Explore the vast range of titles available.

Copper, an essential element for various biological processes, demands precise regulation to avert detrimental health effects and potential cell toxicity. This paper explores the mechanisms of copper-induced cell death, known as cuproptosis, and its potential health and disease implications, including cancer therapy. Copper ionophores, such as elesclomol and disulfiram, increase intracellular copper levels. This elevation triggers oxidative stress and subsequent cell death, offering potential implications in cancer therapy. Additionally, copper ionophores disrupt mitochondrial respiration and protein lipoylation, further contributing to copper toxicity and cell death. Potential targets and biomarkers are identified, as copper can be targeted to those proteins to trigger cuproptosis. The role of copper in different cancers is discussed to understand targeted cancer therapies using copper nanomaterials, copper ionophores, and copper chelators. Furthermore, the role of copper is explored through diseases such as Wilson and Menkes disease to understand the physiological mechanisms of copper. Exploring cuproptosis presents an opportunity to improve treatments for copper-related disorders and various cancers, with the potential to bring significant advancements to modern medicine.

Social networks such as twitter have emerged as social platforms that can impart a massive knowledge base for people to share their unique ideas and perspectives on various topics and issues with friends and families. Sentiment analysis based on machine learning has been successful in discovering the opinion of the people using redundantly available data. However, recent studies have pointed out that imbalanced data can have a negative impact on the results. In this paper, we propose a framework for improved sentiment analysis through various ordered preprocessing steps with the combination of resampling of minority classes to produce greater performance. The performance of the technique can vary depending on the dataset as its initial focus is on feature selection and feature combination. Multiple machine learning algorithms are utilized for the classification of tweets into positive, negative, or neutral. Results have revealed that random minority oversampling can provide improved performance and it can tackle the issue of class imbalance.

Objectives: C5 inhibitors such as eculizumab and ravulizumab are the first-line treatment in the management of paroxysmal nocturnal hemoglobinuria (PNH). However, some patients develop novel symptoms as part of their treatment with eculizumab, and the disease is termed as eculizumab refractory PNH. The aim of this study was to conduct a systematic review on the available treatment modalities for the management of eculizumab refractory PNH. Methods: Two authors independently searched two databases according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. A total of 70 studies were obtained: 4 out 70 studies were found to meet the inclusion criteria. Results: Four studies were found to meet the inclusion criteria of our study. Two studies were published in 2021 and two studies were published in 2020. All four studies were multicenter clinical trials. Two studies were phase III clinical trials, one study was a phase II clinical trial, and one study was a phase I clinical trial. Two studies were about pegcetacoplan, one was about danicopan, and one was about iptacopan. Conclusion: Based upon the findings of our systematic review, we recommend an individualized treatment plan based on the mechanism of eculizumab refractoriness and the mechanism of PNH breakthrough. This recommendation is subject to the available resources and clinical expertise available at different hospitals. More studies using study designs such as randomized controlled trials comparing multiple drugs should be performed to accurately assess the different medications and aid in designing guidelines of the management of eculizumab refractory PNH. Level of evidence: Level I