BioMed Research International最新文献

[This retracts the article DOI: 10.1155/2015/856349.].

Background: Endometriosis is a chronic gynecological disorder characterized by the presence of endometrial-like tissue outside the uterine cavity, causing chronic pain and infertility. Hypoxia plays a significant role in the progression of endometriosis.

Methods: We performed bioinformatics analysis on GEO datasets to identify differentially expressed genes (DEGs) in endometriosis, using weighted gene coexpression network analysis (WGCNA)and GeneCards for hypoxia-related genes. Machine learning models identified key hub genes. CCK-8, EdU, and Transwell assays assessed cell proliferation, migration, and invasion. Molecular docking was performed to investigate the interactions between the drug and the protein.

Results: In the GEO dataset analysis, 2834 DEGs were identified. Using WGCNA, a green module strongly correlated with endometriosis was identified. Intersecting this module with the hypoxia-related genes resulted in the selection of 449 key genes. Machine learning models, including support vector machines (SVMs), were employed to identify hypoxia-related DEGs with significant predictive value. LASSO and SVM-RFE were used to refine this list, ultimately selecting six hub genes: DDR2, ENO3, ESM1, NMBR, PRKAB1, and PRPF19. Validation with an independent dataset confirmed DDR2 as a promising diagnostic biomarker. Functional assays demonstrated that DDR2 knockdown significantly inhibited cell proliferation, migration, and invasion in the endometriosis cell lines VK2/E6E7 and 12Z. DDR2, a receptor tyrosine kinase, mediates extracellular matrix remodeling and cell invasion under hypoxia. By interacting with collagen and HIFs, DDR2 activates pathways that promote MMP secretion, angiogenesis, and migration, facilitating endometriotic cell progression in the hypoxic microenvironment. Molecular docking identified key amino acids near DDR2's binding pocket that form hydrophobic interactions, hydrogen bonds, and π-stacking with baicalein, cavidine, sitogluside, and stigmasterol, further supporting DDR2's potential as a therapeutic target.

Conclusion: DDR2 is a key hypoxia-related gene in endometriosis and a promising diagnostic and therapeutic biomarker.

Background: Despite the increasing incidence of cancer worldwide, the knowledge about the trend of cancer incidence in Ethiopia is limited. The paucity of core cancer diagnostic services like pathology, diagnostic imaging technology, and the absence of a comprehensive national cancer registry masked the exact magnitude of cancer incidence in Ethiopia in general and the Wolaita area in particular. This study is aimed at filling the gap by analyzing diagnostic data from a referral clinic. The clinic used to serve as a primary diagnostic center for patients referred from over 25 healthcare facilities in the region.

Methods: A pathology sample retrospective analysis-based prevalence study was conducted for the period between December 2017 and February 2022. Records saved in computers were subjected to analysis by using Statistical Package for Social Sciences (SPSS) software Version 22. The data were used to analyze the types and distribution of cancers in the region across age, sex, and diagnosis.

Results and discussions: The results showed notable gender disparities, with women experiencing a greater prevalence of breast cancer and men mostly receiving diagnoses for soft tissue sarcomas. The most prevalent forms of cancer were determined, along with the locations of each. The study also emphasized how different referral facilities, such as general hospitals, primary hospitals, and medium-sized clinics, had varying cancer incidence rates. Although generalizability may be limited by the study's clinic-based design, its relevance to comparable healthcare settings in Ethiopia and other low-resource locations is strengthened by the large and diverse sample drawn from a variety of referral institutions. This study emphasizes the necessity of focused screening programs and greater cancer awareness in Wolaita Zone, particularly in rural regions. The results also suggest possible directions for future investigation, such as population-based studies to confirm and build upon these findings.

Conclusions: This study provides crucial insights into the cancer burden in Wolaita Zone and emphasizes the importance of improving diagnostic and preventive measures. Further research, including broader, population-based studies, is necessary to confirm these findings and inform regional cancer control strategies.

Sirtuin 1 (SIRT1) is a crucial regulator of cellular processes, including inflammation, metabolism, and stress responses, playing a significant role in the body's defense mechanisms. During SARS-CoV-2 infection, SIRT1 plays a crucial role in modulating the immune response. This protein helps to enhance the antiviral response through deacetylating key transcription factors and regulating proinflammatory cytokines, thereby reducing the cytokine storm (an overwhelming immune response) associated with severe COVID-19 cases. SIRT1 influences the expression of angiotensin-converting enzyme 2 (ACE2), the primary receptor for SARS-CoV-2, thereby potentially mitigating viral entry and replication. Natural activators of SIRT1, such as resveratrol, have been shown to enhance its activity, offering promising avenues for therapeutic interventions aimed at bolstering the immune response during COVID-19. Understanding the multifaceted role of SIRT1 in human defense mechanisms against SARS-CoV-2 could pave the way for innovative strategies to manage COVID-19 and similar viral infections, emphasizing the importance of SIRT1 as a potential target for future therapeutic approaches.

