Limited Overlap Research Articles

Limited Evidence of Spillover of Antimicrobial-Resistant Klebsiella pneumoniae from Animal/Environmental Reservoirs to Humans in Vellore, India.

Klebsiella pneumoniae is a common opportunistic pathogen in humans, often associated with both virulence and antimicrobial resistance (AMR) phenotypes. K. pneumoniae have a highly plastic genome and can act as a vehicle for disseminating genetic information. Aiming to assess the impact of the human-animal-environment interface on AMR dissemination in K. pneumoniae we sampled and genome sequenced organisms from a range of environments and compared their genetic composition. Representative K. pneumoniae isolated from clinical specimens (n = 59), livestock samples (n = 71), and hospital sewage samples (n = 16) during a two-year surveillance study were subjected to whole genome sequencing. We compared the taxonomic and genomic distribution of K. pneumoniae, AMR gene abundance, virulence gene composition, and mobile genetic elements between the three sources. The K. pneumoniae isolates originating from livestock were clonally distinct from those derived from clinical/hospital effluent samples. Notably, the clinical and hospital sewage isolates typically possessed a greater number of resistance/virulence genes than those from animals. Overall, we observed a limited overlap of K. pneumoniae clones, AMR genes, virulence determinants, and plasmids between the different settings. In this setting, the spread of XDR and hypervirulent clones of K. pneumoniae appears to be restricted to humans with no obvious association with non-clinical sources. Emergent clones of K. pneumoniae carrying both resistance and virulence determinants are likely to have emerged in hospital settings rather than in animal or natural environments. These data challenge the current view of AMR transmission in K. pneumoniae in a One-Health context.

Measurement Invariance for Multilingual Learners Using Item Response and Response Time in PISA 2018

AbstractThis study examines potential assessment bias based on students' primary language status in PISA 2018. Specifically, multilingual (MLs) and nonmultilingual (non‐MLs) students in the United States are compared with regard to their response time as well as scored responses across three cognitive domains (reading, mathematics, and science). Differential item functioning (DIF) analysis reveals that 7–14% of items exhibit DIF‐related problems in scored responses between the two groups, aligning with PISA technical report results. While MLs generally spend more time on the test than non‐MLs across cognitive levels, differential response time (DRT) functioning identifies significant time differences in 7–10% of items for students with similar cognitive levels. It was noticeable that items with DIF and DRT issues show limited overlap, suggesting diverse reasons for student struggles in the assessment. A deeper examination of item characteristics is recommended for test developers and teachers to gain a better understanding of these nuances.

Widespread forest-savanna coexistence but limited bistability at a landscape scale in Central Africa

Abstract Tropical forest and savanna frequently coexist under the same climatic conditions, which has led to the hypothesis that they could represent alternative ecosystem states, stabilized by internal feedbacks. An implication of this hypothesis is that forest and savanna may be bistable and exhibit tipping behavior in response to changing conditions. However, we pose that the local presence of forest and savanna within coexistence landscapes is not sufficient evidence that these are alternative stable states at larger ecosystem scales. Therefore, we explore forest-savanna coexistence and bistability at landscape scale in Central Africa. Using remote sensing data on tree cover, we classify 0.1° x 0.1° (approx. 10 x 10 km) landscapes as homogeneous forest, homogeneous savanna, or coexistence, and analyze the roles of climate, topography and soil sand content in driving their distributions. We find that local coexistence of forest and savanna within landscapes is common and occurs for the whole range of mean annual precipitation in our study area. At low precipitation, however, coexistence increases with topographic roughness and is therefore likely driven by local redistribution of resources rather than internal feedbacks. Coexistence within intermediate and high precipitation landscapes remains unexplained by the studied variables, and may be caused either by heterogeneity in unmeasured drivers or by feedback-driven bistability. At landscape scale, the precipitation ranges for which homogeneous forest and savanna occur have only limited overlap, and this overlap can be largely explained by other external drivers, such as seasonality, soil sand content, and topography. This lack of evidence that homogeneous forest and savanna in Central Africa are alternative ecosystem states at this landscape scale means that transitions between them may be mostly local, resulting in coexistence states. Therefore, we conclude that the likelihood of large-scale tipping between homogeneous forest and savanna ecosystems may be lower than previously thought.

