Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

Hepatocellular carcinoma (HCC) is a leading cause of cancer-associated death globally. Liver transplantation (LT) has emerged as a key treatment for patients with HCC, and the Milan criteria have been adopted as the cornerstone of the selection policy. To allow more patients to benefit from LT, a number of expanded criteria have been proposed, many of which use radiologic morphological characteristics with larger and more tumors as surrogates to predict outcomes. Other groups developed indices incorporating biological variables and dynamic markers of response to locoregional treatment. These expanded selection criteria achieved satisfactory results with limited liver supplies. In addition, a number of prognostic models have been developed using clinicopathological characteristics, imaging radiomics features, genetic data, and advanced techniques such as artificial intelligence. These models could improve prognostic estimation, establish surveillance strategies, and bolster long-term outcomes in patients with HCC. In this study, we reviewed the latest findings and achievements regarding the selection criteria and post-transplant prognostic models for LT in patients with HCC.

This study established the HPLC fingerprints, characteristic chromatograms, and a multi-component content determination method for Bidens bipinnata and B. biternata. The chemical pattern recognition analysis was then employed to clarify the characteristic indexes of quality differences between the two original plants of Bidentis Herba, providing a reference for establishing the quality standards of Bidentis Herba. HPLC was launched on an Agilent Poroshell 120 EC-C_(18) chromatographic column(4.6 mm×250 mm, 4 μm) by gradient elution with a mobile phase of 0.1% aqueous phosphoric acid-acetonitrile at a flow rate of 0.7 mL·min~(-1), detection wavelength of 270 nm, column temperature of 25 ℃, and an injection volume of 5 μL. The similarity between the fingerprints of 18 batches of Bidentis Herba samples and the common pattern(R) ranged from 0.572 to 0.933. A total of 23 chromatographic peaks were calibrated. Through comparison with the reference substances, six components(neochlorogenic acid, chlorogenic acid, isochlorogenic acid A, isochlorogenic acid B, rutin, and hyperoside) were identified and subjected to quantitative analysis. The characteristic fingerprints of B. bipinnata and B. biternata were calibrated with 20 and 17 characteristic peaks, respectively. Among them, peaks 8, 9, 22, and 23 were the characteristic peaks of B. bipinnata, and peak 7 was the characteristic peak of B. biternata, which can be used to distinguish the two original plants of Bidentis Herba. The relative standard deviation of the content of the above-mentioned six components ranged from 36% to 123%. The cluster analysis, principal component analysis, and orthogonal partial least squares-discriminant analysis(OPLS-DA) classified the 18 batches of Bidentis Herba samples into two categories. Additionally, through the analysis of variable importance in projection(VIP) under OPLS-DA, three characteristic indexes, rutin, isochlorogenic acid A, and isochlorogenic acid B, were identified. The analytical method established in this study can comprehensively evaluate the consistency of Bidentis Herba samples derived from different original plants, specifically identify the differential components between them, and effectively distinguish the two original plants of Bidentis Herba, providing a basis for the differentiation between different original plants and the quality control of Bidentis Herba.
Chromatography, High Pressure Liquid/methods* ; Drugs, Chinese Herbal/chemistry* ; Quality Control ; Bidens/chemistry*

OBJECTIVE: The relationship between fish consumption and stroke is inconsistent, and it is uncertain whether this association varies across predicted stroke risks. METHODS: A cohort study comprising 95,800 participants from the Prediction for Atherosclerotic Cardiovascular Disease Risk in China project was conducted. A standardized questionnaire was used to collect data on fish consumption. Participants were stratified into low- and moderate-to-high-risk categories based on their 10-year stroke risk prediction scores. Hazard ratios ( HRs) and 95% confidence intervals ( CIs) were estimated using Cox proportional hazard models and additive interaction by relative excess risk due to interaction (RERI), attributable proportion (AP), and synergy index (SI). RESULTS: During 703,869 person-years of follow-up, 2,773 incident stroke events were identified. Higher fish consumption was associated with a lower risk of stroke, particularly among moderate-to-high-risk individuals ( HR = 0.53, 95% CI: 0.47-0.60) than among low-risk individuals ( HR = 0.64, 95% CI: 0.49-0.85). A significant additive interaction between fish consumption and predicted stroke risk was observed (RERI = 4.08, 95% CI: 2.80-5.36; SI = 1.64, 95% CI: 1.42-1.89; AP = 0.36, 95% CI: 0.28-0.43). CONCLUSION Higher fish consumption was associated with a lower risk of stroke, and this beneficial association was more pronounced in individuals with moderate-to-high stroke risk.
Humans ; China/epidemiology* ; Male ; Female ; Stroke/etiology* ; Middle Aged ; Prospective Studies ; Incidence ; Aged ; Animals ; Fishes ; Risk Factors ; Diet ; Seafood ; Adult ; Cohort Studies

