Visual Observation of Abdominal Adhesion Progression Based on an Optimized Mouse Model of Postoperative Abdominal Adhesions.

Background: There is no clear description of the evolution of the progression of abdominal adhesions over time.Method: The optimized model was selected using different adhesion scoring systems. Then, this model was used to observe the progression of abdominal adhesions. Visualized observation of abdominal adhesion evolution was performed by laparoscopy and computed tomography. The inflammatory cell infiltration and collagen fibers in adhesion tissues at different times were evaluated by hematoxylin-eosin and picrosirius red staining. RNA sequencing was used to predict potential key targets of abdominal adhesions at different times.Results: The abdominal adhesion model showed the highest reproducibility when it was established using a circular tool and an electric brush. Based on this model, we found that the inflammatory response was activated early in the process of adhesion formation, peaking on day 3 and then gradually decreasing until stabilization on day 7. Collagen and fibronectin formed on day 1 and gradually increased until remaining stable on day 7. In addition, the characteristic changes in the adhesion zone from initial congestion, edema and fragile tissue to later dense and stable tissue could be vividly observed in live mice by laparoscopy and artificial pneumoperitoneum CT. The RNA sequencing results revealed that Hck on day 1, Ndufs3 and Ndufs8 on day 3 and Aif1 on day 7 might play key roles in abdominal adhesion formation.Conclusion: The construction of a standard process for describing the evolution of abdominal adhesions based on an optimized mouse model will help to facilitate subsequent adhesion-related studies.

Prognostic nomogram for primary splenic lymphoma: a SEER database-based study.

This study aimed to establish a nomogram model based on the clinicopathological factors affecting the prognosis of patients with primary splenic lymphoma (PSL) to predict the overall survival (OS) and cancer-specific survival (CSS) of patients. A total of 4074 patients diagnosed with PSL were included in this study. Among them, 4052 cases from the SEER (Surveillance, Epidemiology, and End Results) database were randomized into a training set and an internal validation set in a 7:3 ratio. Another 22 patients from the First Affiliated Hospital of Xi'an Jiaotong University were used as an external validation set. The prognostic factors affecting the OS and CSS of patients were analyzed using univariate and multivariate Cox regression models. Survival analysis was performed using Kaplan-Meier (KM) method and compared by Log-rank test. Then, a nomogram model was established to predict OS and CSS. Finally, the model was validated both internally and externally using the concordance index (C-index), receiver operating characteristic curve (ROC), and calibration curve to evaluate its predictive value, and the decision curve analysis (DCA) was conducted to assess its clinical utility. Our results showed that the model displayed a good prediction ability. In the training set, the OS rates at 1, 3, and 5 years were 85.9%, 75.8% and 70.1%, respectively, while the CSS rates at 1, 3, and 5 years were 91.9%, 86.2% and 82.3%, respectively. Predictors in the prediction model of OS included age, sex, marital status, Ann Arbor stage, histology, surgery, chemotherapy and year at diagnosis. On the other hand, predictors in the model of CSS included age, Ann Arbor stage, histology, chemotherapy, and year at diagnosis. Internal and external validation of the nomogram model showed that the C-index for predicting OS was 0.678 (0.662, 0.694) in the training set, 0.672 (0.648, 0.696) in the internal validation set, and 0.704 (0.565, 0.843) in the external validation set; the C-index for predicting CSS was 0.685 (0.661, 0.709) in the training set, 0.683 (0.650, 0.716) in the internal validation set, and 0.676 (0.488, 0.864) in the external validation set. The calibration curves for several groups showed good consistency, and DCA suggested its clinical usability. In conclusion, the nomogram constructed in this study has a good predictive value for the survival of patients with PSL, and can be a clinically applicable and practical prediction tool, facilitating rapid and accurate individualized predictions of the patient survival.

Huaier suppresses pancreatic cancer progression via activating cell autophagy induced ferroptosis.

Purpose: The anti-tumour effect of Huaier has been demonstrated in a variety of tumours. Ferroptosis is a newly identified type of programmed cell death accompanied by the accumulation of reactive oxygen species (ROS) and iron in cells and plays a key role in the therapeutic process against malignant tumours. We aimed to explore the potential therapeutic role of Huaier in pancreatic cancer and uncover the relationship between Huaier and ferroptosis. Methods: CCK8 and colony formation assays were used to determine the proliferation of pancreatic cancer cells (PCs). The levels of cellular ROS were analysed by a fluorescence probe, and the accumulation of cellular iron was showed by Prussian blue staining. The autophagosomes and mitochondrial morphology were characterised by transmission electron microscopy (TEM). The levels of intracellular glutathione (GSH) and lipid peroxidation were measured by the corresponding kits. Results: The growth inhibitory effect of Huaier on PCs was concentration- and time-dependent, but this effect was significantly attenuated by ferroptosis inhibitors. In addition, Huaier effectively inhibited the GSH-GPX4 antioxidation system and resulted in the massive accumulation of ROS in PCs As shown by TEM, Huaier-treated PCs exhibited a decrease in mitochondrial cristae and a smaller mitochondrion, accompanied by an increase in autophagosomes. Indeed, we found that autophagy can induce ferroptosis in PCs and that Huaier-induced ferroptosis can be suppressed by the autophagosome inhibitor, Wortmannin. Conclusion: Huaier can activate ferroptosis by inducing autophagy in PCs.