Immune control in Kawasaki disease knowledge mapping: a bibliometric analysis.

BACKGROUND: Kawasaki disease is a systemic vascular disease with an unclear pathophysiology that primarily affects children under the age of five. Research on immune control in Kawasaki disease has been gaining attention. This study aims to apply a bibliometric analysis to examine the present and future directions of immune control in Kawasaki disease. METHODS: By utilizing the themes "Kawasaki disease," "Kawasaki syndrome," and "immune control," the Web of Science Core Collection database was searched for publications on immune control in Kawasaki disease. This bibliometric analysis was carried out using VOSviewers, CiteSpace, and the R package "bibliometrix." RESULTS: In total, 294 studies on immune control in Kawasaki disease were published in Web of Science Core Collection. The three most significant institutions were Chang Gung University, the University of California San Diego, and Kaohsiung Chang Gung Memorial Hospital. China, the United States, and Japan were the three most important countries. In this research field, Clinical and Experimental Immunology was the top-referred journal, while the New England Journal of Medicine was the most co-cited journal. The Web of Science Core Collection document by McCrindle BW et al. published in 2017 was the most cited reference. Additionally, the author keywords concentrated on "COVID-19," "SARS-CoV-2," and "multisystem inflammatory syndrome in children" in recent years. CONCLUSION: The research trends and advancements in immune control in Kawasaki disease are thoroughly summarised in this bibliometric analysis, which is the first to do so. The data indicate recent research frontiers and hot directions, making it easier for researchers to study the immune control of Kawasaki disease.

Knowledge mapping of neonatal electroencephalogram: A bibliometric analysis (2004-2022).

BACKGROUND: Electroencephalography (EEG), a widely used noninvasive neurophysiological diagnostic tool, has experienced substantial advancements from 2004 to 2022, particularly in neonatal applications. Utilizing a bibliometric methodology, this study delineates the knowledge structure and identifies emergent trends within neonatal EEG research. METHODS: An exhaustive literature search was conducted on the Web of Science Core Collection (WoSCC) database to identify publications related to neonatal EEG from 2004 to 2022. Analytical tools such as VOSviewer, CiteSpace, and the R package "bibliometrix" were employed to facilitate this investigation. RESULTS: The search yielded 2501 articles originating from 79 countries, with the United States and England being the predominant contributors. A yearly upward trend in publications concerning neonatal EEG was observed. Notable research institutions leading this field include the University of Helsinki, University College London, and University College Cork. Clinical Neurophysiology is identified as the foremost journal in this realm, with Pediatrics as the most frequently co-cited journal. The collective body of work from 9977 authors highlights Sampsa Vanhatalo as the most prolific contributor, while Mark Steven Scher is recognized as the most frequently co-cited author. Key terms such as "seizures," "epilepsy," "hypoxic-ischemic encephalopathy," "amplitude-integrated EEG," and "brain injury" represent the focal research themes. CONCLUSION: This bibliometric analysis offers the first comprehensive review, encapsulating research trends and progress in neonatal EEG. It reveals current research frontiers and crucial directions, providing an essential resource for researchers engaged in neonatal neuroscience.

Assessing causal relationships between gut microbiota and psoriasis: evidence from two sample Mendelian randomization analysis.

