Search

Accurately estimating the state of health (SOH) of lithium-ion batteries plays a significant role in the safe operation of electric vehicles. Deep learning (DL)-based approaches for estimating state of health (SOH) have consistently been the focus of study in recent years. In the current era of electric mobility, the utilization of lithium-ion batteries (LIBs) has evolved into a necessity for energy storage. Ensuring the safe operation of EVs requires a precise assessment of the state-of-health (SOH) of LIBs. To estimate battery SOH accurately, this paper employs a deep learning (DL) algorithm to enhance the estimation accuracy of SOH to obtain accurate SOH measurements. This research introduces the Diffusion Convolutional Recurrent Neural Network (DCRNN) with a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm (DCRNN + SVM-RFE) for enhancing classification and feature selection performance. The data gathered from the dataset were pre-processed using the min–max normalization method. The Center for Advanced Life Cycle Engineering (CALCE) dataset from the University of Maryland was employed to train and evaluate the model. The SVM-RFE algorithm was used for feature selection of pre-processed data. DCRNN algorithm was used for the classification process to enhance prediction precision. The DCRNN + SVM-RFE model's performance was calculated using Mean Absolute Percentage Error (MAPE), Mean Squared Error (MAE), Mean Squared Error (MSE), and Root MSE (RMSE) metric values. The proposed model generates accurate results for SOH prediction; all RMSEs are within 0.02%, MAEs are within 0.015%, MSEs were within 0.032%, and MAPEs are within 0.41%. The mean values of RMSE, MSE, MAE, and MAPE were 0.014, 0.026, 0.011, and 0.32, respectively. Experiments confirmed that the DCRNN + SVM-RFE model has the highest accuracy among those that predict SOH. [ABSTRACT FROM AUTHOR]

Abstract Background: Nonspecific Orbital Inflammation (NSOI) remains a perplexing enigma among proliferative inflammatory disorders. Its etiology is idiopathic, characterized by distinctive and polymorphous lymphoid infiltration within the orbital region. Preliminary investigations suggest that PALMD localizes within the cytosol, potentially playing a crucial role in cellular processes, including plasma membrane dynamics and myogenic differentiation. The potential of PALMD as a biomarker for NSOI warrants meticulous exploration. Methods: PALMD was identified through the intersection analysis of common DEGs from datasets GSE58331 and GSE105149 from the GEO database, alongside immune-related gene lists from the ImmPort database, using Lasso regression and SVM-RFE analysis. GSEA and GSVA were conducted with gene sets co-expressed with PALMD. To further investigate the correlation between PALMD and immune-related biological processes, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate immune microenvironment characteristics of each sample. The expression levels of PALMD were subsequently validated using GSE105149. Results: Among the 314 DEGs identified, several showed significant differences. Lasso and SVM-RFE algorithms pinpointed 15 hub genes. Functional analysis of PALMD emphasized its involvement in cell-cell adhesion, leukocyte migration, and leukocyte-mediated immunity. Enrichment analysis revealed that gene sets positively correlated with PALMD were enriched in immune-related pathways. Immune infiltration analysis indicated that resting dendritic cells, resting mast cells, activated NK cells, and plasma cells positively associate with PALMD expression. Conversely, naive B cells, activated dendritic cells, M0 and M1 macrophages, activated mast cells, activated CD4 memory T cells, and naive CD4 T cells showed a negative correlation with PALMD expression. PALMD demonstrated significant diagnostic potential in differentiating NSOI. Conclusions: This study identifies PALMD as a potential biomarker linked to NSOI, providing insights into its pathogenesis and offering new avenues for tracking disease progression.

Abstract Background Asthma, a prevalent chronic inflammatory disorder, is shaped by a multifaceted interplay between genetic susceptibilities and environmental exposures. Despite strides in deciphering its pathophysiological landscape, the intricate molecular underpinnings of asthma remain elusive. The focus has increasingly shifted toward the metabolic aberrations accompanying asthma, particularly within the domain of pyrimidine metabolism (PyM)—a critical pathway in nucleotide synthesis and degradation. While the therapeutic relevance of PyM has been recognized across various diseases, its specific contributions to asthma pathology are yet underexplored. This study employs sophisticated bioinformatics approaches to delineate and confirm the involvement of PyM genes (PyMGs) in asthma, aiming to bridge this significant gap in knowledge. Methods Employing cutting-edge bioinformatics techniques, this research aimed to elucidate the role of PyMGs in asthma. We conducted a detailed examination of 31 PyMGs to assess their differential expression. Through Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), we explored the biological functions and pathways linked to these genes. We utilized Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) to pinpoint critical hub genes and to ascertain the diagnostic accuracy of eight PyMGs in distinguishing asthma, complemented by an extensive correlation study with the clinical features of the disease. Validation of the gene expressions was performed using datasets GSE76262 and GSE147878. Results Our analyses revealed that eleven PyMGs—DHODH, UMPS, NME7, NME1, POLR2B, POLR3B, POLR1C, POLE, ENPP3, RRM2B, TK2—are significantly associated with asthma. These genes play crucial roles in essential biological processes such as RNA splicing, anatomical structure maintenance, and metabolic processes involving purine compounds. Conclusions This investigation identifies eleven PyMGs at the core of asthma's pathogenesis, establishing them as potential biomarkers for this disease. Our findings enhance the understanding of asthma’s molecular mechanisms and open new avenues for improving diagnostics, monitoring, and progression evaluation. By providing new insights into non-cancerous pathologies, our work introduces a novel perspective and sets the stage for further studies in this field.

Abstract Idiopathic orbital inflammation, formerly known as NSOI (nonspecific orbital inflammation), is characterized as a spectrum disorder distinguished by the polymorphic infiltration of lymphoid tissue, presenting a complex and poorly understood etiology. Recent advancements have shed light on the HLF (Human lactoferrin), proposing its critical involvement in the regulation of hematopoiesis and the maintenance of innate mucosal immunity. This revelation has generated significant interest in exploring HLF's utility as a biomarker for NSOI, despite the existing gaps in our understanding of its biosynthetic pathways and operational mechanisms. Intersecting multi-omic datasets—specifically, common differentially expressed genes between GSE58331 and GSE105149 from the Gene Expression Omnibus and immune-related gene compendiums from the ImmPort database—we employed sophisticated analytical methodologies, including Lasso regression and support vector machine-recursive feature elimination, to identify HLF. Gene set enrichment analysis and gene set variation analysis disclosed significant immune pathway enrichment within gene sets linked to HLF. The intricate relationship between HLF expression and immunological processes was further dissected through the utilization of CIBERSORT and ESTIMATE algorithms, which assess characteristics of the immune microenvironment, highlighting a noteworthy association between increased HLF expression and enhanced immune cell infiltration. The expression levels of HLF were corroborated using data from the GSE58331 dataset, reinforcing the validity of our findings. Analysis of 218 HLF-related differentially expressed genes revealed statistically significant discrepancies. Fifteen hub genes were distilled using LASSO and SVM-RFE algorithms. Biological functions connected with HLF, such as leukocyte migration, ossification, and the negative regulation of immune processes, were illuminated. Immune cell analysis depicted a positive correlation between HLF and various cells, including resting mast cells, activated NK cells, plasma cells, and CD8 T cells. Conversely, a negative association was observed with gamma delta T cells, naive B cells, M0 and M1 macrophages, and activated mast cells. Diagnostic assessments of HLF in distinguishing NSOI showed promising accuracy. Our investigation delineates HLF as intricately associated with NSOI, casting light on novel biomarkers for diagnosis and progression monitoring of this perplexing condition.

Abstract Background Nonspecific Orbital Inflammation (NSOI) represents a persistent and idiopathic proliferative inflammatory disorder, characterized by polymorphous lymphoid infiltration within the orbit. The transcription factor Interferon Regulatory Factor 8 (IRF8), integral to the IRF protein family, was initially identified as a pivotal element for the commitment and differentiation of myeloid cell lineage. Serving as a central regulator of innate immune receptor signaling, IRF8 orchestrates a myriad of functions in hematopoietic cell development. However, the intricate mechanisms underlying IRF8 production remain to be elucidated, and its potential role as a biomarker for NSOI is yet to be resolved. Methods IRF8 was extracted from the intersection analysis of common DEGs of GSE58331 and GSE105149 from the GEO and immune- related gene lists in the ImmPort database using The Lasso regression and SVM-RFE analysis. We performed GSEA and GSVA with gene sets coexpressed with IRF8, and observed that gene sets positively related to IRF8 were enriched in immune-related pathways. To further explore the correlation between IRF8 and immune-related biological process, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate TME characteristics of each sample and confirmed that high IRF8 expression might give rise to high immune cell infiltration. Finally, the GSE58331 was utilized to confirm the levels of expression of IRF8. Results Among the 314 differentially expressed genes (DEGs), some DEGs were found to be significantly different. With LASSO and SVM-RFE algorithms, we obtained 15 hub genes. For biological function analysis in IRF8, leukocyte mediated immunity, leukocyte cell-cell adhesion, negative regulation of immune system process were emphasized. B cells naive, Macrophages M0, Macrophages M1, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD4 naive, and T cells gamma delta were shown to be positively associated with IRF8. While, Mast cells resting, Monocytes, NK cells activated, Plasma cells, T cells CD8, and T cells regulatory (Tregs) were shown to be negatively linked with IRF8. The diagnostic ability of the IRF8 in differentiating NSOI exhibited a good value. Conclusions This study discovered IRF8 that are linked to NSOI. IRF8 shed light on potential new biomarkers for NSOI and tracking its progression.

