The devastating impact of the Severe Acute Respiratory Syndrome-Coronavirus 2 (SARS-CoV-2) pandemic almost halted the global economy and is responsible for 6 million deaths with infection rates of over 524 million. With significant reservations, initially, the SARS-CoV-2 virus was suspected to be infected by and closely related to Bats. However, over the periods of learning and critical development of experimental evidence, it is found to have some similarities with several gene clusters and virus proteins identified in animal-human transmission. Despite this substantial evidence and learnings, there is limited exploration regarding the SARS-CoV-2 genome to putative microRNAs (miRNAs) in the virus life cycle. In this context, this paper presents a detection method of SARS-CoV-2 precursor-miRNAs (pre-miRNAs) that helps to identify a quick detection of specific ribonucleic acid (RNAs). The approach employs an artificial neural network and proposes a model that estimated accuracy of 98.24%. The sampling technique includes a random selection of highly unbalanced datasets for reducing class imbalance following the application of matriculation artificial neural network that includes accuracy curve, loss curve, and confusion matrix. The classical approach to machine learning is then compared with the model and its performance. The proposed approach would be beneficial in identifying the target regions of RNA and better recognising of SARS-CoV-2 genome sequence to design oligonucleotide-based drugs against the genetic structure of the virus. In late 2019, few patients were affected in pneumonia with a nescient symptoms known as respiratory syndrome coronavirus 2 (SARS-CoV-2) and later named as coronavirus disease 2019 (COVID-19). There is still in debate exactly where it grown up, but Epidemiological evidence shown that the virous spread from Wuhan, Hubei province from local sea food market. It is also confirmed the gene sequence was identified from Bats. According to World Health Organization (WHO), the virus was rapidly spread in worldwide over 6 million deaths and still growing continuously. The special attention of this virus was to spread very fast, adapt rapidly and affected with the infection of the major symptoms in fever, cough, muscle pain, and diarrhea. The similar symptoms can also be seen in mice, dogs, cats, camels, pigs, chickens, and bats [SARS-Cov-2 are an encapsulated and carrying a positive sense of single-stranded RNA genome that belongs to subfamily Coronavirdiae. However, Micro ribonucleic acid (miRNAs) were initially identified in 1993 which controlls the timing of nematode Caenorhabditis elegans . Micro ribonucleic acid are literally quite small with an average 22 nucleotides in length available in plants, animals and some viruses including HSV, HIV-1, Dengue, Influenza, and SARS-COV-2 involved in biological processes [According to the literature –7, microConsidering the various conditions to investigate how micro ribonucleic acid regulated under various conditions to comprehend the gene expression and disease phenotypes. The miRNAs can be produced by the most deoxyribonucleic acid (DNA) viruses, but miRNA expression is controversial in the case of RNA viruses because of their cytoplasmic replication and insufficient knowledge of the nuclear miRNA complex structure . TherefoMicroRNAs are consequent from pri-miRNAs more than 1000 nt in length and pri-miRNAs comprises a hairpin structure that realises from 60–120 nt . The strIn order to detect miRNAs, it is important to distinguish pre-miRNAs from other hairpin-like sequences . In ordeAccording to the literature , SARS-CoThe major issue with using machine learning to detect pre-miRNAs is that the number of well-known pre-miRNAs is typically few in comparison to the hundreds of thousands of candidate sequences in a genome, making this a high-class imbalanced classification challenge . H. sapiPerformance is the main research gap in SARS-CoV-2 pre-miRNA identification. Other relevant limitations include artificial negative class. In this research, the RUNN-COV  models are presented. The proposed model performance was established, and their significant comparison, limitation and major challenges are introduced along with other existing methods. It is anticipated that this model will contribute to the fight against COVID-19 by improving its detection and subsequent study of the biological functions of SARS-CoV-2 pre-miRNAs, leading to effective and robust treatments.One of the major contributions of our research lies in data visualization through exploratory data analysis and substantial research to understand the high-class imbalance problem and through investigation and experimentation finding the best technique which in our case is random undersampling to solve this tenacious problem thus decreasing the likelihood of overfitting and increasing classifier performance in the process. While the main contribution lies in fine-tuning the model performance, but associated experimentation’s of exploratory data analysis specifically t-distributed stochastic neighbor embedding (t-SNE) were investigated thoroughly to understand the data loss patterns as well as separation pattern between negative and positive data points which ultimately aided us in identifying a more robust, decision boundary that generated better model performance. All these exploratory data analysis mechanisms, aided us in selecting the best parameters and algorithms, which when fed into our own model produced a substantially superior performance outperforming several limitations discussed above. The final contribution of our research is performance comparison, where we compared our approach and results with existing literature and their performance that would aid the researchers in understanding the latest state of research in this field and where to go next.To summarize, this paper presents a random undersampling technique that deals with the high-class imbalance problem. In addition, several techniques including correlation matrix, t-SNE are investigated for data loss visualization, data point visualization, and identifying hidden patterns. The RUNN-COV model has represented with an extraordinary result which compared to the other existing techniques and possible recommendation of their limitations. This paper also shows a performance comparison with other relevant machine learning models. Finally, the results were systematically evaluated using nine evaluation metrics.The rest of the paper is organized as follows. Section 2 introduces the inspiration of the SAR-CoV-2 pre-miRNA identification. Section 3 presents a survey of the literature for using computational approaches in the COVID-19 and relevant miRNA context. Section 4 presents a description of the SARS-CoV-2 dataset. Section 5 presents sampling strategy, clustering analysis, and RUNN-COV model architecture. Section 6 presents a detailed evaluation strategy, performance analysis, and statistical investigation. Finally, the paper concluded in Section 7.SARS-CoV-2 has had a tremendous impact in the world, not just in terms of health care but also in others including agriculture and food security, economic and financial, educational, industrial, power and energy, oil market, employment, and environmental . TherefoAs mentioned above, MicroRNAs (miRNAs) are the most powerful regulators of gene expression that play a role in practically all forms of gene regulation. Cellular miRNAs can be applied as therapeutic options for COVID-19  as well Unfortunately, currently there is one Food and Drug Administration (FDA) approved antiviral drug, Veklury (Remdesivir), for the treatment of COVID-19 under Emergency Use Authorization (EUA)  along wiOne possible avenue of attack is the design and synthesis of oligonucleotides against the genetic structure of SARS-CoV-2 with the aim to impede its replication or to degrade its genome , This isThe proposed RUNN-COV model will assist to detect of SARs-CoV-2 and potentially many other relevant RNAs as of interest. The findings demonstrate that contemporary machine learning technologies can be used to assist in responding to public health emergencies by helping to discover the characteristics of any viral agent and in devising novel therapeutic approaches.Being able to reliably test for SARS-CoV-2 is essential to stopping its spread. Several types of tests exist to identify SARS-CoV-2, varying in how rapidly they give results, how sensitive they are, and how often they can be performed . The NucAmong ML methods, support vector machine (SVM) was used to classify real human pre-miRNAs from pseudo pre-miRNAs with 90% accuracy . When feHuman pre-miRNA classification was also attempted using deep learning, and contrasted with other machine learning techniques such as naive Bayes classifiers, k-nearest neighbors and random forest . The undPlant miRNA detection has also been demonstrated, achieving 97.54% identification accuracy . Human mThe majority pre-miRNAs detection models are SVM classifier-based , 52. SVMWhen performing machine learning, most algorithms require both positive and negative examples. Databases of positive examples are readily available, but negative examples are scarce, forcing researchers to employ tactics such as creating negative examples through various means. This has various problems, among which is that there is no guarantee that an example that has been generated to be negative is not actually an undiscovered positive example .This problem of class imbalance in SARS-CoV-2 pre-miRNA detection was demonstrated using various algorithms such as one-class SVM (OC-SVM), deeSOM, and mirDNN . The imbThe RUNN-COV method presented in this work attempts to overcome the class imbalance problem through undersampling rather than negative example generation or weight adjustment.The dataset was used based upon pre-miRNA detection using machine learning techniques . It was derived by applying various techniques the SARS-CoV-2 genome from National Center for Biotechnology Information (NCBI) Reference Sequence NC_045512.2, resulting in 569 pre-miRNA samples with 73 features. The dataset also included 999888 hairpin-like sequences from the human genome as the negative class. The dataset detailed can be found in  where thPearson correlation was applied to the features in the dataset. It was found that many of the features are uncorrelated. Random undersampling and oversampling are two techniques that are used to overcome the problem of class imbalance. Random oversampling involves duplicating the examples in the minority class, but this increases the likelihood of overfitting, decreases the classifier performance, and increases the computational effort . Random One concern with random undersampling is information loss when samples are removed. To demonstrate the effect of random undersampling, heatmaps of correlation matrices of the dataset before and after the procedure were taken. A random instance of from various runs of the experiment is given in After preparing the data through random undersampling, a visual representation is required to obtain a high-level understanding of the distribution of the positive and