Journal of Experimental Biology

Reactive nitrogen species (RNS), particularly peroxynitrite generated from the reaction of superoxide and nitric oxide, are implicated in thermally-induced oxidative stress but remain difficult to resolve in live coral cells. We optimized fluorescent dye strategies to directly quantify superoxide, nitric oxide, and peroxynitrite production in thermally stressed Pocillopora acuta cell suspensions. Thermal stress was associated with an increase in intracellular peroxynitrite concentration, but not in its precursors, nitric oxide and superoxide, highlighting challenges with the application of fluorescent probes and their controls to live coral cells. Compounds developed for mammalian systems often translate poorly to non-model systems such as corals: strong endogenous fluorescence and multiple membrane barriers within the coral symbiocyte, for instance, limited the function of the nitric oxide probe, DAF-2DA. Despite these limitations, the detection of peroxynitrite in live, thermally stressed P. acuta cells represents a step forward in understanding the mechanism of coral bleaching. We also outline strategies for improving the performance of commercial dyes in non-model systems, including media optimization with EDTA treatment to preserve both cell viability and probe performance.

Sleep deprivation is associated with impaired endurance performance and an increased risk of running-related injury. Previous research has identified alterations in running biomechanics following a single night of sleep deprivation under laboratory conditions. However, whether these biomechanical changes can be detected using wearable technology remains unknown. Twenty-one recreationally active runners completed submaximal treadmill running under both normal sleep and total sleep deprivation conditions in a randomized crossover design. Biomechanical features were extracted simultaneously using a full-body motion capture system and a trunk-mounted wearable sensor. Five machine learning classifiers were evaluated in two classification tasks: a within-subject task using paired recordings from the same individual, and a between-subject task performed without individual baseline data. Within-subject classification consistently exceeded chance level for both measurement systems, with best accuracies of 85% for the wearable sensor (Logistic Regression) and 83% for the motion capture system (Random Forest). These findings indicate that sleep deprivation produces a systematic and individually consistent biomechanical signature during running. In contrast, between-subject classification failed across nearly all models and systems, with accuracies remaining close to chance level ([~]50%), demonstrating that inter-individual variability obscures the sleep-deprivation signal in the absence of personalized baseline data. Both systems converged on temporal organization, loading-related variables, and stride-to-stride variability as the most discriminative feature domains. Contrary to expectations, the laboratory motion capture system did not outperform the wearable sensor. Together, these findings demonstrate that individualized, baseline-referenced monitoring is essential for detecting sleep-deprivation-related changes in running gait, and suggest that a single trunk-mounted wearable sensor may provide a practical solution for real-world monitoring when paired recordings are available.

ABSTRACT Background: Athletes who return to soccer after anterior cruciate ligament reconstruction (ACLR) remain at elevated risk of secondary injury despite meeting conventional discharge criteria, and neuromuscular deficits in the reconstructed limb are known to be exposed by fatigue. Objective: To determine whether match-play fatigue differentially affects muscle stiffness, countermovement jump (CMJ) force symmetry, and rate of force development (RFD) asymmetry between soccer players with a history of ACLR and uninjured teammates. Methods: A prospective, cross-sectional, matched-control study enrolled 128 competitive soccer players (64 ACLR, 6-22 months post-surgery; 64 uninjured controls) across five recruitment waves (February-June 2026). Bilateral CMJ peak vertical force, jump height, RFD, and myotonometric stiffness of the rectus femoris (RF), vastus medialis (VM), and biceps femoris (BF) were recorded immediately before and after a standardized competitive match. Fatigue was quantified from second-half heart rate (percentage of age-predicted maximum) and end-match rating of perceived exertion (RPE). Within-group pre-to-post changes were evaluated with paired t-tests, between-group differences in the magnitude of change with independent-samples t-tests, and associations between fatigue indices and asymmetry changes with Pearson correlations. Results: Match play reduced CMJ limb symmetry index (LSI) in both groups, but the decline was more than three-fold greater in the ACLR group, 92.6% (SD 5.4%) to 85.1% (SD 7.1%), than in control group, 97.3% (SD 3.9%) to 95.0% (SD 4.2%), group-by-time difference, p < 0.001, (d = 0.64). RFD asymmetry approximately doubled in the ACLR group, 10.6% (SD 4.1%) to 17.6% (SD 6.5%), compared with a smaller rise in control group, 4.6% (SD 2.4%) to 6.3% (SD 3.7%); p < 0.001, d = 0.77). Involved-limb stiffness losses in the ACLR group exceeded those of controls for the RF (-21.2 vs. -9.2 N/m, p < 0.001), VM (-17.7 vs. -6.1 N/m, p < 0.001), and BF (-13.3 vs. -6.6 N/m, p < 0.001), whereas uninvolved-limb stiffness losses did not differ between groups (all p > 0.05). Fatigue markers (heart rate, RPE) were not significantly correlated with the magnitude of individual asymmetry change (|r| [&le;] 0.18, p > 0.15). Conclusions: In competitive soccer players 6-22 months after ACLR, match-play fatigue selectively compromises stiffness and explosive force output of the reconstructed limb, widening inter-limb asymmetries beyond what is seen in uninjured teammates, even though global cardiovascular and perceptual fatigue were comparable between groups. These findings suggest that return-to-sport testing performed only in a rested state may underestimate residual neuromuscular deficits, and support fatigue-inclusive assessment protocols before athletes are cleared for unrestricted competition. Abbreviations: ACL: anterior cruciate ligament, ACLR: anterior cruciate ligament reconstruction, BF: biceps femoris, CMJ: countermovement jump, HRmax: maximum heart rate, LSI: limb symmetry index, RF: rectus femoris, RFD: rate of force development, RPE: rating of perceived exertion, RTS: return to sport, VM: vastus medialis, SD: standard deviation. Keywords: Anterior cruciate ligament reconstruction, muscle fatigue, muscle stiffness, countermovement jump, limb symmetry index, rate of force development, soccer, return to sport.

