Symptoms of adult chronic and acute leukaemia before diagnosis: large primary care case-control studies using electronic records

This is the author accepted manuscript. The final version is available from Royal College of General Practitioners via the DOI in this record.BACKGROUND: Leukaemia is the eleventh commonest UK cancer. The four main subtypes have different clinical profiles, particularly between chronic and acute types. AIM: To identify the symptom profiles of chronic and acute leukaemia in adults in primary care. DESIGN AND SETTING: Matched case-control studies using Clinical Practice Research Datalink records. METHOD: Putative symptoms of leukaemia were identified in the year before diagnosis. Conditional logistic regression was used for analysis, and positive predictive values (PPVs) were calculated to estimate risk. RESULTS: Of cases diagnosed between 2000 and 2009, 4655 were aged ≥40 years (2877 chronic leukaemia (CL), 937 acute leukaemia (AL), 841 unreported subtype). Ten symptoms were independently associated with CL, the three strongest being: lymphadenopathy (odds ratio [OR] 22, 95% confidence interval [CI] = 13 to 36), weight loss (OR 3.0, 95% CI = 2.1 to 4.2), and bruising (OR 2.3, 95% CI = 1.6 to 3.2). Thirteen symptoms were independently associated with AL, the three strongest being: nosebleeds and/or bleeding gums (OR 5.7, 95% CI = 3.1 to 10), fever (OR 5.3, 95% CI = 2.7 to 10), and fatigue (OR 4.4, 95% CI = 3.3 to 6.0). No individual symptom or combination of symptoms had a PPV >1%. CONCLUSION: The symptom profiles of CL and AL have both overlapping and distinct features. This presents a dichotomy for GPs: diagnosis, by performing a full blood count, is easy; however, the symptoms of leukaemia are non-specific and of relatively low risk. This explains why many leukaemia diagnoses are unexpected findings.This article presents independent research funded by the National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0608– 10045). Fiona M Walter is part-funded by an NIHR Clinician Scientist award. Richard D Neal is part-funded by Public Health Wales and Betsi Cadwaladr University Health Board. Willie Hamilton is supported by the NIHR Collaboration for Leadership in Applied Health Research and Care South West Peninsula at the Royal Devon and Exeter NHS Foundation Trust. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health

The risk of oesophago-gastric cancer in symptomatic patients in primary care: a large case-control study using electronic records

ArticleBACKGROUND: Over 15 000 new oesophago-gastric cancers are diagnosed annually in the United Kingdom, with most being advanced disease. We identified and quantified features of this cancer in primary care. METHODS: Case-control study using electronic primary-care records of the UK patients aged ≥40 years was performed. Cases with primary oesophago-gastric cancer were matched to controls on age, sex and practice. Putative features of cancer were identified in the year before diagnosis. Odds ratios (ORs) were calculated for these features using conditional logistic regression, and positive predictive values (PPVs) were calculated. RESULTS: A total of 7471 cases and 32 877 controls were studied. Sixteen features were independently associated with oesophago-gastric cancer (all P5% in patients ≥55 years was for dysphagia. In patients <55 years, all PPVs were <1%. CONCLUSION: Symptoms of oesophago-gastric cancer reported in secondary care were also important in primary care. The results should inform guidance and commissioning policy for upper GI endoscopy

A novel GLM-based method for the Automatic IDentification of functional Events (AIDE) in fNIRS data recorded in naturalistic environments.

