ソウサクキュウナンオヨビリハビリテーションヘノロボットコウガクヲトオシタカダイカイケツ

博士（工学）東京農工大

Towards an understanding of GPs' viewpoint on diagnosing postnatal depression in general practice: a small-scale realist evaluation

BACKGROUND: Less than half of postnatal depression cases are identified in routine clinical assessment. Guidelines and current literature suggest that general practitioners (GPs) may have an opportunistic role in detecting postnatal depression due to their early contact and existing rapport with many new mothers. There is limited research on the diagnostic approaches chosen by GPs in different GP-patient contexts. Our small-scale study evaluates the thought processes of seven GPs based in one practice when forming a clinical diagnosis of postnatal depression under different contexts. METHODS: Seven GP participants were interviewed using case vignettes about postnatal depression, based on an adapted Johari's window framework. A realist approach to analysis was undertaken with the intention of understanding GPs' responses to different situations. Context-mechanism-outcome configurations were constructed, and a programme theory was formed to consolidate the findings. FINDINGS: Findings suggest that diagnoses may be a clinician-led or collaborative process between GP and patient. In collaborative contexts, stigmatising views were addressed by GPs, time for self-reflection was encouraged and mothers' views were accounted for. Clinician-led diagnoses often occurred in contexts where there was a lack of acknowledgement of symptoms on behalf of the patient or where safety was a concern. The personal and clinical experience of GPs themselves, as well as effective communication channels with other primary care professionals, was significant mechanisms. CONCLUSION: GPs use a variety of strategies to support patient disclosure and acceptance of their condition. The complexity of GP-patient contexts may influence the clinical thought process. We address some of the gaps in existing literature by exploring postnatal depression diagnosis in primary care and provide tentative explanations to suggest what works, for whom and in what contexts

Sound source localization through shape reconfiguration in a snake robot

This paper describes a snake robot system that uses sound source localization. We show in this paper as to how we can localize a sound source in 3D and solve the classic forward backward problem in sound source localization using minimum number of audio sensors by using the multiple degrees of freedom of the snake robot. We describe the hardware and software architecture of the robot and show the results of several sound tracking experiments we did with our snake robot. We also present biologically inspired sound tracking behavior in different postures of a biological snake robot as "Digital Snake Charming"

Emotional impact of genetic trials in progressive paediatric disorders: a dose-ranging exon-skipping trial in Duchenne muscular dystrophy.

Gene-modifying trials offer hope for improvement in chronic paediatric disorders, but they may also lead to disappointment and have an adverse emotional effect on families. This study aimed to examine emotional impact on participants in a paediatric exon-skipping trial

Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events

open20siAcknowledgements: EPVL is undertaking a PhD funded by the Cambridge School of Clinical Medicine, Frank Edward Elmore Fund and the Medical Research Council’s Doctoral Training Partnership [award reference: 1966157]. JMT is supported by a Wellcome Trust Clinical Research Career Development Fellowship [211100/Z/18/Z], the National Institute for Health Research (NIHR) Imperial Biomedical Research Centre and the British Heart Foundation Cambridge Centre of Research Excellence. NRE was supported by a Research Training Fellowship from The Dunhill Medical Trust [RTF44/0114]. MMC was supported by fellowships from the Royal College of Surgeons of England, and the British Heart Foundation [BHF; FS/16/29/31957]. HP is undertaking a PhD with a BHF CRE studentship. FJG is an NIHR Senior Investigator. LR and ES were supported by The Mark Foundation for Cancer Research and Cancer Research UK (CRUK) Cambridge Centre [C9685/A25177]. MR is supported by AstraZeneca Oncology R&D. ES receives additional support provided by the NIHR Cambridge Biomedical Research Centre. FAG receives funding from CRUK. EAW receives support from the NIHR CRN. CBS acknowledges support from the Leverhulme Trust project on ‘Breaking the non-convexity barrier’, the Philip Leverhulme Prize, the EPSRC grants EP/S026045/1 and EP/T003553/1, the EPSRC Centre Nr. EP/N014588/1, the Wellcome Innovator Award RG98755, European Union Horizon 2020 research and innovation programmes under the Marie Skodowska-Curie grant agreement No. 777826 NoMADS and No. 691070 CHiPS, the Cantab Capital Institute for the Mathematics of Information and the Alan Turing Institute. JHFR is part-supported by the NIHR Cambridge Biomedical Research Centre, the British Heart Foundation, HEFCE, the Wellcome Trust and the EPSRC grant [EP/N014588/1] for the University of Cambridge Centre for Mathematical Imaging in Healthcare. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health and Social Care.Radiomics, quantitative feature extraction from radiological images, can improve disease diagnosis and prognostication. However, radiomic features are susceptible to image acquisition and segmentation variability. Ideally, only features robust to these variations would be incorporated into predictive models, for good generalisability. We extracted 93 radiomic features from carotid artery computed tomography angiograms of 41 patients with cerebrovascular events. We tested feature robustness to region-of-interest perturbations, image pre-processing settings and quantisation methods using both single- and multi-slice approaches. We assessed the ability of the most robust features to identify culprit and non-culprit arteries using several machine learning algorithms and report the average area under the curve (AUC) from five-fold cross validation. Multi-slice features were superior to single for producing robust radiomic features (67 vs. 61). The optimal image quantisation method used bin widths of 25 or 30. Incorporating our top 10 non-redundant robust radiomics features into ElasticNet achieved an AUC of 0.73 and accuracy of 69% (compared to carotid calcification alone [AUC: 0.44, accuracy: 46%]). Our results provide key information for introducing carotid CT radiomics into clinical practice. If validated prospectively, our robust carotid radiomic set could improve stroke prediction and target therapies to those at highest risk.noneLe E.P.V.; Rundo L.; Tarkin J.M.; Evans N.R.; Chowdhury M.M.; Coughlin P.A.; Pavey H.; Wall C.; Zaccagna F.; Gallagher F.A.; Huang Y.; Sriranjan R.; Le A.; Weir-McCall J.R.; Roberts M.; Gilbert F.J.; Warburton E.A.; Schonlieb C.-B.; Sala E.; Rudd J.H.F.Le E.P.V.; Rundo L.; Tarkin J.M.; Evans N.R.; Chowdhury M.M.; Coughlin P.A.; Pavey H.; Wall C.; Zaccagna F.; Gallagher F.A.; Huang Y.; Sriranjan R.; Le A.; Weir-McCall J.R.; Roberts M.; Gilbert F.J.; Warburton E.A.; Schonlieb C.-B.; Sala E.; Rudd J.H.F

Assessing robustness of carotid artery CT angiography radiomics in the identification of culprit lesions in cerebrovascular events

Funder: School of Clinical Medicine, University of Cambridge; doi: http://dx.doi.org/10.13039/501100007552Funder: Frank Edward Elmore FundFunder: National Institute for Health Research (NIHR) Imperial Biomedical Research CentreFunder: British Heart Foundation Cambridge Centre of Research ExcellenceFunder: Royal College of Surgeons of England; doi: http://dx.doi.org/10.13039/501100000297Funder: Cancer Research UK; doi: http://dx.doi.org/10.13039/501100000289Funder: AstraZeneca Oncology RFunder: National Institute for Health Research; doi: http://dx.doi.org/10.13039/501100000272Funder: Leverhulme Trust; doi: http://dx.doi.org/10.13039/501100000275Funder: Cantab Capital Institute for the Mathematics of InformationFunder: Alan Turing Institute; doi: http://dx.doi.org/10.13039/100012338Funder: NIHR Cambridge Biomedical Research CentreFunder: Higher Education Funding Council for England; doi: http://dx.doi.org/10.13039/501100000384Abstract: Radiomics, quantitative feature extraction from radiological images, can improve disease diagnosis and prognostication. However, radiomic features are susceptible to image acquisition and segmentation variability. Ideally, only features robust to these variations would be incorporated into predictive models, for good generalisability. We extracted 93 radiomic features from carotid artery computed tomography angiograms of 41 patients with cerebrovascular events. We tested feature robustness to region-of-interest perturbations, image pre-processing settings and quantisation methods using both single- and multi-slice approaches. We assessed the ability of the most robust features to identify culprit and non-culprit arteries using several machine learning algorithms and report the average area under the curve (AUC) from five-fold cross validation. Multi-slice features were superior to single for producing robust radiomic features (67 vs. 61). The optimal image quantisation method used bin widths of 25 or 30. Incorporating our top 10 non-redundant robust radiomics features into ElasticNet achieved an AUC of 0.73 and accuracy of 69% (compared to carotid calcification alone [AUC: 0.44, accuracy: 46%]). Our results provide key information for introducing carotid CT radiomics into clinical practice. If validated prospectively, our robust carotid radiomic set could improve stroke prediction and target therapies to those at highest risk

