DeepKEGG: a multi-omics data integration framework with biological insights for cancer recurrence prediction and biomarker discovery.

Deep learning-based multi-omics data integration methods have the capability to reveal the mechanisms of cancer development, discover cancer biomarkers and identify pathogenic targets. However, current methods ignore the potential correlations between samples in integrating multi-omics data. In addition, providing accurate biological explanations still poses significant challenges due to the complexity of deep learning models. Therefore, there is an urgent need for a deep learning-based multi-omics integration method to explore the potential correlations between samples and provide model interpretability. Herein, we propose a novel interpretable multi-omics data integration method (DeepKEGG) for cancer recurrence prediction and biomarker discovery. In DeepKEGG, a biological hierarchical module is designed for local connections of neuron nodes and model interpretability based on the biological relationship between genes/miRNAs and pathways. In addition, a pathway self-attention module is constructed to explore the correlation between different samples and generate the potential pathway feature representation for enhancing the prediction performance of the model. Lastly, an attribution-based feature importance calculation method is utilized to discover biomarkers related to cancer recurrence and provide a biological interpretation of the model. Experimental results demonstrate that DeepKEGG outperforms other state-of-the-art methods in 5-fold cross validation. Furthermore, case studies also indicate that DeepKEGG serves as an effective tool for biomarker discovery. The code is available at https://github.com/lanbiolab/DeepKEGG.

Benchmarking of computational methods for predicting circRNA-disease associations.

Accumulating evidences demonstrate that circular RNA (circRNA) plays an important role in human diseases. Identification of circRNA-disease associations can help for the diagnosis of human diseases, while the traditional method based on biological experiments is time-consuming. In order to address the limitation, a series of computational methods have been proposed in recent years. However, few works have summarized these methods or compared the performance of them. In this paper, we divided the existing methods into three categories: information propagation, traditional machine learning and deep learning. Then, the baseline methods in each category are introduced in detail. Further, 5 different datasets are collected, and 14 representative methods of each category are selected and compared in the 5-fold, 10-fold cross-validation and the de novo experiment. In order to further evaluate the effectiveness of these methods, six common cancers are selected to compare the number of correctly identified circRNA-disease associations in the top-10, top-20, top-50, top-100 and top-200. In addition, according to the results, the observation about the robustness and the character of these methods are concluded. Finally, the future directions and challenges are discussed.

MRUNet-3D: A multi-stride residual 3D UNet for lung nodule segmentation.

Obtaining an accurate segmentation of the pulmonary nodules in computed tomography (CT) images is challenging. This is due to: (1) the heterogeneous nature of the lung nodules; (2) comparable visual characteristics between the nodules and their surroundings. A robust multi-scale feature extraction mechanism that can effectively obtain multi-scale representations at a granular level can improve segmentation accuracy. As the most commonly used network in lung nodule segmentation, UNet, its variants, and other image segmentation methods lack this robust feature extraction mechanism. In this study, we propose a multi-stride residual 3D UNet (MRUNet-3D) to improve the segmentation accuracy of lung nodules in CT images. It incorporates a multi-slide Res2Net block (MSR), which replaces the simple sequence of convolution layers in each encoder stage to effectively extract multi-scale features at a granular level from different receptive fields and resolutions while conserving the strengths of 3D UNet. The proposed method has been extensively evaluated on the publicly available LUNA16 dataset. Experimental results show that it achieves competitive segmentation performance with an average dice similarity coefficient of 83.47 % and an average surface distance of 0.35 mm on the dataset. More notably, our method has proven to be robust to the heterogeneity of lung nodules. It has also proven to perform better at segmenting small lung nodules. Ablation studies have shown that the proposed MSR and RFIA modules are fundamental to improving the performance of the proposed model.

KGANCDA: predicting circRNA-disease associations based on knowledge graph attention network.

