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Arrhythmias after paediatric cardiac surgery occur frequently and contribute to postoperative morbidity and mortality. There is limited literature assessing the safety and efficacy of common antiarrhythmics administered in this population. We systematically searched PubMed and EMBASE for literature on antiarrhythmic use in children <18 years of age after cardiac surgery from 2000 to 2024. Two reviewers independently screened abstracts and then reviewed full-text manuscripts to determine eligibility. We identified 28 studies of 3,752 patients across 11 different antiarrhythmics: flecainide, procainamide, esmolol, landiolol, propranolol, amiodarone, sotalol, dexmedetomidine, digoxin, ivabradine, and magnesium. Most studies were small, with 17 enrolling fewer than 100 children. Only eight studies were randomised, 16 were retrospective, 12 were prospective, and one was multicenter. Safety and efficacy endpoints varied widely, limiting our ability to combine data for meta-analysis. Overall, evidence supporting the use of these drugs in children after cardiac surgery was limited. Although antiarrhythmics are commonly used in children after cardiac surgery, randomised trials with standardised endpoints to guide choice of therapy are lacking. Pragmatic trials to generate real-world data should be considered to further evaluate the safety and efficacy of various antiarrhythmics in this population.

We aimed to develop a machine learning model to predict activities of daily living (ADL) at discharge in stroke patients and identify key predictors to guide rehabilitation decisions. Data of 589 stroke inpatients (2019-2024) were split into good (BI&#x2009;&#x2265;&#x2009;60) and poor (BI&#x2009;<&#x2009;60) ADL groups. Continuous variables were processed using Z-score normalization, followed by preliminary univariate regression screening (P&#x2009;<&#x2009;0.05) and final feature selection via LASSO regression (lambda.1se&#x2009;=&#x2009;0.0488). The screened features were used to train and validate ten machine learning algorithms; 30% of the dataset (n&#x2009;=&#x2009;177) was allocated as an independent test set for model evaluation, and SHAP analysis was performed to interpret the optimal model. Six of 41 features were retained. Random forest achieved the best performance (AUC&#x2009;=&#x2009;0.958; accuracy&#x2009;=&#x2009;0.936; sensitivity&#x2009;=&#x2009;0.934; specificity&#x2009;=&#x2009;0.950). SHAP identified the top drivers: admission Barthel Index, standing balance, Brunnstrom stages (upper and lower limb), dressing, and grooming abilities. The ADL risk prediction model constructed using machine learning, particularly the random forest model, shows excellent predictive performance and clinical interpretability, making it valuable for individualized risk assessment of daily living skills in stroke patients at discharge.

Coarse-grain Lagrangian methods, such as Dissipative Particle Dynamics (P. J. Hoogerbrugge et al., EPL, 1992, 19, 155), are suitable to describe mesoscopic fluid systems with the inclusion of thermal fluctuations. However, the realistic simulation of liquids using these methods represents a longstanding problem. In this work, we develop a local thermodynamic (LTh) model for the description of condensed phases within the framework of the Generalized Dissipative Particle Dynamics with Energy Conservation (GenDPDE) method (J. Bonet Avalos et al., Phys. Chem. Chem. Phys. 2019, 21, 24891). Such a model is appropriate for the analysis of liquids, due to the explicit account of the thermal expansion coefficient and isothermal compressibility at the mesoscale. We demonstrate the accuracy of the LTh model by inspecting the thermodynamic properties of argon at both liquid and supercritical conditions, through equilibrium simulations carried out around two characteristic reference states (T = 125.7 K, P = 85.31 MPa, &#x3f1; = 1419.7 kg/m3 for liquid Ar, and T = 418.8 K, P = 85.31 MPa, &#x3f1; = 695.99 kg/m3 for supercritical Ar). Remarkably, we show that the model is also valid in a range of thermodynamic conditions near the reference states, allowing for a correct description of the physics of systems with spatial density and temperature variations. We furthermore derive analytical expressions for the macroscopic pressure and energy equations of state in terms of the model parameters, discussing their validity and limitations. We show that, even at the mean-field level, a correct account of the local particle arrangements is necessary to obtain accurate predictions of the macroscopic thermodynamic quantities from mesoscopic properties. Thus, we also investigate the applicability of the Hypernetted Chain approximation as a tool to predict the radial distribution function of the GenDPDE system, examining the strengths and deficiencies of this approach. With the proposed LTh model, GenDPDE provides a reliable and flexible tool for the analysis of condensed phases through coarse-grain techniques.

