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Evaluating the cost-effectiveness of replacing lansoprazole with vonoprazan for treating erosive oesophagitis
No full textObjective This cost-effectiveness analysis compares vonoprazan against lansoprazole, a gold-standard proton pump inhibitor, in managing erosive oesophagitis.
Methods The economic evaluation was carried out using data from a double-blind, randomised control trial. Costs were measured in pounds sterling. Effectiveness was assessed on a binary scale, resolution versus non-resolution of disease, after 32 weeks.
Results The primary analysis produced an incremental cost-effectiveness ratio (ICER) of £3421.27 per resolution. After applying quality-adjusted life year (QALY) data from the REFLUX trial (2008), we derived an ICER/QALY of £34 747.32, marginally exceeding the £30 000 threshold set by the National Institute for Health and Care Excellence. However, further subgroup analysis showed cost-effectiveness when healing severe grades of oesophagitis (ICER/QALY of £22 165.56). The first sensitivity analysis considers the typically non-invasive determination of disease resolution; the ICER/QALY of £15 826.98 supports vonoprazan’s use in treating severe oesophagitis. The second considers a longer healing phase alongside a stronger 30 mg maintenance dose of lansoprazole, concordant with current guidelines; the ICER/QALY of £43 998.39 suggests the guidelines (regarding dosage, frequency and duration) must be optimised for vonoprazan. The final sensitivity analysis accounts for variations in quality-of-life measures, which grossly inflate the ICER/QALY (£118 216.32); this emphasises that vonoprazan should mainly be considered for patients with persistent symptoms and high severity.
Conclusion Vonoprazan is potentially cost-effective for the initial healing of severe oesophagitis, after endoscopic diagnosis. Further trials and economic evaluations are necessary for the symptom-based prescription of vonoprazan and to determine the optimal dosage, frequency and duration
What is the cost and potential of low carbon electricity transition in the MENA region?
No full textReplacing fossil fuels with renewable energy sources, including wind and solar power, is a validated approach for mitigating carbon emissions. To maximize renewable energy potential and account for costs and impacts of low carbon energy transitions, research models or modelling tools must provide an accurate representation of the technological and economic capabilities of renewable energy technologies. Hence, this study assesses the electricity generation potential, and costs associated with onshore and offshore wind power, and solar photovoltaic (PV) system, in the Middle East and North Africa (MENA) region based on wind and solar atlas datasets. Unlike existing works in literature, the assessment of offshore wind energy potential and the calculation of the levelized cost of electricity (LCOE) in this study incorporates factors like water depth, distance from shore, and wind speed. Also, the potential of onshore wind energy and solar photovoltaic systems is evaluated by considering variables such as population density, availability of suitable land area, and geographical location, for every grid cell in the MENA region. We found that the average LCOE can be as low as US45/MWh for wind power, achieved in Saudi Arabia and Kuwait respectively. This work contributes to existing literature by providing reliable LCOE, capacity factor, and productivity data for solar and wind energy potential estimation in the MENA region
Deep learning classification models demonstrate high accuracy and clinical potential in radiograph interpretation in the arthroplasty clinical pathway: a systematic review and meta-analysis
No full textArtificial intelligence (AI) is set to transform medical imaging, streamlining, and
improving the delivery of care. Used to diagnose disease, plan surgery, and monitor
patients post-operatively, imaging is a cornerstone of the osteoarthritis-arthroplasty
clinical pathway. To date, no systematic review has examined AI’s diagnostic and
prognostic role in interpreting radiographs and cross-sectional imaging in the
arthroplasty pathway. With growing interest from the orthopaedic community, this
meta-analysis broadly evaluates the performance of deep learning (DL) algorithms in
these imaging tasks. Ovid Medline, Ovid Embase, Scopus, and Web of Science were
systematically searched for studies published between January 1, 2012, and April 1,
2024, evaluating DL algorithms for diagnostic and prognostic tasks along the
osteoarthritis-arthroplasty pathway. Eligible studies included those that used
established diagnostic or surgical candidacy assessments as ground truth. Study
quality was assessed using the Quality Assessment of Diagnostic Accuracy Studies 2
tool, and pooled sensitivity and specificity were determined. Hierarchical summary
receiver operating characteristic curves assessed diagnostic performance.
