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Assessment of the prioritisation of diagnostics in National Action Plans for antimicrobial resistance
No full textBackground
Clinically diagnosing antimicrobial resistance (AMR) remains a challenge, with significant variation in countries’ readiness and ability to address it. This study explores how diagnostic capabilities feature in National Action Plans (NAPs) for AMR, to respond to and manage infections in human health.
Methods
A targeted analysis was conducted of available NAPs of WHO member states. NAPs were analysed using pre-defined search terms and an analytical framework for diagnostic modality, key diagnostic activities (laboratory capacity, external quality assessment, surveillance, antimicrobial stewardship, and research) and key performance indicators specific to diagnostics.
Results
Of the 142 NAPs analysed, 136/142 (95.8%) included diagnostics, mostly in the context of surveillance (131/142; 92.3%) and building laboratory capacity (113/142; 79.6%). Culture-based diagnostics appeared in 124/142 (87.3%) NAPs, point-of-care (POC) diagnostics in 47/142 (33.1%), and laboratory-based molecular diagnostics in 47/142 (33.1%). POC diagnostics were included most frequently in high-income country NAPs (24/44; 54.5%), and NAPs of the European region (21/37; 56.8%). Diagnostic specific KPIs were found in 35/142 (24.6%) NAPs, and KPIs specific to POC diagnostics in 9/142 (6.3%). Diagnostic KPIs were included most frequently in NAPs of the African region (15/36; 41.7%) and low- and middle-income countries (13/42; 31.0%).
Conclusions
While diagnostics feature in most NAPs, POC diagnostics are underrepresented, particularly in LMICs lacking diagnostic infrastructure and resources. The lack of KPIs, particularly in HICs, prevents effective evaluation. This study highlights the need for improved strategic planning to ensure NAP objectives are translated into real-world deliverable action to manage AMR
Where are the affective psychoses?: A systematic review and meta-analysis of the proportion of people with primary affective disorders presenting to early intervention services
No full textBackground: People with affective psychotic disorders often face diagnostic delays and presentations are under-recognised at first contact with Early Intervention Services (EIS). Despite their clinical significance, most research and service models for first-episode psychosis (FEP) have focused on non-affective psychoses. Aim: Clarifying the relative prevalence of affective psychoses in EIS.
Method: A systematic review and random-effects meta-analysis of observational studies reporting proportion of affective psychotic disorders among individuals presenting to EIS with FEP (PROSPERO: CRD42021257473) was conducted. Eligible studies included treated FEP populations diagnosed using DSM /ICD criteria. Searches were conducted in Web of Science, MEDLINE, and PsycINFO (inception to July 2025). The primary outcome was pooled proportion of affective psychotic disorders. Heterogeneity was assessed using Q statistics and I². Meta-regressions examined potential moderators, including urbanicity, national income level, and geographical region.
Results: Eighty-three studies (n = 30,946; mean age 24.95 years; 34.78% female) were included. Random-effects pooled proportion was 18.0% (95% CI 15.4–20.6; 95% prediction interval 3.6–39.4%; I² = 95.6%). Schizoaffective disorder represented 7.4% (k = 49; 95% CI: 5.8–9.2). Schizophrenia was the most frequent diagnosis, with a pooled proportion of 45.5% (k = 79; 95% CI: 40.3–50.7). Meta-regression analyses identified that affective psychoses were less common in Asia and more common in North America compared to Europe. Higher urbanicity and national income were also associated with increased prevalence.
Conclusions: Affective psychotic disorders constitute a meaningful subgroup within EIS. This suggests better screening, targeted treatments and adaptive service models of care
Climate change, extractivist infrastructure and environmental conflicts at the Northern Sea-Polar Silk Road intersection
No full textThis study employs a political ecology lens to analyze the interconnections between extractive and infrastructural developments, reported climate change impacts and the experiences of affected communities in socio-environmental conflicts along the Northern Sea-Polar Silk Road. Python programming was employed to process Network Common Data Form (NetCDF) datasets, generating density information on ship movements and applying logarithmic scaling to capture both sparse ship activity in remote areas and high concentrations in key traffic zones. The findings reveal a notable increase in infrastructure developments and areas of concentrated maritime traffic, including existing projects such as Yamal LNG or the newly emerging Vostok mega‑carbon complex. This research offers critical insights into the intersections of climate change and infrastructure-led development corridors, with important implications for Indigenous Peoples and local environmental justice organizations. The results underscore the need to address the colonial dimensions of socio-environmental transformations, especially in the context of the climate crisis that is reshaping both the polar region and global systems
Optimizing membrane-substrate buckling to control surface deformation pattern
No full textBuckling of membrane–substrate structures can result in complex deformation patterns on their top surfaces. Traditionally, control over these patterns has relied on altering the material or thickness ratios of the system, which imposes considerable constraints on design flexibility. Inspired by studies on Winkler foundation optimization, this work presents a novel framework for tailoring membrane–substrate buckling modes via topology optimization. In this approach, the material distribution within the substrate is optimized to tune the mechanical response, while filtering and projection techniques are incorporated to enhance manufacturability. Post-processing of the optimized layout yields a physically realizable structure that preserves the desired mechanical behavior. A two-dimensional case study demonstrates that the optimized design successfully generates the prescribed deformation pattern without modifying material properties or thickness ratios, thus enabling precise control over buckling-induced surface morphologies. Experimental validation using fabricated prototypes subjected to compression further confirms that the observed buckling modes closely match the targeted patterns, underscoring the practical effectiveness of the proposed method
Vegetarian and vegan diets and cancer incidence: a systematic review and meta-analysis of prospective studies
No full textSeveral studies have suggested that vegetarian and vegan vs. non-vegetarian diets are associated with lower cancer risk overall, however, results for specific cancer sites have been less consistent. We conducted a systematic review and meta-analysis of prospective studies on vegetarian and vegan diets and cancer incidence to clarify the associations across cancer sites. PubMed and Embase databases were searched for relevant studies up to 5 July 2025. Summary relative risks (RRs) and 95% confidence intervals (95% CIs) were calculated for the association between vegetarian and vegan diets and cancer incidence. World Cancer Research Fund (WCRF) criteria was used to evaluate the strength of the evidence.