Background: Asthma, a prevalent chronic inflammatory disorder, is shaped by a multifaceted interplay between genetic susceptibilities and environmental exposures. Despite strides in deciphering its pathophysiological landscape, the intricate molecular underpinnings of asthma remain elusive. The focus has increasingly shifted toward the metabolic aberrations accompanying asthma, particularly within the domain of pyrimidine metabolism (PyM)—a critical pathway in nucleotide synthesis and degradation. While the therapeutic relevance of PyM has been recognized across various diseases, its specific contributions to asthma pathology are yet underexplored. This study employs sophisticated bioinformatics approaches to delineate and confirm the involvement of PyM genes (PyMGs) in asthma, aiming to bridge this significant gap in knowledge. Methods: Employing cutting-edge bioinformatics techniques, this research aimed to elucidate the role of PyMGs in asthma. We conducted a detailed examination of 31 PyMGs to assess their differential expression. Through Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), we explored the biological functions and pathways linked to these genes. We utilized Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) to pinpoint critical hub genes and to ascertain the diagnostic accuracy of eight PyMGs in distinguishing asthma, complemented by an extensive correlation study with the clinical features of the disease. Validation of the gene expressions was performed using datasets GSE76262 and GSE147878. Results: Our analyses revealed that eleven PyMGs—DHODH, UMPS, NME7, NME1, POLR2B, POLR3B, POLR1C, POLE, ENPP3, RRM2B, TK2—are significantly associated with asthma. These genes play crucial roles in essential biological processes such as RNA splicing, anatomical structure maintenance, and metabolic processes involving purine compounds. Conclusions: This investigation identifies eleven PyMGs at the core of asthma's pathogenesis, establishing them as potential biomarkers for this disease. Our findings enhance the understanding of asthma's molecular mechanisms and open new avenues for improving diagnostics, monitoring, and progression evaluation. By providing new insights into non-cancerous pathologies, our work introduces a novel perspective and sets the stage for further studies in this field. [ABSTRACT FROM AUTHOR]

Background: Increasing evidence have highlighted the biological significance of mRNA N6-methyladenosine (m6A) modification in regulating tumorigenicity and progression. However, the potential roles of m6A regulators in tumor microenvironment (TME) formation and immune cell infiltration in liver hepatocellular carcinoma (LIHC or HCC) requires further clarification. Method: RNA sequencing data were obtained from TCGA-LIHC databases and ICGC-LIRI-JP databases. Consensus clustering algorithm was used to identify m6A regulators cluster subtypes. Weighted gene co-expression network analysis (WGCNA), LASSO regression, Random Forest (RF), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) were applied to identify candidate biomarkers, and then a m6Arisk score model was constructed. The correlations of m6Arisk score with immunological characteristics (immunomodulators, cancer immunity cycles, tumor-infiltrating immune cells (TIICs), and immune checkpoints) were systematically evaluated. The effective performance of nomogram was evaluated using concordance index (C-index), calibration plots, decision curve analysis (DCA), and receiver operating characteristic curve (ROC). Results: Two distinctm6A modification patterns were identified based on 23m6A regulators, which were correlated with different clinical outcomes and biological functions. Based on the constructed m6Arisk score model, HCC patients can be divided into two distinct risk score subgroups. Further analysis indicated that the m6Arisk score showed excellent prognostic performance. Patients with a high m6Arisk score was significantly associated with poorer clinical outcome, lower drug sensitivity, and higher immune infiltration. Moreover, we developed a nomogram model by incorporating the m6Arisk score and clinicopathological features. The application of the m6Arisk score for the prognostic stratification of HCC has good clinical applicability and clinical net benefit. Conclusion: Our findings reveal the crucial role of m6A modification patterns for predicting HCC TME status and prognosis, and highlight the good clinical applicability and net benefit of m6Arisk score in terms of prognosis, immunophenotype, and drug therapy in HCC patients. [ABSTRACT FROM AUTHOR]

Background: Surgical site infection (SSI) is a common complication in HIV-positive fracture patients undergoing surgery, leading to increased morbidity, mortality, and healthcare costs. Accurate prediction of SSI risk can help guide clinical decision-making and improve patient outcomes. However, there is a lack of user-friendly, Web-based calculator for predicting SSI risk in this patient population. Objective: This study aimed to develop and validate a novel web-based risk calculator for predicting SSI in HIV-positive fracture patients undergoing surgery in China. Method: A multicenter retrospective cohort study was conducted using data from HIV-positive fracture patients who underwent surgery in three tertiary hospitals in China between May 2011 and September 2023. We used patients from Beijing Ditan Hospital as the training cohort and patients from Chengdu Public Health and Changsha First Hospital as the external validation cohort. Univariate, multivariate logistic regression analyses and SVM-RFE were performed to identify independent risk factors for SSIs. A web-based calculator was developed using the identified risk factors and validated using an external validation cohort. The performance of the nomogram was evaluated using the area under the receiver operating characteristic (AUC) curves, calibration plots, and decision curve analysis (DCA). Results: A total of 338 HIV-positive patients were included in the study, with 216 patients in the training cohort and 122 patients in the validation cohort. The overall SSI incidence was 10.7%. The web-based risk calculator (https://sydtliubo.shinyapps.io/DynNom_for_SSI/) incorporated six risk factors: HBV/HCV co-infection, HIV RNA load, CD4+ T-cell count, Neu and Lym level. The nomogram demonstrated good discrimination, with an AUC of 0.890 in the training cohort and 0.853 in the validation cohort. The calibration plot showed good agreement between predicted and observed SSI probabilities. The DCA indicated that the nomogram had clinical utility across a wide range of threshold probabilities. Conclusion: Our study developed and validated a novel web-based risk calculator for predicting SSI risk in HIV-positive fracture patients undergoing surgery in China. The nomogram demonstrated good discrimination, calibration, and clinical utility, and can serve as a valuable tool for risk stratification and clinical decision-making in this patient population. Future studies should focus on integrating this nomogram into hospital information systems for real-time risk assessment and management. [ABSTRACT FROM AUTHOR]

Background: Nonspecific Orbital Inflammation (NSOI) represents a persistent and idiopathic proliferative inflammatory disorder, characterized by polymorphous lymphoid infiltration within the orbit. The transcription factor Interferon Regulatory Factor 8 (IRF8), integral to the IRF protein family, was initially identified as a pivotal element for the commitment and differentiation of myeloid cell lineage. Serving as a central regulator of innate immune receptor signaling, IRF8 orchestrates a myriad of functions in hematopoietic cell development. However, the intricate mechanisms underlying IRF8 production remain to be elucidated, and its potential role as a biomarker for NSOI is yet to be resolved. Methods: IRF8 was extracted from the intersection analysis of common DEGs of GSE58331 and GSE105149 from the GEO and immune- related gene lists in the ImmPort database using The Lasso regression and SVM-RFE analysis. We performed GSEA and GSVA with gene sets coexpressed with IRF8, and observed that gene sets positively related to IRF8 were enriched in immune-related pathways. To further explore the correlation between IRF8 and immune-related biological process, the CIBERSORT algorithm and ESTIMATE method were employed to evaluate TME characteristics of each sample and confirmed that high IRF8 expression might give rise to high immune cell infiltration. Finally, the GSE58331 was utilized to confirm the levels of expression of IRF8. Results: Among the 314 differentially expressed genes (DEGs), some DEGs were found to be significantly different. With LASSO and SVM-RFE algorithms, we obtained 15 hub genes. For biological function analysis in IRF8, leukocyte mediated immunity, leukocyte cell-cell adhesion, negative regulation of immune system process were emphasized. B cells naive, Macrophages M0, Macrophages M1, T cells CD4 memory activated, T cells CD4 memory resting, T cells CD4 naive, and T cells gamma delta were shown to be positively associated with IRF8. While, Mast cells resting, Monocytes, NK cells activated, Plasma cells, T cells CD8, and T cells regulatory (Tregs) were shown to be negatively linked with IRF8. The diagnostic ability of the IRF8 in differentiating NSOI exhibited a good value. Conclusions: This study discovered IRF8 that are linked to NSOI. IRF8 shed light on potential new biomarkers for NSOI and tracking its progression. [ABSTRACT FROM AUTHOR]

Abstract Background This study delves into the intricate landscape of atherosclerosis (AS), a chronic inflammatory disorder with significant implications for cardiovascular health. AS poses a considerable burden on global healthcare systems, elevating both mortality and morbidity rates. The pathological underpinnings of AS involve a marked metabolic disequilibrium, particularly within pyrimidine metabolism (PyM), a crucial enzymatic network central to nucleotide synthesis and degradation. While the therapeutic relevance of pyrimidine metabolism in diverse diseases is acknowledged, the explicit role of pyrimidine metabolism genes (PyMGs) in the context of AS remains elusive. Utilizing bioinformatics methodologies, this investigation aims to reveal and substantiate PyMGs intricately linked with AS. Methods A set of 41 candidate PyMGs was scrutinized through differential expression analysis. GSEA and GSVA were employed to illuminate potential biological pathways and functions associated with the identified PyMGs. Simultaneously, Lasso regression and SVM-RFE were utilized to distill core genes and assess the diagnostic potential of four quintessential PyMGs (CMPK1, CMPK2, NT5C2, RRM1) in discriminating AS. The relationship between key PyMGs and clinical presentations was also explored. Validation of the expression levels of the four PyMGs was performed using the GSE43292 and GSE9820 datasets. Results This investigation identified four PyMGs, with NT5C2 and RRM1 emerging as key players, intricately linked to AS pathogenesis. Functional analysis underscored their critical involvement in metabolic processes, including pyrimidine-containing compound metabolism and nucleotide biosynthesis. Diagnostic evaluation of these PyMGs in distinguishing AS showcased promising results. Conclusion In conclusion, this exploration has illuminated a constellation of four PyMGs with a potential nexus to AS pathogenesis. These findings unveil emerging biomarkers, paving the way for novel approaches to disease monitoring and progression, and providing new avenues for therapeutic intervention in the realm of atherosclerosis.

