Biomedical Signal Processing and Control

Coronary flow waveforms have a distinct diastolic-dominant shape with periods of low or retrograde flow during systole. While the general waveform shape has been attributed to complex interactions between cardiac and vascular mechanics, there is limited research into the variability in coronary flow waveforms and what this variability may reveal about cardiac function. This work presents a shape analysis of left anterior descending artery (LAD) flow waveforms using Fourier transforms and Singular Value Decomposition (SVD) performed on baseline data collected from 32 pigs. Pigs included in the study reflect two breeds (Ossabaw and Yorkshire) and three different experimental conditions (lean-control, lean-paced, and obese-paced). Fourier transforms were used to decompose the waveforms into 15 harmonics for each pig. An SVD analysis is then used to extract temporal patterns of the waveforms. Correlations between pig-specific coefficients for the SVD modes and clinical metrics were used to investigate physiological explanations of LAD waveform variability. Temporal LAD flow patterns of the second SVD mode are significantly correlated with heart rate. The third SVD mode significantly correlates with mean blood pressure and maximum hyperemic flow. Furthermore, the fourth SVD mode is weakly correlated with left-ventricular end diastolic pressure and endocardial-epicardial flow ratios. This work demonstrates that LAD flow waveforms can be broken down into temporal patterns that correlate with physiological features. Furthermore, this shape-analysis method allows for waveform reconstruction and simplifies visualization of the temporal patterns identified using SVD, an advantage over existing methods that focus on characterizing flow waveforms by points of interest.

Stroke remains a leading cause of mortality and long-term disability worldwide, yet rapid diagnosis is often limited by the shortage of trained radiologists, particularly in resource-constrained settings. Automated analysis of CT imaging offers a potential solution, but existing methods often struggle to achieve clinically generalisable performance while jointly addressing multiple diagnostic tasks. Here we present the Intelligent Integrated Stroke Diagnosis System IISDS, an end-to-end deep learning framework built upon StrokeGNN, a graph-based architecture that integrates 3D contextual feature extraction with U-Net-based 2D lesion segmentation to enable comprehensive stroke analysis from non-contrast CT scans. IISDS performs stroke subtype classification, lesion segmentation and lesion volume estimation within a unified pipeline. To develop and validate the system, we collected and curated BGD-ISD through a collaboration between AI researchers, neurologists, radiologists and clinicians, resulting in a large multi-centre dataset comprising 1,507 CT scans from 597 stroke cases acquired across six hospitals and medical centres in Bangladesh. Across BGD-ISD and multiple publicly available datasets, IISDS achieves state-of-the-art performance on all tasks, improving segmentation accuracy by [≥]0.011 Dice score, reducing lesion volume estimation error by [≥]0.3 average symmetric surface distance (ASSD), and increasing classification performance by [≥]0.018 area under the receiver operating characteristic curve (AUC) compared with existing approaches. These results demonstrate the potential of graph-based deep learning to enable clinically generalisable, automated and scalable stroke diagnosis from CT imaging, supporting rapid clinical decision-making, particularly in healthcare environments with limited access to expert radiological interpretation.

Hepatocellular carcinoma (HCC) is particularly aggressive and difficult to treat. Due to the lack of early clinical diagnosis and the unsatisfactory clinical treatment effect, it is particularly important to identify novel markers that can predict tumor behavior in HCC. biogenesis of ribosomes BRX1 (BRIX1) is abundant in various tissues of the human body. However, the regulatory mechanisms and its role in various tissues are not fully understood. Here, we analyzed the expression pattern of BRIX1 in HCC from public gene expression databases and tissue samples from clinical HCC. We confirmed that BRIX1 was upregulated in both HCC cell lines and HCC paraffin section samples. BRIX1 depletion significantly dicreased the capacity of cells to grow and migrate in vitro, and knockdown BRIX1 suppressed tumor growth in xenograft tumor model. Mechanistically, BRIX1 depletion suppressed the MAPK/ERK pathway, as reflected by reduced phosphorylated ERK (p-ERK) levels. In summary, we provide a rational clue for the further investigation of BRIX1 as an invaluable biological marker for diagnosing and predicting prognosis of patients with HCC.

