Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Background/Aims: Early detection and effective prognosis prediction in patients with hepatocellular carcinoma (HCC) provide an avenue for survival improvement, yet more effective approaches are greatly needed. We sought to develop the detection and prognosis models with ultra-sensitivity and low cost based on fragmentomic features of circulating cell free mtDNA (ccf-mtDNA). Methods: Capture-based mtDNA sequencing was carried out in plasma cell-free DNA samples from 1168 participants, including 571 patients with HCC, 301 patients with chronic hepatitis B or liver cirrhosis (CHB/LC) and 296 healthy controls (HC). Results: The systematic analysis revealed significantly aberrant fragmentomic features of ccf-mtDNA in HCC group when compared with CHB/LC and HC groups. Moreover, we constructed a random forest algorithm-based HCC detection model by utilizing ccf-mtDNA fragmentomic features. Both internal and two external validation cohorts demonstrated the excellent capacity of our model in distinguishing early HCC patients from HC and highrisk population with CHB/LC, with AUC exceeding 0.983 and 0.981, sensitivity over 89.6% and 89.61%, and specificity over 98.20% and 95.00%, respectively, greatly surpassing the performance of alpha-fetoprotein (AFP) and mtDNA copy number. We also developed an HCC prognosis prediction model by LASSO-Cox regression to select 20 fragmentomic features, which exhibited exceptional ability in predicting 1-year, 2-year and 3-year survival (AUC=0.8333, 0.8145 and 0.7958 for validation cohort, respectively). Conclusions We have developed and validated a high-performing and low-cost approach in a large clinical cohort based on aberrant ccf-mtDNA fragmentomic features with promising clinical translational application for the early detection and prognosis prediction of HCC patients.

Based on current national policies, regulations, standards, relevant literature, and departmental experience regarding the protection against radionuclides in China, this study provides a brief overview of key issues in the management of hospital wards for patients with internal radionuclide contamination. The discussion covers the detection of internal contamination, general requirements for internal radionuclide contamination wards, and inpatient management. In addition, the study explores in depth the daily responsibilities, protective measures, and management protocols for both healthcare staff and patients within such wards. This article summarizes a framework for the construction of internal radionuclide contamination wards, along with specific plans and detailed role-based guidelines. These results provide a reference for the management of hospital wards for patients with internal radionuclide contamination.

Objective To investigate the knowledge of tuberculin skin test(TST)among healthcare workers and provide evidence for improving the standardization of TST screening in primary healthcare staff.Methods A questionnaire survey was conducted among 248 licensed physicians or nurses who were qualified as licensed physicians or nurses and responsible for TST work from 27 districts/counties of Chongqing in 2023.The awareness of TST-related knowledge and its influencing factors were statistically analyzed.Results The average TST knowledge score of 248 healthcare workers was(78.3±10.6)points.The overall awareness rate was 78.9%(8 213/10 416),with specific rates as follows:65.4%(1 135/1 736)for tubercu-losis knowledge,87.3%(3 248/3 720)for TST general knowledge,53.4%(795/1 488)for TST principles,88.0%(1 964/2 232)for TST procedures,and 86.4%(1 071/1 240)for TST result interpretation.Nurses showed higher awareness rates than physicians and other staff(P＞0.05).Healthcare workers from medium-epidemic areas demonstrated significantly higher awareness rates than those from high-and low-epidemic are-as(P＜0.001).No statistically significant differences were observed in gender,age,occupation type,institu-tion type,or regional epidemic level between the qualified group and non-qualified group about TST-related knowl-edge(P＞0.05).Conclusion Healthcare workers exhibit incomplete mastery of TST-related knowledge.Strengthening TST-related knowledge training for standardizing TST implementation.

Chronic limb-threatening ischemia(CLTI)is a serious peripheral arterial disease(PAD)characterized by reduced blood flow in the limbs,resulting in tissue damage and dysfunction.Mesenchymal stem cells(MSCs)have become a research hotspot in the field of CLTI treatment in recent years due to their unique regenerative ability and immunomodulatory properties.In the environment of hypoxia and tissue injury,MSCs can promote angiogenesis,reduce inflammation and promote tissue repair by secreting cytokines,cell differentiation and inter-cellular signal transduction,so as to improve the symptoms and prognosis of patients with CLTI,showing a broad clinical application prospect.

