Expression, purification, and characterization of diacylated Lipo-YcjN from Escherichia coli.

YcjN is a putative substrate-binding protein expressed from a cluster of genes involved in carbohydrate import and metabolism in Escherichia coli. Here, we determine the crystal structure of YcjN to a resolution of 1.95 Å, revealing that its three-dimensional structure is similar to substrate binding proteins in subcluster D-I, which includes the well-characterized maltose binding protein (MBP). Furthermore, we found that recombinant overexpression of YcjN results in the formation of a lipidated form of YcjN that is posttranslationally diacylated at cysteine 21. Comparisons of size-exclusion chromatography profiles and dynamic light scattering measurements of lipidated and non-lipidated YcjN proteins suggest that lipidated YcjN aggregates in solution via its lipid moiety. Additionally, bioinformatic analysis indicates that YcjN-like proteins may exist in both Bacteria and Archaea, potentially in both lipidated and non-lipidated forms. Together, our results provide a better understanding of the aggregation properties of recombinantly expressed bacterial lipoproteins in solution and establish a foundation for future studies that aim to elucidate the role of these proteins in bacterial physiology.

Dual quorum-sensing control of purine biosynthesis drives pathogenic fitness of Enterococcus faecalis.

Enterococcus faecalis is a resident of the human gut, though upon translocation to the blood or body tissues, it can be pathogenic. Here we discover and characterize two peptide-based quorum-sensing systems that transcriptionally modulate de novo purine biosynthesis in E. faecalis. Using a comparative genomic analysis, we find that most enterococcal species do not encode this system; E. moraviensis, E. haemoperoxidus and E. caccae, three species that are closely related to E. faecalis, encode one of the two systems, and only E. faecalis encodes both systems. We show that these systems are important for the intracellular survival of E. faecalis within macrophages and for the fitness of E. faecalis in a murine wound infection model. Taken together, we combine comparative genomics, microbiological, bacterial genetics, transcriptomics, targeted proteomics and animal model experiments to describe a paired quorum sensing mechanism that directly influences central metabolism and impacts the pathogenicity of E. faecalis.

Hamstrings on focus: Are 72 hours sufficient for recovery after a football (soccer) match? A multidisciplinary approach based on hamstring injury risk factors and histology.

This study aimed to assess acute and residual changes in sprint-related hamstring injury (HSI) risk factors after a football (soccer) match, focusing on recovery within the commonly observed 72-h timeframe between elite football matches. We used a multifactorial approach within a football context, incorporating optical and ultrastructural microscopic analysis of BFlh (biceps femoris long head) muscle fibres, along with an examination of BFlh fibre composition. Changes in sprint performance-related factors and HSI modifiable risk factors were examined until 3 days after the match (MD +3) in 20 football players. BFlh biopsy specimens were obtained before and at MD +3 in 10 players. The findings indicated that at MD +3, sprint-related performance and HSI risk factors had not fully recovered, with notable increases in localized BFlh fibre disruptions. Interestingly, match load (both external and internal) did not correlate with changes in sprint performance or HSI risk factors nor with BFlh fibre disruption. Furthermore, our study revealed a balanced distribution of ATPase-based fibre types in BFlh, with type-II fibres associated with sprint performance. Overall, the results suggest that a 72-h recovery period may not be adequate for hamstring muscles in terms of both HSI risk factors and BFlh fibre structure following a football match.

Recognition of Daily Activities in Adults With Wearable Inertial Sensors: Deep Learning Methods Study.

