Combined impact of sleep and obesity on female infertility in the NHANES 2017-2020.

BACKGROUND: Sleep health and obesity may affect the risk of female infertility. However, few studies focused on the interaction of obesity and sleep health on the female infertility risk. This study aimed to evaluate the combined impact of trouble sleeping / sleep duration and overweight/obesity/ abdominal obesity on the risk of female infertility. METHODS: The data for this cross-sectional study was obtained from National Health and Nutritional Examination Survey, which provided information on trouble sleeping, sleep duration, overweight/obesity, abdominal obesity, and confounding factors. Adopted weighted univariate and multivariate logistic regression models to explore the relationship between trouble sleeping, sleep duration, overweight/obesity, abdominal obesity, and the risk of infertility, respectively, and the combined effect of trouble sleeping and overweight/obesity, trouble sleeping and abdominal obesity, sleep duration and overweight/obesity, sleep duration and abdominal obesity, on the female infertility risk. RESULTS: This study included a total of 1,577 women, and 191 were diagnosed with infertility. Women with infertility had a higher proportion of people with overweight/obesity, abdominal obesity, sleep duration ≤ 7 h and trouble sleeping than those with non-infertility. The result indicated that trouble sleeping [odds ratio (OR) = 2.25, 95% confidence intervals (CI): 1.49-3.39], sleep duration ≤ 7 h (OR = 1.59, 95% CI: 1.03-2.48), and the combined impact of abdominal obesity and trouble sleeping (OR = 2.18, 95% CI: 1.28-3.72), abdominal obesity and sleep duration ≤ 7 h (OR = 2.00, 95% CI: 1.17-3.40), overweight/obesity and trouble sleeping (OR = 2.29, 95% CI: 1.24-4.26), and overweight/obesity and sleep duration ≤ 7 h (OR = 1.88, 95% CI: 1.01-3.49) were associated with increased odds of infertility, respectively. CONCLUSION: There was combined effects of trouble sleeping/sleep duration ≤ 7 h and overweight/obesity/ abdominal obesity on increased odds of female infertility.

Progress in enhancing the remediation performance of microbial fuel cells for contaminated groundwater.

Microbial fuel cells (MFCs) have become more prevalent in groundwater remediation due to their capacity for power generation, removal of pollution, ease of assembly, and low secondary contamination. It is currently being evaluated for practical application in an effort to eliminate groundwater pollution. However, a considerable majority of research was conducted in laboratories. But the operational circumstances including anaerobic characteristics, pH, and temperature vary at different sites. In addition, the complexity of contaminants and the positioning of MFCs significantly affect remediation performance. Taking the aforementioned factors into consideration, this review summarizes a bibliography on the application of MFCs for the remediation of groundwater contamination during the last ten decades and assesses the impact of environmental conditions on the treatment performance. The design of the reactor, including configuration, dimensions, electrodes, membranes, separators, and target contaminants are discussed. This review aims to provide practical guidance for the future application of MFCs in groundwater remediation.

Standard-definition White-light, High-definition White-light versus Narrow-band Imaging Endoscopy for Detecting Colorectal Adenomas: A Multicenter Randomized Controlled Trial.

OBJECTIVE: This study aimed to compare the performance of standard-definition white-light endoscopy (SD-WL), high-definition white-light endoscopy (HD-WL), and high-definition narrow-band imaging (HD-NBI) in detecting colorectal lesions in the Chinese population. METHODS: This was a multicenter, single-blind, randomized, controlled trial with a non-inferiority design. Patients undergoing endoscopy for physical examination, screening, and surveillance were enrolled from July 2017 to December 2020. The primary outcome measure was the adenoma detection rate (ADR), defined as the proportion of patients with at least one adenoma detected. The associated factors for detecting adenomas were assessed using univariate and multivariate logistic regression. RESULTS: Out of 653 eligible patients enrolled, data from 596 patients were analyzed. The ADRs were 34.5% in the SD-WL group, 33.5% in the HD-WL group, and 37.5% in the HD-NBI group (P=0.72). The advanced neoplasm detection rates (ANDRs) in the three arms were 17.1%, 15.5%, and 10.4% (P=0.17). No significant differences were found between the SD group and HD group regarding ADR or ANDR (ADR: 34.5% vs. 35.6%, P=0.79; ANDR: 17.1% vs. 13.0%, P=0.16, respectively). Similar results were observed between the HD-WL group and HD-NBI group (ADR: 33.5% vs. 37.7%, P=0.45; ANDR: 15.5% vs. 10.4%, P=0.18, respectively). In the univariate and multivariate logistic regression analyses, neither HD-WL nor HD-NBI led to a significant difference in overall adenoma detection compared to SD-WL (HD-WL: OR 0.91, P=0.69; HD-NBI: OR 1.15, P=0.80). CONCLUSION: HD-NBI and HD-WL are comparable to SD-WL for overall adenoma detection among Chinese outpatients. It can be concluded that HD-NBI or HD-WL is not superior to SD-WL, but more effective instruction may be needed to guide the selection of different endoscopic methods in the future. Our study's conclusions may aid in the efficient allocation and utilization of limited colonoscopy resources, especially advanced imaging technologies.

