Browse Articles

Synergistic effects of SAE/CaCO3 on enhancing sulfate resistance in High-Strength cementitious composites

Abstract The composite incorporation of polymer and calcium carbonate (CaCO3) in cementitious matrices demonstrates effective toughening effects on high-strength cementitious composites (HSCC). However, its resistance to salt ion erosion remains unverified, which is critical for construction materials in coastal environments and underground engineering. This study systematically evaluates the sulfate attack resistance of SAE/CaCO3 modified HSCC through mass variation analysis, ultrasonic monitoring, durability coefficient determination, and surface morphology characterization. Advanced microstructural investigations including MIP, XRD, DSC, and SEM were employed to elucidate the synergistic mechanisms of SAE/CaCO3 in enhancing durability of the cement matrix. Results reveal that SAE/CaCO3 modified HSCC exhibits exceptional sulfate resistance. Specifically, under sulfate attack, high-SAE dosage specimens showed lower mass fluctuation and less mechanical property degradation compared to control groups. The primary mechanism stems from SAE-induced pore structure refinement. This study elucidates critical structure-performance relationships in SAE/CaCO3 modified HSCC systems, offering dual benefits of special engineering environment durability enhancement and mechanistic insights for polymer-cement composite optimization.

Cocrystal effect-driven ultrafast phase-transition ionogel for dynamically switchable adhesion interfaces

Contrastive learning uncovers cellular interactions and morphologies in the tumor microenvironment of lung adenocarcinoma linked to immunotherapy response

A morphotropic phase boundary in MA1−xFAxPbI3: linking structure, dynamics, and electronic properties

Abstract Understanding the phase behavior of mixed-cation halide perovskites is critical for optimizing their structural stability and optoelectronic performance. Here, we map the phase diagram of MA1−x FA x PbI3 using a machine-learned interatomic potential in molecular dynamics simulations. We identify a morphotropic phase boundary (MPB) at approximately 27% FA content, delineating the transition between out-of-phase and in-phase octahedral tilt patterns. Phonon mode projections reveal that this transition coincides with a mode crossover composition, where the free energy landscapes of the M and R phonon modes become nearly degenerate. This results in nanoscale layered structures with alternating tilt patterns, suggesting minimal interface energy between competing phases. Our results provide a systematic and consistent description of this important system, complementing earlier partial and sometimes conflicting experimental assessments. Furthermore, density functional theory calculations show that band edge fluctuations peak near the MPB, indicating an enhancement of electron-phonon coupling and dynamic disorder effects. These findings establish a direct link between phonon dynamics, phase behavior, and electronic structure, providing a further composition-driven pathway for tailoring the optoelectronic properties of perovskite materials. By demonstrating that phonon overdamping serves as a hallmark of the MPB, our study offers insights into the design principles for stable, high-performance perovskite solar cells.

Non-malarial etiology of acute febrile episodes in children attending five healthcare facilities in Mwanza, Tanzania years 2020–2021

Quantum-enhanced nanodiamond rapid test advances early SARS-CoV-2 antigen detection in clinical diagnostics

Abstract Quantum biosensors, which harness quantum effects to detect biomarkers, could address the urgent need for more sensitive rapid diagnostics. Lateral flow tests using nitrogen-vacancy centres in nanodiamond labels offer high sensitivity and robustness by controlling the spin-dependent fluorescence to remove background. This is particularly important in complex and variable clinical samples. However, to date only model systems have been studied with few clinical samples. Here we show results of a clinical evaluation of a spin-enhanced nanodiamond test for SARS-CoV-2 antigen with 103 upper respiratory tract swab samples. We find 95.1% sensitivity (Ct ≤ 30) and 100% specificity benchmarked against RT-qPCR, with no cross-reactivity to influenza A, RSV, and Rhinovirus. Modelling with patient data yields a mean of 2.0-days earlier detection compared to conventional gold-nanoparticle tests (just 0.6 days after RT-qPCR) with 2.2-fold more patients detected on the first day of symptom onset, potentially reducing the transmission risk and protecting populations.

