Browse Articles

MiR-30a-5p activates the AKT signalling pathway by targeting PHTF2 to inhibit migration and EMT of gastric cancer

Abstract MicroRNAs (miRNAs) play a very important role in the development of gastric cancer (GC). MiR-30a-5p Participates in the formation and progression of various cancers. However, the role and clinical value of miR-30a-5p in GC remain unclear. The expression of miR-30a-5p in GC cells and Gastric Epithelial Strain-1 (GES-1) was detected by quantitative real-time PCR (qPCR). Wound healing assay, transwell assay and western blot analyses were used to examined the effects of miR-30a-5p on GC cells in vitro. In silico prediction, qRT-PCR, dual luciferase reporter assays and western blot were applied to confirm the target genes of miR-30a-5p. The results indicated that miR-30a-5p inhibited the migration and Epithelial-Mesenchymal Transition (EMT) of GC cells by activating the AKT signalling pathway. Putative homeodomain transcriptional factor2 (PHTF2) was identified to be a direct target of miR-30a-5p. Knockdown of PHTF2 also suppressed the migration and EMT of GC cells, while overexpression of PHTF2 could promote the migration and EMT of GC cells and impede the AKT signalling pathway. miR-30a-5p can suppress the migration and EMT of GC cells by directly targeting PHTF2. Hence, miR-30a-5p may be a potential target for GC treatment.

Sigma1R restores mitochondrial energy metabolism via the IRE1α/XBP1 pathway

Oral sodium hyaluronate improves skin hydration, barrier function and signs of aging: a randomized, double-blind, placebo-controlled trial in 150 healthy adults

Abstract Oral hyaluronan has been reported to improve various aspects of skin physiology, but existing trials often lack methodological rigor, comprehensive outcome assessment, and diverse populations. This randomized, double-blind, placebo-controlled trial evaluated the effects of sodium hyaluronate (SH) supplementation at two daily doses on skin parameters in healthy Caucasian adults. A total of 150 participants were randomized to receive SH (1.8 MDa) at 60 mg/day (SH60), 120 mg/day (SH120), or placebo for 12 weeks. Facial hydration, transepidermal water loss (TEWL), sebum, elasticity, wrinkle depth, skin gloss, colorimetric parameters, epidermal thickness, dermal density, red areas, and pore size were assessed at baseline and monthly. Subjective skin condition was evaluated every two weeks, and components of natural moisturizing factor (NMF) in forearm skin were quantified by LC-MS/MS. SH120 significantly enhanced skin hydration and elasticity, while reducing TEWL, sebum, and periorbital wrinkle depth versus placebo. It also improved skin structure by increasing epidermal thickness, dermal density, and NMF levels. SH60 showed similar but more modest effects. No changes were observed for colorimetric parameters, red areas, pore size, or gloss. In conclusion, oral SH supplementation improved multiple aspects of skin physiology, supporting its use as a functional food ingredient with measurable benefits for skin health and healthy aging. Trial registration: ClinicalTrials.gov, NCT07065110 (retrospectively registered on 15 Jul 2025); EFSA (European Food Safety Authority) registry: EFSA202400027979 (prospectively filed on 06 Jun 2024).

Serum cytokine inflammatory profile in children with ulcerative colitis during 6-month follow-up

A hybrid Daubechies wavelet collocation approach for a fractional-order SIR epidemic model with delay effects

Abstract This paper studies the transmission dynamics of influenza by using a fractional SIR (Susceptible-Infected-Removed) epidemic model with discrete delay to describe the short-term dynamics. The model includes history-dependent effects through Caputo fractional derivative and maturity delays, which are biologically motivated as the incubation periods or delayed immune responses. In this paper, we will solve this model by introducing a hybrid collocation method with the Daubechies wavelet basis that can be used to efficiently take into account the fractional-order system and the delay system. The reliability and efficiency of the presented algorithm are investigated by means of comparison with some well-known numerical methods, such as the classical Runge-Kutta method (RK4), the Rational Polynomial Spectral Method of order 7 (RPSM7), the Generalized Wavelet Collocation Method (GWCM), and the Genocchi wavelet method. Numerical simulations demonstrate. Our Daubechies wavelet-based method is reported to converge more. Stably and better track both memory and delay effects in the context of the numerical simulations. Nonetheless, the technique presumes fixed parameters (specifically transmission rates), simplifying the situation of multiple unknown parameter values, which are often encountered. Such heterogeneity, particularly in transmission rates, is likely to impact the model’s predictions and should be accounted to have a better and realistic epidemic model. In addition, the method’s efficiency may increase in the case of systems on a large scale or real-time simulations. However, it offers a higher approximation accuracy with lower computational overhead when compared to the known methods.

