Wiley最新文献

Systematic reviews play a critical role in evidence-based research but are labor-intensive, especially during title and abstract screening. Compact large language models (LLMs) offer potential to automate this process, balancing time/cost requirements and accuracy. The aim of this study is to assess the feasibility, accuracy, and workload reduction by three compact LLMs (GPT-4o mini, Llama 3.1 8B, and Gemma 2 9B) in screening titles and abstracts. Records were sourced from three previously published systematic reviews and LLMs were requested to rate each record from 0 to 100 for inclusion, using a structured prompt. Predefined 25-, 50-, 75-rating thresholds were used to compute performance metrics (balanced accuracy, sensitivity, specificity, positive and negative predictive value, and workload-saving). Processing time and costs were registered. Across the systematic reviews, LLMs achieved high sensitivity (up to 100%) and low precision (below 10%) for records included by full text. Specificity and workload savings improved at higher thresholds, with the 50- and 75-rating thresholds offering optimal trade-offs. GPT-4o-mini, accessed via application programming interface, was the fastest model (~40 minutes max.) and had usage costs ($0.14-$1.93 per review). Llama 3.1-8B and Gemma 2-9B were run locally in longer times (~4 hours max.) and were free to use. LLMs were highly sensitive tools for the title/abstract screening process. High specificity values were reached, allowing for significant workload savings, at reasonable costs and processing time. Conversely, we found them to be imprecise. However, high sensitivity and workload reduction are key factors for their usage in the title/abstract screening phase of systematic reviews.

Understanding inter- and intraspecific interactions is central to community ecology. Using data from two censuses of a subtropical secondary forest in Huangshan, China, we analysed survival rate for over 54,000 saplings across 83 species. We quantified four neighbourhood density indices, conspecific (CI), heterospecific (HI), phylogenetic (PhyI), and functional (FunI), to test how neighbour density influences tree survival. In parallel, we calculated neighbourhood diversity along species, phylogenetic, and functional axes to test how neighbour diversity shapes species' survival probabilities spanning four different spatial scales. Generalized linear mixed models revealed that HI and FI significantly decreased survival, highlighting intense interspecific competition in early successional stages. In contrast, PhyI positively affected survival, suggesting habitat filtering among related species. Neighbourhood diversity exhibited scale-dependent effects. Finally, we identified species with high specific leaf area and phosphorus content grew faster but experienced higher mortality. These findings underscore the importance of incorporating phylogenetic and functional neighbourhood metrics to understand demographic processes and community assembly, particularly in recovering forest ecosystems.

Hypertension is a known modifiable risk factor for Alzheimer's disease and related dementia (ADRD). However, it is unknown how variance in hypertension control, antihypertensive medications, and social determinants of health, such as social deprivation index (SDI), influence the risk of developing ADRD. Validated hypertension computable phenotype algorithms were applied to electronic health record data from the OneFlorida Data Trust (1/1/2013-12/31/2016), to identify apparent treatment-resistant hypertension (aTRH), and hypertension-control levels (well-controlled hypertension, intermediate-controlled hypertension, uncontrolled hypertension). The primary outcome was a new ADRD diagnosis using validated ICD-9/10 codes. Multiple adjusted stepwise logistic regression models were used to identify factors associated with ADRD development. ADRD cumulative hazard incidence per hypertension control levels was assessed using the Nelson-Aalen estimator and log-rank test. A total of 57,273 hypertension patients with 6401 (11%) incident ADRD cases were included in the analysis. The average age was 67 years, with 57% females and 32% identifying as Black or African American. aTRH was a significant ADRD predictor (OR: 1.327, 95% CI: 1.234-1.427), compared to other hypertension phenotypes. aTRH was also significantly associated with a higher incidence of ADRD over time (P < 0.0001). Patients prescribed thiazide diuretics (OR: 0.894, 95% CI: 0.837-0.956) and fixed-dose combination medications (OR: 0.804, 95% CI: 0.732-0.882) had a lower risk of ADRD. A linear relationship between SDI quartiles and ADRD risk was found. aTRH was significantly associated with the development of ADRD. Our study also highlights the importance of comprehensive hypertension control and socioeconomic interventions in preventing or reducing ADRD risk in hypertension patients.

