Astragaloside IV derivative HHQ16 ameliorates infarction-induced hypertrophy and heart failure through degradation of lncRNA4012/9456.

Reversing ventricular remodeling represents a promising treatment for the post-myocardial infarction (MI) heart failure (HF). Here, we report a novel small molecule HHQ16, an optimized derivative of astragaloside IV, which effectively reversed infarction-induced myocardial remodeling and improved cardiac function by directly acting on the cardiomyocyte to reverse hypertrophy. The effect of HHQ16 was associated with a strong inhibition of a newly discovered Egr2-affiliated transcript lnc9456 in the heart. While minimally expressed in normal mouse heart, lnc9456 was dramatically upregulated in the heart subjected to left anterior descending coronary artery ligation (LADL) and in cardiomyocytes subjected to hypertrophic stimulation. The critical role of lnc9456 in cardiomyocyte hypertrophy was confirmed by specific overexpression and knockout in vitro. A physical interaction between lnc9456 and G3BP2 increased NF-κB nuclear translocation, triggering hypertrophy-related cascades. HHQ16 physically bound to lnc9456 with a high-affinity and induced its degradation. Cardiomyocyte-specific lnc9456 overexpression induced, but knockout prevented LADL-induced, cardiac hypertrophy and dysfunction. HHQ16 reversed the effect of lnc9456 overexpression while lost its protective role when lnc9456 was deleted, further confirming lnc9456 as the bona fide target of HHQ16. We further identified the human ortholog of lnc9456, also an Egr2-affiliated transcript, lnc4012. Similarly, lnc4012 was significantly upregulated in hypertrophied failing hearts of patients with dilated cardiomyopathy. HHQ16 also specifically bound to lnc4012 and caused its degradation and antagonized its hypertrophic effects. Targeted degradation of pathological increased lnc4012/lnc9456 by small molecules might serve as a novel promising strategy to regress infarction-induced cardiac hypertrophy and HF.

Progress on the role of traditional Chinese medicine in therapeutic angiogenesis of heart failure.

ETHNOPHARMACOLOGICAL RELEVANCE: Cardiovascular diseases are still the leading cause of death worldwide. Heart failure (HF), as the terminal stage of many cardiovascular diseases, has brought a heavy burden to the global medical system. Microvascular rarefaction (decreased myocardial capillary density) with reduced coronary flow reserve is a hallmark of HF and therapeutic myocardial angiogenesis is now emerging as a promising approach for the prevention and treatment in HF. Traditional Chinese medicine (TCM) has made remarkable achievements in the treatment of many cardiovascular diseases. Growing evidence have shown that their protective effect in HF is closely related to therapeutic angiogenesis. AIM OF THE STUDY: This review is to enlighten the therapeutic effect and pro-angiogenic mechanism of TCM in HF, and provide valuable hints for the development of pro-angiogenic drugs for the treatment of HF. MATERIALS AND METHODS: The relevant information about cardioprotective TCM was collected from electronic scientific databases such as PubMed, Web of Science, ScienceDirect, and China National Knowledge Infrastructure (CNKI). RESULTS: The studies showed that TCM formulas, extracts, and compounds from herbal medicines can provide therapeutic effect in HF with their pro-angiogenic activity. Their actions are achieved mainly by regulating the key angiogenesis factors particularly VEGF, as well as related regulators including signal molecules and pathways, non-coding miRNAs and stem cells. CONCLUSION: TCM and their active components might be promising in therapeutic angiogenesis for the treatment of HF.

Programming ability prediction: Applying an attention-based convolutional neural network to functional near-infrared spectroscopy analyses of working memory.

Although theoretical studies have suggested that working-memory capacity is crucial for academic achievement, few empirical studies have directly investigated the relationship between working-memory capacity and programming ability, and no direct neural evidence has been reported to support this relationship. The present study aimed to fill this gap in the literature. Using a between-subject design, 17 programming novices and 18 advanced students performed an n-back working-memory task. During the experiment, their prefrontal hemodynamic responses were measured using a 48-channel functional near-infrared spectroscopy (fNIRS) device. The results indicated that the advanced students had a higher working-memory capacity than the novice students, validating the relationship between programming ability and working memory. The analysis results also showed that the hemodynamic responses in the prefrontal cortex can be used to discriminate between novices and advanced students. Additionally, we utilized an attention-based convolutional neural network to analyze the spatial domains of the fNIRS signals and demonstrated that the left prefrontal cortex was more important than other brain regions for programming ability prediction. This result was consistent with the results of statistical analysis, which in turn improved the interpretability of neural networks.

Enhancing the feasibility of cognitive load recognition in remote learning using physiological measures and an adaptive feature recalibration convolutional neural network.

