Autonomous in silico optimization framework for high-performance micromixers

Effective mixing at the microscale is essential for lab-on-a-chip systems, yet designing micromixers that achieve both high mixing efficiency and low pressure drop remains challenging and resource-intensive. Here, we introduce an autonomous in silico framework for designing obstacle-based micromixers through integrating 3D geometry generation, computational fluid dynamics (CFD) simulations and a multi-objective artificial intelligence optimization algorithm within a fully automated close-loop workflow. A constraint-aware NSGA-II variant is used, incorporating a repair operator to ensure design feasibility. Experimentally validated against hyperspectral imaging-based mixing characterization and pressure-drop measurements, the framework eliminates manual trial-and-error workload and alleviates researchers from the tedious tasks of navigating across 3D modeling, CFD simulations, and optimization algorithms, reducing optimization time by 48% compared to the conventional simulation-assisted approach. By autonomously screening hundreds of designs, it identifies Pareto-optimal micromixers and generates an extensive database that supports inverse design and reveals the mixing structure-performance relationship, facilitating the establishment of general design guidelines. The framework is generally applicable to a wide range of passive micromixers

Radiomics analysis of early pregnancy ultrasound images to predict viability at the end of first trimester

Objective: To determine whether there are radiomic ultrasound features of early pregnancy when viability is unknown, which in combination with clinical features, may predict subsequent loss. Method: Multi-centre retrospective cohort study, which included 500 cases of pregnancies of unknown viability (PUV) collected from January 2021 to January 2023. Longitudinal ultrasound images were identified from Queen Charlotte’s and Chelsea Hospital (QCCH), London (n=400, split 8:2 for training and validation) and St Mary’s Hospital (SMH), London (test data set n=100). Images were extracted and segmented to include firstly the gestation sac and secondly the sac endometrial border. A segmentation model was developed using a deep learning (DL) model (multi-task nnUNet v2) and standard Dice Coefficient (DICE) was used to measure performance. A prediction model, using clinical and radiomic features, was developed by comparing several machine learning (ML) methods. The area under the ROC curve (AUC), F1-score, and recall were used to assess model performance. Results: The QCCH and SMH data sets were in the majority well matched and consisted of 53.3% and 53.0% miscarriage cases by the end of first trimester, respectively. The DL segmentation model for gestation sac achieved a mean DICE score of 0.950 and 0.940 in the training and test data sets respectively. The segmentation model for the sac endometrial border achieved a DICE mean score of 0.917 (QCCH) and 0.922 (SMH). The best performing PUV outcome classification model (XGBoost and LASSO) for predicting miscarriage (PUVPS model); achieved an AUC of 1.00 (F1-score 1.00), 0.92 (F1-score 0.79) and 0.84 (F1-score 0.76) in the QCCH training, QCCH validation and SMH test set respectively. Conclusions: We have developed an end-to-end radiomics-based model to segment and predict early pregnancy outcomes. The main limitation of this study is its sample size, which can make a ML model prone to overfitting. This study sets the stage for future trials to prospectively evaluate the performance of the PUVPS model, in a large multi-centre cohort, which can then be used to help patients navigate the uncertainty of a PUV early pregnancy classification

The protective stepping response in patients with bilateral vestibular hypofunction

Objective: Protective stepping following postural instability is a defence mechanism that prevents falls. Vestibular patients have increased risk of falling but little is known about their stepping response. Here, we investigate whether the protective stepping response is preserved in patients with bilateral vestibular hypofunction (BVH). Methods: This cross-sectional study, conducted in a balance-research facility, measured body sway and protective stepping responses during a dynamic postural task (platform oscillations at different velocities). Patients diagnosed with BVH were recruited from neuro-otology clinics. As stepping may be dictated by instability perception, objective sway and subjective instability were also analysed for each participant. Results: 12 patients (4 males, age:65.1, SD:14.2 years) and 12 healthy age and gender matched controls (age:64.8, SD:5.3) were recruited. Patients swayed more than controls (t:-2.153,p=0.03,d=-0.39) and showed marginally steeper objective-subjective instability curves than controls (t:-2.082,p=0.053,d=-0.85), meaning they felt slightly more unstable than controls for the same amount of sway. However, stepping velocity thresholds (t:-1.013,p=0.324,d=0.45) and latencies (t:0.062,p=0.951,d=-0.02) were not different between patients and controls. Discussion: These results indicate that the protective stepping response is preserved in BVH patients, hence not critically dependent on vestibular input. Only chronic patients were included, which limits the generalisation of the results to acute phases of the vestibular loss

