Phenotype-based targeted treatment of SGLT2 inhibitors and GLP-1 receptor agonists in type 2 diabetes.

AIMS/HYPOTHESIS: A precision medicine approach in type 2 diabetes could enhance targeting specific glucose-lowering therapies to individual patients most likely to benefit. We aimed to use the recently developed Bayesian causal forest (BCF) method to develop and validate an individualised treatment selection algorithm for two major type 2 diabetes drug classes, sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP1-RA). METHODS: We designed a predictive algorithm using BCF to estimate individual-level conditional average treatment effects for 12-month glycaemic outcome (HbA1c) between SGLT2i and GLP1-RA, based on routine clinical features of 46,394 people with type 2 diabetes in primary care in England (Clinical Practice Research Datalink; 27,319 for model development, 19,075 for hold-out validation), with additional external validation in 2252 people with type 2 diabetes from Scotland (SCI-Diabetes [Tayside & Fife]). Differences in glycaemic outcome with GLP1-RA by sex seen in clinical data were replicated in clinical trial data (HARMONY programme: liraglutide [n=389] and albiglutide [n=1682]). As secondary outcomes, we evaluated the impacts of targeting therapy based on glycaemic response on weight change, tolerability and longer-term risk of new-onset microvascular complications, macrovascular complications and adverse kidney events. RESULTS: Model development identified marked heterogeneity in glycaemic response, with 4787 (17.5%) of the development cohort having a predicted HbA1c benefit >3 mmol/mol (>0.3%) with SGLT2i over GLP1-RA and 5551 (20.3%) having a predicted HbA1c benefit >3 mmol/mol with GLP1-RA over SGLT2i. Calibration was good in hold-back validation, and external validation in an independent Scottish dataset identified clear differences in glycaemic outcomes between those predicted to benefit from each therapy. Sex, with women markedly more responsive to GLP1-RA, was identified as a major treatment effect modifier in both the UK observational datasets and in clinical trial data: HARMONY-7 liraglutide (GLP1-RA): 4.4 mmol/mol (95% credible interval [95% CrI] 2.2, 6.3) (0.4% [95% CrI 0.2, 0.6]) greater response in women than men. Targeting the two therapies based on predicted glycaemic response was also associated with improvements in short-term tolerability and long-term risk of new-onset microvascular complications. CONCLUSIONS/INTERPRETATION: Precision medicine approaches can facilitate effective individualised treatment choice between SGLT2i and GLP1-RA therapies, and the use of routinely collected clinical features for treatment selection could support low-cost deployment in many countries.

Modelling the probability of meeting IUCN Red List criteria to support reassessments.

Comparative extinction risk analysis-which predicts species extinction risk from correlation with traits or geographical characteristics-has gained research attention as a promising tool to support extinction risk assessment in the IUCN Red List of Threatened Species. However, its uptake has been very limited so far, possibly because existing models only predict a species' Red List category, without indicating which Red List criteria may be triggered. This prevents such approaches to be integrated into Red List assessments. We overcome this implementation gap by developing models that predict the probability of species meeting individual Red List criteria. Using data on the world's birds, we evaluated the predictive performance of our criterion-specific models and compared it with the typical criterion-blind modelling approach. We compiled data on biological traits (e.g. range size, clutch size) and external drivers (e.g. change in canopy cover) often associated with extinction risk. For each specific criterion, we modelled the relationship between extinction risk predictors and species' Red List category under that criterion using ordinal regression models. We found criterion-specific models were better at identifying threatened species compared to a criterion-blind model (higher sensitivity), but less good at identifying not threatened species (lower specificity). As expected, different covariates were important for predicting extinction risk under different criteria. Change in annual temperature was important for criteria related to population trends, while high forest dependency was important for criteria related to restricted area of occupancy or small population size. Our criteria-specific method can support Red List assessors by producing outputs that identify species likely to meet specific criteria, and which are the most important predictors. These species can then be prioritised for re-evaluation. We expect this new approach to increase the uptake of extinction risk models in Red List assessments, bridging a long-standing research-implementation gap.

Groundwater is a hidden global keystone ecosystem.

Groundwater is a vital ecosystem of the global water cycle, hosting unique biodiversity and providing essential services to societies. Despite being the largest unfrozen freshwater resource, in a period of depletion by extraction and pollution, groundwater environments have been repeatedly overlooked in global biodiversity conservation agendas. Disregarding the importance of groundwater as an ecosystem ignores its critical role in preserving surface biomes. To foster timely global conservation of groundwater, we propose elevating the concept of keystone species into the realm of ecosystems, claiming groundwater as a keystone ecosystem that influences the integrity of many dependent ecosystems. Our global analysis shows that over half of land surface areas (52.6%) has a medium-to-high interaction with groundwater, reaching up to 74.9% when deserts and high mountains are excluded. We postulate that the intrinsic transboundary features of groundwater are critical for shifting perspectives towards more holistic approaches in aquatic ecology and beyond. Furthermore, we propose eight key themes to develop a science-policy integrated groundwater conservation agenda. Given ecosystems above and below the ground intersect at many levels, considering groundwater as an essential component of planetary health is pivotal to reduce biodiversity loss and buffer against climate change.

