The Christie Research Publications Repository: Recent submissions
Now showing items 41-60 of 15369
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Development and application of a slot-blot assay using the damage sensing protein Atl1 to detect and quantify <i>O</i><SUP>6</SUP>-alkylated guanine bases in DNAHumans are unavoidably exposed to numerous different mutagenic DNA alkylating agents (AAs), but their role in the initiation of cancers is uncertain, in part due to difficulties in assessing human exposure. To address this, we have developed a screening method that measures promutagenic O-6-alkylguanines (O-6-AlkGs) in DNA and applied it to human DNA samples. The method exploits the ability of the Schizosaccharomyces pombe alkyltransferase-like protein (Atl1) to recognise and bind to a wide range of O-6-AlkGs in DNA. We established an Atl1-based slot-blot (ASB) assay and validated it using calf thymus DNA alkylated in vitro with a range of alkylating agents and both calf thymus and human placental DNA methylated in vitro with temozolomide (TMZ). ASB signals were directly proportional to the levels of O-6-meG in these controls. Pre-treatment of DNA with the DNA repair protein O-6-methylguanine-DNA methyltransferase (MGMT) reduced binding of Atl1, confirming its specificity. In addition, MCF 10A cells were treated with 500 mu M TMZ and the extracted DNA, analysed using the ASB, was found to contain 1.34 fmoles O-6 -meG/mu g DNA. Of six human breast tumour DNA samples assessed, five had detectable O-6-AlkG levels (mean +/- SD 1.24 +/- 0.25 O-6-meG equivalents/mu g DNA. This study shows the potential usefulness of the ASB assay to detect and quantify total O-6-AlkGs in human DNA samples.
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Risk of fractures and falls in men with advanced or metastatic prostate cancer receiving androgen deprivation therapy and treated with novel androgen receptor signalling inhibitors: a systematic review and meta-analysis of randomised controlled trialsContext: The addition of androgen receptor signalling inhibitors (ARSIs) to standard androgen deprivation therapy (ADT) has improved survival outcomes in patients with advanced prostate cancer (PCa). Advanced PCa patients have a higher incidence of osteoporosis, compounded by rapid bone density loss upon commencement of ADT resulting in an increased fracture risk. The effect of treatment intensification with ARSIs on fall and fracture risk is unclear. Objective: To assess the risk of falls and fractures in men with PCa treated with ARSIs. Evidence acquisition: A systematic review of EMBASE, MEDLINE, The Cochrane Library, and The Health Technology Assessment Database for randomised control trials between 1990 and June 2023 was conducted in accordance with Preferred Reporting Items for Systematic Review and Meta-analyses guidance. Risk ratios were estimated for the incidence of fracture and fall events. Subgroup analyses by grade of event and disease state were conducted. Evidence synthesis: Twenty-three studies were eligible for inclusion. Fracture outcomes were reported in 17 studies (N N = 18 811) and fall outcomes in 16 studies (N N = 16 537). A pooled analysis demonstrated that ARSIs increased the risk of fractures (relative risk [RR] 2.32, 95% confidence interval [CI] 2.00-2.71; p < 0.01) and falls (RR 2.22, 95% CI 1.81- 2.72; p < 0.01) compared with control. A subgroup analysis demonstrated an increased risk of both fractures (RR 2.13,95% CI 1.70-2.67; p < 0.01) and falls (RR 2.19,95% CI 1.53- 3.12; p < 0.0001) in metastatic hormone-sensitive PCa patients, and an increased risk of fractures in the nonmetastatic (RR 2.27, 95% CI 1.60-3.20; p < 0.00001) and metastatic castrate-resistant (RR 2.85, 95% CI 2.16-3.76; p <0.00001) settings. The key limitations include an inability to distinguish fragility from pathological fractures and potential for a competing risk bias. Conclusions: Addition of an ARSI to standard ADT significantly increases the risk of fractures and falls in men with prostate cancer.
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Relation extraction in underexplored biomedical domains: a diversity-optimized sampling and synthetic data generation approachThe sparsity of labeled data is an obstacle to the development of Relation Extraction (RE) models and the completion of databases in various biomedical areas. While being of high interest in drug-discovery, the literature on natural products, reporting the identification of potential bioactive compounds from organisms, is a concrete example of such an overlooked topic. To mark the start of this new task, we created the first curated evaluation dataset and extracted literature items from the LOTUS database to build training sets. To this end, we developed a new sampler, inspired by diversity metrics in ecology, named Greedy Maximum Entropy sampler (https://github.com/idiap/gme-sampler). The strategic optimization of both balance and diversity of the selected items in the evaluation set is important given the resource-intensive nature of manual curation. After quantifying the noise in the training set, in the form of discrepancies between the text of input abstracts and the expected output labels, we explored different strategies accordingly. Framing the task as an end-to-end Relation Extraction, we evaluated the performance of standard fine-tuning (BioGPT, GPT-2, and Seq2rel) and few-shot learning with open Large Language Models (LLMs) (LLaMA 7B-65B). In addition to their evaluation in few-shot settings, we explore the potential of open LLMs as synthetic data generators and propose a new workflow for this purpose. All evaluated models exhibited substantial improvements when fine-tuned on synthetic abstracts rather than the original noisy data. We provide our best performing (F1-score = 59.0) BioGPT-Large model for end-to-end RE of natural products relationships along with all the training and evaluation datasets. See more details at https://github.com/idiap/abroad-re.