The "signal-off" detection mode in nanozyme-based sensors is widely adopted but inherently suffers from interference due to the "cyclic oxidation" of the chromogenic substrate-namely, the nanozyme continues to catalyze the oxidation of the substrate even as the analyte reduces the colored product during the signal readout step-which compromises sensitivity and accuracy. To address this challenge, this study designed a readily separable nanozyme system for controllable catalysis. Platinum (Pt) nanozymes were immobilized on polydopamine (PDA)-modified glass beads (GBs) via in-situ reduction stabilized by sodium citrate (Cis), constructing a peroxidase-mimic designated GB@PDA@Pt-Cis. This immobilized nanozyme can be separated from the reaction mixture, enabling precise on-demand initiation and termination of the catalytic oxidation of 3,3',5,5'-tetramethylbenzidine (TMB). The system exhibits high peroxidase-like activity, follows Michaelis-Menten kinetics, and demonstrates excellent stability and reusability. Leveraging these advantages, a "signal-off" colorimetric sensor was developed for the sensitive detection of ascorbic acid (AA), uric acid (UA) and alkaline phosphatase (ALP). The strategy's transferability was further demonstrated by transferring the design to a plastic pipette tip platform (P@PDA@Pt-Cis), achieving similar controllable catalysis and ALP detection. This work provides a simple solution to the "cyclic oxidation" problem in signal-off sensing, paving the way for the development of more reliable and user-friendly nanozyme-based biosensors.

The escalating threat of antibiotic-resistant bacteria in environmental and clinical settings poses a serious risk to public health and ecosystem safety. Conventional nanozyme designs often struggle to balance catalytic activity and substrate adsorption, limiting their efficacy in mitigating such hazards. Herein, we report a proximal-distal coordination engineering strategy that achieves synergistic enhancement through first-sphere symmetry breaking (S doping) and second-sphere electronic regulation (-OH incorporation). This dual modification elevates peroxidase-like activity to 5.5 times that of pristine Fe-NC (5.00 mg-1), alongside excellent catalase-like, glutathione oxidase-like activities, and photothermal performance. Density functional theory reveals a shifted Fe d-band center (from -1.97 eV to -0.53 eV), enhancing substrate adsorption, while -OH reduces Fe-S bond localization, facilitating charge transfer. The engineered nanozyme eliminates > 99.9% of S. aureus and E. coli under near-infrared irradiation with trace H2O2, disrupting bacterial tricarboxylic acid cycle, ATP synthesis, and central carbon metabolism. In a mouse wound infection model, it accelerates wound closure, promotes collagen deposition, and reduces IL-6 and TNF-α, with good biocompatibility. This work provides a sustainable, non-antibiotic antimicrobial platform for managing bacterial contamination and mitigating the risk of resistance propagation in environmental matrices.

Intervertebral disc degeneration, the leading cause of chronic low back pain, remains incurable with traditional conservative therapies limited to symptomatic alleviation. We present an ECM-mimetic injectable hydrogel (HPTC) synthesized via dynamic crosslinking of hyaluronic acid-phenylboronic acid (HA-PBA) and tannic acid-cerium(III) metal-polyphenol networks (TA-Ce³⁺ MPNs), which faithfully recapitulates native nucleus pulposus ECM to enable functional tissue regeneration. In vitro, HPTC presented broad-spectrum reactive oxygen species scavenging and downregulated pro-inflammatory cytokine expression (TNF-α, IL-1β, IL-6), while upregulating anti-inflammatory markers (IL-4, IL-10). Crucially, Ce³⁺ effectively reduced dissolved oxygen levels (to 105

