ObjectiveTo analyze the risk factors for long-term cardiovascular events in patients undergoing long-term peritoneal dialysis （PD）， and to construct and validate a visual nomogram prediction model based on multiple parameters. MethodsA prospective cohort study was conducted， consecutively enrolling 248 maintenance PD patients （dialysis duration ≥ 3 months）. Demographic characteristics， clinical indicators， laboratory parameters， and echocardiographic indices （including left ventricular ejection fraction ［LVEF］， ratio of early diastolic mitral inflow velocity to early diastolic mitral annular velocity （E/e’）， etc.） were collected. The composite endpoint was defined as the occurrence of cardiovascular events or cardiovascular death， with non-cardiovascular death as the competing risk and loss to follow-up or the end of follow-up as censoring events. Fine-Gray competing risks model was used to screen independent predictors， based on which a nomogram model was constructed. Internal validation was performed using the Bootstrap method （1 000 resamplings）， and the concordance index （C-index） and time-dependent receiver operating characteristic （time-dependent ROC） curve were calculated to evaluate the model performance. ResultsWith a median follow-up of 29 months （interquartile range： 24–35 months）， 88 patients （35.48%） reached the composite endpoint， including 80 cases of cardiovascular events and 8 cases of cardiovascular death， and 4 patients died of non-cardiovascular causes. Multivariate Fine-Gray analysis revealed that age， diabetes mellitus， hemoglobin （HGB） level and E/e' ratio were independent influencing factors of the composite endpoint. Specifically， each 1-year increase in age was associated with a 3.0% increase in the risk of the composite endpoint （HR=1.030， P=0.006）； patients with diabetes mellitus had a 167.9% higher risk compared with non-diabetic patients （HR=2.679， P=0.007）； each 1g/L increase in HGB level contributed to a 1.5% reduction in the risk （HR=0.985， P=0.003）； and each 0.1 increase in E/e' ratio led to a 7.2% increase in the risk （HR=1.072， P=0.045）. The nomogram model had a C-index of 0.76 （95% CI： 0.698–0.820）， and the AUC of the time-dependent ROC curve reached 0.849 at 23 months of follow-up. ConclusionIncreased age， complicated with diabetes mellitus， decreased HGB， and elevated E/e' ratio are independent risk factors of long-term occurrence of cardiovascular events and cardiovascular death in patients undergoing long-term PD. The nomogram model constructed based on the above variables has good predictive value and clinical applicability， which can provide a reference for cardiovascular risk stratification and individualized intervention in long-term PD patients.

Liver fibrosis is the common pathological process in the progression of various chronic liver diseases to liver cirrhosis， and it greatly affects the prognosis of patients with chronic liver diseases. As a novel adipokine， Chemerin participates in the metabolism of glucose and lipids and inflammation， and various studies have shown that the expression level of Chemerin is correlated with the degree of liver fibrosis， suggesting that Chemerin may be involved in the process of liver fibrosis by regulating metabolism and inflammation. Chemerin has shown certain potential in the auxiliary diagnosis of liver fibrosis and the intervention against the progression of liver fibrosis. This article reviews the potential role and mechanism of action of Chemerin in the process of liver fibrosis， in order to provide new ideas for the diagnosis and treatment of liver fibrosis.

CT-guided thermal ablation is a critical modality for treating lung cancer.Invasive thermometry techniques demonstrated sufficient accuracy for monitoring temperature of the target area during ablation,but their clinical application were limited since procedural invasiveness and relatively high risk of complications.Being non-invasive,safe and capable of global monitoring,non-invasive thermometry techniques have broad application prospects.The status of non-invasive thermometry techniques for CT-guided thermal ablation for lung cancer were reviewed in this article.

