MRTF-A alleviates myocardial ischemia reperfusion injury by inhibiting the inflammatory response and inducing autophagy.

Myocardin-related transcription factor A (MRTF-A) has an inhibitory effect on myocardial infarction; however, the mechanism is not clear. This study reveals the mechanism by which MRTF-A regulates autophagy to alleviate myocardial infarct-mediated inflammation, and the effect of silent information regulator 1 (SIRT1) on the myocardial protective effect of MRTF-A was also verified. MRTF-A significantly decreased cardiac damage induced by myocardial ischemia. In addition, MRTF-A decreased NLRP3 inflammasome activity, and significantly increased the expression of autophagy protein in myocardial ischemia tissue. Lipopolysaccharide (LPS) and 3-methyladenine (3-MA) eliminated the protective effects of MRTF-A. Furthermore, simultaneous overexpression of MRTF-A and SIRT1 effectively reduced the injury caused by myocardial ischemia; this was associated with downregulation of inflammatory factor proteins and when upregulation of autophagy-related proteins. Inhibition of SIRT1 activity partially suppressed these MRTF-A-induced cardioprotective effects. SIRT1 has a synergistic effect with MRTF-A to inhibit myocardial ischemia injury through reducing the inflammation response and inducing autophagy.

miR-8485 alleviates the injury of cardiomyocytes through TP53INP1.

MicroRNAs (miRNAs) feature prominently in regulating the progression of chronic heart failure (CHF). This study was performed to investigate the role of miR-8485 in the injury of cardiomyocytes and CHF. It was found that miR-8485 level was markedly reduced in the plasma of CHF patients, compared with the healthy controls. H2 O2 treatment increased tumor necrosis factor-α, interleukin (IL)-6, and IL-1ß levels, inhibited the viability of human adult ventricular cardiomyocyte cell line AC16, and increased the apoptosis, while miR-8485 overexpression reversed these effects. Tumor protein p53 inducible nuclear protein 1 (TP53INP1) was identified as a downstream target of miR-8485, and TP53INP1 overexpression weakened the effects of miR-8485 on cell viability, apoptosis, as well as inflammatory responses. Our data suggest that miR-8485 attenuates the injury of cardiomyocytes by targeting TP53INP1, suggesting it is a protective factor against CHF.

NetNMSP: Nonoverlapping maximal sequential pattern mining.

Nonoverlapping sequential pattern mining, as a kind of repetitive sequential pattern mining with gap constraints, can find more valuable patterns. Traditional algorithms focused on finding all frequent patterns and found lots of redundant short patterns. However, it not only reduces the mining efficiency, but also increases the difficulty in obtaining the demand information. To reduce the frequent patterns and retain its expression ability, this paper focuses on the Nonoverlapping Maximal Sequential Pattern (NMSP) mining which refers to finding frequent patterns whose super-patterns are infrequent. In this paper, we propose an effective mining algorithm, Nettree for NMSP mining (NetNMSP), which has three key steps: calculating the support, generating the candidate patterns, and determining NMSPs. To efficiently calculate the support, NetNMSP employs the backtracking strategy to obtain a nonoverlapping occurrence from the leftmost leaf to its root with the leftmost parent node method in a Nettree. To reduce the candidate patterns, NetNMSP generates candidate patterns by the pattern join strategy. Furthermore, to determine NMSPs, NetNMSP adopts the screening method. Experiments on biological sequence datasets verify that not only does NetNMSP outperform the state-of-the-arts algorithms, but also NMSP mining has better compression performance than closed pattern mining. On sales datasets, we validate that our algorithm guarantees the best scalability on large scale datasets. Moreover, we mine NMSPs and frequent patterns in SARS-CoV-1, SARS-CoV-2 and MERS-CoV. The results show that the three viruses are similar in the short patterns but different in the long patterns. More importantly, NMSP mining is easier to find the differences between the virus sequences.

NetNCSP: Nonoverlapping closed sequential pattern mining.