Mcadet: A feature selection method for fine-resolution single-cell RNA-seq data based on multiple correspondence analysis and community detection.

Single-cell RNA sequencing (scRNA-seq) data analysis faces numerous challenges, including high sparsity, a high-dimensional feature space, and biological noise. These challenges hinder downstream analysis, necessitating the use of feature selection methods to identify informative genes, and reduce data dimensionality. However, existing methods for selecting highly variable genes (HVGs) exhibit limited overlap and inconsistent clustering performance across benchmark datasets. Moreover, these methods often struggle to accurately select HVGs from fine-resolution scRNA-seq datasets and minority cell types, which are more difficult to distinguish, raising concerns about the reliability of their results. To overcome these limitations, we propose a novel feature selection framework for scRNA-seq data called Mcadet. Mcadet integrates Multiple Correspondence Analysis (MCA), graph-based community detection, and a novel statistical testing approach. To assess the effectiveness of Mcadet, we conducted extensive evaluations using both simulated and real-world data, employing unbiased metrics for comparison. Our results demonstrate the superior performance of Mcadet in the selection of HVGs in scenarios involving fine-resolution scRNA-seq datasets and datasets containing minority cell populations. Overall, we demonstrate that Mcadet enhances the reliability of selected HVGs, although the impact of HVG selection on various downstream analyses varies and needs to be further investigated.

Improved protein interaction models predict differences in complexes between human cell lines.

The interactions of proteins to form complexes play a crucial role in cell function. Data on protein-protein or pairwise interactions (PPI) typically come from a combination of sample separation and mass spectrometry. Since 2010, several extensive, high-throughput mass spectrometry-based experimental studies have dramatically expanded public repositories for PPI data and, by extension, our knowledge of protein complexes. Unfortunately, challenges of limited overlap between experiments, modality-oriented biases, and prohibitive costs of experimental reproducibility continue to limit coverage of the human protein assembly map, both underscoring the need for and spurring the development of relevant computational approaches. Here, we present a new method for predicting the strength of protein interactions. It addresses two important issues that have limited past PPI prediction approaches: incomplete feature sets and incomplete proteome coverage. For a given collection of protein pairs, we fused data from heterogeneous sources into a feature matrix and identified the minimal set of feature partitions for which a non-empty set of protein pairs had complete values. For each such feature partition, we trained a classifier to predict PPI probabilities. We then calculated an overall prediction for a given protein pair by weighting the probabilities from all models that applied to that pair. Our approach accurately identified known and highly probable PPI, far exceeding the performance of current approaches and providing more complete proteome coverage. We then used the predicted probabilities to assemble complexes using previously-described graph-based tools and clustering algorithms and again obtained improved results. Lastly, we used features for three human cell lines to predict PPI and complex scores and identified complexes predicted to differ between those cell lines.

Gene signatures for cancer research: A 25-year retrospective and future avenues.

Over the past two decades, extensive studies, particularly in cancer analysis through large datasets like The Cancer Genome Atlas (TCGA), have aimed at improving patient therapies and precision medicine. However, limited overlap and inconsistencies among gene signatures across different cohorts pose challenges. The dynamic nature of the transcriptome, encompassing diverse RNA species and functional complexities at gene and isoform levels, introduces intricacies, and current gene signatures face reproducibility issues due to the unique transcriptomic landscape of each patient. In this context, discrepancies arising from diverse sequencing technologies, data analysis algorithms, and software tools further hinder consistency. While careful experimental design, analytical strategies, and standardized protocols could enhance reproducibility, future prospects lie in multiomics data integration, machine learning techniques, open science practices, and collaborative efforts. Standardized metrics, quality control measures, and advancements in single-cell RNA-seq will contribute to unbiased gene signature identification. In this perspective article, we outline some thoughts and insights addressing challenges, standardized practices, and advanced methodologies enhancing the reliability of gene signatures in disease transcriptomic research.