The impact of confounders, spillovers and interactions on social distancing policy effects estimates

Cross-ancestral GWAS identifies 29 variants across head and neck cancer subsites

Abstract Head and neck squamous cell carcinoma (HNSCC) includes diverse cancers arising in the oral cavity, oropharynx, and larynx, with the main risk factors being environmental exposures such as tobacco, alcohol, and human papillomavirus (HPV) infection. The genetic factors contributing to susceptibility across different populations and tumour subsites remain incompletely understood. Here we show, through a genome-wide association and fine mapping study of over 19,000 HNSCC cases and 38,000 controls from multiple ancestries, 18 genetic risk variants and 11 signals from fine mapping of the human leukocyte antigen (HLA) region, all previously unreported. rs78378222, a regulatory variant for TP53 is associated with a 40% reduction in overall HNSCC risk. We also identify gene-environment interactions, with BRCA2 and ADH1B variants showing effects modified by smoking and alcohol use. Subsite-specific analysis of the HLA region reveals distinct immune-related associations across HPV-positive and HPV-negative tumours. These findings refine the genetic architecture of HNSCC and highlight mechanisms linking inherited variation, immunity, and environmental exposures.

Development of migraine self-care scale

Fabrication of cytotoxic mirror image nanopores

Abstract Synthetic nanopores composed of mirror-image peptides have been reported, but not fully functional mirror-image pores. Here, we construct a monodisperse mirror-image nanopore, DpPorA and characterise its functional properties. Importantly, we alter the charge pattern and assemble a superior mirror-image pore with enhanced conductance and selectivity under different salt conditions. This pore is used for single-molecule sensing of structurally divergent biomolecules, including peptides, PEGylated polypeptides, full-length alpha-synuclein protein and cyclic sugars. Molecular dynamics simulations confirm these DpPorA are exact mirror-images of LpPorA, further revealing their structurally stable conformation. Fluorescence imaging of giant vesicles reconstituted with mirror-image peptides reveals the formation of large flexible pores facilitating size-dependent molecular transport. To explore biomedical applications, the differential cytotoxic effect of mirror-image peptides and their fluorescently tagged forms on cancer cells demonstrates a significant effect on membrane disruption and cell viability, as opposed to no effect on normal cells. We emphasize that this class of mirror-image pores can advance the development of molecular sensors and therapeutics.

Nurturing care group approach for improving animal faeces management in Ghana

Abstract Animal faeces management (AFM) is key to avert zoonotic risks especially when integrated into water, sanitation, and hygiene interventions although largely neglected by conventional projects. This study focused on understanding the influence of Nurturing Care Group (NCG) approach on improving AFM in some communities within Savelugu Municipal and Sekyere East District Assemblies of Ghana. The study adopted a cross-sectional qualitative design using 12 focus group discussions and 20 in-depth-interviews with predominantly female members from 12 communities. Data analyses used inductive content analysis based on pre-structured themes from interview tools. We recorded 171 female and 4 male participants in the study. Household livestock were reared – fowls, goat, sheep, cattle and others (e.g., pets – cats, dogs, doves). The main motivation for keeping livestock was economic, nutritional and socio-cultural benefits including ritual functions, security and companionship. NCG intervention heightened awareness of safe AFM including penning, faeces composting, and other linked safe environmental sanitation practices. Barriers to uptake included lack of resources for constructing livestock shelters, lack of fodders during dry season hence scavenging, difficulty in securing veterinary services, and women inability to secure male household heads’ support on AFM decisions. Local governance structures were not fully integrated particularly in Savelugu, and even in Sekyere East, leaders’ commitment was unsustainable over time. Intervention could improve awareness on safe AFM, leading to some immediate but unsustainable behaviour change practices due to constraints that project designers should not overlook. In future, contextualized technical solutions and active involvement of key stakeholders with defined roles would be critical for sustainable uptake and impact.

Machine learning applied to biocatalysis research

Advanced deep learning approach for the fault severity classification of rolling-element bearings

Intrinsic nanoparticle-single-atom interplays steering radical versus nonradical pathways in catalytic ozonation

Diagnostic accuracy of artificial intelligence models for imaging detection of hepatic steatosis through systematic review and meta analysis

