A novel defined pyroptosis-related gene signature predicts prognosis and correlates with the tumour immune microenvironment in lung adenocarcinoma

Abstract Lung adenocarcinoma (LUAD) is one of the most common causes of cancer-related death. The role of pyroptosis in LUAD remains unclear. Our study aimed to identify a prognostic signature of pyroptosis-related genes (PRGs) and explore the connection of PRGs with the tumour microenvironment in LUAD. Gene expression and clinical information were obtained from The Cancer Genome Atlas database. Consensus clustering was applied to classify LUAD patients. The least absolute shrinkage and selection operator Cox and multivariate Cox regression models were used to generate a PRG-related prognostic signature. The correlations between PRGs and tumour-infiltrating immune cells or the tumour mutational burden were analysed by Spearman’s correlation analysis. In this study, 44 PRGs significantly differed in expression between LUAD and normal tissues. Based on these genes, patients were clustered into three clusters with significantly different distributions of tumour-infiltrating immune cells and immune checkpoint regulators. A total of four PRGs (NLRP1, HMGB1, CYCS, and BAK1) were used to construct a prognostic model. Significant correlations were observed between these prognostic PRGs and immune cell infiltration or the tumour mutational burden. Predictive nomogram results showed that BAK1 could be an independent prognostic biomarker in LUAD. Additionally, the expression level of BAK1 was validated in two independent Gene Expression Omnibus cohorts. Our identified prognostic PRG signature may provide insight for future studies targeting pyroptosis and the tumour microenvironment in LUAD. Future studies are needed to verify our current findings

Improving Vehicle Positioning Performance in Urban Environment with Tight Integration of Multi-GNSS PPP-RTK/INS

Global navigation satellite system (GNSS) signals are easily blocked by urban canyons, tree-lined roads, and overpasses in urban environments, making it impossible to ensure continuous and reliable positioning using only GNSS, even with the widely used precise point positioning and real-time kinematic (PPP-RTK). Since the inertial navigation system (INS) and GNSS are complementary, a tightly coupled PPP-RTK/INS model is developed to improve the positioning performance in these GNSS-challenged scenarios, in which the atmospheric corrections are used to achieve a rapid ambiguity resolution and the mechanization results from INS are utilized to assist GNSS preprocessing, re-fixing, and reconvergence. The experiment was conducted using three sets of vehicle-mounted data, and the performance of low-cost receiver and microelectromechanical system (MEMS) inertial measurement unit (IMU) was compared. The result shows that the positioning accuracy of PPP-RTK/INS can reach 2 cm in the horizontal component and 5 cm in the vertical component in the open environment. In the complex urban environment, continuous and reliable positioning can be ensured during GNSS short interruption, ambiguity can be instantaneously re-fixed with the assistance of INS, and decimeter-level positioning accuracy can be achieved. As a result, the horizontal positioning errors of more than 95% of the total epochs were within 20 cm. In addition, average positioning accuracy better than 15 cm and 30 cm in the horizontal and vertical components, respectively, can be obtained using the low-cost receiver and MEMS IMU. Compared with tactical IMU, the improvements in positioning accuracy and the ambiguity fixing rate using the geodetic receiver were more significant

Improving Vehicle Positioning Performance in Urban Environment with Tight Integration of Multi-GNSS PPP-RTK/INS

Global navigation satellite system (GNSS) signals are easily blocked by urban canyons, tree-lined roads, and overpasses in urban environments, making it impossible to ensure continuous and reliable positioning using only GNSS, even with the widely used precise point positioning and real-time kinematic (PPP-RTK). Since the inertial navigation system (INS) and GNSS are complementary, a tightly coupled PPP-RTK/INS model is developed to improve the positioning performance in these GNSS-challenged scenarios, in which the atmospheric corrections are used to achieve a rapid ambiguity resolution and the mechanization results from INS are utilized to assist GNSS preprocessing, re-fixing, and reconvergence. The experiment was conducted using three sets of vehicle-mounted data, and the performance of low-cost receiver and microelectromechanical system (MEMS) inertial measurement unit (IMU) was compared. The result shows that the positioning accuracy of PPP-RTK/INS can reach 2 cm in the horizontal component and 5 cm in the vertical component in the open environment. In the complex urban environment, continuous and reliable positioning can be ensured during GNSS short interruption, ambiguity can be instantaneously re-fixed with the assistance of INS, and decimeter-level positioning accuracy can be achieved. As a result, the horizontal positioning errors of more than 95% of the total epochs were within 20 cm. In addition, average positioning accuracy better than 15 cm and 30 cm in the horizontal and vertical components, respectively, can be obtained using the low-cost receiver and MEMS IMU. Compared with tactical IMU, the improvements in positioning accuracy and the ambiguity fixing rate using the geodetic receiver were more significant