BACKGROUND: Activities of daily living (ADL) are essential for independence and personal well-being, reflecting an individual's functional status. Impairment in executing these tasks can limit autonomy and negatively affect quality of life. The assessment of physical function during ADL is crucial for the prevention and rehabilitation of movement limitations. Still, its traditional evaluation based on subjective observation has limitations in precision and objectivity. OBJECTIVE: The primary objective of this study is to use innovative technology, specifically wearable inertial sensors combined with artificial intelligence techniques, to objectively and accurately evaluate human performance in ADL. It is proposed to overcome the limitations of traditional methods by implementing systems that allow dynamic and noninvasive monitoring of movements during daily activities. The approach seeks to provide an effective tool for the early detection of dysfunctions and the personalization of treatment and rehabilitation plans, thus promoting an improvement in the quality of life of individuals. METHODS: To monitor movements, wearable inertial sensors were developed, which include accelerometers and triaxial gyroscopes. The developed sensors were used to create a proprietary database with 6 movements related to the shoulder and 3 related to the back. We registered 53,165 activity records in the database (consisting of accelerometer and gyroscope measurements), which were reduced to 52,600 after processing to remove null or abnormal values. Finally, 4 deep learning (DL) models were created by combining various processing layers to explore different approaches in ADL recognition. RESULTS: The results revealed high performance of the 4 proposed models, with levels of accuracy, precision, recall, and F1-score ranging between 95% and 97% for all classes and an average loss of 0.10. These results indicate the great capacity of the models to accurately identify a variety of activities, with a good balance between precision and recall. Both the convolutional and bidirectional approaches achieved slightly superior results, although the bidirectional model reached convergence in a smaller number of epochs. CONCLUSIONS: The DL models implemented have demonstrated solid performance, indicating an effective ability to identify and classify various daily activities related to the shoulder and lumbar region. These results were achieved with minimal sensorization-being noninvasive and practically imperceptible to the user-which does not affect their daily routine and promotes acceptance and adherence to continuous monitoring, thus improving the reliability of the data collected. This research has the potential to have a significant impact on the clinical evaluation and rehabilitation of patients with movement limitations, by providing an objective and advanced tool to detect key movement patterns and joint dysfunctions.

Entropy-Driven Crystallization of Hard Colloidal Mixtures of Polymers and Monomers.

The most trivial example of self-assembly is the entropy-driven crystallization of hard spheres. Past works have established the similarities and differences in the phase behavior of monomers and chains made of hard spheres. Inspired by the difference in the melting points of the pure components, we study, through Monte Carlo simulations, the phase behavior of athermal mixtures composed of fully flexible polymers and individual monomers of uniform size. We analyze how the relative number fraction and the packing density affect crystallization and the established ordered morphologies. As a first result, a more precise determination of the melting point for freely jointed chains of tangent hard spheres is extracted. A synergetic effect is observed in the crystallization leading to synchronous crystallization of the two species. Structural analysis of the resulting ordered morphologies shows perfect mixing and thus no phase separation. Due to the constraints imposed by chain connectivity, the local environment of the individual spheres, as quantified by the Voronoi polyhedron, is systematically more spherical and more symmetric compared to that of spheres belonging to chains. In turn, the local environment of the ordered phase is more symmetric and more spherical compared to that of the initial random packing, demonstrating the entropic origins of the phase transition. In general, increasing the polymer content reduces the degree of crystallinity and increases the melting point to higher volume fractions. According to the present findings, relative concentration is another determining factor in controlling the phase behavior of hard colloidal mixtures based on polymers.

Evaluating the generalizability of graph neural networks for predicting collision cross section.

Ion Mobility coupled with Mass Spectrometry (IM-MS) is a promising analytical technique that enhances molecular characterization by measuring collision cross-section (CCS) values, which are indicative of the molecular size and shape. However, the effective application of CCS values in structural analysis is still constrained by the limited availability of experimental data, necessitating the development of accurate machine learning (ML) models for in silico predictions. In this study, we evaluated state-of-the-art Graph Neural Networks (GNNs), trained to predict CCS values using the largest publicly available dataset to date. Although our results confirm the high accuracy of these models within chemical spaces similar to their training environments, their performance significantly declines when applied to structurally novel regions. This discrepancy raises concerns about the reliability of in silico CCS predictions and underscores the need for releasing further publicly available CCS datasets. To mitigate this, we introduce Mol2CCS which demonstrates how generalization can be partially improved by extending models to account for additional features such as molecular fingerprints, descriptors, and the molecule types. Lastly, we also show how confidence models can support by enhancing the reliability of the CCS estimates.Scientific contributionWe have benchmarked state-of-the-art graph neural networks for predicting collision cross section. Our work highlights the accuracy of these models when trained and predicted in similar chemical spaces, but also how their accuracy drops when evaluated in structurally novel regions. Lastly, we conclude by presenting potential approaches to mitigate this issue.

Ovarian Sex Cord Stromal Tumor in a Free-Ranging Brown Bear (Ursus arctos).

