Journals
2026 EN
Yan Wenci · Quinn Terence · McConnachie Alex
+6 more
ABSTRACT Objective Cognitive decline can occur following ischaemic stroke. How cognition changes over time and associations with cognitive change are poorly understood. This study aimed to explore these issues over 2 years following ischaemic stroke. Methods This analysis used data from the XILO‐FIST study, a clinical trial of allopurinol versus placebo in people with ischaemic stroke according to Tissue‐Based Definition. Participants underwent clinical assessment, brain MRI at baseline, and Montreal Cognitive Assessment (MoCA) at baseline, year 1 and year 2. We defined cognitive impairment as a MoCA score < 26 and cognitive change as a difference in MoCA score of 2 points or more at year 1 or year 2 after randomisation. Associations with cognitive impairment and cognitive change were assessed by univariable analysis and multiple logistic regression. Results Three hundred and sixty participants with complete MoCA data were included. Mean age was 65.4 (SD 8.36) years, and mean baseline MoCA score was 26.4 (SD 2.7). Seventy‐seven participants had second‐year cognitive improvement. Eighty‐four had second‐year cognitive decline. After adjustment for age and education year, second‐year cognitive improvement was associated with smaller brain volume, lower albumin level, smoking and greater white‐matter hyperintensity, and second‐year cognitive decline was associated with peripheral arterial disease, higher cholesterol level, small‐vessel stroke and greater white‐matter hyperintensity. Interpretation Cognition is dynamic following stroke, with different patterns of change. Brain reserve and vascular risk factors relate to later post‐stroke cognitive change. This complex nature of cognitive trajectory has implications for cognitive rehabilitation provision and cognitive impairment detection after stroke.
Journals
2026 EN
Ganesan Sukirth M. · Yadav Meeta · Ghimire Sudeep
+10 more
ABSTRACT Objective Multiple sclerosis (MS) is a chronic autoimmune disorder characterized by inflammation, demyelination, and neurological impairment. While the gut microbiota's role in MS is extensively studied, the association between the oral microbiota and MS remains underexplored, particularly in North American cohorts. This study aimed to investigate the microbiota (bacterial) composition as well as functional pathways and immune profiles of the oral cavity in 60 patients with relapsing–remitting MS (RRMS), stratified by treatment status, compared to 44 healthy controls (HC). Methods Unstimulated saliva was collected for genomic DNA extraction and salivary cytokine quantification. Oral bacterial composition and diversity were analyzed using 16S rRNA sequencing, with functional pathways inferred using PICRUSt2. Salivary cytokine levels were measured via multiplex immunoassays. LEfSe and random forest models identified key discriminatory taxa, and correlations between microbiota and cytokines were assessed using Spearman's rank analysis. Results RRMS patients exhibited distinct microbial communities compared to HC and a higher Bacteroidota to Firmicutes ratio. Key taxa such as Campylobacter, Lachnoanaerobaculum , and Porphyromonas were enriched in RRMS. Functional profiling revealed 49 differentially abundant pathways, including the enrichment of lipopolysaccharide biosynthesis in MS. Elevated levels of IFN‐γ, IL‐6, and other cytokines correlated with the altered microbiome. IL‐21, elevated in HC, correlated with anti‐inflammatory pathways, suggesting a protective role in immune homeostasis. Interpretation This study provides, for the first time, insights into oral microbiome‐host interactions in North American RRMS patients, underscoring the interplay between microbial dysbiosis, functional pathways, and immune dysregulation. The oral microbiome shows potential as a biomarker for MS‐related immune alterations.
Journals
2026 EN
Hu Manyue · Robinson Oliver · Lill Christina M.