Multimodal objective assessment of a porcine limbal stem cell deficiency model for corneal therapy research

Abstract Reliable preclinical models that recapitulate human limbal stem cell deficiency (LSCD) and provide objective outcome measures are essential for advancing corneal cell therapies. We induced LSCD in four porcine eyes by total corneal epithelial debridement, tracked healing with serial anterior-segment optical coherence tomography (OCT) (preinjury, 0, 5–11, 17, 23, 28 days), and quantified histological changes via computer-assisted digital pathology. OCT confirmed complete epithelial removal and a reproducible stromal edema peak on days 7–9, followed by incomplete regression by day 28. Histology revealed epithelial hyperplasia, keratinization, inflammatory infiltration and neovascularization, whereas cytokeratin-3 staining revealed patchy loss of the corneal phenotype. The automated analysis revealed significant increases in epithelial roughness ( p  = 0.049), thickness heterogeneity ( p  = 0.0001), and stromal cellularity ( p  = 0.0179) in experimental eyes compared with control eyes. These multimodal, bias-free metrics clearly distinguish healthy corneas from LSCD corneas and provide quantifiable end points for preclinical testing of limbal epithelial, iPSC-derived or mesenchymal stromal cell advanced therapy medicinal products. All the data and analysis codes are provided in the Supplementary Information.

The effect of high-dose long-term therapy of intravenous immunoglobulins in autoimmune autonomic and sensory small fiber neuropathy: a retrospective open-label controlled study

A comparative mathematical modeling study of phenotypic approaches to T cell activation

Abstract T cells use their T cell antigen receptors (TCRs) to recognize peptides presented by major histocompatibility complex molecules (pMHC). These peptides may be low-affinity self-peptides or high-affinity foreign peptides from pathogens. Despite recognizing a broad range of affinities, TCRs trigger significant immune responses only to strongly binding foreign peptides. The mechanisms enabling T cells to distinguish diverse antigens with high sensitivity remain a key focus of research. Our goal is to analyze mathematical models of T-cell activation for their ability to replicate key experimental features like optimal response, specificity, sensitivity, and antigen discrimination. We analyzed nine models using mathematical and numerical methods to examine their solutions, responses, and parameter sensitivity. We found that in all models, except kinetic proofreading with negative feedback, solutions converged to a unique steady state. Most response functions defined by ligand concentration and dissociation time showed an optimum value, except for the Occupancy, KPR, and stabilizing activation chain models. Models like KPR with negative feedback, limited/sustained signaling, and incoherent feedforward loops effectively reproduced the key features of specificity, sensitivity, and antigen discrimination. Our sensitivity analysis identified phosphorylation rate as a key parameter influencing most model outcomes. This study highlights the strengths and limitations of current T-cell activation models, suggests directions for improving to enhance their predictive accuracy in future research.

Climate change health risks and workplace protective strategies for construction workers

Assessing extreme sea level rise impacts on coastal agriculture in Europe and North Africa

Abstract Sea Level Rise refers to the long-term increase of sea level. This phenomenon is primarily driven by the melting of ice caps and the thermal expansion of the oceans. Extreme Sea Level Rise (ESLR) events occur when SLR combines with temporary phenomena such as storm surges, tides, and waves, creating potentially damaging coastal flooding. The accelerating impact of climate change has raised the attention on ESLR and its effects on coastal regions. This study focuses on ESLR and its potential impacts on Europe and North Africa up to 2100, with particular attention to agriculture. Utilising Joint Research Centre (JRC) Global Extreme Sea Level projections and fine-scale DTM, we mapped areas vulnerable to ESLR under Representative Concentration Pathway (RCP) scenarios 4.5 and 8.5. Through a topological approach, we generated spatially explicit maps of at-risk regions. Then, the magnitude of ESLR’s impact on local agricultural systems was estimated by overlaying crop production data from FAO (GAEZ 2015+) with different flood scenarios. Findings reveal that ESLR can severely affect coastal agriculture, suggesting significant potential agricultural losses (from $800 million up to $1.5 billion per year in the next 100 years), impacting food security and economic stability. This research underscores the urgent need for adaptive strategies, including the construction of dykes and sea barriers and the shift of agriculture to salt tolerant crops to mitigate ESLR impacts.

