In 2025,the International Association of Pancreatology(IAP),in collaboration with the American Pancreatic Association,European Pancreatic Club,Indian Pancreas Club,and Japan Pancreas Society,released the International Association of Pancreatology revised guidelines on acute pancreatitis 2025.This edition represents a comprehensive revision of the 2013 guidelines,based on high-quality evidence accumulated over the past decade,particularly randomized controlled trials.The guidelines encompass 18 key areas-including pain management,fluid therapy,nutritional support,management of infected necrosis,complication control,discharge and follow-up,and recurrence prevention-offering a total of 96 recommendations that emphasize individualized treatment.These updates provide important guidance for standardizing clinical practice and improving outcomes in acute pancreatitis,while also indicating future research directions such as the development of targeted therapies.However,some recommendations remain limited by lower evidence quality,uncertain applicability in specific clinical settings,and insufficient consideration of economic burden and cost-effectiveness.

Objective To explore the value of deep learning image reconstruction(DLIR)algorithm to improve the quality of low keV monochromatic portal vein images of energy spectrum CT.Methods Fifty patients who underwent enhanced upper abdominal energy spectrum CT scan were selected.Mixed-model adaptive statistical iterative reconstruction-Veo(50%ASIR-V)algorithm and high-deep learning image reconstruction(DLIR-H)algorithm were used to obtain monochromatic images at 40-70 keV(with intervals of 10 keV).The CT and standard deviation(SD)values of the portal vein trunk,left and right branches,and erector spinae muscle were measured in the transverse position,and the signal-to-noise ratio(SNR)and portal vein contrast-to-noise ratio(CNR)were calculated for objective evaluation.The portal vein image quality between the two algorithms and different energy was subjectively scored by two physicians.Results In terms of objective evaluation:compared with 50%ASIR-V,the CNR and SNR of portal vein in monochromatic DLIR-H images at the same keV between 40-70 keV energy levels were increased while the SD value was decreased(P＜0.05),and the CT value was unchanged;there was no statistical difference in the magnitude of change in CNR between the two algorithms at different energy levels(P＞0.05);there was a statistically significant difference in the magnitude of change in SNR and SD value(P＜0.05)and the magnitude of change was the largest at 40 keV;comparison between different energy levels of DLIR-H,the CNR and SD value of 40 keV DLIR-H were the highest(P＜0.05),and there was no significant difference in the SNR(P＞0.05).In terms of subjective evaluation:there was no significant difference between the subjective scores of the two algorithms at the same keV from 40-70 keV(P＞0.05),and the two reconstruction algorithms at 40 keV and 50 keV had the highest subjective scores between different keV.Conclusion The DLIR algorithm can reduce the noise of low keV monochromatic images,improve the image quality of portal vein.

In 2025,the International Association of Pancreatology(IAP),in collaboration with the American Pancreatic Association,European Pancreatic Club,Indian Pancreas Club,and Japan Pancreas Society,released the International Association of Pancreatology revised guidelines on acute pancreatitis 2025.This edition represents a comprehensive revision of the 2013 guidelines,based on high-quality evidence accumulated over the past decade,particularly randomized controlled trials.The guidelines encompass 18 key areas-including pain management,fluid therapy,nutritional support,management of infected necrosis,complication control,discharge and follow-up,and recurrence prevention-offering a total of 96 recommendations that emphasize individualized treatment.These updates provide important guidance for standardizing clinical practice and improving outcomes in acute pancreatitis,while also indicating future research directions such as the development of targeted therapies.However,some recommendations remain limited by lower evidence quality,uncertain applicability in specific clinical settings,and insufficient consideration of economic burden and cost-effectiveness.

Objective To explore the value of deep learning image reconstruction(DLIR)algorithm to improve the quality of low keV monochromatic portal vein images of energy spectrum CT.Methods Fifty patients who underwent enhanced upper abdominal energy spectrum CT scan were selected.Mixed-model adaptive statistical iterative reconstruction-Veo(50%ASIR-V)algorithm and high-deep learning image reconstruction(DLIR-H)algorithm were used to obtain monochromatic images at 40-70 keV(with intervals of 10 keV).The CT and standard deviation(SD)values of the portal vein trunk,left and right branches,and erector spinae muscle were measured in the transverse position,and the signal-to-noise ratio(SNR)and portal vein contrast-to-noise ratio(CNR)were calculated for objective evaluation.The portal vein image quality between the two algorithms and different energy was subjectively scored by two physicians.Results In terms of objective evaluation:compared with 50%ASIR-V,the CNR and SNR of portal vein in monochromatic DLIR-H images at the same keV between 40-70 keV energy levels were increased while the SD value was decreased(P＜0.05),and the CT value was unchanged;there was no statistical difference in the magnitude of change in CNR between the two algorithms at different energy levels(P＞0.05);there was a statistically significant difference in the magnitude of change in SNR and SD value(P＜0.05)and the magnitude of change was the largest at 40 keV;comparison between different energy levels of DLIR-H,the CNR and SD value of 40 keV DLIR-H were the highest(P＜0.05),and there was no significant difference in the SNR(P＞0.05).In terms of subjective evaluation:there was no significant difference between the subjective scores of the two algorithms at the same keV from 40-70 keV(P＞0.05),and the two reconstruction algorithms at 40 keV and 50 keV had the highest subjective scores between different keV.Conclusion The DLIR algorithm can reduce the noise of low keV monochromatic images,improve the image quality of portal vein.