Detection of Plasmodium spp. in asymptomatic blood donors by the new Brazilian NAT PLUS HIV/HBV/HCV/Malaria Bio-Manguinhos kit.

BACKGROUND: Transfusion-transmitted malaria (TTM) is a public health problem in endemic and nonendemic areas. The Brazilian Ministry of Health (MH) requested the development of a nucleic acid amplification test (NAT) for the detection of Plasmodium spp. in public blood centers to increase blood safety. STUDY DESIGN AND METHODS: The new Brazilian NAT kit named NAT PLUS HIV/HBV/HCV/Malaria Bio-Manguinhos was first implemented in HEMORIO, a public blood center in the city of Rio de Janeiro. Since October 1, 2022, this blood center has been testing all its blood donations for malaria in a pool of six plasma samples to detect Plasmodium spp. by real-time polymerase chain reaction (PCR). RESULTS: Since the implementation of the NAT PLUS platform until February 2023, HEMORIO has successfully received and tested 200,277 donations. The platform detected two asymptomatic donors in the city of Rio de Janeiro, which is a nonendemic region for malaria. Our analyses suggested a malaria from the Amazon region caused by Plasmodium vivax, in the first case, while an autochthonous transmission case by Plasmodium malariae was identified in the rural area of Rio de Janeiro state. DISCUSSION: The NAT PLUS platform detects Plasmodium spp. in plasma samples with sensitivity capable of detecting subpatent infections. This is the first time worldwide that a group developed and implemented molecular diagnosis for Plasmodium spp. to be used by public blood centers to avoid TTM.

Time-travelling pathogens and their risk to ecological communities.

Permafrost thawing and the potential 'lab leak' of ancient microorganisms generate risks of biological invasions for today's ecological communities, including threats to human health via exposure to emergent pathogens. Whether and how such 'time-travelling' invaders could establish in modern communities is unclear, and existing data are too scarce to test hypotheses. To quantify the risks of time-travelling invasions, we isolated digital virus-like pathogens from the past records of coevolved artificial life communities and studied their simulated invasion into future states of the community. We then investigated how invasions affected diversity of the free-living bacteria-like organisms (i.e., hosts) in recipient communities compared to controls where no invasion occurred (and control invasions of contemporary pathogens). Invading pathogens could often survive and continue evolving, and in a few cases (3.1%) became exceptionally dominant in the invaded community. Even so, invaders often had negligible effects on the invaded community composition; however, in a few, highly unpredictable cases (1.1%), invaders precipitated either substantial losses (up to -32%) or gains (up to +12%) in the total richness of free-living species compared to controls. Given the sheer abundance of ancient microorganisms regularly released into modern communities, such a low probability of outbreak events still presents substantial risks. Our findings therefore suggest that unpredictable threats so far confined to science fiction and conjecture could in fact be powerful drivers of ecological change.

Comparison of Bayesian approaches for developing prediction models in rare disease: application to the identification of patients with Maturity-Onset Diabetes of the Young.

BACKGROUND: Clinical prediction models can help identify high-risk patients and facilitate timely interventions. However, developing such models for rare diseases presents challenges due to the scarcity of affected patients for developing and calibrating models. Methods that pool information from multiple sources can help with these challenges. METHODS: We compared three approaches for developing clinical prediction models for population screening based on an example of discriminating a rare form of diabetes (Maturity-Onset Diabetes of the Young - MODY) in insulin-treated patients from the more common Type 1 diabetes (T1D). Two datasets were used: a case-control dataset (278 T1D, 177 MODY) and a population-representative dataset (1418 patients, 96 MODY tested with biomarker testing, 7 MODY positive). To build a population-level prediction model, we compared three methods for recalibrating models developed in case-control data. These were prevalence adjustment ("offset"), shrinkage recalibration in the population-level dataset ("recalibration"), and a refitting of the model to the population-level dataset ("re-estimation"). We then developed a Bayesian hierarchical mixture model combining shrinkage recalibration with additional informative biomarker information only available in the population-representative dataset. We developed a method for dealing with missing biomarker and outcome information using prior information from the literature and other data sources to ensure the clinical validity of predictions for certain biomarker combinations. RESULTS: The offset, re-estimation, and recalibration methods showed good calibration in the population-representative dataset. The offset and recalibration methods displayed the lowest predictive uncertainty due to borrowing information from the fitted case-control model. We demonstrate the potential of a mixture model for incorporating informative biomarkers, which significantly enhanced the model's predictive accuracy, reduced uncertainty, and showed higher stability in all ranges of predictive outcome probabilities. CONCLUSION: We have compared several approaches that could be used to develop prediction models for rare diseases. Our findings highlight the recalibration mixture model as the optimal strategy if a population-level dataset is available. This approach offers the flexibility to incorporate additional predictors and informed prior probabilities, contributing to enhanced prediction accuracy for rare diseases. It also allows predictions without these additional tests, providing additional information on whether a patient should undergo further biomarker testing before genetic testing.