Sequential pattern mining (SPM) has been applied in many fields. However, traditional SPM neglects the pattern repetition in sequence. To solve this problem, gap constraint SPM was proposed and can avoid finding too many useless patterns. Nonoverlapping SPM, as a branch of gap constraint SPM, means that any two occurrences cannot use the same sequence letter in the same position as the occurrences. Nonoverlapping SPM can make a balance between efficiency and completeness. The frequent patterns discovered by existing methods normally contain redundant patterns. To reduce redundant patterns and improve the mining performance, this paper adopts the closed pattern mining strategy and proposes a complete algorithm, named Nettree for Nonoverlapping Closed Sequential Pattern (NetNCSP) based on the Nettree structure. NetNCSP is equipped with two key steps, support calculation and closeness determination. A backtracking strategy is employed to calculate the nonoverlapping support of a pattern on the corresponding Nettree, which reduces the time complexity. This paper also proposes three kinds of pruning strategies, inheriting, predicting, and determining. These pruning strategies are able to find the redundant patterns effectively since the strategies can predict the frequency and closeness of the patterns before the generation of the candidate patterns. Experimental results show that NetNCSP is not only more efficient but can also discover more closed patterns with good compressibility. Furtherly, in biological experiments NetNCSP mines the closed patterns in SARS-CoV-2 and SARS viruses. The results show that the two viruses are of similar pattern composition with different combinations.

Anti-apoptotic effects of myocardin-related transcription factor-A on rat cardiomyocytes following hypoxia-induced injury.

Myocardin-related transcription factor-A (MRTF-A) can transduce both biomechanical and humoral signals, which can positively modulate cardiac damage induced by acute myocardial infarction. However, the molecular mechanism that underlies the contribution that MRTF-A provides to the myocardium is not completely understood. The objective of this study was to investigate the effects of MRTF-A on myocardium apoptosis and its mechanisms. Our experiment results showed that MRTF-A expression increased and Bcl-2 expression reduced during myocardial ischemia-reperfusion in rat. Meanwhile, primary cardiomyocytes were pretreated with wild-type MRTF-A or siRNA of MRTF-A before exposure to hypoxia. We found that overexpression of MRTF-A in myocardial cells inhibited apoptosis and the release of cytochrome c. MRTF-A enhanced Bcl-2, which contributes to MRTF-A interaction with Bcl-2 in the nuclei of cardiomyocytes. MRTF-A upregulation expression of Bcl-2 in cardiomyocytes induced by hypoxia was inhibited by PD98059, an ERK1/2 inhibitor. In conclusions, MRTF-A improved myocardial cell survival in a cardiomyocyte model of hypoxia-induced injury; this effect was correlated with the upregulation of anti-apoptotic gene Bcl-2 through the activation of ERK1/2.

Twitter K-H networks in action: Advancing biomedical literature for drug search.

The importance of searching biomedical literature for drug interaction and side-effects is apparent. Current digital libraries (e.g., PubMed) suffer infrequent tagging and metadata annotation updates. Such limitations cause absence of linking literature to new scientific evidence. This demonstrates a great deal of challenges that stand in the way of scientists when searching biomedical repositories. In this paper, we present a network mining approach that provides a bridge for linking and searching drug-related literature. Our contributions here are two fold: (1) an efficient algorithm called HashPairMiner to address the run-time complexity issues demonstrated in its predecessor algorithm: HashnetMiner, and (2) a database of discoveries hosted on the web to facilitate literature search using the results produced by HashPairMiner. Though the K-H network model and the HashPairMiner algorithm are fairly young, their outcome is evidence of the considerable promise they offer to the biomedical science community in general and the drug research community in particular.

[Impact of mesenchymal stem cells transplantation on myocardial myocardin-related transcription factor-A and bcl-2 expression in rats with experimental myocardial infarction].