Mounting data hints that the gut microbiota's role may be pivotal in understanding the emergence of psoriasis. However, discerning a direct causal link is yet elusive. In this exploration, we adopted a Mendelian randomization (MR) strategy to probe the prospective causal interplay between the gut's microbial landscape and the predisposition to psoriasis. Genetic markers acting as instrumental variables for gut microbiota were extrapolated from a genome-wide association study (GWAS) encompassing 18,340 individuals. A separate GWAS yielded summary data for psoriasis, which covered 337,159 patients and 433,201 control subjects. The primary analysis hinged on inverse variance weighting (IVW). Additional methods like the weighted median approach and MR-Egger regression were employed to validate the integrity of our findings. Intriguing correlations emerged between psoriasis risk and eight specific bacterial traits. To illustrate: Mollicutes presented an odds ratio (OR) of 1.003 with a 95% confidence interval (CI) spanning 1.001-1.005 (p = 0.016), while the family. Victivallaceae revealed an OR of 0.998 with CI values between 0.997 and 0.999 (p = 0.023). Eubacterium (coprostanoligenes group) revealed an OR of 0.997 with CI values between 0.994 and 0.999 (p = 0.027). Eubacterium (fissicatena group) revealed an OR of 0.997 with CI values between 0.996 and 0.999 (p = 0.005). Holdemania revealed an OR of 1.001 with CI values 1-1.003 (p = 0.034). Lachnospiraceae (NK4A136 group) revealed an OR of 0.997 with CI values between 0.995 and 0.999 (p = 0.046). Lactococcus revealed an OR of 0.998 with CI values between 0.996 and 0.999 (p = 0.008). Tenericutes revealed an OR of 1.003 with CI values between 1.001 and 1.006 (p = 0.016). Sensitivity analysis for these bacterial features yielded congruent outcomes, reinforcing statistically significant ties between the eight bacterial entities and psoriasis. This comprehensive probe underscores emerging evidence pointing towards a plausible causal nexus between diverse gut microbiota and the onset of psoriasis. It beckons further research to unravel the intricacies of how the gut's microbial constituents might sway psoriasis's pathogenesis.

Development and trends in metabolomics studies in psoriasis: A bibliometric analysis of related research from 2011 to 2024.

Background: Psoriasis is a chronic, inflammatory skin disease with autoimmune characteristics. Recent research has made significant progress in the field of psoriasis metabolomics. However, there is a lack of bibliometric analysis on metabolomics of psoriasis. The objective of this study is to utilize bibliometrics to present a comprehensive understanding of the knowledge structure and research hotspots in psoriasis within the field of metabolomics. Methods: We conducted a bibliometric analysis by searching the Web of Science Core Collection database for publications on metabolomics in psoriasis from 2011 to 2024. To perform this analysis, we utilized tools such as VOSviewers, CiteSpace, and the R package "bibliometrix". Results: A total of 307 articles from 47 countries, with the United States and China leading the way, were included in the analysis. The publications focusing on metabolomics in psoriasis have shown a steady year-on-year growth. The Medical University of Bialystok is the main research institution. The International Journal of Molecular Sciences emerges as the prominent journal in the field, while the Journal of Investigative Dermatology stands out as the highly co-cited publication. A total of 2029 authors contributed to these publications, with Skrzydlewska Elzbieta, Baran Anna, Flisiak Iwona, Murakami Makoto being the most prolific contributors. Notably, Armstrong April W. received the highest co-citation. Investigating the mechanisms of metabolomics in the onset and progression of psoriasis, as well as exploring therapeutic strategies, represents the primary focus of this research area. Emerging research hotspots encompass inflammation, lipid metabolism, biomarker, metabolic syndrome, obesity, and arthritis. Conclusion: The results of this study indicate that metabolism-related research is thriving in psoriasis, with a focus on the investigation of metabolic targets and interventions within the metabolic processes. Metabolism is expected to be a hot topic in future psoriasis research.

Machine learning combined with single-cell analysis reveals predictive capacity and immunotherapy response of T cell exhaustion-associated lncRNAs in uterine corpus endometrial carcinoma.

BACKGROUND: The exhaustion of T-cells is a primary factor contributing to immune dysfunction in cancer. Long non-coding RNAs (lncRNAs) play a significant role in the advancement, survival, and treatment of Uterine Corpus Endometrial Carcinoma (UCEC). Nevertheless, there has been no investigation into the involvement of lncRNAs associated with T-cell exhaustion (TEXLs) in UCEC. The goal of this work is to establish predictive models for TEXLs in UCEC and study their related immune features. METHODS: Using transcriptome and single-cell sequencing data from The Cancer Genome Atlas and Gene Expression Omnibus databases, we employed co-expression analysis and univariate Cox regression to identify prognostic-associated TEXLs (pTEXLs). The prognostic model was developed using the Least Absolute Contraction and Selection Operator. The immunotherapy characteristics of the prognostic model risk score were studied. Then molecular subgroups were identified through non-negative Matrix Factorization based on pTEXLs. The identification of co-expressed genes was done using a weighted correlation network analysis. Subsequently, a diagnostic model for UCEC was created. In-depth investigations, both in vitro and in vivo, were carried out to elucidate the molecular mechanism of the key gene within the diagnostic model. RESULTS: Receiver operating characteristic curve, calibration curve, and decision curve analysis proved the validity of the predictive models established according to pTEXLs. The subgroup with lower risk scores in the prognostic model has better responses to blocking immune checkpoint therapy. Single-cell analysis suggests that the expression level of MIEN1 is relatively high in immune cells among diagnostic genes. Furthermore, the targeted suppression of MIEN1 via sh-MIEN1 diminishes the proliferative, migratory, and invasive capacities of UCEC cells, potentially associated with CD8+ T cell exhaustion. CONCLUSIONS: The association between TEXLs and UCEC was methodically elucidated by our investigation. A stable pTEXLs risk prediction model and a diagnosis model for UCEC were also established.