Prolonged Casualty Care (PCC) has become an increasing focus of military medicine as the Joint Force prepares for Large-Scale Combat Operations (LSCO) with mass casualties, contested logistics, and prolonged evacuation timelines. Existing Role 1 medical equipment was largely developed around assumptions of reliable evacuation and permissive resupply and is poorly suited for prolonged casualty holding in highly contested environments. We previously collaborated with the Joint Trauma System (JTS) under a Department of Defense-funded effort to develop the first Prolonged Field Care Kits (PFAKs), including Delphi-based generation of candidate medications and equipment aligned with PCC Clinical Practice Guidelines (CPGs). However, the resulting content recommendations exceeded 50 lbs and 80 L, rendering them operationally impractical for distributed Role 1 operations (defined as providers organic to combat arms units, from line medical personnel up to the Battalion Aid Station level) during LSCO. To translate PCC guidance into fieldable systems, a multidisciplinary working group with extensive operational experience refined the survey-derived candidate contents using repeated tabletop exercises, field testing, and end-user feedback. Design priorities included reduction of weight, volume, administration burden, cognitive burden, and logistical footprint while maintaining adherence to PCC CPG intent. This process resulted in 2 complementary systems: the PFAK Basic for typical Role 1 PCC and the PFAK Advanced for teams capable of mechanical ventilation. Representative design tradeoffs included simplification of medication strategies, minimization of sharps use, and emphasis on oral and enteral alternatives to reduce reliance on intravenous administration. The PFAKs represent the first standardized PCC equipment configurations specifically designed for conventional forces operating under LSCO assumptions. Their development demonstrates a practical framework for translating PCC guidelines into distributed, scalable, and tactically survivable Role 1 capabilities. The concept is now being operationally implemented within the 1st Marine Division through an Improvised PFAK loadout fielded alongside a PCC course and handbook, demonstrating that conventional formations can begin to generate standardized PCC capabilities using existing organizational structures and inventories while dedicated procurement pathways are developed.

The trade-off between mechanical robustness and ionic conductivity in gel materials impedes their application in flexible electronics. Herein, a eutectogel is engineered via a synergistic strategy that integrates a ternary deep eutectic solvent (DES) (choline chloride/ethylene glycol/zinc chloride) with dynamic Zn2 + coordination. Through in situ photopolymerization of 1-vinylimidazole in the ternary DES, a dynamically cross-linked organic-inorganic hybrid network is constructed. Crucially, Zn2 + ions play a dual role: they form reversible Zn2 +-imidazole coordination sites, enhancing the mechanical properties with an elongation at break of 1100% and a Young's modulus of 0.23&#xa0;MPa, while inducing coordination-driven densification of the amorphous network. This compaction effect tightens the polymer network without triggering crystallization, while accessible ion-transport pathways are retained within the amorphous network. Consequently, the eutectogel exhibits a high ionic conductivity of 0.38 mS cm- 1, overcoming the typical conductivity loss in high-strength gels. Using these properties, a flexible strain-sensing system with Bluetooth transmission is developed. It can capture real-time motor signals and convert them into visual commands, highlighting its potential for wireless assistive monitoring, particularly in rehabilitation for hemiplegic patients. This work provides a promising strategy for achieving a balance between mechanical robustness and ionic conductivity in soft materials by regulating the amorphous structure.