Effects of plant taxonomic position on soil nematode communities in Antarctica.

Antarctica terrestrial ecosystems are facing the most threats from global climate change, which is altering plant composition greatly. These transformations may cause major reshuffling of soil community composition, including functional traits and diversity, and therefore affect ecosystem processes in Antarctica. We used high-throughput sequencing analysis to investigate soil nematodes under 3 dominant plant functional groups (lichens, mosses, and vascular plants) and bare ground in the Antarctic region. We calculated functional diversity of nematodes based on their diet, life histories, and body mass with kernel density n-dimensional hypervolumes. We also calculated taxonomic and functional beta diversity of the nematode communities based on Jaccard dissimilarity. The presence of plants had no significant effect on the taxonomic richness of nematodes but significantly increased nematode functional richness. The presence of plants also significantly decreased taxonomic beta diversity (homogenization). Only mosses and vascular plants decreased nematode functional beta diversity, which was mostly due to a decreased effect of the richness difference component. The presence of plants also increased the effect of deterministic processes potentially because environmental filtering created conditions favorable to nematodes at low trophic levels with short life histories and small body size. Increasing plant cover in the Antarctic due to climate change may lead to increased diversity of nematode species that can use the scarce resources and nematode taxonomic and functional homogenization. In a future under climate change, community restructuring in the region is possible.

Efectos de la posición taxonómica de las plantas sobre las comunidades de nemátodos del suelo en la Antártida Resumen Los ecosistemas terrestres de la Antártida enfrentan las mayores amenazas del cambio climático global, que está alterando gravemente la composición de plantas. Estas transformaciones pueden provocar una reorganización importante de la composición de la comunidad del suelo, incluyendo atributos y diversidad funcionales, y por lo tanto afectar los procesos ecosistémicos en la Antártida. Utilizamos análisis de secuenciación de alto rendimiento para investigar nemátodos del suelo debajo de tres grupos funcionales de plantas dominantes (líquenes, musgos y plantas vasculares) y de suelo desnudo en la región de la Antártida. Calculamos la diversidad funcional de nemátodos con base en su dieta, historia de vida y masa corporal mediante hipervolúmenes n­dimensionales de densidad del núcleo. También calculamos la diversidad beta taxonómica y funcional de las comunidades de nemátodos con base en la disimilitud de Jacard. La presencia de plantas no tuvo efecto significativo sobre la riqueza taxonómica de nemátodos, pero incrementó su riqueza funcional significativamente. La presencia de plantas también disminuyó la diversidad beta taxonómica (homogenización) significativamente. Solo musgos y plantas vasculares disminuyeron la diversidad beta funcional de nemátodos, lo cual se debió principalmente a un menor efecto del componente de diferencia de riqueza. La presencia de plantas también incrementó el efecto de los procesos determinísticos posiblemente porque el filtrado ambiental creó condiciones favorables para los nemátodos de niveles tróficos inferiores con historias de vida corta y tamaño corporal pequeño. El incremento de la cobertura de plantas en la Antártida debido al cambio climático puede conducir a una mayor diversidad de especies de nemátodos que pueden utilizar los escasos recursos y a la homogenización taxonómica y funcional de los nemátodos. En un futuro bajo el cambio climático, es posible la reestructuración comunitaria en la región.

Effective conservation of subterranean-roosting bats.

Bats frequently inhabit caves and other subterranean habitats and play a critical role in subterranean food webs. With escalating threats to subterranean ecosystems, identifying the most effective measures to protect subterranean-roosting bats is critical. We conducted a meta-analysis to evaluate the effectiveness of conservation and management interventions for subterranean-roosting bats. We used network analyses to determine to what extent interventions for bats overlap those used for other subterranean taxa. We conducted our analyses with data extracted from 345 papers recommending a total of 910 conservation interventions. Gating of roost entrances was applied to preserve bat populations in 21 studies, but its effectiveness was unclear. Habitat restoration and disturbance reduction positively affected bat populations and bat behavior, respectively, in ≤4 studies. Decontamination was assessed in 2 studies and positively affected bat populations, particularly in studies focused on reducing fungal spores associated with white-nose syndrome in North America. Monitoring of bat populations as an effective conservation strategy was unclear and infrequently tested. Only 4% of bat studies simultaneously considered other subterranean organisms. However, effective interventions for bat conservation had similarities with all other organisms. If other subterranean organisms are considered when applying interventions to conserve bats, they might also benefit.