OBJECTIVE: This study aimed to explore the association between body mass index (BMI) and mortality based on the 10-year population-based multicenter prospective study. METHODS: A general population-based multicenter prospective study was conducted at four sites in rural China between 2013 and 2023. Multivariate Cox proportional hazards models and restricted cubic spline analyses were used to assess the association between BMI and mortality. Stratified analyses were performed based on the individual characteristics of the participants. RESULTS: Overall, 19,107 participants with a sum of 163,095 person-years were included and 1,910 participants died. The underweight (< 18.5 kg/m ²) presented an increase in all-cause mortality (adjusted hazards ratio [ aHR] = 2.00, 95% confidence interval [ CI]: 1.66-2.41), while overweight (≥ 24.0 to < 28.0 kg/m ²) and obesity (≥ 28.0 kg/m ²) presented a decrease with an aHR of 0.61 (95% CI: 0.52-0.73) and 0.51 (95% CI: 0.37-0.70), respectively. Overweight ( aHR = 0.76, 95% CI: 0.67-0.86) and mild obesity ( aHR = 0.72, 95% CI: 0.59-0.87) had a positive impact on mortality in people older than 60 years. All-cause mortality decreased rapidly until reaching a BMI of 25.7 kg/m ² ( aHR = 0.95, 95% CI: 0.92-0.98) and increased slightly above that value, indicating a U-shaped association. The beneficial impact of being overweight on mortality was robust in most subgroups and sensitivity analyses. CONCLUSION This study provides additional evidence that overweight and mild obesity may be inversely related to the risk of death in individuals older than 60 years. Therefore, it is essential to consider age differences when formulating health and weight management strategies.
Humans ; Body Mass Index ; China/epidemiology* ; Male ; Female ; Middle Aged ; Prospective Studies ; Rural Population/statistics & numerical data* ; Aged ; Follow-Up Studies ; Adult ; Mortality ; Cause of Death ; Obesity/mortality* ; Overweight/mortality*

Objective To investigate an automated pretreatment technology for detecting levels of free silica in workplace dust. Methods An fully automated pretreatment workstation for detecting free silica levels in workplace dust was developed by integrating graphite-controlled digestion temperature, online-controlled dilution of digestion solutions, and filtration endpoint recognition based on monitoring technology, combined with multi-channel synchronous measurements. Results The fully automatic pretreatment workstation was used to digest and filter 14 standard samples of free silica produced by three institutions, and then detected by pyrophosphate method. The result range of high-, medium-, and low-level free silica standard samples detection was 66.5%-84.8%, 40.0%-44.5%, and 2.1%-24.8%, respectively. The mean relative standard deviations were 3.9%, 1.4% and 1.5%. Conclusion The fully automated pretreatment workstation produced results that met relevant requirements. It can effectively replace the manual digestion and filtration steps of the pyrophosphate method to measure free silica levels in workplace dust and enable rapid detection of free silica in dust samples.

Objective To explore the effect of multiparameter electroencephalogram(EEG)-guided anesthesia management on EEG burst suppression(BS)and postoperative delirium(POD)in elderly patients undergoing lower abdominal laparoscopic surgery.Methods A total of 100 elderly patients,48 males and 52 females,aged 65-85 years,BMI 18.5-28.0 kg/m2,and ASA physical status Ⅱ or Ⅲ,were enrolled for lower abdominal surgery under general anesthesia.Patients were randomly divided into two groups:multiparameter group and single parameter group,50 patients in each group.In multiparameter group,multiparameter EEG monitoring with patient statu index(PSI),spectral edge frequency(SEF),burst suppression ratio(BSR)and density spectral array(DSA)were used to guide the depth management of anesthesia.In single parameter group,single parameter PSI was used to guide the depth management of anesthesia.The total area under the hypotensive threshold of MAP(AUTMAP)was calculated,and the amount of anesthetic used during the operation and the use of vasoactive drugs,duration of anesthesia,extu-bation time,duration of PACU stay,and postoperative hospitalisation days were recorded.HR,MAP,PSI,and SEF were recorded before the induction of anesthesia,5 minutes after induction of anesthesia,5,30,and 60 minutes after incision,and at the end of surgery.The incidence,duration,and maximum BSR of in-traoperative BS,as well as the incidence of POD 1,2,and 3 days after surgery were recorded.Results There was no significant difference in AUTMAP values between the two groups.Compared with single parame-ter group,intraoperative propofol and remifentanil dosage were significantly decreased(P＜0.05),awak-ening time,PACU stay,and postoperative hospitalization time were significantly shorter in multiparameter group(P＜0.05),the PSI was significantly increased 5,30,and 60 minutes after incision and at the end of surgery,and the SEF was significantly increased 5 minutes after induction of anesthesia,5,30,and 60 minutes after induction and the end of surgery(P＜0.05).Compared with single parameter group,inci-dence of intraoperative BS was significantly decreased,duration of BS was significantly shorter,smaller maximum BSR was significantly decreased,and incidence of POD on 1 day after surgery in multiparameter group(P＜0.05).Conclusion Anesthesia management guided by multiparameter EEG can inhibit the oc-currence of BS,mitigate the degree of BS,and reduce the incidence of POD in elderly patients undergoing abdominal surgery.