Background: Osteoporosis is characterized by reduced bone mineral density and disrupted bone microstructure, leading to an increased risk of fractures. This study aimed to investigate the role of B cell subpopulations in osteoporosis and their effects on bone metabolism using single-cell RNA sequencing.

Methods: Single-cell RNA sequencing data from primary human femoral head tissue cells of three osteoporosis patients and one non-osteoporosis patient were obtained from the GEO dataset (GSE169396). Data preprocessing, integration, dimensionality reduction, clustering, and annotation were conducted using the Seurat package in R. Enrichment analysis, cell trajectory analysis, and intercellular communication analysis were then applied to investigate the role of B cells and the signaling pathways within B cell subpopulations in osteoporosis.

Results: We identified six distinct B cell subpopulations, and further analysis revealed a higher proportion of precursor B cells in osteoporosis patients compared to the normal group. Functional studies indicated that B cells contribute to the progression of osteoporosis through inflammatory activation and the unfolded protein response. Cell communication analysis among these B cell subpopulations demonstrated markedly enhanced intercellular signaling in osteoporosis patients relative to the normal group. Notably, two critical signaling pathways, MIF-(CD74+CXCR4) and LGALS9-CD45, were identified as potential key regulators driving the progression of osteoporosis.

Conclusion: This study underscores the heterogeneity and functions of B cells in osteoporosis, highlighting two signaling pathways implicated in disease progression. These findings offer novel insights into osteoporosis pathogenesis and suggest potential therapeutic targets for its treatment.

Lung cancer is a deadly disease. According to a report of 2024, it is the primary reason for 1.82 million deaths. Given the high disease burden, early detection of lung cancer is crucial for improving survival rates and implementing effective strategies. This paper is aimed at conducting a systematic literature review and developing a highly accurate framework for predicting lung cancer effectively. Tollgate methodology has been used for systematic literature review, and quality assessment criteria were applied to select published articles relevant to the research questions. The paper investigates the effectiveness of machine learning in identifying patterns relevant to lung cancer prediction (Q1), examines the pros and cons of current predictive systems (Q2), compares the use of artificial intelligence in lung cancer prediction with traditional methods (Q3), and identifies key features that distinguish lung cancer from patient symptoms (Q4). Machine learning techniques were employed for the proposed framework. Two publicly available, distinct datasets containing clinical features were obtained. Then, the SelectKBest method was used for feature selection, and SMOTE was used to handle class imbalance. Our proposed framework includes a voting ensemble with random forest, support vector machine, and logistic regression with cross-validation. The results indicate an accuracy of 99% and 92.5% for the first and second datasets, respectively. This study's systematic literature review, based on four research questions and a machine learning model, exhibits high accuracy in predicting lung cancer.

Introduction: Oral mucositis affects up to 80% of patients undergoing bone marrow transplantation, causing painful ulcers, increased infection risk, and impaired quality of life. Current treatments are mainly palliative, highlighting the need for new therapeutic options. Arrabidaea chica Verlot extract has shown wound healing potential through antioxidant activity and collagen synthesis promotion in vitro and significant wound area reduction in vivo.

Objective: The objective of the study is to evaluate the wound healing potential of a mucoadhesive gel containing 2.5% A. chica extract for treating oral mucositis in oncohematological patients undergoing bone marrow transplantation.

Methods: This was a randomized, controlled, open-label, parallel-group clinical trial conducted at HC/UNICAMP from October 2022 to November 2024. Forty-four patients were randomized to the intervention group (mucoadhesive A. chica gel, n = 22) or control group (low-intensity laser therapy, n = 22). The primary outcome was the time to healing of oral mucositis. Statistical analysis compared mean healing times between groups.

Results: Twenty participants in the intervention group and 18 in the control group completed the study. Mean healing time was 5.2 ± 1.5 days for the intervention group and 11.2 ± 6.5 days for the control group, representing a 2.2-fold faster healing with A. chica gel. Some intervention participants reported a mild burning sensation, without affecting treatment adherence or efficacy.

Conclusion: The mucoadhesive gel containing A. chica extract proved to be a safe and effective therapeutic option for treating oral mucositis in patients undergoing bone marrow transplantation.

Trial registration: ReBEC number: RBR-5×4397.

