In 2025， European Association for the Study of the Liver published the clinical practice guidelines on vascular diseases of the liver. The guidelines comprehensively elaborate on the vascular diseases of the liver from the aspects of risk factors， diagnosis， and treatment strategies， in order to provide guidance for the management of patients with these conditions based on the best evidence available. This article gives an excerpt of the recommendations and guidance statements in the clinical practice guidelines.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

ObjectiveHeadspace solid-phase microextraction-gas chromatography-mass spectrometry（HS-SPME-GC-MS） and GC-triple quadrupole MS（GC-QqQ-MS） in combination with non-targeted and targeted metabolomics were employed to systematically analyze the chemical composition differences of Xihuangwan prepared with natural musk and artificial musk， and establish an identification system for them. MethodsThe volatile components of 9 batches of Xihuangwan samples from 8 manufacturers were analyzed by HS-SPME-GC-MS non-targeted metabolomics， and identified by comparing their MS data with the National Institute of Standards and Technology（NIST） spectral library. Orthogonal partial least squares-discriminant analysis（OPLS-DA） was used to identify differential volatile components of Xihuangwan prepared with natural musk and artificial musk. Additionally， GC-QqQ-MS targeted metabolomics was applied to quantify the levels of α-pinene， β-elemene， muscone， dehydroepiandrosterone， bornyl acetate， and octyl acetate in 27 batches of samples from 9 manufacturers. Cluster analysis， principal component analysis（PCA）， and partial least squares-discriminant analysis（PLS-DA） were conducted to further explore the differences in volatile components between Xihuangwan samples prepared with natural musk and artificial musk. ResultsNon-targeted metabolomics identified 291 volatile compounds in Xihuangwan， including alkanes， esters， alkanes， alcohols， ketones， naphthalenes and others. OPLS-DA analysis revealed distinct separation between Xihuangwan samples containing artificial musk（A1， C1， D1， E1， F1， G1， I1） and those containing natural musk（H1， H3）. A total of 30 differential metabolites were identified. The relative contents of these 30 differential metabolites were visualized using a radar chart， revealing significant differences in the levels of octanol， borneol acetate and muscone. Cluster analysis and PCA results from targeted metabolomics indicated that Xihuangwan could be classified into two distinct groups：one composed of natural musk（H1， H3） and the other of artificial musk， sample H2. PLS-DA identified muscone， octyl acetate， and dehydroepiandrosterone as key differential volatile components. Although no significant difference was observed in the content of octyl acetate between the two groups， statistically significant differences were found for muscone and dehydroepiandrosterone（P<0.05）. ConclusionMuscone and dehydroepiandrosterone can be used for the differentiation of Xihuangwan samples containing natural musk from those containing artificial musk. This study systematically and comprehensively analyzed the differences in the types and contents of major volatile components in Xihuangwan prepared with natural musk and artificial musk， providing a scientific basis for quality evaluation and control of Xihuangwan.

Objective:To understand the epidemiological characteristics and spatiotemporal distribution of hepatitis E in Zhejiang Province from 2008 to 2023 and provide scientific evidence for the prevention and control of hepatitis E.Methods:The incidence data of hepatitis E in Zhejiang from 2008 to 2023 were obtained from China Disease Control and Prevention Information System. Joinpoint regression model was used to analyze the trends in incidence rates. Software ArcGIS 10.8 was used to analyze the spatial distribution and autocorrelation of hepatitis E cases, while software SaTScan 10.1 was used to investigate spatial clustering at district level.Results:A total of 33 027 cases of hepatitis E were reported in Zhejiang from 2008 to 2023. The average annual incidence rate was 3.64/100 000. The majority of the cases reported were men (21 739), and higher average annual incidence rate was observed in the age group 50-74 years (32.04/100 000). The overall reported incidence rate declined first and then increased in 2020, showing a seasonal distribution with a high incidence from January to May. Notably, the incidence was particularly high in Hangzhou (6.54/100 000) and Quzhou (5.56/100 000). Spatial autocorrelation analyses revealed that the incidence of hepatitis E exhibited high-high clustering in all the years, with northwestern Zhejiang as main affected area. Prior to 2020, the high-high clustering area in northwestern Zhejiang became smaller, followed by an expansion trend in 2020-2022. Spatiotemporal clustering analyses identified seven clustering areas from 2010 to 2019, one was in 16 counties (cities, districts) in northwestern Zhejiang. And the clustering areas along the coastline shifted from the north to the south.Conclusions:The incidence of hepatitis E in Zhejiang exhibited distinct spatiotemporal clustering pattern. Consequently, targeted prevention and control measures could be taken for hepatitis E in high-risk areas in Zhejiang, including northwestern and coastal areas.