negative classes. In fact, visual understanding of high-dimensional data is crucial in many areas, such as the detailed analysis of single-cell datasets .In this paper, t-distributed stochastic neighbor embedding (t-SNE), a nonlinear algorithm  for highA neural network model was developed and applied to the data to determine if the positive and negative classes could be accurately identified. The model has 18 layers consisting of dense layers and batch normalization layers.The rectified linear unit (ReLu) activation function was used in the dense layers, since it has various benefits such as computational simplicity, sparsity and linear behavior .The batch normalization layers reduce training time and generalization error, minimize the over-fitting problem, increase stability, and smoothen the loss function . The outThe complete architecture has 1,128,994 parameters, of which 1,127,042 are trainable and 1,952 are non-trainable. The Adam optimizer was used to update network weights . Other hVarious measures were chosen for evaluating RUNN-COV. The basic performance measures that are derived from true positive (TP), true negative (TN), false positive (FP) and false negative (FN): accuracy scores, along with precision, recall and F1 score, are shown in Eqs Another more reliable derivative performance measure is the Matthews correlation coefficient (MCC), which only produces a high score if the prediction gained good results in TP, FN, TN, and FP .MCC=(TPThe Cohen’s kappa (CK) is a robust statistic widely used to measure the algorithm performance .CK=AccuHamming loss gives the fraction of all the labels that have been incorrectly identified.F-Beta (beta = 1.0) is the weighted harmonic mean between recall and precision.The dataset was split into 80% training data and 20% test data. Models were run with 90 epochs and the model scored well with the above performance metrics: accuracy 98.24%, Cohen’s kappa score 96.49%, Matthews correlation coefficient 96.50%, hamming loss 0.0175, precision 98%, recall 98%, f1-score 98%, area under the receiver operating characteristic (ROC AUC) score 98.24%, F-beta(beta = 1) score 98.26%. The accuracy curve, loss curve and confusion matrix are shown in Logistic Regression is a popular algorithm used to used to solve classification problems , using gThe k-nearest-neighbours (KNN) classifies based on similarity of examples. It can help reduce the computational cost while maintaining classification accuracy . The KNNSupport Vector Machines (SVM) uses hyperplanes to separate classes . The SVMRandom Forest provides accurate results most of the time without hyper-parameter tuning . The ranThe model’s performance was compared to the following popular machine learning algorithms.By contrasting the confusion matrices Figs  and 5 anWe used the statistical investigation to compare performance and novelty against previous studies. To compare the difference between the previous and proposed studies, we used a paired samples t-test. The statistical paired t-test is appropriate to compare statistical significance’s and differences . To inveμ is the constant for the population mean, N is the number of observations, and x1H0: The deesom (1:50) Vs. RUNN-COV model performance have no significant difference.x2H0: The OC-SVM (1:50) Vs. RUNN-COV model performance have no significant difference.x3H0: The deesom (1:100) Vs. RUNN-COV model performance have no significant difference.x4H0: The OC-SVM (1:100) Vs. RUNN-COV model performance have no significant difference.x5H0: The deesom (1:200) Vs. RUNN-COV model performance have no significant difference.x6H0: The OC-SVM (1:200) Vs. RUNN-COV model performance have no significant difference.In With p-value less than 0.05 for all tests, with 95% confidence our model can be viewed as novel in nature and performs significantly better than existing approaches.In this research work, we have extensively explored the detection method of SARS-CoV-2 precursor-miRNAs through Artificial Neural networks. The initial dataset through experimentation provided a moderate performance which through dataset investigation, we unearthed existing class imbalance problem. Our research on solving high class imbalance problem, led us to further investigate solution manuals regarding class imbalancce problems and opted for random undersampling techniques. Similarly, As the dataset is noisy, it was handy for practitioners to cluster the dataset for which t-SNE was employed. t-SNE uncovers inexact contiguity in an basic high-dimensional complex, so clusters on the low-dimensional representation of the high-dimensional space maximize the probability that bordering data points will not be within the same cluster. Before employing t-SNE, we undertook the tradeoff that, t-SNE does not preserve distances nor density but preserves some form of nearest neighbours. t-SNE gave us 2D maps for the visualization of bias and variance from high-dimensional data. While the difference is subtle, but it affects any distance based algorithms at its core which led us to select distance-exclusionary algorithms that achieved extremely high- performance scores on various measures, outperforming traditional machine learning models and the other existing work on the same dataset. One might argue, why not use random oversampling instead of undersampling. We have investigated these as well albeit not reported in the results. We have realized that duplicating the instances in the minority class although solved class imbalance problem but it increased the likelihood of overfitting, as instances were increased gradually. Eventually, random oversampling contributed to a substantial decrease of classifier performance and increased computational error manifold that led us to stick to undersampling techniques.An interesting instance of our neural network structure is susbsequent utilization of batch normalization. Although, using undersampling instead of oversampling minimized overfitting problem, in our experimentation, we still received anomaly through overfitting which was ultimately solved using batch normalization. Furthermore, this helped the network in reducing training time, smoothing the loss function and increased overall stability by reducing the generalization error. While we proposed the 18-layer exhaustive neural network, we have experimented the model with several variants of layers by including and excluding normalization and dropout layer as well. However, in our investigation, while including these two we have realized some interesting insights about neural networks in general. When using dropouts during training, activations are scaled to maintain the average after the dropout shift. However, the difference is not preserved. Traversing a non-linear slice translates this dispersion shift into an activation average shift, transitioning to the final linear projection slice. The final prediction is trained to fit the training time stats, so if dropout is off, it will fail during validation. This behavior is not an issue for tasks where only relative scaling of the output is important (such as softmax classification). In our case, if the output represents an absolute quantity, this leads to poor inference time performance. This architecture was arrived at after extensive experimentation using different neural network models.The novelty of this research stands at experimentation of exploratory data analysis (EDA) including correlation matrix and t-distributed stochastic neighbor embedding. Efficient exploratory data analysis including data visual representation and data point analysis mechanisms aided us in selecting the best hyperparameters and models. RUNN-COV  models was presented based on previous EDA. Our designed model has produced a substantially superior performance which we have shown through experimentation. Statistical analysis was conducted against previous studies to understand the objective statistical significance of our research work that concluded our work is significant in nature.While Tables COVID research necessitates an all-hands-on-deck strategy in order to eradicate the virus’s impact on the planet in a manner that is environmentally responsible. Among the various research domain specialists, computer scientists play a significant role in developing, analysing, and deploying cutting-edge research to continue the decent battle. In this study, a strategy is suggested that combines deep learning-based efficient pre-processing with neural network-inspired classification structures to identify SARS-CoV-2 pre-miRNAs effectively, therefore enhancing their performance. Our study demonstrates the effective processing and visualisation tools to generate insights, such as random undersampling, t-SNE, and correlation matrix, which gave insightful information that eventually increased the current research performance. The study examines various approaches to the class imbalance, adopted a random undersampling approaches and visualised data with t-SNE to generate better performances. Later, it is compared with existing approaches to classical Machine learning algorithms to provide an understanding of the contribution throughout the study.This study has presented the RUNN-COV model, a neural network model with dense layers and batch normalisation layers for SARS-CoV-2 pre-miRNAs identification based on seventy-three features that enable the rapid detection of COVID. The model is comprised of using random undersampling techniques to the extremely imbalanced dataset in order to decrease class imbalance, followed by the use of a precisely designed artificial neural network. With Matthew’s correlation coefficient (MCC) score of 96.50 percent and an F1 score of 98.26 percent, the model outperformed typical machine learning models and other current work on the same dataset on a variety of other performance metrics. Additionally, the model performance is similar to that of other models that have been performed on distinct datasets.It is believed that RUNN-COV will aid in the sequencing of the SARS-CoV-2 genome and the identification of target sites in an RNA in order to create oligonucleotide-based medicines against the genetic structure of the virus. Comparing this study with previous research, a comprehensive statistical analysis was undertaken to determine the objective statistical significance of the study.Future work will include the development of a pre-miRNA detection technique based on raw RNA sequence data and the application of RUNN-COV to additional organisms’ datasets. Practitioners are also required to research to create more COVID-related data so that the issue of class imbalance may be resolved from the outset. In addition, prospects for study may be identified in the interpretation of results using explainable artificial intelligence, since the physicians, nurses, and patients are the most probable layperson consumers and stakeholders.https://github.com/MdMahadiHasan1/SARS-CoV-2-pre-miRNA for repeatability and seamless replication.The whole research work including data visualization, exploratory data analysis, COVID detection task are available in github S1 File(DOCX)Click here for additional data file.


