RESUMEN
Chronic kidney disease (CKD) presents a substantial global public health challenge, with high morbidity and mortality. CKD patients often experience dyslipidaemia and poor glycaemic control, further exacerbating inflammation and oxidative stress in the kidney. If left untreated, these metabolic symptoms can progress to end-stage renal disease, necessitating long-term dialysis or kidney transplantation. Alleviating inflammation responses has become the standard approach in CKD management. Medications such as statins, metformin, and GLP-1 agonists, initially developed for treating metabolic dysregulation, demonstrate promising renal therapeutic benefits. The rising popularity of herbal remedies and supplements, perceived as natural antioxidants, has spurred investigations into their potential efficacy. Notably, lactoferrin, Boerhaavia diffusa, Amauroderma rugosum, and Ganoderma lucidum are known for their anti-inflammatory and antioxidant properties and may support kidney function preservation. However, the mechanisms underlying the effectiveness of Western medications and herbal remedies in alleviating inflammation and oxidative stress occurring in renal dysfunction are not completely known. This review aims to provide a comprehensive overview of CKD treatment strategies and renal function preservation and critically discusses the existing literature's limitations whilst offering insight into the potential antioxidant effects of these interventions. This could provide a useful guide for future clinical trials and facilitate the development of effective treatment strategies for kidney functions.
RESUMEN
The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer.