ARCHERY: a prospective observational study of artificial intelligence-based radiotherapy treatment planning for cervical, head and neck and prostate cancer - study protocol.

INTRODUCTION: Fifty per cent of patients with cancer require radiotherapy during their disease course, however, only 10%-40% of patients in low-income and middle-income countries (LMICs) have access to it. A shortfall in specialised workforce has been identified as the most significant barrier to expanding radiotherapy capacity. Artificial intelligence (AI)-based software has been developed to automate both the delineation of anatomical target structures and the definition of the position, size and shape of the radiation beams. Proposed advantages include improved treatment accuracy, as well as a reduction in the time (from weeks to minutes) and human resources needed to deliver radiotherapy. METHODS: ARCHERY is a non-randomised prospective study to evaluate the quality and economic impact of AI-based automated radiotherapy treatment planning for cervical, head and neck, and prostate cancers, which are endemic in LMICs, and for which radiotherapy is the primary curative treatment modality. The sample size of 990 patients (330 for each cancer type) has been calculated based on an estimated 95% treatment plan acceptability rate. Time and cost savings will be analysed as secondary outcome measures using the time-driven activity-based costing model. The 48-month study will take place in six public sector cancer hospitals in India (n=2), Jordan (n=1), Malaysia (n=1) and South Africa (n=2) to support implementation of the software in LMICs. ETHICS AND DISSEMINATION: The study has received ethical approval from University College London (UCL) and each of the six study sites. If the study objectives are met, the AI-based software will be offered as a not-for-profit web service to public sector state hospitals in LMICs to support expansion of high quality radiotherapy capacity, improving access to and affordability of this key modality of cancer cure and control. Public and policy engagement plans will involve patients as key partners.

Role of bilayer characteristics on the structural fate of aß(1-40) and aß(25-40).

The ß-amyloid (Aß) peptide is derived from the transmembrane (TM) helix of the amyloid precursor protein (APP) and has been shown to interact with membrane surfaces. To understand better the role of peptide-membrane interactions in cell death and ultimately in Alzheimer's disease, a better understanding of how membrane characteristics affect the binding, solvation, and secondary structure of Aß is needed. Employing a combination of circular dichroism and deep-UV resonance Raman spectroscopies, Aß(25-40) was found to fold spontaneously upon association with anionic lipid bilayers. The hydrophobic portion of the disease-related Aß(1-40) peptide, Aß(25-40), has often been used as a model for how its legacy TM region may behave structurally in aqueous solvents and during membrane encounters. The structure of the membrane-associated Aß(25-40) peptide was found to depend on both the hydrophobic thickness of the bilayer and the duration of incubation. Similarly, the disease-related Aß(1-40) peptide also spontaneously associates with anionic liposomes, where it initially adopts mixtures of disordered and helical structures. The partially disordered helical structures then convert to ß-sheet structures over longer time frames. ß-Sheet structure is formed prior to helical unwinding, implying a model in which ß-sheet structure, formed initially from disordered regions, prompts the unwinding and destabilization of membrane-stabilized helical structure. A model is proposed to describe the mechanism of escape of Aß(1-40) from the membrane surfaces following its formation by cleavage of APP within the membrane.

Multivariate analysis of emission decay matrices for distinguishing ground state heterogeneity and excited state reactions of tryptophan.

The amino acid tryptophan displays emission solvatochromism, an emission maximum that shifts with solvent polarity, which is often used in protein studies to indicate local environment hydrophobicity. Use of tryptophan solvatochromism in time-resolved protein studies has traditionally been complicated due to the undescribed photokinetics that result in a characteristic multiexponential emission decay. For the first time, by application of the photokinetic matrix decomposition (PMD) multivariate curve resolution method to time-resolved emission decay (TRED) data, a distinguishment between ground state heterogeneous (GSH) and excited state reaction (ESR) type photokinetics of tryptophan in solution is made possible. It is found that molecular tryptophan displays two emission spectra that decay independently, suggesting GSH type photokinetics, one at 347 nm with a lifetime of 0.5 ns and one at 363 nm with a lifetime of 3.1 ns. When tryptophan is incorporated into a peptide, mastoparan X, the data similarly contain two emission spectra that decay independently, but are shifted in wavelength. Photobleaching experiments confirm that the PMD method is sensitive to tryptophan emission quenching, and therefore may be applied to determine the photokinetics of tryptophan that occur in proteins. Future applications of PMD analysis of tryptophan TRED data as a bioanalytical tool for further characterizing dynamic protein processes are discussed.