A review: simulation tools for genome-wide interaction studies.

Genome-wide association study (GWAS) is essential for investigating the genetic basis of complex diseases; nevertheless, it usually ignores the interaction of multiple single nucleotide polymorphisms (SNPs). Genome-wide interaction studies provide crucial means for exploring complex genetic interactions that GWAS may miss. Although many interaction methods have been proposed, challenges still persist, including the lack of epistasis models and the inconsistency of benchmark datasets. SNP data simulation is a pivotal intermediary between interaction methods and real applications. Therefore, it is important to obtain epistasis models and benchmark datasets by simulation tools, which is helpful for further improving interaction methods. At present, many simulation tools have been widely employed in the field of population genetics. According to their basic principles, these existing tools can be divided into four categories: coalescent simulation, forward-time simulation, resampling simulation, and other simulation frameworks. In this paper, their basic principles and representative simulation tools are compared and analyzed in detail. Additionally, this paper provides a discussion and summary of the advantages and disadvantages of these frameworks and tools, offering technical insights for the design of new methods, and serving as valuable reference tools for researchers to comprehensively understand GWAS and genome-wide interaction studies.

Smoking, Alcohol Consumption, and Risk of Arterial Stiffness: A Two-Sample Mendelian Randomization Study.

Background: While observational studies have demonstrated connections between cigarette smoking, alcohol consumption, and arterial stiffness, establishing a causal relationship has proven challenging because of potential confounding factors. To address this problem, we employed a two-sample Mendelian randomization approach. Methods: We selected genetic instruments for these risk factors from genome-wide association studies encompassing 3,383,199 individuals at the genome-wide significance level (p < 5 × 10 - 9 ). Arterial stiffness data were acquired from the UK Biobank, which included 127,121 participants. Our primary analysis utilized the inverse variance-weighted method to explore causality. To confirm our results' robustness, we conducted sensitivity analyses using Egger regression, the weighted median method, and Mendelian Randomization Pleiotropy RESidual Sum and Outlier (MR-PRESSO). Results: Our analysis revealed a significant association between genetic inclination to smoking initiation and an increase in the arterial stiffness index ( ß = 0.11; 95% confidence interval [CI], 0.06 to 0.16; p = 1.95 × 10 - 5 ). Additionally, there was a suggestive connection between genetically predicted number of cigarettes per day and the arterial stiffness index ( ß = 0.05; 95% CI, 5.25 × 10 - 4 to 0.10; p = 4.75 × 10 - 2 ). No causal relationships were observed between the genetically predicted age of smoking initiation, smoking cessation, or alcohol consumption and the risk of arterial stiffness index. Conclusions: This Mendelian randomization study indicates that smoking initiation is likely a causative risk factor for arterial stiffness. However, further research is needed to determine if the quantity of daily cigarettes directly contributes to arterial stiffness development. Regarding alcohol consumption, age of smoking initiation, and smoking cessation, there was insufficient evidence to establish causality.

Association of birth weight, childhood obesity, and age at menarche with the risk of ovarian dysfunction: A mendelian randomization study.

BACKGROUND: Observational studies have revealed associations between birth weight, childhood obesity, age at menarche, and ovarian dysfunction. However, these studies are susceptible to unavoidable confounding factors, leading to ongoing debates regarding their conclusions and making causal relationships challenging to infer. In light of these challenges, Mendelian randomization was employed in this study to investigate the causal relationships between birth weight, childhood obesity, age at menarche, and ovarian dysfunction. METHODS: This study employed a two-sample Mendelian randomization approach using genetic variation as instrumental variables to investigate causal relationships. Genetic variation data were sourced from summary data of genome-wide association studies in European populations. Instrumental variables were selected based on the principles of Mendel's three assumptions. The study utilized the inverse variance weighted method to assess the relationships between birth weight, childhood obesity, age at menarche, and ovarian dysfunction. Supplementary analyses were conducted using MR-Egger regression, the weighted median method, and the weighted median mode to complement the IVW results. Furthermore, the study conducted heterogeneity, horizontal pleiotropy, and sensitivity analyses to evaluate the robustness of the results. RESULTS: Based on the inverse variance weighted method, it was found that there exists a causal relationship between childhood obesity (OR = 1.378, 95% CI: 1.113∼1.705, p = 0.003), age at menarche (OR = 0.639, 95% CI: 0.468∼0.871, p = 0.005), and ovarian dysfunction, while no causal relationship was observed between birth weight and ovarian dysfunction. Heterogeneity tests, multiplicity tests, and leave-one-out sensitivity analyses did not detect any heterogeneity or multiplicity effects in the estimated impact of these three exposure factors on the risk of ovarian dysfunction. CONCLUSIONS: This study represents the first evidence suggesting a potential causal relationship between childhood obesity, age at menarche, and ovarian dysfunction. Childhood obesity was found to increase the risk of ovarian dysfunction, while a later age at menarche was associated with a reduced risk of ovarian dysfunction.