Exploration of programmed cell death-associated characteristics and immune infiltration in neonatal sepsis: new insights from bioinformatics analysis and machine learning.

BACKGROUND: Neonatal sepsis, a perilous medical situation, is typified by the malfunction of organs and serves as the primary reason for neonatal mortality. Nevertheless, the mechanisms underlying newborn sepsis remain ambiguous. Programmed cell death (PCD) has a connection with numerous infectious illnesses and holds a significant function in newborn sepsis, potentially serving as a marker for diagnosing the condition. METHODS: From the GEO public repository, we selected two groups, which we referred to as the training and validation sets, for our analysis of neonatal sepsis. We obtained PCD-related genes from 12 different patterns, including databases and published literature. We first obtained differential expressed genes (DEGs) for neonatal sepsis and controls. Three advanced machine learning techniques, namely LASSO, SVM-RFE, and RF, were employed to identify potential genes connected to PCD. To further validate the results, PPI networks were constructed, artificial neural networks and consensus clustering were used. Subsequently, a neonatal sepsis diagnostic prediction model was developed and evaluated. We conducted an analysis of immune cell infiltration to examine immune cell dysregulation in neonatal sepsis, and we established a ceRNA network based on the identified marker genes. RESULTS: Within the context of neonatal sepsis, a total of 49 genes exhibited an intersection between the differentially expressed genes (DEGs) and those associated with programmed cell death (PCD). Utilizing three distinct machine learning techniques, six genes were identified as common to both DEGs and PCD-associated genes. A diagnostic model was subsequently constructed by integrating differential expression profiles, and subsequently validated by conducting artificial neural networks and consensus clustering. Receiver operating characteristic (ROC) curves were employed to assess the diagnostic merit of the model, which yielded promising results. The immune infiltration analysis revealed notable disparities in patients diagnosed with neonatal sepsis. Furthermore, based on the identified marker genes, the ceRNA network revealed an intricate regulatory interplay. CONCLUSION: In our investigation, we methodically identified six marker genes (AP3B2, STAT3, TSPO, S100A9, GNS, and CX3CR1). An effective diagnostic prediction model emerged from an exhaustive analysis within the training group (AUC 0.930, 95%CI 0.887-0.965) and the validation group (AUC 0.977, 95%CI 0.935-1.000).

Application of Genomic Data in Translational Medicine During the Big Data Era.

Advances in gene sequencing technology and decreasing costs have resulted in a proliferation of genomic data as an integral component of big data. The availability of vast amounts of genomic data and more sophisticated genomic analysis techniques has facilitated the transition of genomics from the laboratory to clinical settings. More comprehensive and precise DNA sequencing empowers patients to address health issues at the molecular level, facilitating early diagnosis, timely intervention, and personalized healthcare management strategies. Further exploration of disease mechanisms through identification of associated genes may facilitate the discovery of therapeutic targets. The prediction of an individual's disease risk allows for improved stratification and personalized prevention measures. Given the vast amount of genomic data, artificial intelligence, as a burgeoning technology for data analysis, is poised to make a significant impact in genomics.

Identification of potential biomarkers in the peripheral blood of neonates with bronchopulmonary dysplasia using WGCNA and machine learning algorithms.

