ObjectiveTo investigate the effects of Huanglian Jiedutang （HLJDT） on cognitive function in mice with ischemic stroke （IS） and to elucidate whether its neuroprotective effects are mediated by inhibition of the nuclear factor-κB （NF-κB） signaling pathway and subsequent suppression of NF-κB-regulated neuronal apoptosis. MethodsAn IS model was established using middle cerebral artery occlusion （MCAO）. Sixty C57BL/6J mice were randomly assigned to five groups （n =12 per group）， i.e.， sham operation， model， HLJDT low-dose （3.9 g·kg^-1·d^-1）， HLJDT high-dose （7.8 g·kg^-1·d^-1）， and Ginkgo biloba extract （GBE， 31.2 mg·kg^-1·d^-1）. Post-operatively， neurological deficit scores （Longa score）， cerebral infarct volume assessed by 2，3，5-triphenyltetrazolium chloride （TTC） staining， and brain water content were evaluated. Learning and memory were assessed using new object recognition （NOR） and fear conditioning （FC） tests. Hippocampal pathology was examined via hematoxylin and eosin （HE） staining. Immunofluorescence detected expression of glial fibrillary acidic protein （GFAP， astrocyte marker）， cellular oncogene Fos （c-Fos， neuronal activation marker）， and glutamate decarboxylase 65 （GAD65）. Western blot measured nuclear factor-κB inhibitor protein α （IκBα）， phosphorylated IκBα （p-IκBα）， NF-κB p65， phosphorylated NF-κB p65 （p-NF-κB p65）， ionic calcium binding adapter molecule 1 （Iba-1）， tumor necrosis factor （TNF）-α， interleukin （IL）-1β， and apoptosis-related proteins， such as cleaved cysteinyl aspartate-specific protease 3 （Caspase-3）， B-cell lymphoma 2 （Bcl-2）， and Bcl-2-associated X protein （Bax）. Real-time quantitative PCR （Real-time PCR） was used to assess mRNA levels of Iba-1， TNF-α， IL-1β， NF-κB p65， cleaved Caspase-3， Bax， and Bcl-2. ResultsCompared with the sham group， the model group exhibited significantly increased neurological deficit scores， brain water content， and cerebral infarct volume （P<0.01）. Hippocampal CA1 neurons were disorganized， showing nuclear pyknosis and karyolysis. NOR exploration time and FC freezing time were significantly reduced （P<0.01）. GFAP and c-Fos expression were increased， while GAD65 expression was decreased （P<0.01）. Cleaved Caspase-3 and Bax were upregulated， Bcl-2 was downregulated， and the Bax/Bcl-2 ratio was elevated （P<0.01）. Expression levels of p-IκBα， p-NF-κB p65， IL-1β， TNF-α， and Iba-1 were significantly increased （P<0.01）. Compared with the model group， HLJDT high-dose， low-dose， and GBE groups showed significant improvements in all parameters （P<0.01）. Among them， the HLJDT high-dose group showed the most pronounced neuronal structural recovery and superior performance in NOR and FC tests （P<0.01）. In this group， GFAP and c-Fos decreased， GAD65 increased （P<0.01）， apoptosis-related protein expression was reversed， and NF-κB signaling and related inflammatory factor expression were suppressed （P<0.01）. ConclusionHLJDT ameliorates cognitive dysfunction in mice after IS， potentially by inhibiting the NF-κB signaling pathway， thereby reducing neuroinflammation and hippocampal neuronal apoptosis.

