Lipid metabolism for predicting the recurrence of hypertriglyceridemic acute pancreatitis.

Rationale and objectives: To investigate the predictive value of lipid metabolism in predicting the recurrence of hypertriglyceridemic acute pancreatitis (HTG-AP). Materials and methods: A total of 892 patients were admitted to our hospital for acute pancreatitis (AP) from January 2017 to December 2020, of whom 198 diagnosed with HTG-AP were enrolled in this retrospective study. Demographic information, length of stay, smoking index, alcohol abuse, necrosis, severity, baseline lipid metabolism and other blood biochemical indicators were recorded. The risk factors of recurrence were evaluated using univariate and multivariate Cox proportional risk analyses, and the cumulative recurrence-free survival rate of patients were calculated using Kaplan Meier method and the differences between groups were compared using the log-rank test. Results: Univariate and multivariate analysis showed that triglyceride (hazard ratio, 2.421; 95% CI, 1.152-5.076; P = 0.020), non high-density lipoprotein (hazard ratio, 4.630; 95% CI, 1.692-12.658; P = 0.003) and apolipoprotein A1 (hazard ratio, 1.735; 95% CI, 1.093-2.754; P = 0.019) were important predictors for recurrence of HTG-AP. Subsequently, patients were divided into four groups according to the cut off values of triglyceride, non high-density lipoprotein and apolipoprotein A1. It was found that the cumulative recurrence-free survival rate of patients in highest-risk group or high-risk group was significantly lower than that of medium-risk group (P < 0.001, P = 0.003) or low risk group (P < 0.001). Conclusion: Serum triglycerides, non high-density lipoprotein and apolipoprotein A1 are independent predictors of recurrence in HTG-AP patients, which can provide reference for individualized treatment and prevention of recurrence in HTG-AP patients.

Three-Dimensional Radiomics Features of Magnetic Resonance T2-Weighted Imaging Combined With Clinical Characteristics to Predict the Recurrence of Acute Pancreatitis.

Objective: To explore the diagnostic value of radiomics model based on magnetic resonance T2-weighted imaging for predicting the recurrence of acute pancreatitis. Methods: We retrospectively collected 190 patients with acute pancreatitis (AP), including 122 patients with initial acute pancreatitis (IAP) and 68 patients with recurrent acute pancreatitis (RAP). At the same time, the clinical characteristics of the two groups were collected. They were randomly divided into training group and validation group in the ratio of 7:3. One hundred thirty-four cases in the training group, including 86 cases of IAP and 48 cases of RAP. There were 56 cases in the validation group, including 36 cases of IAP and 20 cases of RAP. Least absolute shrinkage and selection operator (LASSO) were used for feature screening. Logistic regression was used to establish the radiomics model, clinical model and combined model for predicting AP recurrence. The predictive ability of the three models was evaluated by the area under the curve (AUC). The recurrence risk in patients with AP was assessed using the nomogram. Results: The AUCs of radiomics model in training group and validation group were 0.804 and 0.788, respectively. The AUCs of the combined model in the training group and the validation group were 0.833 and 0.799, respectively. The AUCs of the clinical model in training group and validation group were 0.677 and 0.572, respectively. The sensitivities of the radiomics model, combined model, and clinical model were 0.646, 0.691, and 0.765, respectively. The specificities of the radiomics model, combined model, and clinical model were 0.791, 0.828, and 0.590, respectively. There was no significant difference in AUC between the radiomics model and the combined model for predicting RAP (p = 0.067). The AUCs of the radiomics model and combined model were greater than those of the clinical model (p = 0.008 and p = 0.007, respectively). Conclusions: Radiomics features based on magnetic resonance T2WI could be used as biomarkers to predict the recurrence of AP, and radiomics model and combined model can provide new directions for predicting recurrence of acute pancreatitis.

A spiking network model for clustering report in a visual working memory task.

Introduction: Working memory (WM) plays a key role in many cognitive processes, and great interest has been attracted by WM for many decades. Recently, it has been observed that the reports of the memorized color sampled from a uniform distribution are clustered, and the report error for the stimulus follows a Gaussian distribution. Methods: Based on the well-established ring model for visuospatial WM, we constructed a spiking network model with heterogeneous connectivity and embedded short-term plasticity (STP) to investigate the neurodynamic mechanisms behind this interesting phenomenon. Results: As a result, our model reproduced the clustering report given stimuli sampled from a uniform distribution and the error of the report following a Gaussian distribution. Perturbation studies showed that the heterogeneity of connectivity and STP are necessary to explain experimental observations. Conclusion: Our model provides a new perspective on the phenomenon of visual WM in experiments.

Intravoxel Incoherent Motion Model in Differentiating the Pathological Grades of Esophageal Carcinoma: Comparison of Mono-Exponential and Bi-Exponential Fit Model.

PURPOSE: To compare the diagnostic efficiency of the mono-exponential model and bi-exponential model deriving from intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in differentiating the pathological grade of esophageal squamous cell carcinoma (ESCC). METHODS: Fifty-four patients with ESCC were divided into three groups of poorly-differentiated (PD), moderately-differentiated (MD), and well-differentiated (WD), and underwent the IVIM-DWI scan. Mono-exponential (Dmono, D*mono, and fmono) and bi-exponential fit parameters (Dbi, D*bi, and fbi) were calculated using the IVIM data for the tumors. Mean parameter values of three groups were compared using a one-way ANOVA followed by post hoc tests. The receiver operating characteristic curve was drawn for differentiating pathological grade of ESCC. Correlations between pathological grades and IVIM parameters were analyzed. RESULTS: There were significant differences in fmono and fbi among the PD, MD and WD ESCC groups (all p<0.05). The fmono were 0.32 ± 0.07, 0.23 ± 0.08, and 0.16 ± 0.05, respectively, and the fbi were 0.35 ± 0.08, 0.26 ± 0.10, and 0.18 ± 0.07, respectively. There was a significant difference in the Dmono between the WD and the PD group (1.48 ± 0.51* 10-3 mm2/s versus 1.05 ± 0.44*10-3 mm2/s, p<0.05), but there was no significant difference between the WD and MD groups, MD and PD groups (all p>0.05). The D*mono, Dbi, and D*bi showed no significant difference among the three groups (all p>0.05). The area under the curve (AUC) of Dmono, fmono and fbi in differentiating WD from PD ESCC were 0.764, 0.961 and 0.932, and the sensitivity and specificity were 92.9% and 60%, 92.9% and 90%, 85.7% and 100%, respectively. The AUC of fmono and fbi in differentiating MD from PD ESCC were 0.839 and 0.757, and the sensitivity and specificity were 78.6% and 80%, 85.7% and 70%, respectively. The AUC of fmono and fbi in differentiating MD from WD ESCC were 0.746 and 0.740, and the sensitivity and specificity were 65% and 85%, 80% and 60%, respectively. The pathologically differentiated grade was correlated with all IVIM parameters (all p<0.05). CONCLUSIONS: The mono-exponential IVIM model is superior to the bi-exponential IVIM model in differentiating pathological grades of ESCC, which may be a promising imaging method to predict pathological grades of ESCC.