Background: Anoikis is a cell death programmed to eliminate dysfunctional or damaged cells induced by detachment from the extracellular matrix. Utilizing an anoikis-based risk stratification is anticipated to understand melanoma's prognostic and immune landscapes comprehensively. Methods: Differential expression genes (DEGs) were analyzed between melanoma and normal skin tissues in The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression data sets. Next, least absolute shrinkage and selection operator, support vector machine–recursive feature elimination algorithm, and univariate and multivariate Cox analyses on the 308 DEGs were performed to build the prognostic signature in the TCGA–melanoma data set. Finally, the signature was validated in GSE65904 and GSE22155 data sets. NOTCH3, PIK3R2, and SOD2 were validated in our clinical samples by immunohistochemistry. Results: The prognostic model for melanoma patients was developed utilizing ten hub anoikis-related genes. The overall survival (OS) of patients in the high-risk subgroup, which was classified by the optimal cutoff value, was remarkably shorter in the TCGA–melanoma, GSE65904, and GSE22155 data sets. Low-risk patients exhibited low immune cell infiltration and high expression of immunophenoscores and immune checkpoints. They also demonstrated increased sensitivity to various drugs, including dasatinib and dabrafenib. NOTCH3, PIK3R2, and SOD2 were notably associated with OS by univariate Cox analysis in the GSE65904 data set. The clinical melanoma samples showed remarkably higher protein expressions of NOTCH3 (P = 0.003) and PIK3R2 (P = 0.009) than the para-melanoma samples, while the SOD2 protein expression remained unchanged. Conclusions: In this study, we successfully established a prognostic anoikis-connected signature using machine learning. This model may aid in evaluating patient prognosis, clinical characteristics, and immune treatment modalities for melanoma. [ABSTRACT FROM AUTHOR]

Background: Nonspecific orbital inflammation (NSOI) represents a perplexing and persistent proliferative inflammatory disorder of idiopathic nature, characterized by a heterogeneous lymphoid infiltration within the orbital region. This condition, marked by the aberrant metabolic activities of its cellular constituents, starkly contrasts with the metabolic equilibrium found in healthy cells. Among the myriad pathways integral to cellular metabolism, purine metabolism emerges as a critical player, providing the building blocks for nucleic acid synthesis, such as DNA and RNA. Despite its significance, the contribution of Purine Metabolism Genes (PMGs) to the pathophysiological landscape of NSOI remains a mystery, highlighting a critical gap in our understanding of the disease's molecular underpinnings. Methods: To bridge this knowledge gap, our study embarked on an exploratory journey to identify and validate PMGs implicated in NSOI, employing a comprehensive bioinformatics strategy. By intersecting differential gene expression analyses with a curated list of 92 known PMGs, we aimed to pinpoint those with potential roles in NSOI. Advanced methodologies, including Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), facilitated a deep dive into the biological functions and pathways associated with these PMGs. Further refinement through Lasso regression and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) enabled the identification of key hub genes and the evaluation of their diagnostic prowess for NSOI. Additionally, the relationship between these hub PMGs and relevant clinical parameters was thoroughly investigated. To corroborate our findings, we analyzed expression data from datasets GSE58331 and GSE105149, focusing on the seven PMGs identified as potentially crucial to NSOI pathology. Results: Our investigation unveiled seven PMGs (ENTPD1, POLR2K, NPR2, PDE6D, PDE6H, PDE4B, and ALLC) as intimately connected to NSOI. Functional analyses shed light on their involvement in processes such as peroxisome targeting sequence binding, seminiferous tubule development, and ciliary transition zone organization. Importantly, the diagnostic capabilities of these PMGs demonstrated promising efficacy in distinguishing NSOI from nonaffected states. Conclusions: Through rigorous bioinformatics analyses, this study unveils seven PMGs as novel biomarker candidates for NSOI, elucidating their potential roles in the disease's pathogenesis. These discoveries not only enhance our understanding of NSOI at the molecular level but also pave the way for innovative approaches to monitor and study its progression, offering a beacon of hope for individuals afflicted by this enigmatic condition. [ABSTRACT FROM AUTHOR]

Background: This study delves into the intricate landscape of atherosclerosis (AS), a chronic inflammatory disorder with significant implications for cardiovascular health. AS poses a considerable burden on global healthcare systems, elevating both mortality and morbidity rates. The pathological underpinnings of AS involve a marked metabolic disequilibrium, particularly within pyrimidine metabolism (PyM), a crucial enzymatic network central to nucleotide synthesis and degradation. While the therapeutic relevance of pyrimidine metabolism in diverse diseases is acknowledged, the explicit role of pyrimidine metabolism genes (PyMGs) in the context of AS remains elusive. Utilizing bioinformatics methodologies, this investigation aims to reveal and substantiate PyMGs intricately linked with AS. Methods: A set of 41 candidate PyMGs was scrutinized through differential expression analysis. GSEA and GSVA were employed to illuminate potential biological pathways and functions associated with the identified PyMGs. Simultaneously, Lasso regression and SVM-RFE were utilized to distill core genes and assess the diagnostic potential of four quintessential PyMGs (CMPK1, CMPK2, NT5C2, RRM1) in discriminating AS. The relationship between key PyMGs and clinical presentations was also explored. Validation of the expression levels of the four PyMGs was performed using the GSE43292 and GSE9820 datasets. Results: This investigation identified four PyMGs, with NT5C2 and RRM1 emerging as key players, intricately linked to AS pathogenesis. Functional analysis underscored their critical involvement in metabolic processes, including pyrimidine-containing compound metabolism and nucleotide biosynthesis. Diagnostic evaluation of these PyMGs in distinguishing AS showcased promising results. Conclusion: In conclusion, this exploration has illuminated a constellation of four PyMGs with a potential nexus to AS pathogenesis. These findings unveil emerging biomarkers, paving the way for novel approaches to disease monitoring and progression, and providing new avenues for therapeutic intervention in the realm of atherosclerosis. [ABSTRACT FROM AUTHOR]

Copyright of Informatica (03505596) is the property of Slovene Society Informatika and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

BackgroundIncreasing evidence have highlighted the biological significance of mRNA N6-methyladenosine (m6A) modification in regulating tumorigenicity and progression. However, the potential roles of m6A regulators in tumor microenvironment (TME) formation and immune cell infiltration in liver hepatocellular carcinoma (LIHC or HCC) requires further clarification.MethodRNA sequencing data were obtained from TCGA-LIHC databases and ICGC-LIRI-JP databases. Consensus clustering algorithm was used to identify m6A regulators cluster subtypes. Weighted gene co-expression network analysis (WGCNA), LASSO regression, Random Forest (RF), and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) were applied to identify candidate biomarkers, and then a m6Arisk score model was constructed. The correlations of m6Arisk score with immunological characteristics (immunomodulators, cancer immunity cycles, tumor-infiltrating immune cells (TIICs), and immune checkpoints) were systematically evaluated. The effective performance of nomogram was evaluated using concordance index (C‐index), calibration plots, decision curve analysis (DCA), and receiver operating characteristic curve (ROC).ResultsTwo distinct m6A modification patterns were identified based on 23 m6A regulators, which were correlated with different clinical outcomes and biological functions. Based on the constructed m6Arisk score model, HCC patients can be divided into two distinct risk score subgroups. Further analysis indicated that the m6Arisk score showed excellent prognostic performance. Patients with a high m6Arisk score was significantly associated with poorer clinical outcome, lower drug sensitivity, and higher immune infiltration. Moreover, we developed a nomogram model by incorporating the m6Arisk score and clinicopathological features. The application of the m6Arisk score for the prognostic stratification of HCC has good clinical applicability and clinical net benefit.ConclusionOur findings reveal the crucial role of m6A modification patterns for predicting HCC TME status and prognosis, and highlight the good clinical applicability and net benefit of m6Arisk score in terms of prognosis, immunophenotype, and drug therapy in HCC patients.

