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Liver and Breast cancer are ranked as the most prevailing cancers that cause high cancer-related mortality. As cancer is a life-threatening disease that affects the human population globally, there is a need to develop novel therapies. Among the available treatment options include radiotherapy, chemotherapy, surgery, and immunotherapy. The most superlative modern method is the use of plant-derived anticancer drugs that target the cancerous cells and inhibit their proliferation. Plant-derived compounds are generally considered safer than synthetic drugs/traditional therapies and could serve as potential novel targets to treat liver and breast cancer to revolutionize cancer treatment. Alkaloids and Polyphenols have been shown to act as anticancer agents through molecular approaches. They disrupt various cellular mechanisms, inhibit the production of cyclins and CDKs to arrest the cell cycle, and activate the DNA repairing mechanism by upregulating p53, p21, and p38 expression. In severe cases, when no repair is possible, they induce apoptosis in liver and breast cancer cells by activating caspase-3, 8, and 9 and increasing the Bax/Bcl-2 ratio. They also deactivate several signaling pathways, such as PI3K/AKT/mTOR, STAT3, NF-kB, Shh, MAPK/ERK, and Wnt/ß-catenin pathways, to control cancer cell progression and metastasis. The highlights of this review are the regulation of specific protein expressions that are crucial in cancer, such as in HER2 over-expressing breast cancer cells; alkaloids and polyphenols have been reported to reduce HER2 as well as MMP expression. This study reviewed more than 40 of the plant-based alkaloids and polyphenols with specific molecular targets against liver and breast cancer. Among them, Oxymatrine, Hirsutine, Piperine, Solamargine, and Brucine are currently under clinical trials by qualifying as potent anticancer agents due to lesser side effects. As a lot of research is there on anticancer compounds, there is a desideratum to compile data to move towards clinical trials phase 4 and control the prevalence of liver and breast cancer.
ABSTRACT
BACKGROUND: Cirrhosis and end-stage kidney disease (ESKD) are significant global health concerns, contributing to high mortality and morbidity. Haemodialysis (HD) is frequently used to treat ESKD in patients with cirrhosis. However, it often presents challenges such as haemodynamic instability during dialysis sessions, leading to less than optimal outcomes. Peritoneal dialysis (PD), while less commonly used in cirrhotic patients, raises concerns about the risks of peritonitis and mortality. Our systematic review and meta-analysis aimed to assess outcomes in PD patients with cirrhosis. METHODS: We executed a comprehensive search in Ovid MEDLINE, EMBASE and Cochrane databases up to 25 September 2023. The search focused on identifying studies examining mortality and other clinical outcomes in ESKD patients with cirrhosis receiving PD or HD. In addition, we sought studies comparing PD outcomes in cirrhosis patients to those without cirrhosis. Data from each study were aggregated using a random-effects model and the inverse-variance method. RESULTS: Our meta-analysis included a total of 13 studies with 15,089 patients. Seven studies compared ESKD patients on PD with liver cirrhosis (2753 patients) against non-cirrhosis patients (9579 patients). The other six studies provided data on PD (824 patients) versus HD (1943 patients) in patients with cirrhosis and ESKD. The analysis revealed no significant difference in mortality between PD and HD in ESKD patients with cirrhosis (pooled odds ratio (OR) of 0.77; 95% confidence interval (CI), 0.53-1.14). In PD patients with cirrhosis, the pooled OR for peritonitis compared to non-cirrhosis patients was 1.10 (95% CI: 1.03-1.18). The pooled ORs for hernia and chronic hypotension in cirrhosis patients compared to non-cirrhosis controls were 2.48 (95% CI: 0.08-73.04) and 17.50 (95% CI: 1.90-161.11), respectively. The pooled OR for transitioning from PD to HD among cirrhotic patients was 1.71 (95% CI: 0.76-3.85). Mortality in cirrhosis patients on PD was comparable to non-cirrhosis controls, with a pooled OR of 1.05 (95% CI: 0.53-2.10). CONCLUSIONS: Our meta-analysis demonstrates that PD provides comparable mortality outcomes to HD in ESKD patients with cirrhosis. In addition, the presence of cirrhosis does not significantly elevate the risk of mortality among patients undergoing PD. While there is a higher incidence of chronic hypotension and a slightly increased risk of peritonitis in cirrhosis patients on PD compared to those without cirrhosis, the risks of hernia and the need to transition from PD to HD are comparable between both groups. These findings suggest PD as a viable and effective treatment option for ESKD patients with cirrhosis.
