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"I feel full with shame":A qualitative perspective on gastric interoceptive sensibility
Get PDFBACKGROUND: "Am I hungry? Did I overeat at lunch?" Gastric interoception - the sensing, interpretation, and regulation of signals from the gastrointestinal system - is central to daily behavior and homeostasis. Dysfunctional gastric interoception has been proposed as a maintenance factor in both eating disorders and gastrointestinal symptoms. However, no qualitative research has explored how individuals across these groups, and the general population, subjectively experience gastrointestinal signals, known as gastric interoceptive sensibility. This study aimed to investigate how gastric sensations are sensed, interpreted, and regulated among individuals with eating disorders, gastric disorders, and those without such diagnoses, focusing on identifying shared experiences. METHODS: Fifteen semi-structured focus groups (n = 96) were conducted. Transcripts underwent hybrid deductive and inductive thematic analysis. FINDINGS: Four key themes were identified. In "Sensations in the Interoceptive Body", participants described hunger and fullness as physically aversive or reported an absence of cues related to satiation. "Perceiving the Interoceptive Body" captured the noticing, interpreting, attending to, and reacting to sensations of hunger, satiation, and fullness. In "Affective Experiences of the Interoceptive Body", participants discussed how these sensations influenced emotional states positively, negatively, or not at all. "Responding to the Interoceptive Body" described participants strategies relating to relief-seeking, compensation, acceptance, distraction, and body checking in response to gastric sensations. DISCUSSION: These findings shed light on the nuanced components of gastric interoceptive sensibility and suggest that individuals vary in how they experience and manage gastric signals. This work may inform interoceptive exposure therapies targeting maladaptive interpretations and regulation strategies in eating and gastrointestinal symptoms
Contribution to the Discussion of 'New tools for network time series with an application to COVID-19 hospitalisations' by Nason et al.
No full textDelirium identification, prevention and management in intensive care units in England, Wales and Northern Ireland: a survey of practice
Get PDFIntroduction
Delirium is the most common sign of acute brain dysfunction and is prevalent in ICUs. This work is part of a UK National Institute of Health and Social Care Research-funded Programme Development Grant to identify optimal approaches to prevent, identify and manage ICU delirium in the UK. This survey aimed to provide a baseline for contemporary practice.
Methods
A structured online survey was designed and sent to all ICUs in England, Wales and Northern Ireland, identified through the Intensive Care National Audit and Research Centre Case Mix Programme. Participants were asked to provide a response that reflected ICU-level care.
Results
The ICU participant response rate was 249/268 (93%). Of these, 222/249 (89%) ICUs screened for ICU delirium routinely and 208/222 (94%) used the CAM-ICU tool. Delirium care packages were applied by 125/249 (50%) ICUs, but 81/125 (68%) conveyed that this was not consistent for all patients. Both antipsychotics and benzodiazepines are used commonly to manage delirium. All respondents stated that early mobilisation; early removal of invasive catheters; maintenance of hearing aids/glasses; regular mealtimes; and daytime activity were used as non-pharmaceutical delirium management strategies. Enhanced follow-up was reported by 195/249 (79%) respondents, either routinely or for selected cases.
Discussion
Only half of UK ICUs use a standardised care package to prevent and manage ICU delirium, with inconsistent implementation. Future work should focus on the development and evaluation of an evidence-based and sustainable care package
Household food insecurity and its impact on child and adolescent health outcomes in Western high-income countries: a rapid review of mechanisms and associations
Get PDFObjective
The primary aim of this rapid review was to provide a summary of the mechanisms by which HFI is associated with child and adolescent health outcomes. The secondary aim was to identify key HFI determinants, provide an updated account of HFI-associated child/ adolescent health outcomes and build a conceptual map to illustrate and consolidate the findings.
Design
A rapid review was performed using EMBASE, Medline, Web of Science and The Cochrane library. Inclusion criteria were observational High- income English-language studies, studies evaluating the mechanisms and associations between HFI and child health outcomes using statistical methods.
Setting
High income English-speaking countries.
Participants
Child (3-10 years) and adolescent populations (11-24 years) and their parents, if appropriate.
Results
Eight studies reported on the mechanisms by which HFI is related to child health outcomes, suggesting that maternal mental health and parenting stress play mediating roles between HFI and child/adolescent mental health, behaviour and child weight status. Sixty studies reported on associations between HFI and various child health outcomes. HFI had significant impact on diet and mental health, which appeared to be interrelated. Sociodemographic factors were identified as determinants of HFI and moderated the relationship between HFI and child/adolescent health outcomes.