BackgroundNon-specific Orbital Inflammation (NSOI) is a chronic idiopathic condition marked by extensive polymorphic lymphoid infiltration in the orbital area. The integration of metabolic and immune pathways suggests potential therapeutic roles for C-peptide and G protein-coupled receptor 146 (GPR146) in diabetes and its sequelae. However, the specific mechanisms through which GPR146 modulates immune responses remain poorly understood. Furthermore, the utility of GPR146 as a diagnostic or prognostic marker for NSOI has not been conclusively demonstrated.MethodsWe adopted a comprehensive analytical strategy, merging differentially expressed genes (DEGs) from the Gene Expression Omnibus (GEO) datasets GSE58331 and GSE105149 with immune-related genes from the ImmPort database. Our methodology combined LASSO regression and support vector machine-recursive feature elimination (SVM-RFE) for feature selection, followed by Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) to explore gene sets co-expressed with GPR146, identifying a significant enrichment in immune-related pathways. The tumor microenvironment’s immune composition was quantified using the CIBERSORT algorithm and the ESTIMATE method, which confirmed a positive correlation between GPR146 expression and immune cell infiltration. Validation of GPR146 expression was performed using the GSE58331 dataset.ResultsAnalysis identified 113 DEGs associated with GPR146, with a significant subset showing distinct expression patterns. Using LASSO and SVM-RFE, we pinpointed 15 key hub genes. Functionally, these genes and GPR146 were predominantly linked to receptor ligand activity, immune receptor activity, and cytokine-mediated signaling. Specific immune cells, such as memory B cells, M2 macrophages, resting mast cells, monocytes, activated NK cells, plasma cells, and CD8+ T cells, were positively associated with GPR146 expression. In contrast, M0 macrophages, naive B cells, M1 macrophages, activated mast cells, activated memory CD4+ T cells, naive CD4+ T cells, and gamma delta T cells showed inverse correlations. Notably, our findings underscore the potential diagnostic relevance of GPR146 in distinguishing NSOI.ConclusionOur study elucidates the immunological signatures associated with GPR146 in the context of NSOI, highlighting its prognostic and diagnostic potential. These insights pave the way for GPR146 to be a novel biomarker for monitoring the progression of NSOI, providing a foundation for future therapeutic strategies targeting immune-metabolic pathways.

Abstract Background Acute Myeloid Leukemia (AML) generally has a relatively low survival rate after treatment. There is an urgent need to find new biomarkers that may improve the survival prognosis of patients. Machine-learning tools are more and more widely used in the screening of biomarkers. Methods Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), lrFuncs, IdaProfile, caretFuncs, and nbFuncs models were used to screen key genes closely associated with AML. Then, based on the Cancer Genome Atlas (TCGA), pan-cancer analysis was performed to determine the correlation between important genes and AML or other cancers. Finally, the diagnostic value of important genes for AML was verified in different data sets. Results The survival analysis results of the training set showed 26 genes with survival differences. After the intersection of the results of each machine learning method, DNM1, MEIS1, and SUSD3 were selected as key genes for subsequent analysis. The results of the pan-cancer analysis showed that MEIS1 and DNM1 were significantly highly expressed in AML; MEIS1 and SUSD3 are potential risk factors for the prognosis of AML, and DNM1 is a potential protective factor. Three key genes were significantly associated with AML immune subtypes and multiple immune checkpoints in AML. The results of the verification analysis show that DNM1, MEIS1, and SUSD3 have potential diagnostic value for AML. Conclusion Multiple machine learning methods identified DNM1, MEIS1, and SUSD3 can be regarded as prognostic biomarkers for AML.

Abstract Background Nonspecific orbital inflammation (NSOI) is an idiopathic, persistent, and proliferative inflammatory condition affecting the orbit, characterized by polymorphous lymphoid infiltration. Its pathogenesis and progression have been linked to imbalances in tumor metabolic pathways, with glutamine (Gln) metabolism emerging as a critical aspect in cancer. Metabolic reprogramming is known to influence clinical outcomes in various malignancies. However, comprehensive research on glutamine metabolism's significance in NSOI is lacking. Methods This study conducted a bioinformatics analysis to identify and validate potential glutamine-related molecules (GlnMgs) associated with NSOI. The discovery of GlnMgs involved the intersection of differential expression analysis with a set of 42 candidate GlnMgs. The biological functions and pathways of the identified GlnMgs were analyzed using GSEA and GSVA. Lasso regression and SVM-RFE methods identified hub genes and assessed the diagnostic efficacy of fourteen GlnMgs in NSOI. The correlation between hub GlnMgs and clinical characteristics was also examined. The expression levels of the fourteen GlnMgs were validated using datasets GSE58331 and GSE105149. Results Fourteen GlnMgs related to NSOI were identified, including FTCD, CPS1, CTPS1, NAGS, DDAH2, PHGDH, GGT1, GCLM, GLUD1, ART4, AADAT, ASNSD1, SLC38A1, and GFPT2. Biological function analysis indicated their involvement in responses to extracellular stimulus, mitochondrial matrix, and lipid transport. The diagnostic performance of these GlnMgs in distinguishing NSOI showed promising results. Conclusions This study successfully identified fourteen GlnMgs associated with NSOI, providing insights into potential novel biomarkers for NSOI and avenues for monitoring disease progression.

Hongshuo Shi, Xin Yuan, Guobin Liu, Weijing Fan Department of Peripheral Vascular Surgery, Institute of Surgery of Traditional Chinese Medicine, Shuguang Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, People’s Republic of ChinaCorrespondence: Weijing Fan; Guobin Liu, Email fan18811023202@126.com; 15800885533@163.comBackground: A diabetic foot ulcer (DFU) is a serious, long-term condition associated with a significant risk of disability and mortality. However, research on its biomarkers is still limited. This study utilizes bioinformatics and machine learning methods to identify immune-related biomarkers for DFU and validates them through external datasets and animal experiments.Methods: This study used bioinformatics and machine learning to analyze microarray data from the Gene Expression Omnibus (GEO) database to identify key genes associated with DFU. Animal experiments were conducted to validate these findings. This research employs the datasets GSE68183 and GSE80178 retrieved from the GEO database as the training dataset for building a gene machine learning model, and after conducting differential analysis on the data, this study used package glmnet and package e1071 to construct LASSO and SVM-RFE machine learning models, respectively. Subsequently, we validated the model using the training set and validation set (GSE134431). We conducted enrichment analysis, including GSEA and GSVA, on the model genes. We also performed immune functional analysis and immune-related analysis on the model genes. Finally, we conducted immunohistochemistry (IHC) validation on the model genes.Results: This study identifies GSTM5 as a potential immune-related key target in DFU using machine learning and bioinformatics methods. Subsequent validation through external datasets and IHC experiments also confirms GSTM5 as a critical biomarker for DFU. The gene may be associated with T cells regulatory (Tregs) and T cells follicular helper, and it influences the NF-κB, GnRH, and MAPK signaling pathway.Conclusion: This study identified and validated GSTM5 as a biomarker for DFU. This finding may potentially provide a target for immune therapy for DFU.Keywords: bioinformatics, machine learning, diabetic foot ulcer, LASSO, SVM-RFE, GSTM5

Background: Nonspecific orbital inflammation (NSOI) is an idiopathic, persistent, and proliferative inflammatory condition affecting the orbit, characterized by polymorphous lymphoid infiltration. Its pathogenesis and progression have been linked to imbalances in tumor metabolic pathways, with glutamine (Gln) metabolism emerging as a critical aspect in cancer. Metabolic reprogramming is known to influence clinical outcomes in various malignancies. However, comprehensive research on glutamine metabolism's significance in NSOI is lacking. Methods: This study conducted a bioinformatics analysis to identify and validate potential glutamine-related molecules (GlnMgs) associated with NSOI. The discovery of GlnMgs involved the intersection of differential expression analysis with a set of 42 candidate GlnMgs. The biological functions and pathways of the identified GlnMgs were analyzed using GSEA and GSVA. Lasso regression and SVM-RFE methods identified hub genes and assessed the diagnostic efficacy of fourteen GlnMgs in NSOI. The correlation between hub GlnMgs and clinical characteristics was also examined. The expression levels of the fourteen GlnMgs were validated using datasets GSE58331 and GSE105149. Results: Fourteen GlnMgs related to NSOI were identified, including FTCD, CPS1, CTPS1, NAGS, DDAH2, PHGDH, GGT1, GCLM, GLUD1, ART4, AADAT, ASNSD1, SLC38A1, and GFPT2. Biological function analysis indicated their involvement in responses to extracellular stimulus, mitochondrial matrix, and lipid transport. The diagnostic performance of these GlnMgs in distinguishing NSOI showed promising results. Conclusions: This study successfully identified fourteen GlnMgs associated with NSOI, providing insights into potential novel biomarkers for NSOI and avenues for monitoring disease progression. [ABSTRACT FROM AUTHOR]

