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28 records · Page 2Linked to original sources
PubMed2026

Seed-derived mucilage polysaccharides as biomaterials for in vivo tissue regeneration: A systematic review.

Chronic wounds, bone defects, and cartilage injuries represent persistent clinical challenges requiring biomaterial platforms that actively regulate inflammation, oxidative stress, angiogenesis, and extracellular matrix remodeling. Conventional synthetic dressings often provide limited biological activity in these contexts. Seed-derived mucilages - polysaccharide-rich hydrocolloids obtained from chia (Salvia hispanica), flaxseed (Linum usitatissimum), fenugreek (Trigonella foenum-graecum), psyllium (Plantago ovata), guar (Cyamopsis tetragonoloba), quince (Cydonia oblonga) etc. - have emerged as biocompatible, biodegradable, and chemically versatile platforms for tissue engineering. This systematic review, conducted according to PRISMA 2020 guidelines, synthesized in vivo evidence on seed-derived mucilage-based biomaterials across wound healing, bone repair, cartilage regeneration, and related applications. PubMed, Scopus, and Web of Science Core Collection were searched for original in vivo experimental studies published in English between 2020 and 2026. Eligible studies reported at least one measurable regenerative outcome. Data were extracted independently by two reviewers, and methodological quality was assessed using the SYRCLE Risk of Bias tool. Forty-three studies were included. Hydrogels were the dominant biomaterial format, followed by films, scaffolds, sponges, nanoparticle systems, and bilayer or Janus composites. Included systems generally improved wound closure, re-epithelialization, collagen deposition, angiogenesis, antioxidant defense, and inflammatory regulation. However, most studies used small animals with short follow-up periods, and many incorporated nanoparticles or bioactive agents, limiting attribution of efficacy to the mucilage matrix alone. Risk of bias was predominantly unclear due to insufficient reporting of randomization and blinding. Blank mucilage controls, standardized characterization, long-term biosafety data, and clinically relevant models are essential prerequisites for translational progress.

Humans↗
PubMed2026

The hidden threat from food-derived carbon dots: Formation, biodistribution, and potential health risks.

Food-derived carbon dots (CDs) are a new class of carbon-based nanoparticles generated during the thermal processing of food matrices. These nanomaterials have been extensively studied for their unique fluorescence, good biocompatibility, and tunable surface chemistry in food detection, intelligent packaging, and biomedical applications. However, their nanoscale size and high surface activity have raised safety concerns regarding biological interactions, in vivo biodistribution, and potential long-term health hazards. Although CDs have traditionally been regarded as low-toxicity materials due to their favorable biocompatibility, the potential hidden risks of CDs have not received sufficient attention. CDs exhibit dose-dependent toxicity, not only accumulating in various tissues and organs but also potentially inducing oxidative stress and interfering with cellular metabolic functions. Therefore, this review summarizes the advances in sources, synthetic strategies, and core properties of CDs, with a special focus on in vivo biological interactions, fates, and potential safety challenges. In addition, it is proposed that the standardized detection and risk assessment system should be established to further explore the long-term health effects of CDs under real dietary exposure, thereby ensuring their safety and sustainable application.

Carbon Quantum Dots↗
PubMed2026

Biomimetic mesoporous silica nanosphere ameliorate experimental autoimmune uveitis by delivering sCD83.