Femtogram-level VEGF detection via PEG-directed gold nanostructured electrochemical immunosensor

Quantum-enhanced hybrid deep reinforcement learning for real-time volleyball tactical decision making

Quinic acid attenuates arsenic-induced hepatic injury and hyperglycemia in mice via GLUT2 upregulation and suppression of oxidative stress and inflammation

A scalable data driven geospatial framework for climate risk assessment

Abstract Traditional flood risk management approaches often rely on historical data, limiting their ability to account for the increasing severity and frequency of climate-induced hazards. This study presents a scalable, data-driven framework that integrates geospatial analysis and machine learning to dynamically assess climate risks. The framework enables decision-makers to identify vulnerabilities, quantify flood risk under evolving climate scenarios, and develop informed adaptation strategies. Using bias-corrected CMIP5 climate projections as use case, the framework is demonstrated through a case study in Texas, where community flood risk prediction is done under multiple emission scenarios. Results indicate that under RCP 8.5, community vulnerability is projected to increase by 14%, leading to an estimated 28% rise in economic damages ($1.8B per decade by 2050) and heightened socio-economic disruptions, including displacement and infrastructure failures. By identifying the most influential climatological factors that impact community resilience, our approach stresses the urgent need for global action to mitigate extreme climate scenarios. It shows the scalability and flexibility of the framework, emphasizing its potential as decision-support tool, and a step towards a digital twin system for climate risk assessment and adaptation planning.

A computer-aided diagnosis system of parkinson’s disease based on hilbert spectrum features of speech

The serial mediating effects of mindfulness and emotion regulation between physical exercise and subjective well-being in college students

Perceived vulnerability to wildfire diverges from parcel-level hazard assessments: evidence from nordic Valley, Utah (USA)

Prioritized Aczel–Alsina aggregation operators under p, q-quasirung orthopair fuzzy environment for sustainable supplier selection in new energy vehicle industry

Smart room occupancy detection using neural networks and the puma optimization algorithm

Abstract Room occupancy detection with reasonable accuracy is indispensable for developing innovative building systems that provide energy-efficient management, increased security, and greater comfort. The existing occupancy detection solutions based on traditional sensors suffer from high installation costs, a lack of scalability, and the inability to adapt to dynamic environments. This study proposes an optimized machine learning (ML) approach using a Neural Network (NN) model tailored with a Puma Optimizer Sine Cosine Optimizer (POSC) metaheuristic optimization technique to address these challenges. Based on environmental sensor data, such as temperature, humidity, light intensity, and $$\hbox {CO}_2$$ levels, the proposed model achieves high accuracy in predicting room occupancy. The optimization process helps reinforce the training of the NN model through a dynamic equilibrium between exploration and exploitation, achieving faster convergence speed and better classification. The model is evaluated and compared on a publicly available dataset with other optimization techniques such as the Genetic Algorithm (GA) and Grey Wolf Optimization (GWO). Experimental results prove that the POSC-optimized NN model achieves superior classification and significantly outperforms conventional ML methods in terms of accuracy, precision, recall, and F1-score. These findings suggest that the combined use of metaheuristic optimization and deep learning can be a practical approach for real-world applications in intelligent building automation. The solutions proposed in this research may contribute to the growing field of intelligent occupancy detection and energy-efficient systems for future smart environments.

Holistic valorisation of lemon peel into textile materials via fungal chitosan and micro-nano fibrillated cellulose

Abstract Food-waste-derived bio-based materials offer both environmental and economic advantages. We utilised waste lemon peel as substrate to generate value-added materials from chitosan-rich fungal cell wall of Rhizopus delemar and purified cellulose from pre-treated solid residues. Nutrient from lemon peel was used for fungal cultivation and the cell wall was isolated from the obtained fungal biomass using mild alkali treatment. The fungal cell wall was used to develop a hydrogel through protonation of amino groups in chitosan by lactic acid addition. This hydrogel served as spinning dope to produce fungal monofilaments using dry gel spinning with a tensile strength of 85 MPa. Simultaneously, cellulose purified from pre-treated solid residues, converted to micro-nanocellulose suspension via mechanical fibrillation and underwent dry gel spinning to produce cellulose monofilaments with a tensile strength of 298 MPa. Cellulose fraction was analysed using XRD, FTIR, TGA, and elemental analyses. The micro- and nanoscale structures of fibrillated cellulose were verified by SEM and AFM. The findings of this study demonstrate a novel holistic valorisation approach for lemon peel waste as a resource for bio-based monofilaments, which could be used as alternatives to commercial fibres in textiles.