Importance of Mitochondrial-Related Genes in Dilated Cardiomyopathy Based on Bioinformatics Analysis

We designed this study to identify potential key protein interaction networks, genes, and correlated pathways in dilated cardiomyopathy (DCM) via bioinformatics methods. We selected the GSE3586 microarray dataset, consisting of 15 dilated cardiomyopathic heart biopsy samples and 13 nonfailing heart biopsy samples. Initially, the GSE3586 dataset was downloaded and was analyzed with the limma package to identify differentially expressed genes (DEGs). A total of 172 DEGs consisting of 162 upregulated genes and ten downregulated genes in DCM were selected by the criterion of adjusted Pvalues less than 0.01 and the log2-fold change of 0.6 or greater. Gene Ontology functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to view the biological processes, cellular components, molecular function, and KEGG pathways of the DEGs. Next, protein-protein interactions were constructed, and the hub protein modules were identified. Then we selected the key genes DLD , UQCRC2 , DLAT , SUCLA2 , ATP5A1 , PRDX3 , FH , SDHD , and NDUFV1 , which are involved in a wide range of biological activities, such as the citrate cycle, oxidation-reduction processes and cellular respiration, and energy derivation by oxidation of organic compounds in mitochondria. Finally, we found that currently there are no related gene-targeting drugs after exploring the predicted interactions between key genes and drugs, and transcription factors. In conclusion, our study provides greater understanding of the pathogenesis and underlying molecular mechanisms in DCM. This contributes to the exploration of potential gene therapy targets

Suitable ultrasound screening method for older adults with disability to identify low muscle mass

ObjectiveThis study aimed to investigate the accuracy and consistency of different ultrasound protocols for the measurement of gastrocnemius muscle (GM) thickness and to identify a suitable ultrasound scheme that can be used to detect the low muscle mass in older with disability.Materials and methodsIn this cross-sectional study, each participant underwent three different ultrasound protocols for the measurement of the GM thickness, and each measurement was repeated three times. The three measurement schemes were as follows: method A, lying on the examination bed in a prone position with legs stretched and relaxed and feet hanging outside the examination bed; method B, lateral right side lying position with legs separated (left leg flexed and right leg in a relaxed state); and method C, right side lying position with legs together and lower limb muscles in a relaxed state. The low muscle mass was determined by averaging two or three measurements of the GM thickness determined using different sonographic protocols.ResultsThe study included 489 participants. The difference in the prevalence of low muscle mass identified between two and three replicates of the same measurement protocol ranged from 0 to 1.3%. Considering the three repeated measurements of the method A as the reference, the area under the curve (AUC) in different measurement schemes were 0.977-1 and 0.973-1 in males and females, respectively. Furthermore, male and female Kappa values from low to high were 0.773, 0.801, 0.829, 0.839, and 0.967 and 0.786, 0.794, 0.804, 0.819, and 0.984, respectively.ConclusionDifferent ultrasound measurement protocols showed high accuracy and consistency in identifying low muscle mass. Repeating the measurements two or three times was found to be feasible

Evaluation of Urban Green Space Supply and Demand Based on Mobile Signal Data: Taking the Central Area of Shenyang City as an Example

The degree of coordination between the supply and demand for urban green spaces serves as a vital metric for evaluating urban ecological development and the well-being of residents. An essential principle in assessing this coordination is the precise quantification of both the demand and supply of green spaces, as well as the differential representation of their spatiotemporal structures. This study utilizes the entropy weight method (EWM) and principal component analysis (PCA) to comprehensively measure supply indicators for green space quantity and quality in the central urban area of Shenyang, China. To establish reliable and quantifiable demand indicators, mobile signaling spatial-temporal data are corrected by incorporating static population cross-sectional data. The Gaussian two-step floating catchment area method (Ga2SFCA) is employed to calculate the accessibility of green spaces in each community with ArcGIS 10.2 software, while the Gini coefficient is utilized to assess the equity of green space distribution within the study area. This study employs location entropy to determine the levels of supply and demand for green spaces in each subdistrict. Furthermore, the priority of community-scale green space regulation is accurately determined by balancing vulnerable areas of green space supply and replenishing green space resources for the ageing population. The findings suggest a Gini coefficient of 0.58 for the supply and demand of green spaces in Shenyang’s central metropolitan region, indicating a relatively low level of equalization in overall green space allocation. Based on location entropy, the classification of supply and demand at the street level yields the following outcomes: balanced areas comprise 21.98%, imbalanced areas account for 26.37%, and highly imbalanced regions represent 51.65%. After eliminating the balanced regions, the distribution of the elderly population is factored in, highlighting the spatial distribution and proportions of communities with distinct regulatory priorities: Level 1 (S1) constitutes 7.4%, Level 2 (S2) accounts for 60.9%, and Level 3 (S3) represents 31.7%. Notably, the communities in the S1 category exhibit spatial distribution characteristics of aggregation within the inner ring and the northern parts of the third ring. This precise identification of areas requiring urgent regulation and the spatial distribution of typical communities can provide reliable suggestions for prioritizing green space planning in an age-friendly city

