Artificial Intelligence (AI) has revolutionized various domains, including education and research. Natural language processing (NLP) techniques and large language models (LLMs) such as GPT-4 and BARD have significantly advanced our comprehension and application of AI in these fields. This paper provides an in-depth introduction to AI, NLP, and LLMs, discussing their potential impact on education and research. By exploring the advantages, challenges, and innovative applications of these technologies, this review gives educators, researchers, students, and readers a comprehensive view of how AI could shape educational and research practices in the future, ultimately leading to improved outcomes. Key applications discussed in the field of research include text generation, data analysis and interpretation, literature review, formatting and editing, and peer review. AI applications in academics and education include educational support and constructive feedback, assessment, grading, tailored curricula, personalized career guidance, and mental health support. Addressing the challenges associated with these technologies, such as ethical concerns and algorithmic biases, is essential for maximizing their potential to improve education and research outcomes. Ultimately, the paper aims to contribute to the ongoing discussion about the role of AI in education and research and highlight its potential to lead to better outcomes for students, educators, and researchers.

Methanol is a widely used industrial and household alcohol that poses significant health risks upon exposure. Despite its extensive use, methanol poisoning remains a critical public health concern globally, often resulting from accidental or intentional ingestion and outbreaks linked to contaminated beverages. Methanol toxicity stems from its metabolic conversion to formaldehyde and formic acid, leading to severe metabolic acidosis and multiorgan damage, including profound CNS effects and visual impairments. Epidemiological data underscore the widespread impact of methanol poisoning, with alarming case fatality rates reported in various countries. Comprehensive prevention and effective management strategies are urgently needed to address the significant morbidity and mortality associated with methanol poisoning. The clinical manifestations of methanol toxicity vary between adult and pediatric populations and between acute and chronic exposure. Adults typically present with gastrointestinal and neurological symptoms, whereas pediatric patients often exhibit more severe outcomes due to differences in metabolism and body weight. The diagnosis of methanol poisoning involves a combination of clinical evaluation, laboratory testing, and advanced diagnostic techniques. The identification of metabolic acidosis, elevated anion and osmolal gaps, and confirmation through methanol and formate levels are critical for accurate diagnosis. Timely intervention is crucial, and the management of methanol poisoning includes securing the airway, breathing, and circulation; addressing metabolic acidosis with sodium bicarbonate; administering antidotes such as fomepizole or ethanol; and administering hemodialysis, which plays a pivotal role in eliminating methanol and its toxic metabolites, especially in severe cases. The complexity of methanol poisoning necessitates a comprehensive approach encompassing early recognition, prompt intervention, and coordinated care among healthcare providers. Increased awareness, effective prevention strategies, and timely treatment protocols are essential to mitigate severe health consequences and improve patient survival and recovery.

The integration of artificial intelligence (AI) into oncology drug discovery is redefining the traditional pipeline by accelerating discovery, optimizing drug efficacy, and minimizing toxicity. AI has enabled groundbreaking advancements in molecular modeling, simulation techniques, and the identification of novel compounds, including anti-tumor and antibodies, while elucidating mechanisms of drug toxicity. Additionally, AI has emerged as a critical tool in precision medicine, driving the formulation and release of targeted therapies and improving the development of treatments for oncology and central nervous system diseases. Furthermore, AI-assisted clinical trial designs have further optimized the recruitment and stratification of patients, reducing the time and cost of trials. Despite these advancements, challenges such as data integration, transparency, and ethical considerations persist. By synthesizing current innovations, this manuscript provides a comprehensive analysis of AI-driven approaches in drug discovery and their potential to advance oncology therapeutics and precision medicine. It examines the transformative role of AI across the drug development continuum, with a focus on its applications in computer-aided drug design (CADD), generative artificial intelligence (GAI), and high-throughput screening (HTS).

