GDF11 improves hippocampal neurogenesis and cognitive abilities in diabetic mice by reducing neural inflammation.

BACKGROUND: The cognitive decline associated with type 2 diabetes (T2D) is often attributed to compromised hippocampal neurogenesis and exacerbated neural inflammation. This study investigates the therapeutic potential of growth differentiation factor 11 (GDF11) in reversing these neurodegenerative processes in diabetic mice. RESULT: We utilized a murine model of T2D and examined the effects of GDF11 on learning, memory, neurogenesis, and neuroinflammatory markers. Our results indicate that diabetic mice exhibit significant deficits in cognitive function, mirrored by reduced hippocampal neurogenesis and increased neuroinflammation. Chronic administration of GDF11 was observed to significantly enhance cognitive abilities, as evidenced by improved performance in learning and memory tasks. Concurrently, GDF11 treatment restored neural activity and promoted the regeneration of new neurons within the hippocampus. Inflammatory profiling revealed a reduction in neuroinflammatory markers, which was further supported by reduced microglia numbers. To delineate the role of neuroinflammation, we pharmacologically depleted microglia, leading to a restoration of neurogenesis and cognitive functions in diabetic mice. CONCLUSION: These findings endorse the hypothesis that GDF11 exerts its beneficial effects by modulating neuroinflammatory pathways. Consequently, GDF11 represents a promising intervention to ameliorate diabetes-induced cognitive impairments and neural degeneration through its anti-inflammatory properties.

Polysaccharide-Based Coating with Excellent Antibiofilm and Repeatable Antifouling-Bactericidal Properties for Treating Infected Hernia.

In recent years, the utilization of medical devices has gradually increased and implantation procedures have become common treatments. However, patients are susceptible to the risk of implant infections. This study utilized chemical grafting to immobilize polyethylenimine (QPEI) and hyaluronic acid (HA) on the surface of the mesh to improve biocompatibility while being able to achieve antifouling antimicrobial effects. From the in vitro testing, PP-PDA-Q-HA exhibited a high antibacterial ratio of 93% against S. aureus, 93% against E. coli, and 85% against C. albicans. In addition, after five rounds of antimicrobial testing, the coating continued to exhibit excellent antimicrobial properties; PP-PDA-Q-HA also inhibits the formation of bacterial biofilms. In addition, PP-PDA-Q-HA has good hemocompatibility and cytocompatibility. In vivo studies in animal implantation infection models also demonstrated the excellent antimicrobial properties of PP-PDA-Q-HA. Our study provides a promising strategy for the development of antimicrobial surface medical materials with excellent biocompatibility.

Development and validation of a nomogram model of depression and sleep disorders and the risk of disease progression in patients with breast cancer.

BACKGROUND: In this study, we investigated the relationship between the risk of postoperative progressive disease (PD) in breast cancer and depression and sleep disorders in order to develop and validate a suitable risk prevention model. METHODS: A total of 750 postoperative patients with breast cancer were selected from the First People's Hospital of LianYunGang, and the indices of two groups (an event group and a non-event group) were compared to develop and validate a risk prediction model. The relationship between depression, sleep disorders, and PD events was investigated using the follow-up data of the 750 patients. RESULTS: SAS, SDS, and AIS scores differed in the group of patients who experienced postoperative disease progression versus those who did not; the differences were statistically significant and the ability to differentiate prognosis was high. The area under the receiver operating characteristic (ROC) curves (AUC) were: 0.8049 (0.7685-0.8613), 0.768 (0.727-0.809), and 0.7661 (0.724--0.808), with cut-off values of 43.5, 48.5, and 4.5, respectively. Significant variables were screened by single-factor analysis and multi-factor analysis to create model 1, by lasso regression and cross-lasso regression analysis to create model 2, by random forest calculation method to create model 3, by stepwise regression method (backward method) to create model 4, and by including all variables for Cox regression to include significant variables to create model 5. The AUC of model 2 was 0.883 (0.848-0.918) and 0.937 (0.893-0.981) in the training set and validation set, respectively. The clinical efficacy of the model was evaluated using decision curve analysis and clinical impact curve, and then the model 2 variables were transformed into scores, which were validated in two datasets, the training and validation sets, with AUCs of 0.884 (0.848-0.919) and 0.885 (0.818-0.951), respectively. CONCLUSION: We established and verified a model including SAS, SDS and AIS to predict the prognosis of breast cancer patients, and simplified it by scoring, making it convenient for clinical use, providing a theoretical basis for precise intervention in these patients. However, further research is needed to verify the generalization ability of our model.