Reports on neoplasms in bears are scarce, especially concerning ovarian tumors. A large primary ovarian neoplasm with multiple metastasis was found during the necropsy of a 14-year-old free-ranging Eurasian brown bear (Ursus arctos) from Northwestern Spain. Histopathology and immunohistochemistry allowed for the diagnosis of a sex cord stromal tumor. This is a complex group of neoplasms which differ in the predominant cell morphology and immunohistochemical features. The microscopic examination revealed two types of cells, one with eosinophilic cytoplasm, intermingled with larger vacuolated cells rich in lipids. The evaluation of the immunoreactivity to different markers, frequently used in the characterization of gonadal tumors (INHA, inhibin-alpha; PLAP, placental alkaline phosphatase; Ki-67; α-SMA, actin alpha-smooth muscle) and inflammation patterns (IBA1, ionized calcium-binding adapter molecule for macrophages; CD3 for T lymphocytes; CD20 for B lymphocytes), displayed significant INHA positive immunostaining of neoplastic cells, as well as inflammatory cell infiltration, mainly composed of macrophages and B lymphocytes. These findings were consistent with a malignant ovarian steroid cell tumor, not otherwise specified. The present study characterizes an unusual type of neoplasm, and also represents the first report of an ovarian sex cord stromal tumor in Ursidae.

Random close packing of semi-flexible polymers in two dimensions: Emergence of local and global order.

Through extensive Monte Carlo simulations, we systematically study the effect of chain stiffness on the packing ability of linear polymers composed of hard spheres in extremely confined monolayers, corresponding effectively to 2D films. First, we explore the limit of random close packing as a function of the equilibrium bending angle and then quantify the local and global order by the degree of crystallinity and the nematic or tetratic orientational order parameter, respectively. A multi-scale wealth of structural behavior is observed, which is inherently absent in the case of athermal individual monomers and is surprisingly richer than its 3D counterpart under bulk conditions. As a general trend, an isotropic to nematic transition is observed at sufficiently high surface coverages, which is followed by the establishment of the tetratic state, which in turn marks the onset of the random close packing. For chains with right-angle bonds, the incompatibility of the imposed bending angle with the neighbor geometry of the triangular crystal leads to a singular intra- and inter-polymer tiling pattern made of squares and triangles with optimal local filling at high surface concentrations. The present study could serve as a first step toward the design of hard colloidal polymers with a tunable structural behavior for 2D applications.

Natural Products Have Increased Rates of Clinical Trial Success throughout the Drug Development Process.

Natural products (NPs) or their derivatives represent a large proportion of drugs that successfully progress through clinical trials to approval. This study explores the presence of NPs in both early- and late-stage drug discovery to determine their success rate, and the factors or features of natural products that contribute to such success. As a proxy for early drug development stages, we analyzed patent applications over several decades, finding a consistent proportion of NP, NP-derived, and synthetic-compound-based patent documents, with the latter group outnumbering NP and NP-derived ones (approximately 77% vs 23%). We next assessed clinical trial data, where we observed a steady increase in NP and NP-derived compounds from clinical trial phases I to III (from approximately 35% in phase I to 45% in phase III), with an inverse trend observed in synthetics (from approximately 65% in phase I to 55% in phase III). Finally, in vitro and in silico toxicity studies revealed that NPs and their derivatives were less toxic alternatives to their synthetic counterparts. These discoveries offer valuable insights for successful NP-based drug development, highlighting the potential benefits of prioritizing NPs and their derivatives as starting points.

Rethinking plastic entrapment: Misconceptions and implications for ecosystem services in coastal habitats.

This study addresses the pressing issue of plastic pollution in coastal and marine ecosystems, challenging the misconception that the entrapment of plastics can be considered as an ecosystem service. We differentiate between essential natural processes that sustain ecological balance and biodiversity and the detrimental accumulation of synthetic polymers. The pathways through which plastics enter these environments-from terrestrial to maritime sources-are examined, alongside their pervasive impacts on crucial ecosystem services such as habitat quality, the vitality of marine species, and nutrient cycling. Our findings highlight the paradox of resilience and vulnerability in these ecosystems: while capable of accumulating substantial amounts of plastic debris, they suffer long-lasting ecological, socio-economic, and health repercussions. We argue for a paradigm shift in management strategies aimed at reducing plastic production at the source, improving waste management practices, conducting targeted cleanup operations, and rehabilitating impacted ecosystems. Emphasizing a comprehensive understanding of plastic pollution is vital for framing effective solutions and necessitates a reevaluation of societal, industrial, and regulatory frameworks. This shift is imperative not only to address current pollution levels but also to safeguard and sustain the functionality of coastal ecosystems, ensuring their ability to continue providing essential services and supporting biodiversity.