The clinical model of screening, providing a brief psychosocial and/or pharmacological intervention, and directly referring patients to treatment (SBIRT) is a compelling model to address drug use among assault-injured individuals in the busy emergency department (ED) setting. Our objective in this study was to examine the current literature and determine ED-based strategies that have been reported that screen, directly refer to drug mis-use/addiction specialized treatment services, or initiate addiction treatment among individuals injured by non-partner assault in the United States.We conducted a systematic review of ED-based studies using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol. OVID, MEDLINE, OVID Embase, OVID AMED, Web of Science-Core Collection, Cochrane CENTRAL, and CINAHL were systematically searched using keywords and Medical Subject Heading terms. Studies were excluded if they only involved intimate partner assault-injury, tobacco, or alcohol use. We categorized ED-based strategies as screening, direct referral, or treatment initiation.Of the 2,076 non-duplicated studies identified, we included 26 full-text articles in the final analysis. Fourteen studies were cross-sectional, 11 were cohort, and one was case-control in design. The most common drug use screening instrument used was the National Institute on Drug Abuse Quick Screen Question. Cannabis was the most common drug detected upon screening.Drug use, while highly prevalent, is a modifiable risk factor for non-partner assault-injury. The paucity of scientific studies is evidence for the need to intentionally address this area that remains a major challenge for the public’s health. Future research is needed to evaluate ED-based interventions for drug use in this population. The emergency department (ED) is often referred to as the ideal setting to identify patients with high-risk health behaviors, such as substance use, and link them to evidence-based treatment services. The clinical model of screening patients, providing a brief psychosocial and/or pharmacological intervention, and directly referring them to treatment (SBIRT) has become increasingly more common in the acute care setting.Intentional injury, specifically assault-injury, presents a formidable public health burden in the United States (US). Annually, US EDs treat approximately 1.5 million individuals for non-fatal assault injuries.In this review, we sought to assess the prevalence of co-occurring drug use and non-partner assault-injury in the ED. To accomplish this, our study objective was to determine what types of ED-based strategies have been reported in the published literature that screen for drug use and/or prescription medication misuse, deliver a brief intervention that targets drug use and/or prescription medication misuse, or directly refer to specialized treatment services among individuals injured by non-partner assault, each components of the SBIRT model. We further categorized each study as to whether it evaluated screening, a brief intervention, and/or referral to specialized treatment services for drug use in accordance with the SBIRT model.We also determined the screening method for substance use that each study used . For the purposes of this study, we defined non-partner assault-injury as an intentional injury inflicted by another person not considered to be a boy/girlfriend, fiancé(e), or spouse . This includes individuals who may have been either the victim or aggressor. Although many studies have used the term “violence” or “violent-injury” when referring to an intentional injury inflicted by another person, in this review we will use the term “assault” or “assault-injury” for the purposes of maintaining consistency and clarity. We use the term “drug use” to refer solely to the use of drugs  and the term “substance use” to refer to the use of both drugs and alcohol.The research team developed a protocol using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol (PRISMA-P) checklist.The search strategy was then peer-reviewed by another senior librarian. The search strategy used both keywords and controlled vocabulary combining the terms for drug or substance use/abuse, assault/violence or victim, and emergency department. The databases included the following: OVID Medline, OVID Embase, Web of Science, Cochrane CENTRAL, and CINAHL .A flow chart of the study selection results can be seen in the The general characteristics and main results of the 26 studies are displayed in 21Together, the 26 study populations spanned all ages. Fourteen studies focused on both adults and adolescents, nine on adults, and three on adolescents. The mean age of the participants ranged from 14.5–38.6 years. Thirteen studies were secondary analyses of prospective studies, which were included. None of the studies were of multiple sites.All studies screened for self-reported drug use among assault-injured participants either by computerized/written survey or in-person interview. Of the 26 studies, five studies screened for recent drug use by either survey or in-person interview without a formal screening instrument.33Among all studies, drug use was found to be closely linked to assault-injury. Study results reported of this relationship were heterogenous. Four of 26 studies found a range of 25–61% of assault-injured individuals who reported drug use within the preceding 12 months.27Overall, cannabis was the most common drug identified among assault-injured individuals. Eight studies reported cannabis use among assault-injured individuals ranged from 32.1–96.7%.23In this systematic review, we identified ED-based studies that screen, treat, and/or directly refer to specialized treatment services for drug use among assault-injured individuals. Our comprehensive literature search determined that there were 26 studies that met criteria for inclusion. The studies in this review used various screening modalities to identify drug use including an in-person interview as well as computerized and written versions of validated screening instruments for drug use. None of these studies were interventional nor did they provide a direct referral to specialized treatment services. The vast majority of studies found a high prevalence of drug use within this population, with cannabis being the most common drug detected.Although study results were fairly heterogenous, the majority of them found high rates of drug use among assault-injured individuals, especially when compared to those injured by other mechanisms. Previous literature demonstrates a close link between assault-injury and drug use.The practice of SBIRT to facilitate future treatment engagement for drug use in the ED setting has become increasingly common.Despite the ACS mandating the practice of SBIRT at all trauma centers for over two decades,63This gap in literature may be explained by the challenges of engaging the intersection of two exceptionally vulnerable populations that do not often seek healthcare with regularity.73Additionally, our review highlights several knowledge gaps in the existing literature surrounding drug use in the context of non-partner assault-injury. Little is known about the mutual risk factors, notably socioecological and psychological, that may contribute to the co-occurrence of assault-injury and drug use, both considered to be problem behaviors.78The authors of the identified studies noted several limitations. One main limitation was the potential for social desirability bias in self-reported high-risk behaviors including drug use and injury mechanism due to fear of legal repercussions or embarrassment. The studies also cited small sample sizes as well as potentially limited generalizability from performing research at a single study site. Lastly, 13 of the studies included in this review were secondary analyses of two parent studies . Findings from these studies may also potentially limit generalizability.To the best of our knowledge, this review of ED-based literature that focuses on the use of screening, providing a brief intervention, and/or direct referral to specialized treatment for drug use in assault-injured individuals is the first of its kind. Existing literature included within this review supports a close relationship between non-partner assault-injury and drug use. However, results of this review highlight a substantial gap in literature that seeks to understand the complex nature of substance-use behaviors and potential interventions in this exceptionally vulnerable population. Emergency departments should consider implementing routine use of the SBIRT model to identify and treat drug use in assault-injured individuals. Areas of future investigations include ED-based interventions for drug use in this population, their potential effects on preventing re-injury, and the role that specific drugs, such as cannabis, serve in inciting aggressive behaviors and coping with its negative effects.


