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

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

Screening tools to identify a neurogenic cause for pelvic organ dysfunction: a scoping review

Background Pelvic organ dysfunction can be an early sign of neurological disease, potentially preceding recognition of the underlying neurogenic mechanism. Screening tools are widely used in healthcare to aid early detection. Many tools exist for assessing pelvic organ symptoms, but it is unclear if any are designed to identify whether neurological disease is causing pelvic organ symptoms, nor whether there may be a potential neurogenic basis for such symptoms in the absence of prior neurological diagnosis. Objective To identify assessment and diagnostic tools used to evaluate pelvic organ symptoms in neurological disease and determine their intended purposes, including whether any are designed to evaluate likelihood of neurogenic basis. Methods A scoping review was conducted following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) scoping review guidance. Searches of MEDLINE, CINAHL, and Embase databases included terms such as “assessment and screening tools,” “neurological conditions,” and “pelvic organ dysfunction.” Results From 1600 papers screened, 513 were included. Across these, 212 different tools were identified, covering a wide variety of uses. However, none were specifically developed to screen for a neurogenic cause of pelvic organ symptoms in patients with or without a prior neurological diagnosis. Conclusions There are currently no tools designed to establish neurogenic mechanisms underlying pelvic organ symptoms. For undiagnosed individuals, this type of tool would trigger prompt neurology review, potentially improving prognosis. Developing a screening tool focused on detecting neurogenic origins could support earlier recognition and management of many neurological conditions associated with pelvic organ dysfunction

Engineering whole-cell catalysts to use plastic waste as a feedstock

The extensive production, durability and waste mismanagement of plastic polymers have led to a highly concerning environmental problem. Recycling methods aim to reduce the amount of plastic pollution and, among them, biological processes have emerged as an interesting alternative for the management of plastic waste that is difficult to collect or can not be recycled by other methods. While there has been significant progress in the field, in particular related to the enzymatic hydrolysis of polyesters, most biological methods rely on the use of enzymes in vitro, using collected plastics. In this review we explore the status of technologies using whole-cell catalysts that could be used for in vivo upcycling of plastic waste – with plastic becoming a microbial feedstock – and for the development of biodegradation strategies in relevant environments. We have identified a number of barriers related to polymer bioavailability, enzyme activity and secretion, and the use of strains and microbial communities that need to be overcome to materialise a much-needed solution to plastic pollution

Machine learning-driven nanopore sensing for quantitative, label-free miRNA detection

Nanopore sensors offer exceptional sensitivity for detecting single molecules, making them ideal for early disease diagnostics. In this study, we present a multiplexed nanopore-based assay that combines DNA-barcoded probes with advanced computational analysis to detect microRNAs (miRNAs) with high specificity and quantitative accuracy. Each probe selectively binds its target biomarker and induces a characteristic delay in the ionic current signal upon translocation through the nanopore, enabling label-free detection. We evaluated three analytical strategies for classifying delayed versus non-delayed events: (1) moving standard deviation (MSD), (2) spectral entropy (SE), and (3) a convolutional neural network (CNN). While MSD and SE rely on manually defined thresholds and exhibit limited sensitivity, the CNN model, trained on image representations of raw current traces, achieved near-perfect classification performance across all metrics (accuracy = 0.99, precision = 0.99, recall = 0.99). Grad-CAM visualisation confirmed that the CNN focused on biophysically relevant signal regions, enhancing interpretability and generalisability. All methods produced sigmoidal concentration-response curves consistent with expected binding kinetics, and nanopore-derived delay metrics closely matched RT-qPCR validation data. All three methods were capable of distinguishing between signal classes; however, the CNN model demonstrated superior sensitivity and robustness. This work highlights the importance of data interpretation in nanopore sensing and presents a comparative framework for binary event classification. The findings pave the way for the development of machine learning-driven nanopore diagnostics capable of detecting diverse biomarker types at the single-molecule level

Bayesian optimization for high-dimensional coarse-grained model parameterization: a case study on Pebax polymer

Coarse-grained (CG) force field models are extensively utilized in material simulations because of their scalability. Ordinarily, these models are parameterized using hybrid strategies that sequentially integrate top-down and bottom-up approaches. However, this combination restricts the capacity to jointly optimize all parameters. Although Bayesian optimization (BO) has been explored as an alternative search strategy to identify well-optimized CG parameters, its application has conventionally been limited to low-dimensional scenarios. This has contributed to the assumption that BO is unsuitable for more complex CG models, which often involve a large number of parameters. In this study, we challenge this assumption by successfully extending BO, using the tree-structured Parzen estimator (TPE) model, to optimize a high-dimensional CG model. Specifically, we show that a 41-parameter CG model of Pebax-1657, a copolymer composed of alternating polyamide and polyether segments, can be effectively parameterized using BO, resulting in a model that accurately reproduces the key physical properties of its parent atomistic representation. Our optimization framework simultaneously targets structural and thermodynamic properties, namely density, radius of gyration, and glass transition temperature. Compared to traditional search algorithms, BO-TPE not only converges faster but also delivers consistent improvements over more standard parametrization approaches