M3HOGAT: A Multi-View Multi-Modal Multi-Scale High-Order Graph Attention Network for Microbe-Disease Association Prediction.

Numerous scientific studies have found a link between diverse microorganisms in the human body and complex human diseases. Because traditional experimental approaches are time-consuming and expensive, using computational methods to identify microbes correlated with diseases is critical. In this paper, a new microbe-disease association prediction model is proposed that combines a multi-view multi-modal network and a multi-scale feature fusion mechanism, called M3HOGAT. Firstly, a microbe-disease association network and multiple similarity views are constructed based on multi-source information. Then, consider that neighbor information from disparate orders might be more adept at learning node representations. Consequently, the higher-order graph attention network (HOGAT) is devised to aggregate neighbor information from disparate orders to extract microbe and disease features from different networks and views. Given that the embedding features of microbe and disease from different views possess varying importance, a multi-scale feature fusion mechanism is employed to learn their interaction information, thereby generating the final feature of microbes and diseases. Finally, an inner product decoder is used to reconstruct the microbe-disease association matrix. Compared with five state-of-the-art methods on the HMDAD and Disbiome datasets, the results of 5-fold cross-validations show that M3HOGAT achieves the best performance. Furthermore, case studies on asthma and obesity confirm the effectiveness of M3HOGAT in identifying potential disease-related microbes.

A Delayed Spiking Neural Membrane System for Adaptive Nearest Neighbor-Based Density Peak Clustering.

Although the density peak clustering (DPC) algorithm can effectively distribute samples and quickly identify noise points, it lacks adaptability and cannot consider the local data structure. In addition, clustering algorithms generally suffer from high time complexity. Prior research suggests that clustering algorithms grounded in P systems can mitigate time complexity concerns. Within the realm of membrane systems (P systems), spiking neural P systems (SN P systems), inspired by biological nervous systems, are third-generation neural networks that possess intricate structures and offer substantial parallelism advantages. Thus, this study first improved the DPC by introducing the maximum nearest neighbor distance and K-nearest neighbors (KNN). Moreover, a method based on delayed spiking neural P systems (DSN P systems) was proposed to improve the performance of the algorithm. Subsequently, the DSNP-ANDPC algorithm was proposed. The effectiveness of DSNP-ANDPC was evaluated through comprehensive evaluations across four synthetic datasets and 10 real-world datasets. The proposed method outperformed the other comparison methods in most cases.

MLRR-ATV: A Robust Manifold Nonnegative LowRank Representation with Adaptive Total-Variation Regularization for scRNA-seq Data Clustering.

Since genomics was proposed, the exploration of genes has been the focus of research. The emergence of single-cell RNA sequencing (scRNA-seq) technology makes it possible to explore gene expression at the single-cell level. Due to the limitations of sequencing technology, the data contains a lot of noise. At the same time, it also has the characteristics of highdimensional and sparse. Clustering is a common method of analyzing scRNA-seq data. This paper proposes a novel singlecell clustering method called Robust Manifold Nonnegative LowRank Representation with Adaptive Total-Variation Regularization (MLRR-ATV). The Adaptive Total-Variation (ATV) regularization is introduced into Low-Rank Representation (LRR) model to reduce the influence of noise through gradient learning. Then, the linear and nonlinear manifold structures in the data are learned through Euclidean distance and cosine similarity, and more valuable information is retained. Because the model is non-convex, we use the Alternating Direction Method of Multipliers (ADMM) to optimize the model. We tested the performance of the MLRRATV model on eight real scRNA-seq datasets and selected nine state-of-the-art methods as comparison methods. The experimental results show that the performance of the MLRRATV model is better than the other nine methods.

HGTMDA: A Hypergraph Learning Approach with Improved GCN-Transformer for miRNA-Disease Association Prediction.