Preparation of Luvangetin Nanoemulsions: Antimicrobial Mechanism and Role in Infected Wound Healing.

Purpose: Incorporation of luvangetin in nanoemulsions for antimicrobial and therapeutic use in infected wound healing. Patients and Methods: Luvangetin nanoemulsions were prepared by high-speed shear method and characterized based on their appearance structure, average droplet size, polydispersity index (PDI), electric potential, storage stability. Optimized formulation of luvangetin nanoemulsion by Box-Behnken design (BBD). The antimicrobial activity and antimicrobial mechanism of luvangetin nanoemulsions against common hospital pathogens, ie, Staphylococcus aureus (S. aureus) and Escherichia coli (E. coli), were investigated using luvangetin nanoemulsions. The biosafety of luvangetin nanoemulsion was evaluated through cytotoxicity, apoptosis, and reactive oxygen species (ROS) assay experiments using human normal epidermal cells and endothelial cells. Finally, the effect of luvangetin nanoemulsion on healing of infected wounds was investigated in B6 mice. Results: Luvangetin nanoemulsion formulation consists of 2.5% sunflower seed oil, 10% emulsifier Span-20 and 7 minutes of shear time, and with good stability. Luvangetin nanoemulsion produces antibacterial activity against S. aureus and E. coli by disrupting the structure of bacterial cell membranes. Luvangetin nanoemulsion are biologically safe for HaCat and HUVEC. Luvangetin nanoemulsion showed good therapeutic effect on MRSA infected wounds in mice. Conclusion: For the first time, developed a new formulation called luvangetin nanoemulsion, which exhibited superior antibacterial effects against Gram-positive bacteria. Luvangetin nanoemulsion has a favorable effect in promoting infected wound healing. We have combined luvangetin, which has multiple activities, with nanoemulsions to provide a new topical fungicidal formulation, and have comprehensively evaluated its effectiveness and safety, opening up new possibilities for further applications of luvangetin.

Spreading and Capillary Imbibition of Viscous Oil Lens into an Open-Cell Porous Structure.

Oil pollution in the ocean is becoming more and more of a serious issue, which increases interest in both ways for combating its cause and methods for observing and monitoring how oil spreads. A promising approach based on an optical method with empirical relations for selected viscous oil-water systems is presented. Based on a modified melamine sponge (MMS), the microscopic spreading and oil capillary penetration phenomenon of the porous structure were investigated. The objective of this study is 2-fold: (i) to present a more thorough experimental description of the spreading of viscous oil lens on the water surface and capillary action of oil lens into MMS porous structure; and (ii) to provide a theoretical description that helps to explain some of the observed behavior. With knowledge of δ∞2=-2SρW/gρO(ρW-ρO), we can determine the spreading coefficient S. It needs to be pointed out that the oil lens floating on the water surface does satisfy Neumann's rule as the spreading coefficient of the air-oil-water system is negative (- 9.8 mN/m), indicating the ability to form a stable oil lens with thickness δO = 3.04 mm and radius RL = 38.64 mm after 60 min of spreading test. Furthermore, to better understand the capillary phenomena from a mechanical approach, an oil lens in contact with the surface of the MMS porous structure, by in-depth visualization, is properly defined as the balance of forces acting. Finally, as an illustration of this method, we utilized this approach to obtain the equilibrium height of the capillary rise and take it into account in terms of effective material thickness.

Emergence of phase clusters and coexisting states reveals the structure-function relationship.