Conservación eficiente de murciélagos subterráneos Resumen Es común que los murciélagos habiten en cuevas y otros hábitats subterráneos y contribuyan a las redes alimenticias bajo tierra. Ya que estos ecosistemas cada vez se enfrentan a más amenazas, es importante identificar las medidas más efectivas para proteger a los murciélagos subterráneos. Realizamos un metaanálisis para evaluar la eficiencia de la conservación y las intervenciones de manejo para estos mamíferos. Usamos un análisis de redes para determinar el grado al que las intervenciones en pro de los murciélagos se traslapan con aquellas usadas para otros taxones subterráneos. Realizamos nuestros análisis con datos extraídos de 345 artículos que recomendaban 910 intervenciones de conservación. Se aplicó la colocación de compuertas en la entrada de los dormideros para conservar la población de murciélagos en 21 estudios, pero no quedó clara su efectividad. La restauración del hábitat y la reducción de las perturbaciones afectaron, respectivamente, a las poblaciones y al comportamiento de los murciélagos en ≤ 4 cuatro estudios. Se evaluó a la desinfección en dos estudios y ésta tuvo un efecto positivo sobre las poblaciones, particularmente en los estudios enfocados en la reducción de esporas micóticas asociadas con el síndrome de nariz blanca en América del Norte. La eficiencia del monitoreo de las poblaciones de murciélagos como una estrategia de conservación no fue clara y casi nunca se evaluó. Sólo el 4% de los estudios sobre murciélagos consideró simultáneamente a otros organismos subterráneos. Sin embargo, las intervenciones eficientes para la conservación de murciélagos tuvieron similitudes con las de todos los demás organismos. Otros organismos pueden beneficiarse si se les considera cuando se aplican las intervenciones para conservar a los murciélagos.

Dirichlet process mixture models to impute missing predictor data in counterfactual prediction models: an application to predict optimal type 2 diabetes therapy.

BACKGROUND: The handling of missing data is a challenge for inference and regression modelling. A particular challenge is dealing with missing predictor information, particularly when trying to build and make predictions from models for use in clinical practice. METHODS: We utilise a flexible Bayesian approach for handling missing predictor information in regression models. This provides practitioners with full posterior predictive distributions for both the missing predictor information (conditional on the observed predictors) and the outcome-of-interest. We apply this approach to a previously proposed counterfactual treatment selection model for type 2 diabetes second-line therapies. Our approach combines a regression model and a Dirichlet process mixture model (DPMM), where the former defines the treatment selection model, and the latter provides a flexible way to model the joint distribution of the predictors. RESULTS: We show that DPMMs can model complex relationships between predictor variables and can provide powerful means of fitting models to incomplete data (under missing-completely-at-random and missing-at-random assumptions). This framework ensures that the posterior distribution for the parameters and the conditional average treatment effect estimates automatically reflect the additional uncertainties associated with missing data due to the hierarchical model structure. We also demonstrate that in the presence of multiple missing predictors, the DPMM model can be used to explore which variable(s), if collected, could provide the most additional information about the likely outcome. CONCLUSIONS: When developing clinical prediction models, DPMMs offer a flexible way to model complex covariate structures and handle missing predictor information. DPMM-based counterfactual prediction models can also provide additional information to support clinical decision-making, including allowing predictions with appropriate uncertainty to be made for individuals with incomplete predictor data.

A global analysis of avian island diversity-area relationships in the Anthropocene.

Research on island species-area relationships (ISAR) has expanded to incorporate functional (IFDAR) and phylogenetic (IPDAR) diversity. However, relative to the ISAR, we know little about IFDARs and IPDARs, and lack synthetic global analyses of variation in form of these three categories of island diversity-area relationship (IDAR). Here, we undertake the first comparative evaluation of IDARs at the global scale using 51 avian archipelagic data sets representing true and habitat islands. Using null models, we explore how richness-corrected functional and phylogenetic diversity scale with island area. We also provide the largest global assessment of the impacts of species introductions and extinctions on the IDAR. Results show that increasing richness with area is the primary driver of the (non-richness corrected) IPDAR and IFDAR for many data sets. However, for several archipelagos, richness-corrected functional and phylogenetic diversity changes linearly with island area, suggesting that the dominant community assembly processes shift along the island area gradient. We also find that archipelagos with the steepest ISARs exhibit the biggest differences in slope between IDARs, indicating increased functional and phylogenetic redundancy on larger islands in these archipelagos. In several cases introduced species seem to have 're-calibrated' the IDARs such that they resemble the historic period prior to recent extinctions.

Unsupervised physiological noise correction of functional magnetic resonance imaging data using phase and magnitude information (PREPAIR).

