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A test of ecophysiological theories on tropical forest functional traits along a VPD gradient
No full textForest primary production is a crucial process for both ecosystem functioning and global carbon cycling. Primary production responds to both temperature and vapour pressure deficit (VPD) through separate mechanisms. Vegetation models need to quantify both responses. However, due to their often high correlations, most observational data sets used to test models or theories hardly distinguish them. Here we evaluate ecophysiological theories on the effect of VPD using tree trait data collected along a VPD gradient in West Africa. Study sites spanned an annual rainfall range of 1200–2050 mm, with varying seasonality but minimal temperature variation. Most photosynthetic traits show trends consistent with predictions from optimality theory, including higher net CO2 assimilation rates and greater photosynthetic capacity at drier sites. These patterns were associated with greater deciduousness, increased respiration rates and enhanced water transport at drier sites. In contrast, hydraulic traits showed weaker consistency with theoretical predictions or global trends, particularly those based on the xylem efficiency-safety tradeoff. Our findings suggest that vegetation models should account for higher photosynthetic capacity in drier regions, but that further research is needed to incorporate hydraulic traits into models
Signal enhancement in immunoassays via coupling to catalytic nanoparticles
No full textEarly diagnosis is vital for effective disease management, selection of appropriate treatment regimes, and surveillance and control of disease transmission. There is a growing need for point-of-need diagnostic platforms, such as lateral flow immunoassays (LFIAs), to reduce healthcare burdens, particularly in low-resource settings. However, LFIAs often suffer from inadequate sensitivity and exhibit limited dynamic ranges, leading to late-stage diagnosis or misdiagnosis. Here, we present a signal enhancement platform for use in both plate- and paper-based immunoassays, based on the formation of a coupled nanoparticle network. We demonstrate the coupling of an antigen-targeting detection probe with a secondary, catalytically active nanoparticle by utilizing secondary antibody interactions. Here, we show that signal enhancement is achieved through two functional mechanisms: network formation, facilitated by the secondary nanoparticle increasing the relative concentration of nanoparticles immobilized at the test zone; and the inclusion of catalytically active nanoparticles, which catalyze the oxidation of a chromogenic substrate at the test zone. Through this approach, we yielded a 40-fold improvement in the limit of detection (LOD) using 40 nm gold nanoparticle detection probes in spiked pooled human saliva. Further, the signal enhancement platform can be utilized alongside a range of detection probes, including gold nanoparticles, commonly employed for use in LFIAs. This work concludes by showcasing that the signal enhancement mechanism is compatible for use with complex sample matrices, such as human saliva
Understanding the clinical characteristics and timeliness of diagnosis for patients diagnosed with long Covid: a retrospective observational cohort study from North West London
No full textBackground
Long Covid is a multisystem condition first identified in the Covid-19 pandemic, characterised by a wide range of symptoms including fatigue, breathlessness and cognitive impairment. Considerable disagreement exists in who is most at risk of developing long Covid, driven in part by incomplete coding of a long Covid diagnosis in medical records.
Objective
To describe the incidence and impact of long Covid.
Design
A retrospective observational cohort study.
Setting and Participants
An integrated primary and secondary care dataset from North West London, covering over 2.7 million patients. Patients with long Covid were identified through clinical terms in their primary care records.
Main Variables Studied
Multivariate logistic regression was used to identify factors associated with having a long Covid diagnosis, while multivariate quantile regression was used to identify factors predicting the time a long Covid diagnosis was recorded.
Results
A total of 6078 patients were identified with a long Covid clinical term in their primary care record, 0.33% of the total registered adult population. Women, those aged 41–70 years or of Asian or mixed ethnicity, were more likely to have a recorded long Covid diagnosis, alongside those with pre-existing anxiety, asthma, depressive disorder or eczema and those living outside of the least or most socio-economically deprived areas. Men, those aged 41–70 years, or of black ethnicity, were diagnosed earlier in the pandemic, while those with depressive disorder were diagnosed later.
Discussion
Long Covid is poorly coded in primary care records, and significant differences exist between patient groups in the likelihood of receiving a long Covid diagnosis. A recorded long Covid diagnosis is more likely in women, some ethnic minority patients and those with pre-existing long-term conditions.
Conclusion
The experience of patients with long Covid provides a crucial insight into inequities in access to timely care for complex multisystem conditions and the importance of effective health informatics practices to provide robust, timely analytical support for front line clinical services.
Patient and Public Contribution
This study was co-designed, conducted and written in conjunction with people with long Covid
Diagnostic accuracy of the WHO tuberculosis treatment decision algorithms for children with presumptive tuberculosis: an individual participant data meta-analysis
No full textIntroduction
In 2023, almost 200,000 children under 15 years died from tuberculosis, most without appropriate treatment. Treatment decision algorithms (TDAs), developed to facilitate rapid anti-tuberculosis treatment initiation in children, were recommended by the World Health Organization (WHO) in 2022, conditional on validation in different cohorts and settings. We performed a retrospective external evaluation of the WHO TDAs using an individual participant dataset (IPD).
