Repository of Research and Investigative Information Isfahan University of Medical Sciences
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Dispersive liquid-liquid microextraction for the determination of urinary 8-hydroxy 2′-deoxyguanosine in COVID-19 patients by gas chromatography-mass spectrometry
No full textCOVID-19 disease has led to many deaths worldwide and early detection of people at a high risk of severe forms of this disease would greatly help physicians. The presence of oxidative stress biomarkers may help identify high-risk individuals early in the course of the disease. 8-Hydroxy-2 '-deoxyguanosine (8-OHdG) is a widely used biomarker for assessing endogenous oxidative DNA damage. In this study, the urinary 8-OHdG levels were determined in COVID-19 patients and COVID-19 patients with cancer by a dispersive liquid-liquid microextraction (DLLME) method using gas chromatography-mass spectrometry (GC-MS). The effects of essential parameters on the extraction method were investigated. The LOD and LOQ are equal to 1.7 nM and 5.1 nM, respectively. At varied concentrations of 8-OHdG (300, 400, and 600 nM), the relative standard deviation (RSD) ranged from 18.35 to 22.36. The mean urinary 8-OHdG levels of cancer and COVID-19 patients were 13.20 +/- 6.20 nmol mmol-1, while the mean levels in COVID-19 patients and healthy volunteers were 6.67 +/- 5.80 nmol mmol-1 and 1.61 +/- 1.72 nmol mmol-1, respectively. The results of this study showed that the level of 8-OHdG urine biomarkers in people with COVID-19 is significantly higher than in healthy people. In this study, the DLLME approach was used for the first time to determine the value of 8-OHdG using GC-MS. According to the results of this research, the DLLME method was successfully used as a biomarker of DNA oxidative stress for extracting 8-OHdG urine. Compared to other methods, this technique has advantages such as shorter extraction time and low cost
Comment on "Meta-analysis of the association between the dietary inflammatory index and risk of chronic kidney disease" by Chen et al. 2024
No full textAn overview of extracellular field potentials: Different potentiation and measurable components, interpretations, and hippocampal synaptic activity models
No full textThe hippocampus and some other brain regions are critically involved in synaptic plasticity. Electrophysiological recordings using extracellular field potentials (EFPs) reveal diverse synaptic activity within the hippocampus, including input/output functions (reflecting neural excitability), paired-pulse responses (reflecting short-term plasticity), and long-term potentiation (reflecting long-term plasticity). EFP techniques offer various measurable components for assessing multiple neural functions. These include fEPSP slope, amplitude, and area under curve (AUC), as well as latency (fEPSP onset or peak after stimulation), width at half amplitude, fiber volley, decay time, time-course (fEPSP rise and decay time constants; tau), initial slope/initial area and-/late area derived from a fEPSP waveform sample. Each of these parameters is separately evaluated and provides distinct electrophysiological interpretations. Despite the rich data offered by EFP techniques, many studies adopt a limited approach, focusing solely on fEPSP slope, amplitude, and occasionally AUC, thereby neglecting the potential insights provided by other parameters. Given the inherent variability of fEPSP components within a single recording and timeframe, a comprehensive analysis of synaptic activity within a specific hippocampal region is necessary for obtaining the full spectrum of fEPSP-related data. Researchers should consider the potential influence of additional factors contributing to the variability of synaptic activity magnitude. A detailed analysis considering different parts of extracellular fEPSP recordings and their properties is crucial for a deeper understanding of synaptic activity changes within the brain. Therefore, this review aims to provide a comprehensive overview of diverse forms of hippocampal synaptic activity, measurable components of EFP recordings, and their corresponding interpretations
Association between body composition indices and vascular health: a systematic review and meta-analysis
No full textObjectiveThis systematic review explores the intricate relationship between body composition, with a specific focus on skeletal muscle mass, and vascular health indices, including measures of arterial stiffness-pulse wave velocity (PWV) and cardio-ankle vascular index (CAVI)-as well as arterial structure, specifically carotid artery intima-media thickness (cIMT).MethodsAn extensive literature search, encompassing PubMed, Scopus, EMBASE, Web of Science, and Google Scholar, was conducted until January 2024. Inclusion criteria involved original observational studies, with cross-sectional or longitudinal designs, reporting body composition parameters and vascular health measures. The Newcastle-Ottawa Scale (NOS) assessed study quality. Statistical analyses utilized Stata 17.0, employing random-effects meta-analysis, sensitivity analysis, and evaluation of publication bias.ResultsFifteen observational studies (n = 21,215) met the inclusion criteria. Pooled analyses revealed a positive association between fat-free mass (FFM) and carotid intima-media thickness (IMT) (effect size ES: 1.79, 95% CI 1.68-1.91), highlighting a relationship with arterial