Prediction of short-term progression of COVID-19 pneumonia based on chest CT artificial intelligence: during the Omicron epidemic

Abstract Background and purpose The persistent progression of pneumonia is a critical determinant of adverse outcomes in patients afflicted with COVID-19. This study aimed to predict personalized COVID-19 pneumonia progression between the duration of two weeks and 1 month after admission by integrating radiological and clinical features. Methods A retrospective analysis, approved by the Institutional Review Board, encompassed patients diagnosed with COVID-19 pneumonia between December 2022 and February 2023. The cohort was divided into training and validation groups in a 7:3 ratio. A trained multi-task U-Net network was deployed to segment COVID-19 pneumonia and lung regions in CT images, from which quantitative features were extracted. The eXtreme Gradient Boosting (XGBoost) algorithm was employed to construct a radiological model. A clinical model was constructed by LASSO method and stepwise regression analysis, followed by the subsequent construction of the combined model. Model performance was assessed using ROC and decision curve analysis (DCA), while Shapley’s Additive interpretation (SHAP) illustrated the importance of CT features. Results A total of 214 patients were recruited in our study. Four clinical characteristics and four CT features were identified as pivotal components for constructing the clinical and radiological models. The final four clinical characteristics were incorporated as well as the RS_radiological model to construct the combined prediction model. SHAP analysis revealed that CT score difference exerted the most significant influence on the predictive performance of the radiological model. The training group’s radiological, clinical, and combined models exhibited AUC values of 0.89, 0.72, and 0.92, respectively. Correspondingly, in the validation group, these values were observed to be 0.75, 0.72, and 0.81. The DCA curve showed that the combined model exhibited greater clinical utility than the clinical or radiological models. Conclusion Our novel combined model, fusing quantitative CT features with clinical characteristics, demonstrated effective prediction of COVID-19 pneumonia progression from 2 weeks to 1 month after admission. This comprehensive model can potentially serve as a valuable tool for clinicians to develop personalized treatment strategies and improve patient outcomes

MarkBERT: Marking Word Boundaries Improves Chinese BERT

We present a Chinese BERT model dubbed MarkBERT that uses word information. Existing word-based BERT models regard words as basic units, however, due to the vocabulary limit of BERT, they only cover high-frequency words and fall back to character level when encountering out-of-vocabulary (OOV) words. Different from existing works, MarkBERT keeps the vocabulary being Chinese characters and inserts boundary markers between contiguous words. Such design enables the model to handle any words in the same way, no matter they are OOV words or not. Besides, our model has two additional benefits: first, it is convenient to add word-level learning objectives over markers, which is complementary to traditional character and sentence-level pre-training tasks; second, it can easily incorporate richer semantics such as POS tags of words by replacing generic markers with POS tag-specific markers. MarkBERT pushes the state-of-the-art of Chinese named entity recognition from 95.4\% to 96.5\% on the MSRA dataset and from 82.8\% to 84.2\% on the OntoNotes dataset, respectively. Compared to previous word-based BERT models, MarkBERT achieves better accuracy on text classification, keyword recognition, and semantic similarity tasks.Comment: Work in progres

The Effects and Molecular Mechanisms of MiR-106a in Multidrug Resistance Reversal in Human Glioma U87/DDP and U251/G Cell Lines.

Chemotherapy resistance is one of the major obstacles to effective glioma therapy. Currently, the mechanism underlying chemotherapy resistance is unclear. A recent study showed that miR-106a is an important molecule involved in chemotherapy resistance. To explore the effects and mechanisms of miR-106a on multidrug resistance reversal in human glioma cells, we silenced miR-106a expression in the cisplatin-resistant U87 (U87/DDP) and the gefitinib-resistant U251 (U251/G) glioma cell lines and measured the resulting drug sensitivity, cell apoptosis rate and rhodamine 123 content. In addition, we detected decreased expression of P-glycoprotein, MDR1, MRP1, GST-π, CDX2, ERCC1, RhoE, Bcl-2, Survivin and Topo-II, as well as reduced production of IL-6, IL-8 and TGF-β in these cell lines. Furthermore, we found decreased expression of p-AKT and transcriptional activation of NF-κB, Twist, AP-1 and Snail in these cell lines. These results suggest that miR-106a is a promising therapeutic target for the treatment of human multidrug resistant glioma