A systematic review of unique methods for measuring discount rates

AbstractDiscount rates play a pivotal role in various fields, capturing the trade-off between present and future, influencing predictions of unhealthy behaviors. Despite substantial variations in discount rates across studies, methods, and individuals, our goal was to systematically overview diverse methods for measuring discount rates. We conducted a comprehensive literature review across multiple databases, targeting English studies introducing and utilizing discounting measurement methods in human subjects. Two authors screened titles and abstracts, with full text review split between them. Extracted data encompassed bibliographic details, theoretical aspects (e.g., discount function), and operational features (e.g., elicitation procedure). After deduplication, 4976, 218, and 83 records underwent title and abstract screening, full text screening, and inclusion, respectively. A total of 86 unique methods were identified. Predominantly, methods were devised for money (75%) and health (22%) discounting. Network analysis on citations indicated limited cross-disciplinary overlap. Only about one fourth, one third, and one third of methods possessed theoretically desirable traits: i) allowance for negative discount rates, ii) application of multiple discount functions, and iii) correction for non-linear utility of outcomes. Diverse discounting measurement methods exist, showcasing varied theoretical and operational characteristics. These differences may stem from the isolation of development in fields like psychology, experimental economics, and health economics. Our systematic review aids readers in choosing methods aligned with their priorities when measuring discount rates.

Systematic Comparison of CRISPR and shRNA Screens to Identify Essential Genes Using a Graph-Based Unsupervised Learning Model.

Generally, essential genes identified using shRNA and CRISPR are not always the same, raising questions about the choice between these two screening platforms. To address this, we systematically compared the performance of CRISPR and shRNA to identify essential genes across different gene expression levels in 254 cell lines. As both platforms have a notable false positive rate, to correct this confounding factor, we first developed a graph-based unsupervised machine learning model to predict common essential genes. Furthermore, to maintain the unique characteristics of individual cell lines, we intersect essential genes derived from the biological experiment with the predicted common essential genes. Finally, we employed statistical methods to compare the ability of these two screening platforms to identify essential genes that exhibit differential expression across various cell lines. Our analysis yielded several noteworthy findings: (1) shRNA outperforms CRISPR in the identification of lowly expressed essential genes; (2) both screening methodologies demonstrate strong performance in identifying highly expressed essential genes but with limited overlap, so we suggest using a combination of these two platforms for highly expressed essential genes; (3) notably, we did not observe a single gene that becomes universally essential across all cancer cell lines.

A methodology for assessing multiple hazards applied to Sweden

Despite extensive efforts through various international initiatives to reduce global warming, it has been determined that human induced climate change is here, that the present scale of disruption of the climate is unprecedented and will continue. Increasing weather volatility can be expected, which will most likely increase exposure to weather related hazards, e.g. wildfires, flooding. The aim of this paper is to present an index-based multi-hazard risk assessment method to assess wildfires and flooding hazard for two municipalities within Sweden. The method is designed to be used by the Fire and Rescue Service (FRS) for planning purposes and can be modified to take the local FRS’s capabilities and local conditions into account, thereby improving hazard preparedness at a local level. The analysis presented indicates that, while the frequency of multi-hazard overlap from wildfires and flooding is greatest in more northern parts of Sweden, the method provides important information even when applied to areas with limited overlap. A variation of the hazard assessment using a box kernel sliding window was studied to investigate the sensitivity of the model for rapid variations of an individual hazard level. Given that resource needs will typically spread over several days for large scale natural hazards, the box kernel approach is valuable in helping to identify a span of days when resources associated with incident response might be needed. In the future, the model should be expanded to include additional single hazards, the application to additional municipalities and extension to FRS planning exercises for natural hazards.