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INTRODUCTION: Acute kidney injury (AKI) and continuous renal replacement therapy (CRRT) are critical areas in nephrology. The effectiveness of ChatGPT in simpler, patient education-oriented questions has not been thoroughly assessed. This study evaluates the proficiency of ChatGPT 4.0 in responding to such questions, subjected to various linguistic alterations. METHODS: Eighty-nine questions were sourced from the Mayo Clinic Handbook for educating patients on AKI and CRRT. These questions were categorized as original, paraphrased with different interrogative adverbs, paraphrased resulting in incomplete sentences, and paraphrased containing misspelled words. Two nephrologists verified the questions for medical accuracy. A χ2 test was conducted to ascertain notable discrepancies in ChatGPT 4.0's performance across these formats. RESULTS: ChatGPT provided notable accuracy in handling a variety of question formats for patient education in AKI and CRRT. Across all question types, ChatGPT demonstrated an accuracy of 97% for both original and adverb-altered questions and 98% for questions with incomplete sentences or misspellings. Specifically for AKI-related questions, the accuracy was consistently maintained at 97% for all versions. In the subset of CRRT-related questions, the tool achieved a 96% accuracy for original and adverb-altered questions, and this increased to 98% for questions with incomplete sentences or misspellings. The statistical analysis revealed no significant difference in performance across these varied question types (p value: 1.00 for AKI and 1.00 for CRRT), and there was no notable disparity between the artificial intelligence (AI)'s responses to AKI and CRRT questions (p value: 0.71). CONCLUSION: ChatGPT 4.0 demonstrates consistent and high accuracy in interpreting and responding to queries related to AKI and CRRT, irrespective of linguistic modifications. These findings suggest that ChatGPT 4.0 has the potential to be a reliable support tool in the delivery of patient education, by accurately providing information across a range of question formats. Further research is needed to explore the direct impact of AI-generated responses on patient understanding and education outcomes.
ABSTRACT
The accurate interpretation of CRRT machine alarms is crucial in the intensive care setting. ChatGPT, with its advanced natural language processing capabilities, has emerged as a tool that is evolving and advancing in its ability to assist with healthcare information. This study is designed to evaluate the accuracy of the ChatGPT-3.5 and ChatGPT-4 models in addressing queries related to CRRT alarm troubleshooting. This study consisted of two rounds of ChatGPT-3.5 and ChatGPT-4 responses to address 50 CRRT machine alarm questions that were carefully selected by two nephrologists in intensive care. Accuracy was determined by comparing the model responses to predetermined answer keys provided by critical care nephrologists, and consistency was determined by comparing outcomes across the two rounds. The accuracy rate of ChatGPT-3.5 was 86% and 84%, while the accuracy rate of ChatGPT-4 was 90% and 94% in the first and second rounds, respectively. The agreement between the first and second rounds of ChatGPT-3.5 was 84% with a Kappa statistic of 0.78, while the agreement of ChatGPT-4 was 92% with a Kappa statistic of 0.88. Although ChatGPT-4 tended to provide more accurate and consistent responses than ChatGPT-3.5, there was no statistically significant difference between the accuracy and agreement rate between ChatGPT-3.5 and -4. ChatGPT-4 had higher accuracy and consistency but did not achieve statistical significance. While these findings are encouraging, there is still potential for further development to achieve even greater reliability. This advancement is essential for ensuring the highest-quality patient care and safety standards in managing CRRT machine-related issues.