OBJECTIVE: To observe the impact of mesenchymal stem cells (BMSCs) transplantation on myocardial myocardin-related transcription factor-A (MRTF-A) and bcl-2 expression in rats with experimental myocardial infarction (MI). METHODS: Thirty rats were randomly divided into sham, MI and MI + BMSCs (1 × 10(6) injected into 4 infarct points immediately post coronary artery ligation) groups (n = 10 each).One week later, TUNEL was used to detect cardiomyocyte apoptosis, the myocardial expression of MRTF-A and bcl-2 was detected by laser scanning confocal microscope and Western blot. In vitro plasmid of MRTF-A and co-transfection with plasmids of MRTF-A and bcl-2 or mutated bcl-2 transfection into cardiomyocyte was applied to evaluate the relationship between MRTF-A and bcl-2. RESULTS: The number of apoptotic cardiomyocytes in the sham group, MI group and MI + BMSCs group were (4.05 ± 1.56)%, (62.38 ± 8.41)% and (22.36 ± 6.17)%, respectively (P < 0.05). The protein expression of MRTF-A and bcl-2 in the MI group were significantly lower than those in sham group, while significantly upregulated in MI + BMSCs group (P < 0.05 vs. MI). In cultured neonatal rat cardiomyocyte, the expression of bcl-2 protein was significantly upregulated after transfection with MRTF-A plasmid, and bcl-2-luciferase activity significantly increased after co-transfection with plasmids of MRTF-A and bcl-2-luciferase, however, the positive regulatory effect of MRTF-A was abolished after transfection with mutated bcl-2. CONCLUSION: Mesenchymal stem cells transplantation can effectively reduce cardiomyocyte apoptosis in this rat MI model, and upregulate the expression of MRTF-A. Consequent up-regulated bcl-2 expression might be involved in the beneficial effects of BMSCs transplantation in this model.

Robust feature generation for protein subchloroplast location prediction with a weighted GO transfer model.

Chloroplasts are crucial organelles of green plants and eukaryotic algae since they conduct photosynthesis. Predicting the subchloroplast location of a protein can provide important insights for understanding its biological functions. The performance of subchloroplast location prediction algorithms often depends on deriving predictive and succinct features from genomic and proteomic data. In this work, a novel weighted Gene Ontology (GO) transfer model is proposed to generate discriminating features from sequence data and GO Categories. This model contains two components. First, we transfer the GO terms of the homologous protein, and then assign the bit-score as weights to GO features. Second, we employ term-selection methods to determine weights for GO terms. This model is capable of improving prediction accuracy due to the tolerance of the noise derived from homolog knowledge transfer. The proposed weighted GO transfer method based on bit-score and a logarithmic transformation of CHI-square (WS-LCHI) performs better than the baseline models, and also outperforms the four off-the-shelf subchloroplast prediction methods.

Detection of ChatGPT fake science with the xFakeSci learning algorithm.

Generative AI tools exemplified by ChatGPT are becoming a new reality. This study is motivated by the premise that "AI generated content may exhibit a distinctive behavior that can be separated from scientific articles". In this study, we show how articles can be generated using means of prompt engineering for various diseases and conditions. We then show how we tested this premise in two phases and prove its validity. Subsequently, we introduce xFakeSci, a novel learning algorithm, that is capable of distinguishing ChatGPT-generated articles from publications produced by scientists. The algorithm is trained using network models driven from both sources. To mitigate overfitting issues, we incorporated a calibration step that is built upon data-driven heuristics, including proximity and ratios. Specifically, from a total of a 3952 fake articles for three different medical conditions, the algorithm was trained using only 100 articles, but calibrated using folds of 100 articles. As for the classification step, it was performed using 300 articles per condition. The actual label steps took place against an equal mix of 50 generated articles and 50 authentic PubMed abstracts. The testing also spanned publication periods from 2010 to 2024 and encompassed research on three distinct diseases: cancer, depression, and Alzheimer's. Further, we evaluated the accuracy of the xFakeSci algorithm against some of the classical data mining algorithms (e.g., Support Vector Machines, Regression, and Naive Bayes). The xFakeSci algorithm achieved F1 scores ranging from 80 to 94%, outperforming common data mining algorithms, which scored F1 values between 38 and 52%. We attribute the noticeable difference to the introduction of calibration and a proximity distance heuristic, which underscores this promising performance. Indeed, the prediction of fake science generated by ChatGPT presents a considerable challenge. Nonetheless, the introduction of the xFakeSci algorithm is a significant step on the way to combating fake science.

Safeguarding authenticity for mitigating the harms of generative AI: Issues, research agenda, and policies for detection, fact-checking, and ethical AI.