structure. Similarly, body fat percentage (BFP) was positively associated with PWV (ES: 1.45, 95% CI 1.15-1.82), and FFM showed a positive association with CAVI (ES: 1.46, 95% CI 0.78-2.71), both measures of arterial stiffness. Subgroup analyses revealed a non-significant association between appendicular skeletal muscle (ASM) and IMT (ES: 1.01, 95% CI 0.76-1.35).ConclusionThis meta-analysis highlights the complex relationship between body composition and vascular health. Subgroup analyses suggest the need for further research into specific body composition indices and their clinical implications.Level of evidence: III evidence obtained from well-designed cohort and cross-sectional studies.ConclusionThis meta-analysis highlights the complex relationship between body composition and vascular health. Subgroup analyses suggest the need for further research into specific body composition indices and their clinical implications.Level of evidence: III evidence obtained from well-designed cohort and cross-sectional studies
Neuromuscular alterations in subjects with knee osteoarthritis during most common activities of daily living: a literature review
No full textBackgroundKnee osteoarthritis is an age-related, mobility-restricting illness with a number of risk factors that makes walking and climbing stairs more difficult than any other activity. It is associated with numerous biomechanical and neuromuscular alterations. Many studies have examined neuromuscular changes as an essential issue that should be taken into account in KOA. This study provides an evidence-based guideline to restore muscular activity patterns during common daily activities in subjects with KOA.Materials and methodsLiterature was retrieved up to the end of October 2024 from international databases including Scopus, PEDro, ISI Web of Science, and PubMed; additionally, the scholarly-specific search engines, Science Direct and Google Scholar were used. The English-language articles and related content were included. Due to the large number of articles, we only chose fresh and non-duplicated articles that were relevant to the topic.Results2130 articles were founded based on search strategy, and after removing duplicates and based on title 460 articles remained. After removing irrelevant articles, studying the abstract and full text and cross referencing in the newest systematic reviews, 25 articles were selected for review.ConclusionMany studies with different and sometimes contradictory results have been conducted in this field during walking, but the number of studies about stair-up and stair-down and sit-to-stand activities is low. Based on the different results mentioned in the studies, it seems that more and better quality studies are needed in this field, considering the severity of KOA and differences between different grades of KOA based on radiography findings
Developing risk models for predicting incidence of diabetes and prediabetes in the first-degree relatives of Iranian patients with type 2 diabetes and comparison with the finnish diabetes risk score
No full textBackground: We aimed to develop risk models for predicting the onset of developing diabetes and prediabetes in the first-degree relatives (FDRs) of patients with type 2 diabetes, who have normal glucose tolerance (NGT). Materials and Methods: In this study, 1765 FDRs of patients with type 2 diabetes mellitus, who had NGT, were subjected to the statistical analysis. Diabetes risk factors, including anthropometric indices, physical activity, fast plasma glucose, plasma glucose concentrations 2-h after oral glucose administration, glycosylated hemoglobin (HbA1c), blood pressure, and lipid profile at the baseline were considered as independent variables. Kaplan-Meier, log-rank test, univariate, and multivariable proportional hazard Cox regression were used for the data analysis. The optimal cutoff value for risk score was created according to the receiver operating characteristic curve analysis. Results: The best diabetes predictability was achieved by a model in which waist-to-hip ratio, HbA1c, oral glucose tolerance test-area under the curve (OGTT-AUC), and the lipid profile were included. The best prediabetes risk model included HbA1c, OGTT-AUC, systolic blood pressure, and the lipid profile. The predictive ability of multivariable risk models was compared with fasting plasma glucose (FPG), HbA1c, and OGTT. The predictive ability of developed models was higher than FPG and HbA1c; however, it was comparable with OGTT-AUC alone. In addition, our study showed that the developed models predicted diabetes and OGTT-AUC better than the Finnish Diabetes Risk Score (FINDRISC). Conclusion: We recommend regular monitoring of risk factors for the FDRs of patients with type 2 diabetes as an efficient approach for predicting and prevention of the occurrence of diabetes and prediabetes in future. Our developed diabetes risk score models showed precise prediction ability compared to the FINDRISC in Iranian population
Isfahan Artificial Intelligence Event 2023: Macular Pathology Detection Competition
