Fatigue is a common problem that significantly affects intensive care unit (ICU) survivors' physical, psychological, and social functioning. ICU survivors often experience a loss of self-worth and identity, struggle to return to their normal roles, and face ongoing challenges with cognitive and emotional recovery. Despite its profound impact, there are limited rehabilitation interventions targeting this population. This research aims to evaluate the acceptability of implementing the Fatigue After CriTical illness (FACT) self-management intervention into usual care for patients experiencing fatigue after critical illness and the feasibility of the intervention for a future clinical trial. This is a multicentre, open-label, feasibility randomised controlled trial (RCT) with an embedded qualitative evaluation, conducted across. Seventy participants recruited from three United Kingdom (UK) National Health Service (NHS) Trusts will be randomised either to the FACT self-management intervention following hospital discharge, which focuses on fatigue management, goal setting, and personal action planning, in addition to usual care, or to a control group receiving usual care alone. The intervention will be accessible for six months and will include a 30-min phone or video call with an ICU follow-up healthcare professional (HCP) in the third month. HCPs will attend an online training session and follow a protocol to guide patients in goal setting. Outcome assessments will occur at baseline, three months, and six months post-randomisation to evaluate feasibility and acceptability. Semi-structured interviews with patients and HCPs will explore their experiences and acceptability outcomes at 6months. This study aims to provide insights into the feasibility and acceptability of the FACT intervention, with the goal of improving fatigue management among survivors of critical illness. Preliminary findings will inform the design of a larger-scale RCT to evaluate its effectiveness in enhancing recovery from critical illness in patients who are experiencing fatigue. Study ID: ISRCTN1381359 . Date registered: 01/04/2025.

Gene therapies for severe genetic disease are often highly expensive. In deciding whether or when to provide them, one ethical consideration is the benefit of treatment relative to cost. Another and separate consideration is concern for medical need and the desire to benefit those who are worse off. The latter is a prioritarian concern. But how should we apply prioritarianism to decisions about gene therapy, particularly since such treatments might affect which individuals come into existence? That question is the main aim of this paper. I focus on a particular version of prioritarianism articulated and defended by Derek Parfit. My primary aim is to explore how, if we were to adopt such an account, we should interpret and practically apply this in medical ethics. In doing so, I assess how it fits with other elements of Parfit's philosophy. I defend a new "time-relative" version of priority.

Chromosomal instability (CIN), a pervasive feature of esophageal adenocarcinoma (EAC), drives tumor aggressiveness and metastasis. CIN stimulates the cGAS–STING pathway, typically linked to antitumor immunity. However, despite the high CIN burden in EAC, the cGAS–STING pathway remains largely intact. To address this paradox, we interrogated multiple esophageal cancer models, finding myeloid-attracting chemokines—with CXCL8 as a prominent hit—as conserved CIN-driven targets in EAC. Using multiplexed immunofluorescence microscopy, we quantified ongoing CIN in human EAC tumors by measuring cGAS-positive micronuclei, validated by whole-genome sequencing. Coupling in situ CIN detection with single-nucleus RNA sequencing and multiplex immunophenotyping of human EAC, we link CIN to tumor-intrinsic innate immune activation, CXCL8 expression, and myeloid cell–mediated immunosuppression. In patients with EAC, CINhigh, myeloid-dominated tumors correlate with poor outcomes and aberrant cGAS–STING signaling. These insights explain the counterintuitive maintenance of cGAS–STING and highlight the disruption of the CIN–cGAS–inflammation axis as a potential therapeutic strategy in EAC.