Recent technological advances have allowed the development of portable functional Near-Infrared Spectroscopy (fNIRS) devices that can be used to perform neuroimaging in the real-world. However, as real-world experiments are designed to mimic everyday life situations, the identification of event onsets can be extremely challenging and time-consuming. Here, we present a novel analysis method based on the general linear model (GLM) least square fit analysis for the Automatic IDentification of functional Events (or AIDE) directly from real-world fNIRS neuroimaging data. In order to investigate the accuracy and feasibility of this method, as a proof-of-principle we applied the algorithm to (i) synthetic fNIRS data simulating both block-, event-related and mixed-design experiments and (ii) experimental fNIRS data recorded during a conventional lab-based task (involving maths). AIDE was able to recover functional events from simulated fNIRS data with an accuracy of 89%, 97% and 91% for the simulated block-, event-related and mixed-design experiments respectively. For the lab-based experiment, AIDE recovered more than the 66.7% of the functional events from the fNIRS experimental measured data. To illustrate the strength of this method, we then applied AIDE to fNIRS data recorded by a wearable system on one participant during a complex real-world prospective memory experiment conducted outside the lab. As part of the experiment, there were four and six events (actions where participants had to interact with a target) for the two different conditions respectively (condition 1: social-interact with a person; condition 2: non-social-interact with an object). AIDE managed to recover 3/4 events and 3/6 events for conditions 1 and 2 respectively. The identified functional events were then corresponded to behavioural data from the video recordings of the movements and actions of the participant. Our results suggest that "brain-first" rather than "behaviour-first" analysis is possible and that the present method can provide a novel solution to analyse real-world fNIRS data, filling the gap between real-life testing and functional neuroimaging

The diagnostic value of symptoms of possible oesophagogastric cancers: a systematic review and meta-analysis

Abstracts of the Cancer and Primary Care Research International (Ca-PRI) Network Seventh Annual Meeting: New Partnerships in Primary Care Cancer Research. June 10-13, 2014, Winnipeg, Manitoba, CanadaOral presentation abstractThis is the author accepted manuscript. The final version is available from Wiley via the DOI in this record

Quantification of inter-brain coupling: A review of current methods used in haemodynamic and electrophysiological hyperscanning studies

Hyperscanning is a form of neuroimaging experiment where the brains of two or more participants are imaged simultaneously whilst they interact. Within the domain of social neuroscience, hyperscanning is increasingly used to measure inter-brain coupling (IBC) and explore how brain responses change in tandem during social interaction. In addition to cognitive research, some have suggested that quantification of the interplay between interacting participants can be used as a biomarker for a variety of cognitive mechanisms aswell as to investigate mental health and developmental conditions including schizophrenia, social anxiety and autism. However, many different methods have been used to quantify brain coupling and this can lead to questions about comparability across studies and reduce research reproducibility. Here, we review methods for quantifying IBC, and suggest some ways moving forward. Following the PRISMA guidelines, we reviewed 215 hyperscanning studies, across four different brain imaging modalities: functional near-infrared spectroscopy (fNIRS), functional magnetic resonance (fMRI), electroencephalography (EEG) and magnetoencephalography (MEG). Overall, the review identified a total of 27 different methods used to compute IBC. The most common hyperscanning modality is fNIRS, used by 119 studies, 89 of which adopted wavelet coherence. Based on the results of this literature survey, we first report summary statistics of the hyperscanning field, followed by a brief overview of each signal that is obtained from each neuroimaging modality used in hyperscanning. We then discuss the rationale, assumptions and suitability of each method to different modalities which can be used to investigate IBC. Finally, we discuss issues surrounding the interpretation of each method

The present and future use of functional near‐infrared spectroscopy (fNIRS) for cognitive neuroscience

The past few decades have seen a rapid increase in the use of functional near‐infrared spectroscopy (fNIRS) in cognitive neuroscience. This fast growth is due to the several advances that fNIRS offers over the other neuroimaging modalities such as functional magnetic resonance imaging and electroencephalography/magnetoencephalography. In particular, fNIRS is harmless, tolerant to bodily movements, and highly portable, being suitable for all possible participant populations, from newborns to the elderly and experimental settings, both inside and outside the laboratory. In this review we aim to provide a comprehensive and state‐of‐the‐art review of fNIRS basics, technical developments, and applications. In particular, we discuss some of the open challenges and the potential of fNIRS for cognitive neuroscience research, with a particular focus on neuroimaging in naturalistic environments and social cognitive neuroscience

