Objective:To explore the efficacy of deep learning-based automatic segmentation technology in the segmentation of hepatocellular carcinoma (HCC) lesions using gadoxetate disodium-enhanced MRI (EOB-MRI), and to investigate the prognostic value of radiomics analysis in predicting patient outcomes.Methods:This was a cross-sectional, retrospective study that collected data from 352 patients with solitary HCC who underwent imaging at the Harbin Medical University Cancer Hospital between June 2015 and May 2023. The patients were randomly divided into a training set ( n=213) and a validation set ( n=139) in a 3∶2 ratio using weighted random sampling. Two radiologists manually annotated the lesions. Hepatobiliary-phase EOB-MRI images were standardized, and six deep learning models,nnU-Net, nnFormer, UnetR, Swin-UnetR, UnetR++ and MedNeXt,were trained for automatic segmentation on the training set. The segmentation performance was evaluated on the validation set, and the segmentation efficacy was assessed using the Dice coefficient and 95% Hausdorff distance (HD 95), identifying of the optimal model. Radiomics features were extracted from both manual and automatic segmentation regions, and the radiomics score (Radscore) was calculated to stratify patients into high-risk and low-risk groups. Kaplan-Meier curves and log-rank tests were used to analyze the differences in relapse-free survival (RFS) and overall survival (OS) between the different stratified groups. Results:Among the automatic segmentation models, the MedNeXt model performed best in the validation set, with a Dice coefficient of 76.0%, HD 95 of 7.2, and a segmentation success rate of 90.6% (126/139). The nnFormer model was the second-best, with a Dice coefficient of 75.3%, HD 95 of 10.1, and a segmentation success rate of 89.9% (125/139). Other models showed Dice coefficients ranging from 66.3% to 74.1%. A MedNext-nnF model was established by combining the MedNeXt and nnFormer models, achieving a Dice coefficient of 78.2%, HD 95 of 5.9, and a segmentation success rate of 92.1% (128/139) in the validation group. After constructing the automatic segmentation radiomics prognostic model, patients were stratified by Radscore. Both manual and automatic segmentation models showed statistically significant differences in RFS and OS between different risk groups ( P<0.001). Conclusions:The Mednext-nnF fusion model enables efficient and automated segmentation of HCC lesions in EOB-MRI. The radiomics model constructed based on the automated segmentation demonstrates strong performance in predicting and stratifying prognostic risk.

Objective To translate Evidence-Informed Decision-Making Competence Measure for Chinese nurses and test its validity and reliability.Methods A research group was set up to use the Brislin translation model to translate the original scale into Chinese,and the back translation,cross-cultural adaptation,pre-experiment and cognitive interview were conducted to finally form the Chinese version of the Evidence-Informed Decision-Making Competence Measure for nurses.A total of 1 247 nurses from 7 tertiary A hospitals in Beijing,Hubei,Hunan and Xinjiang were selected by convenience sampling method in April 2024 to test its reliability and validity.Results 1 026 effective question-naires were collected,with an effective recovery rate of 82.28％.The Chinese version of the Evidence-Informed Decision-Making Competence Measure included 25 items,including knowledge/skill,attitude and behavior.A total of 3 common factors were extracted from exploratory factor analysis,and the cumulative variance contribution rate was 91.725％.The content validity index at the item level was 0.83-1.00;the content validity index at the scale level was 0.988;the calibration association validity was 0.496.The Cronbach's α coefficient of the whole scale was 0.992;the half-point reliability was 0.930;the retest reliability was 0.927.Conclusion The Chinese version of Evidence-Informed Decision-Making Competence Measure for nurses has good reliability and validity,and it can be used to evaluate the evidence-informed decision-making competence of Chinese nurses,provide references for promoting evidence-based nursing practice and evidence-informed decision-making.