Human type III collagen (Col III) is a critical component for skin tissue repair and anti-aging, yet its heterologous expression often faces challenges such as incomplete structure and poor thermostability. Here, we established a transgenic zebrafish expression system via CRISPR/Cas9 technology, integrating the human Col3a1 gene into a non-functional region of zebrafish chromosome 4. The extraction yield of total zebrafish collagen (Col III-TC), a composite material comprising both recombinant human Col III and endogenous zebrafish collagens, was 45.76%. Structural analysis revealed intact fibrous architecture and a thermal shrinkage temperature of 71.3 °C, significantly superior to conventional systems. Functionally, Col III-TC exhibited remarkable free radical-scavenging capacity and suppressed LPS-induced inflammation in 3T3-L1 cells (downregulating Tnfα, Il1b, and Il6, while upregulating Il10), alongside promoting fibroblast proliferation. In a murine acute wound model, Col III-TC-based dressings achieved outstanding healing efficacy (>95% closure within 15 days), with histological analysis showing improved neoskin thickness and collagen deposition. The Col3a1 transgenic zebrafish system developed in this study not only provides a novel strategy for heterologous expression of fully functional human proteins, but also highlights the broad application potential of its high-yield collagen in biomedical fields, particularly in wound healing and anti-aging therapies.

Background This retrospective study used machine learning to find the risk factors of nosocomial infection in cancer patients with immune checkpoint inhibitor-related pneumonia. Methods We analyzed data of 120 patients with immune-related pneumonia from a specialized cancer hospital collected between January 2020 and December 2023. Linear logistic regression and nonlinear support vector machine (SVM) models were used to evaluate the predictive factors for nosocomial infection risk among the patients. Results We found a nosocomial infection rate of 45.83%, predominantly lower respiratory tract infections, among cancer patients with immune-related pneumonia. Severity and mortality rates for the immune-related pneumonia with nosocomial infection group were significantly higher than those for the non-infected group. Logistic regression analysis showed that immune-related pneumonia was significantly associated with the diagnosis time and with C-reactive protein levels. Nonlinear SVM model SHapley Additive exPlanation graph analysis revealed that diagnosis time, tumor radiotherapy, pulmonary dysfunction, and age were risk factors for nosocomial infections in immune-related pneumonia. Conclusions Our results highlight the potential of using machine learning to predict the infection risk of immune-related pneumonia. Future multicenter prospective studies are needed to optimize and improve the models and methods used in this study.

Injectable hydrogels offer a promising strategy for treating intervertebral disc degeneration (IVDD), a leading cause of chronic low back pain. However, effective intradiscal diffusion and timely in situ gelation remain challenging within the confined high-pressure nucleus pulposus. Inspired by the temporal regulation of blood coagulation, we developed a biomimetic dual-network hydrogel (named HAD-HPTC) that achieves time-programmed diffusion and delayed solidification to enhance delivery and retention. Upon injection, an initial physical network, formed through dynamic hydrogen bonding and coordination between hyaluronic acid-phenylboronic acid (HA-PBA) and tannic acid-cerium metal polyphenol networks (TA-Ce MPNs), enables fluid-like diffusion and conformal defect filling, analogous to initial blood infiltration in wounds. Subsequently, a second chemical network gradually forms via thiol-Michael addition between hyaluronic acid acrylate (HA-AA) and dithiothreitol (DTT) under physiological conditions, achieving stable in situ gelation reminiscent of fibrin formation. This temporally programmed structure provides delayed gelation and improved diffusion, while integrated TA-Ce MPNs confer antioxidant, anti-inflammatory, and anti-senescence functions. Furthermore, in vivo studies in rat and rabbit models demonstrated superior disc height preservation, extracellular matrix restoration, and inflammation suppression compared to conventional preformed hydrogels. These findings establish blood-coagulation-inspired, time-programmed hydrogels as a promising platform for minimally invasive intervertebral disc regeneration.