Novel diclofenac (Dic) hybrids with methyl 2-bromobenzoate (2-DMB) and methyl 4-bromobenzoate (4-DMB) modifications via ester linkages have been synthesized and proven to have anti-inflammatory potential in vitro, suggesting that further development of these compounds may lead to a potential anti-inflammatory drug. In this study, the anti-inflammatory activity of these compounds was evaluated both in vitro and in vivo, and the underlying mechanisms were explored using a RAW 264.7 cell model of LPS-induced inflammation. Compared with 2-DMB, 4-DMB showed more potent anti-inflammatory activity in LPS-treated RAW 264.7 cells, with no cytotoxicity. Moreover, 4-DMB exhibited greater protective efficacy than 5-aminosalicylic acid (5-ASA) against DSS-induced colitis in mice. Notably, the results from cellular studies indicated increased protein and mRNA levels of TNF-&#x3b1; following treatment with 4-DMB, suggesting an effect of 4-DMB on TNFR. Furthermore, cellular thermal shift assays (CETSA), isothermal titration calorimetry (ITC), and molecular docking studies collectively demonstrated that 4-DMB competes with TNF-&#x3b1; for binding to TNFR1. In addition, network pharmacology analysis combined with Western blot validation indicated that the anti-colitis effect of 4-DMB is closely associated with the inhibition of the AKT/ERK/NF-&#x3ba;B signaling pathway. Critically, siRNA-mediated knockdown of TNFR1 significantly suppressed the AKT/ERK/NF-&#x3ba;B signaling and inflammatory responses, demonstrating the essential role of TNFR1 in mediating the AKT/ERK/NF-&#x3ba;B signaling. Taken together, these findings indicate that 4-DMB mitigates colitis by directly binding to TNFR1 and inhibiting the AKT/ERK/NF-&#x3ba;B signaling pathway. This research underscores the potential clinical relevance of 4-DMB for the treatment of inflammatory disorders, particularly ulcerative colitis.

Cortical control of movement is a distributed computation spanning multiple densely interconnected regions. Although we have rich anatomical atlases and a coarse understanding of how function maps to areas and subregions, we lack a detailed account of how behaviorally relevant activity is organized across the cortical sheet. Here, we trained head-fixed mice to perform a 15-target reach-to-grasp task while we performed cellular-resolution, two-photon calcium imaging across five regions of sensorimotor cortex (>39,000 layer 2/3 neurons). We characterized each neuron's trial-averaged peri-event activity with interpretable metrics and mapped these response properties across areas, revealing large-scale spatial structure. Neuronal response profiles often shifted abruptly at anatomical borders: motor areas showed sharper tuning and more linear relationships with target location, whereas somatosensory areas displayed more heterogeneous response patterns. Neural response properties also differed according to somatotopic representation. Nonlinear dimensionality reduction of the neural feature matrix revealed that areas varied in their average response profiles, but that areas did not have well-separated feature distributions; instead, each area contained subpopulations. Neurons in each subpopulation had characteristic response profiles and were distributed across multiple cortical areas. The spatial distributions of the subpopulations overlapped, with neurons from different subpopulations salt-and-pepper intermingled in the overlap zones. Together, these results describe novel activity structure across sensorimotor cortex and identify several distinct but spatially overlapping subpopulations with characteristic activity patterns during reach-to-grasp behavior. Reaching out to grab a cup of coffee may feel effortless, but it requires the brain to coordinate a precise sequence of movement commands. This control is supported by the sensorimotor cortex, the part of the brain's wrinkled outer layer involved in integrating incoming sensory feedback with outgoing movement commands. Researchers have traditionally divided the sensorimotor cortex into separate anatomical areas based on the shapes of neurons as well as their connectivity to other brain structures. This area-based view has shaped our understanding of the sensorimotor cortex based on the idea that different areas generally serve different functions. However, it has been less clear whether these functions change sharply at the borders dividing regions or whether groups of neurons within an area might perform different functions. Anatomical areas have long formed the basic unit for studying cortical function, yet recent work shows that movement-related activity is far more widely distributed than this view assumes. Understanding how the cortex controls movement, therefore, requires mapping the responses of large numbers of individual neurons across different areas, and determining how responses relate to known anatomical and somatotopic boundaries. Salimian, Grier and Kaufman sought to characterize how responses of single neurons are distributed across anatomically defined sensorimotor areas in the cortex of mice while they performed reach-to-grasp movements. Salimian et al. characterized the activity of nearly 40,000 individual neurons across different areas of the sensorimotor cortex of mice using interpretable features obtained from their activity during a challenging forelimb control task. Neural response features were then organized into coherent spatial gradients spanning motor and somatosensory cortical areas. The results showed that the spatial patterns possessed sharp transitions that closely aligned with anatomical and somatotopic borders. Clustering cells based on their features identified four unique subpopulations with characteristic response profiles, whose members were widely distributed across all recorded areas, but with different prevalence in each. The spatial distributions of these subpopulations formed overlapping zones, in which neurons from different subpopulations intermingled. The findings of Salimian et al. suggest that a complete understanding of the sensorimotor cortex requires mapping the distributed neuronal networks, instead of just focusing on separate, specialized areas. In the future, this view could inform clinical technologies such as the placement of recording electrodes for brain-computer interfaces that help paralyzed people move, or stimulation-based maps used to guide surgical resection of brain tissue. In addition, they may inform the design of neural networks for controlling robots, where sensory feedback must be integrated into choosing motor commands. However, realizing such benefits would first require confirming that these subpopulations exist in the human brain and elucidating their contributions to movement control.