Accumulating scientific evidence highlights the pivotal role of miRNA-disease association research in elucidating disease pathogenesis and developing innovative diagnostics. Consequently, accurately identifying disease-associated miRNAs has emerged as a prominent research topic in bioinformatics. Advances in graph neural networks (GNNs) have catalyzed methodological breakthroughs in this field. However, existing methods are often plagued by data noise and struggle to effectively integrate local and global information, which hinders their predictive performance. To address this, we introduce HGTMDA, an innovative hypergraph learning framework that incorporates random walk with restart-based association masking and an enhanced GCN-Transformer model to infer miRNA-disease associations. HGTMDA starts by constructing multiple homogeneous similarity networks. A novel enhancement of our approach is the introduction of a restart-based random walk association masking strategy. By stochastically masking a subset of association data and integrating it with a GCN enhanced by an attention mechanism, this strategy enables better capture of key information, leading to improved information utilization and reduced impact of noisy data. Next, we build an miRNA-disease heterogeneous hypergraph and adopt an improved GCN-Transformer encoder to effectively solve the effective extraction of local and global information. Lastly, we utilize a combined Dice cross-entropy (DCE) loss function to guide the model training and optimize its performance. To evaluate the performance of HGTMDA, comprehensive comparisons were conducted with state-of-the-art methods. Additionally, in-depth case studies on lung cancer and colorectal cancer were performed. The results demonstrate HGTMDA's outstanding performance across various metrics and its exceptional effectiveness in real-world application scenarios, highlighting the advantages and value of this method.

FSCME: A Feature Selection Method Combining Copula Correlation and Maximal Information Coefficient by Entropy Weights.

Feature selection is a critical component of data mining and has garnered significant attention in recent years. However, feature selection methods based on information entropy often introduce complex mutual information forms to measure features, leading to increased redundancy and potential errors. To address this issue, we propose FSCME, a feature selection method combining Copula correlation (Ccor) and the maximum information coefficient (MIC) by entropy weights. The FSCME takes into consideration the relevance between features and labels, as well as the redundancy among candidate features and selected features. Therefore, the FSCME utilizes Ccor to measure the redundancy between features, while also estimating the relevance between features and labels. Meanwhile, the FSCME employs MIC to enhance the credibility of the correlation between features and labels. Moreover, this study employs the Entropy Weight Method (EWM) to evaluate and assign weights to the Ccor and MIC. The experimental results demonstrate that FSCME yields a more effective feature subset for subsequent clustering processes, significantly improving the classification performance compared to the other six feature selection methods. The source codes of the FSCME are available online at https://github.com/CDMBlab/FSCME.

Coronary computed tomography angiography-derived total coronary plaque burden associated with subsequent cardiovascular outcomes following percutaneous coronary intervention.

OBJECTIVE: To investigate the association of coronary plaque burden variables derived from coronary computed tomography angiography (CCTA) before patients underwent their first percutaneous coronary intervention (PCI) procedure and major adverse cardiovascular events (MACEs) after PCI. METHODS: Patients who underwent CCTA before their first PCI were included retrospectively. A radiologist and a cardiologist analyzed CCTA images on a dedicated workstation. The coronary plaque burden variables included total plaque volume, total percent atheroma volume, volumes and fractions of total low-attenuation plaque, total fibrous plaque, and total calcified plaque. The primary outcomes were MACEs, a composite of all-cause death, nonfatal myocardial infarction, nonfatal stroke, and unscheduled coronary revascularization. RESULTS: A total of 230 patients were included in the final analysis. During a median follow-up of 4.8 years, 67 MACEs occurred. Total plaque volume, total percent atheroma volume, volumes of total low-attenuation plaque and total fibrous plaque but not their fractions were independent predictors for MACEs. Compared with the first tertiles, the hazard ratio of the third tertile of total plaque volume, total percent atheroma volume, total low-attenuation plaque volume, and total fibrous plaque volume were 2.06 (95% CI: 1.03-4.15), 2.15 (95% CI: 1.02-4.51), 3.04 (95% CI: 1.45-6.36), and 2.23 (95% CI: 1.11-4.46), respectively. Neither total calcified plaque volume nor fraction was associated with MACEs independently. CONCLUSION: Selected pre-PCI CCTA-derived variables, including total percent atheroma volume, volumes of total plaque, total low-attenuation plaque and total fibrous plaque, were significantly associated with MACEs after PCI, suggesting that CCTA before PCI reveals the residual risk after revascularization. CLINICAL RELEVANCE STATEMENT: The coronary plaque burden variables derived from coronary computed tomography angiography before percutaneous coronary intervention are independently associated with major adverse cardiovascular events, which could be instrumental in optimizing patient management. KEY POINTS: Coronary plaque burden is associated with cardiovascular events in patients with coronary artery disease. Selected total plaque burden variables derived from coronary computed tomography angiography before percutaneous coronary intervention were associated with poor prognosis. Routine coronary computed tomography angiography before percutaneous coronary intervention might be helpful in reducing future risks.