In recent years, anticoagulant and antiplatelet use have increased over the past years for the prevention and treatment of several cardiovascular conditions. Due to the rising use of antithrombotic medications and the complexity of specific clinical cases requiring such therapies, bleeding remains the primary concern among patients using antithrombotics. Direct oral anticoagulants (DOACs) include rivaroxaban, apixaban, edoxaban, and betrixaban. Direct thrombin inhibitors (DTIs) include argatroban, bivalirudin, and dabigatran. DOACs are associated with lower rates of fatal, life-threatening, and significant bleeding risks compared to those of warfarin. The immediate reversal of these agents can be indicated in an emergency setting. Antithrombotic reversal recommendations are still in development. Vitamin K and prothrombin complex concentrate (PCCs) can be used for warfarin reversal. Andexanet alfa and idarucizumab are specific reversal agents for DOACs and DTIs, respectively. Protamine sulfate is the solely approved reversal agent for unfractionated heparin (UFH) and low-molecular-weight heparin (LMWH). However, there are no specific reversal agents for antiplatelets. This article aims to provide a practical guide for clinicians regarding the reversal of anticoagulants and antiplatelets in clinical practice based on the most recent studies.

Electron-rich, nitrogenous heteroaromatic compounds interact more with biological/cellular components than their non-nitrogenous counterparts. The strong intermolecular interactions with proteins, enzymes, and receptors confer significant biological and therapeutic properties to the imidazole derivatives, giving rise to a well-known and extensively used range of therapeutic drugs used for infections, inflammation, and cancer, to name a few. The current study investigates the anti-cancer properties of fourteen previously synthesized nitrogenous heterocycles, derivatives of imidazole and oxazolone, on a panel of cancer cell lines and, in addition, predicts the molecular interactions, pharmacokinetic and safety profiles of these compounds. Method: The MTT and CellTiter-Glo^® assays were used to screen the imidazole and oxazolone derivatives on six cancer cell lines: HL60, MDA-MB-321, KAIMRC1, KMIRC2, MCF-10A, and HCT8. Subsequently, in vitro tubulin staining and imaging were performed, and the level of apoptosis was measured using the Promega ApoTox-Glo^® triplex assay. Furthermore, several computational tools were utilized to investigate the pharmacokinetics and safety profile, including PASS Online, SEA Search, the QikProp tool, SwissADME, ProTox-II, and an in silico molecular docking study on tubulin to identify the critical molecular interactions. Results: In vitro analysis identified compounds 8 and 9 to possess the most significant potent cytotoxic activity on the HL60 and MDA-MB-231 cell lines, supported by PASS Online anti-cancer predictions with pa scores of 0.413 and 0.434, respectively. In addition, compound 9 induced caspase 3/7 dependent-apoptosis and interfered with tubulin polymerization in the MDA-MB-231 cell line, consistent with in silico docking results, identifying binding similarity to the native ligand colchicine. All the derivatives, including compounds 8 and 9, had acceptable pharmacokinetics; however, the safety profile was suboptimal for all the tested derivates except compound 4. Conclusion: The imidazole derivative compound 9 is a promising anti-cancer agent that switches on caspase-dependent apoptotic cell death and modulates microtubule function. Therefore, it could be a lead compound for further drug optimization and development.

Background: Acute myeloid leukemia (AML) is an aggressive blood cancer with a poor prognosis when treated using conventional chemotherapy. CPX‐351, a liposomal formulation of cytarabine and daunorubicin, has demonstrated potential as a novel therapeutic strategy. Aim: This systematic review aims to evaluate the clinical safety and efficacy of CPX‐351 compared to standard induction chemotherapy in patients with AML. Methods: A systematic literature search was conducted using Web of Science, PubMed, Google Scholar, Ovid MEDLINE, and the Cochrane Library from 2011 to 2023. Overall, 14 clinical trials with more than 800 AML patients were included. Data on adverse events, survival outcomes, and response rates were extracted and analyzed. Results: CPX‐351 exhibited an adverse event profile that was comparable to that of conventional chemotherapy. It resulted in complete remission rates of 18%–41% in relapsed/refractory AML patients. For newly diagnosed individuals, CPX‐351 led to complete remission rates of 41%–58%, surpassing the 14%–40% associated with standard chemotherapy. Additionally, it extended overall survival by 3.6–5.7 months, with significant advantages noted in high‐risk cytogenetics and secondary AML cases. Conclusion: This review presents strong evidence supporting CPX‐351 as a therapeutic alternative with superior efficacy and comparable safety to standard chemotherapy across diverse AML populations. This represents a breakthrough in therapy, with demonstrated efficacy in AML cases.