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Wound healing is an evolving and intricate technique that is vital to the restoration of tissue integrity and function. Over the past few decades, chitosan a biopolymer derived from chitin, became known as an emerging biomaterial in the field of healing wounds due to its distinctive characteristics including biocompatibility, biodegradability, affinity to biomolecules, and wound-healing activity. This natural polymer exhibits excellent healing capabilities by accelerating the development of new skin cells, reducing inflammation, and preventing infections. Due to its distinct biochemical characteristics and innate antibacterial activity, chitosan has been extensively researched as an antibacterial wound dressing. Chronic wounds, such as diabetic ulcers and liver disease, are a growing medical problem. Chitosan-based biomaterials are a promising solution in the domain of wound care. The article analyzes the depth of chitosan-based biomaterials and their impact on wound healing and also the methods to enhance the advantages of chitosan by incorporating bioactive compounds. This literature review is aimed to improve the understanding and knowledge about biomaterials and their use in wound healing.
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BACKGROUND: Childhood obesity is a significant health issue that has grown in prominence, particularly in developed countries. Primary hypertension has become the dominant cause, leading to an increased incidence of arterial hypertension. This study examined the associations between sex and obesity with hypertension (HTN). Understanding these associations can provide insights into the risk factors and potential preventive strategies for HTN. MATERIALS AND METHODS: This cross-sectional observational study was conducted at private schools in Chennai, Tamil Nadu, India, for one year. Data analysis was performed on a cohort of participants who underwent health assessments, including blood pressure measurements, self-reported dietary habits, and socio-economic status information. Statistical analyses assessed the associations between sex, dietary habits, socio-economic status, and HTN. RESULTS: There were 255 (51.0%) males and 245 (49%) females. Among the female participants, only two individuals (0.8%) had HTN, while among males, 11 individuals (4.3%) had HTN, indicating a significant association between sex and HTN (P=0.014). In terms of dietary habits, the prevalence of HTN was similar among non-vegetarians (2.5%) and vegetarians (3.1%), and the association was not statistically significant (P=0.777). Among the obese individuals in the study population, eight individuals (8.8%) had HTN, while the remaining 83 individuals (91.2%) did not have HTN, with a P-value of <0.0001, which indicates a significant association between HTN and obesity. CONCLUSION: This study revealed a significant association between sex and HTN, with males exhibiting a higher prevalence of HTN than females. Furthermore, the study showed a significant association between obesity and hypertension, indicating that obese individuals were more likely to have HTN.
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Phyto-trichobezoar is a rare disorder characterized by the formation of mass in the gastrointestinal tract (GIT) by the ingested inedible material, mostly hair and thread. The ingestion of hair is a rare psychiatric disorder called trichotillomania, which is more common in girls. An 11-year-old girl presented with clinical features of GIT obstruction, which were diagnosed to be Rapunzel syndrome by computed tomography. The phyto-trichobezoar was removed en masse by the anterior gastrotomy, which extended beyond the duodenum. The girl, after recovery, was counseled and treated for subclinical depression.
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Artificial intelligence (AI) has demonstrated significant promise for the present and future diagnosis of diseases. At the moment, AI-powered diagnostic technologies can help physicians decipher medical pictures like X-rays, magnetic resonance imaging, and computed tomography scans, resulting in quicker and more precise diagnoses. In order to make a prospective diagnosis, AI algorithms may also examine patient information, symptoms, and medical background. The application of AI in disease diagnosis is anticipated to grow as the field develops. In the future, AI may be used to find patterns in enormous volumes of medical data, aiding in disease prediction and prevention before symptoms appear. Additionally, by combining genetic data, lifestyle data, and environmental variables, AI may help in the diagnosis of complicated diseases. It is crucial to remember that while AI can be a powerful tool, it cannot take the place of qualified medical personnel. Instead, AI ought to support and improve diagnostic procedures, enhancing patient care and healthcare results. Future research and the use of AI for disease diagnosis must take ethical issues, data protection, and ongoing model validation into account.