Of the sources of noise affecting blood oxygen level-dependent functional magnetic resonance imaging (fMRI), respiration and cardiac fluctuations are responsible for the largest part of the variance, particularly at high and ultrahigh field. Existing approaches to removing physiological noise either use external recordings, which can be unwieldy and unreliable, or attempt to identify physiological noise from the magnitude fMRI data. Data-driven approaches are limited by sensitivity, temporal aliasing, and the need for user interaction. In the light of the sensitivity of the phase of the MR signal to local changes in the field stemming from physiological processes, we have developed an unsupervised physiological noise correction method using the information carried in the phase and the magnitude of echo-planar imaging data. Our technique, Physiological Regressor Estimation from Phase and mAgnItude, sub-tR (PREPAIR) derives time series signals sampled at the slice TR from both phase and magnitude images. It allows physiological noise to be captured without aliasing, and efficiently removes other sources of signal fluctuations not related to physiology, prior to regressor estimation. We demonstrate that the physiological signal time courses identified with PREPAIR agree well with those from external devices and retrieve challenging cardiac dynamics. The removal of physiological noise was as effective as that achieved with the most used approach based on external recordings, RETROICOR. In comparison with widely used recording-free physiological noise correction tools-PESTICA and FIX, both performed in unsupervised mode-PREPAIR removed significantly more respiratory and cardiac noise than PESTICA, and achieved a larger increase in temporal signal-to-noise-ratio at both 3 and 7 T.

Collective and harmonized high throughput barcoding of insular arthropod biodiversity: Toward a Genomic Observatories Network for islands.

Current understanding of ecological and evolutionary processes underlying island biodiversity is heavily shaped by empirical data from plants and birds, although arthropods comprise the overwhelming majority of known animal species, and as such can provide key insights into processes governing biodiversity. Novel high throughput sequencing (HTS) approaches are now emerging as powerful tools to overcome limitations in the availability of arthropod biodiversity data, and hence provide insights into these processes. Here, we explored how these tools might be most effectively exploited for comprehensive and comparable inventory and monitoring of insular arthropod biodiversity. We first reviewed the strengths, limitations and potential synergies among existing approaches of high throughput barcode sequencing. We considered how this could be complemented with deep learning approaches applied to image analysis to study arthropod biodiversity. We then explored how these approaches could be implemented within the framework of an island Genomic Observatories Network (iGON) for the advancement of fundamental and applied understanding of island biodiversity. To this end, we identified seven island biology themes at the interface of ecology, evolution and conservation biology, within which collective and harmonized efforts in HTS arthropod inventory could yield significant advances in island biodiversity research.

Shrub and precipitation interactions shape functional diversity of nematode communities on the Qinghai-Tibet Plateau.

Land use and climate change alter biodiversity patterns and ecosystem functioning worldwide. Land abandonment with consequent shrub encroachment and changes in precipitation gradients are known factors in global change. Yet, the consequences of interactions between these factors on the functional diversity of belowground communities remain insufficiently explored. Here, we investigated the dominant shrub effects on the functional diversity of soil nematode communities along a precipitation gradient on the Qinghai-Tibet Plateau. We collected three functional traits (life-history C-P value, body mass, and diet) and calculated the functional alpha and beta diversity of nematode communities using kernel density n-dimensional hypervolumes. We found that shrubs did not significantly alter the functional richness and dispersion, but significantly decreased the functional beta diversity of nematode communities in a pattern of functional homogenization. Shrubs benefited nematodes with longer life-history, larger body mass, and higher trophic levels. Moreover, the shrub effects on the functional diversity of nematodes depended strongly on precipitation. Increasing precipitation reversed the effects shrubs have on the functional richness and dispersion from negative to positive but amplified the negative effects shrubs have on functional beta diversity of nematodes. Benefactor shrubs had stronger effects on the functional alpha and beta diversity of nematodes than allelopathic shrubs along a precipitation gradient. A piecewise structural equation model showed that shrubs and its interactions with precipitation indirectly increased the functional richness and dispersion through plant biomass and soil total nitrogen, whereas it directly decreased the functional beta diversity. Our study reveals the expected changes in soil nematode functional diversity following shrub encroachment and precipitation, advancing our understanding of global climate change on nematode communities on the Qinghai-Tibet Plateau.

Prioritizing the reassessment of data-deficient species on the IUCN Red List.