Specific generation of reactive oxygen species (ROS) is important for signalling and defence in many organisms. In plants, different types of ROS serve useful biological functions in the extracellular space (apoplast), influencing polymer structures as well as signalling during immune responses. The current knowledge of apoplastic ROS dynamics is limited, as dynamic monitoring of extracellular redox processes in vivo remains difficult. We employed evolutionary distant land plant model species from bryophytes and flowering plants to test whether the genetically encoded redox biosensor roGFP2-Orp1 can be used to assess extracellular redox dynamics. Secreted roGFP2-Orp1 can provide information about local diffusion barriers and protein cysteinyl oxidation rate in the apoplast, after pre-reduction. Observed re-oxidation rates were slow - within the range of hours. Compared to Physcomitrium patens, re-oxidation in Arabidopsis thaliana was faster and increased after triggering an immune response. Comparing roGFP2-Orp1 signals in tip-growing P. patens protonema and Nicotiana tabacum pollen tubes, we consistently find no intracellular redox gradient, but a partially reduced extracellular sensor in pollen tubes. Our data indicate differences in extracellular oxidative processes between species and within a species, depending on cell type and immune signalling.

The evolutionary limits to generalization in plant pollination systems are often determined by trade-offs in which adaptations to one set of flower visitors reduces the effectiveness of another set of visitors. A key question is whether flowers can be pollinated equally effectively during the day and the night, given that the attractants for diurnal visitors are expected to be very different to those for nocturnal visitors. To address this question, we investigated the pollination system of the mass-flowering desert geophyte Nerine laticoma (Amaryllidaceae) over 2 years. We measured floral traits, including colour, scent, dimensions, floral rewards, visitation and reproductive traits. Finally, we exposed a subset of flowers exclusively to either diurnal or nocturnal visitors to establish their relative contributions to reproduction. Nerine laticoma has relatively open flowers, with exposed nectar, attracting a wide diversity of pollinators, including bees, butterflies, nocturnal settling moths and hawkmoths. We established that N. laticoma is reliant on pollinators for seed production. Flowers exposed only during the day set a similar number of seeds to those exposed only during the night, indicating that the plant is effectively pollinated by both diurnal and nocturnal animals. The results highlight the importance of multiple pollinators and their contribution to reproductive success in desert environments with variable pollinator communities. The contribution of all possible pollinators in a system, including frequently overlooked nocturnal visitors, should thus be taken into account.

Nepenthes, a major genus of carnivorous plants, secrete floral and extrafloral nectars (FN and EFN) in their flowers and pitchers, respectively. Recently we demonstrated Nepenthes khasiana EFN as a sugar mix with minimal nitrogenous metabolites and vitamin C. N. khasiana EFN showed insect toxicity due to its strong acetylcholinesterase (AChE) inhibition, and the active principle has been characterized as the naphthoquinone derivative, (+)-isoshinanolone. These findings prompted us to study the chemical composition and toxicity of N. khasiana FN. N. khasiana FN was analyzed for its sugars, amino acids, proteins, fatty acids and vitamin C by HPTLC-densitometry, UFLC, GC-MS and biochemical assays. C:N ratio and naphthoquinones were analyzed by CHNS analyzer, headspace-GC-MS, and GC-FID, respectively. Toxicity of FN and the naphthoquinone droserone, found in its pitcher fluid, was evaluated using the AChE inhibition assay. N. khasiana FN showed a similar chemical pattern as its EFN, viz., sugar (Glc-Fru-Suc)-mix with minimal amino acids, vitamin C, and moderate protein and fatty acid contents. It showed fairly high C:N ratio. The naphthoquinone, plumbagin, was the major volatile constituent in N. khasiana flowers. Crucially, the AChE inhibitors, (+)-isoshinanolone and plumbagin, were absent in N. khasiana FN, which showed no AChE inhibition. Droserone, however, exhibited strong AChE inhibitory activity. The chemical profiles of EFN and FN reflect a nitrogen-limited metabolism in N. khasiana. Notably, the functional allocation of neurotoxic naphthoquinones - abundant in EFN, absent in FN, and present in the pitcher fluid - reflects an adaptive strategy that optimizes both nutritional gain through prey capture and reproductive success through pollination.