OBJECTIVE: To reveal the effects and potential mechanisms by which synaptic vesicle glycoprotein 2A (SV2A) influences the distribution of amyloid precursor protein (APP) in the trans-Golgi network (TGN), endolysosomal system, and cell membranes and to reveal the effects of SV2A on APP amyloid degradation. METHODS: Colocalization analysis of APP with specific tagged proteins in the TGN, ensolysosomal system, and cell membrane was performed to explore the effects of SV2A on the intracellular transport of APP. APP, β-site amyloid precursor protein cleaving enzyme 1 (BACE1) expressions, and APP cleavage products levels were investigated to observe the effects of SV2A on APP amyloidogenic processing. RESULTS: APP localization was reduced in the TGN, early endosomes, late endosomes, and lysosomes, whereas it was increased in the recycling endosomes and cell membrane of SV2A-overexpressed neurons. Moreover, Arl5b (ADP-ribosylation factor 5b), a protein responsible for transporting APP from the TGN to early endosomes, was upregulated by SV2A. SV2A overexpression also decreased APP transport from the cell membrane to early endosomes by downregulating APP endocytosis. In addition, products of APP amyloid degradation, including sAPPβ, Aβ _1-42, and Aβ _1-40, were decreased in SV2A-overexpressed cells. CONCLUSION These results demonstrated that SV2A promotes APP transport from the TGN to early endosomes by upregulating Arl5b and promoting APP transport from early endosomes to recycling endosomes-cell membrane pathway, which slows APP amyloid degradation.
Amyloid beta-Protein Precursor/genetics* ; Membrane Glycoproteins/genetics* ; Animals ; Protein Transport ; Nerve Tissue Proteins/genetics* ; Humans ; Mice ; Endosomes/metabolism* ; trans-Golgi Network/metabolism*

This study aims to explore the mechanism of Huanglian Jiedu Decoction(HJD) in treating acute liver injury(ALI) in the mouse model of sepsis induced by lipopolysaccharide(LPS). Fifty-four male C57BL/6 mice were randomized into six groups: blank group, model group, low-, medium-, and high-dose group HJD, and dexamethasone group. The mouse model of sepsis was established by intraperitoneal injection of LPS after 7 days of gavage with HJD, and dexamethasone(0.2 mL) was injected intraperitoneally 1.5 h after modeling. The murine sepsis score(MSS) was recorded 12 h after modeling. The levels of alanine aminotransferase(ALT) and aspartate aminotransferase(AST) in the liver tissue and tumor necrosis factor-α(TNF-α) and interleukin-6(IL-6) in the serum were measured by ELISA. Hematoxylin-eosin(HE) staining was used to observe the pathological changes of the mouse liver. The content of light chain 3 of microtubule-associated protein 1(LC3) was detected by immunofluorescence, and that of sirtuin 1(SIRT1) was detected by immunohistochemistry. The mRNA levels of adenosine 5'-monophosphate-activated protein kinase(AMPK), LC3, and P62 were detected by RT-PCR. Western blot was employed to determine the protein levels of AMPK, p-AMPK, and SIRT1 in the liver tissue. The results showed that compared with model group, drug interventions decreased the MSS and liver injury indicators, lowered the levels of inflammatory cytokines, improved the liver tissue structure, upregulated the protein levels of of p-AMPK/AMPK and SIRT1 and the mRNA levels of AMPK and LC3, and downregulated the mRNA level of P62. To sum up, HJD can regulate the autophagy level and reduce inflammation to ameliorate acute liver injury in septic mice by activating the AMPK/SIRT1 autophagy pathway.
Animals ; Drugs, Chinese Herbal/administration & dosage* ; Sirtuin 1/genetics* ; Male ; Mice ; Sepsis/metabolism* ; Mice, Inbred C57BL ; Autophagy/drug effects* ; AMP-Activated Protein Kinases/genetics* ; Liver/metabolism* ; Humans ; Signal Transduction/drug effects* ; Disease Models, Animal ; Tumor Necrosis Factor-alpha/genetics*