Of the
2355 studies identified, 138 studies were included. Of these, 66 studies were used in
the meta-analysis for the results of AI-only interpretation and 11 studies for the results
of human-only interpretation. The AI studies had a pooled sensitivity of 0.88 (95% CI:
0.81 to 0.92) and a pooled specificity of 0.91 (95% CI: 0.87 to 0.94). In comparison, the
clinician interpretation studies had a pooled sensitivity of 0.76 (95% CI: 0.64 to 0.85)
and a pooled specificity of 0.79 (95% CI: 0.59 to 0.90). This meta-analysis highlights
the potential of DL algorithms to improve efficiency in osteoarthritis classification and
prognosis in the arthroplasty pathway based on low-to-moderate quality evidence.
Although the results are not generalizable, the findings suggest DL models have the
potential to be adopted in osteoarthritis treatment pathways, warranting further
exploration of its role in patient care
Contextual adaptation and implementation of who guideline on self-care interventions for SRH in Kenya, Nigeria and Uganda
No full textBackground Self-care interventions for sexual and reproductive health and rights (SRHR) are critical to advancing individual wellbeing and achieving universal health coverage. This study assesses how three countries (Kenya, Nigeria and Uganda) have adapted and implemented the World Health Organization (WHO) Guideline on Self-Care Interventions for SRHR within their national policy and practice landscapes. Objectives The primary objective was to develop and pilot a novel policy mapping and implementation analysis tool. Secondary aims included using a mixed-methods approach comprising policy document review, surveys and interviews to evaluate the contextualisation and uptake of WHO recommendations at the country level. Methods We designed a Policy Mapping and Implementation Matrix (PMIM) to assess alignment with 24 WHO SRHR self-care recommendations. Data were collected from 316 stakeholders through surveys and interviews, and 47 policy documents were reviewed. Findings were synthesised using a Red Amber Green (RAG) matrix to assess implementation across the guidelines five domains. Results Implementation varied by country and recommendation. Family planning and infertility services (Category 2) showed the strongest uptake, while areas such as unsafe abortion management and STI self-sampling (Categories 3 and 4) were less consistently addressed. Kenya demonstrated broad alignment through multiple policies, while Nigeria and Uganda showed promising progress, particularly with the development of dedicated national self-care guidelines. Key barriers included supply chain challenges, low health literacy and legal constraints. Critical enablers were provider training and task shifting. Conclusion This study introduces a novel, pragmatic framework for assessing national self-care policy and practice. It highlights the importance of contextual adaptation rather than mechanical adoption of global guidelines. While study limitations are acknowledged, the methodology offers a replicable approach for monitoring and strengthening self-care integration in diverse settings.</jats:p
Effect of cholecalciferol on immune and vascular function in non-diabetic chronic kidney disease
No full textBackground and aims: Vitamin D deficiency, widely prevalent in patients with chronic kidney disease (CKD) could play a role in the pathogenesis of cardiovascular disease (CVD) by causing alterations in endothelial and immune function. We investigated the change in immune and vascular functions following vitamin D supplementation in non-diabetic subjects with stage 3-4 CKD and vitamin D deficiency.
Methods: In this single-arm study, non-diabetic CKD subjects aged 18–75 years, eGFR 15-60 ml/min/1.73m2, and serum 25-hydroxyvitamin D3 levels <20 ng/ml were enrolled. Enrolled subjects received a directly observed oral dose of 300,000 IU cholecalciferol at baseline and 8 weeks. Outcome assessments, including immunological, vascular, endothelial, inflammatory, and biochemical parameters, were measured at baseline and 16 weeks.