The effect of deep and awake extubation on emergence agitation after nasal surgery: a randomized controlled trial.

BACKGROUND: Post-anesthetic emergence agitation is common after general anesthesia and may cause adverse consequences, such as injury as well as respiratory and circulatory complications. Emergence agitation after general anesthesia occurs more frequently in nasal surgery than in other surgical procedures. This study aimed to assess the occurrence of emergence agitation in patients undergoing nasal surgery who were extubated under deep anesthesia or when fully awake. METHODS: A total of 202 patients (18-60 years, American Society of Anesthesiologists classification: I-II) undergoing nasal surgery under general anesthesia were randomized 1:1 into two groups: a deep extubation group (group D) and an awake extubation group (group A). The primary outcome was the incidence of emergence agitation. The secondary outcomes included number of emergence agitations, sedation score, vital signs, and incidence of adverse events. RESULTS: The incidence of emergence agitation was lower in group D than in group A (34.7% vs. 72.8%; p < 0.001). Compared to group A, patients in group D had lower Richmond Agitation-Sedation Scale scores, higher Ramsay sedation scores, fewer agitation episodes, and lower mean arterial pressure when extubated and 30 min after surgery, whereas these indicators did not differ 90 min after surgery. There was no difference in the incidence of adverse events between the two groups. CONCLUSIONS: Extubation under deep anesthesia can significantly reduce emergence agitation after nasal surgery under general anesthesia without increasing the incidence of adverse events. TRIAL REGISTRATION: Registered in Clinicaltrials.gov (NCT04844333) on 14/04/2021.

Combining EEG Features and Convolutional Autoencoder for Neonatal Seizure Detection.

Neonatal epilepsy is a common emergency phenomenon in neonatal intensive care units (NICUs), which requires timely attention, early identification, and treatment. Traditional detection methods mostly use supervised learning with enormous labeled data. Hence, this study offers a semi-supervised hybrid architecture for detecting seizures, which combines the extracted electroencephalogram (EEG) feature dataset and convolutional autoencoder, called Fd-CAE. First, various features in the time domain and entropy domain are extracted to characterize the EEG signal, which helps distinguish epileptic seizures subsequently. Then, the unlabeled EEG features are fed into the convolutional autoencoder (CAE) for training, which effectively represents EEG features by optimizing the loss between the input and output features. This unsupervised feature learning process can better combine and optimize EEG features from unlabeled data. After that, the pre-trained encoder part of the model is used for further feature learning of labeled data to obtain its low-dimensional feature representation and achieve classification. This model is performed on the neonatal EEG dataset collected at the University of Helsinki Hospital, which has a high discriminative ability to detect seizures, with an accuracy of 92.34%, precision of 93.61%, recall rate of 98.74%, and F1-score of 95.77%, respectively. The results show that unsupervised learning by CAE is beneficial to the characterization of EEG signals, and the proposed Fd-CAE method significantly improves classification performance.

Role and molecular mechanism of Salvia miltiorrhiza associated with chemical compounds in the treatment of diabetes mellitus and its complications: A review.

Diabetes mellitus (DM) is one of the most prevalent diseases worldwide, greatly impacting patients' quality of life. This article reviews the progress in Salvia miltiorrhiza, an ancient Chinese plant, for the treatment of DM and its associated complications. Extensive studies have been conducted on the chemical composition and pharmacological effects of S miltiorrhiza, including its anti-inflammatory and antioxidant activities. It has demonstrated potential in preventing and treating diabetes and its consequences by improving peripheral nerve function and increasing retinal thickness in diabetic individuals. Moreover, S miltiorrhiza has shown effectiveness when used in conjunction with angiotensin-converting enzyme inhibitors, angiotensin receptor blockers (ARBs), and statins. The safety and tolerability of S miltiorrhiza have also been thoroughly investigated. Despite the established benefits of managing DM and its complications, further research is needed to determine appropriate usage, dosage, long-term health benefits, and safety.