Increasing evidences have proved that circRNA plays a significant role in the development of many diseases. In addition, many researches have shown that circRNA can be considered as the potential biomarker for clinical diagnosis and treatment of disease. Some computational methods have been proposed to predict circRNA-disease associations. However, the performance of these methods is limited as the sparsity of low-order interaction information. In this paper, we propose a new computational method (KGANCDA) to predict circRNA-disease associations based on knowledge graph attention network. The circRNA-disease knowledge graphs are constructed by collecting multiple relationship data among circRNA, disease, miRNA and lncRNA. Then, the knowledge graph attention network is designed to obtain embeddings of each entity by distinguishing the importance of information from neighbors. Besides the low-order neighbor information, it can also capture high-order neighbor information from multisource associations, which alleviates the problem of data sparsity. Finally, the multilayer perceptron is applied to predict the affinity score of circRNA-disease associations based on the embeddings of circRNA and disease. The experiment results show that KGANCDA outperforms than other state-of-the-art methods in 5-fold cross validation. Furthermore, the case study demonstrates that KGANCDA is an effective tool to predict potential circRNA-disease associations.

Subgroups of relational job characteristics and their differences in turnover intention and subjective well-being among nurses: a latent profile analysis.

OBJECTIVE: Relational job characteristics include perceived social worth and perceived social influence. Good relational job characteristics mean that nurses have high prosocial behavior. The purpose of this study was to explore the potential profile of nurses' relational job characteristics, influencing factors and their differences in turnover intention and subjective well-being, thus finding the most suitable clinical relationship job characteristics. METHODS: A cross-sectional survey was conducted among 1013 clinical nurses using the general demographic data questionnaire, Relational Job Characteristics scale, Turnover Intention Questionnaire and Campbell index of well-being. A latent profile analysis was performed to explore relational job characteristics latent profiles. Multinomial logistic regression analysis was conducted to examine the predictors of profile membership, and a one-way analysis of variance was applied to compare the turnover intention and subjective well-being in each latent profile. RESULTS: Five latent profiles were identified and labeled 'High prosocial job characteristics' profile (20.7%), 'Moderate prosocial job characteristics' profile (41.7%), 'High social worth-low social impact perceived' profile (6.3%), 'Low social worth-high social impact perceived' profile (18.8%) and 'Low prosocial job characteristics' profile (12.5%). Factors affecting the different types of nurse relationship job characteristics include age, marital status, hospital department, nursing years, professional title and hospital position. Among them, chief nurse, nurses with more than 20 years of nursing experience and obstetrics and gynecology nurses were more likely to be 'high prosocial job characteristics' profile. The turnover intention of nurses in 'high prosocial job characteristics' profile was significantly lower than that of other profiles, and their subjective well-being was significantly higher than that of other profiles. CONCLUSION: Improving nurses' perception of social worth and social impact on clinical work can improve nurses' prosocial behavior and subjective well-being, and reduce their turnover intention. Nursing managers or policy makers can formulate targeted intervention measures according to the influencing factors of potential profiles.

Prediction of drug adverse events using deep learning in pharmaceutical discovery.

Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus on detecting the association between drugs and their side effects or classifying drug-drug interaction. Motivated by technological advancements and the availability of big data, we provide a review on the detection and classification of side effects using deep learning approaches. It is shown that the effective integration of heterogeneous, multidimensional drug data sources, together with the innovative deployment of deep learning approaches, helps reduce or prevent the occurrence of adverse drug reactions (ADRs). Deep learning approaches can also be exploited to find replacements for drugs which have side effects or help to diversify the utilization of drugs through drug repurposing.

Negative life events and aggression among Chinese rural left-behind adolescents: do self-esteem and resilience mediate the relationship?

