The predictive performance of artificial intelligence on the outcome of stroke: a systematic review and meta-analysis.

Objectives: This study aimed to assess the accuracy of artificial intelligence (AI) models in predicting the prognosis of stroke. Methods: We searched PubMed, Embase, and Web of Science databases to identify studies using AI for acute stroke prognosis prediction from the database inception to February 2023. Selected studies were designed cohorts and had complete data. We used the Quality Assessment of Diagnostic Accuracy Studies tool to assess the qualities and bias of included studies and used a random-effects model to summarize and analyze the data. We used the area under curve (AUC) as an indicator of the predictive accuracy of AI models. Results: We retrieved a total of 1,241 publications and finally included seven studies. There was a low risk of bias and no significant heterogeneity in the final seven studies. The total pooled AUC under the fixed-effects model was 0.872 with a 95% CI of (0.862-0.881). The DL subgroup showed its AUC of 0.888 (95%CI 0.872-0.904). The LR subgroup showed its AUC 0.852 (95%CI 0.835-0.869). The RF subgroup showed its AUC 0.863 (95%CI 0.845-0.882). The SVM subgroup showed its AUC 0.905 (95%CI 0.857-0.952). The Xgboost subgroup showed its AUC 0.905 (95%CI 0.805-1.000). Conclusion: The accuracy of AI models in predicting the outcomes of ischemic stroke is good from our study. It could be an assisting tool for physicians in judging the outcomes of stroke patients. With the update of AI algorithms and the use of big data, further AI predictive models will perform better.

Ultrathin g-C3N4 nanosheet-CoOOH nanocomposite for fluorescence imaging of ascorbic acid in living cells.

Ascorbic acid (AA), a critical cellular metabolite involved in many biochemical pathways, is an important antioxidant in human body. Therefore, it is of great significance to monitor AA in living cells. Nowadays, there are various technologies developed for the detection of AA, but few methods could sensitively and selectively detect the intracellular AA. Here, we reported a highly efficient biosensor (g-C3N4-CoOOH nanocomposite) based on ultrathin graphitic carbon nitride (g-C3N4) nanosheets and CoOOH nanoflakes, for sensitive detection and fluorescence imaging of AA in living cell. The g-C3N4 used here as fluorescence donor is a promising bioimaging nanomaterial because of their high fluorescence quantum yield, good biocompatibility and low toxicity. In addition, the CoOOH was used to be perfect fluorescence quencher. Herein, we enabled the CoOOH in situ to form a layer on the surface of g-C3N4, resulting in fluorescence quench of the g-C3N4. Upon the addition of AA, the CoOOH nanoflakes were reduced to Co2+, and the system gave a "turn on" fluorescence signal. It developed as an efficient sensing platform for AA, and the linear range was from 5 to 50 µM with a 1.6 µM detection limit. This novel biosensor, g-C3N4-CoOOH nanocomposite exhibited highly selective response toward AA relative to other biomolecules. Furthermore, this biosensor was used successfully to visualize and monitor AA in living cells. Hopefully, we believe that this biosensor would provide a low-cost and highly sensitive platform for AA detection and bioimaging. Schematic illustration of the sensing strategy based on the g-C3N4-CoOOH nanocomposite for AA detection.

Long-lasting and Sex-dependent Effects of Postweaning Swimming Exercise on Social Dominance in Adult Mice.

Increasing evidence has shown that early life events exert long-lasting effects on brain function and mental diseases. Exercise has been proven to have many positive effects on behaviors, such as reducing anxiety- and depression-like behaviors and alleviating cognitive impairment. However, the long-lasting and even short-term effects of regular swimming exercise on social dominance remain unclear. The purpose of this study was to investigate the potential effects of postweaning swimming exercise on social dominance and metabolic adaptation in adult mice. Three-week-old mice performed 1 h of swimming exercise in warm water for 4 weeks. A series of behavioral tests, such as the social dominance test (SDT), open field test (OFT), and forced swim test (FST), were conducted. Behavioral test results showed that both male and female mice in the swimming group had a higher rank than those in the sedentary group in the SDT of early adulthood, while only female mice in the swimming group maintained the social dominance in late adulthood. There was no difference between the swimming and sedentary groups in anxiety- and depression-like behaviors. Metabolomics analysis showed that there were alterations in particular metabolites and signaling pathways after one month of swimming exercise, including sphingolipid metabolism, neuroactive ligand-receptor interaction and caffeine metabolism. In conclusion, our results provide the first evidence that postweaning swimming exercise has long-lasting and sex-dependent effects on social dominance, which may be caused by metabolic adaptation.