Objective To explore the pathogenesis and prognostic factors of liposarcoma in the head and neck region, and simultaneously analyze the efficacy of different treatment regimens. Methods A retrospective analysis was performed on all patients with primary untreated head and neck liposarcoma who were diagnosed and underwent surgical treatment at our hospital from January 2008 to January 2024. All patients were monitored during follow-up, and their prognoses were analyzed using SPSS software. Results A total of 30 patients were included in the study. Liposarcoma accounted for up to 60% of the cases in the orbit, while the remaining liposarcomas were primarily located in various interspaces of the neck. Dedifferentiated liposarcoma was the most common type, comprising 33%, while myxoid pleomorphic liposarcoma was the rarest at 4%. The tumor pathological type (P<0.001) and Ki67 (P=0.014) significantly affected the tumor control rate. However, an analysis of disease-specific survival rates revealed no significant differences across various factors (all P>0.05). Conclusion The prognosis of head and neck liposarcoma is better compared to that of liposarcomas in other parts of the body. However, myxoid pleomorphic liposarcoma, pleomorphic fat sarcoma, and high Ki67 levels are indicators of poor prognosis. Additionally, postoperative adjuvant radiotherapy does not significantly enhance disease-specific survival rates.

Objective To understand the willingness to engage in the combine medical care with old-age care institutions of medical students in vocational colleges and analyze their influencing factors.Methods By convenient sampling method,1 266 medical students from 3 vocational colleges in Hunan Province were select-ed as the research objects,and their occupational cognition and occupational willingness were investigated and analyzed.Through objective sampling,20 medical students who were not willing to work in medical institu-tions were selected for semi-structured interviews.Results 62.1%of medical students intend to work in med-ical institutions after graduation;The results of multi-factor analysis showed that the experience of caring for the elderly,understanding of the nursing institution,participating in the volunteer service of the nursing insti-tution,and cognition score of the combination of medical care and nursing were the influencing factors of med-ical students'career intention(P＜0.05).The qualitative interview found that professional identity,job abili-ty,salary,social security and promotion were the main influencing factors.Conclusion Medical students in medical nursing institutions do not have strong willingness,so they should improve their professional cogni-tion of old-age service,enhance their professional identity and post competency,fully implement salary,social security and professional title promotion,attract more medical students to engage in the combined service in-dustry,and promote the combined development of medical care.

OBJECTIVE To investigate the prognostic factors of chondrosarcoma of the larynx,deeply analyze its clinical data,and provide a theoretical basis for better treatment of chondrosarcoma of the larynx.METHODS A retrospective analysis was conducted on the complete clinical data of patients with primary chondrosarcoma of the larynx admitted to the Department of Otolaryngology Head and Neck Surgery,Beijing Tongren Hospital,Capital Medical University from January 2010 to December 2024.RESULTS A total of 15 patients were included,including 11 males and 4 females,with a gender ratio of 11∶4.The average age of onset was 57.3 years,and the average clinical symptom duration was 12.2 months.The tumors were mainly located in the cricoid cartilage in 11 patients,in the arytenoid cartilage in 2 patients,and in the thyroid cartilage in 2 patients.Tumor grading showed that 7 patients were grade I and 8 were grade II.Four patients underwent transoral laser minimally invasive surgery,2 patients underwent partial laryngectomy+tracheotomy,and 9 patients underwent total laryngectomy/cervical lymph node dissection+tracheostomy.The 5-year overall survival rate was 85.7%,the 5-year disease-specific survival rate was 100%,and the 5-year local-regional control rate was 90.9%.Gender,tumor location,tumor grade,Ki-67,tumor size,and whether larynx preservation surgery was performed did not affect the local-regional control rate or disease-specific survival rate.CONCLUSION Laryngeal chondrosarcoma generally has a longer disease history and is difficult to detect.The pathological type is mostly well-differentiated.Regional or distant metastasis is rare,and the long-term survival rate is good.Surgical resection is the preferred treatment option.On the basis of not reducing the tumor control rate,surgery that prioritizes preserving laryngeal function should be given priority,while comprehensive treatment is generally not recommended.