Barrett's esophagus(BE),a precancerous state of esophageal adenocarcinoma,poses a major challenge for prevention and treatment owing to its complex mechanism of inflammation-cancer transformation and the lack of effective clinical treatment and torsion strategies.Building upon the"preventing disease progression"theory,this study aimed to address the critical clinical challenge of intercepting the pathological progression during the inflammation-cancer transformation of BE by proposing an innovative"two critical nodes-three stages"pathomechanism framework.The pathogenesis of BE originates from liver depression and qi stagnation.The pathological progression evolves through two critical nodes:liver depression transforming into heat and heat transforming into blood stasis,representing a three-stage evolutionary pattern of qi stagnation,heat transformation,and blood stasis formation.Acidic bile salts,acting as a pathogenic toxin,permeate the entire process and catalyze carcinogenesis.Based on this understanding,the therapeutic principles of"treatment from the liver"and"truncation and torsion"were established,emphasizing stage-specific interventions.For the qi stagnation stage,treatment focuses on soothing the liver and regulating qi,as well as moistening,harmonizing,and descending the qi.This is achieved by combining modified Chaihu Shugan Powder with Xuanfu Daizhe Decoction,while using pungent and drying herbs cautiously and supplementing them with light and floral herbs.In the heat transformation stage,the strategy aims to clear the liver and drain heat while protecting yin and harmonizing the stomach,employing modified Huaganjian combined with Yiguanjian and supplemented with Jinlingzi Powder to clear depressed fire.For the blood stasis formation stage,treatment involves activating blood and resolving stasis,combined with supporting healthy qi and removing toxins.This is achieved using a modified Gexia Zhuyu Decoction,supplemented with Liujunzi Decoction,and additions such as Radix Salviae Miltiorrhizae and turtle carapace to disperse nodules and reduce masses.This theoretical framework establishes a diagnostic and therapeutic model characterized by the integration of disease mechanisms with pathology and the mutual reference of macro-level signs with micro-level indicators.It provides a comprehensive clinical practice pathway,complete with principles,methods,formulas,and herbs,for the stage-specific interception of inflammation-cancer transformation in BE using traditional Chinese medicine.

Objective To translate Evidence-Informed Decision-Making Competence Measure for Chinese nurses and test its validity and reliability.Methods A research group was set up to use the Brislin translation model to translate the original scale into Chinese,and the back translation,cross-cultural adaptation,pre-experiment and cognitive interview were conducted to finally form the Chinese version of the Evidence-Informed Decision-Making Competence Measure for nurses.A total of 1 247 nurses from 7 tertiary A hospitals in Beijing,Hubei,Hunan and Xinjiang were selected by convenience sampling method in April 2024 to test its reliability and validity.Results 1 026 effective question-naires were collected,with an effective recovery rate of 82.28％.The Chinese version of the Evidence-Informed Decision-Making Competence Measure included 25 items,including knowledge/skill,attitude and behavior.A total of 3 common factors were extracted from exploratory factor analysis,and the cumulative variance contribution rate was 91.725％.The content validity index at the item level was 0.83-1.00;the content validity index at the scale level was 0.988;the calibration association validity was 0.496.The Cronbach's α coefficient of the whole scale was 0.992;the half-point reliability was 0.930;the retest reliability was 0.927.Conclusion The Chinese version of Evidence-Informed Decision-Making Competence Measure for nurses has good reliability and validity,and it can be used to evaluate the evidence-informed decision-making competence of Chinese nurses,provide references for promoting evidence-based nursing practice and evidence-informed decision-making.