The Brain Connectome Project has made significant strides in uncovering the structural connections within the brain on various levels. This has led to the question of how brain structure and function are related. Our research explores this relationship in an adaptive neural network in which synaptic conductance between neurons follows spike-time synaptic plasticity rules. By adjusting the plasticity boundary, the network exhibits diverse collective behaviors, including phase synchronization, phase locking, hierarchical synchronization (phase clusters), and coexisting states. Using graph theory, we found that hierarchical synchronization is related to the community structure, while coexisting states are related to the hierarchical self-organizing and core-periphery structure. The network evolves into several tightly connected modules, with sparsely intermodule connections resulting in the formation of phase clusters. In addition, the hierarchical self-organizing structure facilitates the emergence of coexisting states. The coexistence state promotes the evolution of the core-periphery structure. Our results point towards the equivalence between function and structure, with function emerging from structure, and structure being influenced by function in a complex dynamic process.

Guest Editorial Introduction to the Special Section on Weakly-Supervised Deep Learning and Its Applications.

Researchers in biomedical engineering are increasingly turning to weakly-supervised deep learning (WSDL) techniques [1] to tackle challenges in biomedical data analysis, which often involves noisy, limited, or imprecise expert annotations [2]. WSDL methods have emerged as a solution to alleviate the manual annotation burden for structured biomedical data like signals, images, and videos [3] while enabling deep neural network models to learn from larger-scale datasets at a reduced annotation cost. With the proliferation of advanced deep learning techniques such as generative adversarial networks (GANs), graph neural networks (GNNs) [4], vision transformers (ViTs) [5], and deep reinforcement learning (DRL) models [6], research endeavors are focused on solving WSDL problems and applying these techniques to various biomedical analysis tasks.

Enhancing Machine-Learning Prediction of Enzyme Catalytic Temperature Optima through Amino Acid Conservation Analysis.

Enzymes play a crucial role in various industrial production and pharmaceutical developments, serving as catalysts for numerous biochemical reactions. Determining the optimal catalytic temperature (Topt) of enzymes is crucial for optimizing reaction conditions, enhancing catalytic efficiency, and accelerating the industrial processes. However, due to the limited availability of experimentally determined Topt data and the insufficient accuracy of existing computational methods in predicting Topt, there is an urgent need for a computational approach to predict the Topt values of enzymes accurately. In this study, using phosphatase (EC 3.1.3.X) as an example, we constructed a machine learning model utilizing amino acid frequency and protein molecular weight information as features and employing the K-nearest neighbors regression algorithm to predict the Topt of enzymes. Usually, when conducting engineering for enzyme thermostability, researchers tend not to modify conserved amino acids. Therefore, we utilized this machine learning model to predict the Topt of phosphatase sequences after removing conserved amino acids. We found that the predictive model's mean coefficient of determination (R2) value increased from 0.599 to 0.755 compared to the model based on the complete sequences. Subsequently, experimental validation on 10 phosphatase enzymes with undetermined optimal catalytic temperatures shows that the predicted values of most phosphatase enzymes based on the sequence without conservative amino acids are closer to the experimental optimal catalytic temperature values. This study lays the foundation for the rapid selection of enzymes suitable for industrial conditions.

BLAM6A-Merge: Leveraging Attention Mechanisms and Feature Fusion Strategies to Improve the Identification of RNA N6-methyladenosine Sites.

RNA N6-methyladenosine is a prevalent and abundant type of RNA modification that exerts significant influence on diverse biological processes. To date, numerous computational approaches have been developed for predicting methylation, with most of them ignoring the correlations of different encoding strategies and failing to explore the adaptability of various attention mechanisms for methylation identification. To solve the above issues, we proposed an innovative framework for predicting RNA m6A modification site, termed BLAM6A-Merge. Specifically, it utilized a multimodal feature fusion strategy to combine the classification results of four features and Blastn tool. Apart from this, different attention mechanisms were employed for extracting higher-level features on specific features after the screening process. Extensive experiments on 12 benchmarking datasets demonstrated that BLAM6A-Merge achieved superior performance (average AUC: 0.849 for the full transcript mode and 0.784 for the mature mRNA mode). Notably, the Blastn tool was employed for the first time in the identification of methylation sites. The data and code can be accessed at https://github.com/DoraemonXia/BLAM6A-Merge.

Identifying allergic-rhinitis-associated genes with random-walk-based method in PPI network.

