Browse Articles

Green synthesis and characterization of bioactive silver nanoparticles from Stachys tibetica

HOPX regulates the invasion and migration abilities of hepatocellular carcinoma by targeting SNAIL

Hyperspectral magnetite grade based on characteristic band screening inversion study

Multimodal ensemble machine learning predicts neurological outcome within three hours after out of hospital cardiac arrest

Classifying AI-Powered prediction models for disability progression using the Tamir-Based complex fuzzy Aczel–Alsina WASPAS method

Abstract Tracking the development of disability conditions presents significant challenges due to uncertainty, imprecision, and dynamic health progression patterns. Traditional multi-criteria decision-making (MCDM) techniques often struggle with such complex and fuzzy medical data. To address this gap, we propose a novel classification framework based on Tamir’s complex fuzzy Aczel-Alsina weighted aggregated sum product assessment (WASPAS) approach. This hybrid model incorporates complex fuzzy logic to handle multidimensional uncertainty and utilizes the Aczel-Alsina function for flexible aggregation. We apply this method to evaluate and classify AI-powered predictive models used for monitoring disability progression. The proposed framework not only improves classification accuracy but also enhances decision support in healthcare planning. A case study validates the robustness, sensitivity, and effectiveness of the proposed method in real-world disability tracking scenarios.

Influence of solar extreme ultraviolet radiations on artificial very-low-frequency waves in near-earth space

Abstract Ground-based very low frequency (VLF) transmitter waves (3 – 30 kHz) can cause the precipitation loss of high-energy electrons in Earth’s radiation belts. Although the propagation and attenuation of artificial VLF waves have been studied for more than half a century, it is not clear whether solar extreme ultraviolet (EUV) radiations can modify the VLF wave intensity in inner radiation belt and slot region (L ~ 1.1 – 3RE). Here, by analyzing satellite observations and quantitative calculations, we find that the enhanced solar EUV radiations cause global attenuation of the artificial VLF waves radiated from low-latitude transmitters (λ < 44.2° or L < 1.8 RE), whereas those waves radiated from middle-latitude transmitters (λ > 44.2° or L > 1.8 RE) weaken slightly around noon. Under high solar EUV radiations, the large attenuation of artificial VLF waves in the low L region is due to enhanced collisional damping of ionospheric plasmas at low latitudes.

Genomic decoding of drug-resistant tuberculosis transmission in Thailand over three decades

Abstract Thailand has a high burden of tuberculosis, with control efforts hindered by drug-resistant Mycobacterium tuberculosis (Mtb). The increasing use of whole-genome sequencing (WGS) of Mtb offers valuable insights for clinical management and public health surveillance. WGS can be used to profile drug resistance, identify circulating sub-lineages, and trace transmission pathways or outbreaks. We analysed WGS data from 2,005 Mtb isolates collected across Thailand from 1994–2020, including 816 retrieved and 1,189 newly sequenced samples, with most isolates being multidrug-resistant (MDR-TB). Most isolates are lineage two strains (78·3%), primarily the Beijing sub-lineage (L2.2.1). Drug resistance profiling revealed substantial isoniazid and rifampicin resistance, and 67·3% classified as MDR-TB. Phenotypic and genotypic drug susceptibility testing showed high concordance (91·1%). Clustering analysis identified 206 transmission clades (maximum size 288), predominantly with MDR-TB, especially in Central and Northeastern regions. One cluster (n = 22) contains the ddn  Gly81Ser mutation, linked to delamanid resistance, with some members pre-dating drug roll-out. In the largest cluster (n = 288), containing isolates spanning two decades, we applied transmission reconstruction methods to estimate a mutation rate of 1·1 × 10 –7 substitutions per site per year. Overall, this study demonstrates the value of WGS in uncovering TB transmission and drug resistance, offering key data to inform better control strategies in Thailand and elsewhere.

