Diseases of failed inflammation resolution are common and largely incurable. Therapeutic induction of inflammation resolution is an attractive strategy to bring about healing without increasing susceptibility to infection. However, therapeutic targeting of inflammation resolution has been hampered by a lack of understanding of the underlying molecular controls. To address this drug development challenge, we developed an in vivo screen for proresolution therapeutics in a transgenic zebrafish model. Inflammation induced by sterile tissue injury was assessed for accelerated resolution in the presence of a library of known compounds. Of the molecules with proresolution activity, tanshinone IIA, derived from a Chinese medicinal herb, potently induced inflammation resolution in vivo both by induction of neutrophil apoptosis and by promoting reverse migration of neutrophils. Tanshinone IIA blocked proinflammatory signals in vivo, and its effects are conserved in human neutrophils, supporting a potential role in treating human inflammation and providing compelling evidence of the translational potential of this screening strategy.

Background Breast cancer surgery in older women is variable and sometimes non‐standard owing to concerns about morbidity. Bridging the Age Gap in Breast Cancer is a prospective multicentre cohort study aiming to determine factors influencing treatment selection and outcomes from surgery for older patients with breast cancer. Methods Women aged at least 70 years with operable breast cancer were recruited from 57 UK breast units between 2013 and 2018. Associations between patient and tumour characteristics and type of surgery in the breast and axilla were evaluated using univariable and multivariable analyses. Oncological outcomes, adverse events and quality‐of‐life (QoL) outcomes were monitored for 2 years. Results Among 3375 women recruited, surgery was performed in 2816 patients, of whom 24 with inadequate data were excluded. Sixty‐two women had bilateral tumours, giving a total of 2854 surgical events. Median age was 76 (range 70–95) years. Breast surgery comprised mastectomy in 1138 and breast‐conserving surgery in 1716 procedures. Axillary surgery comprised axillary lymph node dissection in 575 and sentinel node biopsy in 2203; 76 had no axillary surgery. Age, frailty, dementia and co‐morbidities were predictors of mastectomy (multivariable odds ratio (OR) for age 1·06, 95 per cent c.i. 1·05 to 1·08). Age, frailty and co‐morbidity were significant predictors of no axillary surgery (OR for age 0·91, 0·87 to 0·96). The rate of adverse events was moderate (551 of 2854, 19·3 per cent), with no 30‐day mortality. Long‐term QoL and functional independence were adversely affected by surgery. Conclusion Breast cancer surgery is safe in women aged 70 years or more, with serious adverse events being rare and no mortality. Age, ill health and frailty all influence surgical decision‐making. Surgery has a negative impact on QoL and independence, which must be considered when counselling patients about choices.

Following neutralization of infectious threats, neutrophils must be removed from inflammatory sites for normal tissue function to be restored. Recently, a new paradigm has emerged, in which viable neutrophils migrate away from inflammatory sites by a process best described as reverse migration. It has generally been assumed that this process is the mirror image of chemotaxis, where neutrophils are drawn into the areas of infection or tissue damage by gradients of chemotactic cues. Indeed, efforts are underway to identify cues that drive neutrophils away by the reverse process, fugetaxis. By using photoconvertible pigments expressed in neutrophils in transparent zebrafish larvae, we were able to image the position of each neutrophil during inflammation resolution in vivo. These neutrophil coordinates were analysed within a dynamic modelling framework, using different forms of the drift-diffusion equation with model selection and parameter estimation based on approximate Bayesian computation. This analysis revealed the experimental data were best fitted by a model incorporating a diffusion term but no drift term-where the presence of drift would indicate fugetaxis. This result, for the first time, provides rigorous data-driven evidence that reverse migration of neutrophils in vivo is not a form of fugetaxis, but rather a stochastic redistribution.

Background: Primary endocrine therapy is used as an alternative to surgery in up to 40 per cent of women with early breast cancer aged over 70 years in the UK. This study investigated the impact of surgery versus primary endocrine therapy on breast cancer-specific survival (BCSS) in older women.