Stroke is the leading cause of death and disability worldwide, with ischemic stroke (IS) being the most prevalent type. Circular RNAs (circRNAs) are involved in the pathological process of IS and are promising biomarkers for the diagnosis of IS. However, studies focusing on circRNAs acting as microRNAs (miRNAs) sponges in regulating mRNA expression are currently scarce.In this study, expression profiles of circRNAs (GSE195442), miRNAs (GSE117064), and mRNAs (GSE58294) from the Gene Expression Omnibus (GEO) database were analyzed. Differentially expressed circRNAs (DEcircRNAs), differentially expressed miRNAs (DEmiRNAs), and differentially expressed mRNAs (DEmRNAs) were identified by R software. The target miRNAs and target genes were predicted by several bioinformatics methods. Then, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of the DEmRNAs. Subsequently, the protein-protein interaction (PPI) network and the competing endogenous RNA (ceRNA) regulatory network were visualized by Cytoscape software. Finally, we further constructed an immune-related circRNA-miRNA-mRNA regulatory sub-network in IS.A total of 35 DEcircRNAs, 141 DEmiRNAs, and 356 DEmRNAs were identified. By comprehensive analysis of bioinformatics methods, we constructed a circRNA-miRNA-mRNA regulatory network, including 15 DEcircRNAs, eight DEmiRNAs, and 39 DEmRNAs. FGF9 was identified as an immune-related hub gene. Immune cell analysis indicated a significantly higher level of neutrophils in IS, and the expression of FGF9 was significantly negatively correlated with the level of neutrophils. Eventually, miR-767-5p was predicted as the upstream molecules of FGF9, and circ_0127785 and circ_0075008 were predicted as the upstream circRNAs of miR-767-5p.Our study provides novel insights into the molecular mechanisms governing the progression of IS from the perspective of immune-related ceRNA networks. Stroke is the primary cause of mortality and disability worldwide . AlthougCircular RNA (circRNA) is a highly conserved and stable non-coding RNA with a circular structure that does not contain 5′ end caps and 3′ end poly (A) tails . CircRNAOur study conducted a comprehensive analysis of the IS microarray data from the Gene Expression Omnibus (GEO) database. Eventually, we identified a novel potential circRNA-miRNA-mRNA regulatory network in IS. In addition, we screened out immune-related hub genes in the ceRNA network and further constructed an immune-related ceRNA sub-network. Our study provides a novel perspective on the molecular mechanisms of ceRNA immunoregulation in IS.https://www.ncbi.nlm.nih.gov/geo/)  were downloaded from the Gene Expression Omnibus (GEO) database (ov/geo/) . The datp value <0.01 and |log2fold change| >1.2 to select DEcircRNAs from GSE195442. Next, the GSE117064 was analyzed with adjusted p value <0.05 and |log2fold change| >2 and set as the threshold for selecting DEmiRNAs. Finally, DEmRNAs were screened by the cut-off point of adjusted p value <0.05 and |log2fold change| >1 in GSE58294.DEcircRNAs, DEmiRNAs, and DEmRNAs were screened by the “limma” package in R software . The thrp-Values lower than 0.05 were considered statistically significant.The “clusterProfiler” package in R was applied for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis . p-Valuehttp://string-db.org) (A PPI network of the DEmRNAs was established by the STRING database (-db.org) . Interac-db.org) . Then, t-db.org) .https://circinteractome.nia.nih.gov/) (via the online tool – Venny 2.1.0 (https://bioinfogp.cnb.csic.es/tools/venny/index.html). Then, the target mRNAs of the overlapping miRNAs were predicted by the miRDB database (We predicted the target miRNAs interacted with the DEcircRNAs by the Circular RNA Interactome (CircInteractome) database (ih.gov/) . The predatabase , and theImmune-related genes (IRGs) were obtained from the ImmPort database . We intevia the “pROC” package in R  curves age in R . In addiThe relative expression of different immune cell subtypes in 46 samples (23 controls vs. 23 IS patients of 5 h onset) in GSE58294 was assessed with CIBERSORT in R . First, The flow diagram for the whole study is shown in Gene Ontology terms are classified into three main categories: biological processes (BP), cellular components (CC), and molecular functions (MF). The top three terms in the BP contain neutrophil activation, neutrophil degranulation, and neutrophil activation involved in immune response. The top three terms in the CC contain external side of plasma membrane, specific granule, and tertiary granule. The top three terms in MF contains: channel activity, ion channel activity, and glycosaminoglycan binding . The resThe PPI network of the DEmRNAs was constructed by the STRING database and imported into the Cytoscape software . AccordiA total of 253 potential target miRNAs of the DEcircRNAs were predicted in the CircInteractome database. Then, nine overlapping miRNAs were obtained from the intersection of predicted miRNAs and DEmiRNAs . Next, bTo further explore immune-related ceRNA regulatory relationships in IS, we obtained immune-related genes (IRGs) in the ImmPort database and intersected the IRGs with mRNAs in the ceRNA network and the hub genes of DEmRNAs . As a rep < 0.001; We analyzed the relative expression of 22 immune cells in each of the included samples of GSE58294 through CIBERSORT, and the result showed that 17 kinds of immune cells were expressed in the included samples. The distribution of immune cell subtypes in each sample is shown in Stroke carries a severe burden on society and the economy . IS is tNumerous studies revealed that abnormal expression of circRNAs is involved in various diseases . Recent In this study, we identified 35 DEcircRNAs, 141 DEmiRNAs, and 356 DEmRNAs in the expression profiles from the GEO database. Then, we screened circRNA-miRNA interaction pairs through the CircIteractome database and miRNA-mRNA interaction pairs through the miRDB database. By intersecting the predicted miRNAs and DEmiRNAs, and intersecting the predicted mRNAs and DEmRNAs, were finally identified a potential circRNA-miRNA-mRNA regulatory network in IS, which contained 15circRNAs (eight up-regulated and seven down-regulated), eight miRNAs (five up-regulated and three down-regulated), and 39 mRNAs (21 up-regulated and 18 down-regulated).To understand the function of DEmRNAs in IS, we performed the Gene Ontology and KEGG pathway enrichment analysis. As a result, neutrophil activation was significantly enriched in the BP module of GO, and cytokine–cytokine receptor interaction was significantly enriched by KEGG pathway enrichment. Enrichment results confirm the reliability of DEmRNAs, as numerous studies have suggested that the inflammatory response has an essential role in the various phases after the onset of IS, which affects the prognosis and treatment of stroke . Thus weThe CIBERSORT algorithm revealed a significant increase in the level of neutrophils after the onset of IS, consistent with previous studies. Studies indicated that properties of circulating neutrophils significantly increased after IS, and the neutrophil alterations were notably correlated with the IS severity . An elevInterestingly, certain recent studies indicated the potential role of FGF9 in IS. It has been reported that long non-coding RNA SNHG7 can mediate up-regulation of FGF9 to alleviate the oxygen and glucose deprivation-induced neuron cell injury . DownregWe identified a potential circRNA-miRNA-mRNA regulatory network in IS, including 15 DEcircRNAs, eight DEmiRNAs, and 39 DEmRNAs. In addition, circ_0127785/ circ_0075008/miR-767-5p/FGF9 was identified as an immune-related regulatory sub-network of IS. Our study provides new insights into the pathological processes of IS mediated by circRNA, and the current findings require validation in the future.The original contributions presented in the study are included in the article/SC designed the study and wrote the manuscript. YZ assisted in analyzing the data and revising the manuscript. MC and WO critically read and edited the manuscript. All authors contributed to the article and approved the submitted version.This study was supported by the National Natural Science Foundation of China (No. 81971028).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Background: Prior studies have reported inconsistent results or less well-explored associations between sex hormones and non-alcoholic fatty liver disease (NAFLD). Here, we aimed to investigate the associations of NAFLD with sex steroids and sex hormone-binding globulin (SHBG) in the population-based study and conduct a comprehensive systematic review and meta-analysis of all published observational studies. Methods: Analyses included 755 men and 1109 women with available data on sex steroids, SHBG, and ultrasound-based NAFLD from the Rotterdam Study. Multivariable regression models were used to examine the associations. Additionally, we searched five databases from inception to 1 April 2022 and performed a systematic review and meta-analysis. Random-effects (DerSimonian-Laird) method was used for meta-analysis, odds ratios (ORs) were calculated for the effect estimate, subgroup and leave-one-out sensitivity analyses were conducted, and meta-regression was performed to explore the pooled statistics with high heterogeneity. Results: In the Rotterdam Study, lower levels of SHBG were associated with NAFLD in both sexes, while lower testosterone was associated with NAFLD only among women. Similarly, the meta-analysis of 16 studies indicated no sex-specific association between SHBG and NAFLD , yet there was a sex-specific association between testosterone and NAFLD . Moreover, men with NAFLD had lower estradiol levels than those without NAFLD. Conclusions: Lower SHBG levels were associated with NAFLD in both sexes, but testosterone levels were associated in a sex-specific manner. In addition, our results showed estradiol with the potential as a protective factor for NAFLD in healthy men. Non-alcoholic fatty liver disease (NAFLD) is the most common cause of chronic liver disease worldwide with a substantial health care burden . NAFLD iPrior studies indicated that higher levels of testosterone and sex hormone-binding globulin (SHBG) in middle-aged males are associated with a reduced risk of metabolic syndrome  and womeThe aim of this study was to explore the association of NAFLD with circulating sex steroids  and SHBG in the prospective population-based Rotterdam Study. Additionally, we performed a comprehensive systematic review and meta-analyzed all published observational studies on sex hormones and NAFLD among adults. N = 3932). We excluded 626 participants from the first visit of the third cohort (RS-III-1) because of missing data for sex steroids and SHBG, leaving 3306 eligible participants. After a median follow-up period of 5.6 years, 816 participants who did not undergo abdominal ultrasound due to diet or moving to other cities, 200 participants who did not provide information about alcohol consumption, and 426 individuals who had excessive alcohol consumption, were stetaogenic drug