Bronchopulmonary dysplasia (BPD) is often seen as a pulmonary complication of extreme preterm birth, resulting in persistent respiratory symptoms and diminished lung function. Unfortunately, current diagnostic and treatment options for this condition are insufficient. Hence, this study aimed to identify potential biomarkers in the peripheral blood of neonates affected by BPD. The Gene Expression Omnibus provided the expression dataset GSE32472 for BPD. Initially, using this database, we identified differentially expressed genes (DEGs) in GSE32472. Subsequently, we conducted gene set enrichment analysis on the DEGs and employed weighted gene co-expression network analysis (WGCNA) to screen the most relevant modules for BPD. We then mapped the DEGs to the WGCNA module genes, resulting in a gene intersection. We conducted detailed functional enrichment analyses on these overlapping genes. To identify hub genes, we used 3 machine learning algorithms, including SVM-RFE, LASSO, and Random Forest. We constructed a diagnostic nomogram model for predicting BPD based on the hub genes. Additionally, we carried out transcription factor analysis to predict the regulatory mechanisms and identify drugs associated with these biomarkers. We used differential analysis to obtain 470 DEGs and conducted WGCNA analysis to identify 1351 significant genes. The intersection of these 2 approaches yielded 273 common genes. Using machine learning algorithms, we identified CYYR1, GALNT14, and OLAH as potential biomarkers for BPD. Moreover, we predicted flunisolide, budesonide, and beclomethasone as potential anti-BPD drugs. The genes CYYR1, GALNT14, and OLAH have the potential to serve as diagnostic biomarkers for BPD. This may prove beneficial in clinical diagnosis and prevention of BPD.

Highly water-soluble and biocompatible hyaluronic acid functionalized upconversion nanoparticles as ratiometric nanoprobes for label-free detection of nitrofuran and doxorubicin.

Antibiotic detection is crucial and challenging because the widespread consumption of antibiotics has shown extensive harmful effects on food, environment and human health. Here, we propose highly water-soluble and biocompatible hyaluronic acid (HYA) functionalized upconversion nanoparticles (UCNPs) for ratiometric detection of multiple antibiotics. The ultraviolet upconversion luminescence (UCL) from UCNPs was significantly quenched by nitrofurazone (NFZ)/nitrofurantoin (NFT), and blue UCL was quenched by doxorubicin (DOX), while red UCL remained unchanged for internal reference. The UCNPs-HYA nanoprobes exhibit excellently sensitive and selective NFZ, NFT and DOX detection in linear range of 2.5-100 µM, 2.5-80 µM, and 2.5-200 µM with the LOD at 0.28 µM (55 µg/kg), 0.20 µM (48 µg/kg) and 0.17 µM (97 µg/kg), respectively. The nanoprobes achieved detecting real samples of NFZ in lake water, liquid milk and chicken meat with satisfactory results, and UCL bioimaging of DOX in HeLa cells. The UCNPs-HYA ratiometric nanoprobes are promising for food samples detection and potential biosensing in the cellular environment.

Genetic analysis of potential biomarkers and therapeutic targets associated with ferroptosis from bronchopulmonary dysplasia.

Ferroptosis is a recently identified form of cell death that is distinct from the conventional modes such as necrosis, apoptosis, and autophagy. Its role in bronchopulmonary dysplasia (BPD) remains inadequately understood. To address this gap, we obtained BPD-related RNA-seq data and ferroptosis-related genes (FRGs) from the GEO database and FerrDb, respectively. A total of 171 BPD-related differentially expressed ferroptosis-related genes (DE-FRGs) linked to the regulation of autophagy and immune response were identified. Least absolute shrinkage and selection operator and SVM-RFE algorithms identified 23 and 14 genes, respectively, as marker genes. The intersection of these 2 sets yielded 9 genes (ALOX12B, NR1D1, LGMN, IFNA21, MEG3, AKR1C1, CA9, ABCC5, and GALNT14) with acceptable diagnostic capacity. The results of the functional enrichment analysis indicated that these identified marker genes may be involved in the pathogenesis of BPD through the regulation of immune response, cell cycle, and BPD-related pathways. Additionally, we identified 29 drugs that target 5 of the marker genes, which could have potential therapeutic implications. The ceRNA network we constructed revealed a complex regulatory network based on the marker genes, further highlighting their potential roles in BPD. Our findings offer diagnostic potential and insight into the mechanism underlying BPD. Further research is needed to assess its clinical utility.

Landscape of PCOS co-expression gene and its role in predicting prognosis and assisting immunotherapy in endometrial cancer.

