View clinical trials related to Inflammatory Response.
Filter by:Polymethylmethacrylate (PMMA) has been the material of choice for fabricating complete dentures for more than 80 years now.(1,2) As a material it is easy to handle and can be easily repaired and polished. PMMA while being a low-cost material has good esthetics. Some disadvantages of PMMA as a material are surface roughness and porosities which lead to staining, plaque accumulation and bacterial adhesion over time. Among the various factors that can influence microbial attachment to surfaces, surface roughness, hydrophilicity and free surface energy of PMMA are most important. Carboxylate and methyl ester groups found in PMMA make it a very hydrophilic material with a large amount of free surface energy. Despite the influence of the chemical composition, processing methods play the most important role in developing surface roughness.
Stress is defined as hormonal and metabolic changes in the biological system that follow any injury. The stress response occurs as a general systemic response to injury and includes a wide range of endocrinological, immunological and hematological effects. The level of stress in the surgical process can affect not only the patients outcome but also the overall health system. The two main agents used in maintenance for general anesthesia are inhalation and intravenous anesthetics. Both inhalation anesthesia and TIVA (Total Intravenous Anesthesia) approaches provide general anesthesia suitable for surgical operations. However, the mechanisms of action of these two methods differ and are not fully understood. The aim of investigators' for this study was to investigate the effect of different anesthetic agents on stress inflammatory response in the preoperative, peroperative and postoperative periods.
Fetal growth restriction (FGR) is a serious complication in pregnancy that can lead to various adverse outcomes. It's classified into early-onset (before 32 weeks) and late-onset (after 32 weeks), with late-onset associated with long-term risks like hypoxemia and developmental delays. The study focuses on the role of inflammation in FGR, introducing new blood markers for better understanding and diagnosis. It also addresses the challenges of using advanced diagnostic tools in low-resource settings and explores the use of machine learning to predict FGR based on inflammatory markers, highlighting the potential of artificial intelligence in overcoming these challenges.
The goal of this observational study is to provide exploratory research into the in vivo physiological and psychological effects, if any, of cannabigerol (CBG) in healthy human adults age 21 or over. The main questions it aims to answer are: - What effect, if any, does daily oral consumption of 50mg of full spectrum CBG have on the mental, physical, and emotional wellbeing of healthy individuals, as measured by self-report Medical Symptom Questionnaire and 36-Item Short Form Health Survey scores? - Is CBG effective at reducing inflammation in the body, as measured by HSCRP, ESR, and PSA inflammatory markers? - Do age, gender, weight, or state of body inflammation have an effect on the perceived efficacy of CBG? - What adverse effects, if any, are associated with CBG use? Over the course of the 12-week study, participants will: - Take baseline MSQ and SF-36 surveys, as well as a clinical visit with blood draws for HSCRP, ESR, and PSA testing - Consume one (1) 50mg capsule of full spectrum CBG daily by mouth with food for 8 weeks, followed by a 4-week washout period - Complete biweekly SF-36 surveys as well as MSQ surveys every 4 weeks - Attend a clinical visit every 4 weeks for clinical observation and blood draws for HSCRP, ESR, and PSA (male subjects)
The purpose of this study is to determine the effects of meat on inflammatory and metabolite profiles in middle-aged individuals after an acute meal. Up to 36 adults, who are overweight or obese and between 30-60 years old will undergo a consent/screening visit, followed by three study visits. On separate visits to the clinical research facilities at the Center for Human Nutrition Studies, participants will consume either 9 oz (250 grams) of cooked grain-fed beef, grass-fed beef, or a plant-based meat alternative (Impossible Burger). Blood will be drawn prior to food consumption and three more times after eating the meal at 1h, 3h, and 5h after participants finishing their meal. The investigators will use those blood samples to determine the impacts of these foods on inflammatory markers and metabolite profiles (compounds that circulate in our blood such as amino acids, fatty acids, and phenolics).
