Factors contributing to the time taken to consult with symptoms of lung cancer: a cross-sectional study

<b>Objectives</b>: To determine what factors are associated with the time people take to consult with symptoms of lung cancer, with a focus on those from rural and socially deprived areas. <b>Methods</b>: A cross-sectional quantitative interview survey was performed of 360 patients with newly diagnosed primary lung cancer in three Scottish hospitals (two in Glasgow, one in NE Scotland). Supplementary data were obtained from medical case notes. The main outcome measures were the number of days from (1) the date participant defined first symptom until date of presentation to a medical practitioner; and (2) the date of earliest symptom from a symptom checklist (derived from clinical guidelines) until date of presentation to a medical practitioner. <b>Results</b>: 179 participants (50%) had symptoms for more than 14 weeks before presenting to a medical practitioner (median 99 days; interquartile range 31–381). 270 participants (75%) had unrecognised symptoms of lung cancer. There were no significant differences in time taken to consult with symptoms of lung cancer between rural and/or deprived participants compared with urban and/or affluent participants. Factors independently associated with increased time before consulting about symptoms were living alone, a history of chronic obstructive pulmonary disease (COPD) and longer pack years of smoking. Haemoptysis, new onset of shortness of breath, cough and loss of appetite were significantly associated with earlier consulting, as were a history of chest infection and renal failure. <b>Conclusion</b>: For many people with lung cancer, regardless of location and socioeconomic status, the time between symptom onset and consultation was long enough to plausibly affect prognosis. Long-term smokers, those with COPD and/or those living alone are at particular risk of taking longer to consult with symptoms of lung cancer and practitioners should be alert to this

State of the world’s plants and fungi 2020

Kew’s State of the World’s Plants and Fungi project provides assessments of our current knowledge of the diversity of plants and fungi on Earth, the global threats that they face, and the policies to safeguard them. Produced in conjunction with an international scientific symposium, Kew’s State of the World’s Plants and Fungi sets an important international standard from which we can annually track trends in the global status of plant and fungal diversity

Developing a complex intervention to reduce time to presentation with symptoms of lung cancer

Item does not contain fulltextBACKGROUND: Lung cancer is the commonest cause of cancer in Scotland and is usually advanced at diagnosis. Median time between symptom onset and consultation is 14 weeks, so an intervention to prompt earlier presentation could support earlier diagnosis and enable curative treatment in more cases. AIM: To develop and optimise an intervention to reduce the time between onset and first consultation with symptoms that might indicate lung cancer. DESIGN AND SETTING: Iterative development of complex healthcare intervention according to the MRC Framework conducted in Northeast Scotland. METHOD: The study produced a complex intervention to promote early presentation of lung cancer symptoms. An expert multidisciplinary group developed the first draft of the intervention based on theory and existing evidence. This was refined following focus groups with health professionals and high-risk patients. RESULTS: First draft intervention components included: information communicated persuasively, demonstrations of early consultation and its benefits, behaviour change techniques, and involvement of spouses/partners. Focus groups identified patient engagement, achieving behavioural change, and conflict at the patient-general practice interface as challenges and measures were incorporated to tackle these. Final intervention delivery included a detailed self-help manual and extended consultation with a trained research nurse at which specific action plans were devised. CONCLUSION: The study has developed an intervention that appeals to patients and health professionals and has theoretical potential for benefit. Now it requires evaluation

A vector expressing feline mature IL-18 fused to IL-1 beta antagonist protein signal sequence is an effective adjuvant to a DNA vaccine for feline leukaemia virus

DNA vaccination using vectors expressing the gag/pol and env genes of feline leukaemia virus (FeLV) and plasmids encoding feline interleukin-12 (IL-12) and IL-18 completely protected cats from viraemia following challenge [Hanlon L, Argyle D, Bain D, Nicolson L, Dunham S, Golder MC, et al. Feline leukaemia virus DNA vaccine efficacy is enhanced by coadministration with interleukin-12 (IL-12) and IL-18 expression vectors. J Virol 2001;75:8424–33]. However, the relative contribution of each cytokine gene towards protection is unknown. This study aimed to resolve this issue. IL-12 and IL-18 constructs were modified to ensure effective expression, and bioactivity was demonstrated using specific assays. Kittens were immunised intramuscularly with FeLV DNA and various cytokine constructs. Together with control kittens, these were challenged oronasally with FeLV and monitored for 15 weeks. All six kittens given FeLV, IL-12 and IL-18 were protected from the establishment of persistent viraemia and four from latent infection. Of six kittens immunised with FeLV DNA and IL-18, all were protected from viraemia and five from latent infection. In contrast, three of five kittens given FeLV DNA and IL-12 became persistently viraemic. Therefore, the adjuvant effect on the FeLV DNA vaccine appears to reside in the expression of IL-18

Systematic comparisons of different quality control approaches applied to three large pediatric neuroimaging datasets.

INTRODUCTION: Poor quality T1-weighted brain scans systematically affect the calculation of brain measures. Removing the influence of such scans requires identifying and excluding scans with noise and artefacts through a quality control (QC) procedure. While QC is critical for brain imaging analyses, it is not yet clear whether different QC approaches lead to the exclusion of the same participants. Further, the removal of poor-quality scans may unintentionally introduce a sampling bias by excluding the subset of participants who are younger and/or feature greater clinical impairment. This study had two aims: (1) examine whether different QC approaches applied to T1-weighted scans would exclude the same participants, and (2) examine how exclusion of poor-quality scans impacts specific demographic, clinical and brain measure characteristics between excluded and included participants in three large pediatric neuroimaging samples. METHODS: We used T1-weighted, resting-state fMRI, demographic and clinical data from the Province of Ontario Neurodevelopmental Disorders network (Aim 1: n = 553, Aim 2: n = 465), the Healthy Brain Network (Aim 1: n = 1051, Aim 2: n = 558), and the Philadelphia Neurodevelopmental Cohort (Aim 1: n = 1087; Aim 2: n = 619). Four different QC approaches were applied to T1-weighted MRI (visual QC, metric QC, automated QC, fMRI-derived QC). We used tetrachoric correlation and inter-rater reliability analyses to examine whether different QC approaches excluded the same participants. We examined differences in age, mental health symptoms, everyday/adaptive functioning, IQ and structural MRI-derived brain indices between participants that were included versus excluded following each QC approach. RESULTS: Dataset-specific findings revealed mixed results with respect to overlap of QC exclusion. However, in POND and HBN, we found a moderate level of overlap between visual and automated QC approaches (r(tet)=0.52-0.59). Implementation of QC excluded younger participants, and tended to exclude those with lower IQ, and lower everyday/adaptive functioning scores across several approaches in a dataset-specific manner. Across nearly all datasets and QC approaches examined, excluded participants had lower estimates of cortical thickness and subcortical volume, but this effect did not differ by QC approach. CONCLUSION: The results of this study provide insight into the influence of QC decisions on structural pediatric imaging analyses. While different QC approaches exclude different subsets of participants, the variation of influence of different QC approaches on clinical and brain metrics is minimal in large datasets. Overall, implementation of QC tends to exclude participants who are younger, and those who have more cognitive and functional impairment. Given that automated QC is standardized and can reduce between-study differences, the results of this study support the potential to use automated QC for large pediatric neuroimaging datasets