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 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 study the clinical efficacy of Sini-Moxibustion in the treatment of cancer-induced fatigue in patients with yang- deficiency gastrointestinal cancer. Methods: A total of 120 patients with gastrointestinal cancer treated in our department from January 2017 to January 2018 were randomly divided into 2 groups: the fire moxibustion group and the conventional group. The conventional group and the fire therapy group were treated with basic treatments such as anti-cancer and nutritional support. The conventional group added Sini-Moxibustion to the basic treatment, and the fire therapy group added"Sini-Moxibustion"therapy for a period of 1 month. Tthe indicators of the 2 groups of patients with Piper fatigue scale and grade, quality of life, symptoms of yang deficiency symptoms, clinical efficacy and blood tests of patients with chemotherapy were evaluated. Results: After the treatment, the degrees of fatigue in the fire moxibustion group was lower than that in the conventional group with statistically significant difference ( χ2 =4.24, P =0.037 ＜ 0.05). The scores of improvement in the quality of life scale and five subscales in the fire moxibustion were higher than those in the conventional group with statistically significant difference (P ＜ 0.01), and the improvement score of the body yang deficiency in the fire moxibustion group was greater than that of the conventional group (P ＜ 0.01). The scores of fatigue, nausea and vomiting, insomnia, anorexia, and diarrhea in the fire moxibustion group were higher than those in the conventional group with statistically significant difference (P ＜ 0.05 or P ＜ 0.01). After treatment, the total effective rate was 76.67％ in the fire moxibustion treatment group, which was higher than the conventional group 91.53％ with statistically significant difference ( χ2 =5.64, P =0.012 ＜ 0.01). Hemoglobin improvement value of 3.92 ± 1.18 in the fire moxibustion group was higher than that of the conventional group 1.02 ± 0.52 with statistically significant difference (t =7.212, P =0.003 ＜ 0.01). Conclusion: Sini-moxibustion can improve the CRF of patients with yangdeficiency gastrointestinal cancer, reduce the symptoms of yang deficiency, improve the quality of life, and increase the hemoglobin content in patients with chemotherapy.