Seventeen publications (seven prospective studies) were included. The summary RRs (95% CIs) for vegetarians vs. non-vegetarians was 0.87 (0.84-0.91, I2=0%, n=4 studies) for total cancer incidence, 0.55 (0.36-0.86, I2=32%, n=4) for stomach cancer, 0.86 (0.76-0.97, I2=14%, n=6) for colorectal cancer, 0.79 (0.67-0.93, I2=38%, n=7) for colon cancer, 0.55 (0.31-0.97, I2=0%, n=2) for proximal colon cancer, 0.77 (0.62-0.95, I2=0%, n=5) for pancreatic cancer, 0.79 (0.66-0.94, I2=0%, n=4) for melanoma, 0.92 (0.86-0.99, I2=0%, n=7) for breast cancer, 0.81 (0.69-0.95, I2=0%, n=3) for postmenopausal breast cancer, 0.78 (0.62-0.98, I2=0%, n=5) for bladder cancer, and 0.76 (0.63-0.93, I2=0%, n=4) for non-Hodgkin's lymphoma. In addition, non-statistically significant inverse associations were observed for some cancers, with summary RRs of 0.85 (0.70-1.04, I2=0%, n=6) for lung cancer, 0.83 (0.68-1.02, I2=0%, n=5) for ovarian cancer, and 0.87 (0.75-1.00, I2=43%, n=6) for prostate cancer. Results for other cancer sites were imprecise or near the null. The summary RRs (95% CIs) for vegans vs. non-vegetarians were 0.77 (0.70-0.85, I2=0%, n=3) for total cancer, 1.02 (0.71-1.48, I2=42%, n=3) for colorectal cancer, 0.80 (0.64-1.00, I2=0%, n=4) for breast cancer, and 0.87 (0.50-1.49, I2=49%, n=3) for prostate cancer. BMI explained a moderate part of the associations. The strength of evidence [judging the likelihood of causality] for vegetarian diets and total, colorectal, colon and breast cancer was judged as probable, and limited suggestive for stomach, pancreatic, and bladder cancers, melanoma and non-Hodgkin's lymphoma, and for vegan diets and total and breast cancer was considered limited-suggestive. Vegetarian diets compared to non-vegetarian diets are associated with reduced risk of total cancer and seven specific cancer types, while vegan diets are associated with reduced risk of total and breast cancer. Although further studies are needed to assess the long-term adherence to vegetarian and vegan diets and cancer incidence and across less investigated cancers, these results support recommendations to adopt much more plant-based diets for cancer prevention
Passivity-based control of underactuated systems with non-integrable state-dependent matched disturbances
No full textThis work investigates the passivity-based control of a class of underactuated mechanical systems subject to matched disturbances that enter the dynamics through a state-dependent vector-valued function that is not integrable. The main contributions include a new passivity-based controller with a dynamic extension, designed with the port-Hamiltonian formalism, and, most importantly, a suitably defined function of the states, serving the purpose of estimating the disturbance parameters while circumventing the usual integrability assumption. A corresponding controller is designed with the Lagrangian formalism, and key differences are discussed. Numerical simulations on two examples demonstrate the effectiveness of the new controllers
Aqueous sulfur/carbon nanotube composite material and nanostructure for the cathode of lithium-sulfur batteries
No full textThe 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
Bayesian optimization for high-dimensional coarse-grained model parameterization: a case study on Pebax polymer
No full textCoarse-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
The role of soot aggregate fusing in laminar flames: a study employing fusing models derived from carbon black
No full textThe evolving morphology of carbonaceous aggregates has been previously ignored to a large extent in sooting flame studies. Soot aggregate fusing causes a reduction in surface area and the number of primary particles per aggregate. Despite soot aggregate fusing directly impacting the morphology of aggregates, there is no agreed model for the characteristic timescale for this process. Previous flame simulations used fusing timescale models based on other materials such as titania and silica, which feature different fusing mechanisms. Recently, a series of carbonaceous-based soot fusing models have been proposed that are intrinsically derived from carbonaceous materials such as carbon black or lab-produced ethylene soot. In the present work, these carbonaceous-material-based soot fusing models are implemented in a two population balance equation (PBE) framework and are further coupled with a reactive computational fluid dynamics (CFD) code in order to simulate the laminar Santoro series of flames across all three smoking conditions (non-smoking, incipient smoking, smoking). Predictions of soot volume fraction, total number density, number of primary particles per aggregate, and primary particle diameter are presented across all three flames and compared against experimental observations where possible. The importance of soot fusing is elucidated across various regions within flames and the predictive power of each fusing model is discussed