Abstract Non-alcoholic fatty liver disease (NAFLD) is an increasingly prevalent chronic liver condition affecting nearly 30% of the global population. Characterized by hepatic steatosis in the absence of significant alcohol intake, NAFLD can progress to non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and hepatocellular carcinoma. Although liver biopsy is the diagnostic gold standard, its invasiveness, cost, and associated risks limit widespread application. Artificial intelligence (AI) offers promising non-invasive alternatives by leveraging large datasets to enhance diagnostic precision. To evaluate the diagnostic accuracy of artificialintelligence algorithms for the imaging detection of hepatic steatosis, the essential first step in the metabolic dysfunctionassociated steatotic liver disease (MASLD) spectrum. A comprehensive literature search was conducted across PubMed, Scopus, Embase, Cochrane Library, and Google Scholar for studies published between January 2016 and January 2025. Studies were included if they involved adult populations, employed AI algorithms for NAFLD diagnosis, and reported sufficient diagnostic accuracy measures. Quality assessment was performed using the QUADAS-2 tool. Meta-analysis was conducted using a bivariate random-effects model to estimate pooled sensitivity, specificity, and area under the hierarchical summary receiver operating characteristic (HSROC) curve. Out of 29 studies included in the systematic review, 19 met the criteria for meta-analysis, comprising a total of 344,266 participants. AI-based diagnostic models showed excellent performance, with pooled sensitivity of 91% (95% CI: 84–95%), specificity of 92% (95% CI: 86–96%), and an AUC of 0.97 (95% CI: 0.95–0.98). The diagnostic odds ratio was 123.7, indicating high discriminatory capacity. Convolutional neural networks (CNNs) demonstrated superior accuracy (AUC = 1.00) compared to other AI classifiers. Subgroup analysis revealed higher diagnostic accuracy in studies validated with imaging standards compared to those using liver biopsy. Model performance was also influenced by the type of classifier and validation method used. AI-based models, particularly CNNs, exhibit high diagnostic accuracy for detecting hepatic steatosis and offer promising non-invasive alternatives to traditional modalities. These tools have the potential to transform early detection and screening, especially in resource-limited settings. Future research should focus on external validation, multicentric trials, and standardized reporting for clinical integration.

Direct synthesis of an iron metal-organic framework antiferromagnetic glass

A stacked custom convolution neural network for voxel-based human brain morphometry classification

Abstract The precise identification of brain tumors in people using automatic methods is still a problem. While several studies have been offered to identify brain tumors, very few of them take into account the method of voxel-based morphometry (VBM) during the classification phase. This research aims to address these limitations by improving edge detection and classification accuracy. The proposed work combines a stacked custom Convolutional Neural Network (CNN) and VBM. The classification of brain tumors is completed by this employment. Initially, the input brain images are normalized and segmented using VBM. A ten-fold cross validation was utilized to train as well as test the proposed model. Additionally, the dataset’s size is increased through data augmentation for more robust training. The proposed model performance is estimated by comparing with diverse existing methods. The receiver operating characteristics (ROC) curve with other parameters, including the F1 score as well as negative prediction value, are used to determine the proposed model effectiveness. The proposed VBM and stacked custom CNN model achieves significant improvements. The proposed model performed with a higher accuracy level of 98%. The results confirm that the proposed VBM and stacked custom CNN model outperforms other existing approaches in brain tumor classification.

CeOx-Integrated dual site enhanced urea electrosynthesis from nitrate and carbon dioxide

Evaluation of organ and skeletal physiological characteristics for improved classification of growth-restricted newborn piglets

Stabilized D2R G protein-coupled receptor oligomers identify multi-state β-arrestin complexes

Abstract The G protein-coupled receptor (GPCR) superfamily directs central roles in many physiological and pathophysiological processes via diverse and complex mechanisms. GPCRs can exhibit signal pleiotropy via formation of di/oligomers both with themselves and other GPCRs. A deeper understanding of the molecular basis and functional role of oligomerization would facilitate rational design of activity-selective ligands. A structural model of the D2 dopamine receptor (D2R) homomer identified distinct combinations of substitutions likely to stabilize protomer interactions. Molecular modelling of β-arrestin-2 (βarr2) bound to predicted dimer models suggests a 2:2 receptor: βarr2 stoichiometry, with the dimer favouring βarr2 over Gαi coupling. A combination of biochemical, biophysical and super-resolution, single molecule imaging approaches demonstrated that the D2R mutant homomers exhibited greater stability. The mutant D2R homomers also exhibited bias towards recruitment of the GPCR adaptor protein βarr2 with either faster or ligand-independent βarr2 recruitment, increased internalization and reprogrammed regulation of ERK signaling. Through GPCR dimer-stabilization, we propose that D2R di/oligomerization has a role in βarr2-biased signaling.