BACKGROUND: Left-behind adolescents (LBAs) are adolescents aged 11-18 years who are separated from their parents and left behind in local cities by one or both parents for a period of more than 6 months. LBAs in rural areas are likely to engage in aggressive behavior, which can affect interpersonal relationships, reduce academic performance, and even lead to anxiety and depression. To our knowledge, no studies have examined the mediating effect of resilience and self-esteem on the relationship between negative life events and aggression among Chinese rural LBAs. Therefore, this study aimed to explore the relationship between negative life events and aggression among Chinese rural LBAs and how self-esteem and resilience mediate the association. METHODS: Using a stratified random sampling method, 1344 LBAs in Hunan Province of China were investigated. Information was collected by a self-designed sociodemographic questionnaire, Adolescent Self-Rating Life Events Checklist, Resilience Scale Chinese Adolescent, Rosenberg Self-Esteem Scale and Aggression Scales to assess the psychology of LBAs. Data analysis was conducted using descriptive statistics, Pearson correlation, and regression analysis to estimate direct and indirect effects using bootstrap analysis. RESULTS: Negative life events were significantly related to self-esteem (r = - 0.338), resilience (r = - 0.359), and aggression (r = 0.441). Aggression was directly affected by self-esteem (ß = - 0.44) and resilience (ß = - 0.34). Negative life events were not only directly related to aggression (ß = 0.34, 95% CI: 0.275 ~ 0.398) but also showed an indirect effect on aggression through self-esteem and resilience. The direct effect, total effect and indirect effect of negative life events on aggression through self-esteem and resilience were 0.3364, 0.4344 and 0.0980, respectively. The mediating effect of self-esteem and resilience accounted for 22.56% of the relationship between negative life events and aggression. CONCLUSIONS: We found that self-esteem and resilience mediated most negative life events on aggression. It is imperative for educators and families to improve LBAs' self-esteem and resilience to reduce the occurrence of aggression. Future intervention studies should be designed to strengthen self-esteem and resilience.

LncRNA HCG18 Promotes Osteosarcoma Cells Proliferation, Migration, and Invasion in by Regulating miR-34a/RUNX2 Pathway.

Osteosarcoma (OS) is a type of tumor with high malignant behaviors. Increasing investigates have confirmed that long non-coding RNA HLA complex group 18 (lncRNA HCG18) acted as a tumor-promoting factor in multiple tumors. Nevertheless, the underlying mechanism of HCG18 on OS remains largely unclear. HCG18, miR-34a, and runt-related transcription factor 2 (RUNX2) expressions were detected by quantitative real-time PCR (RT-qPCR) or western blotting assays, respectively. The underlying tumorigenic phenotypes were detected by MTT, wound healing, transwell invasion, western blotting assays. Molecular interactions were verified by dual-luciferase report assay. HCG18 and RUNX2 were notably enhanced, whereas miR-34a was decreased in OS tumor tissues and cell lines. Functional experiments uncovered that HCG18 silencing significantly inhibited the capabilities of proliferation, migration, and invasion, while overexpression of HCG18 play the opposite roles. Furthermore, HCG18 directly bound to miR-34a, and miR-34a was confirm to be a negative regulator of RUNX2. Interestingly, the anti-tumor effects of HCG18 silencing were attenuated by miR-34a inhibitor and RUNX2 overexpression. Taken together, the present study suggested that HCG18 promoted the malignant biological behaviors of OS through regulating the miR-34a/RUNX2 pathway, implying HCG18 might serve as a new target for OS treatment.

The mediating role of organizational commitment between workplace bullying and turnover intention among clinical nurses in China: a cross-sectional study.

