Objective:To analyze the current situation of medical consumables management policy in China,and to provide a reference for the refined management of medical consumables.Methods:Through the policy triangle model and policy tool theory,it comprehensively analyzes the reimbursement medical consumables catalogue and payment management policy of medical insurance in China,covering the policy background,content,process,and participant dimensions.Results:The use frequency of medical consumables policy tools is not balanced,the payment management rules need to be refined,and the participation of multi-stakeholders such as patients is lacking.Conclusion:It is necessary to further strengthen the foundational management of reimbursement medical consumables catalogue,improve the access mechanism of medical consumables for medical insurance,and explore the formulation of categorized payment standards and innovative payment mechanisms.

Objective:To investigate the mechanisms of baicalin in treating septic acute liver injury through a combination of network pharmacology and animal experiments.Methods:Thirty male C57BL/6 mice (6 weeks old) were divided into five groups ( n=6): control group (normal saline), model group [lipopolysaccharide (LPS) 10 mg/kg, intraperitoneal injection], low-dose baicalin group (10 mg/kg), high-dose baicalin group (20 mg/kg), and baicalin-only group (20 mg/kg, without LPS). Baicalin was administered orally for 14 consecutive days prior to modeling. Mice were sacrificed 24 h after LPS injection. Alanine transaminase, aspartate transaminase liver tissue histopathology were measured; neutrophil infiltration was visualized using immunofluorescence; mRNA expression levels of interleukin (IL)-1β, IL-17, IL-6, and tumor necrosis factor (TNF)-α were detected by RT-qPCR; and the expression of Toll-like receptor 4 (TLR4) and phosphorylated nuclear factor (NF)-κB proteins were analyzed by Western blotting. Results:In the LPS model group, the ALT, AST, and histopathological injury score were (148.60±22.02) U/L, (81.58±11.59) U/L, and 8.50(7.75, 9.25), respectively. These indicators were significantly reduced in the high-dose baicalin group with (77.90±16.79) U/L, (49.92±14.89) U/L, and 1.00(1.00, 2.25) (all P<0.05). Compared with the LPS group, neutrophil infiltration in the liver of high-dose baicalin group was also significantly reduced [1.18%(0.98%, 1.22%) vs. 6.13%(5.41%, 8.69%), P<0.05]. RT-qPCR results showed that the relative mRNA expression levels of inflammatory cytokines IL-1β [(1.03±0.06) vs. (2.60±0.34)], IL-17 [(1.21±0.12) vs. (2.94 ± 0.39)], IL-6 [(1.37±0.26) vs. (2.73±0.18)], and TNF-α [(1.18±0.10) vs. (3.30±0.92)] were significantly decreased in the high-dose baicalin group compared with the LPS group (all P<0.05). Western blot analysis revealed that the relative protein expression levels of TLR4 [(1.25±0.13) vs. (1.73±0.06)] and phosphorylated NF-κB [(1.25±0.25) vs. (1.79±0.12)] were also significantly lower in the high-dose baicalin group (both P<0.05). Conclusion:Baicalin reduces liver injury in septic mice by downregula-ting the expression of pro-inflammatory cytokines IL-1β, IL-6, TNF-α, and IL-17, potentially through the inhibition of the TLR4/NF-κB signaling pathway.

Severe acute pancreatitis (SAP), a critical subtype of acute pancreatitis (AP), is one of the common acute abdomen in gastroenterology. Intestinal dysfunction or failure is a significant contributor to the progression of SAP, increasing the development of systemic inflammatory response syndrome and multiple organ failure. Traditional Chinese medicine (TCM) offers unique insights into the etiology, pathophysiology, and disease pattern of SAP, making it promising in preserving the intestines, lowering inflammation, and preventing severe instances. The integrated TCM and western medicine could benefits the treatment of SAP, lowering the morbidity and mortality. This article mainly reviews the application of integrated TCM and Western medicine in the diagnosis and treatment of SAP-related intestinal dysfunction and failure, and elaborates on the pathological mechanisms of SAP in Western medicine, the etiology and pathogenesis of SAP in TCM, and the treatment advantages of integrated TCM and western medicine, providing new perspectives and insights into the management of SAP.