The diagnosis of tuberculosis depends on detectingMycobacteriumtuberculosis (Mtb). Unfortunately, recognizing patients with extrapulmonary tuberculosis (EPTB) remains challenging due to the insidious clinical presentation and poor performance of diagnostic tests. To identify biomarkers for EPTB, the GSE83456 dataset was screened for differentially expressed genes (DEGs), followed by a gene enrichment analysis. One hundred and ten DEGs were obtained, mainly enriched in inflammation and immune -related pathways. Weighted gene co-expression network analysis (WGCNA) was used to identify 10 co-expression modules. The turquoise module, correlating the most highly with EPTB, contained 96 DEGs. Further screening with the least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE) narrowed down the 96 DEGs to five central genes. All five key genes were validated in the GSE144127 dataset. CARD17 and GBP5 had high diagnostic capacity, with AUC values were 0.763 (95% CI: 0.717-0.805) and 0.833 (95% CI: 0.793-0.869) respectively. Using single sample gene enrichment analysis (ssGSEA), we evaluated the infiltration of 28 immune cells in EPTB and explored their relationships with key genes. The results showed 17 immune cell subtypes with significant infiltrations in EPTB. CARD17, GBP5, HOOK1, LOC730167, and HIST1H4C were significantly associated with 16, 14, 12, 6, and 4 immune cell subtypes, respectively. The RT-qPCR results confirmed that the expression levels of GBP5 and CARD17 were higher in EPTB compared to control. In conclusion, CARD17 and GBP5 have high diagnostic efficiency for EPTB and are closely related to immune cell infiltration. [ABSTRACT FROM AUTHOR]

Background: Acute Myeloid Leukemia (AML) generally has a relatively low survival rate after treatment. There is an urgent need to find new biomarkers that may improve the survival prognosis of patients. Machine-learning tools are more and more widely used in the screening of biomarkers. Methods: Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine-Recursive Feature Elimination (SVM-RFE), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), lrFuncs, IdaProfile, caretFuncs, and nbFuncs models were used to screen key genes closely associated with AML. Then, based on the Cancer Genome Atlas (TCGA), pan-cancer analysis was performed to determine the correlation between important genes and AML or other cancers. Finally, the diagnostic value of important genes for AML was verified in different data sets. Results: The survival analysis results of the training set showed 26 genes with survival differences. After the intersection of the results of each machine learning method, DNM1, MEIS1, and SUSD3 were selected as key genes for subsequent analysis. The results of the pan-cancer analysis showed that MEIS1 and DNM1 were significantly highly expressed in AML; MEIS1 and SUSD3 are potential risk factors for the prognosis of AML, and DNM1 is a potential protective factor. Three key genes were significantly associated with AML immune subtypes and multiple immune checkpoints in AML. The results of the verification analysis show that DNM1, MEIS1, and SUSD3 have potential diagnostic value for AML. Conclusion: Multiple machine learning methods identified DNM1, MEIS1, and SUSD3 can be regarded as prognostic biomarkers for AML. [ABSTRACT FROM AUTHOR]

Background: Heart failure (HF) and idiopathic pulmonary fibrosis (IPF) are global public health concerns. The relationship between HF and IPF is widely acknowledged. However, the interaction mechanisms between these two diseases remain unclear, and early diagnosis is particularly difficult. Through the integration of bioinformatics and machine learning, our work aims to investigate common gene features, putative molecular causes, and prospective diagnostic indicators of IPF and HF. Methods: The Gene Expression Omnibus (GEO) database provided the RNA-seq datasets for HF and IPF. Utilizing a weighted gene co-expression network analysis (WGCNA), possible genes linked to HF and IPF were found. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were then employed to analyze the genes that were shared by HF and IPF. Using the cytoHubba and iRegulon algorithms, a competitive endogenous RNA (ceRNA) network was built based on seven basic diagnostic indicators. Additionally, hub genes were identified using machine learning approaches. External datasets were used to validate the findings. Lastly, the association between the number of immune cells in tissues and the discovered genes was estimated using the CIBERSORT method. Results: In total, 63 shared genes were identified between HF- and IPF-related modules using WGCNA. Extracellular matrix (ECM)/structure organization, ECM-receptor interactions, focal, and protein digestion and absorption, were shown to be the most enrichment categories in GO and KEGG enrichment analysis of common genes. Furthermore, a total of seven fundamental genes, including COL1A1, COL3A1, THBS2, CCND1, ASPN, FAP, and S100A12, were recognized as pivotal genes implicated in the shared pathophysiological pathways of HF and IPF, and TCF12 may be the most important regulatory transcription factor. Two characteristic molecules, CCND1 and NAP1L3, were selected as potential diagnostic markers for HF and IPF, respectively, using a support vector machine-recursive feature elimination (SVM-RFE) model. Furthermore, the development of diseases and diagnostic markers may be associated with immune cells at varying degrees. Conclusions: This study demonstrated that ECM/structure organisation, ECM-receptor interaction, focal adhesion, and protein digestion and absorption, are common pathogeneses of IPF and HF. Additionally, CCND1 and NAP1L3 were identified as potential diagnostic biomarkers for both HF and IPF. The results of our study contribute to the comprehension of the co-pathogenesis of HF and IPF at the genetic level and offer potential biological indicators for the early detection of both conditions.

Accurately estimating the state of health (SOH) of lithium-ion batteries plays a significant role in the safe operation of electric vehicles. Deep learning (DL)-based approaches for estimating state of health (SOH) have consistently been the focus of study in recent years. In the current era of electric mobility, the utilization of lithium-ion batteries (LIBs) has evolved into a necessity for energy storage. Ensuring the safe operation of EVs requires a precise assessment of the state-of-health (SOH) of LIBs. To estimate battery SOH accurately, this paper employs a deep learning (DL) algorithm to enhance the estimation accuracy of SOH to obtain accurate SOH measurements. This research introduces the Diffusion Convolutional Recurrent Neural Network (DCRNN) with a Support Vector Machine-Recursive Feature Elimination (SVM-RFE) algorithm (DCRNN + SVM-RFE) for enhancing classification and feature selection performance. The data gathered from the dataset were pre-processed using the min–max normalization method. The Center for Advanced Life Cycle Engineering (CALCE) dataset from the University of Maryland was employed to train and evaluate the model. The SVM-RFE algorithm was used for feature selection of pre-processed data. DCRNN algorithm was used for the classification process to enhance prediction precision. The DCRNN + SVM-RFE model’s performance was calculated using Mean Absolute Percentage Error (MAPE), Mean Squared Error (MAE), Mean Squared Error (MSE), and Root MSE (RMSE) metric values. The proposed model generates accurate results for SOH prediction; all RMSEs are within 0.02%, MAEs are within 0.015%, MSEs were within 0.032%, and MAPEs are within 0.41%. The mean values of RMSE, MSE, MAE, and MAPE were 0.014, 0.026, 0.011, and 0.32, respectively. Experiments confirmed that the DCRNN + SVM-RFE model has the highest accuracy among those that predict SOH.

Abstract Background Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is a chronic, inflammatory, and autoimmune disease, but its specific etiology and pathogenesis are still unclear. This study aimed to better discover the causative basement membrane (BM) genes of their subtypes and their associations. Methods The differential expression of BM genes between CD and UC was analyzed and validated by downloading relevant datasets from the GEO database. We divided the samples into 3 groups for comparative analysis. Construction of PPI networks, enrichment of differential gene functions, screening of Lasso regression models, validation of ROC curves, nomogram for disease prediction and other analytical methods were used. The immune cell infiltration was further explored by ssGSEA analysis, the immune correlates of hub BM genes were found, and finally, the hub central genes were screened by machine learning. Results We obtained 6 candidate hub BM genes related to cellular immune infiltration in the CD and UC groups, respectively, and further screened the central hub genes ADAMTS17 and ADAMTS9 through machine learning. And in the ROC curve models, AUC > 0.7, indicating that this characteristic gene has a more accurate predictive effect on IBD. We also found that the pathogenicity-related BM genes of the CD and UC groups were mainly concentrated in the ADAMTS family (ADAMTS17 and ADAMTS9). Addition there are some differences between the two subtypes, and the central different hub BM genes are SPARC, POSTN, and ADAMTS2. Conclusions In the current study, we provided a nomogram model of CD and UC composed of BM genes, identified central hub genes, and clarified the similarities and differences between CD and UC. This will have potential value for preclinical, clinical, and translational guidance and differential research in IBD.

Abstract Introduction This cross-sectional study aimed to determine the factors related to hallux valgus (HV) and their importance using support vector machine-recursive feature elimination (SVM-RFE). Methods A total of 864 participants aged ≥ 18 years were enrolled. The Manchester scale was used to determine the presence of HV (summed scores for both feet ≥ 4). The questionnaire included items such as age, sex, height, weight, and foot measurements. These internal factors were analyzed to determine if they are related to HV using SVM-RFE. Results The results of tenfold cross-validation using SVM-RFE revealed that the numbers of feature selections were 10, 10, and 9 for age, sex, and body weight, respectively, and these factors were shown to be related to HV. HV was found to be more common in women than in men (women, 24.9%; men, 7.6%), but the sex difference was not significant in older people. Conclusion Age and sex were found to be important factors associated with HV identified via feature selection using SVM-RFE.