users or had viral hepatitis were excluded from the study in the second visit of the third cohort (RS-III-2). Finally, 1864 participants (755 men and 1109 women) were included in the analysis (The current study was embedded within the framework of the Rotterdam Study (RS), a prospective cohort study of individuals aged ≥40 years living in the Ommoord district of Rotterdam, the Netherlands. The objectives, design, and methods of the Rotterdam Study have been described in detail elsewhere . For thianalysis . Follow-Abdominal ultrasound was performed by a certified and skilled technician using Hitachi HI VISION 900. Images were stored digitally and re-assessed by a single hepatologist with more than 10 years of experience in ultrasonography. Diagnosis of hepatic steatosis was determined dichotomously as the presence of hyperechogenic liver parenchyma according to the protocol by Hamaguchi et al. . NAFLD wTM MSMS Steroids Kit . The inter-assay CVs of DHEA and DHEAS were <6.5%.All blood samples were drawn in the morning (≤11 a.m.) after fasting. Total estradiol levels were measured with a COBAS 8000 Modular Analyzer (Roche Diagnostics GmbH). The minimum detection limit for total estradiol was 18.35 pmol/L. Undetectable estradiol was scored as 18.35 pmol/L. Serum SHBG with the Immulite platform  and a lower limit detection of the assays was 0.02 nmol/L. Serum levels of total testosterone were measured with liquid chromatography-tandem mass spectrometry (LC-MS/MS) with a corresponding inter-assay of less than 5% and a lower limit of qualification of 0.07 nmol/L. The corresponding inter-assay coefficients of variations (CVs) for total estradiol, SHBG, and total testosterone were <7%, <5%, and <5%, respectively. Serum DHEA and DHEAS were measured on a Waters XEVO-TQ-S system  using CHSAssessment of covariates and statistical analysis in the Rotterdam Study were described in detail in The study was conducted using a predesigned protocol (which was not registered on online platforms) and following the published guidelines on how to perform a systematic review and meta-analysis , and wasStudies were eligible for inclusion if they (i) were cross-sectional, case-control, or cohort studies; (ii) assessed sex hormones ; (iii) were conducted with participants aged ≥18 years and without comorbidity of NAFLD; (iv) collected data on NAFLD, and; (v) reported the association of any of the above-mentioned sex hormones with NAFLD. We screened the retrieved titles and/or abstracts and selected eligible studies according to the predefined selection criteria . The fulhttps://jbi.global/critical-appraisal-tools (accessed on 1 July 2021)). Study quality was assessed based on these items: the selection criteria of participants, comparability of cases and controls, and exposure and outcome assessments. The NOS assigns a maximum of 4 points for selection, 2 points for comparability, and 3 points for exposure or outcome. Nine points on the NOS reveal the highest study quality , lower SHBG levels , and lower DHEA levels  were associated with NAFLD. Following further adjustment for hypertension, T2D, HDL, and triglycerides in model 2, only lower SHBG levels  were significantly associated with NAFLD. Furthermore, when we adjusted for cardiometabolic risk factors, the association of total testosterone and DHEA with NAFLD was no longer statistically significant. No associations were found between total estradiol, DHEAS, and NAFLD in any of the models. All associations mentioned above remained significant after applying a strict Bonferroni correction for 5 tests , except the association between DHEA and NAFLD among men in the basic model , p trend = 2.23 × 10−16] were associated with NAFLD. In model 2, lower SHBG levels  were associated with NAFLD. There was no association between total estradiol, DHEA, DHEAS, and NAFLD in any of the models. The associations between SHBG levels and NAFLD remained significant after applying a strict Bonferroni correction for 5 tests .Among women  in the bIn the sensitivity analyses, after adjustment for waist circumference, use of exogenous hormones, or lipid-lowering medication, we did not observe any significant change in the identified associations of sex steroids and SHBG with NAFLD in men  and womeAs shown in 2 = 93%, p-value < 0.01) among men and 1.06  among women  among men and 0.40  among women  among men , the details are shown in training.cochrane.org/handbook (accessed on 8 July 2021).We performed a subgroup analysis of included studies for testosterone with NAFLD among men by continents, diagnostic methods, study size, and study design . In addip-value = 0.011)  . This su* Quality assessment based on the Newcastle-Ottawa Scale. Range 0 to 9, a higher score is higher quality. Abbreviations: FLD, fatty liver disease; ALD, alcoholic liver disease; NA, not available; NAFLD, non-alcoholic fatty liver disease; NASH, Non-alcoholic steatohepatitis; MAFLD, metabolic dysfunction-associated fatty liver disease; BMI, body mass index; LDL, low-density lipoprotein; TG, triglycerides; HOMA-IR, homeostatic model assessment for insulin resistance; WHR, waist-to-hip ratio; ALP, alkaline phosphatase; DHEAS, testosterone, and dehydroepiandrosterone sulfate levels; HDL, high-density lipoprotein; UA, uric acid; ALT, alanine aminotransferase; MAP, mean arterial pressure; FPG, fasting plasma glucose; γGT, γ-glutamyl transpeptidase; CRP, C-reactive protein; hs-CRP, high-sensitivity C-reactive protein; VAT, variance inflation factors; ESR, erythrocyte sedimentation rate. FBG, fasting blood glucose; HBeAg, hepatitis B e antigen.In this study, our results showed that people with NAFLD had lower levels of SHBG than people without NAFLD in the Rotterdam Study. The pooled results from the meta-analysis of 16 studies reinforced our finding, suggesting that lower SHBG levels could be associated with NAFLD, however, not in a sex-specific manner. Moreover, men with NAFLD had lower testosterone levels than those without NAFLD, while lower testosterone levels were inversely associated with NAFLD among women. In addition, the pooled results also showed that men with NAFLD had lower estradiol levels than controls. Findings from previous studies reported that the incidence and severity of NAFLD increased among older women, particularly postmenopausal women ,50. EstrTo date, it remains unclear which exact molecular mechanisms underlie the sex-specific associations between testosterone and NAFLD. Some studies have shown that men with simple steatosis or advanced NAFLD had lower testosterone levels than healthy controls of the same age ,54. MoreInterestingly, we observed in our cohort that women with NAFLD had lower testosterone levels than those without NAFLD, while in the meta-analysis, women with NAFLD had higher testosterone levels than those without. In the study performed by Yim et al., NAFLD was diagnosed by ultrasound fatty liver index and liver enzymes. Among the same number of participants, the overall prevalence of suspected NAFLD was different . We included twice the Yim et al. study based on different diagnostic methods and with the different OR and CI, so it is displayed with different weights. We only included seven studies on testosterone and NAFLD for women in the meta-analysis, further research should investigate the levels of testosterone that may contrition to the development of NAFLD.SHBG has traditionally been considered to function as a transporter of sex steroids, controlling circulating free hormone concentrations . ConsistAMPK-PGC-1α-NRF-1 and IRS1-AKT-GLUT2 signaling pathways [n = 44), found that more advanced NAFLD, as indicated by the presence of NASH with advanced fibrosis stage, is strongly associated with low circulating levels of DHEAS [A study on animal models has shown that the protective effect of DHEA on high-fat-induced hepatic glycolipid metabolic disorder and insulin resistance might be achieved through the activation of the pathways . Anotherof DHEAS . In the Our study has several strengths. First, it is embedded within the Rotterdam Study which has a prospective design and we included a comprehensive assessment of sex steroids and SHBG. Second, we were able to perform adequate adjustments for a broad range of possible confounders. We also conducted several sensitivity analyses and investigated the association of sex steroids and SHBG with not only NAFLD but also MAFLD (the new definition of NAFLD). Additionally, our study used the LC-MS/MS method to measure serum total testosterone levels that are specific, accurate, and suitable in routine clinical practice and offer advantages over immunoassays regarding lack of interference . Third, 2 resulted when leaving out the N. Wang et al. study by the leave-one-out sensitivity analysis. The study size was the most important factor among these factors by multivariable meta-regression.However, some limitations of our study need to be addressed here. On one hand, most eligible studies had cross-sectional study designs, as such, the ability to assess causality or temporality was limited. Moreover, in the Rotterdam Study, total estradiol was measured by using an immunoassay with a detection limit of 18.35 pmol/L, which is considered suboptimal, especially in men and postmenopausal women. To address this issue, we performed logistic regression by estradiol tertiles and estradiol continuous data, which provided similar results. On the other hand, most of the included studies in the meta-analysis used ultrasonography to diagnose NAFLD. Although this imaging technique is widely accepted as the diagnostic method of screening NAFLD due to its low cost, safety, and accessibility, it has limited accuracy in detecting mild steatosis and operator dependency. Finally, significant heterogeneity was observed in the meta-analysis and the possible sources of these heterogeneities include the differences in the continents, methodology, study size, and study design, as demonstrated by the subgroup analyses, and the biggest changes in the recalculated OR and IIn this study, we observed that lower SHBG levels were associated with NAFLD in both sexes. In addition, men with NAFLD had lower levels of testosterone, and women with NAFLD had higher testosterone levels than non-NAFLD controls. Moreover, our results showed that estradiol may play a potential protective role in the progression of NAFLD among healthy men. Future research should involve larger sample sizes and longitudinal designs to confirm the association of sex steroids and SHBG with NAFLD and uncover underlying molecular mechanisms by which sex hormones may protect against NAFLD. Inconsistent findings across different studies included in this study may be a consequence of variations in study protocols  and the characteristics of participants . Further well-designed studies are needed to clarify which type and dose of hormone medication used may affect the sex hormone levels, time since menopause onset in women, metabolic status, and standardized methods to assess sex hormones and NAFLD to account for investigating the associations between sex steroids, SHBG, and NAFLD.