AIM:To investigate the role of S100 calcium-binding protein A8(S100A8)in a canine model of atrial fibrillation(AF)induced by obstructive sleep apnea(OSA).METHODS:Ten adult Beagle dogs were randomly as-signed to OSA(n=5)and control(n=5)groups.The OSA model was established by daily tracheal intubation with alternat-ing airway obstruction and ventilation recovery for 4 h per day,sustained over 12 weeks.Model validation was conducted through arterial blood gas analysis,airway pressure monitoring,and esophageal pressure measurements.Open-chest elec-trophysiological studies were performed to assess atrial effective refractory period(ERP),dispersion of ERP(dERP),and AF inducibility.Tandem mass tag-based quantitative proteomics was used to identify differentially expressed proteins in atrial tissue.Key protein expression and localization were verified using immunohistochemistry and immunofluorescence.RESULTS:Compared with the control group,the OSA group exhibited significantly lower arterial blood pH and partial pressure of oxygen,and higher partial pressure of carbon dioxide in arterial blood,confirming successful model establish-ment.Histopathological analysis revealed disorganized cardiomyocyte architecture,fatty degeneration,inflammatory cell infiltration,and a significant increase in myocardial fibrosis in the OSA group(P<0.05).Electrophysiological data showed increased AF inducibility and dERP,and decreased ERP(P<0.05).Proteomic analysis identified 267 differen-tially expressed proteins,including 128 up-regulated and 139 down-regulated proteins.Immunohistochemical analysis showed significant upregulation of S100A8,S100A9,myeloperoxidase,and nuclear factor-κB p65(P<0.05),while im-munofluorescence demonstrated increased expression of matrix metalloproteinase-9 and transforming growth factor-β1 in the OSA group(P<0.01).CONCLUSION:The OSA promotes upregulation of S100A8 in myocardial tissue,enhances atrial electrical remodeling and fibrosis,and increases susceptibility to AF.These findings suggest that S100A8 may play a key role in the pathogenesis and progression of OSA-related AF.

Objective:To develop a preoperative differentiation model for colorectal mucinous adenocarcinoma and non-mucinous adenocarcinoma using a combination of contrast-enhanced CT radiomics and deep learning methods.Methods:This is a retrospective cohort study. Clinical data of colorectal cancer patients confirmed by postoperative pathological examination were retrospectively collected from January 2016 to December 2023 at Shanghai General Hospital Affiliated to Shanghai Jiao Tong University School of Medicine (Center 1, n=220) and the First Affiliated Hospital of Bengbu Medical University (Center 2, n=51). Among them, there were 108 patients diagnosed with mucinous adenocarcinoma, including 55 males and 53 females, with an age of (68.4±12.2) years (range: 38 to 96 years); and 163 patients diagnosed with non-mucinous adenocarcinoma, including 96 males and 67 females, with an age of (67.9±11.0) years (range: 43 to 94 years). The cases from Center 1 were divided into a training set ( n=156) and an internal validation set ( n=64) using stratified random sampling in a 7∶3 ratio, and the cases from Center 2 were used as an independent external validation set ( n=51). Three-dimensional tumor volume of interest was manually segmented on venous-phase contrast-enhanced CT images. Radiomics features were extracted using PyRadiomics, and deep learning features were extracted using the ResNet-18 network. The two sets of features were then combined to form a joint feature set. The consistency of manual segmentation was assessed using the intraclass correlation coefficient. Feature dimensionality reduction was performed using the Mann-Whitney U test and the least absolute shrinkage and selection operator regression. Six machine learning algorithms were used to construct models based on radiomics features, deep learning features, and combined features, including support vector machine, logistic regression, random forest, extreme gradient boosting, k-nearest neighbors, and decision tree. The discriminative performance of each model was evaluated using receiver operating characteristic curves, the area under the curve (AUC), DeLong test, and decision curve analysis. Results:After feature selection, 22 features with the most discriminative value were finally retained, among which 12 were traditional radiomics features and 10 were deep learning features. In the internal validation set, the Random Forest algorithm based on the combined features model achieved the best performance (AUC=0.938, 95% CI: 0.875 to 0.984), which was superior to the single-modality radiomics feature model (AUC=0.817, 95% CI: 0.702 to 0.913, P=0.048) and the deep learning feature model (AUC=0.832, 95% CI: 0.727 to 0.926, P=0.087); in the independent external validation set, the Random Forest algorithm with the combined features model maintained the highest discriminative performance (AUC=0.891, 95% CI: 0.791 to 0.969), which was superior to the single-modality radiomics feature model (AUC=0.770, 95% CI: 0.636 to 0.890, P=0.045) and the deep learning feature model (AUC=0.799, 95% CI: 0.652 to 0.911, P=0.169). Conclusion:The combined model based on radiomics and deep learning features from venous-phase enhanced CT demonstrates good performance in the preoperative differentiation of colorectal mucinous from non-mucinous adenocarcinoma.

