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This research explores the solution of the time-fractional Newell–Whitehead–Segel equation using two separate methods: the optimal auxiliary function method and a new iterative method. The Newell–Whitehead–Segel equation holds significance in modeling nonlinear systems, particularly in delineating stripe patterns within two-dimensional systems. Employing the Caputo fractional derivative operator, we address two case study problems pertaining to this equation through our proposed methods. Comparative analysis between the numerical results obtained from our techniques and an exact solution reveals a strong alignment. Graphs and tables illustrate this alignment, showcasing the effectiveness of our methods. Notably, as the fractional orders vary, the results achieved at different fractional orders are compared, highlighting their convergence toward the exact solution as the fractional order approaches an integer. Demonstrating both interest and simplicity, our proposed methods exhibit high accuracy in resolving diverse nonlinear fractional order partial differential equations.

BackgroundMental health disorders in patients with multi-drug or rifampicin-resistant tuberculosis (MDR/RR-TB) receive consistent attention. Anxiety and depression can manifest and may impact disease progression in patients with MDR/RR-TB. Given the heightened stressors resulting from the COVID-19 pandemic, this scenario is even more concerning.ObjectiveTo evaluate the prevalence of and risk factors associated with anxiety and depression among patients with MDR/RR-TB in southern China.MethodsA facility-based cross-sectional study was undertaken at Guangzhou Chest Hospital in southern China, encompassing a cohort of 219 patients undergoing outpatient and inpatient treatment for MDR/RR-TB. Anxiety and depressive symptoms were assessed using the 7-Item Generalized Anxiety Disorder (GAD-7) scale and Patient Health Questionnaire-9 (PHQ-9). The ramifications of anxiety and depression were examined using univariate and multivariate logistic regression analyses, with odds ratios (ORs) and age- and sex-adjusted ORs (AORs) employed to quantify their influence. All data underwent statistical analysis using SPSS 25.0, with statistical significance established at P < 0.05.ResultsTwo hundred and nineteen individuals with MDR/RR-TB were included in the study. The prevalence of anxiety and depression was 57.53% (n = 126) and 65.75% (n = 144), respectively, with 33.3% (n = 73) of the participants experiencing both conditions simultaneously. Multivariate logistic regression analysis revealed that an age of 20–40 years [anxiety AOR = 3.021, 95% confidence interval (CI): 1.240–7.360; depression AOR = 3.538, 95% CI: 1.219–10.268], disease stigma (anxiety AOR = 10.613, 95% CI: 2.966–37.975; depression AOR = 4.514, 95% CI: 2.051–10.108) and poor physical health (anxiety AOR = 7.636, 95% CI: 2.938–19.844; depression AOR = 6.190, 95% CI: 2.468–15.529) were significant risk factors for moderate levels of anxiety and depression.ConclusionsWe found that individuals with MDR/RR-TB had an elevated risk of anxiety and depression. To decrease the likelihood of unfavorable treatment outcomes, it is imperative to carefully monitor the psychological wellbeing of patients with MDR/RR-TB and promptly address any detrimental psychiatric conditions.

Spinal cord injury (SCI) results in a large amount of tissue cell debris in the lesion site, which interacts with various cytokines, including inflammatory factors, and the intrinsic glial environment of the central nervous system (CNS) to form an inhibitory microenvironment that impedes nerve regeneration. The efficient clearance of tissue debris is crucial for the resolution of the inhibitory microenvironment after SCI. Macrophages are the main cells responsible for tissue debris removal after SCI. However, the high lipid content in tissue debris and the dysregulation of lipid metabolism within macrophages lead to their transformation into foamy macrophages during the phagocytic process. This phenotypic shift is associated with a further pro-inflammatory polarization that may aggravate neurological deterioration and hamper nerve repair. In this review, we summarize the phenotype and metabolism of macrophages under inflammatory conditions, as well as the mechanisms and consequences of foam cell formation after SCI. Moreover, we discuss two strategies for foam cell modulation and several potential therapeutic targets that may enhance the treatment of SCI.