Topic recognition method of comment text based on density Canopy

The method, which combines Sentence-BERT and LDA, takes the topic number of LDA as the k value in K-means algorithm, resulting in poor interpretability and low topic consistency. To solve this problem, a Sentence-BERT and LDA optimization method based on density Canopy(SBERT-LDA-DC) was proposed, which used density Canopy to improve the K-means algorithm. The experimental results indicate that this method is superior to similar methods using K-means and K-means++ to cluster feature vectors on the consistency index. Compared with the SBERT-LDA method, the consistency index is improved by 229% on the 1 852 drama comment dataset. The proposed SBERT-LDA-DC method is effective, which provides a new method for product or service providers to better understand user opinions and improve their own products or services, and has strong practical application value

An Economical Route Planning Method for Plug-In Hybrid Electric Vehicle in Real World

Relieving the adverse effects of automobiles on the environment and natural resources has drawn the attention of numerous researchers. This paper seeks a new path to reach a target by focusing on the synergy of the vehicle and the environment. A real-time economical route planning method for a plug-in hybrid electric vehicle (PHEV) is proposed. Three main contributions have been made. Firstly, a real comparison test is performed to provide rudimentary understanding of the difference in energy usage and route planning between PHEVs and conventional vehicles. Secondly, an approach to obtain PHEV customized data is developed for road weight calculation, which is the essential step in route planning. This method incorporates traffic data from conventional vehicles with the PHEV simulation model, obtaining the required data. Thirdly, the travel expense estimation model (TEEM) is designed. The TEEM could be applied to calculate the road weight of each road segment considering the impact on energy consumption with respect to environmental factors, providing the grounds for route planning. The proposed method to plan an economical route is evaluated, and the results justify its validation and ability to improve fuel economy

Machine learning based biomarker discovery for chronic kidney disease–mineral and bone disorder (CKD-MBD)

Abstract Introduction Chronic kidney disease-mineral and bone disorder (CKD-MBD) is characterized by bone abnormalities, vascular calcification, and some other complications. Although there are diagnostic criteria for CKD-MBD, in situations when conducting target feature examining are unavailable, there is a need to investigate and discover alternative biochemical criteria that are easy to obtain. Moreover, studying the correlations between the newly discovered biomarkers and the existing ones may provide insights into the underlying molecular mechanisms of CKD-MBD. Methods We collected a cohort of 116 individuals, consisting of three subtypes of CKD-MBD: calcium abnormality, phosphorus abnormality, and PTH abnormality. To identify the best biomarker panel for discrimination, we conducted six machine learning prediction methods and employed a sequential forward feature selection approach for each subtype. Additionally, we collected a separate prospective cohort of 114 samples to validate the discriminative power of the trained prediction models. Results Using machine learning under cross validation setting, the feature selection method selected a concise biomarker panel for each CKD-MBD subtype as well as for the general one. Using the consensus of these features, best area under ROC curve reached up to 0.95 for the training dataset and 0.74 for the perspective dataset, respectively. Discussion/Conclusion For the first time, we utilized machine learning methods to analyze biochemical criteria associated with CKD-MBD. Our aim was to identify alternative biomarkers that could serve not only as early detection indicators for CKD-MBD, but also as potential candidates for studying the underlying molecular mechanisms of the condition

Targeting epigenetic and posttranslational modifications regulating ferroptosis for the treatment of diseases

Abstract Ferroptosis, a unique modality of cell death with mechanistic and morphological differences from other cell death modes, plays a pivotal role in regulating tumorigenesis and offers a new opportunity for modulating anticancer drug resistance. Aberrant epigenetic modifications and posttranslational modifications (PTMs) promote anticancer drug resistance, cancer progression, and metastasis. Accumulating studies indicate that epigenetic modifications can transcriptionally and translationally determine cancer cell vulnerability to ferroptosis and that ferroptosis functions as a driver in nervous system diseases (NSDs), cardiovascular diseases (CVDs), liver diseases, lung diseases, and kidney diseases. In this review, we first summarize the core molecular mechanisms of ferroptosis. Then, the roles of epigenetic processes, including histone PTMs, DNA methylation, and noncoding RNA regulation and PTMs, such as phosphorylation, ubiquitination, SUMOylation, acetylation, methylation, and ADP-ribosylation, are concisely discussed. The roles of epigenetic modifications and PTMs in ferroptosis regulation in the genesis of diseases, including cancers, NSD, CVDs, liver diseases, lung diseases, and kidney diseases, as well as the application of epigenetic and PTM modulators in the therapy of these diseases, are then discussed in detail. Elucidating the mechanisms of ferroptosis regulation mediated by epigenetic modifications and PTMs in cancer and other diseases will facilitate the development of promising combination therapeutic regimens containing epigenetic or PTM-targeting agents and ferroptosis inducers that can be used to overcome chemotherapeutic resistance in cancer and could be used to prevent other diseases. In addition, these mechanisms highlight potential therapeutic approaches to overcome chemoresistance in cancer or halt the genesis of other diseases

Comparison of SNR, CNR, and vessel sharpness between FSD, QISS, and CE-MRA in three arterial segments of the calf.

<p>Each column represents average measurements and error is shown as standard deviation. Asterisks indicated significant difference (P < 0.05). SNR: signal-to-noise ratio; CNR: contrast-to-noise ratio; FSD: flow-sensitive dephasing; QISS: quiescent-interval single-shot; CE-MRA: contrast-enhanced MR angiography.</p