Objective:To establish and evaluated a logistic regression model for predicting the acute biliary pancreatitis (ABP) based on liver-injury related indexes.Methods:Clinical data of 210 patients diagnosed with acute pancreatitis (AP) at the Affiliated Hospital of Jiangnan University from October 2020 to December 2022 were retrospectively analyzed, including 113 males and 97 females, with a median age of 52 years (range, 43 to 58). Among these, 88 were diagnosed with ABP and 122 with acute non-biliary pancreatitis (ANBP). Additionally, a test cohort was created using data from 101 AP patients diagnosed between January and December 2023, including 60 males and 41 females, with a median age of 53 years (range, 43 to 63). Based on the original dataset, univariate and multivariate logistic regression analyses were conducted to identify the factors influencing ABP. A prediction probability formula (Pre) was then established based on the multivariate results. The effectiveness of each indicator in predicting ABP was evaluated using the receiver operating characteristic (ROC) curve. The ROC curve analysis determined the optimal cutoff value of Pre, which was subsequently used to diagnose ABP and ANBP in the test cohort.Results:Multivariate logistic regression analysis showed the factors influencing ABP include direct bilirubin (DBIL), alanine aminotransferase (ALT), aspartate aminotransferase (AST), cholinesterase (CHE), and fibrinogen (FIB). Based on the multivariate analysis results, the prediction probability formula (Pre) for ABP was established as follows: P=1/{1+ exp[-(4.807+ 0.134×DBIL-1.859×AST/ALT-0.0003×CHE-0.387×FIB)]}. ROC curve analysis revealed that the area under the curve (AUC) for Pre in predicting ABP was 0.858, with an optimal cutoff value of 0.56, at which the sensitivity was 69.3% and the specificity was 91.0%. Using the cutoff value of 0.56 for Pre, ABP was diagnosed when Pre≥0.56 and ANBP was diagnosed when Pre<0.56. This criterion was applied to diagnose patients in the test cohort, where the sensitivity and specificity of Pre for diagnosing ABP were 86.1% and 92.3%, respectively.Conclusion:The logistic regression model based on liver injury-related indicators is a valuable tool for clinically assessing the incidence of ABP.

Objective:To analyze the changes of serum metabolites in patients with acute pancreatitis (AP) by non-targeted metabolomics method.Methods:Serum samples and clinical data of 15 AP patients hospitalized in the Affiliated Hospital of Jiangnan University from August to September 2024 were collected and included in the AP group, including 9 males and 6 females, aged (55.4±15.3) years. The serum and clinical data of 25 patients with colon polyps in the same hospital during the same period of time were collected, including 15 males and 10 females, aged (61.2±11.5) years, and were included in the control group. Serum metabolomic detection was performed using the ultra-high performance liquid chromatography tandem Fourier transform mass spectrometer. The modeling method was orthogonal partial least square discriminant analysis, and principal component analysis was performed on the data matrix to screen the differential metabolites in serum of AP patients. The Kyoto Encyclopedia database of Genes and Genomes was used to annotate differential metabolites, and the pathway of differential metabolite enrichment was analyzed by software.Results:The principal component analysis showed that the contribution ratio of the first principal component was 15.1%, the proportion of the second principal component was 10.8%, and the total proportion of the two was 25.9%. In principal component analysis, two groups of samples can be clearly distinguished and show obvious clustering characteristics. According to the analysis of OPLS-DA model, there were significant differences in serum metabolic profiles between AP group and control group. There were 683 differentially expressed metabolites between the two groups, with 367 differentially expressed metabolites up-regulated compared with the control group and 316 differentially expressed metabolites down-regulated compared with the control group. It is mainly Phosphatidic Acid (Lte4/8: 0) (+ 218%), Omeprazole Sulphone (-38%), and 2-(Propylthio) Nicotinic Acid (2-propyl thionicotinic acid) (-58%), Gein (salicyricetin) (-47%) and so on. Pathway enrichment analysis showed that the differential metabolites in AP patients were mainly concentrated in citric acid cycle, arginine biosynthesis and glycerophospholipid metabolism pathways.Conclusion:Serum metabolites in AP patients change significantly, including citric acid cycle, arginine biosynthesis, glycerophospholipid metabolism.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective:To analyze the current situation of medical consumables management policy in China,and to provide a reference for the refined management of medical consumables.Methods:Through the policy triangle model and policy tool theory,it comprehensively analyzes the reimbursement medical consumables catalogue and payment management policy of medical insurance in China,covering the policy background,content,process,and participant dimensions.Results:The use frequency of medical consumables policy tools is not balanced,the payment management rules need to be refined,and the participation of multi-stakeholders such as patients is lacking.Conclusion:It is necessary to further strengthen the foundational management of reimbursement medical consumables catalogue,improve the access mechanism of medical consumables for medical insurance,and explore the formulation of categorized payment standards and innovative payment mechanisms.