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P < 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Choline Kinase Alpha as an Androgen Receptor Chaperone and Prostate Cancer Therapeutic Target.

BACKGROUND: The androgen receptor (AR) is a major drug target in prostate cancer (PCa). We profiled the AR-regulated kinome to identify clinically relevant and druggable effectors of AR signaling. METHODS: Using genome-wide approaches, we interrogated all AR regulated kinases. Among these, choline kinase alpha (CHKA) expression was evaluated in benign (n = 195), prostatic intraepithelial neoplasia (PIN) (n = 153) and prostate cancer (PCa) lesions (n = 359). We interrogated how CHKA regulates AR signaling using biochemical assays and investigated androgen regulation of CHKA expression in men with PCa, both untreated (n = 20) and treated with an androgen biosynthesis inhibitor degarelix (n = 27). We studied the effect of CHKA inhibition on the PCa transcriptome using RNA sequencing and tested the effect of CHKA inhibition on cell growth, clonogenic survival and invasion. Tumor xenografts (n = 6 per group) were generated in mice using genetically engineered prostate cancer cells with inducible CHKA knockdown. Data were analyzed with χ(2) tests, Cox regression analysis, and Kaplan-Meier methods. All statistical tests were two-sided. RESULTS: CHKA expression was shown to be androgen regulated in cell lines, xenografts, and human tissue (log fold change from 6.75 to 6.59, P = .002) and was positively associated with tumor stage. CHKA binds directly to the ligand-binding domain (LBD) of AR, enhancing its stability. As such, CHKA is the first kinase identified as an AR chaperone. Inhibition of CHKA repressed the AR transcriptional program including pathways enriched for regulation of protein folding, decreased AR protein levels, and inhibited the growth of PCa cell lines, human PCa explants, and tumor xenografts. CONCLUSIONS: CHKA can act as an AR chaperone, providing, to our knowledge, the first evidence for kinases as molecular chaperones, making CHKA both a marker of tumor progression and a potential therapeutic target for PCa.This work was supported by a Cancer Research UK program grant (to DEN) and also by the US Department of Defense (Prostate Cancer Research Program Transformative Impact Award, grant ID W81XWH-13-2-0093; WDT and SMD), PCFA/Cancer Australia/Movember (grant IDs 1012337 and 1043482; WDT and LAS), Cancer Australia (grant ID 1043497; WDT and JC) and The Ray and Shirl Norman Cancer Research Trust (WDT and LAS). The Dame Roma Mitchell Cancer Research Laboratories were supported by an establishment grant from the PCFA (ID 2011/0452). FO was supported by a PhD project grant from Prostate Cancer UK (S10-10). LAS is supported by a Young Investigator Award from the Prostate Cancer Foundation (the Foundation 14 award)

CameraRoach : a WiFi- and camera-enabled cyborg cockroach for search and rescue

We describe here our design and implementation of a cyborg insect, called CameraRoach, with onboard camera feedback that can be navigated via remote control providing a first-person view. The camera pack is mounted on the Madagascar hissing cockroach, which is small enough to fit into crevices but also can carry a printed circuit boards with power, communication, and sensor components (visual camera). For navigating the cockroach, we implemented a unique electronic backpack neural stimulator, which allows the cockroach to be maneuvered on a desired path with a joystick. A high-resolution wireless camera, also included in the backpack, sends live images via a WiFi (Wireless Fidelity) network. We present the results of an evaluation experiment with the CameraRoach and compare it with the other state of the art systems like the Beetle-Cam