Dual-Mechanism Tuned Engineered Polyphenols with Cascade Photocatalytic Self-Fenton Reaction for Sustainable Biocidal Coatings.

Traditional disposable personal protective equipment (PPE) only blocks pathogenic bacteria by mechanical filtration, with the risk of recontamination and transmission remaining. Herein, inspired by phenolic-enabled nanotechnology (PEN), we proposed engineered polyphenol coatings by plant-derived aromatic aldehydes and metal involvement, denoted as FQM, to obtain the desired photocatalysis-self-Fenton antibacterial performance. Experiments and theoretical analysis proved the dual mechanism of Fe-induced enhancement: (1) tuning of molecular structure realized improved optical properties; (2) Fe(III)/Fe(II) triggered photocatalytic cascade self-Fenton reaction. Mechanism study reveals FQM killing bacteria by direct-contact ROS attack and gene regulation. Further, the FQM was developed as the ideal antibacterial coating on different fabrics (cloth cotton, polyester, and N95 mask), killing more than 93% of bacteria after 5 cycles of use. Such photocatalysis-self-Fenton coatings based on engineered polyphenols endowed with desirable safety, sustainability, and efficient antibacterial features are promising solutions to meet the challenges of the currently available PPE.

Deep learning image reconstruction algorithm reduces image noise while alters radiomics features in dual-energy CT in comparison with conventional iterative reconstruction algorithms: a phantom study.

OBJECTIVES: To compare image quality between a deep learning image reconstruction (DLIR) algorithm and conventional iterative reconstruction (IR) algorithms in dual-energy CT (DECT) and to assess the impact of these algorithms on radiomics robustness. METHODS: A phantom with clinical-relevant densities was imaged on seven DECT scanners with the same voxel size using typical abdominal-pelvis examination protocols. On one DECT scanner, raw data were reconstructed using both conventional IR (adaptive statistical iterative reconstruction-V, ASIR-V) and DLIR. Nine sets of corresponding images were generated on other six DECT scanners using scanner-equipped conventional IR. Regions of interest were delineated through rigid registrations. Image quality was compared. Pyradiomics platform was used for radiomics feature extraction. Test-retest repeatability was assessed by Bland-Altman analysis for repeated scans. Inter-reconstruction algorithm reproducibility between conventional IR and DLIR was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). Robust features were identified. RESULTS: DLIR significantly improved image quality. Ninety-four radiomics features were extracted and nine features were considered as robust. 93.87% features were repeatable between repeated scans. ASIR-V images showed higher reproducibility to other conventional IR than DLIR (ICC mean, 0.603 vs 0.558, p = 0.001; CCC mean, 0.554 vs 0.510, p = 0.004). 7.45% and 26.83% features were reproducible among scanners evaluated by CV and QCD, respectively. CONCLUSIONS: DLIR improves quality of DECT images but may alter radiomics features compared to conventional IR. Nine robust DECT radiomics features were identified. KEY POINTS: • DLIR improves DECT image quality in terms of signal-to-noise ratio and contrast-to-noise ratio compared with ASIR-V and showed the highest noise reduction rate and lowest peak frequency shift. • Most of radiomics features are repeatable between repeated DECT scans, while inter-reconstruction algorithm reproducibility between conventional IR and DLIR, and inter-scanner reproducibility, are low. • Although DLIR may alter radiomics features compared to IR algorithms, nine radiomics features survived repeatability and reproducibility analysis among DECT scanners and reconstruction algorithms, which allows further validation and clinical-relevant analysis.

Robustness of CT radiomics features: consistency within and between single-energy CT and dual-energy CT.

OBJECTIVES: To evaluate inter- and intra- scan mode and scanner repeatability and reproducibility of radiomics features within and between single-energy CT (SECT) and dual-energy CT (DECT). METHODS: A standardized phantom with sixteen rods of clinical-relevant densities was scanned on seven DECT-capable scanners and three SECT-only scanners. The acquisition parameters were selected to present typical abdomen-pelvic examinations with the same voxel size. Images of SECT at 120 kVp and corresponding 120 kVp-like virtual monochromatic images (VMIs) in DECT which were generated according to scanners were analyzed. Regions of interest were drawn with rigid registrations to avoid variations due to segmentation. Radiomics features were extracted via Pyradiomics platform. Test-retest repeatability was evaluated by Bland-Altman analysis for repeated scans. Intra-scanner reproducibility for different scan modes was tested by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). Inter-scanner reproducibility among different scanners for same scan mode was assessed by coefficient of variation (CV) and quartile coefficient of dispersion (QCD). RESULTS: The test-retest analysis presented that 92.91% and 87.02% of the 94 assessed features were repeatable for SECT 120kVp and DECT 120 kVp-like VMIs, respectively. The intra-scanner analysis for SECT 120kVp vs DECT 120 kVp-like VMIs demonstrated that 10.76% and 10.28% of features were with ICC > 0.90 and CCC > 0.90, respectively. The inter-scanner analysis showed that 17.09% and 27.73% of features for SECT 120kVp were with CV < 10% and QCD < 10%, and 15.16% and 32.78% for DECT 120 kVp-like VMIs, respectively. CONCLUSIONS: The majority of radiomics features were non-reproducible within and between SECT and DECT. KEY POINTS: • Although the test-retest analysis showed high repeatability for radiomics features, the overall reproducibility of radiomics features within and between SECT and DECT was low. • Only about one-tenth of radiomics features extracted from SECT images and corresponding DECT images did match each other, even their average photon energy levels were considered alike, indicating that the scan mode potentially altered the radiomics features. • Less than one-fifth of radiomics features were reproducible among multiple SECT and DECT scanners, regardless of their fixed acquisition and reconstruction parameters, suggesting the necessity of scanning protocol adjustment and post-scan harmonization process.