As the influence of transformer-based approaches in general and generative artificial intelligence (AI) in particular continues to expand across various domains, concerns regarding authenticity and explainability are on the rise. Here, we share our perspective on the necessity of implementing effective detection, verification, and explainability mechanisms to counteract the potential harms arising from the proliferation of AI-generated inauthentic content and science. We recognize the transformative potential of generative AI, exemplified by ChatGPT, in the scientific landscape. However, we also emphasize the urgency of addressing associated challenges, particularly in light of the risks posed by disinformation, misinformation, and unreproducible science. This perspective serves as a response to the call for concerted efforts to safeguard the authenticity of information in the age of AI. By prioritizing detection, fact-checking, and explainability policies, we aim to foster a climate of trust, uphold ethical standards, and harness the full potential of AI for the betterment of science and society.

Shared Manifold Regularized Joint Feature Selection for Joint Classification and Regression in Alzheimer's Disease Diagnosis.

In Alzheimer's disease (AD) diagnosis, joint feature selection for predicting disease labels (classification) and estimating cognitive scores (regression) with neuroimaging data has received increasing attention. In this paper, we propose a model named Shared Manifold regularized Joint Feature Selection (SMJFS) that performs classification and regression in a unified framework for AD diagnosis. For classification, unlike the existing works that build least squares regression models which are insufficient in the ability of extracting discriminative information for classification, we design an objective function that integrates linear discriminant analysis and subspace sparsity regularization for acquiring an informative feature subset. Furthermore, the local data relationships are learned according to the samples' transformed distances to exploit the local data structure adaptively. For regression, in contrast to previous works that overlook the correlations among cognitive scores, we learn a latent score space to capture the correlations and employ the latent space to design a regression model with l2,1 -norm regularization, facilitating the feature selection in regression task. Moreover, the missing cognitive scores can be recovered in the latent space for increasing the number of available training samples. Meanwhile, to capture the correlations between the two tasks and describe the local relationships between samples, we construct an adaptive shared graph to guide the subspace learning in classification and the latent cognitive score learning in regression simultaneously. An efficient iterative optimization algorithm is proposed to solve the optimization problem. Extensive experiments on three datasets validate the discriminability of the features selected by SMJFS.

Intracardiac leakage of cement during artificial femoral head replacement: a case report and review.

Bone cement leakage from the femoral medullary cavity is a rare complication following hip replacement. Currently, there are no reports of bone cement leakage into the heart. Here, we report an 81-year-old female patient with right femoral neck fracture. A thorough preoperative examination showed that bone cement had leaked into the heart during right femoral head replacement, leading to the death of the patient that night. Postoperative cardiac ultrasound showed that bone cement entered the vascular system through the femoral medullary cavity and subsequently entered the heart. Extreme deterioration in the patient's condition resulted in death that night. Unfortunately, the patient's family abandoned the idea of surgical removal of foreign bodies, leading to inevitable death. This case emphasizes the risk of clinical manifestations of cardiac embolism of bone cement after artificial femoral head replacement, suggesting that the risk of such embolism might be underestimated. We propose routine real-time C-arm X-ray guidance and injection of an appropriate amount of bone cement to prevent serious cardiopulmonary failure.

Are Graph Convolutional Networks With Random Weights Feasible?

Graph Convolutional Networks (GCNs), as a prominent example of graph neural networks, are receiving extensive attention for their powerful capability in learning node representations on graphs. There are various extensions, either in sampling and/or node feature aggregation, to further improve GCNs' performance, scalability and applicability in various domains. Still, there is room for further improvements on learning efficiency because performing batch gradient descent using the full dataset for every training iteration, as unavoidable for training (vanilla) GCNs, is not a viable option for large graphs. The good potential of random features in speeding up the training phase in large-scale problems motivates us to consider carefully whether GCNs with random weights are feasible. To investigate theoretically and empirically this issue, we propose a novel model termed Graph Convolutional Networks with Random Weights (GCN-RW) by revising the convolutional layer with random filters and simultaneously adjusting the learning objective with regularized least squares loss. Theoretical analyses on the model's approximation upper bound, structure complexity, stability and generalization, are provided with rigorous mathematical proofs. The effectiveness and efficiency of GCN-RW are verified on semi-supervised node classification task with several benchmark datasets. Experimental results demonstrate that, in comparison with some state-of-the-art approaches, GCN-RW can achieve better or matched accuracies with less training time cost.