[Relationship Between Tim-3 and Galectin-9 Expression Levels, Clinical Pathological Characteristics, and Prognosis in Patients After Radical Resection of Colorectal Cancer]. / ç»“ç›´è‚ ç™Œæ ¹æ²»æœ¯åŽæ‚£è€ Tim-3、galectin-9è¡¨è¾¾æ°´å¹³ä¸Žå ¶ä¸´åºŠç— ç†ç‰¹å¾åŠé¢„åŽçš„å ³ç³».

Objective: Some colorectal cancer patients still face high recurrence rates and poor prognoses even after they have undergone the surgical treatment of radical resection. Identifying potential biochemical markers and therapeutic targets for the prognostic evaluation of patients undergoing radical resection of colorectal cancer is crucial for improving their clinical outcomes. Recently, it has been reported that the T cell immunoglobulin and mucin domain protein 3 (Tim-3) and its ligand galactose lectin 9 (galectin-9) play crucial roles in immune dysfunction caused by various tumors, such as colorectal cancer. However, their expressions, biological functions, and prognostic value in colorectal cancer are still unclear. This study aims to investigate the relationship between Tim-3 and galectin-9 expression levels and the clinicopathological characteristics and prognosis of patients undergoing radical resection of colorectal cancer. Methods: A total of 171 patients who underwent radical resection of colorectal cancer at Chengdu Fifth People's Hospital between February 2018 and March 2019 were selected. Immunohistochemistry was performed to assess the expression levels of Tim-3 and galectin-9 in the cancer tissue samples and the paracancerous tissue samples of the patients. The relationship between Tim-3 and galectin-9 expression levels and the baseline clinical parameters of the patients was analyzed accordingly. Kaplan-Meier analysis was performed to assess the association between Tim-3 and galectin-9 expression levels and the relapse-free survival (RFS) and the overall survival (OS) of colorectal cancer patients. Cox regression analysis was conducted to identify factors associated with adverse prognosis in the patients. Results: The immunohistochemical results showed that the high expression levels of Tim-3 and galectin-9 were observed in 70.18% (120/171) and 32.16% (55/171), respectively, of the colorectal cancer tissues, whereas the low expression levels were 29.82% (51/171) and 67.84% (116/171), respectively. Furthermore, the expression score of Tim-3 was significantly higher in colorectal cancer tissues than that in the paracancerous tissues, while the expression score of galectin-9 was lower than that in the paracancerous tissues (P<0.05). Further analysis revealed that the expression of Tim-3 and galectin-9 was associated with the depth of tumor infiltration, vascular infiltration, and clinical staging (P<0.05). During the follow-up period of 14-63 months, 7 out of 171 patients were lost to follow-up. Among the remaining patients, 49 and 112 cases presented abnormally low expression of Tim-3 and galectin-9, respectively, whereas 115 and 52 cases presented high expression of Tim-3 and galectin-9, respectively. Kaplan-Meier survival analysis demonstrated that patients with high Tim-3 expression in colorectal cancer tissues had significantly lower RFS and OS than those with low expression did (RFS: log-rank=22.66, P<0.001; OS: log-rank=19.71, P<0.001). Conversely, patients with low galectin-9 expression had significantly lower RFS and OS than those with high expression did (RFS: log-rank=19.45, P<0.001; OS: log-rank=22.24, P<0.001). Cox multivariate analysis indicated that TNM stage Ⅲ (HR=2.26, 95% CI: 1.20-5.68), high expression of Tim-3 (HR=0.80, 95% CI: 0.33-0.91), and low expression of galectin-9 (HR=1.80, 95% CI: 1.33-4.70) were independent risk factors affecting RFS and OS in patients (P<0.05). Conclusion: Aberrant expression of Tim-3 and galectin-9 is observed in colorectal cancer tissues. High expression of Tim-3 and low expression of galectin-9 are closely associated with adverse clinico-pathological characteristics and prognosis. They are identified as independent influencing factors that may trigger adverse prognostic events in patients. These findings suggest that Tim-3 and galectin-9 have potential as new therapeutic targets and clinical indicators.

Deep Self-Reconstruction Fusion Similarity Hashing for the Diagnosis of Alzheimer's Disease on Multi-Modal Data.