Ambient and direct mass spectrometry (MS) methods are becoming increasingly used for the rapid analysis of food, beverage and agricultural samples. Novel ionization approaches combined with targeted, or untargeted workflows provide analytical outcomes within a greatly reduced time period compared to traditional separation science coupled with MS detection. This review will provide an overview of atmospheric pressure ionization MS based techniques for analysis of food, beverage and agricultural samples, with an emphasis on direct and rapid analysis including ambient ionization. The review will be completed through presentation of relevant examples of the use of ambient ionization techniques for food and beverage analysis along with the authors perspectives for future challenges relevant to the field.

β-Adrenoceptors are important G protein-coupled receptors involved in cardiovascular, metabolic, and neurological regulation. To study their function with high precision, light-based molecular tools have been developed offering precise spatiotemporal control. Fluorescence and bioluminescence techniques allow real-time monitoring of receptor activation and organization, while photopharmacology and optogenetics enable precise external modulation of their activity. A particularly valuable approach involves photoswitchable ligands, which can be switched on and off by specific wavelengths and provide reversible control over receptor activity. In general, the combination of optical biosensing and photopharmacology enhances our ability to analyze GPCR signaling dynamics and function with minimal perturbation. In particular, these approaches open new avenues for targeted research and therapeutic interventions, offering a powerful framework for understanding β-adrenoceptors-related diseases.

Meta-research and evidence synthesis require considerable resources. Large language models (LLMs) have emerged as promising tools to assist in these processes, yet their performance varies across models, limiting their reliability. Taking advantage of the large availability of small size (<10 billion parameters) open-source LLMs, we implemented an agreement-based framework in which a decision is taken only if at least a given number of LLMs produce the same response. The decision is otherwise withheld. This approach was tested on 1020 abstracts of randomized controlled trials in rheumatology, using 2 classic literature review tasks: (1) classifying each intervention as drug or nondrug based on text interpretation and (2) extracting the total number of randomized patients, a task that sometimes required calculations. Re-examining abstracts where at least 4 LLMs disagreed with the human gold standard (dual review with adjudication) allowed constructing an improved gold standard. Compared to a human gold standard and single large LLMs (>70 billion parameters), our framework demonstrated robust performance: several model combinations achieved accuracies above 95% exceeding the human gold standard on at least 85% of abstracts (e.g., 3 of 5 models, 4 of 6 models, or 5 of 7 models). Performance variability across individual models was not an issue, as low-performing models contributed fewer accepted decisions. This agreement-based framework offers a scalable solution that can replace human reviewers for most abstracts, reserving human expertise for more complex cases. Such frameworks could significantly reduce the manual burden in systematic reviews while maintaining high accuracy and reproducibility.

Bayesian hierarchical models offer a principled framework for adjusting for study-level bias in meta-analysis, but their complexity and sensitivity to prior specifications necessitate a systematic framework for robust application. This study demonstrates the application of a Bayesian workflow to this challenge, comparing a standard random-effects model to a bias-adjustment model across a real-world dataset and a targeted simulation study. The workflow revealed a high sensitivity of results to the prior on bias probability, showing that while the simpler random-effects model had superior predictive accuracy as measured by the widely applicable information criterion, the bias-adjustment model successfully propagated uncertainty by producing wider, more conservative credible intervals. The simulation confirmed the model's ability to recover true parameters when priors were well-specified. These results establish the Bayesian workflow as a principled framework for diagnosing model sensitivities and ensuring the transparent application of complex bias-adjustment models in evidence synthesis.