Cerebral hemodynamics are difficult to monitor continuously outside the laboratory. Optical head-worn wearables have been proposed for tracking cerebral blood-flow signals, but they require comparison with an established cerebrovascular reference before they can be interpreted. We evaluated a temple-worn optical wearable, Temple, that outputs a proprietary, dimensionless Brain Flow index, intended as a proxy for relative changes in cerebral hemodynamics, against transcranial Doppler (TCD) ultrasound, which measures blood-flow velocity in the middle cerebral artery (MCAv). Twenty-three healthy adults completed two physiological challenges that elicit distinct and acute cerebral hemodynamic responses: a cycle-ergometer exercise protocol and a stand-to-supine postural transition protocol. Twenty participants were analyzed per protocol. The Brain Flow index tracked MCAv in both protocols, with significant within-subject temporal correlations (median Pearson r = 0.795 and 0.799 for exercise and postural transition; p < 0.001) and directionally concordant, statistically significant transition responses for both increases and decreases in flow. Bland-Altman analysis of the normalized transition responses showed small mean biases between the two devices, consistent with similar relative response shapes. Because both signals were standardized within session before this comparison, it addresses the shape of the relative change rather than agreement in absolute units. The Brain Flow index reproduced the direction and time course of MCAv under both perturbations, including the postural transition, where heart rate moved in the opposite direction. Further studies using complementary modalities and additional cerebrovascular reactivity challenges are required to establish clinical use cases and cerebral specificity of the Brain Flow index.

Objectives This study aimed to assess sleep patterns and life satisfaction among employees of a private company in Dubai, United Arab Emirates, and to examine the relationships among sleep quality, life satisfaction, and selected demographic variables. A quantitative descriptive cross-sectional survey design was adopted. Methods A convenience sample of 110 male employees participated in the study. Data were collected using the Sleep Disorder Assessment Scale (16 items; Cronbachs = 0.89) and the Life Satisfaction Scale (5 items). Statistical analysis was performed using SPSS version 29, including descriptive statistics, chi-square tests, and Pearson correlation analysis. Results Most participants (66.4%) were aged 20-30 years, and 82.7% experienced moderate sleep-related problems. Mobile phone use before bedtime was common, with 60.9% reporting occasional use and 35.5% reporting regular use. Overall, 41.8% reported neutral life satisfaction, while 25.5% and 24.6% were slightly and extremely satisfied, respectively. A significant negative correlation was found between poor sleep patterns and life satisfaction (r = -0.389, p < 0.001). Mobile phone use before bedtime and shift work were significantly associated with sleep patterns (p = 0.048). Conclusion Poor sleep quality, particularly among shift workers and frequent bedtime mobile phone users, is associated with lower life satisfaction. Workplace interventions promoting sleep hygiene may enhance employee well-being.