Background: Multiple myeloma (MM) ranks second among the most prevalent hematological malignancies. Recent studies have unearthed the promise of cuproptosis as a novel therapeutic intervention for cancer. However, no research has unveiled the particular roles of cuproptosis‐related genes (CRGs) in the prediction of MM diagnosis. Methods: Microarray data and clinical characteristics of MM patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed gene analysis, least absolute shrinkage and selection operator (LASSO) and support vector machine‐recursive feature elimination (SVM‐RFE) algorithms were applied to identify potential signature genes for MM diagnosis. Predictive performance was further assessed by receiver operating characteristic (ROC) curves, nomogram analysis, and external data sets. Functional enrichment analysis was performed to elucidate the involved mechanisms. Finally, the expression of the identified genes was validated by quantitative real‐time polymerase chain reaction (qRT‐PCR) in MM cell samples. Results: The optimal gene signature was identified using LASSO and SVM‐RFE algorithms based on the differentially expressed CRGs: ATP7A, FDX1, PDHA1, PDHB, MTF1, CDKN2A, and DLST. Our gene signature‐based nomogram revealed a high degree of accuracy in predicting MM diagnosis. ROC curves showed the signature had dependable predictive ability across all data sets, with area under the curve values exceeding 0.80. Additionally, functional enrichment analysis suggested significant associations between the signature genes and immune‐related pathways. The expression of the genes was validated in MM cells, indicating the robustness of these findings. Conclusion: We discovered and validated a novel CRG signature with strong predictive capability for diagnosing MM, potentially implicated in MM pathogenesis and progression through immune‐related pathways. [ABSTRACT FROM AUTHOR]

Background. Suicidal attempts in patients with major depressive disorder (MDD) have become an important challenge in global mental health affairs. To correctly distinguish MDD patients with and without suicidal attempts, a multimodal prediction model was developed in this study using multimodality data, including demographic, depressive symptoms, and brain structural imaging data. This model will be very helpful in the early intervention of MDD patients with suicidal attempts. Methods. Two feature selection methods, support vector machine-recursive feature elimination (SVM-RFE) and random forest (RF) algorithms, were merged for feature selection in 208 MDD patients. SVM was then used as a classification model to distinguish MDD patients with suicidal attempts or not. Results. The multimodal predictive model was found to correctly distinguish MDD patients with and without suicidal attempts using integrated features derived from SVM-RFE and RF, with a balanced accuracy of 77.78%, sensitivity of 83.33%, specificity of 70.37%, positive predictive value of 78.95%, and negative predictive value of 76.00%. The strategy of merging the features from two selectionmethods outperformed traditional methods in the prediction of suicidal attempts in MDD patients, with hippocampal volume, cerebellar vermis volume, and supracalcarine volume being the top three features in the prediction model. Conclusions. This study not only developed a new multimodal prediction model but also found three important brain structural phenotypes for the prediction of suicidal attempters in MDD patients. This prediction model is a powerful tool for early intervention in MDD patients, which offers neuroimaging biomarker targets for treatment in MDD patients with suicidal attempts. [ABSTRACT FROM AUTHOR]

Abstract Background Multiple myeloma (MM) ranks second among the most prevalent hematological malignancies. Recent studies have unearthed the promise of cuproptosis as a novel therapeutic intervention for cancer. However, no research has unveiled the particular roles of cuproptosis‐related genes (CRGs) in the prediction of MM diagnosis. Methods Microarray data and clinical characteristics of MM patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed gene analysis, least absolute shrinkage and selection operator (LASSO) and support vector machine‐recursive feature elimination (SVM‐RFE) algorithms were applied to identify potential signature genes for MM diagnosis. Predictive performance was further assessed by receiver operating characteristic (ROC) curves, nomogram analysis, and external data sets. Functional enrichment analysis was performed to elucidate the involved mechanisms. Finally, the expression of the identified genes was validated by quantitative real‐time polymerase chain reaction (qRT‐PCR) in MM cell samples. Results The optimal gene signature was identified using LASSO and SVM‐RFE algorithms based on the differentially expressed CRGs: ATP7A, FDX1, PDHA1, PDHB, MTF1, CDKN2A, and DLST. Our gene signature‐based nomogram revealed a high degree of accuracy in predicting MM diagnosis. ROC curves showed the signature had dependable predictive ability across all data sets, with area under the curve values exceeding 0.80. Additionally, functional enrichment analysis suggested significant associations between the signature genes and immune‐related pathways. The expression of the genes was validated in MM cells, indicating the robustness of these findings. Conclusion We discovered and validated a novel CRG signature with strong predictive capability for diagnosing MM, potentially implicated in MM pathogenesis and progression through immune‐related pathways.

Purpose: Lung adenocarcinoma (LUAD) is a malignant tumor with a high lethality rate. Immunotherapy has become a breakthrough in cancer treatment and improves patient survival and prognosis. Therefore, it is necessary to find new immune-related markers. However, the current research on immune-related markers in LUAD is not sufficient. Therefore, there is a need to find new immune-related biomarkers to help treat LUAD patients. Methods: In this study, a bioinformatics approach combined with a machine learning approach screened reliable immune-related markers to construct a prognostic model to predict the overall survival (OS) of LUAD patients, thus promoting the clinical application of immunotherapy in LUAD. The experimental data were obtained from The Cancer Genome Atlas (TCGA) database, including 535 LUAD and 59 healthy control samples. Firstly, the Hub gene was screened using a bioinformatics approach combined with the Support Vector Machine Recursive Feature Elimination algorithm; then, a multifactorial Cox regression analysis by constructing an immune prognostic model for LUAD and a nomogram to predict the OS rate of LUAD patients. Finally, the regulatory mechanism of Hub genes in LUAD was analyzed by ceRNA. Results: Five genes, ADM2, CDH17, DKK1, PTX3, and AC145343.1, were screened as potential immune-related genes in LUAD. Among them, ADM2 and AC145343.1 had a good prognosis in LUAD patients (HR < 1) and were novel markers. The remaining three genes screened were associated with poor prognosis in LUAD patients (HR > 1). In addition, the experimental results showed that patients in the low-risk group had better OS rates than those in the high-risk group (P < 0.001). Conclusion: In this paper, we propose an immune prognostic model to predict OS rate in LUAD patients and show the correlation between five immune genes and the level of immune-related cell infiltration. It provides new markers and additional ideas for immunotherapy in patients with LUAD. [ABSTRACT FROM AUTHOR]

Background: Inflammatory bowel disease (IBD), including Crohn's disease (CD) and ulcerative colitis (UC), is a chronic, inflammatory, and autoimmune disease, but its specific etiology and pathogenesis are still unclear. This study aimed to better discover the causative basement membrane (BM) genes of their subtypes and their associations. Methods: The differential expression of BM genes between CD and UC was analyzed and validated by downloading relevant datasets from the GEO database. We divided the samples into 3 groups for comparative analysis. Construction of PPI networks, enrichment of differential gene functions, screening of Lasso regression models, validation of ROC curves, nomogram for disease prediction and other analytical methods were used. The immune cell infiltration was further explored by ssGSEA analysis, the immune correlates of hub BM genes were found, and finally, the hub central genes were screened by machine learning. Results: We obtained 6 candidate hub BM genes related to cellular immune infiltration in the CD and UC groups, respectively, and further screened the central hub genes ADAMTS17 and ADAMTS9 through machine learning. And in the ROC curve models, AUC > 0.7, indicating that this characteristic gene has a more accurate predictive effect on IBD. We also found that the pathogenicity-related BM genes of the CD and UC groups were mainly concentrated in the ADAMTS family (ADAMTS17 and ADAMTS9). Addition there are some differences between the two subtypes, and the central different hub BM genes are SPARC, POSTN, and ADAMTS2. Conclusions: In the current study, we provided a nomogram model of CD and UC composed of BM genes, identified central hub genes, and clarified the similarities and differences between CD and UC. This will have potential value for preclinical, clinical, and translational guidance and differential research in IBD. [ABSTRACT FROM AUTHOR]

Introduction: This cross-sectional study aimed to determine the factors related to hallux valgus (HV) and their importance using support vector machine-recursive feature elimination (SVM-RFE). Methods: A total of 864 participants aged ≥ 18 years were enrolled. The Manchester scale was used to determine the presence of HV (summed scores for both feet ≥ 4). The questionnaire included items such as age, sex, height, weight, and foot measurements. These internal factors were analyzed to determine if they are related to HV using SVM-RFE. Results: The results of tenfold cross-validation using SVM-RFE revealed that the numbers of feature selections were 10, 10, and 9 for age, sex, and body weight, respectively, and these factors were shown to be related to HV. HV was found to be more common in women than in men (women, 24.9%; men, 7.6%), but the sex difference was not significant in older people. Conclusion: Age and sex were found to be important factors associated with HV identified via feature selection using SVM-RFE. [ABSTRACT FROM AUTHOR]

