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Objective:To investigate the risk factors of postoperative recurrence of solid pseudopapillary neoplasms of the pancreas (SPN).Methods:Case-control studies on risk factors for postoperative recurrence in patients with SPN were conducted by searching in China National Knowledge Infrastructure, Wanfang Database, VIP Database, PubMed, Web of Science and Embase database from inception of these databases to November 2022. Two investigators screened the collected literatures independently according to the inclusion and exclusion criteria, extracted the data and evaluated the methodological quality, and then used Review Manager 5.4 for statistical analysis, odds ratio ( OR) was calculated with 95% confidence interval ( CI). Results:A total of 14 articles were included, including 1 409 patients with 67 cases in recurrence group and 1 342 cases in non-recurrence group. Twelve risk factors with predictive value for postoperative recurrence of SPN were extracted from the literatures. The analysis showed that the pooled OR and 95% CI of each risk factor were: gender ( OR=0.75, 95% CI: 0.35-1.59, P=0.450), age( OR=-2.08, 95% CI: -5.24-1.08, P=0.200), tumor diameter( OR=5.29, 95% CI: 4.71-5.87, P<0.001), tumor location( OR=0.56, 95% CI: 0.28-1.13, P=0.100), synchronous metastasis( OR=86.84, 95% CI: 22.64-333.05, P<0.001), lymph node metastasis ( OR=7.55, 95% CI: 2.58-22.06, P<0.001), perineural invasion ( OR=2.10, 95% CI: 0.98-4.48, P=0.060), positive margin( OR=7.00, 95% CI: 2.56-19.15, P<0.001), calcification( OR=0.49, 95% CI: 0.11-2.23, P=0.360), lymphovascular invasion( OR=11.22, 95% CI: 4.81-26.18, P<0.001), peripancreatic soft tissue invasion( OR=1.38, 95% CI: 0.48-4.00, P=0.550), capsular invasion( OR=1.72, 95% CI: 0.53-5.65, P=0.370). Conclusion:Large tumor diameter, synchronous metastasis, lymph node metastasis, positive margin, lymphovascular invasion increase the risk of recurrence of pancreatic SPN after resection, and patients with these characteristics should receive long-term follow-up.
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Objective:To develop a grading prediction model of traumatic hemorrhage volume based on deep learning and assist in predicting traumatic hemorrhage volume.Methods:A retrospective observational study was conducted based on the experimental data of pig gunshot wounds in the time-effect assessment database for experiments on war-traumatized animals constructed by the General Hospital of the Chinese People's Liberation Army. The hemorrhage volume data of the study population were extracted, and the animals were divided into 0-300 mL, 301-600 mL, and > 600 mL groups according to the hemorrhage volume. Using vital signs indexes as the predictive variables and hemorrhage volume grading as the outcome variable, trauma hemorrhage volume grading prediction models were developed based on four traditional machine learning and ten deep learning methods. Using laboratory test indexes as predictive variables and hemorrhage volume grading as outcome variables, trauma hemorrhage volume grading prediction models were developed based on the above fourteen methods. The effect of the two groups of models was evaluated by accuracy and area under the receiver operator characteristic curve (AUC), and the optimal models in the two groups were mixed to obtain hybrid model 1. Feature selection was conducted according to the genetic algorithm, and hybrid model 2 was constructed according to the best feature combination. Finally, hybrid model 2 was deployed in the animal experiment database system.Results:Ninety-six traumatic animals in the database were enrolled, including 27 pigs in the 0-300 mL group, 40 in the 301-600 mL group, and 29 in the > 600 mL group. Among the fourteen models based on vital signs indexes, fully convolutional network (FCN) model was the best [accuracy: 60.0%, AUC and 95% confidence interval (95% CI) was 0.699 (0.671-0.727)]. Among the fourteen models based on laboratory test indexes, recurrent neural network (RNN) model was the best [accuracy: 68.9%, AUC (95% CI) was 0.845 (0.829-0.860)]. After mixing the FCN and RNN models, the hybrid model 1, namely RNN-FCN model was obtained, and the performance of the model was improved [accuracy: 74.2%, AUC (95% CI) was 0.847 (0.833-0.862)]. Feature selection was carried out by genetic algorithm, and the hybrid model 2, namely RNN-FCN* model, was constructed according to the selected feature combination, which further improved the model performance [accuracy: 80.5%, AUC (95% CI) was 0.880 (0.868-0.893)]. The hybrid model 2 contained ten indexes, including mean arterial pressure (MAP), hematocrit (HCT), platelet count (PLT), lactic acid, arterial partial pressure of carbon dioxide (PaCO 2), Total CO 2, blood sodium, anion gap (AG), fibrinogen (FIB), international normalized ratio (INR). Finally, the RNN-FCN* model was deployed in the database system, which realized automatic, continuous, efficient, intelligent, and grading prediction of hemorrhage volume in traumatic animals. Conclusion:Based on deep learning, a grading prediction model of traumatic hemorrhage volume was developed and deployed in the information system to realize the intelligent grading prediction of traumatic animal hemorrhage volume.
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Objective:To observe the changes of arterial blood gas indexes in pigs with the free-field primary blast lung injury (PBLI) model, and to explore the value of arterial blood gas indexes in predicting moderate to severe PBLI.Methods:Nine adult healthy Landrace pigs were selected to construct the pig free-field PBLI model. Arterial blood samples were taken 15 minutes before the explosion (before injury) and 10, 30, 60, 120, and 180 minutes after the explosion (after injury). Arterial blood gas indexes and pulse oxygen saturation (SpO 2) were measured, compare the changes of blood gas analysis indexes and SpO 2 levels at different time points, and observe the changes of gross injury scores and pathological injury scores of lung tissue. Analyze the correlation between the blood gas indicators. Results:As time prolonged, at each time point, pH, arterial partial pressure of oxygen (PaO 2), and SpO 2 were lower than those before the injury, and blood lactic acid (Lac) and arterial partial pressure of carbon dioxide (PaCO 2) were higher than those before the injury. Compared with that before the injury, the pH value in the blood decreased significantly 10 minutes after the injury (7.39±0.06 vs. 7.46±0.02, P < 0.05), and the Lac increased significantly (mmol/L: 3.61±2.89 vs. 1.10±0.28, P < 0.05), and lasts until 180 minutes after injury (pH value: 7.37±0.07 vs. 7.46±0.02, Lac (mmol/L): 2.40±0.79 vs. 1.10±0.28, both P < 0.05); while PaO 2 and SpO 2 decreased significantly at 180 minutes after injury [PaO 2 (mmHg, 1 mmHg = 0.133 kPa): 59.40±10.94 vs. 74.81±9.39, P < 0.05; SpO 2: 0.75±0.11 vs. 0.89±0.08, P < 0.05], PaCO 2 increased significantly (mmHg: 56.17±5.38 vs. 48.42±4.93, P < 0.05). Correlation analysis showed that the gross injury score of lung blast injury animals was positively correlated with the pathological injury score ( r = 0.866, P = 0.005); PaO 2 and SpO 2 were positively correlated ( r = 0.703, P = 0.000); pH value and Lac were negative Correlation ( r = -0.400, P = 0.006); pH value is negatively correlated with PaCO 2 ( r = -0.844, P = 0.000). Conclusion:This study successfully established a large mammalian free-field PBLI model, arterial blood gas analysis is helpful for the early diagnosis of PBLI, whether SpO 2 can be used to evaluate the severity of lung injury remains to be further verified.