Objective:To understand the epidemiological characteristics and spatiotemporal distribution of hepatitis E in Zhejiang Province from 2008 to 2023 and provide scientific evidence for the prevention and control of hepatitis E.Methods:The incidence data of hepatitis E in Zhejiang from 2008 to 2023 were obtained from China Disease Control and Prevention Information System. Joinpoint regression model was used to analyze the trends in incidence rates. Software ArcGIS 10.8 was used to analyze the spatial distribution and autocorrelation of hepatitis E cases, while software SaTScan 10.1 was used to investigate spatial clustering at district level.Results:A total of 33 027 cases of hepatitis E were reported in Zhejiang from 2008 to 2023. The average annual incidence rate was 3.64/100 000. The majority of the cases reported were men (21 739), and higher average annual incidence rate was observed in the age group 50-74 years (32.04/100 000). The overall reported incidence rate declined first and then increased in 2020, showing a seasonal distribution with a high incidence from January to May. Notably, the incidence was particularly high in Hangzhou (6.54/100 000) and Quzhou (5.56/100 000). Spatial autocorrelation analyses revealed that the incidence of hepatitis E exhibited high-high clustering in all the years, with northwestern Zhejiang as main affected area. Prior to 2020, the high-high clustering area in northwestern Zhejiang became smaller, followed by an expansion trend in 2020-2022. Spatiotemporal clustering analyses identified seven clustering areas from 2010 to 2019, one was in 16 counties (cities, districts) in northwestern Zhejiang. And the clustering areas along the coastline shifted from the north to the south.Conclusions:The incidence of hepatitis E in Zhejiang exhibited distinct spatiotemporal clustering pattern. Consequently, targeted prevention and control measures could be taken for hepatitis E in high-risk areas in Zhejiang, including northwestern and coastal areas.

After organ transplantation, graft rejection is one of the most common complications, which will cause adverse injuries to grafts regardless of etiology. Tertiary lymphoid structures (TLS) occur in non-lymphoid tissues under pathological conditions. Identification and characterization of mechanisms regulating graft rejection can not only promote understanding of the survival and prognosis of organ grafts, but also provide ideas for treating graft rejection reaction.

Objective·To develop a machine learning approach for early identification of metabolic syndromes associated with inflammatory metabolic state changes in breast cancer patients after neoadjuvant therapy,using common laboratory and transthoracic echocardiography indices.Methods·Female patients with primary invasive breast cancer diagnosed at the Department of Breast Surgery,Renji Hospital,Shanghai Jiao Tong University School of Medicine,between September 2020 and September 2022,were included.General patient information,laboratory test results,and transthoracic echocardiography data were collected.After feature extraction,five machine learning algorithms,including random forest(RF),gradient boosting(GB),support vector machine(SVM),K-nearest neighbor(KNN),and decision tree(DT),were applied to construct a prediction model for the changes of the patients' metabolic state after neoadjuvant therapy,and the prediction performances of the five models were compared.Results·A total of 232 cases with valid clinical data were included,comprising 135 cases before neoadjuvant therapy and 97 cases after completing 4 cycles of neoadjuvant therapy.Feature extraction identified five key features:white blood cell count,hemoglobin,high-density lipoprotein(HDL),interleukin-2 receptor,and interleukin-8.In the multi-feature analysis,the area under the receiver operating characferistic curve(AUC)was higher in the combination of white blood cell count,hemoglobin and HDL compared to the combination of interleukin-2 receptor and interleukin-8(RF:0.928 vs 0.772,GB:0.900 vs 0.792,SVM:0.941 vs 0.764,KNN:0.907 vs 0.762,DT:0.799 vs 0.714).The RF,SVM,and GB models showed higher AUC(0.928,0.941,0.900)and accuracy(0.914,0.897,0.776).The SVM model exhibited superior accuracy in the training data compared to the RF and GB models(P=0.394,0.122 and 0.097,respectively).Conclusion·The SVM model can be used to establish a prediction model for identifying breast cancer patients at high risk of developing inflammatory metabolic state-related metabolic syndrome after neoadjuvant therapy by incorporating five common clinical indicators,namely,white blood cell count,hemoglobin,high-density lipoprotein,interleukin-2 receptor,and interleukin-8.SVM modeling may be useful for clinicians to establish individualized screening protocols based on a patient's inflammatory metabolic state.