Systemic inflammation and its associations in acute moderate-severe Traumatic Brain Injury: a cross-sectional study

Traumatic Brain Injury (TBI) triggers an acute systemic inflammatory response, which may impact outcomes. This response may interact with pre-existing factors linked to inflammation, such as age, to influence outcomes. Previous studies have typically measured few cytokines, but high-dimensional proteomic approaches can sensitively detect a broad range of inflammatory markers, to better characterise post-TBI inflammation. We analysed plasma from BIO-AX-TBI study participants (n = 37 acute moderate–severe TBI (Mayo Criteria), n = 22 acute non-TBI trauma (NTT), n = 28 non-injured controls (CON)) using the Alamar NULISA™ panel (>200 inflammatory markers). The NTT group enabled differentiation of TBI-specific versus general injury-related responses. Inflammatory markers were correlated with plasma NFL, GFAP, total tau, UCH-L1 (Simoa®), S100B (Millipore), and subacute (10 days–6 weeks) 3T MRI measures of lesion volume and white matter injury. Differential expression analysis identified four markers elevated specifically in TBI (VSNL1, IL1RN/IL-1Ra, GFAP, IKBKG), while other derangements reflected non-specific injury responses. Higher VSNL1 correlated with greater lesion volume (rs = 0.53) and higher IL1RN/IL-1Ra with greater white matter injury (rs = −0.66, both FDR-adjusted p < 0.05). IL33, part of the non-specific injury response was higher in participants with good (GOS-E 5–8) versus poor (GOS-E 1–4) outcomes (W = 47, FDR-adjusted p = 0.0024). Using an Elastic Net model trained on healthy controls, we show that “inflammation age” exceeded chronological age in TBI, particularly in younger participants. In summary, acute post-TBI inflammation includes both TBI-specific and non-specific components, linked to structural brain injury and functional outcome. Age modulates the inflammatory response. VSNL1, IL1RN/IL-1Ra, and IL33 are potential mediators of post-TBI pathophysiology

Anthropogenic emissions of volatile Cd detected in western tropical North Atlantic surface seawater

The Cd concentrations and isotope compositions of open ocean surface waters are generally thought to be governed by internal cycling, and particularly by the balance between upwelling of more Cd-rich deeper water masses and Cd uptake by phytoplankton. Here we present a new dataset of coupled Cd isotope compositions and concentrations for seawater depth profiles sampled in the western tropical Atlantic Ocean during Leg 2 of the GEOTRACES GA02 section. A box model for the Cd source and sink fluxes of the oligotrophic surface waters of the study area shows that the observed light Cd isotope compositions and low Cd concentrations are a consequence of biological Cd uptake and atmospheric deposition of isotopically light anthropogenic Cd. Aerosols enriched in anthropogenic Cd thereby contributed at least 19%, and possibly more than 45%, to the dissolved surface water Cd inventory during the sampling period. This reveals that anthropogenic emissions of volatile Cd can have a key impact on the distribution of Cd in open ocean surface waters

Glucocorticoid-induced adrenal insufficiency: physiological dose tapering promotes recovery