Background. Whether predictors of intraoperative hypotension (IOH) add information beyond the mean arterial pressure (MAP) already displayed on the monitor is contested: selection bias in common evaluation designs inflates apparent performance, and the field has called for comparisons against simple MAP-based references under bias-resistant protocols. Existing predictors also depend on proprietary waveform analysis or pulse-contour monitors, restricting both deployment and external validation. Methods. Using 807 non-cardiac surgery patients from the open VitalDB database, we derived an additive gradient boosting model (one split per tree: a learned shape function per variable, no interactions) from three variables computable from an arterial line alone: current MAP, its drift from the patient's own 20-minute baseline, and the growth of its rolling variance (critical slowing down). Evaluation used patient-level 5-fold cross-validation under a strict protocol - exclusion of the 65-75 mmHg grey zone and of all samples already hypotensive at prediction time - with MAP alone (same learner class) as comparator. The frozen model was then validated, without any refitting, on an independent cohort from another continent (MOVER, University of California Irvine) following a pre-registered plan sealed before external data access. Results. In development the pressure-only model reached AUROC 0.907 vs. 0.884 for MAP alone (Delta AUROC +0.023, 95% CI +0.017 to +0.029) at 5 min, with +0.031 and +0.032 at 10 and 15 min, and good calibration (Brier skill +0.418 vs. prevalence). In external validation on 3,069 patients (442,194 samples, 1-minute charting, event prevalence 5.8%), the advantage not only transferred but was larger than in development: AUROC 0.696 vs. 0.638, Delta AUROC +0.058 (95% CI +0.051 to +0.064), meeting both pre-registered gates. Discrimination transferred; calibration did not (external Brier skill -0.014), requiring local recalibration. In the unrestricted scenario, where samples already at threshold are retained, the advantage collapsed (+0.007), reproducing the selection effect this paper documents. A secondary model adding pulse-contour cardiac output and stroke volume variation improved development discrimination further (Delta AUROC +0.035) but could be externally validated in only 39 patients, because those signals are rarely recorded. Conclusions. The dynamics of arterial pressure itself - drift from a patient-specific baseline and variance growth - carry predictive information beyond its current value, in a fully interpretable additive model that requires only an arterial line, no waveform access and no proprietary hardware. The advantage is confirmed in a pre-registered frozen-model external validation of over three thousand patients, and is largest at coarse recording cadence, where instantaneous pressure is least informative.

Ultrathin endoscopy is highly attractive for real-time tissue imaging in narrow and hard-to-reach regions of the body. A single multimode fibre (MMF) is an attractive probe because of its small diameter, flexibility, and diffraction-limited spatial resolution enabled by the large number of transverse modes guided within a single core. Because the distal fibre tip is inaccessible during endoscopy, reflection-mode imaging, in which the same fibre delivers illumination and collects backscattered light, is more practical than transmission-mode imaging. However, image recovery from the resulting speckle pattern is challenging because light undergoes double-pass propagation through the MMF, with mode coupling and dispersion; the backscattered signal is weak, and the camera records intensity only, without phase information. Here, we propose a single-shot reflection-mode MMF imaging framework that combines a reflected real-valued intensity transmission matrix (reflected-RVITM) with an image restoration network. The reflected-RVITM is calibrated using intensity-only measurements, without interferometry or phase retrieval, and provides a physics-guided initial reconstruction from a single backscattered speckle frame. A restoration network then refines this initial reconstruction instead of inverting the raw speckle. Four restoration backbones are evaluated: HPM-Attention-UNet, GAM, MambaIRv2, and CICPNet. On matched datasets, hybrid models outperformed corresponding networks trained to map raw speckle directly to images. For example, HPM-Attention-UNet on MNIST improved mean PCC from 0.572 to 0.944 (+65.1%). Under domain shift, with training only on Fashion-MNIST and tested on unseen CIFAR scenes, hybrid models achieved mean PCC of 0.61-0.65, compared with 0.36-0.50 for direct learning. This framework is further demonstrated using physical objects at the distal fibre tip. These results demonstrate that a reflected-RVITM physics prior combined with a restoration network enables single-shot image recovery after intensity-only calibration, offering a phase-retrieval-free and generalisable route towards minimally invasive reflection-mode MMF endoscopy.

Muscle architecture shapes muscle function and changes with age, growth, training and disease, yet quantifying three-dimensional (3D) muscle architecture in vivo remains challenging. We introduce a hybrid fascicle tractography approach for freehand 3D ultrasound data that accurately reconstructs 3D muscle fascicles with respect to an objective, anatomically relevant coordinate system defined by the muscle's central aponeurosis. The hybrid approach combines Hessian-based fascicle detection with wavelet-based refinement to generate volumetric fascicle orientations. In a synthetic dataset with known ground truth, fascicle orientations and lengths were estimated with errors of [&le;]2{degrees} and ~1.5%, respectively. In vivo, the approach detected physiologically plausible fascicle lengthening in the human tibialis anterior following a passive plantar flexion rotation, whereas diffusion tensor imaging of the same muscle did not. The proposed method enables anatomically relevant, objective and non-invasive quantification of 3D muscle architecture in vivo, providing a practical framework for applications in clinical and applied muscle physiology.

