Therapeutic monoclonal antibodies (mAbs) have become integral in the treatment of various diseases, but their formulation and stability remain challenges for long-term storage and distribution. Traditionally, lyophilization (freeze-drying) is employed to convert mAbs into solid formulations; however, this process is time-consuming, costly, and can lead to protein instability. In contrast, a precipitation-based process offers a potential alternative for generating solid mAb formulations. This study explores the use of ammonium sulfate as a precipitating agent to solidify monoclonal antibodies while maintaining their bioactivity and stability. The process involved optimizing precipitation conditions, followed by characterizing the precipitated product using size exclusion chromatography (SEC), dynamic light scattering (DLS), and circular dichroism (CD) spectroscopy. Results demonstrated that the precipitation method achieved high yields (over 85%), with minimal aggregation and preserved structural integrity of the mAb. The solid formulation showed excellent reconstitution properties, and bioactivity assays confirmed that the mAb retained its therapeutic function. Additionally, the precipitated mAb exhibited long-term stability at room temperature, making this precipitation-based method a promising alternative to lyophilization for mAb formulation. This approach is cost-effective, scalable, and provides an efficient solution for producing stable monoclonal antibody therapies.

This study investigates the feasibility and safety of using a controlled electromagnetic launcher (EML) for remote drug delivery, focusing on its potential to deliver pharmaceutical agents to targeted areas without the need for invasive procedures. The traditional methods of drug administration, such as injections or oral tablets, often present challenges, including the risk of infection, discomfort, and inefficient targeting. A controlled electromagnetic launcher offers a novel, non-invasive alternative for delivering drugs, particularly in hard-to-reach areas. The research involves the design and development of an EML system, followed by a series of tests to evaluate its accuracy, safety, and efficiency in drug delivery. The results demonstrate that the EML is capable of delivering drugs to specific locations safely, with promising implications for future medical applications.

The Living Library is an innovative process-based tool designed to facilitate open literature reviews within the context of open science. Unlike traditional static reviews, the Living Library is a dynamic and continuously evolving repository of research, allowing for real-time contributions, updates, and collaborative synthesis. This tool harnesses the power of community-driven knowledge sharing, enabling researchers to contribute, annotate, and vet scholarly articles on an ongoing basis. The platform’s iterative nature ensures that the review remains current and relevant, addressing the fast-paced nature of modern scientific advancements. While the Living Library fosters interdisciplinary collaboration and accelerates the integration of new research, it also faces challenges related to information overload and maintaining quality control. This article explores the design, implementation, and potential of the Living Library to redefine the way literature reviews are conducted in open science, promoting transparency, accessibility, and continuous engagement in the research process.

Artificial Intelligence (AI) is revolutionising higher education by generating novel opportunities for inclusive learning, skill enhancement, and equal access to information. This chapter analyses the influence of AI on higher education, emphasising its contribution to fostering diversity, improving employability skills, and mitigating gender inequities. AI-driven solutions, including adaptive learning platforms, automated feedback systems, and natural language processing technologies, provide the capability to customise the learning experience, assist students with varied needs, and enhance academic English proficiency. Moreover, AI-powered career development solutions are transforming employability training through real-time feedback, workplace simulations, and focused skill enhancement interventions. Nonetheless, whereas AI presents opportunity to bridge educational disparities, it simultaneously engenders worries about algorithmic bias, ethical dilemmas, and data privacy vulnerabilities. While reviewing literature, this chapter examines AI's capacity to enhance inclusive education while critically evaluating the dangers of unequal access and algorithmic bias. It promotes a balanced approach that emphasises human-centred education, gender equality, and preparedness for employment. The findings enhance the overarching dialogue regarding AI in education, impacting policy deliberations and institutional approaches to fostering equal learning environments in the digital era.