The traditional Chinese medicine （TCM） myocardial infarction （MI） unit is a standardized， regulated， and continuous integrated care unit guided by TCM theory and built upon existing chest pain centers or emergency care units. This unit emphasizes multidisciplinary collaboration and forms a restructured clinical entity without altering current departmental settings， offering comprehensive diagnostic and therapeutic services with full participation of TCM in the treatment of MI. Its core medical model is patient-centered and disease-focused， providing horizontally integrated TCM-based care across multiple specialties and vertically constructing a full-cycle treatment unit for MI， delivering prevention， treatment， and rehabilitation during the acute， stable， and recovery phases. Additionally， the unit establishes a TCM-featured education and prevention mechanism for MI to guide patients in proactive health management， reduce the incidence of myocardial infarction， and improve quality of life.

OBJECTIVE To study the safety of Shuangshu decoction in rats and its efficacy in improving functional dyspepsia （FD） in rats. METHODS In safety test， 40 rats were divided into blank control group， Shuangshu decoction low-dose， medium- dose and high-dose groups [108， 216， 324 g/（kg·d）， calculated by raw medicine， the same applies below]； they were given relevant medicine intragastrically， for continuous 14 days. The mortality and toxic reactions of rats were recorded， and the organ indexes of the liver， kidney， spleen， lung and heart of rats were calculated； the pathological morphological changes in the liver， kidney， spleen， lung， heart， stomach， duodenum， and colon were observed to evaluate the acute toxicity of Shuangshu decoction. Another 40 rats were grouped and administered in the same way for 30 consecutive days. The mortality and toxic reactions of the rats were recorded， and the corresponding organ indexes were calculated. The pathological morphological changes in the corresponding organs were observed， and blood routine and serum biochemical indicators were measured， in order to assess the subacute toxicity of Shuangshu decoction. In pharmacodynamic experiments: 50 rats were divided into blank control group， model group， and Shuangshu decoction low-， medium-， and high-dose groups （9.45， 18.9， 37.8 g/kg）， with 10 rats in each group. Except for blank control group， rats in all other groups were used to establish the FD rat model by subcutaneous injection of loperamide （3.5 mg/kg）. Rats in each group were administered the corresponding drug solution/normal saline intragastrically， once a day， for 14 consecutive days. After the last medication， fecal moisture content， intestinal propulsion rate， gastric emptying rate and serum level of motilin were all detected， and interstitial cell of Cajal （ICC） ultrastructure of rats was observed in colon tissue. RESULTS The safety experiments showed that no death occurred in each dose group， and no significant difference was found in organ coefficient， routine blood and serum biological index， compared to blank control group （P＞0.05）； no abnormality was found in organ appearance and pathological sections. The results of the pharmacodynamic experiments showed that， compared with the blank control group， the fecal moisture content， gastric emptying rate， intestinal propulsion rate， and serum motilin levels in the model group were significantly decreased （P＜0.05）； in the colonic tissue， the mitochondria in the ICC exhibited severe swelling with the disappearance of cristae， and the endoplasmic reticulum was dilated. Compared with model group， the rats in Shuangshu decoction high-dose group showed significant increases in the above quantitative indicators （P＜ 0.05）； additionally， there was a large number of mitochondria in the ICC of the colonic tissue， with clear cristae and regular arrangement. CONCLUSIONS Shuangshu decoction is safe and has a beneficial improving effect on FD rats； its mechanism of action may be related to the regulation of gastrointestinal hormone expression to promote gastric emptying and intestinal propulsion， as well as the repair of mitochondrial structure in ICCs to restore gastrointestinal function.