OBJECTIVE To investigate the mechanisms by which Pinggan Yishen Decoction(PGYSD)contributes to alleviating target organ damage in spontaneously hypertensive rats.METHODS The chemical components of PGYSD were determined by ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry(UPLC-Q-TOF/MS)and were analyzed by target a-nalysis and functional enrichment combined with network pharmacology methods to predict the potential mechanism of PGYSD in trea-ting hypertension and its target organ damage.Spontaneously hypertensive rats were randomly divided into the model group,low-dose PGYSD group(2 g·kg-1),high-dose PGYSD group(5 g·kg-1),and valsartan group(7.2 mg·kg-1),with 6 rats in each group.Wistar-Kyoto rats were used as the control group,and the control group and the model group were gavaged with normal saline for 8 consecutive weeks.HE and Masson staining were used to observe the pathological damage and fibrosis degree of rat heart and tho-racic aorta.Immunohistochemical staining and Western blot were used to detect the expression level of EGFR in the heart,liver and kidney of rats.Immunofluorescence staining was used to detect the co-localization of EGFR and EEA1 in the heart,liver and kidney of rats.RESULTS Twenty-six components of PGYSD were detected by UPLC-Q-TOF/MS.Network pharmacology revealed that EG-FR,PIK3R1 and EP300 may be key therapeutic targets of action of PGYSD for the treatment of hypertension and its target organ dam-age,and that the treatment of hypertension and its target organ damage by PGYSD may be closely related to EGFR tyrosine kinase in-hibitor resistance,lipids and atherosclerosis and HIF-1 signaling pathway.The high-dose group of PGYSD significantly reduced sys-tolic blood pressure and mean blood pressure in rats(P<0.05,P<0.01),attenuated pathological damage and fibrosis in the heart and thoracic aorta(P<0.01,P<0.001),significantly reduced the expression level of EGFR in the liver and kidney of rats(P<0.01),and treated fibrosis in liver and kidney,reduced the co-localization of EGFR and EEA1 in the kidney of rats(P<0.001),attenuated fibro-sis in kidney.CONCLUSION The paper integrates UPLC-Q-TOF/MS,network pharmacology and spontaneously hypertensive rat model and preliminarily explores the effect mechanism of PGYSD in the treatment of hypertension and its target organ damage,provi-ding a scientific basis for further mechanism research and clinical application of PGYSD in the treatment of hypertension.

Background and purpose:Human epidermal growth factor receptor 2(HER2)is closely associated with drug efficacy and prognosis in urothelial carcinoma(UC).HER2 is a significant biomarker and therapeutic target in various tumors.In recent years,anti-HER2 antibody-drug conjugates have shown significant clinical efficacy in UC patients with HER2 overexpression.Therefore,an in-depth understanding of HER2 expression and its characteristics in Chinese UC patients is crucial to guide treatment decision-making,optimize treatment strategies and achieve personalized therapy.This study aimed to thoroughly investigate correlation of HER2 expression and clinicopathological characteristics in Chinese patients with UC.Methods:This study was a multicenter study that retrospectively included UC patients from urology departments of 8 tertiary hospitals in 5 geographical regions of China(North China,East China,South China,Central China and Northwest)whose tissue samples were collected from January 2023 to March 2024.Inclusion criteria:① age above 18 years;② UC diagnosed by histopathological or cytological examination;③ complete results of HER2 expression detection using immunohistochemistry(IHC)in the primary tumor site were required.Exclusion criteria:① diagnosed patients with tumors in other parts of the body;② physicians evaluated other situations that were not suitable for inclusion in this study.IHC results for HER2 expression and clinicopathological data were collected.HER2 expression was determined according to the criteria outlined in"Clinical pathological expert consensus on HER2 testing in urothelial carcinoma in China",with HER2 2+and 3+defined as HER2 overexpression.The HER2 expression and clinicopathological features were analyzed.This study was approved by the medical ethics committee of Fudan University Shanghai Cancer Center(ethical number:2301268-12)and was registered at China Clinical Trial Registry(registration number:ChiCTR2300069746).Results:A total of 1054 patients with UC were included.Most of the tumors were bladder UC(n=807,76.6%).The mean age of patients was(66.8±10.5)years,and the majority were male(78.5%).The HER2 overexpression rate was 58.4%(n=616),with an additional 23%of patients having HER2 1+expression(n=242),and a small proportion exhibiting negative HER2 expression(n=196,18.6%).HER2 expression was significantly associated with various clinical and pathological characteristics such as Eastern Cooperative Oncology Group(ECOG)performance status,history of cardiovascular disease,history of metabolic disorders,smoking,UC disease location,differentiation grade,pathological type,and tumor stage.Conclusion:Retrospective analysis of multi-center data shows that HER2 expression is frequently observed in Chinese UC patients,with an overexpression rate of up to 58.4%.Furthermore,HER2 expression is closely associated with various clinical and pathological features of UC patients.This study underscores the critical importance of accurately assessing HER2 expression in UC patient to guide personalized therapies.