Advances in artificial intelligence (AI) systems suggest that they can be used to improve healthcare outcomes via diagnosis, prognostication, patient management, risk assessment, etc. AI systems could be particularly useful in emergency healthcare (EHC) by synthesizing data to generate accurate conclusions rapidly. But the use of AI in EHC raises ethical, legal, and social concerns. The present study undertakes a scoping review to collate, map, and synthesize existing literature on the ethical, legal and social issues (ELSIs) associated with AI in EHC. The aim was to assess which ELSI issues were recognized and analyzed in the current literature and which were under-explored. Online databases were used to identify papers published on the identified topic. An initial search strategy of IEEE, Pubmed, and Scopus yielded 156 unique records; 40 records underwent textual review, after which another 7 were excluded due to scope. The final 33 were analysed for content. Overall, the literature was mostly positive towards AI applications on EHC, with key themes aligning with the general AI ethics literature: transparency, bias, benefit/harm, justice, accountability, privacy and trust. Analyses of these issues, however, were mostly superficial and did not substantially engage with some of the distinctive features of EHC like urgency and high-stakes decision-making. In particular, urgency and stakes were under-recognised or under-explored in the EHC AI literature. Arguably, urgency in some emergency scenarios could justify more flexible ethical/regulatory standards, while conversely high-stakes contexts might require more stringent standards. Lack of discussion of these contextual nuances suggests a significant gap in the literature of deeper research into the unique ethical, legal and social issues arising from AI use in EHC. This paper extends current knowledge by highlighting the need for deeper and more contextualized investigation of AI ethics in EHC. Not applicable.

Interspecific mating between Aedes aegypti and Aedes albopictus, which act as vectors of a wide range of arboviruses, is predicted to have an important influence on their future distributions and disease control efforts. While interspecific insemination has been documented in a range of laboratory and field studies, rates vary between populations. It has been suggested that the outcome of mating interactions is driven by evolved variation in both the male ability to mate interspecific females and ability of females to resist interspecific males. However, there has been relatively little dissection of the behaviours that underlie interspecific insemination rates. We compared insemination rates between sympatric, allopatric, and lab-adapted strains of Ae. aegypti (Colombia, Arizona, and Liverpool, respectively) and allopatric and sympatric strains of Ae. albopictus (Montpellier and Colombia, respectively). We then used both live observations and high-speed videography to compare intraspecific and interspecific mating interactions. We found very low rates of interspecific mating across all strains used. Both behavioural observations and high-speed videography suggested that female resistance behaviours were not responsible for low interspecific mating. Interestingly, we documented male Ae. albopictus consistently aborting interspecific mating attempts. This study provides additional evidence for strong pre-copulatory species barriers between Ae. aegypti and Ae. albopictus and adds male avoidance as a possible mechanism to maintain these barriers. Considering female resistance, the ability of males to overcome that resistance, and male avoidance of interspecific matings together will be important for informing understanding of how we expect interspecific mating rates to vary across populations and respond to selective pressures such as mass-release strategies.

Children and young people with a parent in contact with the justice system rarely receive the support they need; many are judged and stigmatised. Child Impact Assessments were developed by the Prison Reform Trust in collaboration with 28 children and young people from across the United Kingdom with experience of parental imprisonment, who said in interviews, focus groups, and an online survey that they want to be seen, listened to, and considered at all stages of a parent’s journey through the justice system: arrest, court, and sentencing, prison or community sentence, and prior to a parent’s release. They want to be supported, and they want to be included in decisions about that support. This paper lays out why Child Impact Assessments are needed; what they are; and crucially how they can be used in practice to ensure the right support is offered to meet a child’s needs at the earliest opportunity. The paper will provide evidence of how Child Impact Assessments can improve children’s wellbeing and will give an overview of recent developments to the resources. The authors will also explore future developments, including adapting the resources to understand the impact on unborn babies, babies, and very young children (the first 1001 days) when a pregnant woman or mother is in contact with the justice system.