In this study, we explore the evolution of long-duration transpolar arcs observed during an exceptionally quiet period by the Fengyun-3D and Defense Meteorological Satellite Program satellites on 17 January 2019. Leveraging the extensive field of view of WAI, we captured the complete structure and evolution of the TPA over a very quiet period exceeding 7 h, with AE

Summary: Disruption of circadian rhythms during fetal development may predispose mice to developing heart disease later in life. Here, we report that male, but not female, mice that had experienced chronic circadian disturbance (CCD) in utero were more susceptible to pathological cardiac remodeling compared with mice that had developed under normal intrauterine conditions. CCD-treated males showed ventricular chamber dilatation, enhanced myocardial fibrosis, decreased contractility, higher rates of induced tachyarrhythmia, and elevated expression of biomarkers for heart failure and myocardial remodeling. In utero CCD exposure also triggered sex-dependent changes in cardiac gene expression, including upregulation of the secretoglobin gene, Scgb1a1, in males. Importantly, cardiac overexpression of Scgb1a1 was sufficient to induce myocardial hypertrophy in otherwise naive male mice. Our findings reveal that in utero CCD exposure predisposes male mice to pathological remodeling of the heart later in life, likely as a consequence of SCGB1A1 upregulation.

Hitherto, research on iron(III)-reduction has mainly focused on bacteria rather than fungal communities. To acquire insight into fungi involved in iron(III) reduction, typical organic matters (containing cellulose, glucose, lactate, and acetate) and ferrihydrite were used as electron donors and acceptors, respectively, in the presence of antibiotics. After antibiotic addition, microbial iron(III) reduction was still detected at quite high rates. In comparison, rates of iron(III) reduction were significantly lower in cellulose-amended groups than those with glucose, lactate, and acetate under the antibiotic-added condition. Patterns of intermediate (e.g., acetate, pyruvate, glucose) turnover were markedly different between treatments with and without antibiotics during organic degradation. A total of 20 genera of potential respiratory and fermentative iron(III)-reducing fungi were discovered based on ITS sequencing and genome annotation. This study provided an insight into the diversity of iron(III)-reducing fungi, indicating the underestimated contribution of fungi to iron and the coupled carbon biogeochemical cycling in environments.

Background: Endometriosis (EM) is a long-lasting inflammatory disease that is difficult to treat and prevent. Existing research indicates the significance of immune infiltration in the progression of EM. Efferocytosis has an important immunomodulatory function. However, research on the identification and clinical significance of efferocytosis-related genes (EFRGs) in EM is sparse.Methods: The EFRDEGs (differentially expressed efferocytosis-related genes) linked to datasets associated with endometriosis were thoroughly examined utilizing the Gene Expression Omnibus (GEO) and GeneCards databases. The construction of the protein-protein interaction (PPI) and transcription factor (TF) regulatory network of EFRDEGs ensued. Subsequently, machine learning techniques including Univariate logistic regression, LASSO, and SVM classification were applied to filter and pinpoint diagnostic biomarkers. To establish and assess the diagnostic model, ROC analysis, multivariate regression analysis, nomogram, and calibration curve were employed. The CIBERSORT algorithm and single-cell RNA sequencing (scRNA-seq) were employed to explore immune cell infiltration, while the Comparative Toxicogenomics Database (CTD) was utilized for the identification of potential therapeutic drugs for endometriosis. Finally, immunohistochemistry (IHC) and reverse transcription quantitative polymerase chain reaction (RT-qPCR) were utilized to quantify the expression levels of biomarkers in clinical samples of endometriosis.Results: Our findings revealed 13 EFRDEGs associated with EM, and the LASSO and SVM regression model identified six hub genes (ARG2, GAS6, C3, PROS1, CLU, and FGL2). Among these, ARG2, GAS6, and C3 were confirmed as diagnostic biomarkers through multivariate logistic regression analysis. The ROC curve analysis of GSE37837 (AUC = 0.627) and GSE6374 (AUC = 0.635), along with calibration and DCA curve assessments, demonstrated that the nomogram built on these three biomarkers exhibited a commendable predictive capacity for the disease. Notably, the ratio of nine immune cell types exhibited significant differences between eutopic and ectopic endometrial samples, with scRNA-seq highlighting M0 Macrophages, Fibroblasts, and CD8 Tex cells as the cell populations undergoing the most substantial changes in the three biomarkers. Additionally, our study predicted seven potential medications for EM. Finally, the expression levels of the three biomarkers in clinical samples were validated through RT-qPCR and IHC, consistently aligning with the results obtained from the public database.Conclusion: we identified three biomarkers and constructed a diagnostic model for EM in this study, these findings provide valuable insights for subsequent mechanistic research and clinical applications in the field of endometriosis.