BACKGROUND: Workplace bullying experienced by clinical nurses is a critical and pervasive issue that not only detrimentally impacts nurses but also poses a significant threat to the overall quality of nursing services and patient care. This study aimed to determine the mediating role of organizational commitment in the relationship between workplace bullying and turnover intention among clinical nurses in China. METHODS: Participants were recruited from 40 hospitals in various provinces of China from December 2, 2021 to February 25, 2023, using convenience sampling. After obtaining hospital ethical approval and participants' informed consent, clinical nurses (n = 585) from different nursing departments in different hospitals completed the questionnaire. The Socio-demographic Questionnaire, Negative Acts Qestionnaire, Chinese Workers' Organizational Commitment Scale and Turnover Intention Questionnaire were used to collect general demographic data of nurses and assess workplace bullying they experienced, their level of organizational commitment and turnover intention. Descriptive statistics, Pearson correlation analyses and structural equation model were adopted to analyze the data. RESULTS: Pearson's correlation analysis showed that that workplace bullying was significantly negatively correlated with organizational commitment (r = - 0.512, P<0.01) and significantly positively correlated with turnover intention (r = 0.558, P<0.01), organizational commitment was significantly negatively correlated with turnover intention (r = - 0.539, P<0.01). Mediation analysis indicated organizational commitment partially mediated the association between workplace bullying and turnover intention. The total effect (ß = 0.69) of workplace bullying on turnover intention consisted of its direct effect (ß = 0.41) and the indirect effect mediated through organizational commitment (ß = 0.280), with the mediating effect accounting for 40.58% of the total effect. CONCLUSION: Organizational commitment mediated the associations of workplace bullying and turnover intention. Therefore, healthcare organizations and nursing managers should develop appropriate strategies to enhance nurses' organizational commitment in order to reduce their turnover intention.

Development of Dual-Nanopore Biosensors for Detection of Intracellular Dopamine and Dopamine Efflux from Single PC12 Cell.

Detection of neurotransmitters at the single-cell level is essential for understanding the related biological processes and neurodegenerative diseases. We report a dual-nanopore biosensor utilizing a DNA aptamer probe to specifically interact with dopamine, enabling detection of intracellular dopamine and dopamine efflux (extracellular dopamine) in a single pheochromocytoma (PC12) cell. We demonstrate the ability to form an intrapipette electric circuit with the dual-nanopore configuration, which is crucial to achieving both intracellular and extracellular dopamine detection. The sensor allowed rapid detection of dopamine in 10 min with a limit of detection of 0.4 nM. We show the dual-nanopore biosensor was able to monitor single-cell dopamine concentration change under different stimulations. The developed dual-nanopore biosensor represents a novel strategy for time-dependent monitoring of neuron behavior at the single-cell level and potentially can be extended to other platforms for single-cell analysis.

Ensuring privacy and security of genomic data and functionalities.

In recent times, the reduced cost of DNA sequencing has resulted in a plethora of genomic data that is being used to advance biomedical research and improve clinical procedures and healthcare delivery. These advances are revolutionizing areas in genome-wide association studies (GWASs), diagnostic testing, personalized medicine and drug discovery. This, however, comes with security and privacy challenges as the human genome is sensitive in nature and uniquely identifies an individual. In this article, we discuss the genome privacy problem and review relevant privacy attacks, classified into identity tracing, attribute disclosure and completion attacks, which have been used to breach the privacy of an individual. We then classify state-of-the-art genomic privacy-preserving solutions based on their application and computational domains (genomic aggregation, GWASs and statistical analysis, sequence comparison and genetic testing) that have been proposed to mitigate these attacks and compare them in terms of their underlining cryptographic primitives, security goals and complexities-computation and transmission overheads. Finally, we identify and discuss the open issues, research challenges and future directions in the field of genomic privacy. We believe this article will provide researchers with the current trends and insights on the importance and challenges of privacy and security issues in the area of genomics.

Genetic source completeness of HIV-1 circulating recombinant forms (CRFs) predicted by multi-label learning.

MOTIVATION: Infection with strains of different subtypes and the subsequent crossover reading between the two strands of genomic RNAs by host cells' reverse transcriptase are the main causes of the vast HIV-1 sequence diversity. Such inter-subtype genomic recombinants can become circulating recombinant forms (CRFs) after widespread transmissions in a population. Complete prediction of all the subtype sources of a CRF strain is a complicated machine learning problem. It is also difficult to understand whether a strain is an emerging new subtype and if so, how to accurately identify the new components of the genetic source. RESULTS: We introduce a multi-label learning algorithm for the complete prediction of multiple sources of a CRF sequence as well as the prediction of its chronological number. The prediction is strengthened by a voting of various multi-label learning methods to avoid biased decisions. In our steps, frequency and position features of the sequences are both extracted to capture signature patterns of pure subtypes and CRFs. The method was applied to 7185 HIV-1 sequences, comprising 5530 pure subtype sequences and 1655 CRF sequences. Results have demonstrated that the method can achieve very high accuracy (reaching 99%) in the prediction of the complete set of labels of HIV-1 recombinant forms. A few wrong predictions are actually incomplete predictions, very close to the complete set of genuine labels. AVAILABILITY AND IMPLEMENTATION: https://github.com/Runbin-tang/The-source-of-HIV-CRFs-prediction. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

