Browse Articles

Effect of inclusions on polished Si removal mechanism via MD

Peritoneal MSCs-derived exosomes suppress CCL24 synthesis through miR-320d delivery contributing to the improvement of peritoneal dialysis-associated fibrosis

Abstract Mesothelial-to-mesenchymal transition (MMT) is a key pathological process driving peritoneal fibrosis in patients undergoing peritoneal dialysis (PD). Although Chemokine ligand 24 (CCL24) is implicated in multi-organ fibrosis, its role in MMT and PD-related fibrosis is still unknown. And the therapeutic potential of peritoneal mesenchymal stem cells (pMSCs) also remains unexplored. To elucidate the mechanistic role of CCL24 in MMT and PD-related fibrosis, and the reversal effects of human pMSCs-derived exosomes (HpMSCs-Exo) loaded with miRNAs, a series of experiments, including qRT-PCR, western blotting, ELISA, immunofluorescence, hematoxylin-eosin, and Masson’s trichrome staining, were employed. In a PD-induced peritoneal fibrosis rat model, CCL24 was significantly upregulated in peritoneal tissues, predominantly localized to macrophages. Macrophage-derived CCL24 promoted MMT via the CCR3/P38 MAPK pathway, an effect reversed by HpMSCs and their exosomes. Mechanistically, HpMSCs-Exo delivered miR-320d into macrophages. KLF7, the target gene of miR-320d, abolished the suppressive effects of miR-320d mimics and HpMSCs-Exo on STAT3 phosphorylation level and CCL24 expression in macrophages. Furthermore, HpMSCs-Exo attenuated MMT and PD-related fibrosis, with miR-320d-enriched exosomes exhibiting superior efficacy. HpMSCs-Exo deliver miR-320d into macrophages, suppressing CCL24 synthesis and secretion via the KLF7/STAT3 pathway, and thereby ameliorating MMT-driven peritoneal fibrosis. Autologous pMSCs-Exo engineered with miR-320d represent a promising therapeutic strategy for halting PD-related fibrosis progression.

Physical and respiratory training in patients with myasthenia gravis: a systematic review with meta-analysis

Deep learning for vessel segmentation and flow analysis to identify clusters associated with adverse outcomes in a fontan patient registry

Abstract We introduce a deep learning framework comprising two models for automated segmentation (DCS) and large-scale deep temporal clustering (DTC) within a registry of single ventricle patients. The DCS model performs simultaneous classification and segmentation of velocity-encoded phase-contrast magnetic resonance (PCMR) data for five individual blood vessels, the left and right pulmonary arteries, aorta, superior vena cava, and inferior vena cava. Trained, validated and tested on 260 cardiac MRI exams (each containing 5 PCMR scans), it demonstrated a median Dice score of 0.91 on 50 unseen test exams. Integrated into a fully automated pipeline, the DCS model processed over 4500 registry exams without manual intervention, reaching 98% classification accuracy and 90% segmentation accuracy in cases with all five vessels present. Flow curves obtained from successful segmentations were used to train the DTC model, which performs deep temporal clustering to uncover unique flow patterns. Survival analysis showed that these groups were statistically correlated to increased risk of mortality or transplantation and to liver disease, highlighting the clinical relevance of the proposed framework.

Tenoxicam-loaded bioglass/chitosan composites for bone tissue engineering: in vitro characterization, sustained drug release, and antimicrobial activity

RASIP1-positive TECs in pancreatic adenocarcinoma: a potential novel type of endothelial cells correlated with “hot” tumors

Sustainable stabilization of sandy soil using alkali-activated construction waste binders

Validation of AI-enhanced ECG image analysis for identifying extreme cardiac magnetic resonance metrics in a cross-ethnic UK biobank study

The Effect of Allosteric Conformational Regulation on the Crystal Packing of Aromatic Amide Metallomacrocycles via Ligand-Modulated Remote Intramolecular Hydrogen Bonds

Electrochemical studies on Cocos nucifera (coconut hair oil) derived carbon soot as an electrode material for EDLC application using non-aqueous NaPF6 electrolyte