OPP-Miner: Order-Preserving Sequential Pattern Mining for Time Series.

Traditional sequential pattern mining methods were designed for symbolic sequence. As a collection of measurements in chronological order, a time series needs to be discretized into symbolic sequences, and then users can apply sequential pattern mining methods to discover interesting patterns in time series. The discretization will not only cause the loss of some important information, which partially destroys the continuity of time series, but also ignore the order relations between time-series values. Inspired by order-preserving matching, this article explores a new method called order-preserving sequential pattern (OPP) mining, which does not need to discretize time series into symbolic sequences and represents patterns based on the order relations of time series. An inherent advantage of such representation is that the trend of a time series can be represented by the relative order of the values underneath time series. We propose an OPP-Miner algorithm to mine frequent patterns in time series with the same relative order. OPP-Miner employs the filtration and verification strategies to calculate the support and uses the pattern fusion strategy to generate candidate patterns. To compress the result set, we also study to find the maximal OPPs. Experimental results validate that OPP-Miner is not only efficient but can also discover similar subsequences in time series. In addition, case studies show that our algorithms have high utility in analyzing the COVID-19 epidemic by identifying critical trends and improve the clustering performance. The algorithms and data can be downloaded from https://github.com/wuc567/Pattern-Mining/tree/master/OPP-Miner.

Difficult Novel Class Detection in Semisupervised Streaming Data.

Streaming data mining can be applied in many practical applications, such as social media, market analysis, and sensor networks. Most previous efforts assume that all training instances except for the novel class have been completely labeled for novel class detection in streaming data. However, a more realistic situation is that only a few instances in the data stream are labeled. In addition, most existing algorithms are potentially dependent on the strong cohesion between known classes or the greater separation between novel class and known classes in the feature space. Unfortunately, this potential dependence is usually not an inherent characteristic of streaming data. Therefore, to classify data streams and detect novel classes, the proposed algorithm should satisfy: 1) it can handle any degree of separation between novel class and known classes (both easy and difficult novel class detection) and 2) it can use limited labeled instances to build algorithm models. In this article, we tackle these issues by a new framework called semisupervised streaming learning for difficult novel class detection (SSLDN), which consists of three major components: an effective novel class detector based on random trees, a classifier by using the information of nearest neighbors, and an efficient updating process. Empirical studies on several datasets validate that SSLDN can accurately handle different degrees of separation between the novel and known classes in semisupervised streaming data.

Horizontal Federated Density Peaks Clustering.

Density peaks clustering (DPC) is a popular clustering algorithm, which has been studied and favored by many scholars because of its simplicity, fewer parameters, and no iteration. However, in previous improvements of DPC, the issue of privacy data leakage was not considered, and the "Domino" effect caused by the misallocation of noncenters has not been effectively addressed. In view of the above shortcomings, a horizontal federated DPC (HFDPC) is proposed. First, HFDPC introduces the idea of horizontal federated learning and proposes a protection mechanism for client parameter transmission. Second, DPC is improved by using similar density chain (SDC) to alleviate the "Domino" effect caused by multiple local peaks in the flow pattern dataset. Finally, a novel data dimension reduction and image encryption are used to improve the effectiveness of data partitioning. The experimental results show that compared with DPC and some of its improvements, HFDPC has a certain degree of improvement in accuracy and speed.

Adaptive Prototype Interaction Network for Few-Shot Knowledge Graph Completion.

Few-shot knowledge graph completion (FKGC), which aims to infer new triples for a relation using only a few reference triples of the relation, has attracted much attention in recent years. Most existing FKGC methods learn a transferable embedding space, where entity pairs belonging to the same relations are close to each other. In real-world knowledge graphs (KGs), however, some relations may involve multiple semantics, and their entity pairs are not always close due to having different meanings. Hence, the existing FKGC methods may yield suboptimal performance when handling multiple semantic relations in the few-shot scenario. To solve this problem, we propose a new method named adaptive prototype interaction network (APINet) for FKGC. Our model consists of two major components: 1) an interaction attention encoder (InterAE) to capture the underlying relational semantics of entity pairs by modeling the interactive information between head and tail entities and 2) an adaptive prototype net (APNet) to generate relation prototypes adaptive to different query triples by extracting query-relevant reference pairs and reducing the data inconsistency between support and query sets. Experimental results on two public datasets demonstrate that APINet outperforms several state-of-the-art FKGC methods. The ablation study demonstrates the rationality and effectiveness of each component of APINet.

