Mass spectrometry detection of inhaled drug in distal fibrotic lung

BACKGROUND: Currently the only available therapies for fibrotic Interstitial Lung Disease are administered systemically, often causing significant side effects. Inhaled therapy could avoid these but to date there is no evidence that drug can be effectively delivered to distal, fibrosed lung. We set out to combine mass spectrometry and histopathology with rapid sample acquisition using transbronchial cryobiopsy to determine whether an inhaled drug can be delivered to fibrotic, distal lung parenchyma in participants with Interstitial Lung Disease. METHODS: Patients with radiologically and multidisciplinary team confirmed fibrotic Interstitial Lung Disease were eligible for this study. Transbronchial cryobiopsies and endobronchial biopsies were taken from five participants, with Interstitial Lung Disease, within 70 min of administration of a single dose of nebulised ipratropium bromide. Thin tissue cryosections were analysed by Matrix Assisted Laser Desorption/Ionization-Mass Spectrometry imaging and correlated with histopathology. The remainder of the cryobiopsies were homogenised and analysed by Liquid Chromatography—tandem Mass Spectrometry. RESULTS: Drug was detected in proximal and distal lung samples from all participants. Fibrotic regions were identified in research samples of four of the five participants. Matrix Assisted Laser Desorption/Ionization-Mass Spectrometry imaging showed co-location of ipratropium with fibrotic regions in samples from three participants. CONCLUSIONS: In this proof of concept study, using mass spectrometry, we demonstrate for the first-time that an inhaled drug can deposit in distal fibrotic lung parenchyma in patients with Interstitial Lung Disease. This suggests that drugs to treat pulmonary fibrosis could potentially be administered by the inhaled route

An empirical investigation of the emergent issues around OER adoption in Sub-Saharan Africa

In the past few years, Africa has joined the rest of the world as an active participant in the Open Educational Resource (OER) movement with a number of home-grown and externally driven initiatives. These have the potential to make an immense contribution to teaching and learning in Sub-Saharan Africa (SSA). However, certain barriers prevent full participation. This paper reports on qualitative research that sought to investigate SSA's readiness to adopt OERs. This study involves three case studies based in higher education institutions involved in OER projects and located in Kenya, Uganda and South Africa. Contrary to the popular belief, findings indicate that low technological levels in Africa do not necessarily impede the adoption of such educational technologies; the real challenges facing the readiness to adopt OERs appear to be related to socio-economic, cultural, institutional and national issues. This paper argues for a complete mind shift in how people perceive OERs. It also proposes raising awareness of OERs at all levels, involving institutions and government, versioning OERs for the African context and conducting more research on OER adoption

ChatGPT sits the DFPH exam: large language model performance and potential to support public health learning

Background Artificial intelligence-based large language models, like ChatGPT, have been rapidly assessed for both risks and potential in health-related assessment and learning. However, their applications in public health professional exams have not yet been studied. We evaluated the performance of ChatGPT in part of the Faculty of Public Health’s Diplomat exam (DFPH). Methods ChatGPT was provided with a bank of 119 publicly available DFPH question parts from past papers. Its performance was assessed by two active DFPH examiners. The degree of insight and level of understanding apparently displayed by ChatGPT was also assessed. Results ChatGPT passed 3 of 4 papers, surpassing the current pass rate. It performed best on questions relating to research methods. Its answers had a high floor. Examiners identified ChatGPT answers with 73.6% accuracy and human answers with 28.6% accuracy. ChatGPT provided a mean of 3.6 unique insights per question and appeared to demonstrate a required level of learning on 71.4% of occasions. Conclusions Large language models have rapidly increasing potential as a learning tool in public health education. However, their factual fallibility and the difficulty of distinguishing their responses from that of humans pose potential threats to teaching and learning

ChatGPT sits the DFPH exam: large language model performance and potential to support public health learning

BackgroundArtificial intelligence-based large language models, like ChatGPT, have been rapidly assessed for both risks and potential in health-related assessment and learning. However, their applications in public health professional exams have not yet been studied. We evaluated the performance of ChatGPT in part of the Faculty of Public Health’s Diplomat exam (DFPH).MethodsChatGPT was provided with a bank of 119 publicly available DFPH question parts from past papers. Its performance was assessed by two active DFPH examiners. The degree of insight and level of understanding apparently displayed by ChatGPT was also assessed.ResultsChatGPT passed 3 of 4 papers, surpassing the current pass rate. It performed best on questions relating to research methods. Its answers had a high floor. Examiners identified ChatGPT answers with 73.6% accuracy and human answers with 28.6% accuracy. ChatGPT provided a mean of 3.6 unique insights per question and appeared to demonstrate a required level of learning on 71.4% of occasions.ConclusionsLarge language models have rapidly increasing potential as a learning tool in public health education. However, their factual fallibility and the difficulty of distinguishing their responses from that of humans pose potential threats to teaching and learning