Attributes of higher- and lower-performing hospitals in the Consult for Addiction Treatment and Care in Hospitals (CATCH) program implementation: A multiple-case study

IntroductionSix hospitals within the New York City public hospital system implemented the Consult for Addiction Treatment and Care in Hospitals (CATCH) program, an interprofessional addiction consult service. A stepped-wedge cluster randomized controlled trial tested the effectiveness of CATCH for increasing initiation and engagement in post-discharge medication for opioid use disorder (MOUD) treatment among hospital patients with opioid use disorder (OUD). The objective of this study was to identify facility characteristics that were associated with stronger performance of CATCH. MethodsThis study used a mixed methods multiple-case study design. The six hospitals in the CATCH evaluation were each assigned a case rating according to intervention reach. Reach was considered high if ≥50 % of hospitalized OUD patients received an MOUD order. Cross-case rating comparison identified attributes of high-performing hospitals and inductive and deductive approaches were used to identify themes. ResultsHigher-performing hospitals exhibited attributes that were generally absent in lower-performing hospitals, including (1) complete medical provider staffing; (2) designated office space and resources for CATCH; (3) existing integrated OUD treatment resources; and (4) limited overlap between the implementation period and COVID-19 pandemic. ConclusionsHospitals with attributes indicative of awareness and integration of OUD services into general care were generally higher performing than hospitals that had siloed OUD treatment programs. Future implementations of addiction consult services may benefit from an increased focus on hospital- and community-level buy-in and efforts to integrate MOUD treatment into general care.

Towards a universal method for middle-down analysis of antibodies via proton transfer charge reduction-Orbitrap mass spectrometry.

Modern mass spectrometry technology allows for extensive sequencing of the ~ 25kDa subunits of monoclonal antibodies (mAbs) produced by IdeS proteolysis followed by disulfide bond reduction, an approach known as middle-down mass spectrometry (MD MS). However, the spectral congestion of tandem mass spectra of large polypeptides dramatically complicates fragment ion assignment. Here, we report the development and benchmark of an MD MS strategy based on the combination of different ion fragmentation techniques with proton transfer charge reduction (PTCR) to simplify the gas-phase sequencing of mAb subunits. Applied on the liquid chromatography time scale using an Orbitrap Tribrid mass spectrometer, PTCR produces easy-to-interpret mass spectra with limited ion signal overlap. We demonstrate that the accurate estimation of the number of charges submitted to the Orbitrap mass analyzer after PTCR allows for the detection of charge-reduced product ions over a wide mass-over-charge (m/z) window with low parts per million m/z accuracy. Therefore, PTCR-based MD MS analysis increases not only sequence coverage, number of uniquely identified fragments, and number of assigned complementary ion pairs, but also the general confidence in the assignment of subunit fragments. This data acquisition method can be readily applied to any class of mAbs without an apparent need for optimization, and benefits from the high resolving power of the Orbitrap mass analyzer to return sequence coverage of individual subunits exceeding 80% in a single run, and > 90% when just two experiments are combined.

Induced and natural variation affect traits independently in hybrid Populus.

The genetic control of many plant traits can be highly complex. Both allelic variation (sequence change) and dosage variation (copy number change) contribute to a plant's phenotype. While numerous studies have investigated the effect of allelic or dosage variation, very few have documented both within the same system, leaving their relative contribution to phenotypic effects unclear. The Populus genome is highly polymorphic, and poplars are fairly tolerant of gene dosage variation. Here, using a previously established Populus hybrid F1 population, we assessed and compared the effect of natural allelic variation and induced dosage variation on biomass, phenology and leaf morphology traits. We identified QTLs for many of these traits, but our results indicate limited overlap between the QTLs associated with natural allelic variation and induced dosage variation. Additionally, the integration of data from both allelic and dosage variation identifies a larger set of QTLs that together explain a larger percentage of the phenotypic variance. Finally, our results suggest that the effect of the large indels might mask that of allelic QTLs. Our study helps clarify the relationship between allelic and dosage variation and their effects on quantitative traits.