Methods & Findings
The IPD comprised four paediatric cohorts, restricted to children with presumptive pulmonary TB <10 years, and including children in high-risk groups (children living with HIV “CLHIV”, children with severe acute malnutrition “SAM”, and children <2 years). All children in the IPD were retrospectively evaluated using both TDA A (an algorithm including chest X-ray) and TDA B (without chest X-ray), excluding the triage step. The diagnostic accuracy against a composite reference standard (confirmed and unconfirmed tuberculosis versus unlikely tuberculosis) was determined and reported as sensitivities and specificities.
Of 1,886 children included (RaPaed-TB: n=740, Umoya: n=474, TB-Speed HIV: n=204, TB-Speed Decentralization: n=468), the median age was 2.9 years (interquartile range [IQR]:1.3,5.5), 741 (39.3%) were <2 years, 382 (20.3%) were CLHIV, and 284 (15.1%) had SAM. 281 (14.9%) had confirmed TB, 672 (35.6%) were classified as unconfirmed TB (clinically diagnosed, microbiological investigations negative), and 933 (49.5%) as unlikely tuberculosis. For TDAs A and B, algorithm sensitivity was 84.3% (95%CI: 74.8,90.6) and 90.6% (95%CI: 83.8,94.7) respectively, with a specificity of 50.6% (95%CI: 30.4-70.7) and 30.8% (95%CI: 21.5,42.0), respectively. For TDA A, estimated sensitivity in children in high-risk groups was lower than those with low-risk (83.0%, 95%CI: 79.4%, 86.1%; vs 88.0%, 95%CI: 84.8%, 90.6%), while having a gain in specificity (50.0%, 95%CI: 44.9%, 55.1%; vs 36.6%, 95%CI: 32.7%, 40.7%). Trends were similar for TDA B.
As for limitations, most diagnostic tuberculosis studies in children, including two of those included in the IPD, are performed at secondary or tertiary hospitals with higher levels of health care and thus the target population might differ somewhat from the IPD, potentially limiting the generalisability of our results.
Conclusions
This retrospective external evaluation of WHO TDAs in a large IPD shows high sensitivity but sub-optimal specificity for both TDAs, in line with the meta-analyses that generated the algorithms. Prospective studies that evaluate the entire TDA, including triage step are needed. Additionally, the integration of novel diagnostic tools within the TDAs should aim to enhance the accuracy, especially the specificity
Comment on “Atlas of fshr expression from novel reporter mice”
No full textIn recent years, there have been controversial claims of extragonadal effects of follicle-stimulating hormone (FSH). A paper by Chen et al (1) recently published in eLife further muddies the waters, though its intent was to provide clarity. Chen et al developed a new transgenic reporter mouse designed to map FSH receptor (FSHR) expression throughout the body. Using CRISPR/Cas9, the authors reportedly inserted a DNA fragment encoding a P2A-ZsGreen fluorescent protein between the end of the Fshr coding sequence and its 3′ untranslated region. These mice were expected to express a bicistronic Fshr/ZsGreen transcript under the control of the endogenous Fshr regulatory sequences. If successful, FSHR and ZsGreen should be coexpressed in cells as two distinct proteins via ribosomal skipping driven by the P2A sequence, with the former faithfully reporting the endogenous expression of the latter. The ZsGreen signal was unexpectedly observed in nearly all extragonadal tissues and cells examined. Even within the gonads, the reporter was more abundant in noncanonical FSH targets than in Sertoli or granulosa cells
COVID-19 disease and economic burden to healthcare systems in adults in six Latin American countries before nationwide vaccination program: Ministry of Health database assessment and literature review
No full textThe COVID-19 pandemic imposed a substantial burden on healthcare systems worldwide, yet reliable data on COVID-19 morbidity, mortality, and healthcare costs in Latin America remain limited. This study explored the disease and economic burden of COVID-19 in Argentina, Brazil, Chile, Colombia, Mexico, and Peru during the pre-vaccination period. Using national databases and a systematic review of the literature, we analyzed data on adults aged 18 and older, reporting cases, death rates, years of life lost, excess mortality, and direct medical costs. Before vaccination programs began, the average COVID-19 incidence rate was 6741 per 100,000 adults. Of these, 91% were mild cases, 7% moderate/severe, and 2% critical. Among 2,201,816 hospitalizations, 27.8% required intensive care, and 17.5% required mechanical ventilation. Excess mortality ranged from 76 to 557 per 100,000, and years of life lost spanned 241,089 to 3,312,346. Direct medical costs ranged from USD 258 million to USD 10,437 million, representing 2–5% of national health expenditures. The findings highlight significant variability across countries and provide crucial insights to help policymakers to make informed decisions and allocate resources effectively to improve national strategies around surveillance, preventive and treatment strategies to control the spread of COVID-19 disease in the future
Children with post COVID-19 multisystem inflammatory syndrome display unique pathophysiological metabolic phenotypes
No full textSARS-CoV-2 infections in children lead to symptoms from mild respiratory illness to severe postacute sequelae of COVID-19, including multisystem inflammatory syndrome in Children (MIS-C). We conducted a metabolic profiling of 147 children’s serum samples, including acute COVID-19 patients, MIS-C patients, and healthy controls. Using nuclear magnetic resonance spectroscopy and liquid chromatography–mass spectrometry, we measured 1101 metabolites. The results revealed distinct metabolic profiles in acute COVID-19 and MIS-C patients, with significant alterations in lipid classes. Both conditions exhibited an elevated Apo-B100/Apo-A1 ratio and increased serum inflammatory markers. MIS-C patients showed unique disruptions, including increased triglycerides and altered lipoprotein composition. Despite milder clinical respiratory symptoms, children’s metabolic disturbances mirrored those seen in severe adult COVID-19 patients, indicating a shared inflammatory response to SARS-CoV-2. This suggests potential long-term health impacts, underscoring the need for continued research into the metabolic consequences of COVID-19 in children