Interception of moving objects requires the nervous system to compensate for sensory delays and uncertainty, yet how behavior is controlled remains debated. Key questions concern whether predictive processes play any role at all and, if so, whether they rely on simple motion extrapolation or incorporate internalized physical priors, such as gravity. Another open question is whether observers adopt a single control strategy or flexibly switch between predictive and reactive control - or between different predictive strategies - depending on task demands. To address these questions, we developed a virtual interception task in which participants intercepted moving targets under systematically varied conditions. We manipulated gravity (1g vs. 0g), visual availability (occluded vs. non-occluded), target velocity, and the initial spatial configuration of the ball and paddle (same vs. opposite side). Results indicate that interception is supported by predictive mechanisms across conditions. Behavioral patterns during occluded 0g trials suggest that participants extrapolate target motion using expectations consistent with gravity. Target velocity, visual occlusion, and task geometry modulated movement strategies, indicating that predictive control is flexibly adapted to task demands. These findings support the view that interception relies on predictive internal models incorporating structured physical priors while revealing flexible, context-dependent adaptations to sensory and task constraints.

Cooling the skin may increase its tolerance to mechanical loading and decrease the risk of developing pressure ulcers. Yet, the mechanisms of action (e.g. cooling-modulation of cytotoxic, post-occlusive hyperaemia), and their individual variability, remain unclear. We investigated the effects of different cooling levels (24{degrees}C and 16{degrees}C) on microvascular, inflammatory and perceptual responses to mechanical loading of the sacrum in healthy young (N=23) and older adults (N=19), and in spinal cord injury patients (SCI; N=10). Healthy participants underwent 45-min loading (~60 mmHg) and 20-min unloading of the sacrum, using an instrumented indenter probe set at either 38{degrees}C (control condition), 24{degrees}C or 16{degrees}C. SCI participants completed a more conservative protocol (i.e. 25min, ~45mmHg loading, 38{degrees}C and 16{degrees}C conditions). Pre-insult skin structure was characterised with optical coherence tomography; skin blood flow (SkBF) at the loading site was continuously measured, alongside thermal acceptability; and post-insult inflammatory responses were determined via skin-sebum cytokines analyses. Compared to control, 24{degrees}C- and 16{degrees}C-cooling induced a similar ~8-fold decrease in peak post-occlusive reactive hyperaemia in healthy participants, with similar temperature-related differences observed in SCI. Pro-inflammatory cytokines decreased post-insult; yet this occurred similarly across all temperatures and groups. The majority of participants ([&ge;]70%) rated both 24{degrees}C- and 16{degrees}C-cooling as thermally acceptable. We conclude that cooling is a potent modulator of the skin microvascular response to mechanical loading in younger, older, and vulnerable skin (SCI). These findings can inform design parameters for thermal technology aimed at preventing the loss of skin integrity (e.g. integrating 24{degrees}C-cooling in support surfaces and skin wearables).