Objective Glucocorticoid discontinuation is complicated by glucocorticoid-induced adrenal insufficiency. Guidelines discourage tapering below physiological doses (prednisolone 3-6 mg) when morning cortisol is ≤300 nmol/L, with values <150 nmol/L thought to indicate persistent adrenal insufficiency, though this may underestimate hypothalamic-pituitary-adrenal axis suppression from such doses. We aim to evaluate how hypothalamic-pituitary-adrenal axis function evolves during physiological dose tapering and assess whether current cortisol thresholds restrict successful discontinuation. Design Retrospective cohort study. Methods Adults (n=65) with long-term glucocorticoid use for inflammatory disease undergoing prednisolone tapering between 2019 and 2024 were included. Serial short Synacthen tests (n=52) on reducing prednisolone doses (≤5 mg) were analysed using linear mixed-effects modelling. Nadir morning cortisol values at doses ≤5 mg from successful weans were compared with guideline thresholds. Results At referral, mean age was 55.4±16.4 years, with median prednisolone dose and duration of therapy being 5 [3.5-5] mg and 23 [6.5-66.5] months, respectively. For each 1 mg dose reduction, morning and post-Synacthen cortisol rose by 48.8 nmol/L and 57.5 nmol/L (both p2 mg producing larger cortisol increases than 1 mg reductions (both p<0.05). Among completed wean attempts (n=47), 81% (n=38) were successful. Of these, 42% (n=16) had a nadir morning cortisol <150 nmol/L, including six with values <28 nmol/L. No adrenal crises occurred. Conclusions Physiological dose tapering in glucocorticoid-induced adrenal insufficiency enables, rather than follows, hypothalamic-pituitary-adrenal axis recovery, with structured, symptom-led tapering being safe and effective. Future guidelines should recognise the axis suppression from physiological doses

Aqueous sulfur/carbon nanotube composite material and nanostructure for the cathode of lithium-sulfur batteries

The use of multi-wall carbon nanotubes (CNTs) in lithium sulfur batteries (LSB) provides advantages of structural integrity (to account for volume expansion) and better electronic conductivity (to aid the insulating nature of sulfur active material), however, how to efficiently utilise CNTs remains elusive. Here, sulfur/CNT composites are synthesised via scalable melt diffusion and cathodes are fabricated by a sustainable aqueous approach. CNTs are used as the carbon host and carbon black C65 as the electrical additive. Different ratios of CNT (in the melt diffusion step) and C65 (in the cathode coating step) are investigated. The formation of C–S bonds and thiophene-like sulfur in the sulfur/CNT composite material during melt diffusion promotes redox reactions and mitigates polysulfide dissolution. The CNT host forms a hierarchical nanostructure covering a range of pore widths to promote sulfur infiltration into the CNT matrix and increase surface area and porosity, resulting in improved ion diffusion kinetics, polysulfide confinement, and better ability to accommodate sulfur volume changes during (dis)charging. The initial discharge capacity is 1350 mA h g−1 at 0.05 C with the cathode containing 17.5 wt% CNT (capacity based on the total mass of the cathode including both active and inactive materials) and the capacity maintains at 550 mA h g−1 at 1 C

The economic burden of dengue: a systematic literature review of unit costs for non-fatal episodes treated in the formal healthcare system

Background: Dengue, a vector-borne disease caused by the dengue virus, has emerged as a global public health concern, given the tenfold rise in reported cases over the last two decades. In light of the upcoming dengue interventions, country-specific cost-of-illness estimates are required to evaluate the cost-effectiveness of new interventions against dengue. This study aims to conduct an updated systematic review of dengue cost-of-illness studies, extracting the relevant data, and conducting regression analysis to explore potential factors contributing to the cost variations among countries. Methods: We used the MEDLINE, EMBASE, PubMed, and Web of Science databases to systematically search for published dengue cost-of-illness studies reporting primary data on costs per dengue episode. A descriptive analysis was conducted across all extracted studies. Linear regression analysis was performed to investigate the association between the GDP per capita and cost per episode. The quality of the included studies was also assessed. Results: Fifty-six studies were included, of which 22 used the societal perspective. The reported total cost per episode ranged from $15.0 for outpatients in Burkina Faso to$9,386.1 for intensive care unit patients in Mexico. Linear regression analysis revealed that the cost of dengue illness varies significantly across countries and regions, and was positively related to the setting’s GDP per capita. The quality assessment demonstrated that improvements are needed in future studies, particularly in the reporting of the methodology. Conclusions: Cost of dengue illness varies widely across countries and regions. Future research should focus on understanding other drivers of cost variations beyond GDP per capita to improve the cost estimates for economic evaluation studies. The results presented in this study can serve as crucial input parameters for future economic evaluations, supporting decision makers in allocating resources for dengue intervention programmes