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Accurate information regarding oxalate levels in foods is essential for managing patients with hyperoxaluria, oxalate nephropathy, or those susceptible to calcium oxalate stones. This study aimed to assess the reliability of chatbots in categorizing foods based on their oxalate content. We assessed the accuracy of ChatGPT-3.5, ChatGPT-4, Bard AI, and Bing Chat to classify dietary oxalate content per serving into low (<5 mg), moderate (5-8 mg), and high (>8 mg) oxalate content categories. A total of 539 food items were processed through each chatbot. The accuracy was compared between chatbots and stratified by dietary oxalate content categories. Bard AI had the highest accuracy of 84%, followed by Bing (60%), GPT-4 (52%), and GPT-3.5 (49%) (p < 0.001). There was a significant pairwise difference between chatbots, except between GPT-4 and GPT-3.5 (p = 0.30). The accuracy of all the chatbots decreased with a higher degree of dietary oxalate content categories but Bard remained having the highest accuracy, regardless of dietary oxalate content categories. There was considerable variation in the accuracy of AI chatbots for classifying dietary oxalate content. Bard AI consistently showed the highest accuracy, followed by Bing Chat, GPT-4, and GPT-3.5. These results underline the potential of AI in dietary management for at-risk patient groups and the need for enhancements in chatbot algorithms for clinical accuracy.
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The rapid advancement of artificial intelligence (AI) technologies, particularly machine learning, has brought substantial progress to the field of nephrology, enabling significant improvements in the management of kidney diseases. ChatGPT, a revolutionary language model developed by OpenAI, is a versatile AI model designed to engage in meaningful and informative conversations. Its applications in healthcare have been notable, with demonstrated proficiency in various medical knowledge assessments. However, ChatGPT's performance varies across different medical subfields, posing challenges in nephrology-related queries. At present, comprehensive reviews regarding ChatGPT's potential applications in nephrology remain lacking despite the surge of interest in its role in various domains. This article seeks to fill this gap by presenting an overview of the integration of ChatGPT in nephrology. It discusses the potential benefits of ChatGPT in nephrology, encompassing dataset management, diagnostics, treatment planning, and patient communication and education, as well as medical research and education. It also explores ethical and legal concerns regarding the utilization of AI in medical practice. The continuous development of AI models like ChatGPT holds promise for the healthcare realm but also underscores the necessity of thorough evaluation and validation before implementing AI in real-world medical scenarios. This review serves as a valuable resource for nephrologists and healthcare professionals interested in fully utilizing the potential of AI in innovating personalized nephrology care.
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The emergence of artificial intelligence (AI) has greatly propelled progress across various sectors including the field of nephrology academia. However, this advancement has also given rise to ethical challenges, notably in scholarly writing. AI's capacity to automate labor-intensive tasks like literature reviews and data analysis has created opportunities for unethical practices, with scholars incorporating AI-generated text into their manuscripts, potentially undermining academic integrity. This situation gives rise to a range of ethical dilemmas that not only question the authenticity of contemporary academic endeavors but also challenge the credibility of the peer-review process and the integrity of editorial oversight. Instances of this misconduct are highlighted, spanning from lesser-known journals to reputable ones, and even infiltrating graduate theses and grant applications. This subtle AI intrusion hints at a systemic vulnerability within the academic publishing domain, exacerbated by the publish-or-perish mentality. The solutions aimed at mitigating the unethical employment of AI in academia include the adoption of sophisticated AI-driven plagiarism detection systems, a robust augmentation of the peer-review process with an "AI scrutiny" phase, comprehensive training for academics on ethical AI usage, and the promotion of a culture of transparency that acknowledges AI's role in research. This review underscores the pressing need for collaborative efforts among academic nephrology institutions to foster an environment of ethical AI application, thus preserving the esteemed academic integrity in the face of rapid technological advancements. It also makes a plea for rigorous research to assess the extent of AI's involvement in the academic literature, evaluate the effectiveness of AI-enhanced plagiarism detection tools, and understand the long-term consequences of AI utilization on academic integrity. An example framework has been proposed to outline a comprehensive approach to integrating AI into Nephrology academic writing and peer review. Using proactive initiatives and rigorous evaluations, a harmonious environment that harnesses AI's capabilities while upholding stringent academic standards can be envisioned.