The pathogenesis of Alzheimer's disease (AD) is extremely intricate, which makes AD patients almost incurable. Recent studies have demonstrated that analyzing multi-modal data can offer a comprehensive perspective on the different stages of AD progression, which is beneficial for early diagnosis of AD. In this paper, we propose a deep self-reconstruction fusion similarity hashing (DS-FSH) method to effectively capture the AD-related biomarkers from the multi-modal data and leverage them to diagnose AD. Given that most existing methods ignore the topological structure of the data, a deep self-reconstruction model based on random walk graph regularization is designed to reconstruct the multi-modal data, thereby learning the nonlinear relationship between samples. Additionally, a fused similarity hash based on anchor graph is proposed to generate discriminative binary hash codes for multi-modal reconstructed data. This allows sample fused similarity to be effectively modeled by a fusion similarity matrix based on anchor graph while modal correlation can be approximated by Hamming distance. Especially, extracted features from the multi-modal data are classified using deep sparse autoencoders classifier. Finally, experiments conduct on the AD Neuroimaging Initiative database show that DS-FSH outperforms comparable methods of AD classification. To conclude, DS-FSH identifies multi-modal features closely associated with AD, which are expected to contribute significantly to understanding of the pathogenesis of AD.

Diagnosis-Guided Deep Subspace Clustering Association Study for Pathogenetic Markers Identification of Alzheimer's Disease Based on Comparative Atlases.

The roles of brain region activities and genotypic functions in the pathogenesis of Alzheimer's disease (AD) remain unclear. Meanwhile, current imaging genetics methods are difficult to identify potential pathogenetic markers by correlation analysis between brain network and genetic variation. To discover disease-related brain connectome from the specific brain structure and the fine-grained level, based on the Automated Anatomical Labeling (AAL) and human Brainnetome atlases, the functional brain network is first constructed for each subject. Specifically, the upper triangle elements of the functional connectivity matrix are extracted as connectivity features. The clustering coefficient and the average weighted node degree are developed to assess the significance of every brain area. Since the constructed brain network and genetic data are characterized by non-linearity, high-dimensionality, and few subjects, the deep subspace clustering algorithm is proposed to reconstruct the original data. Our multilayer neural network helps capture the non-linear manifolds, and subspace clustering learns pairwise affinities between samples. Moreover, most approaches in neuroimaging genetics are unsupervised learning, neglecting the diagnostic information related to diseases. We presented a label constraint with diagnostic status to instruct the imaging genetics correlation analysis. To this end, a diagnosis-guided deep subspace clustering association (DDSCA) method is developed to discover brain connectome and risk genetic factors by integrating genotypes with functional network phenotypes. Extensive experiments prove that DDSCA achieves superior performance to most association methods and effectively selects disease-relevant genetic markers and brain connectome at the coarse-grained and fine-grained levels.

Investigating the mechanisms of drug resistance and prognosis in ovarian cancer using single-cell RNA sequencing and bulk RNA sequencing.

Ovarian cancer stands as a prevalent malignancy within the realm of gynecology, and the emergence of resistance to chemotherapeutic agents remains a pivotal impediment to both prognosis and treatment. Through a single-cell level investigation, we scrutinize the drug resistance and mitotic activity of the core tumor cells in ovarian cancer. Our study revisits the interrelationships and temporal trajectories of distinct epithelial cells (EPCs) subpopulations, while identifying genes associated with ovarian cancer prognosis. Notably, our findings establish a strong association between the drug resistance of EPCs and oxidative phosphorylation pathways. Subsequently, through subpopulation and temporal trajectory analysis, we confirm the intermediate position of EPCs subpopulation C0. Furthermore, we delve into the immunological functions and differentially expressed genes associated with the prognosis of C0, shedding light on the potential for constructing novel ovarian cancer prognosis models and identifying new therapeutic targets.

Study of multi-dimensional problems arising in wave propagation using a hybrid scheme.

Many scientific phenomena are linked to wave problems. This paper presents an effective and suitable technique for generating approximation solutions to multi-dimensional problems associated with wave propagation. We adopt a new iterative strategy to reduce the numerical work with minimum time efficiency compared to existing techniques such as the variational iteration method (VIM) and homotopy analysis method (HAM) have some limitations and constraints within the development of recurrence relation. To overcome this drawback, we present a Sawi integral transform ( S T) for constructing a suitable recurrence relation. This recurrence relation is solved to determine the coefficients of the homotopy perturbation strategy (HPS) that leads to the convergence series of the precise solution. This strategy derives the results in algebraic form that are independent of any discretization. To demonstrate the performance of this scheme, several mathematical frameworks and visual depictions are shown.