+6 more
ABSTRACT Background There is growing recognition of the potential of plasma proteomics for Alzheimer's Disease (AD) risk assessment and disease characterization. However, differences between proteomics platforms introduce uncertainties regarding cross‐platform applicability. Objective We aimed to identify a detailed plasma biosignature for distinguishing AD from cognitively normal (CN) and another signature for classifying mild cognitive impairment (MCI) decliners and non‐decliners. We also explored the cross‐platform applicability of these models between two proteomic platforms. Methods Elastic net was performed on 190 plasma analytes measured using the Luminex xMAP platform in 566 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to model MCI stable/decliner and AD/CN classification. MCI decliner was defined as progression to AD during follow‐up (mean 4.2 ± 3.2 years). External cross‐platform validation was conducted with 1303 participants from the Spanish Ace study, using the SOMAscan 7k platform. Results An 11‐analyte signature for distinguishing AD from CN achieved a 93.5% accuracy on ADNI and 95.2% on Ace. The ApoE and BNP proteins were the two most important contributors to the classifier. The MCI classification signature performed less well, with 65.9% accuracy on ADNI and 51.0% accuracy upon validation testing in Ace. Discussion Compared with prior proteomic‐based studies on the same dataset, our findings attained higher specificity and sensitivity for AD classification while utilizing a smaller panel of analytes. We also confirmed the reliability and consistency of this signature within a different population from a different platform. The plasma proteomic platforms explored were, however, not sufficient to determine MCI decliners versus non‐decliners.
Journals
2026 EN
Walker Andrew · Leung Jerik · Alagappan Aishwarya
+8 more
Objective Patients with chronic illness share their experiences in online communities and generate rich data on pain management. This study applied natural language processing methods, including large language models (LLMs), to Reddit discussions from lupus communities to characterize multidimensional pain experiences framed in the biopsychosocial model. Methods We extracted Reddit posts from the r/Lupus and r/LupusSupport subreddits posted from June 9, 2010, through December 31, 2023. Pain‐related posts were identified using a clinically informed pain lexicon. Topic modeling was used to identify thematic patterns, which were then compared with structured summaries generated by LLM instructions that were fine‐tuned using the biopsychosocial model of pain. Two reviewers conducted content analysis of the LLM‐generated summaries, evaluating thematic accuracy and coverage. Results Data from Reddit included 31,785 posts from 10,857 authors. We identified common pain complaints, management strategies, and sociocultural, affective, and nociplastic dimensions of pain. Instruction fine‐tuned LLMs produced structured summaries with an average thematic accuracy score of 3.1 of 4 (kappa = .09) and content coverage score of 2.9 of 4 (kappa = .38). Sociocultural features presented in 123 posts (33.8%), including peer support and validation (n = 106) and provider interactions or access issues (n = 35). Nociplastic pain presented in 205 posts (56.3%). Conclusion Natural language processing methods can be used to extract rich, multidimensional insights into pain experiences from online communities focused on lupus. These approaches highlight the psychological, social, and cultural facets of pain that may be underrepresented in clinical settings, supporting more patient‐centered approaches to care in rheumatology.
Journals
2026 EN
Zhao Yonggang · Zhang Le · Yuan Xi
+5 more
It is investigated how the addition of Cu influences the pitting corrosion behavior of carbon steel induced by (Ca, Mn)S‐(Al, Mg)O complex inclusions. While the overall microstructure of the steel matrix remained unchanged, Cu markedly altered the morphology and elemental distribution of these inclusions. First‐principles calculations revealed that Cu preferentially adsorbed on the CaS interface due to its lower adsorption energy and stronger Cu‐S orbital hybridization. This preferential adsorption facilitated the phase separation of MnS and CaS and resulted in localized Cu enrichment within CaS regions. At higher Cu contents, a dense and continuous Cu‐rich layer developed around the inclusions. This layer served as a physical barrier, effectively impeding the ingress of corrosive species, retarding the dissolution of sulfide inclusions, and suppressing pit propagation. Immersion tests demonstrated that increasing Cu content resulted in smaller and shallower pits and reduced localized corrosion rates. Electrochemical analyzes further validated the enhanced corrosion resistance at higher Cu content. The synergistic action of interfacial adsorption and barrier‐layer formation accounts for the reduced pitting susceptibility of Cu‐alloyed carbon steel.