Development and advancements in rodent MRI-based brain atlases.

Rodents, particularly mice and rats, are extensively utilized in fundamental neuroscience research. Brain atlases have played a pivotal role in this field, evolving from traditional printed histology atlases to digital atlases incorporating diverse imaging datasets. Magnetic resonance imaging (MRI)-based brain atlases, also known as brain maps, have been employed in specific studies. However, the existence of numerous versions of MRI-based brain atlases has impeded their standardized application and widespread use, despite the consensus within the academic community regarding their significance in mice and rats. Furthermore, there is a dearth of comprehensive and systematic reviews on MRI-based brain atlases for rodents. This review aims to bridge this gap by providing a comprehensive overview of the advancements in MRI-based brain atlases for rodents, with a specific focus on mice and rats. It seeks to explore the advantages and disadvantages of histologically printed brain atlases in comparison to MRI brain atlases, delineate the standardized methods for creating MRI brain atlases, and summarize their primary applications in neuroscience research. Additionally, this review aims to assist researchers in selecting appropriate versions of MRI brain atlases for their studies or refining existing MRI brain atlas resources, thereby facilitating the development and widespread adoption of standardized MRI-based brain atlases in rodents.

Development and Validation of a Clinical Model for Predicting Delay in Postoperative Transfer Out of the Post-Anesthesia Care Unit: A Retrospective Cohort Study.

Objective: We aimed to analyze the factors related to delay in transfer of patients in the post-anesthesia care unit (PACU) and to develop and validate a prediction model for understanding these factors to guide precise clinical intervention. Methods: We collected data from two cohorts of 1153 and 297 patients who underwent surgery and were treated in the PACU at two time points. We examined their clinical features and anesthesia care data using analytical methods such as logistic regression, Random Forest, and eXtreme Gradient Boosting (Xgboost) to screen out variables and establish a prediction model. We then validated and simplified the model and plotted a nomogram. Using LASSO regression, we reduced the dimensionality of the data. We developed multiple models and plotted receiver operating characteristic (ROC) and calibration curves. We then constructed a simplified model by pooling the identified variables, which included hemoglobin (HB), alanine transaminase (ALT), glucose levels, duration of anesthesia, and the minimum bispectral index value (BIS_min). Results: The model had good prediction performance parameters in the training and validation sets, with an AUC of 0.909 (0.887-0.932) in the training set and 0.939 (0.919-0.959) in the validation set. When we compared model 6 with other models, the net reclassification index (NRI) and the integrated discriminant improvement (IDI) index indicated that it did not differ significantly from the other models. We developed a scoring system, and it showed good prediction performance when verified with the training and validation sets as well as external data. Additionally, both the decision curve analysis (DCA) and clinical impact curve (CIC) demonstrated the potential clinical efficacy of the model in guiding patient interventions. Conclusion: Predicting transfer delays in the post-anesthesia care unit using predictive models is feasible; however, this merits further exploration.

Functional study of two ER localized sterol C-14 reductases in Aspergillus oryzae.

Ergosterol is an important component of fungal cell membrane. Ergosterol biosynthesis involves sterol C-14 reductase, a key enzyme in ergosterol biosynthesis, which has been well studied in Saccharomyces cerevisiae. However, little studies about this important enzyme in Aspergillus oryzae. In this study, two sterol C-14 reductases named AoErg24A and AoErg24B were identified in A. oryzae using bioinformatics analysis. Through phylogenetic tree, expression pattern, subcellular localization, and yeast functional complementation analyses, we discovered that both AoErg24A and AoErg24B are conserved and localized to the endoplasmic reticulum (ER). Both enzymes can partially restore the temperature sensitivity phenotype of a S. cerevisiae erg24 weak mutant. Overexpression of AoErg24A in A. oryzae increased 1.6 times of ergosterol content, while overexpression of AoErg24B led to a slight decrease of ergosterol. Both genes affect the sporulation of A. oryzae. These results uncovered that the two genes function differently in ergosterol biosynthesis. Thus, this study further enhances our understanding of ergosterol biosynthesis in A. oryzae and lays a good foundation for A. oryzae to be used in industrial ergosterol production.