Proof of concept for a new sensor to monitor marine litter from space.

Worldwide, governments are implementing strategies to combat marine litter. However, their effectiveness is largely unknown because we lack tools to systematically monitor marine litter over broad spatio-temporal scales. Metre-sized aggregations of floating debris generated by sea-surface convergence lines have been reported as a reliable target for detection from satellites. Yet, the usefulness of such ephemeral, scattered aggregations as proxy for sustained, large-scale monitoring of marine litter remains an open question for a dedicated Earth-Observation mission. Here, we track this proxy over a series of 300,000 satellite images of the entire Mediterranean Sea. The proxy is mainly related to recent inputs from land-based litter sources. Despite the limitations of in-orbit technology, satellite detections are sufficient to map hot-spots and capture trends, providing an unprecedented source-to-sink view of the marine litter phenomenon. Torrential rains largely control marine litter inputs, while coastal boundary currents and wind-driven surface sweep arise as key drivers for its distribution over the ocean. Satellite-based monitoring proves to be a real game changer for marine litter research and management. Furthermore, the development of an ad-hoc sensor can lower the minimum detectable concentration by one order of magnitude, ensuring operational monitoring, at least for seasonal-to-interannual variability in the mesoscale.

Bismuthene - Tetrahedral DNA nanobioconjugate for virus detection.

In this work, we present an electrochemical sensor for fast, low-cost, and easy detection of the SARS-CoV-2 spike protein in infected patients. The sensor is based on a selected combination of nanomaterials with a specific purpose. A bioconjugate formed by Few-layer bismuthene nanosheets (FLB) and tetrahedral DNA nanostructures (TDNs) is immobilized on Carbon Screen-Printed Electrodes (CSPE). The TDNs contain on the top vertex an aptamer that specifically binds to the SARS-CoV-2 spike protein, and a thiol group at the three basal vertices to anchor to the FLB. The TDNs are also marked with a redox indicator, Azure A (AA), which allows the direct detection of SARS-CoV-2 spike protein through changes in the current intensity of its electrolysis before and after the biorecognition reaction. The developed sensor can detect SARS-CoV-2 spike protein with a detection limit of 1.74 fg mL-1 directly in nasopharyngeal swab human samples. Therefore, this study offers a new strategy for rapid virus detection since it is versatile enough for different viruses and pathogens.

Divergent spatio-temporal tree growth trends in Pinus pinaster Ait. in South-Western European forests.

Climate change influences forest ecosystems in several ways, such as modifying forest growth or ecosystem functionality. To fully understand the impact of changing climatic conditions on forest growth it is necessary to undertake long-term spatiotemporal analyses. The main purpose of this work is to describe the major trends in tree growth of Pinus pinaster in Spain over the last 70 years, differentiating homogeneous ecological units using an unsupervised classification algorithm and additive modelling techniques. We also aim to relate these growth trends with temporal series for precipitation and temperature, as well as forest variables. We leverage information from a large data set of tree cores (around 2200) extracted during the field campaign of the Fourth Spanish National Forest Inventory. An unsupervised algorithm classified the plots into five classes, which were consistent in ecological terms. We also found a general decline in growth in three of the five ecoregions since the 1970s, concomitant with an increase in temperature and a reduction in precipitation. However, this tree growth decline has not been observed in the Atlantic influenced ecoregion, where the cooler, more humid climatic conditions are more stable. Certain stand features, such as low basal area through forest management practices, may have alleviated the impact of harsh climatic conditions on some areas of inner Spain, while denser stands display a more pronounced decline in tree growth. We concluded that Southern populations show some degrees of growth decline and low growth trends while Northern populations did not exhibit growth decline and have the largest growth rates. Under a forecasted increment of temperatures, the growth decline can be expanded.

COVID-19 patient profiles over four waves in Barcelona metropolitan area: A clustering approach.