Despite being central to the implementation of conservation policies, the usefulness of the International Union for Conservation of Nature (IUCN) Red List of Threatened Species is hampered by the 14% of species classified as data-deficient (DD) because information to evaluate these species' extinction risk was lacking when they were last assessed or because assessors did not appropriately account for uncertainty. Robust methods are needed to identify which DD species are more likely to be reclassified in one of the data-sufficient IUCN Red List categories. We devised a reproducible method to help red-list assessors prioritize reassessment of DD species and tested it with 6887 DD species of mammals, reptiles, amphibians, fishes, and Odonata (dragonflies and damselflies). For each DD species in these groups, we calculated its probability of being classified in a data-sufficient category if reassessed today from covariates measuring available knowledge (e.g., number of occurrence records or published articles available), knowledge proxies (e.g., remoteness of the range), and species characteristics (e.g., nocturnality); calculated change in such probability since last assessment from the increase in available knowledge (e.g., new occurrence records); and determined whether the species might qualify as threatened based on recent rate of habitat loss determined from global land-cover maps. We identified 1907 species with a probability of being reassessed in a data-sufficient category of >0.5; 624 species for which this probability increased by >0.25 since last assessment; and 77 species that could be reassessed as near threatened or threatened based on habitat loss. Combining these 3 elements, our results provided a list of species likely to be data-sufficient such that the comprehensiveness and representativeness of the IUCN Red List can be improved.

Priorización de la reevaluación de las especies con datos deficientes en la Lista Roja de la UICN Resumen No obstante que es fundamental para la implementación de políticas de conservación, la utilidad de la Lista Roja de Especies Amenazadas de la Unión Internacional para la Conservación de la Naturaleza (UICN) está limitada por el 14% de especies clasificadas con datos deficientes (DD) debido a que la información para evaluar el riesgo de extinción de estas especies no existía cuando fueron evaluadas la última vez o porque los evaluadores no consideraron la incertidumbre apropiadamente. Se requieren métodos robustos para identificar las especies DD con mayor probabilidad de ser reclasificadas en alguna de las categorías en la Lista Roja UICN con datos suficientes. Diseñamos un método reproducible para ayudar a que los evaluadores de la lista roja prioricen la reevaluación de especies DD y lo probamos con 6,887 especies DD de mamíferos, reptiles, anfibios, peces y Odonata (libélulas y caballitos del diablo). Para cada una de las especies DD en estos grupos, calculamos la probabilidad de ser clasificadas en una categoría con datos suficientes si fuera reevaluada hoy a partir de covariables que miden el conocimiento disponible (e.g., número de registros de ocurrencia o artículos publicados disponibles), sustitutos de conocimiento (e.g., extensión del rango de distribución) y características de la especie ((e.g., nocturnidad); calculamos el cambio en tal probabilidad desde la última reevaluación a partir del incremento en el conocimiento disponible (e.g., registros de ocurrencia nuevos); y determinamos si las especies podrían calificar como amenazadas con base en pérdidas de hábitat recientes a partir de mapas globales de cobertura de suelo recientes. Identificamos 1,907 especies con una probabilidad >0.5 de ser reclasificados en una categoría con datos suficientes; 624 especies cuya probabilidad aumentó en >0.25 desde la última evaluación, y 77 especies que podrían ser reclasificadas como casi en peligro con base en la pérdida de hábitat. Combinando estos 3 elementos, nuestros resultados proporcionaron una lista de especies probablemente con datos suficientes de tal modo que la exhaustividad y la representatividad de la Lista Roja de la UICN pueden ser mejoradas.

A roadmap for ladybird conservation and recovery.

Ladybirds (Coleoptera: Coccinellidae) provide services that are critical to food production, and they fulfill an ecological role as a food source for predators. The richness, abundance, and distribution of ladybirds, however, are compromised by many anthropogenic threats. Meanwhile, a lack of knowledge of the conservation status of most species and the factors driving their population dynamics hinders the development and implementation of conservation strategies for ladybirds. We conducted a review of the literature on the ecology, diversity, and conservation of ladybirds to identify their key ecological threats. Ladybird populations are most affected by climate factors, landscape composition, and biological invasions. We suggest mitigating actions for ladybird conservation and recovery. Short-term actions include citizen science programs and education, protective measures for habitat recovery and threatened species, prevention of the introduction of non-native species, and the maintenance and restoration of natural areas and landscape heterogeneity. Mid-term actions involve the analysis of data from monitoring programs and insect collections to disentangle the effect of different threats to ladybird populations, understand habitat use by taxa on which there is limited knowledge, and quantify temporal trends of abundance, diversity, and biomass along a management-intensity gradient. Long-term actions include the development of a worldwide monitoring program based on standardized sampling to fill data gaps, increase explanatory power, streamline analyses, and facilitate global collaborations.