Given their extremely effective unit-catalytic activity, single-atom nanozymes have drawn a lot of attention; yet, its obvious catalytic inefficiencies are caused by low atomic loading rates. While current regulation strategies focus on the coordination structures of individual atoms, the unique bonding structures of carriers and the crucial anchoring role of surface defects in single-atom active sites remain under-explored. Herein, we successfully anchored Pt single atoms (Pt SAs) onto the precisely engineered 111-exposed faces of CeO2 cross-sectional octahedra, thereby obtaining a single-atom nanozyme catalyst with a high loading rate (2.93%). High-density introduction of Pt SAs provided efficient catalytic sites and activated liganded Ce species to synergistically enhance catalytic activity. The cascade reaction provided a new perspective, and the constructed CeO2-anchored Pt SAs with exposed 111-surfaces were found to exhibit efficient haloperoxidase (HPO) activity (2.55 & times; 10- 2 U mu g- 1). Surface coordination analysis combined with density functional theory identified that the d orbitals of Pt hybridize with the f orbitals of Ce to a high degree on the 111 face in a low coordination environment, lowering the reaction energy barrier of the HPO-mimicking enzyme. Antimicrobial agents developed from simulated enzymes demonstrated the advantage of metabolite species-targeted restriction against drug-resistant bacteria. Combined with a microneedle hydrogel, it enabled precise nanozymes delivery in a bacterial infection setting and synchronized modulation of the inflammatory microenvironment. This work gives new horizons for increasing single-atom loading to improve catalytic activity.

BACKGROUND:Adverse pregnancy outcomes (APOs) affect long-term maternal and offspring health. Conventional metabolic markers, including the triglyceride-glucose (TyG) index, inadequately capture pregnancy-related immuno-inflammatory dysregulation. We propose a composite index, TyNIR, integrating metabolic and inflammatory parameters, and assess its associations with APOs and predictive performance using machine learning. METHODS:Data were collected from two tertiary centers in Fujian, China (2023-2025), including 1,741 pregnancies and an external validation cohort of 300. Early-pregnancy parameters were used to derive TyNIR. Primary outcomes were gestational diabetes mellitus (GDM), gestational hypertension (GH), preterm birth (PB), low birth weight (LBW), and premature rupture of membranes (PROM). Multivariable regression, subgroup, and interaction analyses assessed associations between TyNIR and APOs. A feature selection approach developed the PREMIER model, which was compared with TyG and other models and externally validated. RESULTS:TyNIR was positively associated with GDM, GH, and PB. After adjustment, the highest quartile showed increased risks, with odds ratios (ORs) of 3.86 (95% CI 2.33-6.39) for GDM, 3.05 (1.74-5.36) for GH, and 3.45 (1.24-9.66) for PB. No associations were observed for LBW or PROM. The PREMIER model achieved area under the curve (AUC) values of 0.710 ± 0.031, 0.736 ± 0.040, and 0.742 ± 0.073, outperforming TyG and other models. External validation confirmed consistent associations and superior predictive performance. CONCLUSIONS:TyNIR consistently predicts risks of GDM, GH, and PB. The TyNIR-based PREMIER model shows robust predictive performance with external validation. These findings support early identification of high-risk pregnancies and may guide personalized management of APOs.

Sepsis is a type of life-threatening organ dysfunction caused by dysregulated host response to infection. Given the heterogenous nature of sepsis, precise and temporally-optimized drug recommen-dations are critical, yet existing AI-based drug recommendation models often fail to address temporal dynamics in medication administration and in clinical practice. To address this limitation, we propose ICUDrug, a novel time-window-based drug recommendation model that extracts patient-specific clini-cal features at distinct critical time points through focused temporal windows while explicitly modeling drug-drug interaction (DDI) to minimize adverse reactions. ICUDrug achieved performance with Jaccard (0.7068 ± 0.0035), F1-score (0.8166 ± 0.0028), and AUPRC (0.8879 ± 0.0022) at a 24-hour time window, while reducing DDI rate to 0.1914 ± 0.0010, demonstrating significant effectiveness and outperformed baseline models. These results demonstrate the ability of ICUDrug to deliver both clini-cally-relevant timing and safe medication combinations for sepsis management.