Capturing and replicating experts'acupuncture manipulation techniques is a crucial aspect in inheriting acupuncture manipulation techniques.The precision,stability and real-time performance during the capturing process will directly impact the quality of the technique replication.This article summarized the methods and application status of acupuncture technique capturing from the aspects of acupuncture technique measuring instrument,visual collection,and tactile collection,and analyzed the shortcomings of current research,further explored the application prospect of motion capture technologies such as optical technology and electromagnetic navigation technology in acupuncture manipulation acquisition,and provided references for acupuncture manipulation acquisition research.

Capturing and replicating experts'acupuncture manipulation techniques is a crucial aspect in inheriting acupuncture manipulation techniques.The precision,stability and real-time performance during the capturing process will directly impact the quality of the technique replication.This article summarized the methods and application status of acupuncture technique capturing from the aspects of acupuncture technique measuring instrument,visual collection,and tactile collection,and analyzed the shortcomings of current research,further explored the application prospect of motion capture technologies such as optical technology and electromagnetic navigation technology in acupuncture manipulation acquisition,and provided references for acupuncture manipulation acquisition research.

Objective:To explore the influencing factors of left ventricular thrombus (LVT) in patients with non-ischemic heart failure (NIHF) and to construct a nomogram prediction model for NIHF patients with LVT.Methods:This study was a case-control study. A total of 2 592 patients with NIHF hospitalized in Beijing Anzhen Hospital affiliated to Capital Medical University from January 2018 to July 2022 were selected. Fifty-one patients with LVT identified by echocardiography and cardiac magnetic resonance were classified into LVT group. One hundred and sixty patients were selected as the non-LVT group using a 1∶3 propensity score matching based on age and gender. Multivariate logistic regression analysis was used to explore the influencing factors of LVT in patients with NIHF. A nomogram prediction model was constructed, and the area under (AUC) the receiver operating characteristic (ROC) curve was calculated to evaluate the predictive effect of the model.Results:A total of 211 patients were enrolled, with a median age of 40 years old and 160 males (76%). Compared with non-LVT group, LVT group had lower systolic blood pressure ((112±20) mmHg vs. (120±19) mmHg; 1 mmHg=0.133 kPa), lower left ventricular ejection fraction (LVEF; (27±12)% vs. (39±14)% ), lower proportion of patients with history of hypertension (28% (14/51) vs. 44% (70/160)) and atrial fibrillation (8% (4/51)vs.39% (62/160)), higher proportion of patients with New York Heart Association functional class Ⅲ to Ⅳ (class Ⅲ: 59% (30/51) vs. 41% (66/160); class Ⅳ: 28% (14/51) vs. 19% (31/160)), and larger left ventricular end-systolic diameter (LVESD; (56±14) mm vs. (50±15) mm). The levels of hemoglobin ((152±23) g/L vs. (142±30) g/L), D-dimer (508 (300, 1 105) μg/L vs. 158 (68, 379) μg/L), and N-terminal pro-brain natriuretic peptide (3 429 (2 462, 4 734) ng/L vs. 1 288 (422, 2 544) ng/L) were higher in LVT group than in non-LVT group ( P all<0.05). LVT group had a higher proportion of patients using beta-blockers (92% (47/51) vs. 78% (124/160)), angiotensin-converting enzyme inhibitors or angiotensin receptor blockers or angiotensin receptor neprilysin inhibitors (88% (45/51) vs. 72% (115/160)), and anticoagulant drugs (98% (50/51) vs. 32% (51/160)) than non-LVT group (all P <0.05). Multivariate logistic regression showed that reduced LVEF ( OR=1.08, 95% CI 1.02-1.15, P=0.008), decreased LVESD ( OR=1.07, 95% CI 1.01-1.12, P=0.013), and increased D-dimer levels ( OR=5.40, 95% CI 1.98-14.74, P=0.001) were independent influencing factors for LVT in patients with NIHF. The ROC curve showed that the AUC of the nomogram for predicting LVT in patients with NIHF was 0.793 (95% CI 0.710-0.876, P<0.001). Conclusion:Reduced LVEF, decreased LVESD, and elevated D-dimer are associated with LVT in NIHF patients. The predictive model developed based on the above indicators has certain value in predicting LVT in NIHF patients.