Low cost inside out wearable trackers can be deployed at scale to measure body motion, but errors in estimated sensor position propagate through coordinate transformations into derived gait and dynamic-stability metrics. Healthy adults walked on a treadmill at 0.5 to 2.0 m/s while VIVE Ultimate Tracker (VUT) and Vicon data were recorded. Data-driven calibration models were developed to correct tracker coordinates and to estimate full body centre of mass (CoM) from a sacrum-only configuration. Agreement with Vicon was assessed using RMSE, mixed-effects Bland-Altman limits of agreement, MAE, and intraclass correlation coefficients. Calibration improved coordinate-level agreement. For gait parameters, model-corrected VUT showed small errors against Vicon (MAE: 0.24 to 0.71 mm step height, 1.73 to 4.63 mm step length, 0.15 to 0.95 mm step width, 0.26 to 0.88 mm foot clearance). Proxy CoM-derived margin of stability (MoS) agreed excellently with Vicon. For the sacrum-only pipeline, calibration reduced CoM RMSE from 103.65 to 104.04 mm to 7.55 to 8.95 mm, and markedly reduced systematic error in stability outcomes, with extrapolated CoM bias decreasing from 172.92 to 0.29 mm and MoS bias from -75.09 to -3.54 mm. Data-driven calibration improved the measurement utility of low-cost VUTs, enabling inexpensive, relatively simple gait and stability measurement from a sacrum-only setup in controlled settings.

Humans and other animals often act in environments that are at least partly familiar, where aspects of the spatial layout are known. Although such knowledge is known to support navigation and spatial cognition, its role in the online control of action remains unclear. We investigated whether drivers use knowledge of road layout to guide steering in high and low visibility and, if so, the form of such knowledge. In two simulated driving experiments (total N = 90), participants repeatedly drove winding roads containing segments with and without fog. Drivers who repeatedly experienced the same road exhibited more stable steering and lane positioning than drivers encountering novel roads, but only when visibility was reduced. These advantages were accompanied by superior performance on post-tests assessing knowledge of road geometry. We next examined the form of such knowledge by dissociating global knowledge of road layout from local associations between landmarks and road segments. Disrupting landmark-road segment associations produced the largest impairment in steering performance. The benefits of prior experience were largely preserved when road-segment order was scrambled but landmark associations remained intact. These findings show that spatial knowledge can support moment-to-moment steering control when visibility is reduced. Rather than relying on a globally coherent representation of the environment, drivers use local associations between landmarks and upcoming road geometry to anticipate future demands. More broadly, the results elucidate how familiarity with environmental structure contributes to the control of action when visual information is degraded, revealing a close interplay between spatial knowledge and visual control. Significance StatementPeople routinely act within surroundings they have encountered before, from commuting on the same streets to walking familiar hallways. Whether the spatial knowledge acquired from such experience actually shapes online visual control remains an open question. Using a simulated driving task, we show that familiarity with a road improves steering stability specifically when visibility is reduced, and that this benefit depends on learned associations between landmarks and upcoming road geometry rather than a global cognitive map. The results indicate that spatial knowledge plays a key role in moment-to-moment control, letting drivers anticipate road segments they cannot yet see. Unfamiliar roads and impaired spatial learning may compound the risks of poor visibility, suggesting a role for driver-assistance systems that leverage landmarks.

Microcomputed tomography (micro-CT) is a useful tool that can be utilised for 3D structural characterisation and volumetric quantification of small biological specimens. Its potential application is particularly valuable within the field of cardiac development, where phenotypic profiling at the whole organ, cell, and molecular level is often most informative within the same sample. Consequently, this study sought to develop a multimodal imaging protocol to enable 3D phenotypic characterisation of embryonic avian hearts (iodine-based contrast X-ray imaging) prior to immunohistochemistry-based cell and molecular analysis. Micro-CT parameters were tested to establish an optimal protocol for 3D analysis of embryonic cardiac specimens across multiple developmental timepoints. Optimised parameters provided reliable and reproducible 3D analysis of cardiac macrostructures. Sodium thiosulphate treatment of X-ray imaged hearts effectively reversed the iodine-based contrast stain whilst maintaining antigen availability of nuclear, membranous, and cytoplasmic targets in traditional downstream imaging studies. Together, this study demonstrates a robust and highly efficient multimodal imaging strategy to comprehensively characterise cardiac morphology in avian embryos and may serve as a versatile foundation for a broad range of bioimaging applications within the wider scientific community.