Neutrophils must be removed from inflammatory sites for inflammation to resolve. Recent work in zebrafish has shown neutrophils can migrate away from inflammatory sites, as well as die in situ. The signals regulating the process of reverse migration are of considerable interest, but remain unknown. We wished to study the behaviour of neutrophils during reverse migration, to see whether they moved away from inflamed sites in a directed fashion in the same way as they are recruited or whether the inherent random component of their migration was enough to account for this behaviour. Using neutrophil-driven photoconvertible Kaede protein in transgenic zebrafish larvae, we were able to specifically label neutrophils at an inflammatory site generated by tailfin transection. The locations of these neutrophils over time were observed and fitted using regression methods with two separate models: pure-diffusion and drift-diffusion equations. While a model hypothesis test (the F-test) suggested that the datapoints could be fitted by the drift-diffusion model, implying a fugetaxis process, dynamic simulation of the models suggested that migration of neutrophils away from a wound is better described by a zero-drift, “diffusion” process. This has implications for understanding the mechanisms of reverse migration and, by extension, neutrophil retention at inflammatory sites.

Background: Adjuvant chemotherapy is recommended as a treatment for women with high recurrence risk early breast cancer. Older women are less likely to receive chemotherapy than younger women. This study has investigated the impact of chemotherapy on breast cancer specific survival in women aged 70+ using English Registry data. Methods: Cancer registration data were obtained from two English regions from 2002 to 2012 (n=29,728). The impact of patient level characteristics on the probability of receiving adjuvant chemotherapy was explored using logistic regression. Survival modelling was undertaken to show the effect of chemotherapy and age/health status on breast cancer specific survival. Missing data was handled using multiple imputation. Results: 11,735 surgically treated early breast cancer patients were identified. Use of adjuvant chemotherapy has increased over time. Younger age at diagnosis, increased nodal involvement, tumour size and grade, oestrogen receptor negative or HER2 positive disease were all associated with increased probability of receiving chemotherapy. Chemotherapy was associated with a significant reduction in the hazard of breast cancer specific mortality in women with high recurrence risk cancer, after adjusting for patient level characteristics (Hazard Ratio 0.74, 95% CI 0.67-0.81). Discussion: Chemotherapy is associated with an improved breast cancer specific survival in older women with high recurrence risk early breast cancer. Lower rates of chemotherapy use in older women may, therefore, contribute to inferior cancer outcomes. Decisions on potential benefits for individual patients should be made on the basis of life expectancy, treatment tolerance and patient preference.

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This paper investigates the feasibility of using machine learning algorithms to predict the loads experienced by a landing gear during landing. For this purpose, results on drop test data and flight test data will be examined. This paper will focus on the use of Gaussian Process regression for the prediction of loads on components of a landing gear. For the learning task, comprehensive measurement data from drop tests are available. These include measurements of strains at key locations, such as the on the side-stay and torque link, as well as acceleration measurements of the drop carriage and the gear itself, measurements of shock absorber travel, tyre closure, shock absorber pressure and wheel speed. Ground-to-tyre loads are also available through measurements made with a drop test ground reaction platform. The aim is to train the GP to predict load at a particular location from other available measurements, such as accelerations, or measurements of the shock absorber. If models can be successfully trained, then future load patterns may be predicted using only these measurements. The ultimate aim is to produce an accurate model that can predict the load at a number of locations across the landing gear by using measurements that are readily available, or may be measured more easily than directly measuring strain on the gear itself (for example, these may be measurements already available on the aircraft, or from a small number of sensors attached to the gear). The drop test data models provide a positive feasibility test which is the basis for moving on to the critical task of prediction on flight test data. For this, a wide range of available flight test measurements is considered for potential model inputs (excluding strain measurements themselves), before attempting to refine the model or use a smaller number of measurements for the prediction.