The overall primary objective of the PULSE-MI trial is to test the hypothesis that administration of single-dose glucocorticoid pulse therapy in the pre-hospital setting reduces final infarct size in patients with ST-segment elevation myocardial infarction (STEMI) treated with primary percutaneous coronary intervention (PCI)
Aim of this project is to understand clinical features, clinical outcomes and efficacy and safety profiles of different therapies by analyzing a cohort of COVID-19 patients hospitalized and treated in a tertiary-level institution, University hospital Dubrava. Patients' clinical and laboratory characteristics, drug exposure and outcomes are obtained by analysis of written and electronical medical records.
The aging process tends to promote an overall increase in inflammation compromising the immunologic system regulation, sleep/wakefulness pattern, and neurocognitive performance. In elders, there is an increase in repetitive arousals during sleep, secondary to breathing interruption by pharynx collapse, generating a transient reduction in oxygen delivery to the brain known as obstructive sleep apnea. This lack in oxygen supply results in an inflammatory process producing brain damage. Some substances present in the blood seem to be associated to neurocognitive damage, like S100β protein, cortisol, interleukin 1-β,6 and TNF-α. In the other way, a substance called brain-derived neurotrophic factor (BDNF) enhances cognitive function, and memory consolidation improvement.
To build simple and reliable predictive scores for intensive care admissions and deaths in COVID19 patients. These scores adhere to the TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) reporting guidelines. The outcomes of the study are (i) admission in the Intensive Care Unit admission and (ii) death. All patients admitted in the Emergency Department with a positive reverse transcription‐polymerase chain reaction SARS-COV2 test were included in the study. Routine clinical and laboratory data were collected at their admission and during their stay. Chest X-Rays and CT-Scans were performed and analyzed by a senior radiologist. Generalized Linear Models using a binomial distribution with a logit link function (R software version X) were used to develop predictive scores for (i) admission to ICU among emergency ward patients; (ii) death among ICU patients. A first panel of Number Models with the highest AIC (BIC) was preselected. Ten-fold cross-validation was then used to estimate the out-of-sample prediction error among these preselected models. The one with the smallest prediction error was in the end singled out .
The Risk stratification in COVID-19 patients in the ICU (RISC-19-ICU) registry was founded during the emerging SARS-CoV-2 pandemic. COVID-19 is a novel disease caused by infection with the SARS-CoV-2 virus that was first described in December 2019. The disease has spread exponentially in many countries and has reached global pandemic status within three months. According to first experience, hospitalization was required in approximately 20 % of cases and severe, life-threatening illness resulted in approximately 10 %. In some countries, health care systems were overwhelmed by the rapid increase in critically ill patients that far exceeded their capacity. It is thus of utmost importance to gain knowledge about the characteristics and course of critically ill patients with COVID-19 and to stratify these patients according to their risk for further deterioration. A key part of fighting this pandemic is to exchange scientific information and advance our understanding of the disease. The Risk stratification in COVID-19 patients in the ICU (RISC-19-ICU) registry aims to collect an anonymized dataset to characterize patients that develop life-threatening critical illness due to COVID-19 and make it accessible to collaborative analysis. The data collected may be composed of a core dataset and/or an extended dataset. The core dataset consists of a basic set of parameters, of which many are commonly generated during treatment of critically ill patients with COVID-19 in an intensive care unit (the individual parameters are marked yellow in the attached case report forms, and are clearly marked on the electronic case report forms during data entry). The extended dataset consists of parameters that may be measured during treatment of critically ill patients with COVID-19 in an intensive care unit, depending on clinical practice, indication and availability of the measurement method. The data accumulating in the registry as the pandemic or subsequent waves develop are made available to the collaborators to support an optimal response to the pandemic threat. The information gained on the initial characteristics and disease course via the RISC-19-ICU registry may contribute to a better understanding of the risk factors for developing critical illness due to COVID-19 and for an unfavorable disease course, and thus support informed patient triage and management decisions. Initial research questions are (I) to perform risk stratification of critically ill patients with COVID-19 to find predictors associated with the development of critical illness due to COVID-19: characterization of the study population, which are critically ill patients with COVID-19: inflammation, oxygenation, circulatory function, among other parameters collected in the registry, and (II) to perform risk stratification of critically ill patients with COVID-19 to predict outcome after ICU admission (ICU mortality, ICU length of stay): characterization of patients grouped by disease course in the ICU, based on inflammation, oxygenation, circulatory function, and other parameters collected in the registry.