Survey to identify research priorities for primary care in Scotland during and following the COVID-19 pandemic.

Objectives To identify research priorities for primary care in Scotland following the COVID-19 pandemic. Design Modified James Lind Alliance methodology; respondents completed an online survey to make research suggestions and rank research themes in order of priority. Setting Scotland primary care. Participants Healthcare professionals in primary care in Scotland and members of primary care patient and public involvement groups. 512 respondents provided research suggestions; 8% (n=40) did not work in health or social care; of those who did work, 68.8% worked in primary care, 16.3% community care, 11.7% secondary care, 4.5% third sector, 4.2% university (respondents could select multiple options). Of those respondents who identified as healthcare professionals, 33% were in nursing and midwifery professions, 25% were in allied health professions (of whom 45% were occupational therapists and 35% were physiotherapists), 20% were in the medical profession and 10% were in the pharmacy profession. Main outcomes Suggestions for research for primary care made by respondents were categorised into themes and subthemes by researchers and ranked in order of priority by respondents. Results There were 1274 research suggestions which were categorised under 12 themes and 30 subthemes. The following five themes received the most suggestions for research: disease and illness (n=461 suggestions), access (n=202), workforce (n=164), multidisciplinary team (MDT; n=143) and integration (n=108). One hundred and three (20%) respondents to the survey participated in ranking the list of 12 themes in order of research priority. The five most highly ranked research priorities were disease and illness, health inequalities, access, workforce and MDTs. The disease and illness theme had the greatest number of suggestions for research and was scored the most highly in the ranking exercise. The subtheme ranked as the most important research priority in the disease and illness theme was 'mental health'. Conclusions The themes and subthemes identified in this study should inform research funders so that the direction of primary healthcare is informed by evidence

Hyperpolarized 13C-MRI of Tumor Metabolism Demonstrates Early Metabolic Response to Neoadjuvant Chemotherapy in Breast Cancer

Purpose: To compare hyperpolarized carbon-13 (13C)-MRI with dynamic contrast-enhanced MRI (DCE-MRI) for detecting early treatment response in breast cancer. Materials and Methods: In this institutional review board-approved prospective study, one woman with triple-negative breast cancer (age 49) underwent 13C-MRI following injection of hyperpolarized [1-13C]pyruvate and DCE-MRI at 3 T at baseline and after a single cycle of neoadjuvant therapy. The 13C-lactate/13C-pyruvate ratio derived from hyperpolarized 13C-MRI and the pharmacokinetic parameters Ktrans and kep derived from DCE-MRI were compared, before and after treatment. Results: Exchange of the 13C-label between injected hyperpolarized [1-13C]pyruvate and the endogenous lactate pool was demonstrated, catalyzed by the enzyme lactate dehydrogenase. After one cycle of neoadjuvant chemotherapy, a 34% reduction in the 13C-lactate/13C-pyruvate ratio was shown to correctly identify the patient as a responder to therapy, which was subsequently confirmed by a complete pathologic response. However, DCE-MRI showed an increase in the pharmacokinetic parameters Ktrans (132%) and kep (31%), which could be incorrectly interpreted as a poor response to treatment. Conclusion: Hyperpolarized 13C-MRI successfully identified response in breast cancer after a single cycle of neoadjuvant chemotherapy and may improve response prediction when used in conjunction with multiparametric proton MRI.This work was supported by a Wellcome Trust Strategic Award, Cancer Research UK (CRUK; Grants C8742/A18097, C19212/ A16628, C19212/A911376, and C197/A16465), the Austrian Science Fund (Grant J4025-B26), the CRUK Cambridge Centre, the CRUK & Engineering and Physical Sciences Research Council Cancer Imaging Centre in Cambridge and Manchester, the Mark Foundation for Cancer Research and Cancer Research UK Cambridge Centre (Grant C9685/A25177), CRUK National Cancer Imaging Translational Accelerator Award, Addenbrooke’s Charitable Trust, the National Institute for Health Research Cambridge Biomedical Research Centre, Cambridge Experimental Cancer Medicine Centre, and Cambridge University Hospitals National Health Service Foundation Trust

Imaging breast cancer using hyperpolarized carbon-13 MRI.

