VDAC1 Negatively Regulates Floral Transition in Arabidopsis thaliana.

Voltage-dependent anion channels (VDACs) are the most important proteins in mitochondria. They localize to the outer mitochondrial membrane and contribute to the metabolite transport between the mitochondria and cytoplasm, which aids plant growth regulation. Here, we report that Arabidopsis thaliana VDAC1 is involved in the floral transition, with the loss of AtVDAC1 function, resulting in an early-flowering phenotype. AtVDAC1 is expressed ubiquitously in Arabidopsis. To identify the flowering pathway integrators that may be responsible for AtVDAC1's function during the floral transition, an RNA-seq analysis was performed. In total, 106 differentially expressed genes (DEGs) were identified between wild-type and atvdac1-5 mutant seedlings. However, none were involved in flowering-related pathways. In contrast, AtVDAC1 physically associated with FLOWERING LOCUS T. Thus, in the floral transition, AtVDAC1 may function partly through the FLOWERING LOCUS T protein.

A Deep Learning Model Combining Multimodal Factors to Predict the Overall Survival of Transarterial Chemoembolization.

Background: To develop and validate an overall survival (OS) prediction model for transarterial chemoembolization (TACE). Methods: In this retrospective study, 301 patients with hepatocellular carcinoma (HCC) who received TACE from 2012 to 2015 were collected. The residual network was used to extract prognostic information from CT images, which was then combined with the clinical factors adjusted by COX regression to predict survival using a modified deep learning model (DLOPCombin). The DLOPCombin model was compared with the residual network model (DLOPCTR), multiple COX regression model (DLOPCox), Radiomic model (Radiomic), and clinical model. Results: In the validation cohort, DLOPCombin shows the highest TD AUC of all cohorts, which compared with Radiomic (TD AUC: 0.96vs 0.63) and clinical model (TD AUC: 0.96 vs 0.62) model. DLOPCombin showed significant difference in C index compared with DLOPCTR and DLOPCox models (P < 0.05). Moreover, the DLOPCombin showed good calibration and overall net benefit. Patients with DLOPCombin model score ≤ 0.902 had better OS (33 months vs 15.5 months, P < 0.0001). Conclusion: The deep learning model can effectively predict the patients' overall survival of TACE.

Biodegradable MoNx@Mo-foil electrodes for human-friendly supercapacitors.

With the advancement in the field of biomedical research, there is a growing demand for biodegradable electronic devices. Biodegradable supercapacitors (SCs) have emerged as an ideal solution for mitigating the risks associated with secondary surgeries, reducing patient discomfort, and promoting environmental sustainability. In this study, MoNx@Mo-foil was prepared as an active material for biodegradable supercapacitors through high-temperature and nitridation processes. The composite electrode exhibited superior electrochemical performance in both aqueous and solid-state electrolytes. In the case of the solid-state electrolyte, the MoNx@Mo-foil composite electrode-based device demonstrated excellent cycling stability and electrochemical performance. Additionally, the composite electrode exhibited rapid and complete biodegradability in a 3% H2O2 solution. Through detailed experimental analysis and performance testing, we verified the potential application of the MoNx@Mo-foil composite electrode in biodegradable supercapacitors. This work provides a new choice of degradable material for developing biomedical electronic devices.