Identification of novel prognostic signature of recurrent low-grade glioma.

The prognosis of patients with recurrent low-grade glioma (rLGG) varies greatly. Some patients can survive more than 10 years after recurrence, while other patients have less than 1 year of survival. In order to identify the related risk factors affecting the prognosis of rLGG patients, we performed a series of bioinformatics analyses on RNA-sequencing data of rLGG based on the CGGA database, and finally constructed a 12-genes prognostic signature, dividing all the rLGG patients into high- and low-risk subgroups. The result showed an excellent predictive effect in both the training cohort and the validation cohort using LASSO-COX regression. Moreover, multivariate COX analysis identified 4 independent prognostic factors of rLGG, and among them, ZCWPW1 is identified as a high-value protective factor. In all, this prognostic model displayed robust predictive capability for the overall survival (OS) of rLGG patients, providing a new monitoring method for rLGG, and the 4 independent prognostic factors, especially ZCWPW1, can be potential targets for rLGG, bringing new possibilities for the treatment of rLGG patients.

Maternal smoking, consumption of alcohol, and caffeinated beverages during pregnancy and the risk of childhood brain tumors: a meta-analysis of observational studies.

BACKGROUND: We conducted this meta-analysis to investigate the potential association between maternal smoking, alcohol and caffeinated beverages consumption during pregnancy and the risk of childhood brain tumors (CBTs). METHODS: A thorough search was carried out on PubMed, Embase, Web of Science, Cochrane Library, and China National Knowledge Internet to identify pertinent articles. Fixed or random effects model was applied to meta-analyze the data. RESULTS: The results suggested a borderline statistically significant increased risk of CBTs associated with maternal smoking during pregnancy (OR 1.04, 95% CI 0.99-1.09). We found that passive smoking (OR 1.12, 95% CI 1.03-1.20), rather than active smoking (OR 1.00, 95% CI 0.93-1.07), led to an increased risk of CBTs. The results suggested a higher risk in 0-1 year old children (OR 1.21, 95% CI 0.94-1.56), followed by 0-4 years old children (OR 1.12, 95% CI 0.97-1.28) and 5-9 years old children (OR 1.11, 95% CI 0.95-1.29). This meta-analysis found no significant association between maternal alcohol consumption during pregnancy and CBTs risk (OR 1.00, 95% CI 0.80-1.24). An increased risk of CBTs was found to be associated with maternal consumption of caffeinated beverages (OR 1.16, 95% CI 1.07-1.26) during pregnancy, especially coffee (OR 1.18, 95% CI 1.00-1.38). CONCLUSIONS: Maternal passive smoking, consumption of caffeinated beverages during pregnancy should be considered as risk factors for CBTs, especially glioma. More prospective cohort studies are warranted to provide a higher level of evidence.

Co-exposure of decabromodiphenyl ethane and cadmium increases toxicity to earthworms: Enrichment, oxidative stress, damage and molecular binding mechanisms.

The increase of electronic waste worldwide has resulted in the exacerbation of combined decabromodiphenyl ethane (DBDPE) and cadmium (Cd) pollution in soil, posing a serious threat to the safety of soil organisms. However, whether combined exposure increases toxicity remains unclear. Therefore, this study primarily investigated the toxic effects of DBDPE and Cd on earthworms at the individual, tissue, and cellular levels under single and combined exposure. The results showed that the combined exposure significantly increased the enrichment of Cd in earthworms by 50.32-90.42 %. The toxicity to earthworms increased with co-exposure, primarily resulting in enhanced oxidative stress, inhibition of growth and reproduction, intensified intestinal and epidermal damage, and amplified coelomocyte apoptosis. PLS-PM analysis revealed a significant and direct relationship between the accumulation of target pollutants in earthworms and oxidative stress, damage, as well as growth and reproduction of earthworms. Furthermore, IBR analysis indicated that SOD and POD were sensitive biomarkers in earthworms. Molecular docking elucidated that DBDPE and Cd induced oxidative stress responses in earthworms through the alteration of the conformation of the two enzymes. This study enhances understanding of the mechanisms behind the toxicity of combined pollution and provides important insights for assessing e-waste contaminated soils.

Self-supervised contrastive learning for EEG-based cross-subject motor imagery recognition.

Objective. The extensive application of electroencephalography (EEG) in brain-computer interfaces (BCIs) can be attributed to its non-invasive nature and capability to offer high-resolution data. The acquisition of EEG signals is a straightforward process, but the datasets associated with these signals frequently exhibit data scarcity and require substantial resources for proper labeling. Furthermore, there is a significant limitation in the generalization performance of EEG models due to the substantial inter-individual variability observed in EEG signals.Approach. To address these issues, we propose a novel self-supervised contrastive learning framework for decoding motor imagery (MI) signals in cross-subject scenarios. Specifically, we design an encoder combining convolutional neural network and attention mechanism. In the contrastive learning training stage, the network undergoes training with the pretext task of data augmentation to minimize the distance between pairs of homologous transformations while simultaneously maximizing the distance between pairs of heterologous transformations. It enhances the amount of data utilized for training and improves the network's ability to extract deep features from original signals without relying on the true labels of the data.Main results. To evaluate our framework's efficacy, we conduct extensive experiments on three public MI datasets: BCI IV IIa, BCI IV IIb, and HGD datasets. The proposed method achieves cross-subject classification accuracies of 67.32%, 82.34%, and 81.13%on the three datasets, demonstrating superior performance compared to existing methods.Significance. Therefore, this method has great promise for improving the performance of cross-subject transfer learning in MI-based BCI systems.

Motion states identification of underwater glider based on complex networks and graph convolutional networks.

Underwater glider (UG) plays an important role in ocean observation and exploration for a more efficient and deeper understanding of complex ocean environment. Timely identifying the motion states of UG is conducive for timely attitude adjustment and detection of potential anomalies, thereby improving the working reliability of UG. Combining limited penetrable visibility graph (LPVG) and graph convolutional networks (GCN) with self-attention mechanisms, we propose a novel method for motion states identification of UG, which is called as visibility graph and self-attention mechanism-based graph convolutional network (VGSA-GCN). Based on the actual sea trial data of UG, we chose the attitude angle signals of motion states related sensors collected by the control system of UG as the research object and constructed complex networks based on the LPVG method from pitch angle, roll angle, and heading angle data in diving and climbing states. Then, we build a self-attention mechanism-based GCN framework and classify the graphs under different motion states constructed by a complex network. Compared with support vector machines, convolutional neural network, and GCN without self-attention pooling layer, the proposed VGSA-GCN method can more accurately distinguish the diving and climbing states of UG. Subsequently, we analyze the variation of the transitivity coefficient corresponding to these two motion states. The results suggest that the coordination of the various sensors in the attitude adjustment unit during diving becomes closer and more efficient, which corresponds to the higher network measure of the diving state compared to the climbing state.