Allergic rhinitis is a common allergic disease with a complex pathogenesis and many unresolved issues. Studies have shown that the incidence of allergic rhinitis is closely related to genetic factors, and research on the related genes could help further understand its pathogenesis and develop new treatment methods. In this study, 446 allergic rhinitis-related genes were obtained on the basis of the DisGeNET database. The protein-protein interaction network was searched using the random-walk-with-restart algorithm with these 446 genes as seed nodes to assess the linkages between other genes and allergic rhinitis. Then, this result was further examined by three screening tests, including permutation, interaction, and enrichment tests, which aimed to pick up genes that have strong and special associations with allergic rhinitis. 52 novel genes were finally obtained. The functional enrichment test confirmed their relationships to the biological processes and pathways related to allergic rhinitis. Furthermore, some genes were extensively analyzed to uncover their special or latent associations to allergic rhinitis, including IRAK2 and MAPK, which are involved in the pathogenesis of allergic rhinitis and the inhibition of allergic inflammation via the p38-MAPK pathway, respectively. The new found genes may help the following investigations for understanding the underlying molecular mechanisms of allergic rhinitis and developing effective treatments.

Mechanistic exploration of the shenlian formula in the suppression of atherosclerosis progression via network pharmacology and in vivo experimental validation.

ETHNOPHARMACOLOGICAL RELEVANCE: The Shenlian formula (SL) is a Chinese medicine formula used to curb the development of atherosclerosis (AS) and cardiovascular disease in clinical practice. However, owing to the complexity of compounds and their related multiple targets in traditional Chinese medicine (TCM), it remains difficult and urgent to elucidate the underlying mechanisms at a holistic level. AIM: To investigate the intrinsic mechanisms by which SL suppresses AS progression and to gain new insight into its clinical use. METHODS: We proposed a network pharmacology-based workflow to evaluate the mechanism by which SL affects AS via data analysis, target prediction, PPI network construction, GO and KEGG analyses, and a "drug-core ingredient-potential target-key pathway" network. Then, non-targeted lipidomic analysis was performed to explore the differential lipid metabolites in AS rats, revealing the possible mechanism by which SL affects atherosclerotic progression. Moreover, an AS rabbit model was constructed and gavaged for SL intervention. Serum lipid profiles and inflammatory cytokine indices were tested as an indication of the mitigating effect of SL on AS. RESULTS: A total of 89 bioactive compounds and 298 targets related to SL and AS, which play essential roles in this process, were identified, and a component-target-disease network was constructed. GO and KEGG analyses revealed that SL regulated metabolic pathway, lipids and atherosclerosis, the PI3K-Akt pathway, the MAPK pathway and so on. In vivo experimental validation revealed that a total of 43 different lipid metabolites regulated by SL were identified by non-targeted lipidomics, and glycerophospholipid metabolism was found to be an important mechanism for SL to interfere with AS. SL reduced the plaque area and decreased the levels of inflammatory cytokines (TNF-α and IL-4) and blood lipids (TC, TG, LDL-C, and ApoB) in HFD-induced AS models. In addition, HDL and ApoA1 levels are increased. PLA2 and Lipin1 are highly expressed in AS model, indicating their role in destabilizing glycerophosphatidylcholine metabolism and contributing to the onset and progression of ankylosing spondylitis. Moreover, SL intervention significantly reduced the level of pro-inflammatory cytokines; significantly down-regulated NF-kB/p65 expression, exhibiting anti-inflammatory activity. CONCLUSION: The Shenlian formula (SL) plays a pivotal role in the suppression of AS progression by targeting multiple pathways and mechanisms. This study provides novel insights into the essential genes and pathways associated with the prognosis and pathogenesis of AS.

[Research progress on curcumin improving chronic low-grade inflammation and related diseases].

Chronic low-grade inflammation(CLGI), a relatively new concept without a clear definition, refers to a nonspecific, chronic, continuous, and low-grade inflammation state, and it is closely associated with various chronic diseases, including obesity, inflammatory bowel disease, neurodegenerative diseases, and tumors. Improvement of CLGI can slow down disease progression. Anti-inflammatory treatment is an important strategy for prevention and treatment of CLGI. However, there is currently no definitive drug treatment method. Curcumin is a polyphenolic compound extracted from the rhizome of zingiberaceae, with significant anti-inflammatory activity. Research has shown that curcumin can play an anti-inflammatory role by regulating NF-κB, JAK/STAT, PI3K/Akt, MAPK, NLRP3 inflammasome, Nrf2/ARE, and other inflammation-related pathways. This paper summarized the anti-inflammatory mechanisms, pharmacological effect, and clinical application of curcumin in improving CLGI and other diseases, so as to provide a reference for in-depth research and clinical application of curcumin in improving CLGI.