Access to real-world healthcare data is increasingly hindered by privacy concerns and stringent legal frameworks, including the General Data Protection Regulation (GDPR) and the Health Insurance Portability and Accountability Act (HIPAA). Consequently, synthetic healthcare data generators, notably Synthea&#x2122;, have emerged as essential tools to generate high-quality datasets with minimal privacy and legal concerns. However, many simulation engines including, Synthea&#x2122;, predominantly rely on command-line interfaces (CLI), imposing significant technical barriers for clinical researchers. Furthermore, raw output formats such as HL7 Fast Healthcare Interoperability Resources (FHIR) JSON create a substantial "interpretability gap" for end-users. To address these challenges, we present SyntheaWeb, a web-based platform that simplifies the generation and interactive inspection of synthetic patient cohorts. It provides a user interface, visual cohort dashboard, structured longitudinal patient records, semantic terminology linking (e.g., SNOMED CT, LOINC), and the capability for selective subset export.

BackgroundSarcopenia is a progressive skeletal muscle disorder associated with increased disability, morbidity, and mortality. Ultrasound has gained increasing attention as an accessible and repeatable tool for muscle assessment.ObjectiveThis study aimed to map the global research trends and technological evolution in ultrasound-based sarcopenia assessment from 1997 to 2025.MethodsA total of 1574 records were retrieved from the Web of Science Core Collection. Bibliometric analyses were performed using CiteSpace, VOSviewer, and the bibliometrix R package. Sankey diagrams were used to visualize thematic flows between ultrasound assessment dimensions and outcome-related research topics.ResultsPublications showed a prolonged latent phase, steady growth, and rapid acceleration after 2019, peaking in 2025. Keyword and thematic-flow analyses identified a three-stage evolution: an early Morphometry Phase centered on muscle size and atrophy, a Qualitative Transition Phase emphasizing muscle quality and chronic disease contexts, and an Advanced Functional and AI Phase characterized by elastography, AI-assisted analysis, and outcome-related topics. Research contexts expanded across geriatrics, nephrology, critical care, surgical oncology, and rehabilitation-related research.ConclusionUltrasound-based sarcopenia research has shifted toward multidimensional muscle assessment and technology-assisted analysis. Because this study did not appraise individual study quality or clinical effect sizes, the findings should be interpreted as bibliometric patterns and research priorities rather than direct evidence of diagnostic or prognostic effectiveness.