Purpose::To assess the value of the driving pressure variation rate (ΔP%) in predicting the outcome of weaning from invasive mechanical ventilation in patients with acute respiratory distress syndrome.Methods::In this case-control study, a total of 35 patients with moderate-severe acute respiratory distress syndrome were admitted to the intensive care unit between January 2022 and December 2022 and received invasive mechanical ventilation for at least 48 h were enrolled. Patients were divided into successful weaning group and failed weaning group depending on whether they could be removed from ventilator support within 14 days. Outcome measures including driving pressure, PaO 2:FiO 2, and positive end-expiratory pressure, etc. were assessed every 24 h from day 0 to day 14 until successful weaning was achieved. The measurement data of non-normal distribution were presented as median (Q 1, Q 3), and the differences between groups were compared by Wilcoxon rank sum test. And categorical data use the Chi-square test or Fisher's exact test to compare. The predictive value of ΔP% in predicting the outcome of weaning from the ventilator was analyzed using receiver operating characteristic curves. Results::Of the total 35 patients included in the study, 17 were successful vs. 18 failed in weaning from a ventilator after 14 days of mechanical ventilation. The cut-off values of the median ΔP% measured by Operator 1 vs. Operator 2 in the first 4 days were ≥ 4.17% and 4.55%, respectively ( p < 0.001), with the area under curve of 0.804 (sensitivity of 88.2%, specificity of 64.7%) and 0.770 (sensitivity of 88.2%, specificity of 64.7%), respectively. There was a significant difference in mechanical ventilation duration between the successful weaning group and the failure weaning group (8 (6, 13) vs. 12 (7.5, 17.3), p = 0.043). The incidence of ventilator-associated pneumonia in the successful weaning group was significantly lower than in the failed weaning group (0.2‰ vs. 2.3‰, p = 0.001). There was a significant difference noted between these 2 groups in the 28-day mortality (11.8% vs. 66.7%, p = 0.003). Conclusion::The median ΔP% in the first 4 days of mechanical ventilation showed good predictive performance in predicting the outcome of weaning from mechanical ventilation within 14 days. Further study is needed to confirm this finding.

Objective To investigate the feasibility and value of a multi-parametric MRI radiomics-based nomogram model for pretreatment predicting the lymphovascular space invasion(LVSI)of endometrial endometrioid adenocarcinoma(EEA).Methods Preoperative MRI and baseline clinical characteristics of 205 EEA patients were prospectively collected from Oct 2020 to Jan 2022 in the Obstetrics and Gynecology Hospital,Fudan University,and randomly divided into training set(n=123)and validation set(n=82)in a 6∶4 ratio.The whole-tumor region of interest was manually drawn on T2-weighted imaging,diffusion-weighted imaging(apparent diffusion coefficient),and dynamic contrast-enhanced MRI,respectively,for radiomics features extraction.In the training set,univariate and multivariate Logistic regression analysis were used to select independent clinical predictors of LVSI(+)and construct the clinical model.The least absolute shrinkage and selection operator(LASSO)regression and multivariate Logistic regression analysis were used to select optimal radiomics features to form a radiomics signature.A combined nomogram model was established by integrating clinical independent predictors and the radiomics signature,and validated in the validation set.The predicting performance and clinical net benefit were evaluated by using the area under the receiver operating characteristic curve(AUC)and clinical decision curve analysis,respectively.Results Of the 205 EEA cases,144 cases were LVSI(-)and 61 cases were LVSI(+).Menopausal status,CA125,and CA199 were independent clinical predictors for the LVSI(+),and contributing to a clinical model with AUCs of 0.714(training)and 0.731(validation).From 8 240 extracted radiomics features,five were selected to construct a MRI radiomics signature after de-redundancy and LASSO dimensionality reduction,yielding AUCs of 0.860(training)and 0.759(validation).The combined nomogram model showed AUCs of 0.887(training)and 0.807(validation),outperforming others and achieving maximum clinical benefit in a large range of threshold probability in both training and validation sets.Conclusion The multi-parametric MRI-based nomogram model has the potential for pretreatment predicting the LVSI status of EEA,providing valuable information for clinical management decision-making and improving patient's clinical benefits.