Journals
2026 EN
Kushwaha Aditya · Vardhan Shalini · Singh Ritu Raj
+1 more
Abstract 2D transition metal dichalcogenides (2D TMDs) like WS 2 have shown immense potential for optoelectronic applications but face inherent limitations in spectral range, carrier mobility, and recombination losses. To overcome these challenges, a novel heterostructure combining WS 2 with the semimetal NiTeSe is proposed, leveraging its ultrahigh carrier mobility and near‐zero bandgap for enhanced photodetection. Through first‐principles density functional theory (DFT) calculations and COMSOL Multiphysics simulations, the electronic and optical properties of the NiTeSe–WS 2 heterostructure are systematically investigated. The hybrid system has a Schottky barrier at the interface and a smaller bandgap (0.689 eV in NiTeSe–WS 2 compared to 1.809 eV in pure WS 2 ). This helps separate charges more efficiently and absorb a wider range of light. Optical analyses reveal exceptional performance, including a 48% higher absorption coefficient (2.21 × 10⁵ cm −1 ) and 53% enhanced optical conductivity (3.91 Ω −1 cm −1 ) compared to pristine WS 2 . Device simulations reveal outstanding photoresponse performance, with a peak responsivity of 4.3 × 10 4 A W −1 and an external quantum efficiency of 1.06 × 10 5 %, representing a significant enhancement compared to pristine WS 2 . These results establish the NiTeSe–WS 2 heterostructure as a transformative platform for next‐generation photodetectors, offering unprecedented sensitivity, spectral versatility, and speed for applications in communication, imaging, and sensing technologies.
Journals
2026 EN
Yasir Muhammad · Bilal S. · Qi Haitao
ABSTRACT Efficient thermal transmission is a necessary requirement of diversified engineering units to attain optimized output. For this purpose, the induction of tri‐hybrid nanoparticles to achieve advanced heat transfer is considered as an innovative strategy because of an increase in the specific heat capacity of a system in different ways. The dispersion of silver (Ag), copper (Cu), and alumina (Al 2 O 3 ) in water with different physical factors controlled the flow dynamics. A practical approach to enhance heat transfer effectiveness is to improve the thermal properties of the working fluid. Nanofluids, which are suspensions of nanoparticles in a base fluid, are recommended. In addition, the synergistic effects of homogeneous and heterogeneous reactions among nanoparticle interactions are also accounted for to improve overall thermal efficiency. The findings demonstrated that tri‐hybrid nanofluids provide superior thermal conductivity and absorption rates compared with those of conventional fluids. A machine learning framework is employed to predict complex heat transfer behaviors and optimize system performance. Therefore, a machine learning paradigm based on an artificial neural network is used to analyze the particle concentration impacts for the estimation of the friction factor and heat transfer analysis of a water‐based tri‐hybrid nanofluid. The ANN model training for larger needle sizes exhibits higher accuracy and stability than that for smaller needle sizes, owing to the higher sensitivity and non‐linearity, which are also indicated by the larger MSE. Validation failures remain zero in most cases, which represents fitness along with good generalization to the data.