Sunitinib, a first-line tyrosine kinase inhibitor, is widely used for renal cell carcinoma (RCC) therapy; however, sunitinib resistance compromises clinical efficacy. N6-methyladenosine (m6A), the most prevalent internal RNA modification, plays a crucial role in cancer progression and drug response. This study aimed to investigate the regulatory mechanism of an m6A writer ZC3H13 on secreted frizzled-related protein 4 (sFRP-4) and reveal their roles in sunitinib resistance of RCC. The levels of sFRP-4 and ZC3H13 were evaluated by qRT-PCR and western blotting. Cell functional assays and in vivo experiments, were conducted to explore the effects of sFRP-4 and ZC3H13 on sunitinib resistance of RCC. The regulatory relationship between ZC3H13 and sFRP-4 was confirmed via qRT-PCR, western blotting, luciferase, MeRIP and RNA stability assays. Wnt/&#x3b2;-catenin pathway was examined by western blotting. sFRP-4 was downregulated in sunitinib-resistant RCC tissues and cells. Forced expression of sFRP-4 suppressed sunitinib-resistant RCC cell viability, migration, invasion, and tumor growth under sunitinib exposure by attenuating Wnt/&#x3b2;-catenin pathway. Mechanistically, ZC3H13 enhanced sFRP-4 mRNA stability by increasing its m6A modification. Knockdown of ZC3H13 could reverse the inhibitory effects of sFRP-4 on sunitinib resistance of RCC cells. In summary, ZC3H13-mediated m6A modification stabilizes sFRP-4 expression, which suppresses Wnt/&#x3b2;-catenin signaling and attenuates the sunitinib resistance of RCC. The ZC3H13/sFRP-4 axis may represent a promising therapeutic target in RCC.

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Imaging Mass Cytometry (IMC) enables highly multiplexed, spatially resolved single-cell proteomics, providing simultaneous measurement of dozens of protein markers while preserving tissue architecture. Despite its analytical power, IMC data analysis remains fragmented across multiple software environments, requiring researchers to combine independent tools for visualization, preprocessing, segmentation, feature extraction, phenotyping, batch correction, and spatial analysis. This fragmentation increases technical barriers, complicates reproducibility, and limits accessibility for non-computational users. We developed OpenIMC, an open-source platform that integrates the major stages of IMC analysis within a unified graphical and command-line framework. OpenIMC supports image visualization, quality control, preprocessing, segmentation, feature extraction, dimensionality reduction, batch effect correction, clustering, phenotyping, and spatial analysis while maintaining interoperability with established community tools. The platform incorporates automated provenance tracking, records analytical parameters and software versions, and enables export and sharing of complete analytical sessions. Benchmarking demonstrated deterministic behavior across repeated runs, complete concordance between graphical and command-line workflows, and strong agreement with established IMC analysis pipelines. OpenIMC additionally provides support for high-resolution IMC workflows, including signal attenuation modeling and image deconvolution. We apply OpenIMC to two datasets of circulating cells and breast tissue to demonstrate the platform's ability to support integrated single-cell and spatial proteomics analysis. OpenIMC reduces the complexity of IMC data analysis by providing a unified, reproducible, and extensible framework for common IMC workflows. By combining interactive visualization with scalable computational analysis, OpenIMC lowers technical barriers and facilitates reproducible single-cell and spatial proteomics research.

Healthcare systems face increasing pressure to deliver high-quality care while managing rising patient volumes and operational complexity. Although Electronic Medical Records (EMRs) generate large volumes of clinical data, hospitals often lack integrated operational intelligence capable of transforming fragmented information into actionable insights. This study presents the conceptual architecture of the Dedalus Command Centre, a platform designed to support real-time operational coordination in healthcare environments. The architecture was developed using a design-oriented research approach involving a multidisciplinary working group and a structured concept development process including stakeholder engagement, operational workflow analysis, architecture design, prototype validation, and platform implementation. The resulting platform consists of three logical layers: a real-time data layer integrating heterogeneous hospital data streams, an analytics and intelligence layer providing predictive and simulation capabilities, and a user application layer delivering operational dashboards and role-based tools. The proposed architecture enables real-time operational monitoring and predictive insights to support improved situational awareness, patient flow management, and coordinated hospital operations. Ongoing studies aim to evaluate its impact on hospital operational performance.