Objective:To analyze the differences in dosimetric quality and plan complexity of volumetric modulated arc therapy (VMAT) plans based on Halcyon 2.0 and Truebeam for different treatment sites of the patients.Methods:Halcyon 2.0 VMAT plans in head & neck, chest, abdomen, and pelvis treatment sites of 49 cases were retrospectively selected and the VMAT plans were re-designed based on Truebeam with the same optimization parameters. The differences in dosimetric metrics and plan complexity between the two types of plans were compared and analyzed. P<0.05 was considered as statistically significant. Results:In terms of PTV, Halcyon 2.0 plans showed better homogeneity index (HI), conformal index (CI) in the head & neck and chest. Besides, Halcyon 2.0 plans yielded better D 98% and CI in the abdomen and better D 2% in the pelvis. For organs at risk (OAR), the D 20% and D mean of bilateral lungs, and D meanof heart for Halcyon 2.0 plans in the chest were lower than those for Truebeam plans (all P<0.05). For the complexity metrics, the median average aperture area variability (AAV) of Halcyon 2.0 plans in the head & neck, abdomen and pelvis were 0.414, 0.425 and 0.432, which were better than 0.385, 0.368 and 0.361 of Truebeam plans in the corresponding sites, respectively. In the abdomen and pelvis, Halcyon 2.0 plans showed better median modulation complexity score (MCS) than Truebeam plans (0.320 vs. 0.268, 0.303 vs. 0.282; both P<0.05). The median small aperture score (SAS) for all plans of Halcyon 2.0 were better than that of Truebeam plans (all P<0.05), and the median plan average beam area (PA) of all plans of Halcyon 2.0 were larger than that of Truebeam plans (all P<0.05). Conclusions:Compared with conventional fractionated VMAT plans based on Halcyon 2.0 and Truebeam, Halcyon 2.0 plans have similar or even better dosimetric quality. However, Halcyon 2.0 plans have lower plan complexity, which makes it an advantage in clinical application.

Objective To observe the application effects of scenario simulation combined with guided feedback teaching method in the teaching of endocrinology nursing interns.Methods A total of 180 nursing students who interned in the endocri-nology department of Southern Medical University Shenzhen Hospital from January 2020 to January 2023 were selected as the re-search subjects.Using a digital randomization method,the students were divided into a control group(90 cases)and an observa-tion group(90 cases).The control group received conventional teaching,while the observation group received scenario-based teaching combined with guided feedback teaching method.The intern performance,satisfaction of teaching instructors toward students,and students'satisfaction with teaching instructors were compared between the two groups.Results The intern per-formance of the observation group was higher and showed a greater improvement after training,with statistically significant differ-ences(P＜0.05).Both the students'satisfaction with teaching instructors and the teaching instructors'satisfaction with students in the observation group were higher than those in the control group,with statistically significant differences(P＜0.05).Conclusion The application of scenario simulation combined with guided feedback teaching method in the teaching of endocri-nology nursing interns can significantly improve students'intern performance,providing a more scientific basis for nursing intern-ship teaching.

Tryptophan (TRP) is an essential amino-acid and the precursor of many signaling molecules. Under the catalysis of indoleamine 2, 3-dioxygenase, kynurenine pathway can form its metabolites uroquinolinic acid and quinolinic acid, which is the main channel of TRP metabolism.Through different mechanisms in N-methyl-D-aspartate receptor, they participate in nervous modulation, affect cognitive processes and play an important role in many central nervous system diseases development. Kynurenine pathway is different under physiological and pathological conditions. In addition, there are many rate-limiting enzymes in the kynurenine pathway, which can interfere kynurenine pathway. This article reviews the relationship between tryptophan/kynurenine pathway and cognitive dysfunction.