In this study, we present the innovative design and comprehensive evaluation of a novel point-of-care testing (POCT) methodology for the rapid and accurate detection of T-2 toxin, a potent mycotoxin with significant implications for food safety and human health. The cornerstone of this approach lies in the integration of a copper-based conductive metal-organic framework (Cu3(HHTP)2) with a target-responsive DNA hydrogel system, creating a dual-signal readout mechanism that significantly enhances detection sensitivity and specificity. Specifically, the hydrogel covers the material's surface metal active sites, which become exposed upon target addition due to hydrogel collapse. The released Cu3(HHTP)2 can catalyze the oxidation of 3,3 ',5,5 '-tetrame-thylbenzidine, resulting in a colorimetric and temperature change in the solution. This dual-mode detection strategy enables both qualitative assessment through direct visual inspection of color change and quantitative analysis by monitoring the solution temperature variation post laser irradiation with a thermometer. Under the optimized conditions, the detection system demonstrates a wide range spanning from 5 to 200 ng/mL, with a detection limit of 1.67 ng/mL. This method has been successfully demonstrated through the analysis of real-world samples, yielding encouraging results that underscore its reliability and effectiveness. The proposed dual-signal approach boasts advantages such as straightforward operational procedures, unambiguous signal outputs, and robust practicality, making it an attractive option for mycotoxin detection in resource-constrained settings and developing regions.

Zearalenone (ZEN) is a common contaminant in crops with serious food safety implications. By leveraging the etching effect of H2O2 on gold nanorods (Au NRs) and the catalysis of Fenton reaction, we have successfully developed two enzyme-linked immunosorbent assay (ELISA) kits with distinct principles. ZEN can be accurately quantified by both methods by measuring changes in the longitudinal surface plasmon resonance (LSPR) absorption peaks of Au NRs. Direct detection kit is simpler and faster, with a linear range of 0.2–4000 ng/mL, while indirect competitive kit is more complex and has a narrower linear range of 0.02–10 ng/mL. This study is expected to provide an effective analytical strategy for rapid screening and accurate monitoring of mold contaminants in food and agricultural products. In this study, a novel dual-mode ELISA system was developed for the detection of zearalenone (ZEN) using Au NRs and their longitudinal surface plasmon resonance (LSPR) properties. By integrating H₂O₂-mediated nano-etching and Fenton reaction catalysis, the system enables both direct (0.2–4000 ng/mL) and indirect competitive (0.02–10 ng/mL) detection modes. The main work includes: (1) replacing the traditional enzyme colorimetric signal with an LSPR shift for accurate UV-visible quantification and visual multicolor readout; (2) optimizing the nanoenzyme catalytic system for improved reaction sensitivity; (3) eliminating the need for enzyme-labeled secondary antibodies in direct mode, thereby reducing cost and improving stability. The system was validated on grain samples with recoveries of 93.1-107.1

Herein, we present an innovative electrochemiluminescence (ECL) biosensor for the ultrasensitive detection of N-nitrosodimethylamine (NDMA). The biosensor utilizes a triple signal amplification strategy, combining rolling circle amplification (RCA), CRISPR/Cas12a-driven hyperbranched rolling circle amplification (HRCA), and electrostatic repulsion with size exclusion effects from vertically ordered mesoporous silica film (VMSF)/indium tin oxide (ITO) on double-stranded DNA (dsDNA)-Ru(phen)32+ complexes. In this system, aptamers and circular DNA undergo RCA reactions, followed by the CRISPR/Cas12a-mediated HRCA process, producing abundant dsDNA. The electropositive ECL indicator, namely Ru(phen)32+, was subsequently adsorbed onto the electronegative dsDNA, forming dsDNA-Ru(phen)32+ complexes. These complexes are subjected to electrostatic repulsion and size exclusion by the VMSF-modified ITO electrode, resulting in a lower ECL intensity. Upon introducing NDMA, the aptamer preferentially binds to NDMA, thereby preventing the formation of long dsDNA. This process releases free Ru(phen)32+, which diffuses to the electrode surface through narrow mesoporous channels via electrostatic adsorption. Consequently, an enhanced and strong ECL signal is observed. The integration of VMSF enhances selectivity and sensitivity by excluding larger impurities and promoting the electrostatic repulsion of dsDNA-Ru(phen)32+ complexes near the electrode surface. Additionally, the CRISPR/Cas12a system eliminates the formation of primer dimers and reduces false positives through its unique cis- and trans-cleavage activities. The biosensor demonstrated excellent performance with a linear correlation between the ECL signal and NDMA concentration in the range spanning from 10 pg/mL to 10 μg/mL, achieving a low limit of detection of 5.33 pg/mL. This platform offers a reliable and robust solution for detecting NDMA in complex matrices, making it a promising tool for environmental monitoring, public health, and safety applications.