Background::Pulmonary embolism (PE) is a severe and acute cardiovascular syndrome with high mortality among patients with autoimmune inflammatory rheumatic diseases (AIIRDs). Accurate prediction and timely intervention play a pivotal role in enhancing survival rates. However, there is a notable scarcity of practical early prediction and risk assessment systems of PE in patients with AIIRD.Methods::In the training cohort, 60 AIIRD with PE cases and 180 age-, gender-, and disease-matched AIIRD non-PE cases were identified from 7254 AIIRD cases in Tongji Hospital from 2014 to 2022. Univariable logistic regression (LR) and least absolute shrinkage and selection operator (LASSO) were used to select the clinical features for further training with machine learning (ML) methods, including random forest (RF), support vector machines (SVM), neural network (NN), logistic regression (LR), gradient boosted decision tree (GBDT), classification and regression trees (CART), and C5.0 models. The performances of these models were subsequently validated using a multicenter validation cohort.Results::In the training cohort, 24 and 13 clinical features were selected by univariable LR and LASSO strategies, respectively. The five ML models (RF, SVM, NN, LR, and GBDT) showed promising performances, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.962-1.000 in the training cohort and 0.969-0.999 in the validation cohort. CART and C5.0 models achieved AUCs of 0.850 and 0.932, respectively, in the training cohort. Using D-dimer as a pre-screening index, the refined C5.0 model achieved an AUC exceeding 0.948 in the training cohort and an AUC above 0.925 in the validation cohort. These results markedly outperformed the use of D-dimer levels alone.Conclusion::ML-based models are proven to be precise for predicting the onset of PE in patients with AIIRD exhibiting clinical suspicion of PE.Trial Registration::Chictr.org.cn: ChiCTR2200059599.

Malnutrition, as an independent predictor of incidence and mortality in patients with chronic heart failure (CHF), has a serious impact on the treatment effect of patients. Conducting nutritional risk assessment and providing nutritional interventions for CHF patients can effectively improve their quality of life. On the basis of elaborating on the risk factors for malnutrition in CHF patients, this paper reviews the assessment tools for malnutrition in CHF patients, aiming to promote the localization of malnutrition assessment tools and the research and clinical application of malnutrition specific assessment tools, making nutrition assessment a routine part of overall health assessment for CHF patients.

Objective:To explore the risk factors of oral-mucosal pressure injury (OMPI) in patients with tracheal intubation in ICU and to establish a nomogram model.Methods:Using the convenient sampling method, a total of 640 patients with oral tracheal intubation admitted to ICU of the First Affiliated Hospital of Xinjiang Medical University from January to May 2023 were selected as the research objects. They were divided into the occurrence group ( n=286) and the non-occurrence group ( n=354) according to whether OMPI occurred or not. Binomial Logistic regression analysis was used to explore the risk factors for OMPI in patients with tracheal intubation in ICU. A risk nomogram model was created based on independent risk factors, and internal verification was conducted by Bootstrap repeated sampling method. Results:OMPI occurred in 286 of 640 ICU patients with tracheal intubation. Binomial Logistic regression analysis showed that high APACHEⅡ score, modified Beck oral score greater than or equal to 11 points, use of sedative drugs, prone ventilation, long retention time of tracheal catheter, low oxygenation index less than 200 mmHg (1 mmHg=0.133 kPa) and tracheal catheter fixation frequency of 1 time /24 h were the risk factors for OMPI in patients with tracheal intubation in ICU ( P<0.05). A risk nomogram model for OMPI in patients with tracheal intubation in ICU was established based on independent risk factors. The results showed that the predictive performance (area under the receiver operating characteristic curve of subjects was 0.918, 95% confidence interval was 0.897 to 0.938) and calibration (χ 2 value of 4.647, P=0.795) of the risk nomogram model for OMPI in patients with tracheal intubation in ICU were good. When the threshold probability was 0 to 1, the decision curve showed that the model had good clinical effectiveness. Conclusions:The OMPI risk nomogram model of tracheal intubation patients in ICU established in this study has good calibration and differentiation, which can be used as an effective tool for screening high-risk patients.