Objective: To examine how medial plantar nerve shear wave speed (Cs) and viscosity coefficient (Vi) are associated with the severity of diabetic peripheral neuropathy (DPN), and to assess their ability to differentiate adjacent severity categories. Materials and Methods: Based on TCSS, the 113 patients with type 2 diabetes mellitus were assigned to the non-DPN (n = 33), mild DPN (n = 46), and moderate DPN (n = 34) groups. Medial plantar nerve Cs and Vi were measured using shear wave elastography and viscosity imaging. Receiver operating characteristic analysis evaluated Cs, Vi, and their logistic regression-based combination; areas under the curves (AUCs) were compared using DeLong tests. Results: Cs and Vi increased progressively across the three groups (both P < 0.001). For non-DPN versus mild DPN, the AUCs of Cs, Vi, and the combined model were 0.688 (95% CI, 0.604-0.772), 0.741 (0.660-0.822), and 0.745 (0.665-0.826), respectively, without significant pairwise differences. For mild versus moderate DPN, the corresponding AUCs were 0.707 (0.625-0.789), 0.794 (0.724-0.865), and 0.799 (0.731-0.867). The combined model outperformed Cs (P = 0.045), whereas Cs versus Vi and Vi versus the combined model did not differ significantly (P = 0.162 and 1.000, respectively). Conclusion: Medial plantar nerve Cs and Vi increased with DPN severity. Their combination improved discrimination between mild and moderate DPN compared with Cs alone but not with Vi alone. Quantitative medial plantar nerve viscoelastic assessment may complement clinical severity grading.

Ethnopharmacological relevance: Australian Indigenous medicinal plants represent a valuable yet underexplored source of bioactive compounds with potential therapeutic relevance. The Iningai community of Central Queensland has traditionally used native plants to manage conditions associated with inflammation, pain, infection, and general illness. Scientific evaluation of these plants may provide evidence for their customary applications and identify bioactivities relevant to anticancer biodiscovery. Aim of the study: This study evaluated leaf and stem extracts of seven medicinal plants-Pittosporum angustifolium, Alphitonia excelsa, Calytrix microcoma, Geijera parviflora, Melaleuca uncinata, Gossypium australe, and Eucalyptus similis-traditionally used by the Iningai community, focusing on three biological processes relevant to cancer: oxidative stress, inflammation, and cellular proliferation. Materials and methods: Antioxidant activity was assessed using DPPH radical-scavenging and ferric reducing antioxidant power (FRAP) assays. Anti-inflammatory activity was evaluated in lipopolysaccharide (LPS)-stimulated THP-1 macrophage-like cells by profiling IFN-, TNF-, IL-6, IL-12, IL-18, and IL-23. Antiproliferative activity was assessed using MTT-based viability assays in human and murine liver cancer cell lines (Huh7, Hep3B, Hep-55.1c, and A52). Results: The extracts exhibited distinct biological activity profiles. G. parviflora stem and C. microcoma leaf extracts showed the strongest antioxidant activities, whereas P. angustifolium stem exhibited the weakest radical-scavenging capacity. Cytokine responses were extract-specific, with E. similis leaf extract demonstrating broad and pronounced suppression of multiple LPS-induced pro-inflammatory cytokines. Several extracts produced concentration-dependent reductions in liver cancer cell viability, with P. angustifolium stem exhibiting the most consistent and potent antiproliferative activity across the cell lines tested. Notably, strong antioxidant or anti-inflammatory activity did not necessarily correspond with antiproliferative activity. Conclusion: Australian Indigenous medicinal plant extracts demonstrated distinct antioxidant, immunomodulatory, and antiproliferative activities rather than uniform bioactivity across experimental systems. The divergent activities of G. parviflora, C. microcoma, E. similis, and P. angustifolium highlight the importance of integrated biological screening and support the value of Indigenous knowledge-guided biodiscovery. These plants represent promising sources for further investigation of selective bioactive compounds with potential relevance to anticancer drug discovery.