Journals
2026 EN
Du Kang · Huang Xiaopeng · Yao Xin
+3 more
ABSTRACT This study presents, a comprehensive investigation into the failure behavior and parametric optimization of novel 3D‐printed Auxetic Tubular Re‐entrant Structures (ATRS), using an integrated experimental–numerical framework. Compression tests are performed on four ATRS designs featuring different unit cell angles, thicknesses, and widths to confirm simulation accuracy and evaluate mechanical performance. Excellent agreement is observed between experimental and simulation results, capturing both the initial linear response and the nonlinear buckling behavior. The simulations revealed exceptional auxetic responses and showed how geometric parameters govern stress localization and failure initiation. Reduced unit cell widths led to earlier buckling owing to a smaller load‐bearing area and increased soft mode activation, whereas larger angles raised buckling forces but triggered instability sooner. Also, energy absorption capacity rose significantly with increases in unit cell width, angle, and thickness, reaching as much as four times higher in thicker samples. According to Response Surface Methodology (RSM) and Analysis of Variance (ANOVA), thickness and width are the primary parameters influencing buckling force, stiffness, and energy absorption, with thickness having the greatest impact. These findings facilitate accurate predictive modeling of ATRS mechanical behavior driven by geometric design and offer new pathways for designing damage‐tolerant structures with tunable mechanical responses.
Journals
2026 EN
Nair Manikantan R. · Chandran Anusree S. · Shukla Nishant
+2 more
ABSTRACT Climate change and global targets to achieve net‐zero carbon goals need efficient energy storage systems that are capable of powering green mobility and have the capacity to mitigate the irregular supply of renewable sources. Among the various available technologies, energy storage devices such as batteries, supercapacitors, etc., provide a sustainable solution. However, these devices currently lack the required performance to meet the growing demands, particularly in terms of power and energy densities, fast charging capabilities, longer cycle life, etc. Their electrochemical performance is mainly determined by how many electrolyte ions are stored within the electrodes without the crowding effect. The shape and size of electrolyte ions, along with electrode pore connectivity, are two major factors that significantly affect charge storage capabilities. Determining these relations using traditional experimental methods is time‐consuming, expensive, complex, and ineffective. Hence, this study uses molecular dynamics simulation integrated with density functional theory calculations to gain insights into how ion shape and size affect the charge storage mechanism. Further, the significance of electrode pore interconnectivity in governing charge storage is also explored in this work. The study employed a microporous carbon electrode with a pore size distribution ranging from ∼0.3 to 1.0 nm. Results indicate that [EMIM] + (∼0.76 × 0.43 nm) with [Cl] − (∼0.18 nm) is significantly smaller than [C 4 C 1 Pyrr] + (∼1.10 × 0.60 nm) and [TFSI] − (∼0.79 × 0.29 nm). Further, the ionic size of [EMIM] + /[Cl] − fall within the pore size distribution and was thus diffuse more readily and yield higher in‑pore ion counts and charge storage. Bulkier ions like [C 4 C 1 Pyrr] + and [TFSI] − systems were found to be locally concentrated near the initial sections of the electrode, partly due to their large size and/or due to their shape. Density functional theory studies also support the results obtained from molecular dynamics simulations and confirm that smaller ions whose size falls within the electrode pore size distribution contribute to double‐layer formation and thereby aid in improving the charge storage of energy storage devices.
Journals
2026 EN
Cristóbal Ana Belén · SanzCuadrado Cristina · Alves e Silva Kiane
+1 more
Achieving meaningful citizen engagement in energy innovation is crucial for a successful energy transition, but realizing high levels of participation remains a challenge. Successful initiatives characterized by high levels of participation—according to Arnstein's ladder—are presented and analyzed. In these cases, citizens play a key role in driving technological and social innovations within the energy sector over several decades. From the development of technical standards to the evaluation of energy yield, the assessment of solar module aging, the creation of in situ repair procedures, or the deployment of solar vehicles, among other examples, the initiatives studied demonstrate how citizens can meaningfully engage with researchers in the successful development of technical innovations. Regarding the deployment of social innovations, 34 energy communities are analyzed to assess engagement levels. The findings reveal a gap—similar to that observed in technical innovations—between current practices and genuine citizen‐led innovation. While many communities fall short of full citizen control, inspiring examples that demonstrate pathways toward deeper and more impactful citizen participation are showcased. By highlighting these successful cases, this study underscores the transformative potential of citizen engagement in accelerating the sustainable energy transition and provides actionable insights for fostering citizen‐driven innovation in the energy sector.