and identified through database searches.Protein-protein interaction(PPI)network analysis was performed using the STRING database,followed by GO and KEGG enrichment analyses via the David plat-form.Molecular docking validation was conducted with AutoDock.An AS model was established in Syrian golden hamsters fed a high-fat diet.Predicted pathways and targets were validated using qPCR,ELISA,and histopathological assessment of aortic and hepatic tis-sues via HE staining.Results Network pharmacology identified 27 potential active components of Polygonum capitatum(primarily flavonoids such as quercetin and luteolin)and 110 drug-disease intersection targets,in-cluding core targets MMP-9,ALB,and AKT1.GO and KEGG analyses enriched 593 and 125 pathways,re-spectively,with the NF-κB inflammatory pathway,TNF signaling pathway and lipid metabolism/atherosclerosis pathways highlighted as key mechanisms.Animal ex-periments demonstrated that Polygonum capitatum im-proved serum lipid profiles(reduced TC,TG,LDL-C)in AS hamsters,suppressed the MMP-9/NF-κB signa-ling pathway(downregulated MMP-9,p65 phosphoryla-tion,TNF-α,and IL-6),and inhibited VSMC synthetic phenotypic transformation(upregulated α-SMA and myocardin)by downregulating MCPIP1.Additionally,Polygonum capitatum ameliorated aortic lesions and he-patic lipid deposition in AS hamsters.Conclusions Polygonum capitatum alleviates AS by synergistically regulating the MMP-9/NF-κB/MCPIP1 axis through flavonoid components,suppressing vascular inflammato-ry cascades and maintaining VSMC contractile pheno-types.This reflects Polygonum capitatum's multi-com-ponent,multi-pathway,and multi-target characteristics in combating AS.

Background ¹³⁷Cs in atmospheric aerosol is the product of past nuclear weapon tests and nuclear accidents. When ¹³⁷Cs is released into the atmosphere, it will deposit in dry land and marine environment, causing pollution of soil surface, water, agricultural products, and animal byproducts, and affecting public health. Objective To identify the variation pattern of ¹³⁷Cs activity concentration in aerosol and its correlation with dust concentration in Beijing area from 2017 to 2020. Methods A total of 958 aerosol samples were collected from November 1, 2017 to June 30, 2020 in Beijing with a high volume air sampler at a sampling flow rate about 600 m³·h⁻¹ and a collection time for each sample about 24 h. The activity concentration of ¹³⁷Cs in the aerosol samples was determined with a low-background high-purity germanium γ spectrometer. The dust concentration was calculated using the difference in the mass of the aerosol filter before and after sampling. The detection rate of ¹³⁷Cs and dust concentration in different seasons were compared. Spearman rank correlation test was used to analyze the correlation between ¹³⁷Cs activity concentration and dust concentration. Results From 2017 to 2020, the ¹³⁷Cs activity concentrations of 33 from 958 aerosol samples in Beijing were above the minimum detectable activityconcentration, the overall detection rate of ¹³⁷Cs was 3.4%, and the activity concentration ranged from 1.86 to 45.53 μBq·m⁻³, with a median value of 4.85 μBq·m⁻³. The detection rate of ¹³⁷Cs was highest in spring, followed by autumn, and lowest in winter and summer (8.4%, 3.0%, 1.1%, and 0.5%, respectively). The dust concentration ranged from 0.03 to 1.55 mg·m⁻³, with an average value of 0.18 mg·m⁻³. There was a statistically significant difference in the dust concentrations in spring, summer, autumn, and winter (F=45.51, P<0.05), and the highest value was 0.24 mg·m⁻³ in spring (P<0.05). The ¹³⁷Cs activity concentration was positively correlated with the dust concentration (P<0.05). Conclusion The ¹³⁷Cs activity concentration in aerosol in Beijing from 2017 to 2020 fluctuates within the range of background level, and its activity concentration is highest in spring, followed autumn, and lowest in summer and winter.