Autoimmune uveitis (AU) is an autoimmune disease that may lead to blindness, but there are currently no precise targeted therapies for its prevention and treatment. Dendritic cell (DC) is key cell involved in the pathogenesis of AU, and specific regulation of their state can help improve AU. In this work, mesoporous silica nanospheres were loaded with the immunomodulator soluble CD83 (sCD83) and subsequently camouflaged with dendritic cell (DC) membranes to fabricate the nanocarrier DCM@MSN/sCD83 for treating experimental autoimmune uveitis (EAU). Research results show that DCM@MSN/sCD83 effectively alleviated the symptoms of uveitis in EAU, reduced the proportion of CD4+CD25-T cell/CD4+CD25+T cell and the percentage of DC in the eyes and cervical lymph nodes. It also decreased the expression of STING in Müller cell. Furthermore, the efficacy of DCM@MSN/sCD83 was found to be primarily targeting DC, and promoted the expression of IL-10 and TGF-β1 in DC by activating the phosphorylated HIF/STAT3 pathway, to induce the production of CD4+CD25+ T. This effect is superior to nanomedicine loaded with dexamethasone. Moreover，DCM enabled the nanocarriers to efficiently cross the blood-eye barrier and reach cervical lymph nodes, thereby regulating peripheral immunity. This research indicate that cell membrane-modified nanoparticles targeting homologous cells can effectively improve treatment efficiency and duration, which is potential therapy strategy for uveitis.

Animals↗
PubMed2026

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61 nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE = 0.0377 mg/kg, RPD = 5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves↗
PubMed2026

Oxidative potential of fresh vs. O₃-aged PM2.5 across urban and rural sources in China.

Fine particulate matter (PM2.5) is a major health risk, yet its impacts are still largely assessed using mass concentration, which does not capture toxicity. Recently, oxidative potential (OP) has emerged as a more relevant metric, reflecting the ability of particles to generate reactive oxygen species. A current challenge, especially in China, is understanding how emission sources and ozone (O3) aging affect PM2.5 toxicity, given that O3 is an increasingly important pollutant there. A work by Ma and co-workers published in J. Environ. Sci. (doi.org/10.1016/j.jes.2024.04.023) addressed this by evaluating the OP of fresh and O3-aged PM2.5 from multiple sources in China using the dithiothreitol (DTT) assay. Biomass burning particles exhibited the highest OP, up to 35 times greater than suburban PM2.5, driven by water-soluble organics and transition metals. While O3 aging generally reduced OP, it also induced complex chemical transformations. These findings highlight that PM2.5 toxicity is dynamic and source-dependent, underscoring the need to move beyond mass-based air quality metrics.

Particulate Matter↗
PubMed2025

Depth-dependent multi-kingdom microbial interactions and biogeochemical cycling genes in eutrophic shallow lake sediments.

Microorganisms are pivotal to lake ecosystem biogeochemical cycles, yet existing research often focuses on single microbial kingdoms or surface sediments, neglecting multi-kingdom interactions and depth-resolved dynamics. To address these gaps, we used metagenomic sequencing to characterize microbial communities and their functional associations across overlying water and 0-45 cm sediments in four shallow lakes of the middle Yangtze River basin, China. Despite increasing bacterial and fungal diversity with depth, the 0-9 cm surface sediments exhibited the strongest multi-kingdom network connectivity and the greatest microbial stability. Functional genes exhibited clear depth-dependent patterns: nitrogen cycling genes, including those involved in dissimilatory nitrate reduction to ammonium, were most enriched in the upper 0-9 cm of sediment; methane cycling genes were positively correlated with depth; phosphorus cycling genes and some sulfur cycling genes, such as assimilatory sulphate reduction, declined with depth. Sediment microbial assembly was dominated by deterministic processes, in which the vertical distribution of functional genes was primarily dictated by heavy metals and conventional environmental indicators. These findings highlight depth-specific multi-kingdom microbial interactions and their associations with biogeochemical cycling, advancing lacustrine microbial ecology understanding and providing references for lake conservation under environmental change.

Lakes↗
PubMed2026

Molecular Landscape and Advanced Diagnostic Technologies for BRAF Mutations in Cancer: From Quantitative PCR and ddPCR to CRISPR-Based Platforms.

