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Microglia originate from myeloid progenitors in the embryonic yolk sac and play an integral role in central nervous system (CNS) development, immune surveillance and repair. The role of microglia in multiple sclerosis (MS) has been complex and controversial, with evidence suggesting that these cells play key roles in both active inflammation and remyelination. Here we will review the most recent histological classification of MS lesions as well as the evidence supporting both inflammatory and reparative functions of these cells. We will also review how microglia may yield new biomarkers for MS activity and serve as a potential target for therapy.

The 5xFAD transgenic mouse model widely used in Alzheimer’s disease (AD) research recapitulates many AD-related phenotypes with a relatively early onset and aggressive age-dependent progression. Besides developing amyloid peptide deposits alongside neuroinflammation by the age of 2 months, as well as exhibiting neuronal decline by the age of 4 months that intensifies by the age of 9 months, these mice manifest a broad spectrum of behavioural impairments. In this review, we present the extensive repertoire of behavioural dysfunctions in 5xFAD mice, organised into four categories: motor skills, sensory function, learning and memory abilities, and neuropsychiatric-like symptoms. The motor problems, associated with agility and reflex movements, as well as balance and coordination, and skeletal muscle function, typically arise by the time mice reach 9 months of age. The sensory function (such as taste, smell, hearing, and vision) starts to deteriorate when amyloid peptide buildups and neuroinflammation spread into related anatomical structures. The cognitive functions, encompassing learning and memory abilities, such as visual recognition, associative, spatial working, reference learning, and memory show signs of decline from 4 to 6 months of age. Concerning neuropsychiatric-like symptoms, comprising apathy, anxiety and depression, and the willingness for exploratory behaviour, it is believed that motivational changes emerge by approximately 6 months of age. Unfortunately, numerous studies from different laboratories are often contradictory on the conclusions drawn and the identification of onset age, making preclinical studies in rodent models not easily translatable to humans. This variability is likely due to a range of factors associated with animals themselves, housing and husbandry conditions, and experimental settings. In the forthcoming studies, greater clarity in experimental details when conducting behavioural testing in 5xFAD transgenic mice could minimise the inconsistencies and could ensure the reliability and the reproducibility of the results.

Nanomaterials based on Pd nanoparticles supported on Vulcan carbon (XC-72R) were prepared by the organometallic approach in one-pot and mild conditions (3 bar hydrogen and room temperature) using Pd(dba)2 (bis (dibenzylideneacetone) palladium (0)) as metal source and hexadecylamine (HDA) as stabilizer. High-resolution transmission electron microscopy (HR-TEM) evidenced the presence of well-dispersed Pd nanoparticles of ca. 4.5 nm mean size onto the carbon support (Pd/HDA/C). Scanning and transmission electron microscopy with electron energy loss spectroscopy (STEM-EELS) allowed to determine the chemical composition of the nanomaterials. When the Pd/HDA/C nanomaterial was submitted to heating treatment (ht) at 400 °C under air (referred as Pd/HDA/C@air-ht), X-ray photoelectron spectroscopy (XPS) and HR-TEM/STEM-EELS analyses suggested the presence of interactions between PdO and Pd(0) as a result of the formation of Pd@PdO core–shell nanoparticles. The highest oxidation current magnitude during methanol oxidation reaction is ascribed to the heat-treated material, linked with a better electron and mass transfer processes at the electrode interface. This can be attributed to electronic interactions at the core–shell formed, which might promote different redox processes at the electrode interface during CH3OH deprotonation in the alkaline electrolyte.

Polymers obtained from biomass are an interesting alternative to petro-based polymers due to their low cost of production, biocompatibility, and biodegradability. This is the case of lignin, which is the second most abundant biopolymer in plants. As a consequence, the exploitation of lignin for the production of new materials with improved properties is currently considered as one of the main challenging issues, especially for the paper industry. Regarding its chemical structure, lignin is a crosslinked polymer that contains many functional hydrophilic and active groups, such as hydroxyls, carbonyls and methoxyls, which provides a great potential to be employed in the synthesis of biodegradable hydrogels, materials that are recognized for their interesting applicability in biomedicine, soil and water treatment, and agriculture, among others. This work describes the main methods for the preparation of lignin-based hydrogels reported in the last years, based on the chemical and/or physical interaction with polymers widely used in hydrogels formulations. Furthermore, herein are also reviewed the current applications of lignin hydrogels as stimuli-responsive materials, flexible supercapacitors, and wearable electronics for biomedical and water remediation applications.