Abstract Cocos nucifera (Coconut hair oil) was burned using wick-and-oil technique (gas-phase combustion) known as flame synthesis method to obtain carbon soot for electrode material. Synthesized carbon soot was chemically activated using optimized ratio of activating agents ZnCl 2 (1:1, wt. /wt.) and KOH (1:2, wt./wt.), followed by thermal treatment at 900 °C. XRD analysis showed that the coconut oil-derived carbon soot (CoCS) has a crystallite size of ~ 1.92 nm with an interlayer spacing of 3.62 Å. After chemical activation, the crystallite size slightly altered only, while a small increase in interlayer spacing was observed (3.65 Å for ZnCl₂ activation and 3.71 Å for KOH activation), revealing subtle structural modification of the carbon framework. SEM analysis revealed a well-connected microporous network with reduced agglomerate size after activation, while EDAX confirmed increased carbon content following chemical treatment. BET results showed a substantially enhanced surface area and mesoporous structure of the KOH-activated CoCS, leading to favorable pathways for electrolyte ion transport. Among the studied samples, KOH-activated CoCS demonstrated the best electrochemical performance, delivering a specific capacitance of ~ 176 F g⁻¹, with an energy density of ~ 6.11 Wh kg⁻¹ and a maximum power density of ~ 395 W kg⁻¹. The simple synthesis route, favorable structural characteristics, and competitive electrochemical performance highlight the potential of coconut oil-derived carbon soot as a scalable and sustainable electrode material for EDLC and other energy storage applications.

Behavioral determinants of climate-smart agriculture adoption among smallholder leafy vegetable agripreneurs in semi-arid Tanzania

Ultrafine Sub-1 nm One-Dimensional Coordination Polymer Nanowires for Boosting Photocatalytic Functionalization of Inert C( <i>sp</i> ³ )–H Bonds

Machine learning and mechanistic studies on p-nitrophenol remediation using sustainable activated carbon

Abstract This study presents a sustainable approach for p -nitrophenol ( p NP) removal by synthesizing activated carbon with a large surface area from waste Pistacia vera shells using H 3 PO 4 activation. Comprehensive characterization confirmed a high specific surface area (670.25 m 2 g − 1 ) and a heterogeneous structure rich in functional groups, which are beneficial for adsorption. Adsorption followed pseudo-second-order kinetics, and the equilibrium dataset followed the Langmuir isotherm, with a maximum adsorption capacity of 142.93 mg g − 1 . The thermodynamic results showed that the adsorption was a spontaneous and exothermic (− 13.43 kJ mol − 1 ) process. ANN and ANFIS models were developed to predict the adsorption behavior. The ANFIS model exhibited the best predictive performance, with an R 2 of 0.9935. ANFIS sensitivity analysis identified that contact time and initial p NP concentration were the key factors. Regeneration tests demonstrated that the adsorbent could be reused for five cycles, supporting its practical applicability. Real water matrix studies have demonstrated robust p NP removal in real-world water scenarios, highlighting its environmental relevance. Thus, waste-derived adsorbents present a low-cost and sustainable option for p NP removal.

Unlabeled NMR Approach with Site-Specific Methyl Assignments for Structural Evaluation of the IgG1 Fc Region

Feasibility and application of an adsorption model for coal pore structure analysis through N2 adsorption

Magnification-independent breast cancer diagnosis using a GWO-enhanced vision transformer with multi-stage stain normalization

Abstract Breast cancer diagnosis from histopathological images remains a critical yet challenging task due to staining variability, magnification differences, and complex tissue morphology. This study presents a comprehensive preprocessing, balancing, and classification framework for the BreakHis dataset, integrating advanced stain normalization, magnification-wise augmentation, and an optimized Vision Transformer architecture. A four-stage normalization pipeline comprising CLAHE, histogram matching, Shades-of-Gray correction, and Macenko stain normalization was developed to standardize color distribution and enhance structural clarity across 7909 images. To address severe class imbalance, a targeted magnification-specific augmentation strategy expanded the dataset to 11,848 images with equal benign and malignant representation. A Vision Transformer (ViT) model was designed for each of the four magnifications (40X, 100X, 200X, 400X), and further optimized using the Grey Wolf Optimizer (GWO) to automatically tune hyperparameters such as transformer depth, attention heads, embedding dimensions, dropout, and learning rate. Experimental results demonstrate high and consistent performance across magnifications, with accuracies of 92.1%, 92.9%, 93.5%, and 94.0%, respectively. Cross-validation reveals minimal variance, confirming model robustness and generalization. The proposed GWO-ViT framework establishes a reliable, magnification-invariant, and computationally efficient solution for automated breast cancer histopathology classification, offering strong potential for clinical integration.

An acetylation-dependent switch underlies host disease tolerance during streptococcal infection

Evaluating mammographic density polygenic risk score for contralateral breast cancer risk prediction

Exploratory randomized trial of Weissella cibaria CMU and oral microbiome changes in peri implant mucositis

Development of a scalable production bioprocess for HIV-1 virus-like particles coupling continuous VLP harvesting with end-to-end downstream processing