OBJECTIVE: To probe the Clinacanthus nutans leaf extract effect on oxidative stress and inflammatory markers and the Langerhans islet area in diabetic rat models.
DESIGN: An experimental laboratory in the animal model.
METHODS: Twenty-five diabetic rat models were randomly assigned into 5 clusters. Clusters 1, 2, and 3 were administered with C. nutans leaf extract in aqueous suspension with vehicle 1% Na-CMC at 75 mg/kg body weight (BW), 150 mg/kg BW, and 300 mg/kg BW, respectively. Cluster 4 was diabetic control rats administered with metformin at a 21 mg/rat dose. Cluster 5 was a control diabetic rat only administered with 1% Na-CMC suspension. Treatment was administered orally for 14 days. On the 15th day, the rats were sacrificed to obtain blood samples and pancreatic tissues. Serum interleukin (IL)-6, malondialdehyde (MDA), and tumor necrosis factor (TNF-α) were measured using the enzyme-linked immunosorbent assay (ELISA) method. Histopathological examination was performed by counting the Langerhans islet areas.
RESULTS: The average IL-6, MDA, and TNF-α levels declined in the cluster receiving C. nutans extract and were significantly different from the untreated cluster (P
OBJECTIVES: To evaluate UAE community pharmacists' knowledge, attitude, and practices (KAP) concerning metformin XR.
METHODS: This is a cross-sectional research study conducted amongst licensed community pharmacists. The survey took place via a questionnaire and physical interviews were held. The survey used in this study included questions on demographics and questions on the participants' attitudes, knowledge and practices concerning metformin XR. The factors influencing KAP regarding metformin XR were examined via simple logistic regression analysis.
RESULTS: Threehundred fifty-three (n = 353) participants were recruited in the study. Independent pharmacies constituted 57.5% of this study sample and 42.5% were chain pharmacies. The average knowledge score about metformin XR tablets was 42.5% with a confidence interval (CI) of 95% [37.3%, 47.4]. Better knowledge scores on metformin XR tablets was observed in respondents aged ⩾40 years (OR 2.97, 95% CI 1.63-5.4), having greater than 10 years in terms of experience (OR 2.28; 95% CI 1.25-4.16) and pharmacist graduated from Regional or international universities (OR 2.08; 95% CI 1.34-3.24). About 78% (n = 275) of the participants believed that metformin XR tablets have better efficacy and 63.2% (n = 233) indicated that metformin IR was associated with greater adverse effects.
CONCLUSION: This study demonstrated a distinct gap in knowledge, attitude and practice pertaining to metformin XR among community pharmacists in the UAE. The community pharmacists need to enhance their practice by receiving accurate and reliable data to support their decision-making on the prescribing of metformin XR. The implementation of novel guidelines and evidence dissemination strategies may help bridge this gap.
OBJECTIVES: To investigate the benefits and risks associated with the application of ChatGPT in managing diabetes and metabolic illnesses by exploring the perspectives of endocrinologists and diabetologists.
METHODS AND MATERIALS: The study employed a qualitative research approach. A semi-structured in-depth interview guide was developed. A convenience sample of 25 endocrinologists and diabetologists was enrolled and interviewed. All interviews were audiotaped and verbatim transcribed; then, thematic analysis was used to determine the themes in the data.
RESULTS: The findings of the thematic analysis resulted in 19 codes and 9 major themes regarding the benefits of implementing AI and ChatGPT in managing diabetes and metabolic illnesses. Moreover, the extracted risks of implementing AI and ChatGPT in managing diabetes and metabolic illnesses were categorized into 7 themes and 14 codes. The benefits of heightened diagnostic precision, tailored treatment, and efficient resource utilization have potential to improve patient results. Concurrently, the identification of potential challenges, such as data security concerns and the need for AI that can be explained, enables stakeholders to proactively tackle these issues.
CONCLUSIONS: Regulatory frameworks must evolve to keep pace with the rapid adoption of AI in healthcare. Sustained attention to ethical considerations, including obtaining patient consent, safeguarding data privacy, ensuring accountability, and promoting fairness, remains critical. Despite its potential impact on the human aspect of healthcare, AI will remain an integral component of patient-centered care. Striking a balance between AI-assisted decision-making and human expertise is essential to uphold trust and provide comprehensive patient care.