Abstract Objective: To synthesize the available evidence on the association between SARS-CoV-2-related microvascular thrombosis and acute kidney injury (AKI), with emphasis on renal outcomes, mortality, and renal replacement therapy requirements. Methods: This systematic review followed the PRISMA 2020 statement and was prospectively registered in PROSPERO (CRD420251132701). PubMed/MEDLINE, Scopus, and Embase were searched for systematic reviews, including meta-analyses, and umbrella reviews investigating the association between SARS-CoV-2-related microvascular thrombosis and acute kidney injury. Two reviewers independently performed study selection, data extraction, and methodological quality assessment using AMSTAR-2 and ROBIS. Evidence was synthesized through a structured narrative synthesis supported by quantitative data extracted from the included reviews. Results: Six evidence syntheses evaluating kidney involvement, thrombotic events, and microvascular mechanisms in COVID-19 were included. AKI incidence was 9.2% (95%CI 4.6-13.9) among hospitalized patients and 32.6% (95%CI 8.5-56.6) among critically ill patients. In children with multisystem inflammatory syndrome associated with SARS-CoV-2, AKI incidence was 20% (95%CI 14-28). Microvascular or thrombotic events were associated with adverse renal outcomes (OR 2.14; 95%CI 1.32-3.48). AKI was associated with increased mortality (OR 4.68; 95%CI 1.06-20.70) and greater likelihood of renal replacement therapy requirement (OR 2.87; 95%CI 1.45-5.68). The certainty of evidence ranged from moderate to high for the principal outcomes. Conclusion: Current evidence supports an important association between microvascular thrombotic injury and COVID-19-associated AKI. These findings reinforce the relevance of endothelial dysfunction and thromboinflammatory pathways in kidney involvement during COVID-19 and highlight the need for early renal monitoring, risk stratification, and kidney-protective strategies in high-risk patients. Keywords: COVID-19; Acute Kidney Injury; Microvascular Thrombosis; SARS-CoV-2; Renal Replacement Therapy; Systematic Review

Objective: Photobiomodulation therapy has emerged as a promising modality to facilitate scar healing and pain management in dermatology and plastic surgery. However, its role in postoperative care following spine surgeries remains understudied. This double-blinded, placebo-controlled study aimed to investigate the effects of photobiomodulation in patients with chronic lower back pain undergoing lumbar decompression, with postoperative wound healing as the primary outcome and pain reduction and functional recovery as secondary outcomes. Methods: Patients were randomized to receive either active photobiomodulation braces (N=13) or placebo braces (N=12). Follow-up assessments were performed at 2, 4, 6, 8, and 12 weeks postoperatively. Outcomes included wound healing (Stony Brook Scar Evaluation Scale), back and leg pain (Visual Analog Scale), quality of life (EuroQol 5D), and functional status (Oswestry Disability Index). Results: Compared to the placebo group, the photobiomodulation treatment group had a 4.12-fold cumulative improvement in final scar scores, with significant between-group differences at postoperative weeks 6, 8, and 12 (p = 0.0062, 0.010, 0.042). Among patients with severe preoperative disability, treatment resulted in a 1.89-fold faster improvement in back pain (p=0.025) and a 1.80-fold faster improvement in ODI scores (p=0.025); and superior treatment effect on wound healing were again observed at weeks 6, 8, and 12. Among patients with poor initial scars, treatment led to a significantly better scar outcome than placebo at week 6 and a 1.94-fold faster EQ5D improvement (p=0.052), with significant gains observed as early as two weeks after surgery. There were no adverse events associated with photobiomodulation treatment. Conclusions: Photobiomodulation significantly promoted postoperative wound healing following lumbar decompression surgery, with therapeutic benefits preserved even in patients with poor baseline scar scores and functional impairment. This indicates that the efficacy of photobiomodulation is not limited by the initial scar condition or disability, supporting its broad clinical applicability. Additionally, patients with severe preoperative disability experienced greater benefits from photobiomodulation than placebo, including faster reduction in back pain and more rapid improvement in functional capacity, highlighting its role in postoperative pain management and rehabilitation. These therapeutic effects are likely mediated by photobiomodulation-induced reduction of inflammation and enhancement of tissue repair. Together, this study suggests that photobiomodulation can be a promising adjunct therapy to facilitate postoperative recovery in patients undergoing spine surgery.