Objective:To establish and validate a multimodal model based on radiomics and deep learning for predicting acute pancreatitis (AP) complicated with acute respiratory distress syndrome (ARDS).Methods:Patients diagnosed with AP from The First Affiliated Hospital of Soochow University, Donghai County People's Hospital and Jintan Affiliated Hospital of Jiangsu University between January 2017 and December 2023 were enrolled. Based on the diagnosis of ARDS within 1 week after admission, the patients were classified into the ARDS group and the non-ARDS group. Patients in the First Affiliated Hospital of Soochow University ( n=406) was used as the training set (non-ARDS group n=212 vs ARDS group n=194), while Donghai and Jintan hospitals served as the test set ( n=175; non-ARDS group n=104 vs ARDS group n=71). Clinical data, laboratory tests and the occurrence of systemic inflammatory response syndrome (SIRS) within 24 hours after admission were collected. Scoring systems such as bedside index for severity in acute pancreatitis (BISAP), Ranson score and modified CT severity index (MCTSI) were calculated. Radiomics features were extracted from three-dimensional CT images to develop a radiomics model based on XGBoost algorithm. At the same time, a deep learning model was constructed using deep convolutional networks to extract deep features. Finally, clinical features and the predictions from the aforementioned models were integrated to establish a multimodal model based on XGBoost algorithm. To enhance model visualization, variable importance ranking and local interpretable visualization were used. The receiver operating characteristic (ROC) curves of the three models and the three scores including BISAP, Ranson and MCTSI were plotted and the area under the curves (AUCs) were calculated to evaluate the prediction performance for ARDS in AP patients, as well as sensitivity and specificity. Results:In the multimodal model for predicting ARDS in AP patients, predictions of the deep learning model and the radiomics model were the most important variables, followed by SIRS, C-reactive protein, procalcitonin, albumin, glucose, creatinine, neutrophil, and Ca 2+. In the training set, the multimodal model achieved an AUC of 0.933 for predicting ARDS in AP patients, higher than the radiomics model (0.727), the deep learning model (0.877), MCTSI (0.870), Ranson (0.620) and BISAP (0.898). In the test set, the model's AUC was 0.916 for predicting ARDS in AP patients, higher than the radiomics model (0.660), the deep learning model (0.864), MCTSI (0.851), Ranson (0.609), and BISAP (0.860). Conclusions:Based on clinical structured data, radiomics and deep learning features, the multimodal model could predict the risk of ARDS in AP patients at an early stage, whose performance is better than the single-modal models and the traditional scoring systems.

Objective:To investigate the mechanisms of baicalin in treating septic acute liver injury through a combination of network pharmacology and animal experiments.Methods:Thirty male C57BL/6 mice (6 weeks old) were divided into five groups ( n=6): control group (normal saline), model group [lipopolysaccharide (LPS) 10 mg/kg, intraperitoneal injection], low-dose baicalin group (10 mg/kg), high-dose baicalin group (20 mg/kg), and baicalin-only group (20 mg/kg, without LPS). Baicalin was administered orally for 14 consecutive days prior to modeling. Mice were sacrificed 24 h after LPS injection. Alanine transaminase, aspartate transaminase liver tissue histopathology were measured; neutrophil infiltration was visualized using immunofluorescence; mRNA expression levels of interleukin (IL)-1β, IL-17, IL-6, and tumor necrosis factor (TNF)-α were detected by RT-qPCR; and the expression of Toll-like receptor 4 (TLR4) and phosphorylated nuclear factor (NF)-κB proteins were analyzed by Western blotting. Results:In the LPS model group, the ALT, AST, and histopathological injury score were (148.60±22.02) U/L, (81.58±11.59) U/L, and 8.50(7.75, 9.25), respectively. These indicators were significantly reduced in the high-dose baicalin group with (77.90±16.79) U/L, (49.92±14.89) U/L, and 1.00(1.00, 2.25) (all P<0.05). Compared with the LPS group, neutrophil infiltration in the liver of high-dose baicalin group was also significantly reduced [1.18%(0.98%, 1.22%) vs. 6.13%(5.41%, 8.69%), P<0.05]. RT-qPCR results showed that the relative mRNA expression levels of inflammatory cytokines IL-1β [(1.03±0.06) vs. (2.60±0.34)], IL-17 [(1.21±0.12) vs. (2.94 ± 0.39)], IL-6 [(1.37±0.26) vs. (2.73±0.18)], and TNF-α [(1.18±0.10) vs. (3.30±0.92)] were significantly decreased in the high-dose baicalin group compared with the LPS group (all P<0.05). Western blot analysis revealed that the relative protein expression levels of TLR4 [(1.25±0.13) vs. (1.73±0.06)] and phosphorylated NF-κB [(1.25±0.25) vs. (1.79±0.12)] were also significantly lower in the high-dose baicalin group (both P<0.05). Conclusion:Baicalin reduces liver injury in septic mice by downregula-ting the expression of pro-inflammatory cytokines IL-1β, IL-6, TNF-α, and IL-17, potentially through the inhibition of the TLR4/NF-κB signaling pathway.

Severe acute pancreatitis (SAP), a critical subtype of acute pancreatitis (AP), is one of the common acute abdomen in gastroenterology. Intestinal dysfunction or failure is a significant contributor to the progression of SAP, increasing the development of systemic inflammatory response syndrome and multiple organ failure. Traditional Chinese medicine (TCM) offers unique insights into the etiology, pathophysiology, and disease pattern of SAP, making it promising in preserving the intestines, lowering inflammation, and preventing severe instances. The integrated TCM and western medicine could benefits the treatment of SAP, lowering the morbidity and mortality. This article mainly reviews the application of integrated TCM and Western medicine in the diagnosis and treatment of SAP-related intestinal dysfunction and failure, and elaborates on the pathological mechanisms of SAP in Western medicine, the etiology and pathogenesis of SAP in TCM, and the treatment advantages of integrated TCM and western medicine, providing new perspectives and insights into the management of SAP.