Postpyloric enteral feeding is recommended for ICU patients at high risk of aspiration. However, blind placement carries a rare yet potentially life-threatening risk of tube misplacement into the pleural cavity. We present a case in which blind insertion of a fine-bore naso-intestinal tube led to intrapleural malposition in an elderly patient with a history of radiotherapy for nasopharyngeal carcinoma (NPC). A 78-year-old Chinese man with severe dysphagia following radiotherapy for NPC underwent tracheotomy due to respiratory failure and was subsequently transferred to the ICU. The naso-intestinal tube was inserted blindly, and its position was considered appropriate based on limited fluid aspiration and auscultation. Nine hours later, chest radiography revealed that the tube had traversed through the trachea, entered the right lower-lobe bronchus, extended into the pleural cavity, and terminated mid-thorax. The tube was promptly removed and accurately repositioned into the stomach under laryngoscopic guidance.&#xa0;Blind postpyloric tube placement in patients with radiation-induced anatomical and neurological changes following NPC may result in silent intrapleural misplacement. Immediate pH testing combined with radiographic confirmation is crucial to prevent this potentially fatal complication.

Docosahexaenoic acid (DHA), a long-chain omega-3 polyunsaturated fatty acid, has well-recognized anti-inflammatory activity; however, the mechanisms underlying its protective effects in inflammatory bowel disease (IBD) remain incompletely understood. In this study, we investigated the effects of DHA in a dextran sulfate sodium (DSS)-induced mouse model of colitis and examined whether these effects were mediated by the gut microbiota. DHA administration markedly alleviated DSS-induced colitis, as indicated by reduced body weight loss, disease activity, mortality, colon shortening, histological injury, intestinal barrier disruption, and colonic inflammatory responses. 16&#xa0;S rRNA gene sequencing showed that DHA reshaped the gut microbial community and increased the abundance of beneficial taxa, including Bifidobacterium. Antibiotic cocktail (ABX)-mediated microbiota depletion largely abolished the protective effects of DHA, whereas fecal microbiota transplantation (FMT) from DHA-treated donors transferred resistance to DSS-induced colitis to recipient mice. DHA also restored tight junction protein expression and increased the frequency of colonic regulatory T cells in a microbiota-dependent manner. These findings indicate that DHA alleviates experimental colitis by modulating the gut microbiota, restoring intestinal barrier integrity, and regulating mucosal immune homeostasis. DHA may therefore represent a promising dietary strategy for the prevention or adjunctive treatment of ulcerative colitis (UC).

Most studies assess emergence delirium (ED) in the postanesthesia care unit (PACU), but immediate post-extubation evaluation and its associated risk factors remain understudied. We aimed to evaluate the incidence of ED immediately after extubation and to identify risk factors associated with its occurrence during this early post-extubation period in surgical patients. This study employed an observational, cross-sectional, correlational design with convenience sampling. A cross-sectional correlational study was conducted involving 299 adult patients who underwent surgery at a tertiary medical center. ED was assessed using the Nursing Delirium Screening Scale (Nu-DESC) immediately following extubation, which was performed once patients regained consciousness and were able to follow commands. Demographic and clinical data were collected, including age, sex, comorbidities, type and duration of surgery, and anesthetic agents used. Descriptive statistics and multivariable logistic regression were used to analyze the data. The incidence of ED was 45.5% (136/299). Multivariable logistic regression analysis demonstrated that anesthetic duration &#x2265;76&#xa0;min (OR 2.33; 95% CI, 1.23 to 4.42; P = 0.009), lower education level (&#x2264;9 years; OR 2.19; 95% CI, 1.17 to 4.08; P = 0.014), and a history of smoking (OR 2.12; 95% CI, 1.16 to 3.88; P = 0.014) were independently associated with an increased risk of ED. Early identification of risk factors facilitates timely recognition of ED. Lower educational attainment, smoking history, and prolonged anesthetic duration were associated with an increased risk. Immediate assessment of delirium after extubation shows potential value. In the future, it may support targeted monitoring, inform preoperative risk stratification, and help improve anesthesia nursing plans to enhance patient safety.