BRAF mutations are key oncogenic alterations across multiple malignancies, including melanoma, thyroid carcinoma, colorectal cancer, non-small cell lung cancer, glioma, and hairy cell leukemia. The most prevalent variant, BRAF-V600E, induces constitutive activation of the MAPK signaling pathway, promoting tumor progression and influencing therapeutic responsiveness. Accurate detection of BRAF alterations is therefore essential for molecular classification, prognostic assessment, treatment selection, and resistance surveillance. This review summarizes the molecular heterogeneity of BRAF mutations and critically evaluates current diagnostic methodologies. Conventional approaches such as allele-specific PCR and Sanger sequencing are compared with advanced quantitative platforms, including high-resolution melting analysis, droplet digital PCR, and next-generation sequencing, with emphasis on analytical sensitivity, mutation coverage, and clinical applicability. Emerging technologies such as CRISPR-based assays, rolling circle amplification systems, and nanoparticle-based biosensors and point-of-care diagnostic platforms are also discussed for their potential to enhance ultra-sensitive detection, particularly in liquid biopsy settings. These emerging tools are highlighted for their potential to enable ultra-sensitive, rapid, and decentralized mutation detection, particularly in liquid biopsy settings. Key challenges, including intratumoral heterogeneity, low allele-frequency variants, FFPE-associated artifacts, and clonal evolution under therapeutic pressure, are examined within a translational framework. In addition, we examine critical barriers to clinical implementation, including standardization, cost, and global accessibility of molecular diagnostics, and outline potential solutions through scalable technologies and decentralized testing strategies. We propose that optimal BRAF testing requires a mutation subclass-informed and clinically integrated strategy combining comprehensive baseline profiling with longitudinal molecular monitoring. Future diagnostic paradigms will likely integrate multi-omics data and artificial intelligence (AI)-assisted interpretation to refine precision oncology implementation. Looking forward, we propose that optimal BRAF testing will require integration of multi-omics profiling with AI-assisted interpretation, enabling automated variant classification, real-time clinical decision support, and improved prediction of therapeutic response and resistance.

Humans↗
PubMed2026

Transformation of antibiotics mediated by iron-bearing minerals: A review.

Iron-bearing minerals are ubiquitous in water, sediments and soil, where their surface chemical properties and redox activity can play an important role in degradation of trace antibiotics. This review systematically summarizes the roles of various iron-bearing minerals in chemical transformation and microbial degradation of antibiotics and reaction mechanisms involved, and refines the critical idea for iron-driven control of antibiotics with trace level in natural environment. Overall, antibiotics removal in the presence of iron-bearing minerals involves combination of adsorption, surface oxidative degradation, photo-induced degradation, Fenton-like reaction and microbial degradation. Adsorption of antibiotics by Fe(III)-minerals involves electrostatic interaction, complexation, H-bonding, π-π interaction and hydrophobic interaction. Adsorbed antibiotics form complexes with Fe(III)-minerals, undergoing electron transfer to generate radical intermediates, subsequently generating final products through hydroxylation, dealkylation, and deamination. Additionally, Fe(III)-minerals can be excited to produce electrons and holes under sunlight and to produce antibiotics-degrading hydroxyl radical through O2 reduction, H2O oxidation and ligand-to-metal charge transfer. Reduced iron minerals can activate oxygen to participate in Fenton-like degradation reactions. Finally, antibiotics are mainly removed by bio-driven Fenton reaction and direct enzyme biodegradation. The presence of iron-bearing minerals can promote antibiotics microbial degradation by providing nutrients for microorganisms or by changing microbial activity and microbial community structure. Existing problems and future research directions are identified. New insights for application of iron-bearing minerals in transformation of antibiotics are proposed. The work aims to suggest new methods and insights for pollution control and remediation of emerging contaminants including trace antibiotics in the natural environment.

Anti-Bacterial Agents↗
PubMed2026

A novel urease-producing strain effectively induces cadmium biomineralization under low-temperature stress.