White matter lesion (WML) identification, assessment, and characterization using magnetic resonance imaging (MRI) are fundamental for diagnosis and monitoring of multiple sclerosis (MS). Portable ultra-low field (pULF) MRI at 64 millitesla (mT) has been shown to visualize WML with at least one dimension greater than 4 mm. An automated WML segmentation tool catered to pULF-MRI can provide standardized and accurate quantitative measurements of WML volume. In this study, we sought to investigate and compare the accuracy of machine-learning (ML) and deep-learning (DL) pULF MRI segmentation tools. Same-day paired pULF (64mT) and high-field (HF, 3T) MRI scans from 84 adults with MS or suspected-MS (mean age {+/-} SD: 48 {+/-} 13, 62 females) included T2-FLAIR and T1w images. Reference WML segmentations were manually annotated on pULF T2-FLAIR for all scans, with WML confirmed with registered HF T2-FLAIR. HF reference WML segmentations were created. Four automated segmentation methods were applied to pULF scans: Method for Inter-Modal Segmentation Analysis (MIMoSA), an ML algorithm trained on HF WML masks; WMH-SynthSeg, a convolutional neural network model with flexible segmentation capabilities across field strengths and resolution; nnU-Net, a DL algorithm trained on pULF reference WML masks; and Pseudo-Label Assisted nnU-Net (PLAn), a DL algorithm pre-trained on HF reference WML masks and refined with 64mT reference WML masks. Two models were trained with nnU-Net, one using T2-FLAIR images only (nnU-Net-FL) and one using T1w and T2-FLAIR images (nnU-Net-FL/T1). The same was done with PLAn, creating PLAn-FL and PLAn-FL/T1. The six automated WML segmentation outputs were compared to the manual segmentations to determine Dice Similarity Coefficient (DSC) scores. Associations of WML volume estimates with clinical measures were investigated. DSC scores with pULF reference WML masks from PLAn-FL (DSC mean {+/-} SD: 0.50 {+/-} 0.24) outperformed MIMoSA (0.24 {+/-} 0.20, p < 0.0001), WMH-SynthSeg (0.30 {+/-} 0.18, p < 0.0001), nnU-Net-FL (0.41 {+/-} 0.24, p < 0.0001), and nnU-Net-FL/T1 (0.41 {+/-} 0.26, p = 0.0004). Worse Expanded Disability Status Scale (EDSS) and Scripps Neurologic Rating Scale (SNRS) scores were correlated with higher WML volumes in the pULF and HF reference masks. They were also correlated with WML volumes derived from WHM-SynthSeg, nnU-Net-FL, nnU-Net-FL/T1, PLAn-FL, and PLAn-FL/T1, but not MIMoSA. After adjusting for age, WHM-SynthSeg, nnU-Net FL, nnU-Net-FL/T1, PLAn-FL, and PLAn-FL/T1 had significant associations with EDSS and SNRS scores. nnU-Net and PLAn performed best in segmenting WML on pULF-MRI at 64 mT, providing accurate quantitative estimates of WML burden. Moreover, WML volumes estimated by these algorithms were associated with clinical measures of disability, underscoring their utility for reflecting clinical and radiological disease severity. Given pULF-MRI's mobility and lower cost, these findings highlight its relevance in clinical trials, particularly in involving more participants who face logistical constraints and barriers.

Introduction Parkinson's disease (PD) is a multifactorial disorder, affecting multiple neurotransmitter systems, including the cholinergic system. Cholinergic denervation is heterogeneous across patients and difficult to predict based on clinical presentation. In this study, we assessed the sensitivity of structural MRI (sMRI) and functional MRI (fMRI) to cholinergic degeneration related to PD and to cognitive functioning in PD. We compared our results to results from previously reported [18F]Fluoroethoxybenzovesamicol ([18F]FEOBV) PET imaging, which is considered the gold standard for cholinergic imaging. Methods 34 PD patients and 10 healthy controls underwent structural T1-weighted MRI. A subset of 14 patients and 9 controls also underwent resting-state fMRI. We extracted the bilateral volumes of the nucleus basalis of Meynert (NBM) from the sMRI images. Functional connectivity (FC) from the NBM to the cortex (NBM-FC) was determined using fMRI data. Principal component analysis (PCA) was applied to reduce the dimensionality of the NBM-FC images. We assessed performances for NBM-FC in distinguishing patients from controls using stepwise logistic regression. Similarly, NBM volume was used using logistic regression. Furthermore, the relation between these measures and cognitive function in several domains was investigated with (stepwise) linear regression. Leave-one-out cross validation (LOOCV) and bootstrapping was performed to assess robustness of the results. Results NBM-FC was well able to discriminate patients from controls with an AUC of 0.84 (95% CI: 0.62-1). NBM volume showed lower performance, but was still better than chance: AUC: 0.75 (95% CI: 0.57-0.93). Significant correlations were found between 1) cognition in the attentional domain and NBM-FC (r=0.63; p=.015) and 2) global cognition and NBM volume (r=0.55, p=.001). These results were inferior to those previously reported using [18F]FEOBV tracer uptake (see Chapter 6). Bootstrapping revealed that NBM volume of only the left hemisphere was stably related to PD diagnosis and global cognition in PD patients. We found that a lower NBM-FC in specific brain areas, including the fusiform gyrus, supramarginal gyrus and dorsolateral prefrontal cortex, was related to PD diagnosis. Bootstrapping revealed no stable NBM-FC pattern related to attention. Conclusion Although MRI results were slightly inferior to [18F]FEOBV PET data, MRI may provide a cheaper and more widely available alternative for cholinergic imaging. We recommend testing the utility of MRI as predictor and monitor of cholinergic treatment effect in a longitudinal study.