Las catarinas (Coleoptera: Coccinellidae) proporcionan servicios que son críticos para la producción de alimento, y juegan un papel ecológico como fuente de alimento para depredadores. Sin embargo, la riqueza, abundancia y distribución de catarinas están en peligro debido a muchas amenazas antropogénicas. La carencia de conocimiento sobre el estatus de conservación de la mayoría de las especies y los factores que inciden en su dinámica poblacional dificulta el desarrollo e implementación de estrategias de conservación para las catarinas. Realizamos una revisión de la literatura sobre la ecología, diversidad y conservación de catarinas para identificar sus amenazas ecológicas clave. Las poblaciones de catarinas fueron afectadas mayormente por factores climáticos, composición del paisaje e invasiones biológicas. Proponemos acciones de mitigación para la conservación y recuperación de catarinas. Acciones a corto plazo incluyen programas de ciencia y educación ciudadana, medidas de protección para la recuperación de hábitat y de especies amenazadas, prevención de la introducción de especies no nativas y el mantenimiento y restauración de áreas naturales y la heterogeneidad del paisaje. Acciones a mediano plazo implican el análisis de datos obtenidos de programas de monitoreo y colecciones de insectos para desenmarañar el efecto de las diferentes amenazas a las poblaciones de catarinas, comprender el uso del hábitat por taxa de los que se tiene conocimiento limitado y cuantifica las tendencias temporales de la abundancia, diversidad y biomasa a lo largo de un gradiente de intensidad de manejo. Acciones a largo plazo incluyen el desarrollo de un programa de monitoreo a nivel mundial basado en muestreos estandarizados para subsanar la falta de datos, incrementar el poder explicativo, optimizar los análisis y facilitar colaboraciones globales.

Spleen stiffness: a new tool to predict high-risk varices in cirrhotic patients.

INTRODUCTION: Cirrhosis is one of the major causes of morbidity and mortality worldwide. Portal hypertension is the major contributor of cirrhosis-related complications and is defined as a hepatic venous pressure gradient (HVPG) > 5 mmHg. Measurement of HVPG is an invasive, difficult, and costly procedure. Therefore, it is only performed in specialized centers. Liver stiffness measured with transient elastography is one of the most studied noninvasive markers of portal hypertension, and spleen elastography has recently emerged as an important adjuvant tool. The development of a new probe (100 Hz) that more reliably reflect the grade of portal hypertension evaluated by spleen stiffness measurement has improved the accuracy of this technique. The aim of this work was to evaluate the accuracy of spleen stiffness with the new dedicated probe to predict the presence of high-risk varices, as well as to determine the ideal cutoff to predict it. METHODS: Prospective study of cirrhotic patients admitted to upper endoscopy that were also submitted to liver and spleen elastography with the 100-Hz probe by the same blinded operator in a tertiary center. RESULTS: We included 209 cirrhotic patients, with mean age of 61.9 years (±9.9), 77.0% male. The most common etiology was alcoholic liver disease (72.7%). The median value of liver elastography was 25.3 [4.5-75] kPa, and the median value of spleen elastography was 42.4 [7.6-100] kPa. At the cutoff of 53.25 kPa, we obtained sensitivity of 100% and specificity of 72.6% to predict high-risk varices, and, according to this cutoff, 133/175 of esophagogastroduodenoscopy could have been spared (76.0%), while according to Baveno guidelines, only 51/175 would have been spared (29.1%). CONCLUSION: In the era of noninvasive exams, spleen elastography with the 100-Hz probe emerges as an excellent tool for prediction of presence of high-risk varices. At the cutoff of 53.25 kPa, spleen elastography avoids upper endoscopy for screening for high-risk varices, promising to be become part of the hepatologists' daily routine.

A comprehensive analysis of the genetic diversity and environmental adaptability in worldwide Merino and Merino-derived sheep breeds.

BACKGROUND: To enhance and extend the knowledge about the global historical and phylogenetic relationships between Merino and Merino-derived breeds, 19 populations were genotyped with the OvineSNP50 BeadChip specifically for this study, while an additional 23 populations from the publicly available genotypes were retrieved. Three complementary statistical tests, Rsb (extended haplotype homozygosity between-populations), XP-EHH (cross-population extended haplotype homozygosity), and runs of homozygosity (ROH) islands were applied to identify genomic variants with potential impact on the adaptability of Merino genetic type in two contrasting climate zones. RESULTS: The results indicate that a large part of the Merino's genetic relatedness and admixture patterns are explained by their genetic background and/or geographic origin, followed by local admixture. Multi-dimensional scaling, Neighbor-Net, Admixture, and TREEMIX analyses consistently provided evidence of the role of Australian, Rambouillet and German strains in the extensive gene introgression into the other Merino and Merino-derived breeds. The close relationship between Iberian Merinos and other South-western European breeds is consistent with the Iberian origin of the Merino genetic type, with traces from previous contributions of other Mediterranean stocks. Using Rsb and XP-EHH approaches, signatures of selection were detected spanning four genomic regions located on Ovis aries chromosomes (OAR) 1, 6 and 16, whereas two genomic regions on OAR6, that partially overlapped with the previous ones, were highlighted by ROH islands. Overall, the three approaches identified 106 candidate genes putatively under selection. Among them, genes related to immune response were identified via the gene interaction network. In addition, several candidate genes were found, such as LEKR1, LCORL, GHR, RBPJ, BMPR1B, PPARGC1A, and PRKAA1, related to morphological, growth and reproductive traits, adaptive thermogenesis, and hypoxia responses. CONCLUSIONS: To the best of our knowledge, this is the first comprehensive dataset that includes most of the Merino and Merino-derived sheep breeds raised in different regions of the world. The results provide an in-depth picture of the genetic makeup of the current Merino and Merino-derived breeds, highlighting the possible selection pressures associated with the combined effect of anthropic and environmental factors. The study underlines the importance of Merino genetic types as invaluable resources of possible adaptive diversity in the context of the occurring climate changes.