Introduction Hearing loss (HL) affects over 1{middle dot}5 billion people globally and India shares a disproportionately high burden including Disabling Hearing Loss (DHL). HL affects an Individual socio-economically, but there are limited studies on the broader societal economic consequences of HL in India.Methods Using Cost-of-Illness (COI) approach, we studied the societal economic burden of HL in India. This study uses epidemiological and macroeconomic data and modelling to estimate the loss of Gross National Income (GNI) due to HL and DHL across three economic pathways. Uncertainty is evaluated using deterministic and Probabilistic Sensitivity Analyses (PSA).Results The model estimates that there are in India, 289 million and 85{middle dot}9 million people with HL and DHL respectively. Direct Loss of GNI and Indirect Loss of GNI (Caregiver burden) are estimated as INR 4,648{middle dot}4 billion (USD 55{middle dot}6 billion) and INR 3,268 billion (USD 39 billion) respectively. The Loss of GNI due to Low Education amongst those with HL is estimated as INR 1,041{middle dot}9 billion (USD 12{middle dot}45 billion).Discussion Economic burden of HL is presented across three pathways with Direct Loss of GNI due to DHL being the greatest. It also presents age stratified caregiver economic burden. The findings of the study help in estimating similar cost pathways, advocacy, and policy decisions towards reducing HL prevalence in India and LMICs. This study also highlights the need for India specific estimations related to the HL attributable low education, state-wise disaggregates, and prevalence studies. Funding This study has not received any funding.

Dynamic positron emission tomography (PET) with long axial field-of-view (LAFOV) scanners enables multi-organ imaging and kinetic quantification beyond static (late-phase) imaging; however, the long times typically required for dynamic acquisitions remain clinically impractical. This study evaluates a deep learning (DL) framework to enable abbreviated dynamic PET acquisitions, comparing single-time-window (STW, early dynamic data only) and dual-time-window (DTW, early dynamic data plus a late 5-min static frame) protocols with early dynamic scan durations of 5-30 min and dose levels ranging from 360 MBq to 18 MBq. Seventeen 60-min dynamic [18F]FDG datasets were first motion-corrected using a staggered FALCON pipeline and then used to train and test a spatiotemporal DL model for autoregressive frame prediction. Performance was assessed across the full quantitative workflow, from DL-predicted frames and time-activity curves to organ-based kinetic modeling and voxel-wise parametric imaging in multiple tissues and two patient cohorts. DTW protocols consistently outperformed STW, better preserving late-phase kinetics. For a 15-min early dynamic scan, adding a late 5-min scan reduced mean absolute Ki difference from 23% (STW) to 17% (DTW) in the liver and from 26% to 15% in the thalamus. DTW + DL further reduced errors to [&le;]10% in the liver, thalamus, and breast lesion, and 16% in muscle. Our recommended protocol, 15-min early dynamic scan plus a 5-min late scan with DL, remained robust to up to a 5-fold dose reduction (~74 MBq). Overall, these findings support DL-enabled abbreviated, low-dose dynamic LAFOV PET as a clinically feasible approach for accurate kinetic quantification