Background: Temporal lobe epilepsy (TLE) is a common chronic episodic illness of the nervous system. However, the precise mechanisms of dysfunction and diagnostic biomarkers in the acute phase of TLE are uncertain and hard to diagnose. Thus, we intended to qualify potential biomarkers in the acute phase of TLE for clinical diagnostics and therapeutic purposes. Methods: An intra-hippocampal injection of kainic acid was used to induce an epileptic model in mice. First, with a TMT/iTRAQ quantitative labeling proteomics approach, we screened for differentially expressed proteins (DEPs) in the acute phase of TLE. Then, differentially expressed genes (DEGs) in the acute phase of TLE were identified by linear modeling on microarray data (limma) and weighted gene co-expression network analysis (WGCNA) using the publicly available microarray dataset GSE88992. Co-expressed genes (proteins) in the acute phase of TLE were identified by overlap analysis of DEPs and DEGs. The least absolute shrinkage and selection operator (LASSO) regression and support vector machine recursive feature elimination (SVM-RFE) algorithms were used to screen Hub genes in the acute phase of TLE, and logistic regression algorithms were applied to develop a novel diagnostic model for the acute phase of TLE, and the sensitivity of the diagnostic model was validated using receiver operating characteristic (ROC) curves. Results: We screened a total of 10 co-expressed genes (proteins) from TLE-associated DEGs and DEPs utilizing proteomic and transcriptome analysis. LASSO and SVM-RFE algorithms for machine learning were applied to identify three Hub genes: Ctla2a, Hapln2, and Pecam1. A logistic regression algorithm was applied to establish and validate a novel diagnostic model for the acute phase of TLE based on three Hub genes in the publicly accessible datasets GSE88992, GSE49030, and GSE79129. Conclusion: Our study establishes a reliable model for screening and diagnosing the acute phase of TLE that provides a theoretical basis for adding diagnostic biomarkers for TLE acute phase genes. [ABSTRACT FROM AUTHOR]

BACKGROUND: Prostate cancer (PCa) and benign prostatic hyperplasia (BPH) are common male diseases whose incidence rates gradually increase with age. They seriously affect men's physical health and quality of life. This study aimed to identify new biomarkers for the diagnosis of BPH and PCa. METHODS: Two datasets, GSE28204 and GSE134051 (including human PCa and BPH), were downloaded from the GEO database. The batch effect was removed for merging, and then differential gene expression analysis was conducted to identify BPH and PCa cases. The diagnostic biomarkers of BPH and PCa were further screened using machine learning and bioinformatics. ROC curves were drawn to evaluate the diagnostic accuracy of the selected biomarkers. An online website and qPCR were used to preliminarily explore the expression levels of PCa biomarkers. The correlations between the expression of biomarkers and the tumor microenvironment, tumor mutation load and immunotherapy drugs were evaluated. RESULTS: We identified fifteen genes (CHRDL1, DES, FLNC, GSTP1, MYL9, TGFB3, NEFH, TAGLN, SPARCL1, SYNM, TRPM8, HPN, PLA2G7, ENTPD5 and GPR160) as critical diagnostic biomarkers. After reviewing the literature on all selected biomarkers, we found few studies on the four genes CHRDL1, NEFH, TAGLN and SYNM in BPH or PCa. We defined these four genes as new potential diagnostic biomarkers (NPDBs) of BPH and PCa. All NPDBs were downregulated in PCa patients and PCa cell lines and upregulated in BPH patients and cell lines. When the immune landscape and mutation frequencies were analyzed, the results showed that the tumor microenvironment (TME), immune landscape, tumor mutation burden, and drug response were significantly correlated with NPDB expressions. CONCLUSIONS: We found four new diagnostic markers of BPH and PCa, which may facilitate the early diagnosis, treatment, and immunotherapeutic responses assessment and may be of major value in guiding clinical practice. [ABSTRACT FROM AUTHOR]

BackgroundAging and immune infiltration have essential role in the physiopathological mechanisms of diabetic nephropathy (DN), but their relationship has not been systematically elucidated. We identified aging-related characteristic genes in DN and explored their immune landscape.MethodsFour datasets from the Gene Expression Omnibus (GEO) database were screened for exploration and validation. Functional and pathway analysis was performed using Gene Set Enrichment Analysis (GSEA). Characteristic genes were obtained using a combination of Random Forest (RF) and Support Vector Machine Recursive Feature Elimination (SVM-RFE) algorithm. We evaluated and validated the diagnostic performance of the characteristic genes using receiver operating characteristic (ROC) curve, and the expression pattern of the characteristic genes was evaluated and validated. Single-Sample Gene Set Enrichment Analysis (ssGSEA) was adopted to assess immune cell infiltration in samples. Based on the TarBase database and the JASPAR repository, potential microRNAs and transcription factors were predicted to further elucidate the molecular regulatory mechanisms of the characteristic genes.ResultsA total of 14 differentially expressed genes related to aging were obtained, of which 10 were up-regulated and 4 were down-regulated. Models were constructed by the RF and SVM-RFE algorithms, contracted to three signature genes: EGF-containing fibulin-like extracellular matrix (EFEMP1), Growth hormone receptor (GHR), and Vascular endothelial growth factor A (VEGFA). The three genes showed good efficacy in three tested cohorts and consistent expression patterns in the glomerular test cohorts. Most immune cells were more infiltrated in the DN samples compared to the controls, and there was a negative correlation between the characteristic genes and most immune cell infiltration. 24 microRNAs were involved in the transcriptional regulation of multiple genes simultaneously, and Endothelial transcription factor GATA-2 (GATA2) had a potential regulatory effect on both GHR and VEGFA.ConclusionWe identified a novel aging-related signature allowing assessment of diagnosis for DN patients, and further can be used to predict immune infiltration sensitivity.

Background: Studies have demonstrated that immune cell activation and their infiltration in the myocardium can have adverse effects on the heart, contributing to the pathogenesis of heart failure (HF). The purpose of this study is used by bioinformatics analysis to determine the potential diagnostic markers of heart failure and establish an applicable model to predict the association between heart failure and immune cell infiltration. Methods: Firstly, gene expression profiles of dilated heart disease GSE3585 and GSE120895 were obtained in Gene Expression Omnibus (GEO) database. This study then selected differentially expressed genes (DEGs) in 54 patients with HF and 13 healthy controls. In this study, biomarkers were identified using Least Absolute Shrinkage and Selector Operation (LASSO) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE). Additionally, we evaluated the prognostic discrimination performance by the receiver operating characteristic (ROC) curve. Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) was used for analyzing immune cell infiltration in HF tissues. Lastly, immune biomarkers were correlated with each other. Result: After 24 DEGs were analyzed using a combinatorial model of LASSO regression and SVM-RFE analysis, four key genes were obtained, namely NSG1, NPPB, PHLDA1, and SERPINE2.The area under the curve (AUC) of these four genes were greater than 0.8. Subsequently, using CIBERPORT, we also found that compared with normal people, the proportion of M1 macrophages and activated mast cells in heart failure tissues decreased. In addition, correlation analysis showed that NPPB, PHLDA1 and SERPINE2 were associated with immune cell infiltration. Conclusion: NSG1, NPPB, PHLDA1 and SERPINE2 were identified as potential biomarkers of heart failure. It reveals the comprehensive role of relevant central genes in immune infiltration, which provides a new research idea for the treatment and early detection in heart failure.

Soil salinization has posed a serious threat to the growth and yield of crops in the national food security. Among them, the Taolai River basin with the widely distributed saline-alkali land has been one of the most important planting areas in northwest China. It is a high demand for the timely acquisition of soil salinity information during the salinization control. In this study, a representative sampling area of soil salinization was taken as the Bianwan Farm in Suzhou District, Jiuquan City, Gansu Province, China. A rapid inversion model of soil salinity was proposed at the soil depths of 0-15, 15-30, and 30-50 cm under the crop cover of alfalfa and corn in the phenological period. The multi-spectral image data of the Unmanned Aerial Vehicle (UAV) was also collected at the same time. The reflectance of the spectral band was extracted in the different acquisition points of plots. The red edge band was also introduced to calculate the spectral index, in order to effectively improve the inversion accuracy. A total of 58 spectral indices were involved in the modeling. The Support Vector Machine-Recursive Feature Elimination (SVM-RFE) was selected to screen the spectral index. Specifically, the SVM was used to sort the feature variables, and then evaluate the importance of each feature variable. The variables with low importance were removed, according to the backward iteration. As such, a better performance was achieved to effectively remove the redundant features for the high running speed of the model. A total of 36 models were constructed to evaluate the accuracy and inversion effect of the models, including Random Forest (RF), Extreme learning machine (ELM), and Back-propagation neural network (BPNN). The model input was taken as the unfiltered full and filtered new variable group. Finally, the best soil-salinity inversion model was determined for the optimal inversion depth under crop coverage. The results show that the SVM-RFE variable selection significantly improved the accuracy of each soil-salinity inversion model. A better performance was achieved in the coefficient of determination (R2), root-mean-square error (RMSE), and training speed of the improved model, compared with the model without variable screening. Overall, the inversion effect of the RF model was better than that of ELM and BPNN models. Among them, the inversion effect was one of the best indicators for real soil salt. Specifically, the RF model presented the best inversion effect in the 0-15 and 30-50 cm soil layers under crop cover, where the Rp 2 values of the validation set in the alfalfa field were 0.71 and 0.58, respectively, RMSEp values were 0.026 and 0.033, respectively; the Rp 2 values in the corn field were 0.67 and 0.64, respectively, the RMSEp values were 0.111 and 0.094, respectively. In the 15-30 cm layer, the ELM model presented the best inversion effect, where the Rp 2 values in the alfalfa and corn fields were 0.58, and 0.039, respectively; the RMSEp values were 0.68, and 0.059, respectively. In the terms of inversion depth, the inversion effects of 0-15 cm and 30-50 cm were better than that of 15-30 cm for the alfalfa-covered soil. The inversion effects of 0-15 cm and 15-30 cm were better than that of 30-50 cm for the corn-covered soil. The comprehensive analysis showed that the 0-15 cm soil layer was the best inversion depth for the soil salt content under crop cover, where the average R² of the validation set was 0.65, and the RMSE was 0.084. A strong reference was offered to manage the saline-alkali land in the arid area of northwest China. The finding can also provide a scientific basis for the rapid inversion of salt in the different soil depth layers under crop mulching. [ABSTRACT FROM AUTHOR]

