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Background: Accurate estimation of perioperative blood transfusion risk in lumbar posterior interbody fusion is essential to reduce the number, cost, and complications associated with blood transfusions. Machine learning algorithms have the potential to outperform traditional prediction methods in predicting perioperative blood transfusion. This study aimed to construct a machine learning-based perioperative transfusion risk prediction model for lumbar posterior interbody fusion in order to improve the efficacy of surgical decision-making.Methods: We retrospectively collected clinical data on 1905 patients who underwent lumbar posterior interbody fusion surgery at the Second Hospital of Shanxi Medical University between January 2021 and March 2023. All the data was randomly divided into a training set and a validation set, and the “feature_importances” method provided by eXtreme Gradient Boosting (XGBoost) algorithm was applied to select statistically significant features on the training set to establish five machine learning prediction models. The optimal model was identified by utilizing the area under the curve (AUC) and the probability calibration curve on the validation set. Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) were employed for interpretable analysis of the optimal model.Results: In the postoperative outcomes of patients, the number of hospital days in the transfusion group was longer than that in the non-transfusion group. Additionally, the transfusion group experienced higher total hospital costs, 90-day readmission rates, and complication rates within 90 days after surgery than the non-transfusion group. A total of 9 features were selected for the models. The XGBoost model performed best with an AUC value of 0.958. The SHAP values showed that intraoperative blood loss, intraoperative fluid infusion, and number of fused segments were the top 3 most important features affecting perioperative blood transfusion in lumbar posterior interbody fusion. The LIME algorithm was used to interpret the individualized prediction.Conclusion: Surgery, ASA class, levels fused, total intraoperative blood loss, operative time, and preoperative Hb are viable predictors of perioperative blood transfusion in lumbar posterior interbody fusion. The XGBoost model has demonstrated superior predictive efficacy compared to the traditional logistic regression model, making it a more effective decision-making tool for perioperative blood transfusion.

Background: Exosomes, as extracellular membrane vesicles, play important roles in intercellular communication and can influence tumour progression. Circular RNAs (circRNAs) have been reported in various malignancies and are also important components of exosomes. However, the role of exosomal circRNAs in gastric cancer (GC) progression has not been completely clarified. Methods: The exosomal circRNAs enriched in GC were identified using exosomal circRNA sequencing. The biological function of circMAN1A2 in GC was investigated using a series of in vitro and in vivo experiments. PKH-67 staining was used to label the exosomes. The molecular mechanism of exosomal circMAN1A2 was investigated via mass spectrometry, immunoprecipitation, Western blot, and single-cell RNA-sequencing data analyses. Results: In our study, we determined that circMAN1A2 (hsa_circ_0000118) was enriched in GC-derived exosomes. Higher circMAN1A2 expression was related to poor survival in GC patients (HR = 2.917, p = 0.0120). Exosomal circMAN1A2 promoted GC progression in vitro and in vivo and suppressed the antitumour activity of T cells. Moreover, circMAN1A2 bound to SFPQ in GC cells and T cells, promoting the G1/S phase transition of the cell cycle in GC cells while inhibiting the activation of the T cell receptor signalling pathway in T cells to decrease antitumour activity. Mechanistically, circMAN1A2 competed with FBXW11 for binding to SFPQ, preventing FBXW11-mediated k48-linked ubiquitination and SFPQ protein degradation, thereby stabilizing SFPQ expression. Conclusions: Our work confirms the critical role of exosomal circMAN1A2 in the progression and immunosuppression of GC. This novel axis of circMAN1A2-SFPQ provides new insights into exosomal circRNA-based GC diagnostic and therapeutic strategies. [ABSTRACT FROM AUTHOR]

Objective To investigate the role of aquaporin-3 (AQP-3) in hyperosmolarity-induced apoptosis of cultured rat nucleus pulposus (NP) cells. Methods Rat NP cells were cultured under different osmotic pressures (330 and 550 mOsm/kg), and the proliferation and apoptotic rate of the cells were determined using CCK-8 assay and flow cytometry, respectively. Western blotting and real-time quantitative PCR were used to detect the expression levels of apoptosis-related molecules (Bcl-2, Bax, cleaved caspase 3/caspase 3), and immunocytochemistry, Western blotting and real-time quantitative PCR were used to detect the expression of AQP-3 in the cells. The effect of lentivirus-mediated AQP-3 overexpression on apoptosis of NP cells was investigated after hyperosmolar exposure. Results An osmotic pressure of 550 mOsm/kg, as compared with 330 mOsm/kg, significantly decreased NP cell proliferation, increased the cell apoptotic rate, upregulated the expression of the pro-apoptotic molecules Bax and cleaved caspase 3/caspase 3 (P < 0.05), and down-regulated the expression of the anti-apoptotic molecule Bcl-2 (P < 0.05). The cells with lentivirus-mediated AQP-3 overexpression showed an obviously lowered apoptotic rate following the hyperosmolar exposure and significantly reduced expression of Bax and cleaved caspase 3/caspase 3 (P < 0.05) with an enhanced expression of Bcl-2 (P < 0.05). Conclusion Hyperosmolar exposure significantly promotes apoptosis of NP cells and reduce the expression of AQP-3. AQP-3 overexpression can partly attenuate apoptosis of NP cells following a hyperosmolar exposure.