BACKGROUND: Endometrial carcinoma (EC) is the sixth most frequent malignancy in women and is often linked to high estrogen exposure. Polycystic ovarian syndrome (PCOS) is a known risk factor for EC, but the underlying mechanisms remain unclear. METHODS: We investigated shared gene signals and potential biological pathways to identify effective therapy options for PCOS- and EC-related malignancies. Weighted gene expression network analysis (WGCNA) was used to identify genes associated with PCOS and EC using gene expression data from the Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) datasets. Enrichment analysis using Cluego software revealed that the steroid hormone biosynthetic process was a critical feature in both PCOS and EC. A predictive signature encompassing genes involved in steroid hormone production was developed using multivariate and least absolute shrinkage and selection operator (LASSO) regression analysis to predict the prognosis of EC. Then, we conducted further experimental verification. RESULTS: Patients in the TCGA cohort with high predictive scores had poorer outcomes than those with low scores. We also investigated the relationship between tumor microenvironment (TME) features and predictive risk rating and found that patients with low-risk scores had higher levels of inflammatory and inhibitory immune cells. Also, we found that immunotherapy against anti-CTLA4 and anti-PD-1/PD-L1 was successful in treating individuals with low risk. Low-risk individuals were more responsive to crizotinib therapy, according to further research performed using the "pRRophetic" R package. We further confirmed that IGF2 expression was associated with tumor cell migration, proliferation, and invasion in EC cells. CONCLUTIONS: By uncovering the pathways and genes linking PCOS and EC, our findings may provide new therapeutic strategies for patients with PCOS-related EC.

Casual association between childhood body mass index and risk of psoriasis: A Mendelian randomization study.

BACKGROUND: Observational studies have suggested that childhood body mass index (BMI) is associated with the risk of psoriasis. However, their causal relationship remains unclear. In this investigation, we aimed to determine whether an association exists between childhood BMI and psoriasis. METHODS: Using summary statistics for childhood BMI of European descent from publicly available GWAS meta-analyses (n = 39 620), we conducted Mendelian randomization (MR) research using the inverse variance weighting (IVW), weighted median, and MR-Egger regression techniques. The outcome was a genome-wide association studies (GWAS) for the self-reported non-cancer disease classification psoriasis in the UK Biobank population (total n = 337 159; case = 3871; control = 333 288). RESULTS: We selected instrumental variables from 16 single-molecule polymorphisms that attained genome-wide significance in GWAS on childhood BMI. Using the IVW method, our findings supported a causal relationship between childhood BMI and psoriasis (beta = 0.003, standard error [SE] = 0.001, p = 0.006). Using MR-Egger regression analysis, we evaluated the potential for directional pleiotropy to bias our results (intercept = 0.00039, p-value = 0.247) and found no causal relationship between childhood BMI and psoriasis (beta = -0.002, SE = 0.004, p = 0.625). The weighted median method, however, provided proof of a causal relationship (beta = 0.003, SE = 0.001, p = 0.029). Cochran's Q test and the funnel plot revealed little proof of heterogeneity or asymmetry, indicating the lack of directional pleiotropy. CONCLUSION: According to the findings of the MR analysis, an increased childhood BMI may be linked to a higher likelihood of psoriasis.

Recovery of severe COVID-19 complicated with cerebral infarction: Considerations on a case report.

RATIONALE: The standardization, individualization, and rationalization of intensive care and treatment for severe patients have improved. However, the combination of corona virus disease 2019 (COVID-19) and cerebral infarction presents new challenges beyond routine nursing care. PATIENT CONCERNS AND DIAGNOSES: This paper examines the rehabilitation nursing of patients with both COVID-19 and cerebral infarction as an example. It is necessary to develop a nursing plan for COVID-19 patients and implement early rehabilitation nursing for cerebral infarction patients. INTERVENTIONS: Timely rehabilitation nursing intervention is essential to enhance treatment outcomes and promote patient rehabilitation. After 20 days of rehabilitation nursing treatment, patients showed significant improvement in visual analogue scale score, drinking test, and upper and lower limb muscle strength. OUTCOMES: Treatment outcomes for complications, motor function, and daily activities also improved significantly. LESSONS: Critical care and rehabilitation specialist care play a positive role in ensuring patient safety and improving their quality of life by adapting measures to local conditions and the timing of care.