Objective To investigate the relationship between the levels of serum suppressor of cytokine signaling 3(SOCS3),growth differentiation factor-15(GDF-15)and liver fibrosis in patients with type 2 dia-betes mellitus(T2DM)combined with non-alcoholic fatty liver disease(NAFLD).Methods A total of 320 patients with T2DM combined with NAFLD who were hospitalized in this hospital from May 2023 to May 2024 were selected as the study group,and another 320 patients with simple T2DM admitted during the same period were selected as the control group.The levels of serum SOCS3 and GDF-15 were determined by en-zyme-linked immunosorbent assay(ELISA).Pearson correlation analysis was used to analyze the correlation between SOCS3,GDF-15 levels and liver fibrosis indicators.Logistic regression analysis was used to analyze the factors affecting the degree of liver fibrosis.The receiver operating characteristic(ROC)curve was used to analyze the diagnostic value of serum SOCS3 and GDF-15 for the degree of liver fibrosis in patients.Results Compared with the control group,the levels of serum SOCS3,GDF-15,5,type Ⅲ procollagen peptide,laminin and type Ⅳ col-lagen in the study group were significantly increased(P＜0.05).There were statistically significant differ-ences in the levels of type Ⅲ procollagen peptide,laminin and type Ⅳ collagen between the mild to moderate group and the severe group(P＜0.05).Compared with the mild to moderate group,the levels of serum SOCS3 and GDF-15 in the severe group were significantly increased(P＜0.05).The results of Pearson corre-lation analysis showed that serum SOCS3 and GDF-15 in patients with T2DM combined with NAFLD were positively correlated with type Ⅲ procollagen peptide,laminin,and type Ⅳ collagen(P＜0.05).Serum SOCS3,GDF-1 5,type Ⅲ procollagen peptide,laminin and type Ⅳ collagen are risk factors affecting the degree of liver fibrosis in patients with T2DM combined with NAFLD(P＜0.05).The results of the ROC curve showed that the combined diagnosis of serum SOCS3 and GDF-15 for the degree of liver fibrosis in patients with T2DM complicated with NAFLD had the highest area under the curve(AUC),which was superior to the individual diagnosis of each(both P＜0.05),with a corresponding sensitivity of 69.08％and a specificity of 85.71％.The combined diagnosis of the degree of liver fibrosis in patients with T2DM complicated with NAFLD by serum SOCS3,GDF-15,type Ⅲ procollagen peptide,laminin,and type Ⅳ collagen had the highest AUC,which was superior to the individual diagnosis of each index(all P＜0.001),with a corresponding sensi-tivity of 89.47％and a specificity of 97.02％.Conclusion The levels of serum SOCS3 and GDF-15 are elevated in patients with T2DM combined with NAFLD,.The combined diagnosis of serum SOCS3,GDF-15,type Ⅲ procolla-gen peptide,laminin,and type Ⅳ collagen has a high value in the degree of liver fibrosis in patients.

Objective:To investigate the clinical value of enhanced magnetic resonance imaging (MRI)-based deep learning model in preoperative prediction of proliferative hepatocellular carcinoma (HCC).Methods:The retrospective cohort study was conducted. The clinical data of 906 HCC patients who were admitted to The First Affiliated Hospital of Army Medical University and The Second Affiliated Hospital of Chongqing Medical University from May 2017 to October 2022 were collected. There were 769 males and 137 females, aged (53.2±10.9)years. Of the 906 patients, 815 cases who were admitted to The First Affiliated Hospital of Army Medical University were divided into the training set of 634 patients and the internal validation set of 181 patients using a random number table method with a ratio of 8:2, and 91 patients who were admitted to The Second Affiliated Hospital of Chongqing Medical University were divided into the external validation set. The training set was used to construct the prediction model, while the validation set was used to validate the prediction model. Observation indicators: (1) analysis of factors influencing the pathological classification of HCC patients; (2) deep learning imaging features of HCC patients; (3) evaluation of the efficacy of prediction model for proliferative HCC; (4) validation of the prediction model for proliferative HCC; (5) prognosis of HCC patients. Comparison of measurement data with normal distribution between groups was conducted using the independent sample t test. Comparison of measurement data with skewed distribution between groups was conducted using the Mann-Whitney