Conclusions
There is a gap in the evidence explaining the mechanistic role of diet quality between HFI and child weight status, as well as the interplay between diet, eating behaviours and mental health on physical child health outcomes. The conceptual map highlights opportunities for intervention and policy evaluations using complex systems approaches
When algorithms and human experts contradict, whom do users follow?
Get PDFDrawing on the theory of planned behavior and the risk-taking theory, the objective of this research is to investigate how attitude toward algorithms, attitude toward humans, and willingness to take risks affect user intention to follow in the situation where recommendations from algorithms and human experts contradict. Set in the context of investment decision-making, a 2 (attitude toward algorithms: algorithm aversion vs. algorithm appreciation) x 2 (attitude toward human experts: unfavorable vs. favorable) x 2 (willingness to take risks: low vs. high) quasi-experiment was conducted online (N=804) where contradictory recommendations were presented from algorithms and human sources. Favorable attitudes toward algorithms and human experts promoted the intention to follow algorithm-generated and human-generated recommendations, respectively. A high willingness to take risks increased the intention to follow regardless of the source of the recommendations. Moreover, willingness to take risks moderated the relationship between attitude toward algorithms and the intention to follow the algorithm-generated recommendation as well as that between attitude toward humans and the intention to follow the human-generated recommendation. While the literature has shed light on how individuals evaluate recommendations from algorithms and humans separately, this is one of the earliest efforts to study the situation where algorithms contradict humans
Nested resolution mesh-graph CNN for automated extraction of liver surface anatomical landmarks
Get PDFThe anatomical landmarks on the liver (mesh) surface, including the falciform ligament and liver ridge, are composed of triangular meshes of varying shapes, sizes, and positions, making them highly complex. Extracting and segmenting these landmarks is critical for augmented reality-based intraoperative navigation and monitoring. The key to this task lies in comprehensively understanding the overall geometric shape and local topological information of the liver mesh. However, due to the liver’s variations in shape and appearance, coupled with limited data, deep learning methods often struggle with automatic liver landmark segmentation. To address this, we propose a two-stage automatic framework combining mesh-CNN and graph-CNN. In the first stage, dynamic graph convolution (DGCNN) is employed on low-resolution meshes to achieve rapid global understanding, generating initial landmark proposals at two levels, “dilation” and “erosion”, and mapping them onto the original high-resolution surface. Subsequently, a refinement network based on mesh convolution fuses these landmark proposals from edge features along the local topology of the high-resolution mesh surface, producing refined segmentation results. Additionally, we incorporate an anatomy-aware Dice loss to address resolution imbalance and better handle sparse anatomical regions. Extensive experiments on two liver datasets, both in-distribution and out-of-distribution, demonstrate that our method accurately processes liver meshes of different resolutions, outperforming state-of-the-art methods. The reconstructed liver mesh dataset and the source code are available at https://github.com/xukun-zhang/MeshGraphCNN
Verification of Multi-Model Stochastic Systems
No full textGiven its ability to analyse stochastic models ranging from discrete and continuous-time Markov chains to Markov decision processes and stochastic games, probabilistic model checking (PMC) is widely used to verify system dependability and performance properties. However, modelling the behaviour of, and verifying these properties for many software-intensive systems requires the joint analysis of multiple interdependent stochastic models of different types, which existing PMC techniques and tools cannot handle. To address this limitation, we introduce a tool-supported UniversaL stochasTIc Modelling, verificAtion and synThEsis (ULTIMATE) framework that supports the representation, verification and synthesis of heterogeneous multi-model stochastic systems with complex model interdependencies. Through its unique integration of multiple PMC paradigms, and underpinned by a novel verification method for handling model interdependencies, ULTIMATE unifies—for the first time—the modelling of probabilistic and nondeterministic uncertainty, discrete and continuous time, partial observability, and the use of both Bayesian and frequentist inference to exploit domain knowledge and data about the modelled system and its context. A comprehensive suite of case studies and experiments confirm the generality and effectiveness of our novel verification framework
BAFF-R Expression as a Potential Biomarker Associated with COVID-19 Vaccine Non-Responsiveness in Antibody-Deficient Patients
No full textINTRODUCTION: Patients with primary and secondary antibody deficiencies exhibit variable responses to vaccination, with many failing to mount optimal immunity to SARS-CoV-2. Mechanisms underpinning vaccine non-responsiveness remain poorly defined and unpredictable. We hypothesised that B-cell-intrinsic features are associated with SARS-CoV-2 vaccine failure.