Results: In total, 62 subjects were studied. The mean age was 44 ± 12 years with 58% men. TH1 cells decreased from 17% (9%, 27%) to 11% (6%, 16%) (p=0.002) and TH2 cells increased from 9% (5%, 16%) to 16% (10%, 27%) (p=0.001) after cholecalciferol treatment. A significant increase in mRNA expression of vitamin D-responsive genes (cathelicidin, IL-10, VDR, and CYP27B1) was observed. The levels of pro-inflammatory cytokines (IFN-γ, TNF-α, IL-23, and IL-6) decreased whereas anti-inflammatory cytokines (IL-4, IL-10, and IL-13) showed an increase. Cholecalciferol treatment improved flow-mediated dilatation (FMD): 8.2% (6.2%, 12.1%) at baseline to 14.1% (10.0%, 20.1%) at 16 weeks (p<0.001).
Conclusions: This study confirms that cholecalciferol supplementation influenced immune function as it favored the TH2/TH1 phenotype, favorably affected the levels of inflammatory markers and mRNA expression of vitamin D responsive genes, and improved vascular function in CKD.
Clinical Trial Registration: https://www.ctri.nic.in, identifier CTRI/2019/10/021494
Understanding the risk of enhanced particle penetration into slow sand filter beds when using underwater skimming techniques
No full textThis study evaluated abiotic slow sand filters (SSFs) to understand the risk of particle penetration during underwater skimming (UWS), focusing on clogging, headloss development, and particle breakthrough. Pilot-scale filters containing clean sand were challenged with dispersed kaolin particles to simulate surface accumulation, and the sand surface was agitated to mimic UWS procedures. The study was undertaken with no maturation period to consider the worst-case scenario corresponding to the period just after filter skimming. Agitating the surface and restarting flow released captured particles, some moving downward through the filter. Shallow filter depths resulted in particles appearing in the filtrate, but increasing the media depth beyond 500 mm minimized this effect. Since 90 % of headloss occurred in the upper layers, deeper particle penetration was insignificant. Increasing the hydraulic loading rate from 0.3 to 0.5 m/h reduced particle retention by 0.72 log, yet all abiotic SSFs achieved over 2 log particle capture. Small particles (2–10 μm) were removed by 2 logs, indicating sufficient non-viral pathogen retention under routine conditions. Effective capture of particles sized 2–125 μm suggested minimal risk to water quality and public health during UWS on full-scale SSFs. Using clean sand and kaolin represented a worst-case scenario, excluding biological maturation and particles. The findings suggest that under normal conditions, UWS does not increase deep particle penetration or breakthrough, supporting its safe implementation to enhance filter maintenance without compromising water quality
Mast cells promote inflammatory Th17 cells and impair Treg cells through an IL-1β and PGE2 axis
No full textPurpose: CD4+ effector T cells (Teffs) play a key role in immune responses by infiltrating the sites of inflammation and modulating local leukocyte activity. In turn resident immune cells shape their response. This study aimed to investigate the influence of mast cells (MCs) on Teff biological responses.
Methods: This study examined human MC-Teff interactions, focusing on how MCs shape Teff responses. Flow cytometry, qRT-PCR, and cytokine assays were used to analyze the impact of primary human MCs on the Teff phenotype and function. MC-Teff crosstalk within Crohn’s disease patient tissues was assessed using confocal microscopy and advanced image analysis.
Results: MCs promoted the differentiation of Th17 cells, particularly the inflammatory Th17.1 subset, that secretes IFN-γ and GM-CSF. This differentiation was driven by the PGE2 and IL-1β axis. Additionally, MCs disrupted the phenotype and impaired the suppressive function of regulatory T cells (Tregs) through PGE2, skewing the Th17/Treg balance. The analysis of biopsies from patients with Crohn’s disease indicated that this MC/Teff crosstalk may play a role in the pathogenesis of auto-inflammatory processes.
Conclusion: MCs influence CD4+ T cell responses by fostering pro-inflammatory Th17 differentiation while impairing Treg function. This interaction underpins a Th17/Treg imbalance, which is significant in auto-inflammatory diseases such as Crohn’s disease, positioning MCs as critical drivers of disease pathogenesis
Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age
No full textWith biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems (RAS) offer technological solutions that may substantially advance terrestrial biodiversity monitoring, but this potential is yet to be considered systematically. We used a modified Delphi technique to synthesize knowledge from 98 biodiversity experts and 31 RAS experts, who identified the major methodological barriers that currently hinder monitoring, and explored the opportunities and challenges that RAS offer in overcoming these barriers. Biodiversity experts identified four barrier categories: site access, species and individual identification, data handling and storage, and power and network availability. Robotics experts highlighted technologies that could overcome these barriers and identified the developments needed to facilitate RAS-based autonomous biodiversity monitoring. Some existing RAS could be optimized relatively easily to survey species but would require development to be suitable for monitoring of more ‘difficult’ taxa and robust enough to work under uncontrolled conditions within ecosystems. Other nascent technologies (for instance, new sensors and biodegradable robots) need accelerated research. Overall, it was felt that RAS could lead to major progress in monitoring of terrestrial biodiversity by supplementing rather than supplanting existing methods. Transdisciplinarity needs to be fostered between biodiversity and RAS experts so that future ideas and technologies can be codeveloped effectively
Careful design of Large Language Model pipelines enables expert-level retrieval of evidence-based information from syntheses and databases
No full textWise use of evidence to support efficient conservation action is key to tackling biodiversity loss with limited time and resources. Evidence syntheses provide key recommendations for conservation decision-makers by assessing and summarising evidence, but are not always easy to access, digest, and use. Recent advances in Large Language Models (LLMs) present both opportunities and risks in enabling faster and more intuitive systems to access evidence syntheses and databases. Such systems for natural language search and open-ended evidence-based responses are pipelines comprising many components. Most critical of these components are the LLM used and how evidence is retrieved from the database. We evaluate the performance of ten LLMs across six different database retrieval strategies against human experts in answering synthetic multiple-choice question exams on the effects of conservation interventions using the Conservation Evidence database. We found that LLM performance was comparable with human experts over 45 filtered questions, both in correctly answering them and retrieving the document used to generate them. Across 1867 unfiltered questions, LLM performance demonstrated a level of conservation-specific knowledge, but this varied across topic areas. A hybrid retrieval strategy that combines keywords and vector embeddings performed best by a substantial margin. We also tested against a state-of-the-art previous generation LLM which was outperformed by all ten current models – including smaller, cheaper models. Our findings suggest that, with careful domain-specific design, LLMs could potentially be powerful tools for enabling expert-level use of evidence syntheses and databases in different disciplines. However, general LLMs used ‘out-of-the-box’ are likely to perform poorly and misinform decision-makers. By establishing that LLMs exhibit comparable performance with human synthesis experts on providing restricted responses to queries of evidence syntheses and databases, future work can build on our approach to quantify LLM performance in providing open-ended responses
Residential greenspace and lung function throughout childhood and adolescence in five European birth cohorts. A CADSET initiative
No full textWhether greenspace affects lung function is unclear. We explored associations between the level of greenness or presence of urban green space near the home with lung function measures taken repeatedly during childhood and adolescence in five European birth cohorts.
Lung function was measured by spirometry between six and 22 years (2–3 times), and 9,206 participants from BAMSE (Sweden), GINI/LISA South and GINI/LISA North (Germany), PIAMA (The Netherlands) and INMA (Spain) contributed at least one lung function measurement. The mean Normalized Difference Vegetation Index (NDVI) in a 300 m buffer and presence of urban green space within a 300 m buffer (yes/no) were estimated at the home address at the time of each spirometry measurement. Cohort-specific associations were assessed using adjusted linear mixed models and combined in a random-effects meta-analysis.
Residential greenness was not associated with forced expiratory volume in one second (FEV1), forced vital capacity (FVC) or FEV1/FVC in the meta-analysis (2.3 ml [-3.2, 7.9], 6.2 ml [-3.4, 15.7] and −0.1 [-0.3, 0.1] per 0.1 increase in NDVI, respectively), nor was having a nearby urban green space (−8.6 ml [–22.3, 5.0], −7.6 ml [-24.7, 9.4] and 0.0 [-0.4, 0.3], respectively). Heterogeneity was low to moderate (I2 = 0 –39 %). Asthma, atopy, air pollution, sex, socioeconomic status and urbanization did not modify the null associations.
Using repeated data from five large independent European birth cohorts, we did not find associations between vegetation levels around the home or the presence of an urban green space and lung function levels during childhood and adolescence