The degree of belief we have in predictions from hydrologic models will normally depend on how well they can reproduce observations. Calibrations with traditional performance measures, such as the Nash-Sutcliffe model efficiency, are challenged by problems including: (1) uncertain discharge data, (2) variable sensitivity of different performance measures to different flow magnitudes, (3) influence of unknown input/output errors and (4) inability to evaluate model performance when observation time periods for discharge and model input data do not overlap. This paper explores a calibration method using flow-duration curves (FDCs) to address these problems. The method focuses on reproducing the observed discharge frequency distribution rather than the exact hydrograph. It consists of applying limits of acceptability for selected evaluation points (EPs) on the observed uncertain FDC in the extended GLUE approach. Two ways of selecting the EPs were tested – based on equal intervals of discharge and of volume of water. The method was tested and compared to a calibration using the traditional model efficiency for the daily four-parameter WASMOD model in the Paso La Ceiba catchment in Honduras and for Dynamic TOPMODEL evaluated at an hourly time scale for the Brue catchment in Great Britain. The volume method of selecting EPs gave the best results in both catchments with better calibrated slow flow, recession and evaporation than the other criteria. Observed and simulated time series of uncertain discharges agreed better for this method both in calibration and prediction in both catchments. An advantage with the method is that the rejection criterion is based on an estimation of the uncertainty in discharge data and that the EPs of the FDC can be chosen to reflect the aims of the modelling application, e.g. using more/less EPs at high/low flows. While the method appears less sensitive to epistemic input/output errors than previous use of limits of acceptability applied directly to the time series of discharge, it still requires a reasonable representation of the distribution of inputs. Additional constraints might therefore be required in catchments subject to snow and where peak-flow timing at sub-daily time scales is of high importance. The results suggest that the calibration method can be useful when observation time periods for discharge and model input data do not overlap. The method could also be suitable for calibration to regional FDCs while taking uncertainties in the hydrological model and data into account.

Predicting students’ performance is one of the most important topics for learning contexts such as schools and universities, since it helps to design effective mechanisms that improve academic results and avoid dropout, among other things. These are benefited by the automation of many processes involved in usual students’ activities which handle massive volumes of data collected from software tools for technology-enhanced learning. Thus, analyzing and processing these data carefully can give us useful information about the students’ knowledge and the relationship between them and the academic tasks. This information is the source that feeds promising algorithms and methods able to predict students’ performance. In this study, almost 70 papers were analyzed to show different modern techniques widely applied for predicting students’ performance, together with the objectives they must reach in this field. These techniques and methods, which pertain to the area of Artificial Intelligence, are mainly Machine Learning, Collaborative Filtering, Recommender Systems, and Artificial Neural Networks, among others.

This phase 3, placebo-controlled platform trial evaluated a single infusion of casirivimab and imdevimab (REGEN-COV) at 2400-mg and 1200-mg doses in outpatients with acute SARS-CoV-2 infection. The incidence of Covid-19–related hospitalization was lower and recovery was faster among patients who received the antibody combination than among those who received placebo.