Online Causal Feature Selection for Streaming Features.

Recently, causal feature selection (CFS) has attracted considerable attention due to its outstanding interpretability and predictability performance. Such a method primarily includes the Markov blanket (MB) discovery and feature selection based on Granger causality. Representatively, the max-min MB (MMMB) can mine an optimal feature subset, i.e., MB; however, it is unsuitable for streaming features. Online streaming feature selection (OSFS) via online process streaming features can determine parents and children (PC), a subset of MB; however, it cannot mine the MB of the target attribute ( T ), i.e., a given feature, thus resulting in insufficient prediction accuracy. The Granger selection method (GSM) establishes a causal matrix of all features by performing excessively time; however, it cannot achieve a high prediction accuracy and only forecasts fixed multivariate time series data. To address these issues, we proposed an online CFS for streaming features (OCFSSFs) that mine MB containing PC and spouse and adopt the interleaving PC and spouse learning method. Furthermore, it distinguishes between PC and spouse in real time and can identify children with parents online when identifying spouses. We experimentally evaluated the proposed algorithm on synthetic datasets using precision, recall, and distance. In addition, the algorithm was tested on real-world and time series datasets using classification precision, the number of selected features, and running time. The results validated the effectiveness of the proposed algorithm.

BLCA prognostic model creation and validation based on immune gene-metabolic gene combination.

BACKGROUND: Bladder cancer (BLCA) is a prevalent urinary system malignancy. Understanding the interplay of immunological and metabolic genes in BLCA is crucial for prognosis and treatment. METHODS: Immune/metabolism genes were extracted, their expression profiles analyzed. NMF clustering found prognostic genes. Immunocyte infiltration and tumor microenvironment were examined. Risk prognostic signature using Cox/LASSO methods was developed. Immunological Microenvironment and functional enrichment analysis explored. Immunotherapy response and somatic mutations evaluated. RT-qPCR validated gene expression. RESULTS: We investigated these genes in 614 BLCA samples, identifying relevant prognostic genes. We developed a predictive feature and signature comprising 7 genes (POLE2, AHNAK, SHMT2, NR2F1, TFRC, OAS1, CHKB). This immune and metabolism-related gene (IMRG) signature showed superior predictive performance across multiple datasets and was independent of clinical indicators. Immunotherapy response and immune cell infiltration correlated with the risk score. Functional enrichment analysis revealed distinct biological pathways between low- and high-risk groups. The signature demonstrated higher prediction accuracy than other signatures. qRT-PCR confirmed differential gene expression and immunotherapy response. CONCLUSIONS: The model in our work is a novel assessment tool to measure immunotherapy's effectiveness and anticipate BLCA patients' prognosis, offering new avenues for immunological biomarkers and targeted treatments.

Multi-Attribute Discriminative Representation Learning for Prediction of Adverse Drug-Drug Interaction.

Adverse drug-drug interaction (ADDI) is a significant life-threatening issue, posing a leading cause of hospitalizations and deaths in healthcare systems. This paper proposes a unified Multi-Attribute Discriminative Representation Learning (MADRL) model for ADDI prediction. Unlike the existing works that equally treat features of each attribute without discrimination and do not consider the underlying relationship among drugs, we first develop a regularized optimization problem based on CUR matrix decomposition for joint representative drug and discriminative feature selection such that the selected drugs and features can well approximate the original feature spaces and the critical factors discriminative to ADDIs can be properly explored. Different from the existing models that ignore the consistent and unique properties among attributes, a Generative Adversarial Network (GAN) framework is then designed to capture the inter-attribute shared and intra-attribute specific representations of adverse drug pairs for exploiting their consensus and complementary information in ADDI prediction. Meanwhile, MADRL is compatible with any kind of attributes and capable of exploring their respective effects on ADDI prediction. An iterative algorithm based on the alternating direction method of multipliers is developed for optimization. Experiments on publicly available dataset demonstrate the effectiveness of MADRL when compared with eleven baselines and its six variants.