Purpose: Otoacoustic emissions (OAEs) are used to assess outer hair cell (OHC) function. Clinical interpretation of OAE responses, however, is often limited to a present/absent binary since both physiological factors and measurement variability affect the measured OAE amplitude. Prior work showed elevated OAE responses in sedated compared to awake chinchillas, pointing to the potential influence of the medial olivocochlear (MOC) efferents on amplitudes, but this finding is inconsistent across species and OAE type. Here, we aimed to further investigate the effect of anesthesia on distortion- and reflection-type emissions in chinchillas using swept stimuli and more reliable calibration methods. Methods: Swept distortion-product (DP) and stimulus-frequency (SF) OAEs were measured in chinchillas with and without ketamine/xylazine sedation. Stimuli were presented using in-ear forward pressure level calibrations. DPOAE and SFOAE amplitudes and estimated Qerb from SFOAE group delays were compared across the two conditions. Results: We found that low-frequency DPOAE amplitudes were elevated when animals were sedated. The difference in SFOAE amplitudes was more variable across animals but appeared mildly reduced in sedated animals. Qerb estimates were slightly higher in sedated animals at some frequencies. The effect of sedation was not different across sexes. Conclusion: Taken together, these findings suggest that sedation impacts OAE measurements in chinchillas. MOC modulation could account for the present findings and differences across species. For diagnostic precision, OAE responses should be considered in the context of not only intrinsic OHC function but also extrinsic physiological processes that can modulate OHCs.

Peripheral artery disease (PAD) spans a continuum from large-vessel obstruction to distal microvascular dysfunction, yet routine non-invasive tests, including the ankle-brachial index (ABI), do not provide structurally resolved assessment of the foot microvascular bed and may be unreliable in the setting of medial arterial calcification or perioperative follow-up. Here we developed a clinic-oriented multispectral compound-scanning photoacoustic tomography system (MCPATS) for compression-free distal toe imaging, and an interpretable photoacoustic tomography distal microcirculation score, termed PACT-DMS, for phenotyping PAD-related distal vascular abnormalities. PACT-DMS was derived from anatomically standardized distal toe sections and integrated seven prespecified vascular features spanning trunk-vessel morphology, microvascular distribution and pulsation-related dynamics through a traceable linear support vector machine. In a prospective single-centre cohort of 45 participants, the bilateral fusion PACT-DMS model distinguished patients with PAD from healthy controls with an area under the receiver operating characteristic curve of 0.964 (95% CI, 0.907-1.000) and an accuracy of 91.1% (95% CI, 82.2%-97.8%) under subject-level leave-one-out cross-validation, supported by complementary robustness analyses. Exploratory analyses further showed that PACT-DMS identified abnormal distal vascular phenotypes in 6 of 9 clinically diagnosed PAD limbs with non-abnormal ABI and visualized distal vascular-bed changes before and after revascularization. These findings support MCPATS-enabled interpretable photoacoustic vascular phenotyping as a candidate adjunctive approach for distal microcirculatory assessment in PAD; larger multicentre studies with external validation and prespecified analysis protocols will be required to define its clinical role.

Conventional sleep electroencephalography (EEG) measures often rely on predefined bands, thresholds, and averages that incompletely capture transient oscillatory dynamics across an entire night. Here, we introduce the Dynamic Oscillation (DYNAM-O) Toolbox, an open-source, cross-platform (MATLAB, Python, and Rust) software package for data-driven characterization of individualized neural dynamics in sleep EEG. DYNAM-O identifies transient oscillations as time-frequency peaks on multitaper spectrograms using a novel multi-resolution procedure, computes intrinsic and sleep-state-dependent extrinsic features for each event, and represents the overnight distributions of tens of thousands of TF-peaks as feature histograms spanning oscillation frequency, slow oscillation power, and slow oscillation phase. This distributional representation preserves continuous brain-state variation that could be obscured by averaging within conventional sleep stages. The toolbox further provides Gaussian and spline basis-based dimensionality reduction, visualization, and whole-histogram statistical testing tools to support both exploratory and hypothesis-driven analyses. To demonstrate its use for group-level inference, we analyzed overnight C3-channel EEG from 133 adults (71 females, 72 males; ages 20-35 years) in the Cleveland Family Study. Whole-histogram and parameterized-mode analyses reproduced the established higher center frequency of fast-spindle activity in females and additionally revealed greater low-alpha transient oscillatory activity in females, a pattern outside the conventional sleep spindle range. By completing the analysis cycle from TF-peak extraction to statistical inference, DYNAM-O provides an accessible and interpretable framework for studying individualized sleep physiology and identifying subtle, reproducible electrophysiological patterns.