A straightforward one-step strategy for SARS-CoV-2 diagnosis and screening of variants of concern: a multicentre study.

BACKGROUND: The prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants of concern (VOCs) has changed unevenly over time around the world. Although whole genome sequencing is the gold standard for virus characterisation, the discovery of alpha VOC causing spike gene target failure (SGTF) result, when tested using an reverse transcription real-time polymerase chain reaction (RT-qPCR) assay, has provided a simple tool for tracking the frequencies of variants. OBJECTIVES: The aim of this study was to investigate if a multiplex RT-qPCR assay (BioM 4Plex VOC) could be used to detect SARS-CoV-2 and to perform a VOC screening test in a single reaction tube. Here, we present the multicentre study evaluating this assay. METHODS: Twelve laboratories have participated in the multicentre study. The BioM 4Plex VOC was distributed to them with detailed instructions of how to perform the test. They were asked to test the BioM 4Plex VOC in parallel with their routine Commercial SARS-CoV-2 diagnostic assay. Additionally, they were requested to select SARS-CoV-2-positive samples with genome sequenced and lineage definition according to PANGO lineage classification. FINDINGS: The BioM 4Plex VOC and commercial RT-PCR assay are equally effective in detecting SARS-CoV-2. Results revealed a specificity of 96.5-100% [95% confidence interval (CI)], a sensitivity of 99.8-100% (95% CI), and an accuracy of 99.8-100% (95% CI). A 99% concordance rate was found between results from the BioM 4Plex VOC and that from available genome sequencing data. MAIN CONCLUSIONS: The BioM 4Plex VOC provides an effective solution to detect SARS-CoV-2 infections and screening for VOCs in a single reaction. It is a straightforward method to help us monitor the frequency and distribution of VOCs and develop strategies to better cope with the pandemics.

In-situ acceleration-speed profile of an elite soccer academy: A cross-sectional study.

Speed is an essential skill in sports performance and an important performance metric in talent identification. This study aims to evaluate and compare the sprint acceleration characteristics across different age groups in an elite soccer academy. A total of 141 elite academy soccer players were recruited to participate in the study, and they were assigned to their respective competitive age groups, ranging from under-14 to the B-team. An individual in-situ acceleration-speed (A-S) profile was assessed and derived from Global Position System (GPS) speed-acceleration raw data, from 10 consecutive football sessions, in the beginning of the season. The results showed that under-14 players exhibited significantly lower theoretical maximum speed (S0) (ηp2 = 0.215, p < 0.01) when compared with all other age groups. However, no differences were found between maximum theoretical acceleration (A0) and A-S slope between age groups. The results suggest that sprint mechanical profiles of young soccer athletes remain stable throughout their athletic development. Nevertheless, younger athletes have less capacity to apply horizontal force at higher speeds (S0).

Artificial intelligence and capsule endoscopy: automatic detection of enteric protruding lesions using a convolutional neural network.

BACKGROUND AND AIMS: capsule endoscopy (CE) revolutionized the study of the small intestine. Nevertheless, reviewing CE images is time-consuming and prone to error. Artificial intelligence algorithms, particularly convolutional neural networks (CNN), are expected to overcome these drawbacks. Protruding lesions of the small intestine exhibit enormous morphological diversity in CE images. This study aimed to develop a CNN-based algorithm for the automatic detection small bowel protruding lesions. METHODS: a CNN was developed using a pool of CE images containing protruding lesions or normal mucosa from 1,229 patients. A training dataset was used for the development of the model. The performance of the network was evaluated using an independent dataset, by calculating its sensitivity, specificity, accuracy, positive and negative predictive values. RESULTS: a total of 18,625 CE images (2,830 showing protruding lesions and 15,795 normal mucosa) were included. Training and validation datasets were built with an 80 %/20 % distribution, respectively. After optimizing the architecture of the network, our model automatically detected small-bowel protruding lesions with an accuracy of 92.5 %. CNN had a sensitivity and specificity of 96.8 % and 96.5 %, respectively. The CNN analyzed the validation dataset in 53 seconds, at a rate of approximately 70 frames per second. CONCLUSIONS: we developed an accurate CNN for the automatic detection of enteric protruding lesions with a wide range of morphologies. The development of these tools may enhance the diagnostic efficiency of CE.