Diagnostic overshadowing in systemic lupus erythematosus (SLE): A qualitative study

Objectives: SLE diagnostic journeys can be protracted, with negative impacts on long-term health. This study explored the role of diagnostic overshadowing (DOS) in delaying SLE diagnoses. Methods: A qualitative analysis of 268 completed SLE patient surveys and 25 in-depth interviews purposively selected from the 2018-2021 Cambridge University Systemic Autoimmune Rheumatic Disease (SARD) studies. Results: The majority of participants appear to have experienced DOS and there were indications that sustained DOS (S-DOS) may add years to some SLE diagnostic journeys. Symptom misattributions which contributed to S-DOS included: (1) “Medical mystery”, particularly when the clinician indicated that it was too expensive to keep investigating. (2) Negative misattributions (e.g. “nothing seriously wrong”), often due to a failure to connect multiple symptoms as possible indicators of an underlying condition. (3) Diagnostic roadblocks, including, in the case of some participants, a mental health, psychosomatic, ME/CFS or fibromyalgia (mis)diagnosis. (4) Moral misattributions, such as to “malingering”, which could undermine patient help-seeking and/or clinician help-giving. Conclusion: Our data suggests that DOS may be an important factor in diagnostic delay in patients with SLE

Guided latent diffusion for universal medical image segmentation

Deep learning based medical segmentation still presents a great challenge due to the lack of large-scale datasets in the medical domain. The existing publicly available datasets vary significantly in terms of imaging modalities and target anatomies. This paper presents a novel guided latent diffusion model for universal medical segmentation, capable of segmenting diverse anatomical structures using a single and unified architecture. Given a Contrastive Language-Image Pretraining (CLIP) embedding specifying the target anatomical structure, the proposed model leverages a collection of datasets covering the diverse structures which can segment any anatomical targets that are presented in the aggregated data. By performing diffusion fully in latent space, we achieve comparable results to pixel-space diffusion with significantly lower computational cost. The proposed methods demonstrates competitive performance against existing deep learning-based discriminative approaches on several benchmarks. Furthermore, it shows strong generalization capabilities on unseen datasets

Predator–Prey Movement Interactions: Jaguars and Peccaries in the Spotlight

Understanding how landscape structure influences predator–prey dynamics is critical for conservation. This study analyzed jaguar‐peccary interactions, revealing uncommon close distances and prevalent 3–5 km ranges, especially away from grasslands. Low peccary densities increased interactions. Findings inform conservation strategies, highlighting landscape structure and prey density roles in maintaining Pantanal's balance

Comparison of Systemic Inflammatory Indices With the Oncotype DX Recurrence Score and the Nottingam Prognostic Index in Early Hormone Receptor Positive Ductal Breast Cancer

Background: Adjuvant therapy decisions in hormone receptor positive and human epidermal growth factor receptor 2 negative breast cancer are evolving. The introduction of gene panel testing has significantly reduced the number of patients recommended for chemotherapy by up to two thirds. However, these tests are expensive, highlighting the need to identify low-risk genomic breast cancer cases before testing, which could represent a significant economic impact. The use of systemic inflammatory indices has shown promise as prognostic markers in early breast cancer. We investigated the potential utility of four systemic inflammatory indices with the Nottingham Prognostic Index to predict the Oncotype DX® recurrence scores threshold level (low score or high score), in a cohort of women aged 50 and over with node negative invasive ductal carcinoma of the breast. Methods: A retrospective review of patients who had Oncotype DX® Recurrence Score testing from 2007 to 2021 were identified. After exclusions, the final sample size was 245. Clinicopathological features were collected to calculate the Nottingham Prognostic Index. The systemic inflammatory indices were estimated from preoperative peripheral blood samples. Results: 22.4% of the cohort had a Recurrence Score in the higher risk group. This cohort had a greater percentage of grade 3 tumours, progesterone receptor negativity, higher Nottingham Prognostic Scores, and inflammatory indices ratios than the lower risk group. A decision tree incorporating the Neutrophil Lymphocyte Ratio with clinicopathological features showed potential as an indicator of a high Oncotype DX® RS score, such that further investigation is warranted to assess whether Recurrence Score testing could be triaged in certain cohorts of patients. In this cohort, 38% of patients might be able to avoid genomic testing based on the decision tree analysis. Conclusion: Utility of the inflammatory indices with clinicopathological features may help triage gene panel testing