U test. Comparison of count data between groups was conducted using the chi-square test. Multivariate analysis was conducted using the binary Logistic regression model. The model perfor-mance was evaluated through five-fold cross-validation, and receiver operating characteristic (ROC) curve was plotted to assess the diagnostic value of the model based on the area under curve (AUC), sensitivity, and specificity. The Delong test was used to compare the diagnostic performance of models. The Hosmer-Lemeshow test was employed to evaluate the calibration of models. The optimal cutoff value of the prediction model was determined by the maximum Youden index, with the value >0.175 indicating high-risk patients and value ≤0.175 indicating low-risk patients.The Kaplan-Meier method was used to calculate the survival rate and the Log-rank test was used for survival analysis. Results:(1) Analysis of factors influencing the pathological classification of HCC patients. Of 634 patients in the training set, there were 190 cases of proliferative HCC and 444 cases of non-proliferative HCC. Results of multivariate analysis showed that alpha fetoprotein (AFP) ≥400 μg/L and tumor diameter >5 cm were independent risk factors for pathological type of HCC as proli-ferative [ odds ratio=1.73, 1.88, 95% confidence interval ( CI) as 1.19-2.50, 1.30-2.71, P<0.05]. (2) Deep learning imaging features of HCC patients. In the training set of 634 patients, the probability predicted by MRI-based deep learning model was 84.8%(30.5%,95.4%) for proliferative HCC and 5.8%(3.2%,12.5%) for non-proliferative HCC, showing a significant difference between them ( Z=-16.01, P<0.05). (3) Evaluation of the efficacy of prediction model for proliferative HCC. In the training set, the AUC of clinical prediction model for proliferative HCC was 0.63(95% CI as 0.59-0.68, P<0.05), with sensitivity of 54.74% and specificity of 64.19%. The AUC of MRI-based deep learning prediction model was 0.90(95% CI as 0.87-0.93, P<0.05), with sensitivity of 80.53% and specificity of 86.94%. The AUC of combined MRI-based deep learning with clinical prediction model was 0.90 (95% CI as 0.87-0.93, P<0.05), with sensitivity of 83.16% and specificity of 86.04%. Results of Delong test showed that there was a significant difference between the combined MRI-based deep learning with clinical prediction model and the clinical prediction model ( P<0.05), and there was no signifi-cant difference between the combined MRI-based deep learning with clinical prediction model and the MRI-based deep learning prediction model ( P>0.05). Results of Hosmer-Lemeshow test showed good calibration for the clinical prediction model, the MRI-based deep learning prediction model and the combined MRI-based deep learning with clinical prediction model ( χ2=0.84, 6.38, 3.93, P>0.05), indicating that the predicted probabilities of these three prediction models matched the actual risk well. (4) Validation of the prediction model for proliferative HCC. Results of validation of the prediction model in internal validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.84(95% CI as 0.77-0.91, P<0.05), with sensitivity of 82.35% and specificity of 77.69%. Results of validation of the prediction model in external validation set showed the AUC of MRI-based deep learning prediction model for proliferative HCC was 0.81(95% CI as 0.71-0.92, P<0.05), with sensitivity of 70.00% and specificity of 81.69%. (5) Prognosis of HCC patients. Of the 906 patients, the 1-, 3-, and 5-year recurrence-free survival rates for 645 proliferative HCC patients were 56.9%, 31.4%, and 29.1%, respectively, and the 1-, 3-, and 5-year recurrence-free survival rates for 261 non-proliferative HCC patients were 88.8%, 68.6%, and 56.0%, respectively. There were significant differences in recurrence-free survival time between proliferative HCC and non-proliferative HCC patients of the training set, internal validation set and external validation set ( P<0.05). The 1-, 3-, 5-year recurrence-free survival rates for 331 high-risk HCC patients were 64.6%, 50.4%, 43.6%, versus 88.5%, 71.9%, 62.7% for 575 low-risk HCC patients. There were significant differences in recurrence-free survival time between high-risk HCC patients and low-risk HCC patients of the training set, internal validation set and external validation set ( P<0.05). Conclusion:The MRI-based deep learning model can effectively predict proliferative HCC and recurrence-free survival of patients before the surgery.