Background: Senescence, as an effective barrier against tumorigenesis, plays a critical role in cancer therapy. However, the role of senescence in colorectal cancer (CRC) has not yet been reported. This study aimed to build a prognostic signature for the prognosis of patients with CRC based on senescence-related genes.Methods: A prognostic signature was built from TCGA based on differentially expressed senescence-related genes by the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses, which were further validated using two Gene Expression Omnibus (GEO) cohorts. The CIBERSORT and ssGSEA algorithms were utilized to analyze the infiltrating abundance of immune cells. The relationship of signature with the immune therapy and the sensitivity of different therapies was explored.Results: We found 93 genes associated with senescence that were differentially expressed. Based on expression and clinical parameters, we developed a senescence-related prognostic signature and its effectiveness was verified using two external validation cohorts. Overall survival was predicted using a prognostic nomogram that incorporated the predictive values of the risk score and clinical traits. Additionally, the risk score was significantly correlated with immune cells infiltration, tumor immune microenvironment (TME) score, immune checkpoints, immunotherapeutic efficacy, and chemotherapy sensitivity.Conclusion: The senescence-related prognostic model can well predict the prognosis, immunotherapeutic response, and identify potential drug targets, which can help guide individualized treatment. Colorectal cancer (CRC) is one of the most common malignant tumors. Its incidence rate ranks third in the world and the mortality rate is ranked second . There wCellular senescence is defined as a permanent state of cell cycle termination. It is a response to endogenous and exogenous stresses, including DNA damage, telomere dysfunction, oncogene activation, and organelle stress, and is associated with processes such as tumor suppression, tissue repair, embryogenesis, and organ aging . The curIn this study, for the first time, we established a prognostic signature based on differentially expressed senescence-related genes (DEGs) and verified its accuracy in two external databases. Following that, we developed a nomogram to predict the OS of patients with CRC. In addition, we investigated the prognostic value, and impact on tumor immune infiltration, immune checkpoint expression, immunotherapy, and chemotherapeutic drug sensitivity of senescence-related genes in HCC.https://genomics.senescence.info/cells/signatures.php?) and were listed in A total of 279 senescence-related genes were collected from CellAge database | > 1.5. To screen senescence-related genes with prognostic significance, a univariate Cox regression analysis was conducted on DEGs. Following that, the Least absolute shrinkage and selection operator (LASSO) and multivariable Cox analysis was performed to build a predictive signature. The following formula was employed to calculate the risk scores of CRC samples:The training cohort was employed to detect the senescence-related DEGs between normal and CRC tissues via the R package “limma” in RStudio, with the following cutoff for adjustment: “Coef”, “exp”, and “n” represented the coefficient of the gene, the expression level, and the number of genes, respectively. The median risk score was used as the threshold. Patients with risk scores greater than the threshold (median risk score) were included in the high-risk group and the rest in the low-risk group. Receiver operating characteristics (ROC) and Kaplan-Meier survival curves were employed to assess the effectiveness of the signature. Principal component analysis (PCA) was conducted to verify whether the risk score could distinguish high- and low-risk score groups.Two GEO validation cohorts were recruited to vertify the predictive accuracy of the model developed from the TCGA set. The above cut-off value was used to divide all CRC patients into high- and low-risk score groups, the same method was employed for the predictive power of the signature in OS prediction.We explored the risk score with the corresponding CRC samples’ clinical information, including age, gender, tumor site, and TNM stage. Additionally, we also explored whether the risk levels would affect the prognosis of patients in distinct clinical variable groups. Univariate and multivariate models were employed to ascertain whether the signature could be an independent predictive indicator for the prognosis of CRC patients. Then, a nomogram integrating risk score and clinical parameters was built using the “rms” R packages. ROC and calibration curves were employed to validate its accuracy is demonstrated.The ssGSEA algorithm was used to quantify the scores of 16 tumor immune infiltration cells (TIICs) and the function of 13 immune-related pathways. The proportion of 22 TIICs in two risk score groups was further quantified with CIBERSORT algorithm. The immune score, stromal score, ESTIMATE score were calculated through ESTIMATE algorithm to quantify the tumors microenvironment . Two immIn this study, the capability of the signature in predicting sensitivity of chemotherapy and immunotherapy was investigated. In the aspect of chemotherapy, half maximal inhibitory concentration (IC50) was used to predict the sensitivity of chemotherapy drugs in the high- and low-risk groups. Meanwhile, potential immune checkpoint inhibitors (ICIs) response was predicted with TIDE algorithm .p-value and Q-value < 0.05 were the cutoff criterion for significant gene enrichment.To investigate the biological pathways of the subgroups, we further generated a gene set enrichment analysis (GSEA) for functional enrichment analysis. Gene sets with Data were analyzed by R software version 4.1.0. Log-rank test was used for survival analysis. Wilcoxon rank-sum or Kruskal–Wallis tests were utilized to compare differences between two or three groups, respectively. The ROC curves were plotted to access the prognostic value of the model.Among 279 senescence-related genes, 93 DEGs in CRC patients of the TCGA cohort were identified with FDR <0.05 and FC > 1.5. Volcano and heatmap representations of senescence-related DEGs are provided in To explore whether these senescence-related genes are related to the prognosis of CRC, univariate COX regression analysis was applied. Based on the TCGA cohort, 15 genes were identified . As showp < 0.001, In the training cohort, the Kaplan-Meier analysis revealed that high-risk group had lower OS compared to low-risk group  using the same formula, and divided patients into the high- and low-risk subgroups according to the cutoff of the training cohort. Likewise, high-risk was associated with OS , and thep < 0.01; p < 0.001, In our study, we analyzed the prognosis of patients in low- and high-risk groups among distinct clinical variable subgroups. As shown in An approach by which 3- and 5-years OS rates could be more accurately predicted was to construct a nomogram model based on Cox regression results , which i+ T cells and NK cells was obviously lower in the high-risk group than that in the low-risk group. On the contrary, the fraction of macrophages and T helper cells were much lower in the low-risk group. We also found substantial variations in immune function in terms of T cell co-stimulation, type I IFN response, and type II IFN response , dasatinib, and imatinib -L.p < 0.05. The top five signaling pathways in the high-risk group were axon guidance, complement and coagulation cascades, ECM receptor interaction, focal adhesion, and hematopoietic cell lineage  is a key structural component of caveolae and plays an important role in a variety of cellular processes including cholesterol homeostasis, vesicle transport, and tumor progression . CAV1 haFurthermore, all CRC patients were categorized into low- and high-risk subgroups depending upon the median value. Internal and external validation results showed that risk scores independently and effectively predicted 3- and 5-years survival in CRC patients. We also conducted univariate and multivariate Cox analyses to explore the effectiveness of the signature and clinical parameters as indicators of patient prognosis. It was concluded that the risk score served as an independent prognostic predictor for CRC patients. To better quantify the 3- and 5-years survival of CRC samples, a nomogram, combined with these independent indicators, was constructed. The predictive accuracy of the nomogram was verified by the ROC curve and calibration plot. Therefore, it may be used as a supplementary tool to better assist the prognosis evaluation and treatment of CRC.+ T cells tended to be downregulated, while the relative contents of macrophages tended to be upregulated. This discovery is in line with prior research that intratumoral T cell density has been shown to be an independent prognostic factor in CRC (+ T cells are considered major drivers of anti-tumor immunity (We calculated the infiltration of immune cells and TME scores in the high-and low-risk groups. The ssGSEA and CIBERSORT results showed the risk score was closely related to the relative contents of TIICs, especially for T cells and macrophages. And with the increase of the risk score in the prognostic signature, relative contents of CD8r in CRC . CD8+ T immunity . Accumulimmunity . Tumor-aimmunity . Tumor-aimmunity . Also, pEmerging therapeutic strategies, including PD-1/PD-L1 inhibitors, are used for treating CRC . In our This study successfully constructed a 5-gene senescence-related signature that could be used to classify CRC patients. The prognostic model shows the convincing clinical value and may provide new ideas for improving the OS rate of CRC patients and facilitating personalized treatment.