Geriatric syndromes (GS) are complex conditions that affect older adults and often require multidisciplinary assessment. Natural language processing (NLP) has emerged as a promising tool for extracting relevant clinical information from unstructured text in electronic health records (EHRs). However, the application of NLP in detecting and monitoring GS remains an evolving area of research. This systematic review explores the role of NLP in the identification and analysis of GS, examining its applications, methodologies, and effectiveness. Furthermore, this review discusses the existing challenges, limitations, and future directions to advance NLP applications in the GS research. We conducted a systematic literature search across ten databases to identify studies that applied NLP to GS detection. Articles were screened using predefined inclusion and exclusion criteria, and relevant studies were evaluated for quality using PROBAST. Data were extracted on study characteristics, datasets, annotation processes, NLP approaches, performance metrics, population demographics, and clinical applications. A PRISMA flow diagram was used to illustrate the study selection process. A total of 65 studies were included, where the majority of the studies used traditional rule-based and machine learning approaches. Publicly available datasets were scarce, and most studies used their private dataset, leading to significant variability in data sources and formats. Annotation methodologies differed across studies, with minimal shared guidelines or standards, making direct comparisons challenging. Performance metrics varied across syndromes, with F1-score, precision, and recall as the most commonly reported. Key challenges included the lack of dataset uniformity, differences in annotation practices, and the absence of external validation. NLP has shown potential in GS analysis, particularly for the detection of syndromes and epidemiological research. However, the majority of studies only focused on one syndrome, and variability in dataset availability, annotation processes, and model performance present challenges to broader implementation. Future research should focus on improving the comprehensiveness of GS identification, dataset standardisation, enhancing model generalisability, and integrating NLP approaches into clinical workflows.

Abstract The effect of supernovae (SNe) on star formation in the interstellar medium (ISM) depends sensitively on where SNe explode with respect to ISM clouds. Observationally, SN ISM environments characterized by spatially resolved gas maps can empirically guide the placement of SNe in subgrid models, but unfortunately such measurements remain scarce, as SNe are rare and often distant. Here we demonstrate a new approach—mapping the ISM around evolved massive stars that are soon to explode. These provide a substantially larger sample of “explosion sites” (than just historical SNe) in nearby galaxies that have high-resolution atomic and molecular ISM maps from the Jansky Very Large Array and Atacama Large Millimeter/submillimeter Array. We demonstrate this technique in the well-resolved Local Group spiral M33 by analyzing the 50 pc scale projected ISM densities around red supergiants (RSGs; 8–30 M ⊙ stars) Wolf–Rayet stars (W-Rs; >30 M ⊙ stars), and supernova remnants. We find a mass-dependent correlation between stars and gas clouds, with at least 45% of W-Rs and up to 77% of RSGs having no detectable H 2 at their pixel locations. In the sample with H 2 detections, we find that more-massive younger progenitors are coincident with denser gas. We show that the density distributions for stars >15 M ⊙ are statistically distinct from random alignment of stars and gas in M33. Our work provides the first observationally derived estimate of the fraction of the SN-producing stellar population correlated with ISM density peaks. We demonstrate how this can be compared with galaxy simulations, and advocate similar comparisons to the community for constraining subgrid models.

Immune responses to parasite infection involve the increased production of basophils and eosinophils. These two myeloid cell types have key roles in type 2 anti-parasite immunity1 and rely on GATA family transcription factors for their specification2,3. The first committed step in basophil and eosinophil production is generation of basophil-eosinophil-mast cell progenitors (BEMPs) from oligopotent erythroid-primed multipotent progenitors (EMPPs). However, it is not well established how immune responses act on progenitors to initiate type 2 myelopoiesis. Here we show that infection with the helminth Heligmosomoides polygyrus increases EMPP commitment to myeloid fate at the expense of erythropoiesis. Uponinfection with H. polygyrus, the IL-33 alarmin accumulated in the bone marrow, causing EMPPs to upregulate the GATA co-factor LMO4 and preferentially differentiate into myeloid cells. LMO4 was sufficient to instruct myeloid fate in EMPPs by interacting with GATA2, displacing the FOG1 co-factor and redistributing GATA binding from megakaryocyte-erythroid-specific to basophil, eosinophil and mast cell (BEM)-specific chromatin. Accordingly, mice carrying a GATA2 mutation that selectively impairs the LMO4-GATA2 interaction were deficient in GATA factor allocation to BEM chromatin, myeloid lineage commitment, basophil and eosinophil production, and parasite control. This identifies LMO4 as an IL-33-regulated master regulator of type 2 myelopoiesis, and transcription factor reallocation as a mechanism of lineage commitment.