LncRNA FGD5-AS1 enhances the proliferation and stemness of hepatocellular carcinoma cells through targeting miR-223 and regulating the expression of ECT2 and FAT1.

AIM: Hepatocellular carcinoma (HCC) is common and causes many deaths worldwide. The aim of this study is to explore the mechanism by which long non-coding RNA FGD5-AS1 regulates HCC cell proliferation and stemness. METHODS: Tumor and normal adjacent tissues were harvested from HCC patients. Real-time quantitative reverse transcription-PCR was applied to examine the expression of FGD5-AS1, miR-223, Epithelial cell transforming sequence 2 (ECT2) and FAT1. The protein levels of ECT2, FAT1, proliferating cell nuclear antigen (PCNA), OCT4, CD133 and CD90 were analyzed by western blot. The localization of FGD5-AS1 was examined by Fluorescence in situ hybridization. Cell proliferation was analyzed with CCK-8 and colony formation assays. Spheroid formation was used for analyzing cell stemness. Gene interaction was examined by RNA immunoprecipitation and luciferase activity assays. A subcutaneous xenograft mouse model was established to analyze HCC growth and stemness in vivo. Immunohistochemistry staining was used to analyze the expression PCNA and OCT4 in subcutaneous tumors. RESULTS: FGD5-AS1 was upregulated in HCC and its high expression indicated poor prognosis of patients. High expression of FGD5-AS1 enhanced HCC cell proliferation and stemness. Knockdown of FGD5-AS1 restrained tumor growth and stemness in mice. FGD5-AS1 directly sponged miR-223 and promoted the expression of ECT2 and FAT1 in HCC. Both knockdown of miR-223 and overexpression of ECT2 and FAT1 reversed FGD5-AS1 silencing-mediated suppression of HCC cell proliferation and stemness. CONCLUSION: FGD5-AS1 directly sponged miR-223 and promoted the expression of ECT2 and FAT1 in HCC, thus enhancing HCC cell proliferation and stemness. Our study identifies potential prognostic biomarkers and therapeutic targets for HCC.

Structural insights into CpG-specific DNA methylation by human DNA methyltransferase 3B.

DNA methyltransferases are primary enzymes for cytosine methylation at CpG sites of epigenetic gene regulation in mammals. De novo methyltransferases DNMT3A and DNMT3B create DNA methylation patterns during development, but how they differentially implement genomic DNA methylation patterns is poorly understood. Here, we report crystal structures of the catalytic domain of human DNMT3B-3L complex, noncovalently bound with and without DNA of different sequences. Human DNMT3B uses two flexible loops to enclose DNA and employs its catalytic loop to flip out the cytosine base. As opposed to DNMT3A, DNMT3B specifically recognizes DNA with CpGpG sites via residues Asn779 and Lys777 in its more stable and well-ordered target recognition domain loop to facilitate processive methylation of tandemly repeated CpG sites. We also identify a proton wire water channel for the final deprotonation step, revealing the complete working mechanism for cytosine methylation by DNMT3B and providing the structural basis for DNMT3B mutation-induced hypomethylation in immunodeficiency, centromere instability and facial anomalies syndrome.

A comprehensive review of federated learning for COVID-19 detection.