No full textBackground: Computer-aided diagnosis (CAD) methods have become of great interest for diagnosing macular diseases over the past few decades. Artificial intelligence (AI)-based CADs offer several benefits, including speed, objectivity, and thoroughness. They are utilized as an assistance system in various ways, such as highlighting relevant disease indicators to doctors, providing diagnosis suggestions, and presenting similar past cases for comparison. Methods: Much specifically, retinal AI-CADs have been developed to assist ophthalmologists in analyzing optical coherence tomography (OCT) images and making retinal diagnostics simpler and more accurate than before. Retinal AI-CAD technology could provide a new insight for the health care of humans who do not have access to a specialist doctor. AI-based classification methods are critical tools in developing improved retinal AI-CAD technology. The Isfahan AI-2023 challenge has organized a competition to provide objective formal evaluations of alternative tools in this area. In this study, we describe the challenge and those methods that had the most successful algorithms. Results: A dataset of OCT images, acquired from normal subjects, patients with diabetic macular edema, and patients with other macular disorders, was provided in a documented format. The dataset, including the labeled training set and unlabeled test set, was made accessible to the participants. The aim of this challenge was to maximize the performance measures for the test labels. Researchers tested their algorithms and competed for the best classification results. Conclusions: The competition is organized to evaluate the current AI-based classification methods in macular pathology detection. We received several submissions to our posted datasets that indicate the growing interest in AI-CAD technology. The results demonstrated that deep learning-based methods can learn essential features of pathologic images, but much care has to be taken in choosing and adapting appropriate models for imbalanced small datasets
Exploring chemokines and soluble adhesion molecules in mustard lung pathogenesis
No full textSulfur mustard (SM), functioning as an alkylating agent, plays a significant role in developing respiratory system pathologies. This study aimed to evaluate serum concentrations of chemokines and soluble adhesion molecules in serious mustard lung (ML) patients 25-30 years after exposure to SM, exploring their roles in ML pathogenesis and disease severity. The study included 275 individuals exposed to SM and 64 unexposed individuals as controls. Serum samples were collected and clinical evaluations categorized disease severity and pulmonary pathogenesis. Serum levels of MCP-1/CCL2, RANTES/CCL5, CX3CL1, CXCL12s, P-selectin, sL-selectin, sE-selectin, sICAM-1 levels were measured using ELISA kits, and mRNA expression of CXCR4 in whole blood was determined via real-time PCR. Data analysis included comparisons between groups. SM-exposed individuals exhibited significantly higher MCP-1/CCL2 and RANTES/CCL5 levels, with decreased CX3CL1 levels compared to controls. CXCL12, selectins, sICAM-1 levels, and the expression level of CXCR4 showed no significant differences. Changes in some of the mentioned factors were observed, along with changes in the severity of the disease, suggesting potential roles in ML progression. The findings suggest a complex interplay of immune responses in ML pathogenesis, with elevated MCP-1/CCL2 and RANTES/CCL5 potentially contributing to inflammation, while decreased CX3CL1 levels and unchanged CXCL12 and CXCR4 may impair immune responses and tissue repair mechanisms. The unique chemokine and adhesion molecule profile observed in SM-exposed subgroups suggests ML as a differentiated pulmonary disease requiring further investigation into its pathogenesis and relationship with inflammatory disorders
Gallstones as a predictor of elevated cardiovascular disease risk: A meta-analysis and meta-regression of over 7.4 million participants
No full textIntroduction Gallstone disease (GD) is a prevalent condition frequently encountered in surgical units worldwide. The objective of this comprehensive systematic review and meta-analysis study was to examine the relationship between gallstones and the risk of cardiovascular diseases (CVDs). Methods To conduct our study, we performed a systematic review and meta-analysis. We gathered relevant studies from reputable databases, including Web of Science, Scopus, PubMed, Cochrane, Google Scholar, and Embase. The quality of the articles was assessed using the Newcastle-Ottawa Scale checklist. To assess heterogeneity among the studies, we utilized statistical tests such as the Chi-square test, I-2 statistic, and forest plots. Meta-regression analysis considered variables such as the year of the study, study design, sample size, study quality assessment score, geographical region, average age of subjects, and follow-up duration. Additionally, we evaluated publication bias using Begg's and Egger's tests. Results Data from 22 studies conducted between 1985 and 2023 were analyzed. The combined number of participants across these studies was 7,496,303. The meta-analysis results revealed that individuals with GD had a higher risk of CVDs (Risk Ratio (RR): 1.29; 95 CI: 1.22-1.36; P 0.10). In a sensitivity analysis, the estimated RR remained consistent, confirming the robustness of the meta-analysis results. Conclusion Our findings suggest an association between gallstone disease and an increased risk of CVDs. It seems that one of the important factors of this relationship is having common causes for the formation of gallstones and cardiovascular diseases. However, gallstones can be considered an important sign of increased risk of cardiovascular diseases