Objective To systematically examine the design, reporting standards, risk of bias, and claims of studies comparing the performance of diagnostic deep learning algorithms for medical imaging with that of expert clinicians. Design Systematic review. Data sources Medline, Embase, Cochrane Central Register of Controlled Trials, and the World Health Organization trial registry from 2010 to June 2019. Eligibility criteria for selecting studies Randomised trial registrations and non-randomised studies comparing the performance of a deep learning algorithm in medical imaging with a contemporary group of one or more expert clinicians. Medical imaging has seen a growing interest in deep learning research. The main distinguishing feature of convolutional neural networks (CNNs) in deep learning is that when CNNs are fed with raw data, they develop their own representations needed for pattern recognition. The algorithm learns for itself the features of an image that are important for classification rather than being told by humans which features to use. The selected studies aimed to use medical imaging for predicting absolute risk of existing disease or classification into diagnostic groups (eg, disease or non-disease). For example, raw chest radiographs tagged with a label such as pneumothorax or no pneumothorax and the CNN learning which pixel patterns suggest pneumothorax. Review methods Adherence to reporting standards was assessed by using CONSORT (consolidated standards of reporting trials) for randomised studies and TRIPOD (transparent reporting of a multivariable prediction model for individual prognosis or diagnosis) for non-randomised studies. Risk of bias was assessed by using the Cochrane risk of bias tool for randomised studies and PROBAST (prediction model risk of bias assessment tool) for non-randomised studies. Results Only 10 records were found for deep learning randomised clinical trials, two of which have been published (with low risk of bias, except for lack of blinding, and high adherence to reporting standards) and eight are ongoing. Of 81 non-randomised clinical trials identified, only nine were prospective and just six were tested in a real world clinical setting. The median number of experts in the comparator group was only four (interquartile range 2-9). Full access to all datasets and code was severely limited (unavailable in 95% and 93% of studies, respectively). The overall risk of bias was high in 58 of 81 studies and adherence to reporting standards was suboptimal (<50% adherence for 12 of 29 TRIPOD items). 61 of 81 studies stated in their abstract that performance of artificial intelligence was at least comparable to (or better than) that of clinicians. Only 31 of 81 studies (38%) stated that further prospective studies or trials were required. Conclusions Few prospective deep learning studies and randomised trials exist in medical imaging. Most non-randomised trials are not prospective, are at high risk of bias, and deviate from existing reporting standards. Data and code availability are lacking in most studies, and human comparator groups are often small. Future studies should diminish risk of bias, enhance real world clinical relevance, improve reporting and transparency, and appropriately temper conclusions. Study registration PROSPERO CRD42019123605.

SummaryVirus infection is sensed by pattern recognition receptors (PRRs) detecting virus nucleic acids and initiating an innate immune response. DNA-dependent protein kinase (DNA-PK) is a PRR that binds cytosolic DNA and is antagonized by vaccinia virus (VACV) protein C16. Here, VACV protein C4 is also shown to antagonize DNA-PK by binding to Ku and blocking Ku binding to DNA, leading to a reduced production of cytokines and chemokines in vivo and a diminished recruitment of inflammatory cells. C4 and C16 share redundancy in that a double deletion virus has reduced virulence not seen with single deletion viruses following intradermal infection. However, non-redundant functions exist because both single deletion viruses display attenuated virulence compared to wild-type VACV after intranasal infection. It is notable that VACV expresses two proteins to antagonize DNA-PK, but it is not known to target other DNA sensors, emphasizing the importance of this PRR in the response to infection in vivo.

BackgroundBoth people with autism spectrum conditions (ASC) and borderline personality disorder (BPD) are significantly challenged in terms of understanding and responding to emotions and in interpersonal functioning.AimsTo compare ASC, BPD, and comorbid patients in terms of autistic traits, empathy, and systemizing.Methods624 ASC, 23 BPD, and 16 comorbid (ASC+BPD) patients, and 2,081 neurotypical controls (NC) filled in the Autism Spectrum Quotient (AQ), the Empathy Quotient (EQ) and the Systemizing Quotient-Revised (SQ-R).ResultsOn the AQ, the ASC group scored higher than the BPD group, who in turn scored higher than the comorbid group, who scored higher than controls. On the EQ, we found the comorbid and ASC groups scored lower than the BPD group, who were not different from controls. Finally, on the SQ-R, we found the ASC and BPD group both scored higher than controls.ConclusionsSimilar to ASC, BPD patients have elevated autistic traits and a strong drive to systemize, suggesting an overlap between BPD and ASC.

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Previous studies have found links between personality and exam scores, job satisfaction and burnout. Now, for the first time, we are able to investigate the relationship between surgeon personality and outcomes.

Introduction The increasing longevity of the Western population means patients with a more advanced age are being diagnosed with resectable disease. With improvements in imaging and diagnostic capabilities, this trend is likely to develop further. As a unit operating on a higher proportion of older patients and with limited literature regarding the population of older than 85 years, we retrospectively compared the outcomes of patients older than 85 years in our unit treated with elective lung resection for non-small cell lung cancer (NSCLC) with those between the age of 80 and 84 years inclusive. Methods All patients who underwent elective lung cancer resection between the years 2012 and 2015 were identified from the National Thoracic Surgical Database. Results A total of 701 elective lung resections were performed during this time frame; 76 patients between the ages of 80 and 84 years and 18 patients older than 85 years. The follow-up period was 3 to 7 years. There was a significant increase in the Thoracic Surgery Scoring System (2.04; 2.96%, p = 0.0015) and a significant reduction in the transfer factor (94.7; 69.5%, p = 0.0001) between the younger and older groups. There were three (3.9%) in-hospital deaths in the 80 to 84 years age group and no in-hospital deaths in the 85 years and older age group. Conclusion This study demonstrates that surgery for early NSCLC can be safely performed in 85 years and older population. This is a higher risk population and parenchymal-sparing procedures should be considered.

There is an error in the first sentence of the Results section of the Abstract. The correct sentence is: On the AQ, the comorbid group scored higher than the ASC group, who in turn scored higher than the BPD group, who scored higher than controls. Reference 1. Dudas RB, Lovejoy C, Cassidy S, Allison C, Smith P, Baron-Cohen S (2017) The overlap between autistic spectrum conditions and borderline personality disorder. PLoS ONE 12(9): e0184447. https://