The coronavirus of 2019 (COVID-19) was declared a global pandemic by World Health Organization in March 2020. Effective testing is crucial to slow the spread of the pandemic. Artificial intelligence and machine learning techniques can help COVID-19 detection using various clinical symptom data. While deep learning (DL) approach requiring centralized data is susceptible to a high risk of data privacy breaches, federated learning (FL) approach resting on decentralized data can preserve data privacy, a critical factor in the health domain. This paper reviews recent advances in applying DL and FL techniques for COVID-19 detection with a focus on the latter. A model FL implementation use case in health systems with a COVID-19 detection using chest X-ray image data sets is studied. We have also reviewed applications of previously published FL experiments for COVID-19 research to demonstrate the applicability of FL in tackling health research issues. Last, several challenges in FL implementation in the healthcare domain are discussed in terms of potential future work.

Structural insights into nanoRNA degradation by human Rexo2.

Human RNA exoribonuclease 2 (Rexo2) is an evolutionarily conserved 3'-to-5' DEDDh-family exonuclease located primarily in mitochondria. Rexo2 degrades small RNA oligonucleotides of <5 nucleotides (nanoRNA) in a way similar to Escherichia coli Oligoribonuclease (ORN), suggesting that it plays a role in RNA turnover in mitochondria. However, how Rexo2 preferentially binds and degrades nanoRNA remains elusive. Here, we show that Rexo2 binds small RNA and DNA oligonucleotides with the highest affinity, and it is most robust in degrading small nanoRNA into mononucleotides in the presence of magnesium ions. We further determined three crystal structures of Rexo2 in complex with single-stranded RNA or DNA at resolutions of 1.8-2.2 Å. Rexo2 forms a homodimer and interacts mainly with the last two 3'-end nucleobases of substrates by hydrophobic and π-π stacking interactions via Leu53, Trp96, and Tyr164, signifying its preference in binding and degrading short oligonucleotides without sequence specificity. Crystal structure of Rexo2 is highly similar to that of the RNA-degrading enzyme ORN, revealing a two-magnesium-ion-dependent hydrolysis mechanism. This study thus provides the molecular basis for human Rexo2, showing how it binds and degrades nanoRNA into nucleoside monophosphates and plays a crucial role in RNA salvage pathways in mammalian mitochondria.

Acupuncture Can Play an Antidepressant Role by Regulating the Intestinal Microbes and Neurotransmitters in a Rat Model of Depression.

BACKGROUND Acupuncture, which has many good effects and few adverse effects, is widely recognized as an alternative therapy for depression in clinical practice. This study aimed to explore the mechanism of acupuncture in antidepressant treatment. MATERIAL AND METHODS In this experiment, Sprague-Dawley rats were randomly divided into 4 groups: control, chronic unpredictable mild stress (CUMS), acupuncture, and fluoxetine groups. The CUMS, acupuncture, and fluoxetine groups were orphaned and subjected to chronic unpredictable stress for 6 weeks, and the acupuncture and fluoxetine groups were treated with their respective intervention in weeks 4-6. The body weight of rats was monitored weekly. After behavioral tests were completed, serum, feces, and hippocampal tissue of rats were collected. RESULTS The results showed that the acupuncture and fluoxetine treatments could alleviate the behavioral changes caused by CUMS. The treatments increased the total distance of rat crossing in the open-field test, prolonged the activity time of the open cross maze in the open arm, and improved the rate of sucrose consumption in the sucrose preference test. In addition, both the decreased level of dopamine (DA) and 5-hydroxytryptamine (5-HT) in serum and hippocampus caused by CUMS were improved after the treatments with acupuncture and fluoxetine, and the decreased expression of brain-derived neurotrophic factor signaling and the astrocytes in the hippocampus caused by CUMS were increased after the treatments with acupuncture and fluoxetine. Acupuncture and fluoxetine also decreased the ß isoform of calmodulin-dependent protein kinase II in the hippocampus, which was increased by CUMS. Furthermore, acupuncture regulated intestinal microbial disorders caused by CUMS, which reduced the relative abundance ratio of Bacteroidetes/Firmicutes in rats. CONCLUSIONS Our experimental results indicate that acupuncture can alleviate depression-like performance in CUMS rats by regulating intestinal microbes and neurotransmitters.