Reaction–Diffusion Problems on Time-Periodic Domains

Reaction-diffusion equations are studied on bounded, time-periodic domains with zero Dirichlet boundary conditions. The long-time behaviour is shown to depend on the principal periodic eigenvalue of a transformed periodic-parabolic problem. We prove upper and lower bounds on this eigenvalue under a range of different assumptions on the domain, and apply them to examples. The principal eigenvalue is considered as a function of the frequency, and results are given regarding its behaviour in the small and large frequency limits. A monotonicity property with respect to frequency is also proven. A reaction-diffusion problem with a class of monostable nonlinearity is then studied on a periodic domain, and we prove convergence to either zero or a unique positive periodic solution

Stochastic weight matrix dynamics during learning and Dyson Brownian motion

We demonstrate that the update of weight matrices in learning algorithms can be described in the framework of Dyson Brownian motion, thereby inheriting many features of random matrix theory. We relate the level of stochasticity to the ratio of the learning rate and the minibatch size, providing more robust evidence to a previously conjectured scaling relationship. We discuss universal and nonuniversal features in the resulting Coulomb gas distribution and identify the Wigner surmise and Wigner semicircle explicitly in a teacher-student model and in the (near-)solvable case of the Gaussian restricted Boltzmann machine

A tri-axial acceleration-based behaviour template for translocated birds: the case of the Asian houbara bustard

Understanding the behaviours and time budgets of translocated animals post-release has the potential to improve rearing and release protocols, and therefore survival rate. Otididae (bustards) inhabit open landscapes across the Middle East and Asia, are highly mobile on the ground and have similar lifestyles and body plans. The Asian houbara Chlamydotis macqueenii is a bustard of conservation concern inhabiting the Middle East to Central Asia and is frequently reared in captivity for population management. We deployed tri-axial accelerometers on 20 captive Asian houbaras in two seasons to catalogue basic behaviours, provide a template applicable to other bustard species and examine seasonal differences in behaviour. We created Boolean algorithms to define the following behaviours using raw acceleration data and derived metrics: stationary, eating/drinking and locomotion. We used video recordings to cross-validate the algorithms, yielding recalls from 95 to 97%, and precisions between 97 and 98%. Houbaras spent significantly more time ‘stationary' and less time on ‘locomotion' in summer (June) compared to spring (March). Simple Boolean algorithms proved useful in identifying several behaviours and have the potential to be applicable to other bustard species, in captivity and in the wild post-release

Decision making in pelvic exenteration for locally advanced and locally recurrent rectal cancer

This thesis relates to locally advanced and locally recurrent rectal cancer (LARC and LRRC) and examines the decision-making process in patients contemplating potential surgical cure with pelvic exenteration (PE). It will determine the factors that influence the decision to undergo or to decline surgery for both patients and clinicians. The aim of this research is to produce a validated Patient Decision Aid (PtDA) for patients with LARC and LRRC to facilitate the shared decision making (SDM) process. Objectives1.To undertake a systematic literature review to identify existing validated PtDAs and grey literature including online forums for PE.2.To explore whether English patient information leaflets (PILs) exist at centres worldwide performing exenterative surgery.3.To explore through qualitative interviews patient views regarding what factors are important to them when making decisions regarding PE surgery.4.To explore through qualitative interviews with clinicians their views regarding what they perceive to be important for the patient to consider when making decisions regarding PE surgery.5.To use the findings from the literature review and qualitative interviews to inform the first stage development of a PtDA that outlines treatment options for patients with LARC or LRRC.6.To develop the PtDA using key stakeholders.7.To assess the content and face validity of the PtDA.8.To assess the reliability and validity of the PtDA.9.To assess the feasibility and efficacy of the PtDA