Background: Post-stroke dysphagia (PSD) contributes to aspiration, pneumonia, malnutrition, prolonged hospitalization and mortality. We evaluated the discrimination, validity and readiness of machine learning and data-driven prediction models for PSD-related outcomes. Methods: Following a prospectively registered protocol (PROSPERO CRD420261419259), we searched PubMed/MEDLINE, Embase, Web of Science Core Collection, CINAHL and CENTRAL from inception through June 7, 2026. Eligible studies developed or validated multivariable prediction models for PSD-related outcomes in adults with stroke. We used PROBAST and PROBAST+AI to assess risk of bias and applicability and TRIPOD+AI to evaluate reporting. Area under the curve (AUC) estimates were pooled on the logit scale with random-effects models. Results: Twenty-four studies were included and ten contributed to meta-analysis. Four studies predicting early or incident PSD yielded a pooled AUC of 0.94 (95% CI 0.60-0.99; I2 = 95.6%). Pooled AUCs were 0.84 (95% CI 0.71-0.92) for aspiration or penetration-aspiration and 0.89 (95% CI 0.24-1.00) for severe dysphagia. The exploratory analysis of all ten risk-prediction models produced an AUC of 0.90 (95% CI 0.80-0.95), but heterogeneity was substantial (I2 = 90.3%) and the prediction interval was 0.51-0.99. Every study had high risk of bias because of analysis-domain concerns; calibration and external validation were uncommon. Conclusions: Reported discrimination was often high, but the evidence does not establish reliable performance in care. Independent validation, calibration, complete model reporting and clinical-impact studies are needed before these models guide post-stroke swallowing care. Keywords: Post-stroke dysphagia; Stroke; Deglutition disorders; Machine learning; Clinical prediction model; Area under the curve; Meta-analysis

Introduction Hospitalised community-acquired pneumonia (CAP) is heterogeneous in aetiology, severity, and outcome. Phenotyping and endotyping approaches offer potential to stratify patients biologically and guide targeted therapy, but require well-characterised cohorts with linked biosamples. We describe the PARIS (Pneumonia: Acute Respiratory Infection +/- Sepsis) study: a prospective observational cohort of hospitalised patients with pneumonia, designed to characterise functional outcomes and to provide a biobank for translational immunological research. Methods Adults admitted with CAP to a single NHS district general hospital were enrolled within 24 hours of admission between December 2020 and March 2022. Clinical, functional, and physiological data were collected at enrolment, hospital discharge, and 6-8 week follow-up. Serial blood samples were collected for flow cytometry, transcriptomics, pathogen DNA detection, and plasma biobanking. Results Forty-seven patients were enrolled (15 without and 32 with sepsis [SOFA >=2] at enrolment); 87% met sepsis criteria by 24 hours post enrolment. Most patients (30/47, 64%) were managed as COVID-19, microbiologically confirmed in 27. Mean age was 57 years (SD 16), 70% were male, and baseline comorbidity burden was low. Severity was moderate (median NEWS2 4 at enrolment, rising to 6 by 24 hours post enrolment; p<0.001). Mortality was 4/47 (8.5%), with 44/47 (94%) alive at hospital discharge. Median length of stay was 8 days (IQR 5.5-11). Translational samples were collected from the majority: fresh flow cytometry (44/47, 94%), transcriptomics from the sepsis subgroup (31/32, 97%), pathogen DNA sampling (35 samples received across study timepoints; see Table 5), and stored plasma (29/47, 62%). The primary outcome of functional decline (Barthel score decrease >=1.85) occurred in only 1/29 patients with paired assessments (3.4%). Persistent CRP elevation (>3 mg/L) at 6-8 week follow-up was present in 16/31 (52%) survivors with available data. Conclusions The PARIS cohort provides a well-characterised clinical platform and linked biobank to support translational studies of pneumonia and sepsis. The low rate of functional decline reflects the younger, lower-comorbidity, COVID-predominant population recruited. Primary protocol endpoints were not achieved owing to pandemic-related disruption. Data and samples underpin a programme of linked translational studies.