The precise assessment of cognitive load during a learning phase is an important pathway to improving students' learning efficiency and performance. Physiological measures make it possible to continuously monitor learners' cognitive load in remote learning during the COVID-19 outbreak. However, maintaining a good balance between performance and computational cost is still a major challenge in advancing cognitive load recognition technology to real-world applications. This paper introduced an adaptive feature recalibration (AFR) convolutional neural network to overcome this challenge by capturing the most discriminative physiological features (EEG and eye-tracking). The results revealed that the optimal average classification accuracy of the feature combination obtained by the AFR method reached 95.56% with only 60 feature dimensions. Additionally, compared with the best result of the conventional correlation-based feature selection (CFS) method, the introduced AFR algorithm achieved higher accuracy and cheaper computational cost, as well as a 2.06% improvement in accuracy and a 51.21% reduction in feature dimension, which is more in line with the requirements of low delay and real-time performance in practical BCI applications.

Emotional Activity Is Negatively Associated With Cognitive Load in Multimedia Learning: A Case Study With EEG Signals.

We aimed to investigate the relationship between emotional activity and cognitive load during multimedia learning from an emotion dynamics perspective using electroencephalography (EEG) signals. Using a between-subjects design, 42 university students were randomly assigned to two video lecture conditions (color-coded vs. grayscale). While the participants watched the assigned video, their EEG signals were recorded. After processing the EEG signals, we employed the correlation-based feature selector (CFS) method to identify emotion-related subject-independent features. We then put these features into the Isomap model to obtain a one-dimensional trajectory of emotional changes. Next, we used the zero-crossing rate (ZCR) as the quantitative characterization of emotional changes ZCR EC . Meanwhile, we extracted cognitive load-related features to analyze the degree of cognitive load (CLI). We employed a linear regression fitting method to study the relationship between ZCR EC and CLI. We conducted this study from two perspectives. One is the frequency domain method (wavelet feature), and the other is the non-linear dynamic method (entropy features). The results indicate that emotional activity is negatively associated with cognitive load. These findings have practical implications for designing video lectures for multimedia learning. Learning material should reduce learners' cognitive load to keep their emotional experience at optimal levels to enhance learning.

Mitochondrial dysfunction and mitochondrial therapies in heart failure.

Cardiovascular diseases remain the leading cause of death worldwide in the last decade, accompanied by immense health and economic burdens. Heart failure (HF), as the terminal stage of many cardiovascular diseases, is a common, intractable, and costly medical condition. Despite significant improvements in pharmacologic and device therapies over the years, life expectancy for this disease remains poor. Current therapies have not reversed the trends in morbidity and mortality as expected. Thus, there is an urgent need for novel potential therapeutic agents. Although the pathophysiology of the failing heart is extraordinarily complex, targeting mitochondrial dysfunction can be an effective approach for potential treatment. Increasing evidence has shown that mitochondrial abnormalities, including altered metabolic substrate utilization, impaired mitochondrial oxidative phosphorylation (OXPHOS), increased reactive oxygen species (ROS) formation, and aberrant mitochondrial dynamics, are closely related to HF. Here, we reviewed the findings on the role of mitochondrial dysfunction in HF, along with novel mitochondrial therapeutics and their pharmacological effects.

Impacts of Color Coding on Programming Learning in Multimedia Learning: Moving Toward a Multimodal Methodology.

In the present study, we tested the effectiveness of color coding on the programming learning of students who were learning from video lectures. Effectiveness was measured using multimodal physiological measures, combining eye tracking and electroencephalography (EEG). Using a between-subjects design, 42 university students were randomly assigned to two video lecture conditions (color-coded vs. grayscale). The participants' eye tracking and EEG signals were recorded while watching the assigned video, and their learning performance was subsequently assessed. The results showed that the color-coded design was more beneficial than the grayscale design, as indicated by smaller pupil diameter, shorter fixation duration, higher EEG theta and alpha band power, lower EEG cognitive load, and better learning performance. The present findings have practical implications for designing slide-based programming learning video lectures; slides should highlight the format of the program code using color coding.

Comprehensive bioinformatics analysis reveals kinase activity profiling associated with heart failure.

Heart failure is a complex clinical syndrome originating from cardiac injury, which leads to considerable morbidity and mortality. Among the dynamic molecular adaptations occurring in heart failure development, aggravation of the disease is often attributed to global or local abnormality of the kinase. Therefore, the overall monitoring of kinase activity is indispensable. In this study, a bioinformatics analysis method was developed to conduct deep mining of transcriptome and phosphoproteome in failing heart tissue. A total of 982 differentially expressed genes and 9781 phosphorylation sites on 3252 proteins were identified. Via upstream regulator relations and kinase-substrate relations, a dendrogram of kinases can be constructed to monitor its abnormality. The results show that, on the dendrogram, the distribution of kinases demonstrated complex kinase activity changes and certain rules that occur during heart failure. Finally, we also identified the hub kinases in heart failure and verified the expression of these kinases by reverse-transcription polymerase chain reaction and Western blot analysis. In conclusion, for the first time, we have systematically analyzed the differences in kinases during heart failure and provided an unprecedented breadth of multi-omics data. These results can bring about a sufficient data foundation and novel research perspectives.