Dynamics of direct inter-pack encounters in endangered African wild dogs

Aggressive encounters may have important life history consequences due to the potential for injury and death, disease transmission, dispersal opportunities or exclusion from key areas of the home range. Despite this, little is known of their detailed dynamics, mainly due to the difficulties of directly observing encounters in detail. Here, we describe detailed spatial dynamics of inter-pack encounters in African wild dogs (Lycaon pictus), using data from custom-built high-resolution GPS collars in 11 free-ranging packs. On average, each pack encountered another pack approximately every 7 weeks and met each neighbour twice each year. Surprisingly, intruders were more likely to win encounters (winning 78.6% of encounters by remaining closer to the site in the short term). However, intruders did tend to move farther than residents toward their own range core in the short-term (1 h) post-encounter, and if this were used to indicate losing an encounter, then the majority (73.3%) of encounters were won by residents. Surprisingly, relative pack size had little effect on encounter outcome, and injuries were rare (<15% of encounters). These results highlight the difficulty of remotely scoring encounters involving mobile participants away from static defendable food resources. Although inter-pack range overlap was reduced following an encounter, encounter outcome did not seem to drive this, as both packs shifted their ranges post-encounter. Our results indicate that inter-pack encounters may be lower risk than previously suggested and do not appear to influence long-term movement and ranging

From the Bloch sphere to phase space representations with the Gottesman-Kitaev-Preskill encoding

In this work, we study the Wigner phase-space representation of qubit states encoded in continuous variables (CV) by using the Gottesman-Kitaev-Preskill (GKP) mapping. We explore a possible connection between resources for universal quantum computation in discrete-variable (DV) systems, i.e. non-stabilizer states, and negativity of the Wigner function in CV architectures, which is a necessary requirement for quantum advantage. In particular, we show that the lowest Wigner logarithmic negativity of qubit states encoded in CV with the GKP mapping corresponds to encoded stabilizer states, while the maximum negativity is associated with the most non-stabilizer states, H-type and T-type quantum states.Comment: (v1) Accepted for publication in the Springer's "Mathematics for Industry" series. (v2) Typo in the abstract fixed; URL of the conference where the paper has been presented added: International Symposium on Mathematics, Quantum Theory, and Cryptography (MQC), held in September 2019 in Fukuoka, Japan (https://www.mqc2019.org/mqc2019/program

Classical kinetic energy, quantum fluctuation terms and kinetic-energy functionals

We employ a recently formulated dequantization procedure to obtain an exact expression for the kinetic energy which is applicable to all kinetic-energy functionals. We express the kinetic energy of an N-electron system as the sum of an N-electron classical kinetic energy and an N-electron purely quantum kinetic energy arising from the quantum fluctuations that turn the classical momentum into the quantum momentum. This leads to an interesting analogy with Nelson's stochastic approach to quantum mechanics, which we use to conceptually clarify the physical nature of part of the kinetic-energy functional in terms of statistical fluctuations and in direct correspondence with Fisher Information Theory. We show that the N-electron purely quantum kinetic energy can be written as the sum of the (one-electron) Weizsacker term and an (N-1)-electron kinetic correlation term. We further show that the Weizsacker term results from local fluctuations while the kinetic correlation term results from the nonlocal fluctuations. For one-electron orbitals (where kinetic correlation is neglected) we obtain an exact (albeit impractical) expression for the noninteracting kinetic energy as the sum of the classical kinetic energy and the Weizsacker term. The classical kinetic energy is seen to be explicitly dependent on the electron phase and this has implications for the development of accurate orbital-free kinetic-energy functionals. Also, there is a direct connection between the classical kinetic energy and the angular momentum and, across a row of the periodic table, the classical kinetic energy component of the noninteracting kinetic energy generally increases as Z increases.Comment: 10 pages, 1 figure. To appear in Theor Chem Ac