Construction and validation of the diagnostic model of keloid based on weighted gene co-expression network analysis (WGCNA) and differential expression analysis.

Keloid is a disease that seriously affects the aesthetic appearance of the body. In contrast to normal skin or hypertrophic scars, keloid tissue extends beyond the initial site of injury. Patients may complain of pain, itching, or burning. Although multiple treatments exist, none is uniformly successful. Genetic advances have made it possible to explore differences in gene expression between keloids and normal skin. Identifying the biomarker for keloid is beneficial to the mechanism exploration and treatment development of keloid. In this study, we identified seven genes with significant differences in keloids through weighted gene co-expression network analysis(WGCNA) and differential expression analysis. Then, by the Lasso regression, we constructed a keloid diagnostic model using five of these genes. Further studies found that keloids could be divided into high-risk and low-risk groups by this model, with differences in immunity, m6A methylation, and pyroptosis. Finally, we verified the accuracy of the diagnostic model in clinical RNA-sequencing data.

Significance of m6A regulatory factor in gene expression and immune function of osteoarthritis.

One of the most prevalent posttranscriptional modifications of eukaryotic mRNA is the RNA N6-methyladenosine (m6A) regulator, which plays a significant role in various illnesses. The involvement of m6A regulators in osteoarthritis (OA) is not fully known. By comparing nonosteoarthritic and osteoarthritic patients, 26 important m6A regulators were identified from the gene expression omnibus GSE48556 dataset. Seven candidate m6A regulators (IGFBP3, WTAP, IGFBP1, HNRNPC, RBM15B, YTHDC1, and METTL3) were screened using a random forest model to assess the likelihood of OA. A column line graph model founded on seven m6A modulator candidates was created. According to decision curve analysis, patients might profit from the column line graph model. Based on chosen relevant m6A modifiers, a consensus clustering approach was utilized to categorize OA into two m6A categories (group A and group B). To measure the m6A pattern, a principal component analysis technique was created to generate the m6A score for every sample. Cluster A patients exhibited more excellent m6A scores than cluster B patients. Furthermore, we discovered that patients with lower and higher m6A scores had varied immunological responses using the m6A type. At last, m6A regulators contribute significantly to the progression of OA. Our research on m6A patterns might help to guide further OA immunotherapeutic techniques.

Identification of a prognostic model based on costimulatory molecule-related subtypes and characterization of tumor microenvironment infiltration in acute myeloid leukemia.

Costimulatory molecules have been found to play significant roles in anti-tumor immune responses, and are deemed to serve as promising targets for adjunctive cancer immunotherapies. However, the roles of costimulatory molecule-related genes (CMRGs) in the tumor microenvironment (TME) of acute myeloid leukemia (AML) remain unclear. In this study, we described the CMRG alterations in the genetic and transcriptional fields in AML samples chosen from two datasets. We next evaluated their expression and identified two distinct costimulatory molecule subtypes, which showed that the alterations of CMRGs related to clinical features, immune cell infiltration, and prognosis of patients with AML. Then, a costimulatory molecule-based signature for predicting the overall survival of AML patients was constructed, and the predictive capability of the proposed signature was validated in AML patients. Moreover, the constructed costimulatory molecule risk model was significantly associated with chemotherapeutic drug sensitivity of AML patients. In addition, the identified genes in the proposed prognostic signature might play roles in pediatric AML. CMRGs were found to be potentially important in the AML through our comprehensive analysis. These findings may contribute to improving our understanding of CMRGs in patients with AML, as well as provide new opportunities to assess prognosis and develop more effective immunotherapies.

A Nomogram for Individualized Prediction of Calf Muscular Vein Thrombosis in Stroke Patients During Rehabilitation: A Retrospective Study.