OBJECTIVES: Identifying profiles of hospitalized COVID-19 patients and explore their association with different degrees of severity of COVID-19 outcomes (i.e. in-hospital mortality, ICU assistance, and invasive mechanical ventilation). The findings of this study could inform the development of multiple care intervention strategies to improve patient outcomes. METHODS: Prospective multicentre cohort study during four different waves of COVID-19 from March 1st, 2020 to August 31st, 2021 in four health consortiums within the southern Barcelona metropolitan region. From a starting point of over 292 demographic characteristics, comorbidities, vital signs, severity scores, and clinical analytics at hospital admission, we used both clinical judgment and supervised statistical methods to reduce to the 36 most informative completed covariates according to the disease outcomes for each wave. Patients were then grouped using an unsupervised semiparametric method (KAMILA). Results were interpreted by clinical and statistician team consensus to identify clinically-meaningful patient profiles. RESULTS: The analysis included nw1 = 1657, nw2 = 697, nw3 = 677, and nw4 = 787 hospitalized-COVID-19 patients for each of the four waves. Clustering analysis identified 2 patient profiles for waves 1 and 3, while 3 profiles were determined for waves 2 and 4. Patients allocated in those groups showed a different percentage of disease outcomes (e.g., wave 1: 15.9% (Cluster 1) vs. 31.8% (Cluster 2) for in-hospital mortality rate). The main factors to determine groups were the patient's age and number of obese patients, number of comorbidities, oxygen support requirement, and various severity scores. The last wave is also influenced by the massive incorporation of COVID-19 vaccines. CONCLUSION: Our study suggests that a single care model at hospital admission may not meet the needs of hospitalized-COVID-19 adults. A clustering approach appears to be appropriate for helping physicians to differentiate patients and, thus, apply multiple care intervention strategies, as another way of responding to new outbreaks of this or future diseases.

Exploratory study of blood biomarkers in patients with post-stroke epilepsy.

INTRODUCTION: In addition to clinical factors, blood-based biomarkers can provide useful information on the risk of developing post-stroke epilepsy (PSE). Our aim was to identify serum biomarkers at stroke onset that could contribute to predicting patients at higher risk of PSE. PATIENTS AND METHODS: From a previous study in which 895 acute stroke patients were followed-up, 51 patients developed PSE. We selected 15 patients with PSE and 15 controls without epilepsy. In a biomarker discovery setting, 5 Olink panels of 96 proteins each, were used to determine protein levels. Biomarkers that were down-regulated and overexpressed in PSE patients, and those that showed the strongest interactions with other proteins were validated using an enzyme-linked immunosorbent assay in samples from 50 PSE patients and 50 controls. A ROC curve analysis was used to evaluate the predictive ability of significant biomarkers to develop PSE. RESULTS: Mean age of the PSE discovery cohort was 68.56 ± 15.1, 40% women and baseline NIHSS 12 [IQR 1-25]. Nine proteins were down-expressed: CASP-8, TNFSF-14, STAMBP, ENRAGE, EDA2R, SIRT2, TGF-alpha, OSM and CLEC1B. VEGFa, CD40 and CCL4 showed greatest interactions with the remaining proteins. In the validation analysis, TNFSF-14 was the single biomarker showing statistically significant downregulated levels in PSE patients (p = 0.006) and it showed a good predictive capability to develop PSE (AUC 0.733, 95% CI 0.601-0.865). DISCUSSION AND CONCLUSION: Protein expression in PSE patients differs from that of non-epileptic stroke patients, suggesting the involvement of several different proteins in post-stroke epileptogenesis. TNFSF-14 emerges as a potential biomarker for predicting PSE.

Hidden impacts of ocean warming and acidification on biological responses of marine animals revealed through meta-analysis.

Conflicting results remain on the impacts of climate change on marine organisms, hindering our capacity to predict the future state of marine ecosystems. To account for species-specific responses and for the ambiguous relation of most metrics to fitness, we develop a meta-analytical approach based on the deviation of responses from reference values (absolute change) to complement meta-analyses of directional (relative) changes in responses. Using this approach, we evaluate responses of fish and invertebrates to warming and acidification. We find that climate drivers induce directional changes in calcification, survival, and metabolism, and significant deviations in twice as many biological responses, including physiology, reproduction, behavior, and development. Widespread deviations of responses are detected even under moderate intensity levels of warming and acidification, while directional changes are mostly limited to more severe intensity levels. Because such deviations may result in ecological shifts impacting ecosystem structures and processes, our results suggest that climate change will likely have stronger impacts than those previously predicted based on directional changes alone.

Structural insights reveal interplay between LAG-3 homodimerization, ligand binding, and function.