A Prospective Study of Fibrosis in the Lung Endpoints (PROFILE): characteristics of an incident cohort of patients with idiopathic pulmonary fibrosis

Background: Idiopathic pulmonary fibrosis (IPF) is a chronic, progressive fibrotic lung disease. The PROFILE study was a prospective, observational cohort study designed to better define the natural history of IPF, understand disease biology and identify biomarkers to support disease management and enhance clinical trial design. Methods: Individuals with an incident diagnosis of IPF were recruited between 2010 and 2017 across two co-ordinating centres in the UK. Demographics, clinical measurements and blood samples were obtained at baseline, and 1, 3, 6, 12, 24, 36 months. Disease progression events were defined as death or relative FVC decline>10% at 12 months. Survival estimates were modelled using cox proportional hazards; longitudinal lung function decline was estimated using mixed effect models, specified with restricted cubic splines, a random intercept for participant and random effect for study visit. All models were adjusted for baseline age, sex and continuous baseline percent predicted forced vital capacity (ppFVC). Results: A total of 632 participants were recruited, 77.1% were male and mean age at enrolment was 70.4 years (SD 8.4). Mean baseline ppFVC was 79.5% (SD 19.2), mean percent predicted DLCO (ppDLCO) was 45.7% (SD 15.1). A total of 304 (48.1%) participants met disease progression criteria at 1 year. Median survival was 3.7 years (95%CI 3.3; 4.0). More severe baseline physiology, 12-month relative lung function decline ≥10%, older age, and short telomeres were independent risk factors for mortality. Twelve-month estimated change in ppFVC was -5.28% (95%CI -6.34; -4.22) with an average FVC decline of 186.9ml (95%CI -225.4.0; -148.5), 12- month estimated change in ppDLCO was -3.35% (95%CI -4.30; -2.40). Conclusion: The PROFILE cohort confirms that untreated, IPF is inexorable progressive and inevitably fatal with a poor median survival from diagnosis

Two-stage Bayesian factor analysis for air pollution source apportionment and health risk assessment

Air pollution, especially particulate matter (PM), presents significant public health challenges and is associated with several Sustainable Development Goals (SDGs), notably SDG 3 (Good Health and Well-being) and SDG 11 (Sustainable Cities and Communities). Effective policy development requires robust statistical approaches to identify pollution sources and quantify their health impacts with appropriate uncertainty. This study develops a novel two-stage Bayesian framework for air pollution source apportionment and health risk assessment, with the aim of quantifying the contribution of distinct particle sources to respiratory health outcomes in children, using particle number size distribution (PNSD) data from London. In the first stage, we construct a Bayesian dynamic factor model with autoregressive components to infer latent pollution sources, incorporating non-negativity constraints and accounting for temporal dependence. In the second stage, we assess the relationship between source-specific exposures and respiratory hospital admissions in children via a Poisson regression model, explicitly propagating uncertainty from the source apportionment stage to the health model. The model identifies four main sources: nucleation, traffic, urban activities, and secondary aerosols. Among these, traffic and secondary sources exhibit the strongest and most consistent associations with increased respiratory hospital admissions. Importantly, models that do not account for uncertainty propagation tend to overestimate health risk associations, underscoring the value of the proposed Bayesian framework. This work illustrates the advantages of integrating Bayesian methods for source apportionment and health effect estimation, with formal uncertainty propagation across model stages. The proposed framework enhances interpretability and supports evidence-based public health and environmental policy. It is readily extensible to other pollutants and settings, contributing to improved air quality management and progress toward global sustainability goals