Background Creatinine is the most widely used test to estimate the glomerular filtration rate (GFR), but muscle mass as key determinant of creatinine next to renal function may confound such estimates. We explored effects of 24‐h height‐indexed creatinine excretion rate (CER index) on GFR estimated with creatinine (eGFRCr), muscle mass‐independent cystatin C (eGFRCys), and the combination of creatinine and cystatin C (eGFRCr‐Cys) and predicted probabilities of discordant classification given age, sex, and CER index. Methods We included 8076 adults enrolled in the PREVEND study. Discordant classification was defined as not having eGFRCr <60 mL/min per 1.73 m2 when eGFRCys was <60 mL/min/1.73 m2. Baseline effects of age and sex on CER index were quantified with linear models using generalized least squares. Baseline effects of CER index on eGFR were quantified with quantile regression and logistic regression. Effects of annual changes in CER index on trajectories of eGFR were quantified with linear mixed‐effects models. Missing observations in covariates were multiply imputed. Results Mean (SD) CER index was 8.0 (1.7) and 6.1 (1.3) mmol/24 h per meter in male and female participants, respectively (Pdifference < 0.001). In male participants, baseline CER index increased until 45 years of age followed by a gradual decrease, whereas a gradual decrease across the entire range of age was observed in female participants. For a 70‐year‐old male participant with low muscle mass (CER index of 2 mmol/24 h per meter), predicted baseline eGFRCr and eGFRCys disagreed by 24.7 mL/min/1.73 m2 (and 30.1 mL/min/1.73 m2 when creatinine was not corrected for race). Percentages (95% CI) of discordant classification in male and female participants aged 60 years and older with low muscle mass were 18.5% (14.8–22.1%) and 15.2% (11.4–18.5%), respectively. For a 70‐year‐old male participant who lost muscle during follow‐up, eGFRCr and eGFRCys disagreed by 1.5, 5.0, 8.5, and 12.0 mL/min/1.73 m2 (and 6.7, 10.7, 13.5, and 15.9 mL/min/1.73 m2 when creatinine was not corrected for race) at baseline, 5 years, 10 years, and 15 years of follow‐up, respectively. Conclusions Low muscle mass may cause considerable overestimation of single measurements of eGFRCr. Muscle wasting may cause spurious overestimation of repeatedly measured eGFRCr. Implementing muscle mass‐independent markers for estimating renal function, like cystatin C as superior alternative to creatinine, is crucial to accurately assess renal function in settings of low muscle mass or muscle wasting. This would also eliminate the negative consequences of current race‐based approaches.

Alzheimer’s disease (AD) is a leading cause of dementia and a growing public health concern worldwide. Despite decades of research, effective disease-modifying treatments remain elusive, partly due to limitations in current experimental models. The purpose of this review is to critically assess and compare existing murine and alternative models of AD to identify key strengths, limitations, and future directions for model development that can enhance translational relevance and therapeutic discovery. Traditional transgenic mouse models have advanced the understanding of amyloid-beta and tau pathologies, but often fail to capture the complexity of sporadic, late-onset AD. In response, alternative models—including zebrafish, Drosophila melanogaster, Caenorhabditis elegans, non-human primates, and human brain organoids—are gaining traction due to their complementary insights and diverse experimental advantages. This review also discusses innovations in genetic engineering, neuroimaging, computational modelling, and drug repurposing that are reshaping the landscape of AD research. By integrating these diverse approaches, the review advocates for a multi-model, multidisciplinary strategy to improve the predictive power, accelerate clinical translation, and inform personalised therapeutic interventions. Ethical considerations and equitable access to diagnostics and emerging treatments are also emphasised. Ultimately, this work aims to support the development of more accurate, effective, and human-relevant models to combat AD.

The Dwarf Ombú, Phytolacca tetramera, is a rare and highly unusual plant endemic to the northeastern Pampean grasslands of Argentina and is currently considered of high conservation priority. In order to better understand its biology, ecology, and conservation requirements, we studied its anatomy, reproductive traits, life history, and distribution based on field observations and herbarium material. Our results show that P. tetramera possesses a combination of traits consistent with the concept of ecological anachronism. The species produces large fleshy fruits whose size and shape are comparable to those interpreted as adapted for dispersal by extinct megafauna. In addition, the plant exhibits morphological and ecological adaptations associated with intense grazing, trampling, and drought tolerance, including robust underground structures and a growth pattern comparable to underground trees from seasonally dry open habitats. These findings suggest that P. tetramera evolved under ecological conditions markedly different from those existing today, including megafaunal disclimax environments that disappeared after the late Pleistocene extinctions. This ecological mismatch may help to explain its present rarity, fragmented distribution, and low population numbers. Our results also indicate that current conservation strategies for P. tetramera should consider the role of disturbance regimes and extinct ecological interactions in shaping the biology of this species.