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: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.

Objective:To develop a new type of detection window protective device for surface contamination monitor,and to verify its testing effect.Methods:The new type of surface contamination monitor detection window protection device was composed of the protective film and fixed frame,which was of integrated design and one-off production.The protective film was made of transparent flat Myra film,thickness≤2.5μm,the fixed frame was a rectangular hollow structure surrounded by four edges and provided with a working surface and a mounting surface.The protective film was pasted on the inner wall of the working surface of the fixed frame by adhesive,and the protective film and fixed frame were installed on the outside of the detection window of the surface contamination monitor by means of fixing buckles.Two commonly used surface contamination detection instruments,RDa150 and Como170,were selected to measure artificial radiation sources under three protection states:no film protection,plastic wrap protection and new protection device to test the detection efficiency and operability of the new surface contamination monitor's detection window protection device.Results:Compared with the traditional protective measures of plastic wrap,the detection efficiency of α,β and γ rays was more effectively ensured by the new detection window protective device for surface contamination monitor,and the detection efficiency of α rays was increased from about 40%to about 70%.Conclusion:The new detection window protection device for surface contamination monitor can significantly improve the detection efficiency of radioactive contamination,especially alpha-ray pollution,and effectively protect the instrument and equipment,and effectively improve the detection efficiency of surface contamination detection operators.

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.

Tuberculosis is caused by Mycobacterium tuberculosis,and the problem of its drug resistance has become increasingly prominent in recent years,attracting widespread attention globally.Currently,the situation of drug-resistant tuberculosis is grim,and effective strategies are urgently needed to deal with it.Understanding the drug resistance mechanism and treatment status of drug-resistant tuberculosis can provide an important basis for clinical prevention and treatment of drug-resistant tuberculosis.This paper reviews the progress of drug resistance mechanism and treatment of drug-resistant tuberculosis,in order to provide a reference for clinical intervention.

In order to maximize the detection efficiency of the surface contamination detector on α rays and make up for the problems of insufficient protective measures of the detector,a new detection window protection device for wound contamination detector was designed.The device was consisted of a protective film and a fixed frame sleeve.The protective film was made of a transparent flat Mylar film with a thickness of≤2.5 μm and a circular shape.Its diameter matched the detection surface diameter of the wound radiation measuring instrument probe and can be used in combination with the appropriate model of wound radiation measuring instrument probe.The sleeve was provided with a closed end and an open end,the protective film was fixed on the inner side of the closed end of the sleeve,and the open end of the sleeve was provided with an annular fixing buckle,so that the sleeve can be firmly installed on the probe of the disposable wound radiation measuring instrument.After installation,the protective film can be closely attached to the detection surface of the probe of the wound radiation measuring instrument.Two commonly used contamination detectors were selected to measure the artificial radioactive sources under three protection conditions:film-free protection,plastic wrap protection and new protective devices,and the detection efficiency and operability of the detection window protection device of the new wound contamination detector were tested.The results showed that compared with the traditional protective measures of plastic wrap,the new device can significantly improve the detection efficiency of wound radioactive contamination,especially α contamination,and effectively protect the instrument and equipment,and improve the detection efficiency of wound pollution operators.

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.