Lung cancer is the deadliest and most aggressive malignancy in the world. Preventing cancer is crucial. Therefore, the new molecular targets have laid the foundation for molecular diagnosis and targeted therapy of lung cancer. PLA2G1B plays a key role in lipid metabolism and inflammation. PLA2G1B has selective substrate specificity. In this paper, the recombinant protein molecular structure of PLA2G1B was studied and novel therapeutic interventions were designed to disrupt PLA2G1B activity and impede tumor growth by targeting specific regions or residues in its structure. Construct protein-protein interaction networks and core genes using R's "STRING" program. LASSO, SVM-RFE and RF algorithms identified important genes associated with lung cancer. 282 deg were identified. Enrichment analysis showed that these genes were mainly related to adhesion and neuroactive ligand-receptor interaction pathways. PLA2G1B was subsequently identified as developing a preventative feature. GSEA showed that PLA2G1B is closely related to α-linolenic acid metabolism. Through the analysis of LASSO, SVM-RFE and RF algorithms, we found that PLA2G1B gene may be a preventive gene for lung cancer. [ABSTRACT FROM AUTHOR]

BackgroundNumerous studies have suggested that ferroptosis plays a significant role in the development of polycystic ovary syndrome (PCOS), but the mechanism remains unclear.MethodsIn this study, we explored the role of ferroptosis-related genes in the pathogenesis of PCOS using a comprehensive bioinformatics method. First, we downloaded several Gene Expression Omnibus (GEO) datasets and combined them into a meta-GEO dataset. Differential expression analysis was performed to screen for significant ferroptosis-related genes between the normal and PCOS samples. The least absolute shrinkage selection operator regression and support vector machine–recursive feature elimination were used to select the best signs to construct a PCOS diagnostic model. Receiver operating characteristic curve analysis and decision curve analysis were applied to test the performance of the model. Finally, a ceRNA network-related ferroptosis gene was constructed.ResultsFive genes, namely, NOX1, ACVR1B, PHF21A, FTL, and GALNT14, were identified from 10 differentially expressed ferroptosis-related genes to construct a PCOS diagnostic model. Finally, a ceRNA network including 117 lncRNAs, 67 miRNAs, and five ferroptosis-related genes was constructed.ConclusionOur study identified five ferroptosis-related genes that may be involved in the pathogenesis of PCOS, which may provide a novel perspective for the clinical diagnosis and treatment of PCOS.

Background: Calcific aortic valve disease (CAVD) is the most common valvular heart disease in the aging population, resulting in a significant health and economic burden worldwide, but its underlying diagnostic biomarkers and pathophysiological mechanisms are not fully understood.Methods: Three publicly available gene expression profiles (GSE12644, GSE51472, and GSE77287) from human Calcific aortic valve disease (CAVD) and normal aortic valve samples were downloaded from the Gene Expression Omnibus database for combined analysis. R software was used to identify differentially expressed genes (DEGs) and conduct functional investigations. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to identify key feature genes as potential biomarkers for Calcific aortic valve disease (CAVD). Receiver operating characteristic (ROC) curves were used to evaluate the discriminatory ability of key genes. The CIBERSORT deconvolution algorithm was used to determine differential immune cell infiltration and the relationship between key genes and immune cell types. Finally, the Expression level and diagnostic ability of the identified biomarkers were further validated in an external dataset (GSE83453), a single-cell sequencing dataset (SRP222100), and immunohistochemical staining of human clinical tissue samples, respectively.Results: In total, 34 identified DEGs included 21 upregulated and 13 downregulated genes. DEGs were mainly involved in immune-related pathways such as leukocyte migration, granulocyte chemotaxis, cytokine activity, and IL-17 signaling. The machine learning algorithm identified SCG2 and CCL19 as key feature genes [area under the ROC curve (AUC) = 0.940 and 0.913, respectively; validation AUC = 0.917 and 0.903, respectively]. CIBERSORT analysis indicated that the proportion of immune cells in Calcific aortic valve disease (CAVD) was different from that in normal aortic valve tissues, specifically M2 and M0 macrophages. Key genes SCG2 and CCL19 were significantly positively correlated with M0 macrophages. Single-cell sequencing analysis and immunohistochemical staining of human aortic valve tissue samples showed that SCG2 and CCL19 were increased in Calcific aortic valve disease (CAVD) valves.Conclusion: SCG2 and CCL19 are potential novel biomarkers of Calcific aortic valve disease (CAVD) and may play important roles in the biological process of Calcific aortic valve disease (CAVD). Our findings advance understanding of the underlying mechanisms of Calcific aortic valve disease (CAVD) pathogenesis and provide valuable information for future research into novel diagnostic and immunotherapeutic targets for Calcific aortic valve disease (CAVD).

Background: To determine effective biomarkers for the diagnosis of acute liver failure (ALF) and explore the characteristics of the immune cell infiltration of ALF. Methods: We analyzed the differentially expressed genes (DEGs) between ALF and control samples in GSE38941, GSE62029, GSE96851, GSE120652, and merged datasets. Co-expressed DEGs (co-DEGs) identified from the five datasets were analyzed for enrichment analysis. We further constructed a PPI network of co-DEGs using the STRING database. Then, we integrated the two kinds of machine-learning strategies to identify diagnostic biomarkers of top hub genes screened based on MCC and Degree methods. And the potential diagnostic performance of the biomarkers for ALF was estimated using the AUC values. Data from GSE14668, GSE74000, and GSE96851 databases was performed as external verification sets to validate the expression level of potential diagnostic biomarkers. Furthermore, we analyzed the difference in the protein level of diagnostic biomarkers between normal and ALF mice models. Finally, we used CIBERSORT to estimate relative infiltration levels of 22 immune cell subsets in ALF samples and further analyzed the relationships between the diagnostic biomarkers and infiltrated immune cells. Results: A total of 200 co-DEGs were screened. Enrichment analyses depicted that they are highly enriched in metabolism and matrix collagen productionassociated processes. The top 28 hub genes were obtained by integrating MCC and Degree methods. Then, the collagen type IV alpha 2 chain (COL4A2) was regarded as the diagnostic biomarker and showed excellent specificity and sensitivity. COL4A2 also showed a statistically significant difference and excellent diagnostic effectiveness in the verification set. In addition, there was a significant upregulation in the COL4A2 protein level in ALF mice models compared with the normal group. CIBERSORT analysis showed that activated CD4 T cells, plasma cells, macrophages, and monocytes may be implicated in the progress of ALF. In addition, COL4A2 showed different degrees of correlation with immune cells. Conclusion: In conclusion, COL4A2 may be a diagnostic biomarker for ALF, and immune cell infiltration may have important implications for the occurrence and progression of ALF. [ABSTRACT FROM AUTHOR]

Since the classification methods mentioned in previous studies are currently unable to meet the accuracy requirements for fault diagnosis in large-scale chemical industries, these methods are gradually being eliminated and rarely used. This research offers a probabilistic neural network (PNN) based on feature selection and a bio-heuristic optimizer as a fault diagnostic approach for chemical industries using artificial intelligence. The sample characteristics are initially simplified using heuristic feature selection and support vector machine recursive feature elimination (SVM-RFE). Using PNN as the principal classifier of the fault diagnostic model and employing a modified salp swarm algorithm (MSSA) linked with the bio-heuristic optimizer to optimize the hidden smoothing factor (σ) of PNN further improves the classification performance of PNN. The MSSA introduces the Lévy flight method, greatly enhancing exploration capabilities and convergence speed compared to the standard SSA. To validate the engineering application of the suggested method, a PSO-SVM-REF-MSSA-PNN model is created, and TE process data are utilized in tests. The model's performance is evaluated by comparing its accuracy and F1-score to other regularly used classification models. The results indicate that the data samples selected by PSO-SVM-RFE features simplify and eliminate redundant features more effectively than other feature selection techniques. The MSSA algorithm's optimization capabilities surpass those of conventional optimization techniques. The PNN network is more suitable for fault detection and classification in the chemical industry. The three considerations listed above make it evident that the proposed approach might greatly help identify TE process problems. [ABSTRACT FROM AUTHOR]