METHODS: Peripheral B-cells from 49 patients enrolled in the COVID-19 in Antibody Deficiency (COV-AD) study underwent a validated in vitro B-cell differentiation assay. We assessed plasmablast and plasma cell (PC) generation, immunoglobulin production, immunoglobulin heavy chain (IGH) repertoire diversity, and BAFF-R expression.
RESULTS: Vaccine non-responders displayed reduced IgA class-switched immunoglobulin production in vitro compared to healthy controls and responders. Moreover, while the relative percentage of PC output was comparable between groups, the overall number of cells obtained from non-responders was reduced. Most non-responders and a subset of responders exhibited reduced BAFF-R surface expression at baseline compared to healthy controls, though with considerable overlap between groups. BAFF-R transcript levels partially corresponded with surface expression but varied and did not clearly distinguish response. No compensatory upregulation of alternative BAFF receptors or elevated serum BAFF was observed. IGH repertoire analysis revealed preserved diversity among patients.
CONCLUSIONS: Diminished BAFF-R expression is associated with vaccine non-responsiveness and may indicate underlying B-cell-intrinsic defects. BAFF-R shows potential as a candidate biomarker that merits further validation in larger, multicentre cohorts to determine its clinical utility for stratifying patients at risk of vaccine failure. These findings suggest that the BAFF/BAFF-R axis may play an important role in vaccine-induced humoral immunity in antibody-deficient patients, warranting further mechanistic investigation
Task-dependent cognitive effects of intermittent theta-burst stimulation across the adult lifespan
Get PDFIntermittent Theta-Burst Stimulation (iTBS) of the Dorsolateral Prefrontal Cortex (DLPFC) has the potential to enhance cognitive function by inducing long-term potentiation-like effects, modulating cortical excitability and network plasticity. However, the precise effects of iTBS across specific cognitive domains, age groups, and hemispheres remains unclear. Fifty-three adults aged 19-73 years, participated in a within-subject crossover designed study, receiving iTBS to the left and right DLPFC across two sessions, spaced one week apart. Cognitive tasks assessed four cognitive domains of attention, working memory, sequence learning, and inhibition. Processing speed and accuracy were assessed using time and hemisphere as fixed effects and age as a continuous variable in linear mixed effects and Bayesian models. There was an overall slowing of reaction time with age across all tasks. Brain stimulation reduced reaction times in attention and working memory tasks in all participants. Accuracy improved for working memory following iTBS, with a right hemisphere advantage. ITBS of the DLPFC influences cognition in a task-dependent manner. Improvements in attention were not influenced by hemisphere, suggesting a facilitation of top-up processing after DLPFC stimulation. Working memory enhancements were a dominant effect, especially following right hemisphere iTBS stimulation supporting lateralized optimization of visuospatial storage. These findings highlight iTBS as a potential noninvasive tool for cognitive enhancement. Future research should explore the longevity of these effects and their applicability in clinical populations
Quantifying environmental impacts of cold chain interruptions using coupled CFD and LCA
Get PDFCold chain interruptions (CCIs) can alter energy use and emissions in cold chain logistics (FCCL), yet their impacts are often overlooked in life cycle assessment (LCA). This study integrates computational fluid dynamics (CFD) with LCA to quantify CCI-related impacts, through a gate-to-gate case study of Zigui navel oranges from precooling to cold storage at destination. CFD reconstructs temperature histories, which are converted to stage-specific refrigeration energy usage and fed into LCA to quantify environmental burdens. Two types of CCIs were evaluated: refrigeration pauses have minimal effects, while ambient exposures, especially after precooling, increase global warming potential (GWP) by up to 5.94%. Overall, refrigerated transport contributes over 85% of total GWP, with a distance–GWP slope of 0.0014 kg CO₂-eq/km. Replacing diesel with B5 biodiesel (5% fatty acid methyl esters) reduces GWP by 3.95%. This CFD–LCA framework enables more accurate CCI impact assessments, supporting the design of sustainable FCCL systems
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