Background: Accurate estimation of perioperative blood transfusion risk in lumbar posterior interbody fusion is essential to reduce the number, cost, and complications associated with blood transfusions. Machine learning algorithms have the potential to outperform traditional prediction methods in predicting perioperative blood transfusion. This study aimed to construct a machine learning-based perioperative transfusion risk prediction model for lumbar posterior interbody fusion in order to improve the efficacy of surgical decision-making. Methods: We retrospectively collected clinical data on 1905 patients who underwent lumbar posterior interbody fusion surgery at the Second Hospital of Shanxi Medical University between January 2021 and March 2023. All the data was randomly divided into a training set and a validation set, and the “feature_ importances” method provided by eXtreme Gradient Boosting (XGBoost) algorithm was applied to select statistically significant features on the training set to establish five machine learning prediction models. The optimal model was identified by utilizing the area under the curve (AUC) and the probability calibration curve on the validation set. Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME) were employed for interpretable analysis of the optimal model. Results: In the postoperative outcomes of patients, the number of hospital days in the transfusion group was longer than that in the non-transfusion group. Additionally, the transfusion group experienced higher total hospital costs, 90-day readmission rates, and complication rates within 90 days after surgery than the non-transfusion group. A total of 9 features were selected for the models. The XGBoost model performed best with an AUC value of 0.958. The SHAP values showed that intraoperative blood loss, intraoperative fluid infusion, and number of fused segments were the top 3 most important features affecting perioperative blood transfusion in lumbar posterior interbody fusion. The LIME algorithm was used to interpret the individualized prediction. Conclusion: Surgery, ASA class, levels fused, total intraoperative blood loss, operative time, and preoperative Hb are viable predictors of perioperative blood transfusion in lumbar posterior interbody fusion. The XGBoost model has demonstrated superior predictive efficacy compared to the traditional logistic regression model, making it a more effective decision-making tool for perioperative blood transfusion. [ABSTRACT FROM AUTHOR]

Background: Chemotherapy can significantly improve the disease‐free survival and overall survival of patients with advanced gastric cancer (GC). 5‐fluorouracil (5‐FU) is frequently applied in the clinic, acting as a first‐line chemotherapy drug of advanced GC, which could be used alone or combining platinum drugs. However, its efficacy is significantly attenuated by chemoresistance, which is associated with patients' poor survival. Recently, there is evidence suggesting that dysregulation of autophagy may contribute to drug resistance in cancer, and circular RNAs (circRNAs) also take part in chemoresistance. However, whether circRNAs participate in 5‐FU chemoresistance through autophagy remains largely unknown. Methods: RNA sequencing technologies and bioinformatics analysis were performed in GC. Sanger sequencing, Actinomycin D assay and RNase R assay confirmed the circular structure of circular CPM (circCPM). Various cell line models and animal models were used to explore related functions in vitro and in vivo. Quantitative Real‐time PCR (qRT‐PCR), fluorescence in situ hybridization, ribonucleic acid; (RNA) pulldown assays, RNA binding protein immunoprecipitation assays and Luciferase reporter assays were applied to explore involved pathways. Results: circCPM was up‐regulated in 5‐FU resistant GC cell lines and tissue. Moreover, high circCPM expression is positively associated with poor survival. Silencing circCPM greatly improved chemosensitivity in vitro and in vivo. Mechanistically, it directly binds to miR‐21‐3p in the cytoplasm and therefore increases the expression of PRKAA2, contributing to the activation of autophagy and chemoresistance. Conclusion: Our results reveal that circCPM has a crucial role in regulating GC autophagy and 5‐FU resistance by targeting PRKAA2. It may function as a new theory basis for assessing the curative effect of GC and reversing 5‐FU chemoresistance. [ABSTRACT FROM AUTHOR]

Gastric cancer (GC) ranks third in mortality among all cancers worldwide. Circular RNAs (circRNAs) play an important role in the occurrence and development of gastric cancer. Forkhead box P2 (FOXP2), as a transcription factor, is closely associated with the development of many types of tumours. However, the regulatory network between FOXP2 and circRNAs remains to be explored. In our study, circST3GAL6 was significantly downregulated in GC and was associated with poor prognosis in GC patients. Overexpression of circST3GAL6 inhibited the malignant behaviours of GC cells, which was mediated by inducing apoptosis and autophagy. In addition, we demonstrated that circST3GAL6 regulated FOXP2 through the mir‐300 sponge. We further found that FOXP2 inhibited MET Proto‐Oncogene (MET), which was the initiating factor that regulated the classic AKT/mTOR pathway of autophagy. In conclusion, our results suggested that circST3GAL6 played a tumour suppressive role in gastric cancer through miR‐300/FOXP2 axis and regulated apoptosis and autophagy through FOXP2‐mediated transcriptional inhibition of the MET axis, which may become a potential target for GC therapy. [ABSTRACT FROM AUTHOR]

Surface plasmon polaritons (SPPs) can be excited, meanwhile some peculiar optical phenomena will appear when light irradiates metal structures under some conditions. Based on photonic band gap theory, in this Letter we present a kind of SPP waveguide with multiple wavelength-channels. By using the Bragg effect and introducing some geometric defect layers into a quasi-periodic metal heterowaveguide, the multiple SPP forbidden bands (SPFBs) in a given waveband can be generated, and the multiple SPP pass bands (SPPBs) with narrow bandwidth in each SPFB can be realized. The SPP propagation in metal heterowaveguide is calculated by FDTD and transfer matrix methods. By selecting appropriate thickness, position and the number of defect layers, two SPPBs can be achieved in the SPFBs around 1.31 and 1.55 mm simultaneously. [ABSTRACT FROM AUTHOR]