Lipids are central at various stages of host–pathogen interactions in determining virulence and modulating plant defense. Free fatty acids may act as substrates for oxidizing enzymes  that synthesize oxylipins. Fatty acids and oxylipins function as modulators of several pathways in cell-to-cell communication; their structural similarity among plant, fungal, and bacterial taxa suggests potential in cross-kingdom communication. We provide a prospect of the known role of fatty acids and oxylipins in fungi and bacteria during plant–pathogen interactions. In the pathogens, oxylipin-mediated signaling pathways are crucial both in development and host infection. Here, we report on case studies suggesting that oxylipins derived from oleic, linoleic, and linolenic acids are crucial in modulating the pathogenic lifestyle in the host plant. Intriguingly, overlapping (fungi-plant/bacteria-plant) results suggest that different inter-kingdom pathosystems use similar lipid signals to reshape the lifestyle of the contenders and occasionally determine the outcome of the challenge. The teres Blée,  refers tes Blée, , which cFusarium spp.) but also as attractors of biocontrol agents (such as Trichoderma spp.) , its precursor 12-oxo-phytodienoic acid (12-OPDA), and JA derivatives such as methyl jasmonate and JA-isoleucine. JA and its derivatives act as defense regulators, influencing the reproductive and pathogenetic processes during the interaction with both beneficial and pathogenic microorganisms  and their interspecific interactions  from where they may be cleaved. Free FAs are diversified by length and degree of unsaturation. PUFA availability is crucial for the cell because, based on the degree of saturation, they alter cell membrane fluidity, the arrangement and availability of receptors, influencing signal transduction regulation. In this regard, fatty acids may act directly on the receptors , a series of fatty acid-derived oxylipins involved in the regulation of the development of spores and conidia  or a consequence of enzymatic activity. The study of oxylipins in fungi followed the discovery of the role of lipid hydroperoxides in Gaeumannomyces graminis, the causal agent of root and crown rot diseases. G. graminis produces oxylipins via FA oxidation with LOXs, DOXs [including the transformation of linoleic acid into dihydroxy-linoleate by 8-DOX ], and cyclooxygenase (COX) , and production of volatile organic compounds. LOX1 in T. atroviride is required during the interaction with the host plant, particularly in Arabidopsis thaliana, to induce systemic resistance against the plant-pathogenic fungus Botrytis cinerea  is largely conserved among filamentous fungi; the synthesis of diol-containing oxylipins, primarily 5,8- and 7,8-diHODE, might represent a crucial step in the organization of fungal “morphology”  and even “escape” from antifungals  increases sporulation and mycotoxin production. In Aspergillus deletion mutants for the DOX ppoA and ppoC, the wild-type phenotype can be restored through the insertion of maize lipoxygenase, suggesting a complementing activity. Inoculation of peanut seeds with Aspergillus ppo-mutants reduces LOX expression in seed, suggesting that fungal oxylipins are related to the plant LOX expression  deletion alters F. verticillioides fitness, decreasing conidiation and fumonisin B1 synthesis , DSF-based QS model promotes biofilm formation and stickiness, determining (a) degradation of pit membranes to enable cross-vessel diffusion in the xylem; (b) twitching motility of bacterial cells; and (c) adhesion to the xylem surface and the switch from the planktonic endophytic lifestyle to the sessile insect-acquisition stage . Lipids play important roles in bacterial cell-to-cell communication by regulating quorum sensing (QS) and in the interactions with the host and the vector   , with different intra/extra-cellular distribution. LOX enzyme-derived oxylipins  are less represented  represents one of the first studies based on the cross talk mediated by the oxylipins during plant–bacteria interaction. The pre-treatment of plant with 9-LOX- and α-DOX-oxylipins from linoleic acid activated SAR and protected plant tissues against infection through a JA-independent signaling pathway . The study of oxylipin metabolism in the X. fastidiosa pathosystem was investigated also in Olea europaea. Symptomatic trees of the susceptible olive cultivar Ogliarola salentina accumulate ten lipid compounds that should be defined as hallmarks of OQDS  and oxylipins to trigger systemic invasion, limiting biofilm formation and acquisition by insect vectors.Details of the oxylipin-mediated communication between plant and phytopathogenic bacteria were provided by several studies on  Rogers, ; Nicotiat vector . LOX accvs. susceptibility and that plant on one side and bacteria on the other try to adapt to the oxylipin signature formed during their interaction to exploit the weaknesses of the opponent.These studies suggest that oxylipins have a significant role in determining the fate of the interactions: Resistance Lipid-mediated signal communication is an issue ongoing with numerous implications in plant–fungi and plant–bacteria disease control. This review focused primarily on oxidized lipids, the oxylipins, implicated in host–pathogen interactions, notably when the plant is exposed to fungal or bacterial disease. In plants, the oxylipins—jasmonates apart—are mainly involved in the defense against pathogens; in pathogens, a dual role emerges: oxylipins are produced as autocrine signals to mediate the development and differentiation , and paracrine signals to entertain a communication during plant diseases. The LOX pathway is the best characterized in plants and seems to be crucial for communicating with both fungi and bacteria, probably antagonizing and modulating the pathogen's response. LDS pathway seems to be crucial for the pathogen invasion . The identification of oxylipins common or structurally similar in plants, fungi, and bacteria consolidates the theory of cross-kingdom communication. We can suggest that the ability to “recognize and react” to specific oxylipins may drive the fate of the interaction for the host: susceptibility vs. resistance.Despite numerous findings regarding the specific oxylipins that are exchanged during the interactions, many questions remain open. One of these concerns the understanding of the receptor and transduction system of oxylipins that remains still unclear.MB, MR, and VS: conceptualization. MSa, MB, and NP: methodology. MR, MSc, and SL: investigation and funding acquisition. MB and VS: writing—original draft. MB, VS, BM, MZ, MSc, NP, and SL: writing—reviewing and editing. MB: visualization. MR and VS: supervision. All authors contributed to the article and approved the submitted version.This study was funded by MIPAAFT, Project Oli.Di.X.I.It , D.M. 23773 of 6/09/2017, Project SALVAOLIVI , D.M.33437 of 12/21/20. MIUR National Operational Program Research and Innovation 2014-2020 (PON RI 2014-2020) Development of Nutraceuticals from Natural Sources - ARS01_01166. PUT National Operational Program “Enterprises and Competitiveness” 2014–2020 ERDF, UR - Development of new technologies in precision agriculture for the sustainable production of potato genotypes with high nutritional qualities (Acronym SOS TATA), no. F / 200088 / 01-03 / X45.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Furthermore, a sub-group analysis based on the internal organization order of CT revealed that there was no statistically significant subgroup effect between CT and ST alone in all parameters. Conclusions: Well-designed CT regimens did not interfere with the development of physical fitness of trained team sports players.Background: Concurrent strength and HIIT-based endurance training (CT) has merit in time-saving in team sports. However, the effect of CT on physical fitness remained equivocal. This meta-analysis aimed to determine whether CT would produce an interference effect on the development of physical fitness when compared to strength training (ST) or HIIT-based endurance training (HET) alone in trained team sports players. Methods: A total of 2478 studies from three databases were screened. 52 full texts were reviewed. Seven studies were finally included and then subgroups were used for quantitative analysis. Results: Compared to ST alone, CT had a significant effect on the development of maximal lower-body strength in trained team sports players (MD 4.20 kg, 95% CI 0.71–7.68,  The most common team sports, such as soccer, rugby, American football, basketball, and handball are based on strength and endurance. Players need to develop multiple aspects of physical fitness to achieve the desired athletic performance in these sports. This necessitates players developing a well-rounded fitness to meet the physical demands of match-play. Within these criteria of physical fitness, maximal lower-body strength, power, and aerobic capacity are the most important determinants of athletic performance . In a 902max or VO2peak and Yo-Yo test performance [Since Robert C. Hickson first conducted concurrent training studies in 1980, an extensive body of research has been produced. Some studies have shown that concurrent training can potentiate the individual effects produced by ST and ET more than ST or ET alone ,15,16,17formance ,12,35,36formance ,13,37,38formance ,42,43,44Given the necessity for trained team sports players to simultaneously develop strength and endurance, as well as the popularity of HIIT-based endurance training in team sports. There is a need for a systematic review and meta-analysis to draw conclusions from the inconsistencies. The purpose of this study was to determine whether concurrent strength and HIIT-based endurance training (CT) would produce an interference effect on physical fitness development compared to ST or HIIT-based endurance training (HET) alone in trained team sports players. The hypothesis was as follows: ST carried out first in CT with an adequate interval time between ST and HET would not induce an interference effect on physical fitness