Cancer remains a major global health challenge, requiring natural therapeutic solutions. Olive tree by-products, like leaves and pomace, are rich in bioactive phenolics with antioxidant and anticancer potential. This study explores the chemical composition and anticancer potential of the hexane fraction of olive leaf extracts and the dichloromethane fraction of olive pomace extracts from two Olea europaea varieties: e cultivated Chemlali (var. europaea) and the wild olive (var. sylvestris). GC-MS analysis identified stigmast-5-en-3-ol, erythrodiol, and phytol as the predominant compounds in wild olive leaf extracts, whereas Chemlali leaf extracts contained only the first two. Analysis of olive pomace extracts revealed four major compounds: coniferyl alcohol, glyceryl monooleate, stigmast-5-en-3-ol, and methylursolate, present in both varieties, with significant variations in their peak areas. Cytotoxic evaluation showed that olive waste extracts significantly inhibited cancer cell growth and viability in both prostate (PC3) and breast (MDA-MB-231) cancer cell lines. Specifically, pomace extracts exhibited the strongest effect against PC3 prostate cancer cells, while leaf extracts were more effective against MDA-MB-231 breast cancer cells. These extracts inhibited cell proliferation, induced morphological and phenotypic alterations, modified the cell cycle progression, promoted apoptotic nuclear changes, and triggered apoptosis. Notably, wild olive extracts demonstrated stronger cytotoxic effects than those derived from the Chemlali cultivar. These findings highlight olive by-products as promising sources of natural anticancer agents for pharmaceutical applications.

Background: Justicia gendarussa is a branched shrub spread across Indian, Sri Lankan, and Malaysian forests. It has been widely used across many countries to treat asthma, rheumatism, colics in children, eczema, and HIV. The study goal was to investigate the phytoconstituents from J. gendarussa and to discover its therapeutic potential against various disease conditions.

Methods: The plant sample was collected, dried, and grinded into coarse powder which was then soaked in methanol for 2 weeks. After the maceration process, the crude methanolic extract was subjected to solvent-solvent partitioning into four different fractions: n-hexane soluble fraction (HSF), dichloromethane soluble fraction (DMSF), ethyl acetate soluble fraction (EASF), and aqueous fraction (AQF). DMSF was chemically evaluated through chromatographic separation, and all the fractions including the crude methanolic extracts were assessed for their potential pharmacological activities against pain, oxidative stress, hyperglycemia, diarrhea, and microbes following standard protocols.

Results: Chemical investigation results in the isolation of lupeol, β-sitosterol, and 1-monostearin. The structures of the compounds were elucidated through meticulous NMR spectroscopic analysis. In a DPPH free radical scavenging assay, prominent action was noticed by EASF, with a median inhibition concentration (IC₅₀) of 24.207 g/mL in comparison to the BHT with an IC₅₀ value of 23.159 g/mL. In central analgesic activity, all the results were highly significant, with the highest (233.47%) time elongation in comparison to the control, observed after 90 min at 600 mg/kg b.w. and maximum peripheral analgesic activity of 61.96% was found at a dose of 600 mg/kg b.w. Two test doses (600 and 400 mg/kg b.w.) demonstrated substantial hypoglycemic and antidiarrheal effects that became more pronounced over time. The isolated compounds demonstrated impressive binding scores when interacting with glutathione reductase (3GRS), mu-opioid receptor (MOR), kappa opioid receptor (KOR), and glucose transporter 3 (GLUT 3) receptors. However, their performance was notably lacking in terms of binding with cyclooxygenase-2 (COX-2) and dihydrofolate reductase (DHFR) receptors.

Conclusion: Three isolated phytochemicals demonstrate promising binding affinities with the receptor molecules that support the pharmacological findings of this study. However, additional research needs to be conducted to isolate more phytoconstituents and affirm the pharmacological potential of J. gendarussa.

Background and aims: The effects of vascular endothelial growth factor (VEGF) overexpression on the prognosis of patients with esophageal squamous cell carcinoma (ESCC) are still unclear. The aim of this study was to construct and evaluate a mutant gene-based model to predict the prognosis of ESCC patients with VEGF overexpression.

Methods: Samples from 50 ESCC patients with VEGF overexpression were subjected to next-generation sequencing (NGS) to identify gene mutations. The associations between the enrichment of these mutations and patient outcomes were also evaluated in a cohort from The Cancer Genome Atlas. Hazard ratios were identified via the Kaplan-Meier and Cox analyses. A support vector machine recursive feature elimination algorithm was used to construct the model, and receiver operating characteristic analysis was carried out to evaluate its prognostic performance.

Results: ESCC patients with FAT1, FGF3, FGF12, and FGF19 mutations; advanced M stage; and high neutrophil counts tended to have poorer prognoses.

Conclusion: A model based on a four-gene signature effectively predicts the prognosis of ESCC patients.