Microbially induced carbonate precipitation (MICP) has been widely used to immobilize Cadmium (Cd) in contaminated soils in mining-affected regions. However, its remediation efficacy under low-temperature stress, as well as the nucleation process that regulates Cd biomineralization via carbonate precipitation by psychrophilic bacteria, has yet to be investigated. Here, we isolated Pseudomonas sp. J-6, a novel urease-producing strain from tailings in high-altitude cold regions, exhibiting unparalleled cold adaptability at 5 °C and achieving 95.85 % Cd removal efficiency by MICP at 10 °C. Furthermore, the coprecipitation process of Ca1-xCdxCO3 was clarified through the continuous observation of the precipitates after the low-temperature MICP reaction. The crystal morphology transitioned from loose vaterite in the early stage to a dense square-block morphology in the middle stage. Cd2+ progressively shifted from a surface-bound state to lattice incorporation, ultimately resulting in the formation of stable Cd-substituted calcite crystals. In this process, low temperatures led to the formation of larger, highly ordered Cd-substituted calcite crystals, thereby strengthening Cd sequestration and its long-term stability. In addition, under low-temperature stress, Pseudomonas sp. J-6 induced MICP reaction decreased the bioavailable Cd in alpine slag soil by 44.85 % and enhanced physical properties. In the freeze-thaw cycles, the remediation efficiency remained stable. This study clarified the biomineralization potential in high-altitude cryogenic environments and the nucleation process of Cd biomineralization by psychrophilic bacteria-induced carbonate precipitation, filling a critical research gap in its application under extreme conditions and highlighting its promise for sustainable remediation of heavy metal pollution under low-temperature stress.

Cadmium↗
PubMed2026

Comparative effectiveness of percutaneous coronary intervention strategies for coronary small-vessel disease: a network meta-analysis of randomized trials.

BACKGROUND: Coronary small-vessel disease (SVD) remains challenging for percutaneous coronary intervention (PCI) because small lumens magnify restenosis and ischemic risk. Multiple devices are available, yet their comparative performance is uncertain. This study evaluated and ranked PCI strategies for SVD. METHODS: A systematic review and network meta-analysis was conducted in accordance with PRISMA. PubMed, Embase, the Cochrane Central Register of Controlled Trials, Web of Science, and Google Scholar were searched from inception to 15 August 2025. Eligible studies were English-language randomized controlled trials enrolling adults with angiographic SVD defined as reference vessel diameter ≤3.0 mm, comparing PCI strategies, and reporting target lesion revascularization (TLR), binary restenosis (BR), or myocardial infarction (MI). A frequentist random-effects network meta-analysis generated odds ratios (ORs) with 95% confidence intervals (CIs) and treatment rankings using the surface under the cumulative ranking curve (SUCRA). RESULTS: Thirty-nine trials including 14,503 patients met the criteria. For TLR (37 studies; 11,980 patients), the highest SUCRA values were observed with sirolimus-eluting stents (SES 90.1%), zotarolimus-eluting stents (ZES 83.9%), and everolimus-eluting stents (EES 82.2%). For BR (32; 6,468), SES, ZES, and paclitaxel-coated balloons (DCB-PTX) ranked highest (95.0%, 80.0%, and 78.3%). For MI (37; 11,602), SES, DCB-PTX, and ZES ranked highest (79.0%, 78.7%, and 68.5%). Representative effects showed SES reduced TLR versus bare-metal stents (BMS) (OR, 0.25; 95% CI, 0.15-0.43) and MI versus BMS (OR, 0.41; 95% CI, 0.21-0.79). Conventional approaches such as BMS, plain old balloon angioplasty (POBA), and gold-plated balloon angioplasty (GPBA) ranked lowest across outcomes. CONCLUSION: SES provides the most consistent clinical benefit for coronary SVD. ZES, EES, and DCB-PTX are effective alternatives in selected settings, whereas BMS, POBA, and GPBA are less effective. These findings offer comparative evidence to guide device selection in SVD.

Humans↗