Barrett's esophagus(BE),a precancerous state of esophageal adenocarcinoma,poses a major challenge for prevention and treatment owing to its complex mechanism of inflammation-cancer transformation and the lack of effective clinical treatment and torsion strategies.Building upon the"preventing disease progression"theory,this study aimed to address the critical clinical challenge of intercepting the pathological progression during the inflammation-cancer transformation of BE by proposing an innovative"two critical nodes-three stages"pathomechanism framework.The pathogenesis of BE originates from liver depression and qi stagnation.The pathological progression evolves through two critical nodes:liver depression transforming into heat and heat transforming into blood stasis,representing a three-stage evolutionary pattern of qi stagnation,heat transformation,and blood stasis formation.Acidic bile salts,acting as a pathogenic toxin,permeate the entire process and catalyze carcinogenesis.Based on this understanding,the therapeutic principles of"treatment from the liver"and"truncation and torsion"were established,emphasizing stage-specific interventions.For the qi stagnation stage,treatment focuses on soothing the liver and regulating qi,as well as moistening,harmonizing,and descending the qi.This is achieved by combining modified Chaihu Shugan Powder with Xuanfu Daizhe Decoction,while using pungent and drying herbs cautiously and supplementing them with light and floral herbs.In the heat transformation stage,the strategy aims to clear the liver and drain heat while protecting yin and harmonizing the stomach,employing modified Huaganjian combined with Yiguanjian and supplemented with Jinlingzi Powder to clear depressed fire.For the blood stasis formation stage,treatment involves activating blood and resolving stasis,combined with supporting healthy qi and removing toxins.This is achieved using a modified Gexia Zhuyu Decoction,supplemented with Liujunzi Decoction,and additions such as Radix Salviae Miltiorrhizae and turtle carapace to disperse nodules and reduce masses.This theoretical framework establishes a diagnostic and therapeutic model characterized by the integration of disease mechanisms with pathology and the mutual reference of macro-level signs with micro-level indicators.It provides a comprehensive clinical practice pathway,complete with principles,methods,formulas,and herbs,for the stage-specific interception of inflammation-cancer transformation in BE using traditional Chinese medicine.

Objective:To explore the efficacy of deep learning-based automatic segmentation technology in the segmentation of hepatocellular carcinoma (HCC) lesions using gadoxetate disodium-enhanced MRI (EOB-MRI), and to investigate the prognostic value of radiomics analysis in predicting patient outcomes.Methods:This was a cross-sectional, retrospective study that collected data from 352 patients with solitary HCC who underwent imaging at the Harbin Medical University Cancer Hospital between June 2015 and May 2023. The patients were randomly divided into a training set ( n=213) and a validation set ( n=139) in a 3∶2 ratio using weighted random sampling. Two radiologists manually annotated the lesions. Hepatobiliary-phase EOB-MRI images were standardized, and six deep learning models,nnU-Net, nnFormer, UnetR, Swin-UnetR, UnetR++ and MedNeXt,were trained for automatic segmentation on the training set. The segmentation performance was evaluated on the validation set, and the segmentation efficacy was assessed using the Dice coefficient and 95% Hausdorff distance (HD 95), identifying of the optimal model. Radiomics features were extracted from both manual and automatic segmentation regions, and the radiomics score (Radscore) was calculated to stratify patients into high-risk and low-risk groups. Kaplan-Meier curves and log-rank tests were used to analyze the differences in relapse-free survival (RFS) and overall survival (OS) between the different stratified groups. Results:Among the automatic segmentation models, the MedNeXt model performed best in the validation set, with a Dice coefficient of 76.0%, HD 95 of 7.2, and a segmentation success rate of 90.6% (126/139). The nnFormer model was the second-best, with a Dice coefficient of 75.3%, HD 95 of 10.1, and a segmentation success rate of 89.9% (125/139). Other models showed Dice coefficients ranging from 66.3% to 74.1%. A MedNext-nnF model was established by combining the MedNeXt and nnFormer models, achieving a Dice coefficient of 78.2%, HD 95 of 5.9, and a segmentation success rate of 92.1% (128/139) in the validation group. After constructing the automatic segmentation radiomics prognostic model, patients were stratified by Radscore. Both manual and automatic segmentation models showed statistically significant differences in RFS and OS between different risk groups ( P<0.001). Conclusions:The Mednext-nnF fusion model enables efficient and automated segmentation of HCC lesions in EOB-MRI. The radiomics model constructed based on the automated segmentation demonstrates strong performance in predicting and stratifying prognostic risk.