Determining the impact of an artificial intelligence tool on the management of pulmonary nodules detected incidentally on CT (DOLCE) study protocol: a prospective, non-interventional multicentre UK study

\ua9 Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. INTRODUCTION: In a small percentage of patients, pulmonary nodules found on CT scans are early lung cancers. Lung cancer detected at an early stage has a much better prognosis. The British Thoracic Society guideline on managing pulmonary nodules recommends using multivariable malignancy risk prediction models to assist in management. While these guidelines seem to be effective in clinical practice, recent data suggest that artificial intelligence (AI)-based malignant-nodule prediction solutions might outperform existing models. METHODS AND ANALYSIS: This study is a prospective, observational multicentre study to assess the clinical utility of an AI-assisted CT-based lung cancer prediction tool (LCP) for managing incidental solid and part solid pulmonary nodule patients vs standard care. Two thousand patients will be recruited from 12 different UK hospitals. The primary outcome is the difference between standard care and LCP-guided care in terms of the rate of benign nodules and patients with cancer discharged straight after the assessment of the baseline CT scan. Secondary outcomes investigate adherence to clinical guidelines, other measures of changes to clinical management, patient outcomes and cost-effectiveness. ETHICS AND DISSEMINATION: This study has been reviewed and given a favourable opinion by the South Central-Oxford C Research Ethics Committee in UK (REC reference number: 22/SC/0142).Study results will be available publicly following peer-reviewed publication in open-access journals. A patient and public involvement group workshop is planned before the study results are available to discuss best methods to disseminate the results. Study results will also be fed back to participating organisations to inform training and procurement activities. TRIAL REGISTRATION NUMBER: NCT05389774

Ultradian rhythmicity of plasma cortisol is necessary for normal emotional and cognitive responses in man

Glucocorticoids (GCs) are secreted in an ultradian, pulsatile pattern that emerges from delays in the feedforward-feedback interaction between the anterior pituitary and adrenal glands. Dynamic oscillations of GCs are critical for normal cognitive and metabolic function in the rat and have been shown to modulate the pattern of GC-sensitive gene expression, modify synaptic activity, and maintain stress responsiveness. In man, current cortisol replacement therapy does not reproduce physiological hormone pulses and is associated with psychopathological symptoms, especially apathy and attenuated motivation in engaging with daily activities. In this work, we tested the hypothesis that the pattern of GC dynamics in the brain is of crucial importance for regulating cognitive and behavioral processes. We provide evidence that exactly the same dose of cortisol administered in different patterns alters the neural processing underlying the response to emotional stimulation, the accuracy in recognition and attentional bias toward/away from emotional faces, the quality of sleep, and the working memory performance of healthy male volunteers. These data indicate that the pattern of the GC rhythm differentially impacts human cognition and behavior under physiological, nonstressful conditions and has major implications for the improvement of cortisol replacement therapy

Glucocorticoid ultradian rhythmicity differentially regulates mood and resting state networks in the human brain: A randomised controlled clinical trial

Adrenal glucocorticoid secretion into the systematic circulation is characterised by a complex rhythm, composed of the diurnal variation, formed by changes in pulse amplitude of an underlying ultradian rhythm of short duration hormonal pulses. To elucidate the potential neurobiological significance of glucocorticoid pulsatility in man, we have conducted a randomised, double-blind, placebo-controlled, three-way crossover clinical trial on 15 healthy volunteers, investigating the impact of different glucocorticoid rhythms on measures of mood and neural activity under resting conditions by recruiting functional neuroimaging, computerised behavioural tests and ecological momentary assessments. Endogenous glucocorticoid biosynthesis was pharmacologically suppressed, and plasma levels of corticosteroid restored by hydrocortisone replacement in three different regimes, either mimicking the normal ultradian and circadian profile of the hormone, or retaining the normal circadian but abolishing the ultradian rhythm of the hormone, or by our current best oral replacement regime which results in a suboptimal circadian and ultradian rhythm. Our results indicate that changes in the temporal mode of glucocorticoid replacement impact (i) the morning levels of self-perceived vigour, fatigue and concentration, (ii) the diurnal pattern of mood variation, (iii) the within-network functional connectivity of various large-scale resting state networks of the human brain, (iv) the functional connectivity of the default-mode, salience and executive control networks with glucocorticoid-sensitive nodes of the corticolimbic system, and (v) the functional relationship between mood variation and underlying neural networks. The findings indicate that the pattern of the ultradian glucocorticoid rhythm could affect cognitive psychophysiology under non-stressful conditions and opens new pathways for our understanding on the neuropsychological effects of cortisol pulsatility with relevance to the goal of optimising glucocorticoid replacement strategies

Determining the impact of an artificial intelligence tool on the management of pulmonary nodules detected incidentally on CT (DOLCE) study protocol: a prospective, non-interventional multicentre UK study.