Breast cancer survivors frequently experience upper-limb impairments, making continuous monitoring essential for effective rehabilitation. We propose REINA (Recognize-Then-Infer Wearable-to-App AI Framework), a two-stage deep-learning approach for remote monitoring of motor function during breast cancer rehabilitation using wearable-device data. Inertial measurement unit (IMU) signals from wearable devices are first used to recognize physical activities via supervised learning, followed by an activity-specific recurrent neural network (RNN) to infer corresponding electromyography (EMG) signals. REINA establishes reliable inference of neuromuscular activity from wearable IMU data, enabling real-time, cost-effective assessment of motor function recovery in real-world settings.

Background Mirror therapy (MT)-specifically paradigms using mirror visual feedback (MVF)-is widely used in neurorehabilitation; however, mechanistic implementations vary substantially in movement content, rhythmicity and attentional demands. This protocol describes an acute mechanistic, within-participant fNIRS screening study designed to compare three prespecified upper-limb mirror-therapy task paradigms and to quantify associated subjective experience after each condition in healthy adults during a single visit. Methods and analysis This is a single-centre, within-participant, randomised crossover study conducted at Wuhan Wuchang Hospital (Wuhan, China). Healthy adults aged 18-35 years will complete three task conditions once each in a counterbalanced order using a 3*3 Latin-square scheme: UMT1 (task-oriented rhythmic functional movement), UMT2 (open-ended free movement with auditory control), and UMT3 (non-functional rhythmic movement). fNIRS will be acquired using the NirSmart-6000A system during a standardised block design. The primary outcome is ROI-level HbO activation quantified as GLM-derived {beta} estimates within the prespecified primary ROIs (bilateral SM1/M1 and bilateral PMC). Secondary outcomes include ROI-level windowed {Delta}HbO (5-20 s post-onset relative to the immediately preceding rest; descriptive only), ROI-level {Delta}HbR, and post-condition subjective ratings (illusion, immersion, confusion and fatigue; 1-7 Likert). Condition effects will be analysed using linear mixed-effects models with fixed effects for condition and period and prespecified multiplicity-adjusted pairwise contrasts. Ethics and dissemination Ethics approval was obtained from the Ethics Committee of Wuchang Hospital Affiliated to Wuhan University of Science and Technology (Approval No.: 2025-112-01; approved on 2025-08-21). The study is expected to be minimal risk. Findings will be disseminated through publication of this protocol manuscript and subsequent results manuscripts and conference presentations. Trial registration number Chinese Clinical Trial Registry (ChiCTR2600116634). This study is conducted as a prespecified mechanistic sub-study under the overarching registered project.