Objective:To develop an Enteral Nutrition Knowledge, Attitude and Practice Questionnaire for ICU Nurses in Prone Position Ventilation Patients, and test its reliability and validity.Methods:Based on the theory of knowledge, attitude and practice, the item pool was initially constructed through literature review, group discussions, and semi-structured interviews. The initial questionnaire was formed through two rounds of Delphi expert consultation, and the initial questionnaire items were adjusted based on semi-structured interviews. From November to December 2023, convenience sampling was used to select 254 ICU nurses from six Class Ⅲ Grade A hospitals in Henan Province to test the reliability and validity of the questionnaire. Project analysis method was used for item screening, exploratory factor analysis and expert consultation method were used to evaluate the validity of the questionnaire, and internal consistency coefficient, split half reliability, and test-retest reliability were used to evaluate the reliability of the questionnaire.Results:The Enteral Nutrition Knowledge, Attitude and Practice Questionnaire for ICU Nurses in Prone Position Ventilation Patients was divided into three levels, with a total of 7 dimensions and 30 items. The knowledge level included three dimensions and 12 items, the attitude level consisted of three dimensions and 8 items, and the practice level includes one dimension and 10 items. Exploratory factor analysis proposed 7 common factors with a cumulative variance contribution rate of 65.516%. The content validity index at the questionnaire level was 0.92, and the content validity index at the item level was 0.80 to 1.00. The overall Cronbach's α coefficient of the questionnaire was 0.874, the split half reliability coefficient was 0.813, and the test-retest reliability coefficient was 0.989.Conclusions:The Enteral Nutrition Knowledge, Attitude and Practice Questionnaire for ICU Nurses in Prone Position Ventilation Patients has good reliability and validity, and can be used as an evaluation tool for ICU nurses to assess the knowledge, attitude, and practice of enteral nutrition for prone position ventilation patients.

Objective:Glioblastoma(GBM)and brain metastases(BMs)are the two most common malignant brain tumors in adults.Magnetic resonance imaging(MRI)is a commonly used method for screening and evaluating the prognosis of brain tumors,but the specificity and sensitivity of conventional MRI sequences in differential diagnosis of GBM and BMs are limited.In recent years,deep neural network has shown great potential in the realization of diagnostic classification and the establishment of clinical decision support system.This study aims to apply the radiomics features extracted by deep learning techniques to explore the feasibility of accurate preoperative classification for newly diagnosed GBM and solitary brain metastases(SBMs),and to further explore the impact of multimodality data fusion on classification tasks. Methods:Standard protocol cranial MRI sequence data from 135 newly diagnosed GBM patients and 73 patients with SBMs confirmed by histopathologic or clinical diagnosis were retrospectively analyzed.First,structural T1-weight,T1C-weight,and T2-weight were selected as 3 inputs to the entire model,regions of interest(ROIs)were manually delineated on the registered three modal MR images,and multimodality radiomics features were obtained,dimensions were reduced using a random forest(RF)-based feature selection method,and the importance of each feature was further analyzed.Secondly,we used the method of contrast disentangled to find the shared features and complementary features between different modal features.Finally,the response of each sample to GBM and SBMs was predicted by fusing 2 features from different modalities. Results:The radiomics features using machine learning and the multi-modal fusion method had a good discriminatory ability for GBM and SBMs.Furthermore,compared with single-modal data,the multimodal fusion models using machine learning algorithms such as support vector machine(SVM),Logistic regression,RF,adaptive boosting(AdaBoost),and gradient boosting decision tree(GBDT)achieved significant improvements,with area under the curve(AUC)values of 0.974,0.978,0.943,0.938,and 0.947,respectively;our comparative disentangled multi-modal MR fusion method performs well,and the results of AUC,accuracy(ACC),sensitivity(SEN)and specificity(SPE)in the test set were 0.985,0.984,0.900,and 0.990,respectively.Compared with other multi-modal fusion methods,AUC,ACC,and SEN in this study all achieved the best performance.In the ablation experiment to verify the effects of each module component in this study,AUC,ACC,and SEN increased by 1.6%,10.9%and 15.0%,respectively after 3 loss functions were used simultaneously. Conclusion:A deep learning-based contrast disentangled multi-modal MR radiomics feature fusion technique helps to improve GBM and SBMs classification accuracy.