BACKGROUND: The American Heart Association (AHA) introduced the concept of cardiovascular-kidney-metabolic (CKM) health and stage, reflecting the interaction among metabolism, chronic kidney disease (CKD), and the cardiovascular system. However, the association between CKM stage and the long-term risk of cardiovascular disease (CVD) has not been validated. This study aimed to evaluate the long-term CVD risk associated with CKM health metrics and CKM stage using data from a population-based cohort study. METHODS: In total, 5293 CVD-free participants were followed up to around 13 years in the Chinese Multi-provincial Cohort Study (CMCS). Considering the pathophysiologic progression of CKM health metrics abnormalities (comprising obesity, central adiposity, prediabetes, diabetes, hypertriglyceridemia, CKD, and metabolic syndrome), participants were divided into CKM stages 0, 1, and 2. The time-dependent Cox regression models were used to estimate the cardiovascular risk associated with CKM health metrics and stage. Additionally, broader CVD outcomes were examined, with a specific assessment of the impact of stage 3 in 2581 participants from the CMCS-Beijing subcohort. RESULTS: Among participants, 91.2% (4825/5293) had at least one abnormal CKM health metric, 8.8% (468/5293), 13.3% (704/5293), and 77.9% (4121/5293) were in CKM stages 0, 1, and 2, respectively; and 710 incident CVD cases occurred during a median follow-up time of 13.3 years (interquartile range: 12.1 to 13.6 years). Participants with each poor CKM health metric exhibited significantly higher CVD risk. Compared with stage 0, the hazard ratio (HR) (95% confidence interval [CI]) for CVD incidence was 1.31 (0.84-2.04) in stage 1 and 2.27 (1.57-3.28) in stage 2. Significant interactive impacts existed between CKM stage and age or sex, with higher CVD risk related to increased CKM stages in participants aged <60 years or females. CONCLUSION These findings highlight the contribution of CKM health metrics and CKM stage to the long-term risk of CVD, suggesting the importance of multi-component recognition and management of poor CKM health in CVD prevention.
Humans ; Female ; Male ; Cardiovascular Diseases/etiology* ; Middle Aged ; Adult ; Cohort Studies ; Renal Insufficiency, Chronic/metabolism* ; Aged ; Risk Factors ; Metabolic Syndrome/metabolism* ; China ; East Asian People

Objective:To analyze the prediction of pathological classification of ground glass nodules based on artificial intelligence computed tomography (CT) quantitative parameters combined with histogram parameters.Methods:The clinical data of 268 suspected patients with ground glass nodules admitted to Foshan Fosun Chancheng Hospital from June 2021 to June 2023 were retrospectively selected as the research subjects. They were divided into pre invasive lesions group (100 cases) and invasive lesions group(168 cases) according to pathological classification. Basic data of patients with different pathological classifications and the CT characteristics were compared, the prediction of pathological classification of ground glass nodules based on CT quantitative parameters combined and histogram parameters were analyzed by receiver operating characteristic (ROC) curve.Results:The edge and boundary of the tumor, shape of the lesion, the peripheral signs of the lesion and the boundary between the two groups had statistical differences ( P<0.05). The CT quantitative parameters of maximum diameter, lesion volume, average CT value in the invasive lesions group and pre invasive lesions group had statistical differences: (15.29 ± 3.20) cm vs. (9.75 ± 2.14) cm, (1.54 ± 0.31) cm 3 vs. (0.51 ± 0.10) cm 3, (- 328.16 ± 46.35) HU vs. (-541.25 ± 100.30) HU, P<0.05. The CT histogram parameters of inproportion of solid components, entropy and maximum CT value in the invasive lesions group and pre invasive lesions group had statistical differences: (66.39 ± 13.25)% vs. (42.65 ± 11.20)%, 4.31 ± 0.52 vs. 3.32 ± 0.39, (-75.34 ± 21.27) HU vs. (-141.72 ± 32.43)HU, P<0.05. Compared with the single prediction of CT quantitative parameters and CT histogram parameters, the combined prediction of the two parameters had higher value in predicting different pathological subtypes of ground glass nodules (the area under the curve was 0.877, P = 0.001). Conclusions:The combined detection of CT quantitative parameters and histogram parameters based on artificial intelligence can effectively evaluate the invasion status of ground glass nodules, which is beneficial for improving the detection of different pathological types of ground glass nodules.