Background: Sickle cell anaemia (SCA) is characterised by chronic haemolysis, inflammation, platelet activation, and recurrent vaso-occlusive complications. Mean platelet volume (MPV) is a readily available platelet index, but evidence regarding its relationship with disease severity in paediatric SCA remains limited and inconsistent, particularly in African populations. Objective: To evaluate the relationship between MPV and disease severity among children with SCA in Kwara State, North-Central Nigeria. Methods: This hospital-based cross-sectional study included 51 clinically stable children with confirmed SCA consecutively recruited from the paediatric haematology clinic of Children Emergency Specialist Hospital, Ilorin. Complete blood count, including MPV, was performed using a Rayto RT-7600 automated haematology analyser. Disease severity was assessed using a composite clinical and laboratory scoring system based on a previously described method. Pearson's correlation, Spearman's rank correlation, simple linear regression, and the Kruskal-Wallis test were used as appropriate. Statistical significance was set at p < 0.05. Results: Of 51 participants, 14 (27.5%) had mild, 33 (64.7%) moderate, and 4 (7.8%) severe disease. Mean MPV was 9.34 +/- 0.76 fL (range, 8.0-11.2). Pearson's correlation showed a weak positive, non-significant linear relationship with severity score (r = 0.231, p = 0.103), whereas Spearman's analysis showed a weak positive monotonic association (rho = 0.286, p = 0.042). Regression explained 5.3% of severity-score variation (R2 = 0.053, p = 0.103). MPV did not differ significantly across severity categories (H = 2.163, p = 0.339). MPV correlated inversely with haemoglobin (r = -0.556, p < 0.001) and positively with platelet count (r = 0.307, p = 0.029). Conclusion: MPV showed a weak relationship with disease severity but inconsistent statistical evidence across analyses. The limited explained variance and absence of significant differences between severity categories do not support MPV as a standalone severity marker. Larger longitudinal studies are warranted. Keywords: Sickle cell anaemia; Mean platelet volume; Disease severity; Platelet indices; Paediatric haematology; Cross-sectional study; Nigeria.

Artificial intelligence applied to routine electrocardiograms (ECGs) has largely focused on detecting existing disease or predicting individual cardiovascular outcomes. Whether ECGs can support prediction of multiple future diseases across organ systems remains unclear. We developed ECG-RISK, a multitask survival model for 67 incident three-character ICD-10 endpoints using ECG waveforms, demographic characteristics and routinely collected laboratory data from 86,673 MIMIC-IV patients. Discrimination was highest for heart, brain, kidney and lung endpoints, with organ-level C-indices ranging from 0.796 to 0.825, whereas liver and pancreatic endpoints showed lower discrimination. The ECG-only model achieved strong discrimination across most endpoints, whereas the incremental improvement gained by incorporating ECG and laboratory inputs beyond demographic information varied substantially across endpoints. Across the nine exploratory aggregated outcomes, Kaplan Meier curves showed clear separation among model-score tertiles. Discrimination was highest for dementia (C-index, 0.891) and heart failure (C-index, 0.857). These findings support the feasibility of ECG-based longitudinal risk prediction across multiple diseases. External validation and competing-risk analyses are required to assess generalisability and clinical utility.

Objectives To evaluate whether access to a certified large language model (LLM)-based clinical decision support system improves physician diagnostic performance in rheumatology compared with conventional diagnostic resources alone. Methods In this multicentre, open-label, randomised controlled trial, 82 physicians from seven hospitals in two countries were randomised 1:1 to conventional diagnostic resources plus Prof. Valmed or conventional resources alone. Participants assessed three rheumatology vignettes before and after assistance. The primary outcome was top-1 diagnostic accuracy. Secondary outcomes included top-3 accuracy, diagnostic reasoning, confidence, case-processing time and perceived support quality. Results Top-1 accuracy increased from 22.2% to 33.3% in the intervention group and from 23.3% to 35.0% in the control group, with no between-group difference in improvement (adjusted OR 0.99, 95% CI 0.45 to 2.19; p=0.979). Differences in top-3 accuracy, diagnostic reasoning and confidence were also not significant. Assisted case-processing time was substantially shorter with LLM support (94 vs 206 s; adjusted mean difference -112 s, 95% CI -141 to -83; p<0.001). Information timeliness and perceived diagnostic support quality were rated significantly higher in the intervention group. Exploratory analyses showed persistent overconfidence and substantial AI over-reliance. Conclusions Certified LLM-based diagnostic support did not improve diagnostic accuracy compared with conventional resources, but substantially reduced case-processing time and improved perceived support quality. These findings suggest potential workflow benefits while highlighting overconfidence and over-reliance as important safety considerations.