Effects of chronic triclosan exposure on social behaviors in adult mice.

Triclosan (TCS), a newly identified environmental endocrine disruptor (EED) in household products, has been reported to have toxic effects on animals and humans. The effects of TCS exposure on individual social behaviors and the potential underlying mechanisms are still unknown. This study investigated the behavioral effects of 42-day exposure to TCS (0, 50, 100 mg/kg) in drinking water using the open field test (OFT), social dominance test (SDT), social interaction test (SIT), and novel object recognition task (NOR). Using 16S rRNA sequencing analysis and transmission electron microscopy (TEM), we observed the effects of TCS exposure on the gut microbiota and ultrastructure of hippocampal neurons and synapses. Behavioral results showed that chronic TCS exposure reduced the social dominance of male and female mice. TCS exposure also reduced social interaction in male mice and impaired memory formation in female mice. Analysis of the gut microbiota showed that TCS exposure increased the relative abundance of the Proteobacteria and Actinobacteria phyla in female mice. Ultrastructural analysis revealed that TCS exposure induced ultrastructural damage to hippocampal neurons and synapses. These findings suggest that TCS exposure may affect social behaviors, which may be caused by altered gut microbiota and impaired plasticity of hippocampal neurons and synapses.

Repeated 3,3-Dimethyl-1-butanol exposure alters social dominance in adult mice.

The influence of gut microbiota on brain function and brain disorders has been attracted more and more attention. Trimethylamine N-oxide (TMAO), an indirect metabolite of gut microbiota, has been linked to aging, cognitive impairment, and other brain disorders. However, the relationship between TMAO and social behaviors are still poorly understood. Adult male mice were exposed to drinking water containing 3,3- Dimethyl-1-butanol (DMB), an indirect inhibitors of TMAO, for 21 continuous days followed by a series of behavioral tests to detect the effect of DMB exposure on social behaviors, mainly including social dominance test (SDT), bedding preference test (BP), sexual preference test (SP), social interaction test (SI), open field test (OFT), tail suspension test (TST), forced swim test (FST), novelty suppressed feeding test (NSF), and novel object recognition (NOR) task. In the SDT, compared with the control group, the mice treated with DMB (both 0.2% and 1.0%), both high-ranked and low-ranked mice, showed a reduction in the number of victories. There is no statistical difference on sexual preference, anxiety, depression-like behavior phenotype, and memory formation. In conclusion, the present findings provide direct evidence, for the first time, that repeated DMB exposure produces significant effects on social dominance of adult mice, without any effects on sexual preference, anxiety, depression-like behavior phenotype or memory formation, highlighting the regulatory effects of gut-brain interaction on social behaviors.

Novel Lidar algorithm for horizontal visibility measurement and sea fog monitoring.

Traditionally, Klett and Fernald inversion estimates an initial value using the slope method for horizontal visibility, which causes inversion uncertainty. We proposed an algorithm to retrieve the extinction coefficient and visibility distribution information from scanning Lidar to overcome instability due to initial atmospheric extinction coefficient choice and assuming the Lidar ratio. Numerical simulations showed that extinction coefficient maximum relative was much larger for inhomogeneous atmosphere using the Klett method, reaching 0.31. In contrast, it is only 0.049 using the proposed algorithm. Experimental showed that the proposed algorithm and scanning Lidar system provide very high stability and accuracy, can work in different weather conditions and monitor sea fog evolution over real time, and is suitable for various situations with different visibility.