Objectives: To develop a nomogram for predicting calf muscle veins thrombosis (CMVT) in stroke patients during rehabilitation. Methods: We enrolled 360 stroke patients from the Rehabilitation Medicine Center from December 2015 to February 2019. Of the participants, 123 were included in the CMVT group and 237 in the no CMVT group. The least absolute shrinkage and selection operator (LASSO) regression model was applied to optimize feature selection for the model. Multivariable logistic regression analysis was applied to construct a predictive model. Performance and clinical utility of the nomogram were generated using the Harrell's concordance index, calibration curve, and decision curve analysis (DCA). Results: Age, Brunnstrom stage (lower extremity), D-dimer, and antiplatelet therapy were associated with the occurrence of CMVT. The prediction nomogram showed satisfactory performance with a concordance index of 0.718 (95% CI: 0.663-0.773) in internal verification. The Hosmer-Lemeshow test, P = .217, suggested that the model was of goodness-of-fit. In addition, the DCA demonstrated that the CMVT nomogram had a good clinical net benefit. Conclusions: We developed a nomogram that could help clinicians identify high-risk groups of CMVT in stroke patients during rehabilitation for early intervention.

Characterization of Fatty Acid Metabolism-Related Genes Landscape for Predicting Prognosis and Aiding Immunotherapy in Glioma Patients.

Glioma is a highly malignant brain tumor with a poor survival rate. The involvement of fatty acid metabolism in glioma was examined to find viable treatment options. The information was gathered from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. A prognostic signature containing fatty acid metabolism-dependent genes (FAMDs) was developed to predict glioma outcome by multivariate and most minor absolute shrinkage and selection operator (LASSO) regression analyses. In the TCGA cohort, individuals with a good score had a worse prognosis than those with a poor score, validated in the CGGA cohort. According to further research by "pRRophetic" R package, higher-risk individuals were more susceptible to crizotinib. According to a complete study of the connection between the predictive risk rating model and tumor microenvironment (TME) features, high-risk individuals were eligible for activating the immune cell-associated receptor pathway. We also discovered that anti-PD-1/PD-L1 and anti-CTLA4 immunotherapy are more effective in high-risk individuals. Furthermore, we demonstrated that CCNA2 promotes glioma proliferation, migration, and invasion and regulates macrophage polarization. Therefore, examining the fatty acid metabolism pathway aids our understanding of TME invasion properties, allowing us to develop more effective immunotherapies for glioma.

Construction and Analysis of a Joint Diagnosis Model of Random Forest and Artificial Neural Network for Obesity.

Obesity is a significant global health concern since it is connected to a higher risk of several chronic diseases. As a consequence, obesity may be described as a condition that reduces human life expectancy and significantly impacts life quality. Because traditional obesity diagnosis procedures have several flaws, it is vital to design new diagnostic models to enhance current methods. More obesity-related markers have been discovered in recent years as a result of improvements and enhancements in gene sequencing technology. Using current gene expression profiles from the Gene Expression Omnibus (GEO) collection, we identified differentially expressed genes (DEGs) associated with obesity and found 12 important genes (CRLS1, ANG, ALPK3, ADSSL1, ABCC1, HLF, AZGP1, TSC22D3, F2R, FXN, PEMT, and SPTAN1) using a random forest classifier. ALPK3, HLF, FXN, and SPTAN1 are the only genes that have never been linked to obesity. We also used an artificial neural network to build a novel obesity diagnosis model and tested its diagnostic effectiveness using public datasets.

A Necroptosis-Related Prognostic Model of Uveal Melanoma Was Constructed by Single-Cell Sequencing Analysis and Weighted Co-Expression Network Analysis Based on Public Databases.

BACKGROUND: Uveal melanoma(UVM) is the most common intraocular malignancy and has a poor prognosis. The clinical significance of necroptosis(NCPS) in UVM is unclear. METHODS: We first identified necroptosis genes in UVM by single-cell analysis of the GSE139829 dataset from the GEO database and weighted co-expression network analysis of TCGA data. COX regression and Lasso regression were used to construct the prognostic model. Then survival analysis, immune microenvironment analysis, and mutation analysis were carried out. Finally, cell experiments were performed to verify the role of ITPA in UVM. RESULT: By necroptosis-related prognostic model, UVM patients in both TCGA and GEO cohorts could be classified as high-NCPS and low-NCPS groups, with significant differences in survival time between the two groups (P<0.001). Besides, the high-NCPS group had higher levels of immune checkpoint-related gene expression, suggesting that they might be more likely to benefit from immunotherapy. The cell experiments confirmed the role of ITPA, the most significant gene in the model, in UVM. After ITPA was knocked down, the activity, proliferation, and invasion ability of the MuM-2B cell line were significantly reduced. CONCLUSION: Our study can provide a reference for the diagnosis and treatment of patients with UVM.