Present and future distribution of the European pond turtle versus seven exotic freshwater turtles, with a focus on Eastern Europe

Freshwater turtles are often used as terrarium pets, especially juveniles of exotic species. At the adult stage they are often released by their owners into the wild despite their high invasion potential. In Europe these thermophilic potentially invasive alien species occupy the habitats of the native European pond turtle Emys orbicularis (Linnaeus, 1758), with new records from the wild being made specifically in Eastern Europe (Latvia and Ukraine) during recent decades. Assessing the potential of alien freshwater turtles to establish in new territories is of great concern for preventing invasion risks while preserving native biodiversity in the present context of climate change. We explored this issue by identifying the present and future (by 2050) suitable habitats of the European pond turtle and several potentially invasive alien species of freshwater turtle already settled in Europe, using a geographic information system (GIS) modelling approach based on datasets from CliMond for climate, Near-global environmental information (NGEI) for freshwater ecosystems (EarthEnv) and Maxent modelling using open-access databases, data from the literature and original field data. Modelling was performed for seven species of alien freshwater turtles occurring from the extreme northern to southern borders of the European range of E. orbicularis: the pond slider Trachemys scripta (Thunberg and Schoepff, 1792), the river cooter Pseudemys concinna (Le Conte, 1830), the Florida red-bellied cooter Pseudemys nelsoni (Carr, 1938), the false map turtle Graptemys pseudogeographica (Gray, 1831), the Chinese softshell turtle Pelodiscus sinensis (Wiegmann, 1835), the Caspian turtle Mauremys caspica (Gmelin, 1774) and the Balkan terrapin Mauremys rivulata (Valenciennes, 1833). In Ukraine, the most Eastern limit of E. orbicularis distribution, were previously reported northern American originated T. scripta, M. rivulata, M. caspica, whereas in Latvia, Emys’ most northern limit, were additionally reported P. concinna, P. nelsoni, G. pseudogeographica and Asia originated P. sinensis. The resulting Species Distribution Models (SDM) were of excellent performance (AUC > 0.8). Of these alien species, the most potentially successful in terms of range expansion throughout Europe were T. scripta (34.3% of potential range expansion), G. pseudogeographica (24.1%), and M. caspica (8.9%) and M. rivulata (4.3%) mainly in Eastern Europe, especially in the south of Ukraine (Odesa, Kherson, Zaporizhzhia regions, and Crimean Peninsula). Correlation between the built SDMs for the native E. orbicularis and the invasive alien T. scripta was reliably high, confirming the highly likely competition between these two species in places they cooccur. Moreover, a Multiple Regression Analysis revealed that by 2050, in most of Europe (from the western countries to Ukraine), the territory overlap between E. orbicularis and potentially invasive alien species of freshwater turtles will increase by 1.2 times, confirming higher competition in the future. Importantly, by 2050, Eastern Europe and Ukraine are predicted to be the areas with most suitable habitats for the European pond turtle yet with most limited overlap with the invasive alien species. We conclude that Eastern Europe and Ukraine are the most relevant priority conservation areas for the European pond turtle where it is now necessary to take protective measures to ensure safe habitat for this native species on the long-term.

Comparative analysis of cortical anatomy inmale participants with internet gaming disorder or tobacco use disorder: Insights fromnormative modeling.

Research on individual differences in brain structural features of internet gaming disorder (IGD) and established addictions such as tobacco use disorder (TUD) is currently limited. This study utilized normative modeling to analyze the cortical thickness (CT) development patterns of male patients with IGD and TUD, aiming to provide further insights into whether IGD qualifies as an addiction. Surface-based brain morphometry (SBM) was used to calculate CT from T1-weighted magnetic resonance imaging data of 804 male participants (665 healthy individuals, 68 IGD and 71 TUD). Gaussian process regression was employed to generate normative models of CT development. Deviation maps were produced to depict deviations of IGD and TUD participants from the typical developmental patterns. Both addiction groups exhibited widespread cortical thinning, particularly in regions such as the bilateral temporal pole and medial orbitofrontal cortex. The TUD group demonstrated a higher degree of individualization and limited spatial overlap compared to the IGD group. Opposite trends in CT changes were observed between the two groups in the bilateral pericalcarine cortex and pars triangularis. These findings regarding the similarities and differences between IGD and TUD provide support for the idea that IGD shares common features with substance-related addictions and contribute to a deeper understanding of the neural mechanisms underlyingIGD.