Herein, we introduced anhydrous 4-aminobenzene sulfonic acid (C6H7NO3S) to passivate the ligands on the surface of CsPbBr3 quantum dots, thereby synthesising a perovskite composite with excellent dispersion and stability in water via a one-pot process, namely CsPbBr3@C6H7NO3S composite. The fluorescence attenuation of CsPbBr3@C6H7NO3S composite was only 2.20 % after 5-day immersion in water, and the CsPbBr3@C6H7NO3S composite maintained excellent luminescence performance in long-term storage (4 months). The CsPbBr3@C6H7NO3S composite was then adopted to construct the fluorescent sensing for the detection of nitrite. Results showed that the fluorescence intensity had a linear relationship with the nitrite concentration in the range of 0.01-50.00 μM, with a detection limit of 3.30 nM. Meanwhile, the detection mechanism was discussed in detail. This work not only provides a sensitive sensing method for the detection of nitrite, but also provides a simple and effective strategy for the synthesis of perovskite.

The persistence of T-2 toxin in food and feed matrices renders it a pervasive contaminant, impacting both human and animal health. Traditional detection methods suffer from cumbersome instrumentation and intricate procedures, rendering on-site detection of T-2 toxin unfeasible. Therefore, we have constructed a real-time detection method for T-2 toxin detection by employing a target-responsive DNA hydrogel in conjunction with potassium iodide starch test paper. This method integrates both colorimetric and distance-based signal outputs, offering a streamlined and effective approach for the on-site detection of T-2 toxin. The specific binding of the target to the aptamer in the DNA hydrogel results in the collapse of the hydrogels structure, which changes the viscosity of the system and released horseradish peroxidase in the hydrogel wrapped, and then produces blue-purple marks of different lengths on the starch iodide papers to achieve the specific detection of T-2 toxin. Under optimized conditions, the assay exhibits a detection range spanning from 10 ng/mL to 10 mg/mL for the toxin, with a detection limit of 12.83 ng/mL. The proposed method has been successfully applied for the detection of real corn samples with satisfied result. Such colorimetric-distance dual signal detection method offers notable advantages, including straightforward operation, clear signal interpretation, and practical utility. Its implementation enables rapid, on-the-spot detection of T-2 toxin, particularly beneficial in resource-limited regions and less developed countries.

The integration of living cells into hydrogels offers a promising approach to enhance functionality of biomaterials for biomedical applications while protecting the encapsulated cells. In this study, we developed a living yeast-based system for in situ production of epidermal growth factor (EGF) to address the challenges associated with diabetic wound healing. Our approach involves surface-initiated polymerization of hyaluronic acid methacrylate (HAMA) from the engineered yeast surface, followed by photo-crosslinking to form a living hydrogel embedded with viable yeast cells. These cells function both as bioactive components and structural anchors within the hydrogel. We demonstrate that the yeast cells remain fully encapsulated for at least 7 days, maintaining high viability and sustained EGF expression with controlled release in vitro. In vitro assays showed that the hydrogel significantly promotes the proliferation and migration of human umbilical vein endothelial cells (HUVECs), resulting in a ~ 20 % increase in proliferation and a ~ 30 % enhancement in migration rate compared to control groups. In vivo evaluation using a diabetic mouse model confirmed that the hydrogel accelerates chronic wound healing, as indicated by improved tissue regeneration and reduced healing time. Specifically, wounds treated with the ELH treated wounds reached a ~ 96 % closure rate by day 14, outperforming a commercially dressing. This leak-free, living-hydrogel platform provides a versatile route to localized, cell-based therapy and holds promise for regenerative medicine and advanced wound care.

