Background And Aim: Cardiovascular diseases are characterized by problems affecting the circulatory system, specifically the heart and blood vessels. This study evaluates the relationship between cardiovascular events and pharmacological treatment for Type II Diabetes Mellitus (T2DM).
Methods: We recruited 227 individuals, 191 with T2DM (EG) and 36 pre-diabetics (CG), with a mean age of 70.3 years (SD=8.3), and 62 years (SD=10.3) respectively. The individuals were distributed into five groups concerning the following variables: body mass index (BMI), age, diagnosis age of T2DM, glycated hemoglobin value (HbA1c), Homeostatic model that estimates the function of β cells value (HOMA2-B), and Homeostatic model that estimates insulin resistance (HOMA-IR) value. At the time of data collection, there were no individuals with T1DM, so it was decided to use prediabetic individuals (with a high risk of developing T2DM).
Results: Group 1 had the pre-diabetic patients (15.9%), while diabetic individuals were divided into groups 2 (1.8%), 3 (17.6%), 4 (21.1%) and 5 (43.6%). It was possible to conclude that most of the patients in the different groups had a history of acute myocardial infarction (AMI). Regarding the prevalence of pharmacological treatment, it was possible to conclude that metformin was the most used drug in most of the groups.
Conclusions: It was possible to create different groups and to observe the existence of dependency relationships between different cardiovascular events and pharmacological treatment.
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http://dx.doi.org/10.23750/abm.v94i2.13685 | DOI Listing |
J Eval Clin Pract
February 2025
California State University Monterey Bay, Seaside, California, USA.
Rationale: Obesity is an increasing medical issue not responding well to behavioural treatments beyond their initial weeks/months.
Aims And Objectives: Before suggesting surgical or pharmacological interventions, medical professionals might consider referrals to cost-effective, community-based behavioural treatments if stronger theoretical/empirical bases were demonstrated. Thus, evaluation of such is warranted.
J Antimicrob Chemother
January 2025
Research Laboratory, Botswana Harvard Health Partnership, Gaborone, Botswana.
Objectives: We assessed HIV-1 drug resistance profiles among people living with HIV (PLWH) with detectable viral load (VL) and on dolutegravir-based antiretroviral therapy (ART) in Botswana.
Methods: The study utilised available 100 residual HIV-1 VL samples from unique PLWH in Francistown who had viraemia at-least 6 months after initiating ART in Botswana's national ART program from November 2023 to January 2024. Viraemia was categorized as low-level viraemia (LLV) (VL: 200-999 copies/mL) or virologic failure (VF) (VL ≥1000 copies/mL).
Free Radic Res
January 2025
Radiation Biology & Health Sciences Division, Bio-science Group, Bhabha Atomic Research Centre, Trombay, Mumbai-400085, India.
Free radicals have been implicated in the pathogenesis of cancer along with cardiovascular, neurodegenerative, pulmonary and inflammatory disorders. Further, the relationship between oxidative stress and disease is distinctively established. Clinical trials using anti-oxidants for the prevention of disease progression have indicated some beneficial effects.
View Article and Find Full Text PDFPhytother Res
January 2025
Laboratory of Immunology and Inflammation, School of Life Sciences and Biopharmaceutics, Guangdong Pharmaceutical University, Guangzhou, China.
Renal fibrosis is the most common pathway for the development of end-stage renal disease (ESRD) in various kidney diseases. Currently, the treatment options for renal fibrosis are limited. Ferroptosis is iron-mediated lipid peroxidation, triggered mainly by iron deposition and ROS generation.
View Article and Find Full Text PDFPer Med
January 2025
Department of Clinical Pharmacy, Zhejiang Provincial Key Laboratory for Drug Evaluation and Clinical Research, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China.
Efforts have been made to leverage technology to accurately identify tumor characteristics and predict how each cancer patient may respond to medications. This involves collecting data from various sources such as genomic data, histological information, functional drug profiling, and drug metabolism using techniques like polymerase chain reaction, sanger sequencing, next-generation sequencing, fluorescence in situ hybridization, immunohistochemistry staining, patient-derived tumor xenograft models, patient-derived organoid models, and therapeutic drug monitoring. The utilization of diverse detection technologies in clinical practice has made "individualized treatment" possible, but the desired level of accuracy has not been fully attained yet.
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