Objective To explore the efficacy and safety of recombinant human anti-severe acute respiratory syn-drome coronavirus 2(anti-SARS-CoV-2)monoclonal antibody injection(F61 injection)in the treatment of patients with coronavirus disease 2019(COVID-19)combined with renal damage.Methods Patients with COVID-19 and renal damage who visited the PLA General Hospital from January to February 2023 were selected.Subjects were randomly divided into two groups.Control group was treated with conventional anti-COVID-19 therapy,while trial group was treated with conventional anti-COVID-19 therapy combined with F61 injection.A 15-day follow-up was conducted after drug administration.Clinical symptoms,laboratory tests,electrocardiogram,and chest CT of pa-tients were performed to analyze the efficacy and safety of F61 injection.Results Twelve subjects(7 in trial group and 5 in control group)were included in study.Neither group had any clinical progression or death cases.The ave-rage time for negative conversion of nucleic acid of SARS-CoV-2 in control group and trial group were 3.2 days and 1.57 days(P=0.046),respectively.The scores of COVID-19 related target symptom in the trial group on the 3rd and 5th day after medication were both lower than those of the control group(both P＜0.05).According to the clinical staging and World Health Organization 10-point graded disease progression scale,both groups of subjects improved but didn't show statistical differences(P＞0.05).For safety,trial group didn't present any infusion-re-lated adverse event.Subjects in both groups demonstrated varying degrees of elevated blood glucose,elevated urine glucose,elevated urobilinogen,positive urine casts,and cardiac arrhythmia,but the differences were not statistica-lly significant(all P＞0.05).Conclusion F61 injection has initially demonstrated safety and clinical benefit in trea-ting patients with COVID-19 combined with renal damage.As the domestically produced drug,it has good clinical accessibility and may provide more options for clinical practice.

Objective China is among the 30 countries with a high burden of tuberculosis(TB)worldwide,and TB remains a public health concern.Kashgar Prefecture in the southern Xinjiang Autonomous Region is considered as one of the highest TB burden regions in China.However,molecular epidemiological studies of Kashgar are lacking. Methods A population-based retrospective study was conducted using whole-genome sequencing(WGS)to determine the characteristics of drug resistance and the transmission patterns. Results A total of 1,668 isolates collected in 2020 were classified into lineages 2(46.0%),3(27.5%),and 4(26.5%).The drug resistance rates revealed by WGS showed that the top three drugs in terms of the resistance rate were isoniazid(7.4%,124/1,668),streptomycin(6.0%,100/1,668),and rifampicin(3.3%,55/1,668).The rate of rifampicin resistance was 1.8%(23/1,290)in the new cases and 9.4%(32/340)in the previously treated cases.Known resistance mutations were detected more frequently in lineage 2 strains than in lineage 3 or 4 strains,respectively:18.6%vs.8.7 or 9%,P<0.001.The estimated proportion of recent transmissions was 25.9%(432/1,668).Multivariate logistic analyses indicated that sex,age,occupation,lineage,and drug resistance were the risk factors for recent transmission.Despite the low rate of drug resistance,drug-resistant strains had a higher risk of recent transmission than the susceptible strains(adjusted odds ratio,1.414;95%CI,1.023-1.954;P = 0.036).Among all patients with drug-resistant tuberculosis(DR-TB),78.4%(171/218)were attributed to the transmission of DR-TB strains. Conclusion Our results suggest that drug-resistant strains are more transmissible than susceptible strains and that transmission is the major driving force of the current DR-TB epidemic in Kashgar.