Our purpose is to investigate the feasibility of imaging tumor metabolism in breast cancer patients using 13C magnetic resonance spectroscopic imaging (MRSI) of hyperpolarized 13C label exchange between injected [1-13C]pyruvate and the endogenous tumor lactate pool. Treatment-naïve breast cancer patients were recruited: four triple-negative grade 3 cancers; two invasive ductal carcinomas that were estrogen and progesterone receptor-positive (ER/PR+) and HER2/neu-negative (HER2-), one grade 2 and one grade 3; and one grade 2 ER/PR+ HER2- invasive lobular carcinoma (ILC). Dynamic 13C MRSI was performed following injection of hyperpolarized [1-13C]pyruvate. Expression of lactate dehydrogenase A (LDHA), which catalyzes 13C label exchange between pyruvate and lactate, hypoxia-inducible factor-1 (HIF1α), and the monocarboxylate transporters MCT1 and MCT4 were quantified using immunohistochemistry and RNA sequencing. We have demonstrated the feasibility and safety of hyperpolarized 13C MRI in early breast cancer. Both intertumoral and intratumoral heterogeneity of the hyperpolarized pyruvate and lactate signals were observed. The lactate-to-pyruvate signal ratio (LAC/PYR) ranged from 0.021 to 0.473 across the tumor subtypes (mean ± SD: 0.145 ± 0.164), and a lactate signal was observed in all of the grade 3 tumors. The LAC/PYR was significantly correlated with tumor volume (R = 0.903, P = 0.005) and MCT 1 (R = 0.85, P = 0.032) and HIF1α expression (R = 0.83, P = 0.043). Imaging of hyperpolarized [1-13C]pyruvate metabolism in breast cancer is feasible and demonstrated significant intertumoral and intratumoral metabolic heterogeneity, where lactate labeling correlated with MCT1 expression and hypoxia

Sodium homeostasis in the tumour microenvironment.

The concentration of sodium ions (Na+) is raised in solid tumours and can be measured at the cellular, tissue and patient levels. At the cellular level, the Na+ gradient across the membrane powers the transport of H+ ions and essential nutrients for normal activity. The maintenance of the Na+ gradient requires a large proportion of the cell's ATP. Na+ is a major contributor to the osmolarity of the tumour microenvironment, which affects cell volume and metabolism as well as immune function. Here, we review evidence indicating that Na+ handling is altered in tumours, explore our current understanding of the mechanisms that may underlie these alterations and consider the potential consequences for cancer progression. Dysregulated Na+ balance in tumours may open opportunities for new imaging biomarkers and re-purposing of drugs for treatment

Federated learning for predicting clinical outcomes in patients with COVID-19

Federated learning (FL) is a method used for training artificial intelligence models with data from multiple sources while maintaining data anonymity, thus removing many barriers to data sharing. Here we used data from 20 institutes across the globe to train a FL model, called EXAM (electronic medical record (EMR) chest X-ray AI model), that predicts the future oxygen requirements of symptomatic patients with COVID-19 using inputs of vital signs, laboratory data and chest X-rays. EXAM achieved an average area under the curve (AUC) >0.92 for predicting outcomes at 24 and 72 h from the time of initial presentation to the emergency room, and it provided 16% improvement in average AUC measured across all participating sites and an average increase in generalizability of 38% when compared with models trained at a single site using that site's data. For prediction of mechanical ventilation treatment or death at 24 h at the largest independent test site, EXAM achieved a sensitivity of 0.950 and specificity of 0.882. In this study, FL facilitated rapid data science collaboration without data exchange and generated a model that generalized across heterogeneous, unharmonized datasets for prediction of clinical outcomes in patients with COVID-19, setting the stage for the broader use of FL in healthcare

Annexin A1 expression in a pooled breast cancer series : Association with tumor subtypes and prognosis

Background: Annexin A1 (ANXA1) is a protein related with the carcinogenesis process and metastasis formation in many tumors. However, little is known about the prognostic value of ANXA1 in breast cancer. The purpose of this study is to evaluate the association between ANXA1 expression, BRCA1/2 germline carriership, specific tumor subtypes and survival in breast cancer patients. Methods: Clinical-pathological information and follow-up data were collected from nine breast cancer studies from the Breast Cancer Association Consortium (BCAC) (n = 5,752) and from one study of familial breast cancer patients with BRCA1/