ObjectiveTo observe the clinical efficacy and safety of Modified Guomin Decoction （加味过敏煎， MGD） in patients with mild to moderate atopic dermatitis （AD） of the traditional Chinese medicine （TCM） pattern of heart fire and spleen deficiency， and to explore its possible mechanisms. MethodsIn this randomized， double-blind， placebo-controlled study， 72 patients with mild to moderate AD and the TCM pattern of heart fire and spleen deficiency were randomly divided into a treatment group and a control group， with 36 cases in each group. The treatment group received oral MGD granules combined with topical vitamin E emulsion， while the control group received oral placebo granules combined with topical vitamin E treatment. Both groups were treated twice daily for 4 weeks. Clinical efficacy， TCM syndrome scores， Visual Analogue Scale （VAS） for pruritus， Dermatology Life Quality Index （DLQI） scores， Scoring Atopic Dermatitis （SCORAD） and serum biomarkers， including interleukin-33 （IL-33）， interleukin-1β （IL-1β）， immunoglobulin E （IgE）， and tumor necrosis factor-α （TNF-α） were compared before and after treatment. Safety indexes was also assessed. ResultsThe total clinical effective rates were 77.78% （28/36） in the treatment group and 38.89% （14/36） in the control group， with cure rates of 19.44% （7/36） and 2.78% （1/36）， respectively. The treatment group showed significantly better clinical outcomes compared to the control group （P＜0.05）. The treatment group exhibited significant reductions in total TCM syndrome scores， including erythema， edema， papules， scaling， lichenification， pruritus， irritability， insomnia， abdominal distension， and fatigue scores， as well as reductions in VAS， DLQI， SCORAD， and serum IgE and IL-33 levels （P＜0.05 or P＜0.01）. Compared to the control group， the treatment group had significantly better improvements in all indicators except for insomnia （P＜0.05）. No adverse events occurred in either group. ConclusionMGD is effective and safe in treating mild to moderate AD patients with heart fire and spleen deficiency pattern. It significantly alleviates pruritus， improves TCM syndromes and quality of life， and enhances clinical efficacy， possibly through modulation of immune responses.

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases. Accurately predicting microbe-disease interactions (MDIs) offers critical insights for disease intervention and pharmaceutical research. Current advanced AI-based technologies automatically generate robust representations of microbes and diseases, enabling effective MDI predictions. However, these models continue to face significant challenges. A major issue is their reliance on complex feature extractors and classifiers, which substantially diminishes the models' generalizability. To address this, we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs. Initially, we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation. Secondly, we employ decoupled representation learning technology, compelling the graph neural network (GNN) to independently learn the weights for each feature subspace, thus enhancing its expressive power. Finally, we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN, reducing information loss due to occlusion. Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models. This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research. Code and data are accessible at: https://github.com/shmildsj/MDI-IFDRL.

In general, bioassay-guided fractionation and isolation of bioactive constituents from botanical materials frequently ended up with the reward of a single compound. However, botanical materials typically exert their therapeutic actions through multi-pathway effects due to the intrinsic complex nature of chemical constituents. In addition, the content of bioactive compounds in botanical materials is largely dependent on humidity, temperature, soil, especially geographical origins, from which rapid and accurate identification of plant materials is pressingly needed. These long-standing obstacles collectively impede the deep exploitation and application of these versatile natural sources. To address the challenges, a new paradigm integrating biogravimetric analyses and machine learning-driven origin classification (BAMLOC) was developed. The biogravimetric analyses are based on absolute qHNMR quantification and in vivo zebrafish model-assisted activity index calculation, by which bioactive substance groups jointly responsible for the bioactivities in all fractions are pinpointed before any isolation effort. To differentiate origin-different botanical materials varying in the content of bioactive substance groups, principal component analysis, linear discriminant analysis, and hierarchical cluster analysis in conjunction with supervised support vector machine are employed to classify and predict production areas based on the detection of volatile organic compounds by E-nose and GC-MS. Expanding BAMLOC to Codonopsis Radix enables the identification of polyacetylenes and pyrrolidine alkaloids as the bioactive substance group for immune restoration effect and accurately determines the origins of plants. This study advances the toolbox for the discovery of bioactive compounds from complex mixtures and lays a more definitive foundation for the in-depth utilization of botanical materials.