Abstract Background: Remote clinical reviews have become an integral component of contemporary nursing practice across community and acute care settings. Nurses increasingly make autonomous clinical decisions using telephone, video, and online/digital systems, often with limited sensory information and under conditions of uncertainty. However, empirical understanding of how nurses make clinical decisions via remote reviews remains limited. Aim: To explore and understand how registered nurses (RNs) make clinical decisions about patient care via remote reviews. Methods: A convergent mixed-methods design was employed. Quantitative data (analytic quantitative sample N=53) were collected using validated questionnaires that measured decision-making processes, physician-nurse collaboration, decision-making stress, and perceived decision-making ability. Qualitative data (N=23) were generated through semi-structured interviews. Data collection took place between October 2024 and April 2025. Quantitative data were analysed using descriptive statistics, correlation, and multiple regression. Qualitative data were analysed using framework analysis. Integration was achieved through pillar-building and theory-driven synthesis and illustrated by joint display tables. Results: Most nurses demonstrated a flexible decision-making style, integrating analytical and intuitive reasoning. Both analytical and intuitive processes were positively associated with perceived decision-making ability. Physician-nurse collaboration emerged as a strong predictor of decision-making confidence, while decision-related stress was not a significant predictor. Qualitative findings identified three themes: characteristics of remote review; making adaptive decisions shaped by both internal and external constraints and enablers; and external influencing factors. The integrated findings informed a theory-informed ICE framework to illustrate how nurses make clinical decisions via remote reviews. Conclusion: Remote clinical decision-making is a dynamic cognitive-environmental process rather than a purely individual cognitive act. The ICE framework conceptualises this interaction, extending existing decision-making theories to digitally mediated care. Impact: Understanding remote decision-making supports training design, clinical governance, and the development of Artificial Intelligence-enhanced decision-support tools grounded in ecological bounded rationality. Patient or Public Contribution: Patient and public representatives contributed to stakeholder discussions that informed the development of the interview topic guide and the theoretical model. Patients or members of the public were not involved in recruitment, data collection, analysis, interpretation of findings, or preparation of the manuscript. Keywords: clinical decision-making, remote reviews, telehealth, nursing, mixed methods, ecological bounded rationality

Background: Actigraphy-derived rest-activity rhythm (RAR) features are widely used to characterize clinical states in bipolar disorder (BD). Both mean levels and temporal variability of these features have been associated with mood episodes; however, variability measures are often statistically coupled with the mean, particularly in skewed distributions. This raises a question as to whether variability reflects a separate characteristic of the data or whether the observed association arises from statistical properties of the data. Objective: In this study, we aim to determine whether temporal variability of actigraphy-derived RAR features provides standalone information on mood episodes in BD beyond mean activity levels after accounting for mean-variance dependence. Methods: We analyzed actigraphy data from a subset of 72 participants with BD drawn from a larger longitudinal study, extracting 22 daily RAR features aggregated weekly as sample mean (MEAN) and within-week temporal variability computed as sample standard deviation (VAR). Variance-stabilizing transformations (Box-Cox or Yeo-Johnson) were applied to the entire study cohort to reduce mean-variance dependence. Associations with mood episodes and remission (mania: n=34; depression: n=58 annotated participants) were evaluated using generalized linear mixed-effects models with a logistic link function, including univariate (MEAN or VAR) and multivariate (MEAN+VAR) specifications, assessed by likelihood-based metrics and the area under the receiver operating characteristic curve (AUC). Results: Transformations reduced mean-absolute correlations from 0.43 to below 0.06. Temporal variability remained significantly associated with clinical state for 11/22 RAR features in mania and 16/22 features in depression, with all significant associations remaining after false discovery rate correction (p<0.05). Joint models showed modest incremental gains (AUC 3%-4% overall; up to 12% in mania, 7% in depression), with absolute performance remaining limited (AUC 0.50-0.66). In both mania and depression, nearly all significant variability-based regressors contributed incremental information beyond mean-based models. Only sleep duration and activity changes around wake time (+-1 hour), did not improve discrimination between mania and remission. Conclusions: Temporal variability in RAR features can be considered a standalone state marker of mood episodes not captured by mean activity. We found it to be more consistently associated with depression than mania. Its incremental discriminative contribution is modest, suggesting greater utility within multivariate or multimodal frameworks.

Background. UK clinical guidance recommends that structured risk prediction tools and risk stratification should not be used in self-harm, to predict suicide or determine who is offered treatment. Underpinning this position is the premise that routinely collected health data contain no useful predictive signal, which has received little direct scrutiny. Objective. To test whether routinely collected electronic health record data can distinguish groups at higher and lower risk of severe outcomes following paracetamol overdose. Methods. We analysed 4,095 adults presenting to NHS Lothian emergency departments with paracetamol overdose (2017-2023). Elastic-net logistic regression was fitted to 37 routinely collected electronic health record features to predict a composite of death or mental health inpatient admission at 0-7, 8-30 and 31-365 days following attendance, evaluated on a held-out 20% test set with bootstrapping. Findings. Events occurred in 5.5% of patients at 0-7 days, 2.0% at 8-30 days and 7.9% at 31-365 days, dominated by mental health admission. Bootstrap AUROC 95% confidence intervals lay above 0.5 in every window (0.65-0.82, 0.63-0.90, 0.71-0.85): models ranked patients better than chance. Calibration slopes (1.04, 1.14, 1.07) were close to one. Ranking drew primarily on mental health-related features. Conclusions. Routinely collected health data carried predictive signal for severe outcomes after paracetamol overdose, although discrimination fell short of what is needed for individual-level clinical use. Clinical implications. These models are not proposed for clinical deployment; however, treating risk prediction as a settled question will redirect research efforts, potentially excluding this patient population from machine learning advances driving improvements in care in other medical specialties.