Comparing lycopene's impact on mortality in adults with or without obesity.

Objective: Given lycopene's anti-inflammatory and antioxidant properties, we investigated its mortality impact in individuals with and without obesity, confirming distinct effects. Methods: This study analyzes the National Health and Nutrition Examination Survey (NHANES) data from 2003-2006 and 2017-2018, linking lycopene levels to all-cause and cardiovascular mortality. Using various statistical methods, three models are sequentially adjusted for confounders, investigating the lycopene-outcome relationship. Results: We studied 11 737 adults for 162 months and found 1537 all-cause deaths (13.1%) and 443 cardiovascular deaths (3.8%). For those without obesity, serum lycopene had an "L" shape relationship with all-cause mortality, being harmful at very low levels but protective above a certain threshold. It consistently protects against cardiovascular mortality. In individuals with obesity, the relationship with all-cause mortality formed a "U" shape, with increased risk at very low and very high lycopene levels and protection in the middle range. Cardiovascular mortality showed a similar pattern in individuals with obesity. Interestingly, dietary lycopene intake had protective effects in both groups. Conclusion: This study reveals that lycopene exhibits distinct associations with all-cause and cardiovascular mortality in populations with or without obesity, emphasizing the importance of considering individual health profiles when assessing its benefits.

KFDAE: CircRNA-Disease Associations Prediction Based on Kernel Fusion and Deep Auto-Encoder.

CircRNA has been proved to play an important role in the diseases diagnosis and treatment. Considering that the wet-lab is time-consuming and expensive, computational methods are viable alternative in these years. However, the number of circRNA-disease associations (CDAs) that can be verified is relatively few, and some methods do not take full advantage of dependencies between attributes. To solve these problems, this paper proposes a novel method based on Kernel Fusion and Deep Auto-encoder (KFDAE) to predict the potential associations between circRNAs and diseases. Firstly, KFDAE uses a non-linear method to fuse the circRNA similarity kernels and disease similarity kernels. Then the vectors are connected to make the positive and negative sample sets, and these data are send to deep auto-encoder to reduce dimension and extract features. Finally, three-layer deep feedforward neural network is used to learn features and gain the prediction score. The experimental results show that compared with existing methods, KFDAE achieves the best performance. In addition, the results of case studies prove the effectiveness and practical significance of KFDAE, which means KFDAE is able to capture more comprehensive information and generate credible candidate for subsequent wet-lab.

HGSMDA: miRNA-Disease Association Prediction Based on HyperGCN and Sørensen-Dice Loss.

Biological research has demonstrated the significance of identifying miRNA-disease associations in the context of disease prevention, diagnosis, and treatment. However, the utilization of experimental approaches involving biological subjects to infer these associations is both costly and inefficient. Consequently, there is a pressing need to devise novel approaches that offer enhanced accuracy and effectiveness. Presently, the predominant methods employed for predicting disease associations rely on Graph Convolutional Network (GCN) techniques. However, the Graph Convolutional Network algorithm, which is locally aggregated, solely incorporates information from the immediate neighboring nodes of a given node at each layer. Consequently, GCN cannot simultaneously aggregate information from multiple nodes. This constraint significantly impacts the predictive efficacy of the model. To tackle this problem, we propose a novel approach, based on HyperGCN and Sørensen-Dice loss (HGSMDA), for predicting associations between miRNAs and diseases. In the initial phase, we developed multiple networks to represent the similarity between miRNAs and diseases and employed GCNs to extract information from diverse perspectives. Subsequently, we draw into HyperGCN to construct a miRNA-disease heteromorphic hypergraph using hypernodes and train GCN on the graph to aggregate information. Finally, we utilized the Sørensen-Dice loss function to evaluate the degree of similarity between the predicted outcomes and the ground truth values, thereby enabling the prediction of associations between miRNAs and diseases. In order to assess the soundness of our methodology, an extensive series of experiments was conducted employing the Human MicroRNA Disease Database (HMDD v3.2) as the dataset. The experimental outcomes unequivocally indicate that HGSMDA exhibits remarkable efficacy when compared to alternative methodologies. Furthermore, the predictive capacity of HGSMDA was corroborated through a case study focused on colon cancer. These findings strongly imply that HGSMDA represents a dependable and valid framework, thereby offering a novel avenue for investigating the intricate association between miRNAs and diseases.