Introduction In a small percentage of patients, pulmonary nodules found on CT scans are early lung cancers. Lung cancer detected at an early stage has a much better prognosis. The British Thoracic Society guideline on managing pulmonary nodules recommends using multivariable malignancy risk prediction models to assist in management. While these guidelines seem to be effective in clinical practice, recent data suggest that artificial intelligence (AI)-based malignant-nodule prediction solutions might outperform existing models. Methods and analysis This study is a prospective, observational multicentre study to assess the clinical utility of an AI-assisted CT-based lung cancer prediction tool (LCP) for managing incidental solid and part solid pulmonary nodule patients vs standard care. Two thousand patients will be recruited from 12 different UK hospitals. The primary outcome is the difference between standard care and LCP-guided care in terms of the rate of benign nodules and patients with cancer discharged straight after the assessment of the baseline CT scan. Secondary outcomes investigate adherence to clinical guidelines, other measures of changes to clinical management, patient outcomes and cost-effectiveness. Ethics and dissemination This study has been reviewed and given a favourable opinion by the South Central—Oxford C Research Ethics Committee in UK (REC reference number: 22/SC/0142). Study results will be available publicly following peer-reviewed publication in open-access journals. A patient and public involvement group workshop is planned before the study results are available to discuss best methods to disseminate the results. Study results will also be fed back to participating organisations to inform training and procurement activities. Trial registration number NCT05389774

The evolution of lung cancer and impact of subclonal selection in TRACERx

Lung cancer is the leading cause of cancer-associated mortality worldwide1. Here we analysed 1,644 tumour regions sampled at surgery or during follow-up from the first 421 patients with non-small cell lung cancer prospectively enrolled into the TRACERx study. This project aims to decipher lung cancer evolution and address the primary study endpoint: determining the relationship between intratumour heterogeneity and clinical outcome. In lung adenocarcinoma, mutations in 22 out of 40 common cancer genes were under significant subclonal selection, including classical tumour initiators such as TP53 and KRAS. We defined evolutionary dependencies between drivers, mutational processes and whole genome doubling (WGD) events. Despite patients having a history of smoking, 8% of lung adenocarcinomas lacked evidence of tobacco-induced mutagenesis. These tumours also had similar detection rates for EGFR mutations and for RET, ROS1, ALK and MET oncogenic isoforms compared with tumours in never-smokers, which suggests that they have a similar aetiology and pathogenesis. Large subclonal expansions were associated with positive subclonal selection. Patients with tumours harbouring recent subclonal expansions, on the terminus of a phylogenetic branch, had significantly shorter disease-free survival. Subclonal WGD was detected in 19% of tumours, and 10% of tumours harboured multiple subclonal WGDs in parallel. Subclonal, but not truncal, WGD was associated with shorter disease-free survival. Copy number heterogeneity was associated with extrathoracic relapse within 1 year after surgery. These data demonstrate the importance of clonal expansion, WGD and copy number instability in determining the timing and patterns of relapse in non-small cell lung cancer and provide a comprehensive clinical cancer evolutionary data resource

Combinatorial hydrogel library enables identification of materials that mitigate the foreign body response in primates

The foreign body response is an immune-mediated reaction that can lead to the failure of implanted medical devices and discomfort for the recipient. There is a critical need for biomaterials that overcome this key challenge in the development of medical devices. Here we use a combinatorial approach for covalent chemical modification to generate a large library of variants of one of the most widely used hydrogel biomaterials, alginate. We evaluated the materials in vivo and identified three triazole-containing analogs that substantially reduce foreign body reactions in both rodents and, for at least 6 months, in non-human primates. The distribution of the triazole modification creates a unique hydrogel surface that inhibits recognition by macrophages and fibrous deposition. In addition to the utility of the compounds reported here, our approach may enable the discovery of other materials that mitigate the foreign body response.Leona M. and Harry B. Helmsley Charitable Trust (3-SRA-2014-285-M-R)United States. National Institutes of Health (EB000244)United States. National Institutes of Health (EB000351)United States. National Institutes of Health (DE013023)United States. National Institutes of Health (CA151884)United States. National Institutes of Health (P41EB015871-27)National Cancer Institute (U.S.) (P30-CA14051