The metabolism of microbial communities is essential for host and environmental health. The rational design of microbiomes with targeted functional properties is an important objective but remains challenging due to complex interactions and environmental heterogeneity. Community-function landscapes address this challenge by statistically inferring impacts of species presence or absence on function. Similar to fitness landscapes, community-function landscapes estimate both additive effects and interactions (epistasis) between species that influence function. We apply landscapes to design synthetic consortia to degrade the toxic contaminant bisphenol-A (BPA). Using synthetic communities of ten BPA-degrading bacteria, we map community-function landscapes across increasing BPA concentrations, where higher BPA means greater toxicity. Epistasis increases with toxicity, indicating that collective effects become more important for degradation. Designed communities are able to remediate BPA in contaminated soils. Our results demonstrate that toxicity can drive epistatic interactions in community-function landscapes and that these landscapes can guide microbial consortia design for bioremediation.

The poor prognosis of high&#x2011;grade gliomas, such as glioblastoma, is largely driven by a notably immunosuppressive tumor microenvironment (TME), wherein microglia and tumor&#x2011;associated macrophages play important roles. The present review summarizes the current evidence supporting triggering receptor expressed on myeloid cells 2 (TREM2) as a key immunoregulator in the glioma TME. The present review explores the context&#x2011;dependent dual role of TREM2, which can either be hijacked to promote immunosuppression and tumor progression or inhibit tumor progression through its notable functions in phagocytosis and antigen presentation. The present review also examines the clinical association of TREM2 expression with glioma grade and patient prognosis, evaluating its potential as a diagnostic and prognostic biomarker. Furthermore, the present review discusses the current landscape of TREM2&#x2011;targeted therapeutic strategies, from direct TREM2 inhibition and myeloid&#x2011;targeted immunocytokines to nano&#x2011;engineered drug delivery systems, and addresses the core translational challenges of these strategies. Looking forward, the importance of leveraging spatial multi&#x2011;omics and artificial intelligence to decipher the functional heterogeneity of TREM2 and to guide precision immunotherapy is highlighted. In conclusion, the present review provides a comprehensive framework for understanding the role of TREM2 in glioma; this receptor serves not only as a biomarker for glioma, but as an important signaling hub in the glioma TME. Therefore, the present review aims to support the development of novel therapeutic strategies targeting the immune microenvironment in glioma.

This paper introduces TESSCCo (TV-control EEG-based Silent Speech Command Corpus), a new dataset including electroencephalography (EEG) signals during Overt Speech (OS) and Covert Speech (CS) in different languages. The dataset comprises repetitions of five different commands pronounced covertly and overtly in English and Spanish from 21 healthy native Spanish speakers (13 male, 8 female, 23 &#xb1; 2 years old), while EEG and audio were recorded. In addition, 3 non-native healthy Spanish speakers were recorded under the same circumstances (3 male, 23.3 &#xb1; 0.6 years old). A total of 7936 available epochs (i.e., 11.02 hours of data) were recorded with a 32 channel, 256 Hz sampling rate Water based EEG device. The database was designed to maximize the number of different analysis involving EEG signals. The final number of epochs, as well as the statistical analysis (showing significance in Broca's and Wernicke's areas) and machine learning experiments (with subjects exceeding the chance level with basic machine learning models), show that this material is a valuable resource for research on future ways of communication.