Disulfidptosis-Related LncRNA Signatures for Prognostic Prediction in Kidney Renal Clear Cell Carcinoma.

INTRODUCTION BACKGROUND: Disulfidptosis is a prevalent apoptotic mechanism, intrinsically linked to cancer prognosis. However, the specific involvement of disulfidptosis-related long non-coding RNA (DRLncRNAs) in Kidney renal clear cell carcinoma (KIRC) remains incompletely understood. This study aims to elucidate the potential prognostic significance of disulfidptosis-related LncRNAs in KIRC. MATERIALS AND METHODS: Expression profiles and clinical data of KIRC patients were retrieved from the TCGA database to discern differentially expressed DRLncRNAs correlated with overall survival. Cox univariate analysis, Lasso Regression, and Cox multivariate analysis were used to construct a clinical prediction model. RESULTS: Six signatures, namely FAM83C.AS1, AC136475.2, AC121338.2, AC026401.3, AC254562.3, and AC000050.2, were established to evaluate overall survival (OS) in the context of Kidney renal clear cell carcinoma (KIRC) in this study. Survival analysis and ROC curves demonstrated the strong predictive performance of the associated signature. The nomogram exhibited accurate prognostic predictions for overall patient survival, offering substantial clinical utility. Gene set enrichment analysis revealed that risk signals were enriched in various immune-related pathways. Furthermore, the risk features exhibited significant correlations with immune cells, immune function, immune cell infiltration, and immune checkpoints. CONCLUSION: This study has unveiled, for the first time, six disulfdptosis-related LncRNA signatures, laying a solid foundation for enhanced and precise prognostic predictions in KIRC.

Aquaculture Performance and Genetic Diversity of a New [(Crassostrea hongkongensis ♀ × C. gigas ♂) ♂ × C. hongkongensis ♀] Variety of the Oyster "South China No. 1" in Beibu Gulf, China.

Crassostrea hongkongensis is an economically important bivalve found in various parts of the South China Sea. A new interspecific backcross ([(Crassostrea hongkongensis ♀ × C. gigas ♂) ♂ × C. hongkongensis ♀]) variety was bred by the South China Sea Institute of Oceanology which named "South China No. 1". This study aims to explore the effects of stocking density on the growth performance of "South China No. 1", compared their growth performance and genetic diversity to C. hongkongensis, and found the best place breeding site for "South China No. 1" in Beibu Gulf. The results showed that stocking a density of 20 oysters/substrate can significantly increase the shell height, shell width, total weight, survival rate, daily shell height gain and daily body mass gain. It was found that the shell height and total weight of "South China No. 1" cultured in Fangchenggang were significantly higher than that of those in Beihai and Qinzhou from September 2018 to November 2018. Similarly, the shell width of oysters in Fangchenggang and Qinzhou was also significantly higher in September 2018, and the interaction between site and stocking density had significant effects on the shell width in March 2018 and November 2018. In addition, the shell height and shell width of "South China No. 1" were significantly higher than that of C. hongkongensis in all three sites. At all three sites, the phytoplankton community structure was mostly dominated by Bacillariophyta. In the Hardy-Weinberg equilibrium test, for the seven populations and ten microsatellites, in 10 of the 70 groups, the segregation distortion was significant. These results suggest that a stocking density of 20 oysters/substrate can promote the shell height, shell width and total weight of "South China No. 1" in Beibu Gulf, China. "South China No. 1" has better growth performance compared with C. hongkongensis. Fangchenggang is a suitable place to cultivate the "South China No. 1" breed according to the total weight and sum of all algal genus abundances. The results of this study can be used as a reference to further understand the stocking density and genetic diversity of the "South China No. 1" breed in Beibu Gulf, China.

An investigation into the abnormal dynamic connection mechanism of generalized anxiety disorders based on non-homogeneous Markov models.

BACKGROUND: The dynamic and hierarchical nature of the functional brain network. The neural dynamical systems tend to converge to multiple attractors (stable fixed points or dynamical states) in long run. Little is known about how the changes in this brain dynamic "long-term" behavior of the connectivity flow of brain network in generalized anxiety disorder (GAD). METHODS: This study recruited 92 patients with GAD and 77 healthy controls (HC). We applied a reachable probability approach combining a Non-homogeneous Markov model with transition probability to quantify all possible connectivity flows and the hierarchical structure of brain functional systems at the dynamic level and the stationary probability vector (10-step transition probabilities) to describe the steady state of the system in the long run. A random forest algorithm was conducted to predict the severity of anxiety. RESULTS: The dynamic functional patterns in distributed brain networks had larger possibility to converge in bilateral thalamus, posterior cingulate cortex (PCC), right superior occipital gyrus (SOG) and smaller possibility to converge in bilateral superior temporal gyrus (STG) and right parahippocampal gyrus (PHG) in patients with GAD compared to HC. The abnormal transition probability pattern could predict anxiety severity in patients with GAD. LIMITATIONS: Small samples and subjects taking medications may have influenced our results. Future studies are expected to rule out the potential confounding effects. CONCLUSION: Our results have revealed abnormal dynamic neural communication and integration in emotion regulation in patients with GAD, which give new insights to understand the dynamics of brain function of patients with GAD.

Multi-Semantic Decoding of Visual Perception with Graph Neural Networks.

Constructing computational decoding models to account for the cortical representation of semantic information plays a crucial role in understanding visual perception. The human visual system processes interactive relationships among different objects when perceiving the semantic contents of natural visions. However, the existing semantic decoding models commonly regard categories as completely separate and independent visually and semantically and rarely consider the relationships from prior information. In this work, a novel semantic graph learning model was proposed to decode multiple semantic categories of perceived natural images from brain activity. The proposed model was validated on the functional magnetic resonance imaging data collected from five normal subjects while viewing 2750 natural images comprising 52 semantic categories. The results showed that the Graph Neural Network-based decoding model achieved higher accuracies than other deep neural network models. Moreover, the co-occurrence probability among semantic categories showed a significant correlation with the decoding accuracy. Additionally, the results suggested that semantic content organized in a hierarchical way with higher visual areas was more closely related to the internal visual experience. Together, this study provides a superior computational framework for multi-semantic decoding that supports the visual integration mechanism of semantic processing.

Structural and functional study on cysteine 495, coordinating ligand to T1Cu site in multicopper oxidase CopA.

Excessive intake of manganese seriously affects human health. Manganese oxidizing bacteria can efficiently remove manganese, among which manganese oxidase plays a decisive role. Multicopper oxidase, one of the manganese oxidases, has 4 copper binding sites, among them, T1Cu coordinates with two histidine, one cysteine and one axial residue, mainly transferring electrons from the substrate to T2Cu and T3Cu. Here, we conducted site-directed mutagenesis on T1Cu coordinating 495 amino acid site from cysteine to aspartic acid, histidine and methionine in multicopper oxidase CopA from Brevibacillus panacihumi MK-8, through the enzyme kinetics and structure models, finding that the enzyme catalytic efficiency (kcat/Km) of the mutated C495H with Mn2+ and ABTS reached 9.03 min-1 mM-1 and 8863 s-1 mM-1, 1.47 times and 1.67 times that of CopA. And it was found strain Rosetta-pET-copAC495H could remove 91.67% manganese after 7-day culture, which was 11.65% higher than the original strain. To sum up, these results provide a vision for the future application of protein engineering in biological manganese removal.