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration.

Objective.Myocarditis poses a significant health risk, often precipitated by viral infections like coronavirus disease, and can lead to fatal cardiac complications. As a less invasive alternative to the standard diagnostic practice of endomyocardial biopsy, which is highly invasive and thus limited to severe cases, cardiac magnetic resonance (CMR) imaging offers a promising solution for detecting myocardial abnormalities.Approach.This study introduces a deep model called ELRL-MD that combines ensemble learning and reinforcement learning (RL) for effective myocarditis diagnosis from CMR images. The model begins with pre-training via the artificial bee colony (ABC) algorithm to enhance the starting point for learning. An array of convolutional neural networks (CNNs) then works in concert to extract and integrate features from CMR images for accurate diagnosis. Leveraging the Z-Alizadeh Sani myocarditis CMR dataset, the model employs RL to navigate the dataset's imbalance by conceptualizing diagnosis as a decision-making process.Main results.ELRL-DM demonstrates remarkable efficacy, surpassing other deep learning, conventional machine learning, and transfer learning models, achieving an F-measure of 88.2% and a geometric mean of 90.6%. Extensive experimentation helped pinpoint the optimal reward function settings and the perfect count of CNNs.Significance.The study addresses the primary technical challenge of inherent data imbalance in CMR imaging datasets and the risk of models converging on local optima due to suboptimal initial weight settings. Further analysis, leaving out ABC and RL components, confirmed their contributions to the model's overall performance, underscoring the effectiveness of addressing these critical technical challenges.

Association of preoperative neutrophil-to-lymphocyte ratio with the risk of postoperative delirium in elderly patients undergoing noncardiac surgery: a systematic review and meta-analysis.

To assess the correlation between preoperative neutrophil-to-lymphocyte ratio (NLR) and risk of postoperative delirium (POD) in older patients undergoing noncardiac surgery. PubMed, Web of Science, Embase, and Scopus were systematically retrieved from inception until February 2023. Two authors independently conducted the selection of literature, data extraction and statistical analysis. In this meta-analysis, Review Manager 5.4 was used for statistical analysis, and the mean difference (MD) and 95% confidence intervals (CIs) of preoperative NLR between the POD group and non-POD group were calculated. We utilised the Newcastle-Ottawa Scale (NOS) to evaluate the quality of literature. Further, our meta-analysis used a random-effects model, and publication bias was evaluated by conducting a funnel plot. The correlation between preoperative NLR and POD was the primary outcome, and the secondary outcome was the association of other prognostic factors with the risk of POD. This meta-analysis included seven studies with 2424 patients, of whom 403 were diagnosed with POD with an incidence of 16.63%. Results indicated a positive correlation between preoperative NLR and the risk of POD (MD = 1.06, 95% CI: 0.64-1.49; P < 0.001). Further, our results found that neutrophil counts, advanced age, longer surgery time, diabetes, and elevated C-reactive protein were significantly associated with POD (MD = 0.98, 95% CI: 0.40-1.56; P = 0.001; MD = 4.20, 95% CI: 2.90-5.51; P < 0.001; MD = 0.15, 95% CI: 0.05-0.25; P < 0.01; OR = 1.42, 95% CI: 1.08-1.86; P = 0.01; MD = 1.26, 95% CI: 0.36-2.16; P < 0.01). Other factors including lymphocyte counts, hypertension and male gender were not significantly associated with POD (MD = -0.11, 95% CI: -0.27 to 0.05; P > 0.05; OR = 1.20, 95% CI: 0.91-1.58, P > 0.05; OR = 1.28, 95% CI: 1.00-1.63; P = 0.05). Our meta-analysis indicated a positive correlation between preoperative NLR and the risk of POD in older noncardiac surgery patients.

Symptoms and Conditions in Children and Adults up to 90 Days after SARS-CoV-2 Infection: A Retrospective Observational Study Utilizing the Common Data Model.