More of the same? How digital food platforms reinforce prevailing eating interests and practices

ABSTRACT Techno-optimistic framings of the digital food economy purport that digital platforms can contribute to individuals’ dietary behavior change. Against this background, we investigate how people’s understandings of ‘eating well’ and their engagements with digital platforms are entangled. To do so, we conducted the first large-scale explorative survey among adults living in Switzerland on digital eating practices to date (n = 449). The survey examines participants’ understanding of ‘eating well’ and their uptake and use of digital food platforms. Our survey analysis revealed six distinct clusters of how participants conceptualize ‘eating well’. We found that participants in each cluster engage with a different set of apps, blogs and social media platforms with limited overlap between the clusters. Moreover, each cluster's engagement with selected digital food platforms responds to and supports their prevailing eating practices and goals. The 21 follow-up interviews supported the finding of the survey analysis that eaters (per cluster) seek out digital food platforms to have ‘more of the same’. In other words, participants’ use of digital platforms reinforces their existing eating interests and practices, nonetheless, these practices are increasingly digitally mediated. Ultimately, our results lead us to problematize techno-optimistic visions that digital platforms can unilaterally alter eaters’ established diet.

Challenges faced by spotted seals born in captivity and released into the wild

The artificial reproduction of endangered marine species populations is becoming increasingly crucial. Determining the potential for reintegration into their natural ecological niche remains a persistent obstacle in conservation biology. Spotted seals (Phoca largha) are a flagship species in the Chinese marine ecosystem. Efforts have been made to artificially breed them to prevent local extinction. Our study monitored 18 captive-born (nine) and rehabilitated (nine) individuals released into the Bohai Sea between 2010 and 2020. We examined the ecological niche-seeking behavior of captive-born seals (CS) using rehabilitated wild seals (RS) as benchmarks. It encompassed three oceanic gradients, habitat patterns, and potential threats. The findings demonstrated that CS did not return to the appropriate latitudes, horizontals, and sea surface salinity gradients in the ocean. Despite a larger potential habitat area for CS, it exhibited high temporal variability (with an average monthly fluctuation of 53 %) and demonstrated limited spatial overlap with the RS habitat (27 % overlap in July), and four indices, including the Splitting and Global Moran's Indices indicated a higher level of habitat fragmentation. Additionally, the number of shipping hotspots and collision risk within the CS habitats were twice those of the RS. These findings suggest that the released CS did not fully integrate into its native ecological niche. Our objective is to offer a pragmatic approach and framework for monitoring and assessing marine species released into the ocean.

Examination of the mild, moderate, and severe alcohol use disorder severity indicators using a nationally representative sample.

The Diagnostic and Statistical Manual of Mental Disorders, fifth edition conceptualizes alcohol use disorder (AUD) as a single continuum with indicators to denote the level of severity along this spectrum with the presence of 2-3, 4-5, or 6 + symptoms indicating mild, moderate, and severe AUD, respectively. However, despite the labels of these indicators, it remains unclear how individuals compare across these indicators, both in terms of AUD severity, but also risk for other related problems (e.g., depression). Confirmatory factor analysis was conducted on past year AUD symptoms to obtain estimates of latent AUD severity using data from the 2020 National Survey on Drug Use and Health (unweighted n = 31,941). The range and distribution of latent trait estimates were then compared across AUD diagnostic statuses (i.e., no AUD, mild, moderate, and severe). Multinomial regressions were then used to compare diagnostic groups based on alcohol use, problems with other substances, treatment utilization, and mental/physical health. Results indicated very limited overlap in latent severity estimates between individuals with different severity indicators. Multinomial regression results demonstrated that some measures increased in a roughly stepwise fashion across AUD indicators (e.g., alcohol use and drinking behavior), while many did not. Results partially support the current AUD indicators as AUD severity and co-occurring problems did broadly increase across the indicators. However, the present study also explores several ways to improve these indicators in future AUD formulations. For example, having indicators that account not only for the quantitative but also the qualitative differences in AUD presentation at different severity levels. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Community ionomics reveals a diversity of mineral nutrition in a species‐rich shrubland on infertile soil