Background with objective: Detecting EGFR mutations is critical for treating lung adenocarcinoma with highly effective targeted therapies. However, standard genetic testing is expensive, complex, and often unavailable in resource-limited settings like Bangladesh. Because elevated serum CEA has been linked to these genetic alterations, it could serve as an accessible screening tool. This study aims to evaluate the association between serum CEA levels and EGFR mutation status to determine if routine CEA testing can reliably predict these mutations and guide treatment. Methodology: In this cross-sectional analytical study, we recruited 58 patients with histologically confirmed treatment naive lung adenocarcinoma. The presence of EGFR mutations in the ctDNA was determined via ARMS (Amplification Refractory Mutation System) PCR. Patient data was statistically analyzed to assess the diagnostic correlation between serum CEA levels and the presence of EGFR mutations. Result: The overall EGFR mutation rate was 43.1% with exon 19 deletion (48%) and exon 21 mutations (44%) were the predominant types. Median serum CEA levels were significantly higher in patients with EGFR mutations compared to wild-type cases (14.6 ng/ml vs 2.8 ng/ml, p<0.001). A multivariate analysis revealed a 14% increased likelihood of an EGFR mutation for 1 ng/ml rise in serum CEA. Furthermore, serum CEA showed strong diagnostic accuracy for ctDNA samples at a 6.39 ng/ml cut-off (AUC 0.82, sensitivity 68.0%, specificity 84.8%). Conclusion: Serum CEA is a valuable, cost-effective, and non-invasive biomarker demonstrating significantly higher levels and strong diagnostic accuracy in EGFR-mutated lung adenocarcinoma compared to wild-type cases.

Magnetic Resonance Imaging (MRI) derived brain age varies substantially between individuals, but it remains unclear whether early deviations from normal brain ageing precede future cognitive decline and whether they provide predictive value beyond conventional MRI measures. Here, we investigated whether MRI-derived brain age gap estimation (BrainAGE) identifies early structural brain ageing differences among cognitively normal individuals who later develop mild cognitive impairment (MCI) or dementia. We analysed longitudinal structural MRI data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and replicated the main findings in the population-based Kuopio Osteoporosis Risk Factor and Prevention Study (OSTPRE). Individuals who later converted to MCI or dementia had higher BrainAGE values several years before diagnosis and, in ADNI, showed steeper longitudinal increases than stable individuals. Elevated BrainAGE values were also associated with increased risk of future conversion to MCI in cognitively healthy individuals and faster subsequent memory decline. Cross-sectional differences and the association between BrainAGE and risk of future conversion were replicated in OSTPRE. Importantly, adding BrainAGE to models including demographic, APOE4, cognitive, and MRI-derived measures consistently improved prediction of future cognitive outcomes, with the greatest benefit observed for individuals who converted after longer follow-up. These findings show that structural brain ageing begins to diverge years before the onset of MCI. BrainAGE captures this early divergence, providing complementary information beyond conventional structural MRI measures that may improve the early identification of cognitively normal individuals at increased risk of future cognitive decline when integrated with other biomarkers.

Chronic kidney disease and heart failure disproportionately burden populations of African ancestry, yet Mendelian randomisation (MR) studies of the causal relationship between kidney function and heart failure subtypes have been conducted exclusively in European ancestry populations. We performed a forward two-sample MR analysis to evaluate the causal effect of genetically predicted estimated glomerular filtration rate (eGFR) on heart failure with preserved ejection fraction (HFpEF) and heart failure with reduced ejection fraction (HFrEF) in individuals of African ancestry. Genetic instruments were selected from an African ancestry eGFR genome-wide association study (N = 67,943) at genome-wide significance, with linkage disequilibrium clumping using an African ancestry reference panel. Heart failure subtype summary statistics were obtained from the Million Veteran Program (HFpEF: 5,379 cases / 113,041 controls; HFrEF: 9,104 cases / 109,632 controls). Six independent SNPs (F-statistics 30.5 &#8211 107.3; R&#178 = 0.62%) were retained as instruments. The primary inverse-variance weighted analysis provided no evidence of a causal effect of eGFR on HFpEF (OR 0.92, 95% CI 0.80 &#8211 1.06, p = 0.248) or HFrEF (OR 0.98, 95% CI 0.78 &#8211 1.23, p = 0.878). Sensitivity analyses were directionally consistent. There was no evidence of heterogeneity or directional pleiotropy. Minimum detectable effects at 80% power were OR 1.28 for HFpEF and OR 1.22 for HFrEF. These null findings should be interpreted as inconclusive given current power constraints; larger ancestry-matched studies are needed.