development. Our findings will enhance understanding regarding the application of CT in team sports and assist strength and conditioning professionals to design better CT regimens.This systematic review and meta-analysis was performed in line with the recommendations of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis statement (PRISMA).A search from 1980, the year that seminal research relating to concurrent training was published, to and including June 2022 was carried out using the following electronic databases: Web of Science, PubMed, and ScienceDirect. The search strategy used the following Boolean search syntax: “(concurrent training or concurrent exercise or combined training or concurrent strength and endurance training) and .” The search strategy is presented in 2max/peak or Yo-Yo test. These indicators represented lower-body maximum muscle strength, maximum immediate muscle power, and aerobic capacity, respectively; (5) randomized controlled or matched trials with at least 5 weeks of follow-up duration; (6) the original study.According to the Cochrane Collaboration methods, the PICOS  approach was used to build inclusion criteria for this systematic review and meta-analysis: (1) cohorts of team sports players aged >13 with no restriction for gender; (2) studies needed to have incorporated a concurrent training regimen, containing at least one group that followed single-mode ST/ET; (3) means and standard deviations of one or more outcome measures had to be reported for all groups pre- and post-intervention; (4) outcome measures included one or more of the following, lower-body 1-repetition maximum(1RM) of half squat, countermovement jump (CMJ) or squat jump (SJ), VOTwo authors (X.Y. and Z.Y.) independently conducted data extraction from the included studies using an electronic data sheet. Data extracted comprised participant characteristics , study characteristics , and outcome measures. Discrepancies about study conditions were discussed until a consensus was reached with a third author (B.G.).T, NC, and SDT, SDC represent the sample size in the experiment group and control group as well as the standard deviation of their responses, respectively. According to Morris [T and SDC here use the standard deviation of the pretest mean of each group.Though included studies reported multiple outcome measures, only those that were relevant to the meta-analysis were extracted. The differences in means in each group were calculated for each study using a comparison of mean change from pre- to post-intervention. The standard deviation (SD) of the mean change was also calculated and used to generate forest plots with study-specific point estimates and respective 95% confidence intervals (CIs). SD of the difference of the means was computed using the following formulation according to Deeks and Higgins : (1)SDcho Morris , the SDTp ≤ 0.05. SMDs were also used to create funnel plots so that all estimates could be placed into one funnel plot. For each study, if the same measures were applied to the same outcome, pooled mean difference (MD) was calculated in Review Manager software . If not, between-group standardized mean differences (SMD) were calculated in Review Manager software using Hedges’ adjusted g, which is corrected for sample size. Analysis of the pooled data was conducted with a random-effects model, where weighting was based on inverse variance. According to Cohen, the overall effect size was interpreted as trivial , small , moderate , or large , respectively. Statistical significance was considered for The Physiotherapy Evidence Database (PEDro) scale was used to rate the risk of bias and methodological quality of trials of the included studies. This rating scheme is reported as valid and reliable . It quan2 statistics for each outcome and interpreted as low, moderate, and high, corresponding to an I2 statistic of 25%, 50%, and 75%, respectively, according to Higgins et al. [Heterogeneity between studies was assessed using Is et al. . Furtherhttp://www.getdata-graph-digitizer.com/, accessed on 16 July 2022). In most studies the ET protocol was mainly repeated-sprint training (RST) and/or sprint interval training (SIT) HIIT models; the ST protocol included combined free weights, machine resistance, circuit resistance training, and plyometrics to maintain and develop maximum strength levels while further improving explosive strength. The intervention duration ranged from 5 to 12 weeks, and two to three times per week. More details can be found in Through systematic database searching, 1365, 921, and 671 relevant articles were initially identified in PubMed, Web of Science, and ScienceDirect, respectively. After duplicates were identified via Endnote software  and screening was performed based on the title and abstract, 52 articles remained. Through a manual search of the reference lists an additional nine studies were selected. After the initial full-text examination, 15 potential studies were further assessed. Further trial details of 15 studies were examined according to the eligibility criteria, and seven studies ,52,53,54This systematic review and meta-analysis aimed to compare the effect of CT versus single-mode ST/HET on lower-body strength, power, and aerobic capacity development in trained team sports players, as well as the influence of internal organization order of CT on training adaptations. Upper-body performance was not included in the primary outcome analysis, because the ET protocols within CT in this study were for the lower extremities.p = 0.02). The heterogeneity was low . In the random effects model, aggregated MD and 95% CI were 4.20 kg . Furthermore, a sub-group analysis on the internal organization order of CT revealed there was no statistically significant subgroup effect , suggesting that the internal organization order did not modify the effect of CT on the development of lower-body strength. However, a smaller number of trials and participants contributed data to the ETfirst sub-group than to the STfirst sub-group. The data are shown in Seven studies used 1 RM half squat as the outcome measure of lower-body strength. The results from 244 participants in the seven studies demonstrated that, compared to ST alone, CT had a significant positive effect on lower-body strength development in trained team sports players (p = 0.62). The heterogeneity was low . In the random effects model, aggregated effect size and 95% CI were 0.08 . A sub-analysis of the internal organization order revealed that there was no statistically significant subgroup effect , indicating that the internal organization order did not modify the effect of CT on power development. However, a smaller number of trials and participants contributed data to the ETfirst sub-group than to the STfirst sub-group. More research should be carried out to establish more robust conclusions. The data are shown in Six studies used CMJ or SJ as outcome measures of lower-body power. The six studies consisted of 225 participants and results suggested that there was no significant difference between CT and ST alone in the training adaptation of lower-body power of trained team sports players  it interferes with some indicators of explosive strength development, such as vertical jump  and angl2max of elite-level professional players [In terms of aerobic capacity, it was not able to perform a meta-analysis due to small sample sizes. However, the development of aerobic capacity may not be as susceptible to the so-called interference effect when HET combined with ST forms a concurrent training protocol according to available literature. Much evidence indicates that HIIT can be more effective for enhancing aerobic capacity compared with traditional continual endurance exercises of low-moderate intensity, long duration, and long-distance ,76,77,78 players . Another players . Based o players . Thus, iAlthough this novel study provides insight to better understand CT regimens of team sports, some limitations should be acknowledged. One of the main drawbacks of this study was the available data from the literature concerning the physiological effect of CT on physical performance in team sports players were limited. Therefore, we were unable to conduct analysis on a greater number of indicators to assist in understanding the topic. In addition, as concurrent training effects are multifactorial, training- and non-training-related variables were important considerations when interpreting the outcomes of concurrent training interventions ; it is pMore high-powered studies are needed to shed light on this intricate subject in team sports. Future studies should be well controlled and distinguish the training-related variables and non-training-related variables, especially in “real-world” daily routines of trainers and athletes, to classify whether any potentiating or interfering effect is caused by methodological factors or by a true underlying mechanism that we do not yet fully understand. Finally, studies of female athletes, especially high-level female players in team sports, should be enhanced so that any effect of sex on concurrent training adaptations can be determined.Despite the limitations of this systematic review and meta-analysis, these findings can be utilized by strength and conditioning professionals to better understand and design concurrent training regimens which can assist players to maximize training adaptations relative to the match-play needs of a team sport. Based on our results, it seems that the physical fitness gains of trained team sports players may be optimized by the use of HIIT-based ET protocols in place of traditional ET protocols in team sport concurrent training regimens. Specifically, regardless of the internal organization order of CT, CT appears to have a significantly positive effect on the development of lower-body maximal strength in trained team sports players compared to ST alone, and does not interfere with their lower-body power development. To sum up, such concurrent training regimens can be time-efficient training protocols for developing physical fitness in trained team sports players.