AbstractQuestionsHigh species richness is observed in certain shrublands on infertile substrates. Mineral nutrients are likely to be the primary limiting resources in these ecosystems, and below‐ground plant interactions may be crucial to understanding their diversity. Using ionomics, we investigated whether there were nutritional variations between plant species that coexist in a shrubland located in an edaphically extreme environment.LocationNew Caledonia.MethodsWe set up a 20 m × 20 m plot in a diverse shrubland (“maquis”) on ultramafic (infertile) substrate, in which we sampled all 475 plants taller than 1 m and characterized their ionome (22 elements).ResultsIn our study, 37 species were identified in the plot, representing all major forms of mycorrhizal symbioses, as well as nitrogen‐fixing plants, cluster rooted and parasitic plants. Notably, both nickel hyperaccumulating and manganese hyperaccumulating species were present. Hypervolume approaches were used to assess ionome overlap among the nine most abundant species, with the results revealing limited overlap. Moreover, it was observed that the rarest species in the plot also had the most functionally distinct features.ConclusionsDifferent nutritional strategies were present in the plot, as demonstrated by the variety of root symbioses and leaf ionomes. Our findings indicate coexistence of multiple species within this infertile shrubland may be achieved by species partitioning into different highly specialized biogeochemical niches.

Limited content overlap between commonly used self-report instruments for central (pain) sensitization in rheumatology.

Central pain mechanisms may be prominent in a considerable subset of rheumatology patients with persistent pain. Several self-report instruments have been used in previous research to infer the presence and severity of central sensitization (CS) that stem from different definitions or approaches of CS. The current study aimed to evaluate and quantify the overlap of actual symptoms measured among self-report measures of CS in rheumatology. We used Fried's (2017) comprehensive systematic approach to analyse the content of five commonly used or typical self-report measures (Generalized Pain Questionnaire, Pain Sensitivity Questionnaire, Central Sensitization Inventory, Central Aspects of Pain in the Knee scale and the painDETECT) used in rheumatology research and to visualize and quantify the overlap in symptoms measured. The five instruments together measured 39 different symptoms, most of which could be grouped into nociplastic pain manifestations (7 symptoms), neuropathic pain qualities (5 symptoms), and psychosomatic symptoms and emotional distress (25 symptoms). Most symptoms (74.4%) were unique to a single instrument. Thermal allodynia was the most frequently measured symptom across the different instruments, assessed in four of the measures. Average content overlap was very low and ranged from no overlap at all to moderate overlap (Jaccard index = 0.43) between pairs of instruments. There is high heterogeneity and limited overlap in the content of self-report measures used to infer central pain sensitization. This may lead to results that are specific to the particular instrument and may limit the generalizability and comparability of study findings in rheumatology research.

Transcriptional Response to Tick-Borne Flavivirus Infection in Neurons, Astrocytes and Microglia In Vivo and In Vitro.

Tick-borne encephalitis virus (TBEV) is a neurotropic member of the genus Orthoflavivirus (former Flavivirus) and is of significant health concern in Europe and Asia. TBEV pathogenesis may occur directly via virus-induced damage to neurons or through immunopathology due to excessive inflammation. While primary cells isolated from the host can be used to study the immune response to TBEV, it is still unclear how well these reflect the immune response elicited in vivo. Here, we compared the transcriptional response to TBEV and the less pathogenic tick-borne flavivirus, Langat virus (LGTV), in primary monocultures of neurons, astrocytes and microglia in vitro, with the transcriptional response in vivo captured by single-nuclei RNA sequencing (snRNA-seq) of a whole mouse cortex. We detected similar transcriptional changes induced by both LGTV and TBEV infection in vitro, with the lower response to LGTV likely resulting from slower viral kinetics. Gene set enrichment analysis showed a stronger transcriptional response in vivo than in vitro for astrocytes and microglia, with a limited overlap mainly dominated by interferon signaling. Together, this adds to our understanding of neurotropic flavivirus pathogenesis and the strengths and limitations of available model systems.