Explainable reinforcement and causal learning for improving trust to 6G stakeholders
No full textFuture telecommunications will increasingly integrate AI capabilities into network infrastructures to deliver seamless and harmonized services closer to end-users. However, this progress also raises significant trust and safety concerns. The machine learning systems orchestrating these advanced services will widely rely on deep reinforcement learning (DRL) to process multi-modal requirements datasets and make semantically modulated decisions, introducing three major challenges: (1) First, we acknowledge that most explainable AI research is stakeholder-agnostic while, in reality, the explanations must cater for diverse telecommunications stakeholders, including network service providers, legal authorities, and end users, each with unique goals and operational practices; (2) Second, DRL lacks prior models or established frameworks to guide the creation of meaningful long-term explanations of the agent’s behaviour in a goal-oriented RL task, and we introduce state-of-the-art approaches such as reward machine and sub-goal automata that can be universally represented and easily manipulated by logic programs and verifiably learned by inductive logic programming of answer set programs; (3) Third, most explainability approaches focus on correlation rather than causation, and we emphasise that understanding causal learning can further enhance 6G network optimisation. Together, in our judgement they form crucial enabling technologies for trustworthy services in 6G. This review offers a timely resource for academic researchers and industry practitioners by highlighting the methodological advancements needed for explainable DRL (X-DRL) in 6G. It identifies key stakeholder groups, maps their needs to X-DRL solutions, and presents case studies showcasing practical applications. By identifying and analysing these challenges in the context of 6G case studies, this work aims to inform future research, transform industry practices, and highlight unresolved gaps in this rapidly evolving field
Creative combinational design through generative AI in different dimensional representations: an exploration
No full textAlthough generative AI models are increasingly integrated into creative work, there is limited evidence on how dimensional representations of generative AI—text, image, and 3D—impact the creative process. Identifying these effects aids researchers in selecting suitable models for developing generative design. Through two empirical studies, this research examines the capacity of generative models with varying dimensions to support combinational ideation and influence the creative design process. The results indicate that generative models in different dimensions demonstrate varying levels of creative combinational ideation. Generative AI supports combinational ideation by providing more ideas in divergent stages rather than helping decision-making in convergent stages. These results help inform AI design developers in choosing models and integrating models for combination
Exploring gaps, biases, and research priorities in the evidence for reptile conservation actions
No full textWith over 21% of reptile species threatened with extinction, there is an urgent need to ensure conservation actions to protect and restore populations are informed by relevant, reliable evidence. We examined the geographic and taxonomic distribution of 707 studies that tested the effects of actions to conserve reptiles synthesized in Conservation Evidence's Reptile Conservation synopsis. More studies were conducted in countries with higher gross domestic product per capita, more reptile species, and higher proportions of threatened reptile species. Studies were clustered in the United States (43%) and Australia (15%), and no studies were conducted in large parts of Southeast Asia, South America, and sub‐Saharan Africa. Taxonomically, 47% of 90 reptile families (mostly Squamata) were not studied at all. Although Squamata and Testudines species featured in approximately 50% of studies, 7 of the 10 most‐studied reptiles (constituting 36% of studies) were turtles or tortoises, and there were significantly more studies per species on Testudines than Squamata. There were also significantly more studies on species: classified as least concern (as opposed to all other International Union for Conservation of Nature categories apart from near threatened); not categorized as endemic or insular; with more Wikipedia page views; and lacking data on venomousness. There was no significant relationship between the number of studies and the evolutionary distinctiveness or body mass of species. Our results highlight pressing evidence needs, particularly for underrepresented regions and threatened and data‐deficient species (e.g., evolutionarily distinct and globally endangered reptiles in South America, sub‐Saharan Africa, and Southeast Asia). To overcome evidence gaps and a lack of basic ecological data, future work should explore how the effects of actions transfer across taxa and regions. We call for greater efforts to coordinate and increase testing and reporting in a strategic manner to inform more effective and efficient conservation actions globally