Lymphocyte activation gene-3 (LAG-3) is an inhibitory receptor expressed on activated T cells and an emerging immunotherapy target. Domain 1 (D1) of LAG-3, which has been purported to directly interact with major histocompatibility complex class II (MHCII) and fibrinogen-like protein 1 (FGL1), has been the major focus for the development of therapeutic antibodies that inhibit LAG-3 receptor-ligand interactions and restore T cell function. Here, we present a high-resolution structure of glycosylated mouse LAG-3 ectodomain, identifying that cis-homodimerization, mediated through a network of hydrophobic residues within domain 2 (D2), is critically required for LAG-3 function. Additionally, we found a previously unidentified key protein-glycan interaction in the dimer interface that affects the spatial orientation of the neighboring D1 domain. Mutation of LAG-3 D2 residues reduced dimer formation, dramatically abolished LAG-3 binding to both MHCII and FGL1 ligands, and consequentially inhibited the role of LAG-3 in suppressing T cell responses. Intriguingly, we showed that antibodies directed against D1, D2, and D3 domains are all capable of blocking LAG-3 dimer formation and MHCII and FGL-1 ligand binding, suggesting a potential allosteric model of LAG-3 function tightly regulated by dimerization. Furthermore, our work reveals unique epitopes, in addition to D1, that can be targeted for immunotherapy of cancer and other human diseases.

An orally bioavailable SARS-CoV-2 main protease inhibitor exhibits improved affinity and reduced sensitivity to mutations.

Inhibitors of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) main protease (Mpro) such as nirmatrelvir (NTV) and ensitrelvir (ETV) have proven effective in reducing the severity of COVID-19, but the presence of resistance-conferring mutations in sequenced viral genomes raises concerns about future drug resistance. Second-generation oral drugs that retain function against these mutants are thus urgently needed. We hypothesized that the covalent hepatitis C virus protease inhibitor boceprevir (BPV) could serve as the basis for orally bioavailable drugs that inhibit SARS-CoV-2 Mpro more efficiently than existing drugs. Performing structure-guided modifications of BPV, we developed a picomolar-affinity inhibitor, ML2006a4, with antiviral activity, oral pharmacokinetics, and therapeutic efficacy similar or superior to those of NTV. A crucial feature of ML2006a4 is a derivatization of the ketoamide reactive group that improves cell permeability and oral bioavailability. Last, ML2006a4 was found to be less sensitive to several mutations that cause resistance to NTV or ETV and occur in the natural SARS-CoV-2 population. Thus, anticipatory design can preemptively address potential resistance mechanisms to expand future treatment options against coronavirus variants.

Antiseizure medication withdrawal in adult patients with idiopathic generalized epilepsy: Performance of two seizure recurrence prediction models.

PURPOSE: Currently, there is a limited availability of tools to predict seizure recurrence after discontinuation of antiseizure medications (ASMs). This study aimed to establish the seizure recurrence rate following ASM cessation in adult patients with idiopathic generalized epilepsy (IGE) and to assess the predictive performance of the Lamberink and the Stevelink prediction models using real-world data. METHODS: Retrospective longitudinal study in IGE patients who underwent ASM withdrawal in a tertiary epilepsy clinic since June 2011, with the latest follow up in January 2024. The minimum follow-up period was 12 months. Clinical and demographic variables were collected, and the seizure recurrence prediction models proposed by Lamberink and Stevelink were applied and evaluated. RESULTS: Forty-seven patients (mean age 33.15 ± 8 [20-55] years; 72.35 % women) were included. During the follow-up period, seizures recurred in 25 patients (53.2 %). Median time to recurrence was 8 months [IQR 3-13.5 months], and 17 patients (68 %) relapsed within the first year. None of the relapsing patients developed drug-resistant epilepsy. The only significant risk factor associated with recurrence was a seizure-free period of less than 2 years before discontinuing medication (91.7 % vs 40 %, p =.005). The Stevelink prediction model at both 2 (p =.015) and 5 years (p =.020) achieved statistical significance, with an AUC of 0.72 (95 % CI 0.56-0.88), while the Lamberink model showed inadequate prognostic capability. CONCLUSION: In our real-world cohort, a seizure-free period of at least 2 years was the only factor significantly associated with epilepsy remission after ASM withdrawal. Larger studies are needed to accurately predict seizure recurrence in IGE patients.