Background/Objectives: There have been widespread reports of persistent symptoms in both children and adults after SARS-CoV-2 infection, giving rise to debates on whether it should be regarded as a separate clinical entity from other postviral syndromes. This study aimed to characterize the clinical presentation of post-acute symptoms and conditions in the Korean pediatric and adult populations. Methods: A retrospective analysis was performed using a national, population-based database, which was encoded using the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). We compared individuals diagnosed with SARS-CoV-2 to those diagnosed with influenza, focusing on the risk of developing prespecified symptoms and conditions commonly associated with the post-acute sequelae of COVID-19. Results: Propensity score matching yielded 1,656 adult and 343 pediatric SARS-CoV-2 and influenza pairs. Ninety days after diagnosis, no symptoms were found to have elevated risk in either adults or children when compared with influenza controls. Conversely, at 1 day after diagnosis, adults with SARS-CoV-2 exhibited a significantly higher risk of developing abnormal liver function tests, cardiorespiratory symptoms, constipation, cough, thrombophlebitis/thromboembolism, and pneumonia. In contrast, children diagnosed with SARS-CoV-2 did not show an increased risk for any symptoms during either acute or post-acute phases. Conclusions: In the acute phase after infection, SARS-CoV-2 is associated with an elevated risk of certain symptoms in adults. The risk of developing post-acute COVID-19 sequelae is not significantly different from that of having postviral symptoms in children in both the acute and post-acute phases, and in adults in the post-acute phase. These observations warrant further validation through studies, including the severity of initial illness, vaccination status, and variant types.

Host proteins interact with viral elements and affect the life cycle of highly pathogenic avian influenza A virus H7N9.

Host-virus interactions can significantly impact the viral life cycle and pathogenesis; however, our understanding of the specific host factors involved in highly pathogenic avian influenza A virus H7N9 (HPAI H7N9) infection is currently restricted. Herein, we designed and synthesized 65 small interfering RNAs targeting host genes potentially associated with various aspects of RNA virus life cycles. Afterward, HPAI H7N9 viruses were isolated and RNA interference was used to screen for host factors likely to be involved in the life cycle of HPAI H7N9. Moreover, the research entailed assessing the associations between host proteins and HPAI H7N9 proteins. Twelve key host proteins were identified: Annexin A (ANXA)2, ANXA5, adaptor related protein complex 2 subunit sigma 1 (AP2S1), adaptor related protein complex 3 subunit sigma 1 (AP3S1), ATP synthase F1 subunit alpha (ATP5A1), COPI coat complex subunit alpha (COP)A, COPG1, heat shock protein family A (Hsp70) member 1A (HSPA)1A, HSPA8, heat shock protein 90 alpha family class A member 1 (HSP90AA1), RAB11B, and RAB18. Co-immunoprecipitation revealed intricate interactions between viral proteins (hemagglutinin, matrix 1 protein, neuraminidase, nucleoprotein, polymerase basic 1, and polymerase basic 2) and these host proteins, presumably playing a crucial role in modulating the life cycle of HPAI H7N9. Notably, ANXA5, AP2S1, AP3S1, ATP5A1, HSP90A1, and RAB18, were identified as novel interactors with HPAI H7N9 proteins rather than other influenza A viruses (IAVs). These findings underscore the significance of host-viral protein interactions in shaping the dynamics of HPAI H7N9 infection, while highlighting subtle variations compared with other IAVs. Deeper understanding of these interactions holds promise to advance disease treatment and prevention strategies.

Comprehensive analysis of CYBB as a prognostic marker and therapeutic target in glioma: A bioinformatics approach.

Background: In the central nervous system, glioma is the most common malignant tumor, and patients have a poor prognosis. Identification of novel marker genes and establishment of prognostic models are important for early diagnosis and prognosis determination. Methods: Download glioma data from the CGGA and TCG databases. Application of bioinformatics to analyze the impact of CYBB on the clinicopathological characteristics, immunological features and prognosis of gliomas. Using single-cell sequencing data from 7 glioblastoma patients in the CGGA database, the role of CYBB in the tumor microenvironment was analyzed. In addition, a prognostic model was constructed based on CYBB high and low differentially expressed genes and mitochondrial genes. Results: The expression of CYBB is closely related to various clinical features, immune cell infiltration level, immune checkpoint and survival time of patients. A 10-gene prediction model was constructed based on the differentially expressed genes of low and high CYBB and mitochondria-related genes. Glioma patients with higher risk scores had significantly lower survival probabilities. Receiver operating characteristic curves and nomograms were plotted over time to show the predictive accuracy and predictive value of the 10-gene prognostic model. Conclusions: Our study shows that CYBB is strongly correlated with clinical characteristics features and prognosis of glioma patients, and can be used as a potential therapeutic target. Prognostic models based on CYBB and mitochondrial genes have good performance in predicting prognosis of glioma patients.