Retrospective cohort study on alkaline phosphatase and ICU mortality rate of multiple myeloma

Pedagogical applications of all-atom molecular dynamics simulation in coal seam seepage mechanics

Intelligent deep learning for human activity recognition in individuals with disabilities using sensor based IoT and edge cloud continuum

HNF4A ameliorates acute liver failure by inhibiting NCOA4-mediated ferritinophagy

Theoretical analysis of MOFs for pharmaceutical applications by using machine learning models to predict loading capacity and cell viability

Analytical insights and physical behavior of solitons in the fractional stochastic Allen-Cahn equations using a novel method

Anomalous softening of 3D printed elastomeric foam irradiated under compressive strain

A quantitative analysis on policies of China’s fuel cell electric vehicle industry

Mechanical flexibility of fertile frond stipes in the rheophytic fern Osmunda lancea

Predictive modeling of ADME properties using M-polynomial based topological indices for biocompatible polysaccharides

Abstract Dextran and chitosan, two natural polysaccharides, are recognized for their biocompatibility, biodegradability, and structural adaptability. Dextran, composed of glucose units with predominant $$\alpha$$ -(1 $$\rightarrow$$ 6) linkages, exhibits flexible conformations influenced by branching and molecular weight. Chitosan, derived from chitin via deacetylation, consists of $$\beta$$ -(1 $$\rightarrow$$ 4)-linked D-glucosamine units and displays semi-crystalline behavior sensitive to pH and ionic conditions. An in-depth understanding of these structural properties is essential for applications in drug delivery, biomedical engineering, and polymer-based therapeutics. In this study, M-polynomial indices were calculated for dextran and chitosan using the edge/connectivity partition technique. Their predictive utility was evaluated through statistical correlations with several ADME-related physico-chemical properties of polycyclic drugs. Multiple regression models−Support Vector Regression, Lasso, Ridge, ElasticNet, and Multiple Linear Regression−were applied to model these relationships. Performance assessment was conducted using cross-validation and external test metrics, including the coefficient of determination ( $$R^2$$ ), Pearson correlation coefficient (R), root mean squared error, and p-values. Findings indicate that M-polynomial indices can reliably predict key properties such as molecular weight, exact mass, molar refractivity, polarization, complexity, and others. Several models demonstrated excellent predictive strength (e.g., $$R^2 > 0.95$$ ) with statistical significance ( $$p < 0.001$$ ), confirmed through both cross-validation and external validation. A Python-based tool was also developed to automate the computation of M-polynomial indices, enhancing efficiency and reproducibility. The results support the biological relevance of topological descriptors in modeling drug behavior and underline their potential utility in computational drug design, especially for biocompatible polysaccharide-based delivery systems.

Influence of the seasons on the chemical composition and biological properties of pistacia lentiscus L. essential oil in the mediterranean region

Fatty acid analysis identifies an aberrant circulating triglyceride composition in patients with hypertriglyceridemia-induced acute pancreatitis

Single cell spatial transcriptomics links Wnt signaling disruption to extracellular matrix development in a cleft palate model

Abstract Despite advances in understanding the morphological disruptions that lead to defects in palate formation, the precise perturbations within the signaling microenvironment of palatal clefts remain poorly understood. To explore in greater depth the genomic basis of palatal clefts, we designed and implemented the first single cell spatial RNA-sequencing study in a cleft palate model, utilizing the Pax9 −/− murine model at multiple developmental timepoints, which exhibits a consistent cleft palate defect. Visium HD, an emerging platform for true single-cell resolution spatially resolved transcriptomics, was employed using custom bins of 2 × 2 μm spatial gene expression data. Validation of spatial gene expression was then validated using custom designed Xenium In Situ mRNA spatial profiling and RNAscope Multiplex assays. Functional enrichment analysis revealed a palate cell-specific perturbation in Wnt signaling effector function in tandem with disrupted expression of extracellular matrix genes in developing mesenchyme. As a key step toward laying the framework for identifying key molecular targets these data can be used for translational studies aimed at developing effective therapies for human palatal clefts.