The intracellular delivery of protein drugs via nanocarriers offers significant potential for expanding their therapeutic applications. However, the unintended activation of innate immune responses and inflammation triggered by the carriers presents a major challenge, often compromising therapeutic efficacy. Here, we present oligoethylenimine-thioketal (OEI-TK), a reactive oxygen species-responsive cationic polymer with intrinsic anti-inflammatory properties, to overcome this challenge. OEI-TK self-assembles electrostatically with bovine serum albumin (BSA) to form stable nanoparticles (OTB NPs) with excellent encapsulation efficiency. In vitro studies confirmed that OTB NPs retained OEI-TK's antioxidant and anti-inflammatory properties, enhanced biocompatibility, and efficiently delivered BSA into cells. Furthermore, OEI-TK facilitated the intracellular delivery of β-galactosidase while preserving its enzymatic activity, demonstrating its potential for functional protein transport. These findings highlight OEI-TK as a promising platform with dual benefits of inflammation modulation and intracellular protein delivery, holding potential for the synergistic treatment of inflammation-related diseases.

Formaldehyde (FA), a known carcinogen, is occasionally used illegally as a preservative in seafood, while traditional detection methods for FA residues often fail to meet the practical needs for nondestructive detection. In this study, a approach was developed by combining a portable Raman spectrometer with the InceptionTime deep learning model without sample pretreatment. Model were trained by FA-negative and FA-positive Raman spectral data from the shrimp surface and achieved accuracies of 84.40 % and 85.17 % at detection thresholds of 5 mg/kg (the primary safety detection threshold) and 100 mg/kg (the abuse-level contamination threshold), respectively. Metabolomic analysis and weight visualization indicated that the model particularly focused on Raman peaks associated with specific amino acids and astaxanthin-binding proteins. Two amino acid metabolites, timonacic and spinacine, were also identified as direct indicators of FA addition. Our model offers a field-deployable and practical approach for real-time and on-site FA detection scenario.

Background Larval zebrafish phenotypes serve as critical research indicators in fields such as ecotoxicology and safety assessment since phenotypic defects are closely related to alterations of underlying pathway. However, identifying these defects is time-consuming and requires specialized knowledge. Method We proposed a deep network model called RECNet, which combines attention mechanisms and residual structures. In terms of data processing, we applied the mixup data augmentation technique and accumulated a collection of 6805 larval zebrafish phenotype images, mostly generated from our laboratory. Our proposed model was deployed to execute two distinct tasks, including a four-classification of zebrafish phenotypes and a seven-classification involving mixed labels for abnormalities. Results In the four-class classification task, the RECNet model achieved an accuracy of 0.949, with a mean area under the curve of 0.986 and an F1-score of 0.966. Through interpretable research, attention mechanisms enable the model to focus more accurately on regions of interest. In the mixed-label seven-classification task for anomalies, our model achieved an accuracy of 0.913 and a mean average precision value of 0.847 by employing the weighted loss function (DFBLoss). Furthermore, in a new test dataset, the RECNet model achieved accuracy rates of 0.924 and 0.876 for the two tasks, respectively. Our RECNet model was trained by orders of magnitude larger dataset than previous studies and also showed better accuracy rates. Conclusions Our method holds promise for diverse applications within zebrafish laboratories and fields such as toxicology, providing indispensable support to scientific research.

Liver fibrosis is a chronic liver disease driven by sustained inflammation, highlighting the need for targeted delivery of anti-inflammatory agents. We report the design of an acid-sensitive, liver-targeted hyperbranched polyprodrug (PPOG) that forms stable unimolecular micelles. Prednisone is conjugated to the hydrophobic core through acid-labile linkers, and glycyrrhetinic acid on the micelle surface enables hepatocyte targeting. PPOG demonstrates excellent colloidal stability under dilution, ionic stress, and long-term storage. In vitro, the micelles show low cytotoxicity and rapid prednisone release under acidic conditions. GA-mediated targeting enhances cellular uptake in hepatocytes, while in vivo imaging in a CCl4-induced mouse fibrosis model confirms higher liver accumulation than nontargeted micelles. PPOG treatment markedly reduces liver injury biomarkers, inflammatory cytokines, and collagen deposition, outperforming free prednisone and nontargeted formulations. These results indicate that PPOG unimolecular micelles offer a promising strategy for targeted anti-inflammatory therapy in liver fibrosis.