Seasonal variation and positive matrix factorization result reveal the sources of giant pandas' exposure to POPs.

Persistent organic pollutant (POPs) contamination was analyzed in samples collected from wild and captive giant pandas to characterize seasonal variation in concentrations of POPs and possible sources. POP concentrations in bamboo and fecal samples collected from captive pandas showed significant fluctuations compared with those collected from wild pandas in each season. The highest polychlorinated biphenyl (PCB) and organochlorine pesticide (OCP) concentrations were 1380 pg g-1 dw and 3140 pg g-1 dw, respectively, which were observed in captive bamboo samples in the summer. PCBs varied seasonally, whereas OCPs did not show apparent seasonal variation. Based on the seasonal variability, component analysis, and the positive matrix factorization results, we determined that the secondary volatilization of POPs during periods of high temperatures was the leading cause of the exposure of pandas to pollutants (45%), and atmospheric transport played a crucial role in the secondary distribution of pollutants in panda food. The other two sources of pollution were historical residues transmitted over long distances to protected areas (28%), as well as UP-POPs and new inputs from agricultural activities (27%). The concentrations of pollutants in bamboo shoots were significantly lower than those in bamboo. Therefore, bamboo shoots should be incorporated into the diet of captive pandas in the spring to reduce their exposure to pollutants. The absorption capacity of pollutants associated with the consumption of bamboo shoots was significantly lower than that associated with the consumption of bamboo. The diet of young captive pandas in the summer should also be managed with caution given their slightly stronger ability to absorb pollutants.

Design, Synthesis, and Evaluation of Chalcone Derivatives as Multifunctional Agents against Alzheimer's Disease.

Fifteen chalcone derivatives 3a-3o were synthesized, and evaluated as multifunctional agents against Alzheimer's disease. In vitro studies revealed that these compounds inhibited self-induced Aß1-42 aggregation effectively ranged from 45.9-94.5 % at 20 µM, and acted as potential antioxidants. Their structure-activity relationships were summarized. In particular, (2E)-3-[4-(dimethylamino)phenyl]-1-(pyridin-2-yl)prop-2-en-1-one (3g) exhibited an excellent inhibitory activity of 94.5 % at 20 µM, and it could disassemble the self-induced Aß1-42 aggregation fibrils with ratio of 57.1 % at 20 µM concentration. In addition, compound 3g displayed good chelating ability for Cu2+ , and could effectively inhibit and disaggregate Cu2+ -induced Aß aggregation. Moreover, compound 3g exerted low cytotoxicity, significantly reversed Aß1-42 -induced SH-SY5Y cell damage. More importantly, compound 3g remarkably ameliorated scopolamine-induced memory impairment in mice. In summary, all the results revealed compound 3g was a potential multifunctional agent for AD therapy.

High vibration frequency of soft tissue occurs during gait in power-trained athletes.

Muscles serve as a critical regulator of locomotion and damping, resulting in changes of soft tissue vibration. However, whether muscle fibre compositions of different individuals will cause different extents of soft tissue vibration during gait is unclear. Therefore, this study investigated the differences in lower extremity vibration frequencies among power-trained and non-power-trained athletes during walking and running. Twelve weightlifting athletes were assigned to the power-trained group and twelve recreational runners were assigned to the non-power-trained group. Accelerometers were used to detect soft tissue compartment vibration frequencies of the rectus femoris (RF) and gastrocnemius medialis (GMS) during walking and running. Results indicated that power-trained athletes, as compared to the non-power-trained, induced significantly (p < 0.05) higher vibration frequencies in their soft tissue compartments during walking and running. This suggests that power-trained athletes, who have higher ratios of fatigable fast-twitch muscle fibres, may have induced higher soft tissue compartment vibration frequencies. As a result, there is a likelihood that power-trained athletes may recruit more fatigable fast-twitch muscle fibres during muscle tuning, causing dysfunctions during prolonged exercises.