Method of upper and lower solutions for fractional differential equations

In this paper, we show the existence and uniqueness of solutions for the boundary-value problems of fractional differential equations, using the upper and lower solutions method and monotone iterative algorithm. An example is also included to illustrate our results

In silico analysis of Hsp70 genes in Ctenopharyngodon idella and their expression profiles in response to environmental stresses

AbstractHeat shock protein 70 (Hsp70) is a crucial member of the Hsp family, which is present in many animals, and acts as a chaperone to protect the organism from damage caused by various environmental stresses, particularly unfavorable temperatures. In this study, we used homologous gene search and domain analysis to identify sixteen Hsp70 genes (named as CiHSP genes) from the genome of grass carp (Ctenopharyngodon idella). These genes were classified into ten subfamilies based on their conserved structures and phylogenetic analysis. To investigate the biological functions of CiHSP genes in grass carp, we analyzed public RNA-Seq data, and found that most members of the CiHSP gene family were highly expressed in the brain and kidney, suggesting potential roles in protecting brain cells and participating in fish immunological processes. Additionally, these CiHSP genes were characterized as responding to high density and high temperature stress, with most members significantly upregulated under high temperature conditions. These findings demonstrate the critical roles of CiHSP genes in grass carp development and their response to environmental stress, which will provide valuable insights for determining their function and potential application in fish production in the future

Deep learning techniques for imaging diagnosis and treatment of aortic aneurysm

ObjectiveThis study aims to review the application of deep learning techniques in the imaging diagnosis and treatment of aortic aneurysm (AA), focusing on screening, diagnosis, lesion segmentation, surgical assistance, and prognosis prediction.MethodsA comprehensive literature review was conducted, analyzing studies that utilized deep learning models such as Convolutional Neural Networks (CNNs) in various aspects of AA management. The review covered applications in screening, segmentation, surgical planning, and prognosis prediction, with a focus on how these models improve diagnosis and treatment outcomes.ResultsDeep learning models demonstrated significant advancements in AA management. For screening and diagnosis, models like ResNet achieved high accuracy in identifying AA in non-contrast CT scans. In segmentation, techniques like U-Net provided precise measurements of aneurysm size and volume, crucial for surgical planning. Deep learning also assisted in surgical procedures by accurately predicting stent placement and postoperative complications. Furthermore, models were able to predict AA progression and patient prognosis with high accuracy.ConclusionDeep learning technologies show remarkable potential in enhancing the diagnosis, treatment, and management of AA. These advancements could lead to more accurate and personalized patient care, improving outcomes in AA management

p16 and p53 can Serve as Prognostic Markers for Head and Neck Squamous Cell Carcinoma

Objective: The present study aimed to explore the expression and clinical significance of human papilloma virus-related pathogenic factors (p16, cyclin D1, p53) in patients with head and neck squamous cell carcinoma (HNSCC) and construct a predictive model. Methods: The Cancer Genome Atlas was used to obtain clinical data for 112 patients with HNSCC. Expression of p16, p53, and cyclin D1 was quantified. We used the survival package of the R program to set the cut-off value. Values above the cut-off were considered positive, while values below the cut-off were negative. Kaplan–Meier analysis and univariate and multivariate Cox regression analyses were performed to investigate prognostic clinicopathological indicators and the expression of p16, p53, and cyclin D1. A predictive model was constructed based on the results of multifactor Cox regression analysis, and the accuracy of the predictive model was verified through final calibration analysis. Follow-up of patients with HNSCC at the Affiliated Hospital of Binzhou Medical University was conducted from 2015 to 2017, and reliability of the predictive model was validated based on follow-up data and molecular expression levels. Results: According to the results, expression of p16 and p53 was significantly associated with prognosis (P < .05). The predictive model constructed based on the expression levels of p16 and p53 was useful for evaluating the prognosis of patients with HNSCC. The predictive model was validated using follow-up data obtained from the hospital, and the trend of the follow-up results was consistent with the predictive model. Conclusion: p16 and p53 can be used as key indicators to predict the prognosis of HNSCC patients and as critical immunohistochemical indicators in clinical practice. The survival model constructed based on p16 and p53 expression levels reliably predicts patient prognosis