Quantifying functional impact of non-coding variants with multi-task Bayesian neural network.

MOTIVATION: Advances in high-throughput genotyping and sequencing technologies during recent years have revealed essential roles of non-coding regions in gene regulation. Genome-wide association studies (GWAS) suggested that a large proportion of risk variants are located in non-coding regions and remain unexplained by current expression quantitative trait loci catalogs. Interpreting the causal effects of these genetic modifications is crucial but difficult owing to our limited knowledge of how regulatory elements function. Although several computational methods have been designed to prioritize regulatory variants that substantially impact human phenotypes, few of them achieve consistently high performance even when large-scale multi-omic data are integrated. RESULTS: We propose a novel multi-task framework based on Bayesian deep neural networks, MtBNN, to quantify the deleterious impact of single nucleotide polymorphisms in non-coding genomic regions. With the high-efficiency provided by the multi-task Bayesian framework to integrate information from different sources, MtBNN is capable of extracting features from genomic sequences of large-scale chromatin-profiling data, such as chromatin accessibility and transcript factor binding affinities, and calculating the distribution of the probability that a non-coding variant disrupts regulatory activities. A series of comprehensive experiments show that MtBNN quantifies the functional impact of cis-regulatory variations with high accuracy, including expression quantitative trait locus, DNase I sensitivity quantitative trait locus and functional genetic variants located within ATAC-peaks that affect the accessibility of the corresponding peak and achieves significantly better performance than the existing methods. Moreover, MtBNN has applications in the discovery of potentially causal disease-associated single-nucleotide polymorphisms (SNPs), thus helping fine-map the GWAS SNPs. AVAILABILITY AND IMPLEMENTATION: Code can be downloaded from https://github.com/Zoesgithub/MtBNN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

[New Model for Intelligent Imaging Screening of Pulmonary Nodules].

The artificial intelligence based on medical aid diagnosis has been in full swing in these years. How to better and more safely utilize this new technology to improve the diagnostic efficiency and quality of doctors poses new challenges for our hospital management. This paper aims to explore relevant management problems and corresponding solutions from seven aspects:data security, system integration, technical parameters, risks, workflows and diagnosis results by introducing a new intelligent image screening system. After these management problems have been better solved, we found that the intelligent image screening system can improve the diagnostic efficiency and quality of doctors.

Imputation for transcription factor binding predictions based on deep learning.

Understanding the cell-specific binding patterns of transcription factors (TFs) is fundamental to studying gene regulatory networks in biological systems, for which ChIP-seq not only provides valuable data but is also considered as the gold standard. Despite tremendous efforts from the scientific community to conduct TF ChIP-seq experiments, the available data represent only a limited percentage of ChIP-seq experiments, considering all possible combinations of TFs and cell lines. In this study, we demonstrate a method for accurately predicting cell-specific TF binding for TF-cell line combinations based on only a small fraction (4%) of the combinations using available ChIP-seq data. The proposed model, termed TFImpute, is based on a deep neural network with a multi-task learning setting to borrow information across transcription factors and cell lines. Compared with existing methods, TFImpute achieves comparable accuracy on TF-cell line combinations with ChIP-seq data; moreover, TFImpute achieves better accuracy on TF-cell line combinations without ChIP-seq data. This approach can predict cell line specific enhancer activities in K562 and HepG2 cell lines, as measured by massively parallel reporter assays, and predicts the impact of SNPs on TF binding.

Canonical nucleosome organization at promoters forms during genome activation.

The organization of nucleosomes influences transcriptional activity by controlling accessibility of DNA binding proteins to the genome. Genome-wide nucleosome binding profiles have identified a canonical nucleosome organization at gene promoters, where arrays of well-positioned nucleosomes emanate from nucleosome-depleted regions. The mechanisms of formation and the function of canonical promoter nucleosome organization remain unclear. Here we analyze the genome-wide location of nucleosomes during zebrafish embryogenesis and show that well-positioned nucleosome arrays appear on thousands of promoters during the activation of the zygotic genome. The formation of canonical promoter nucleosome organization is independent of DNA sequence preference, transcriptional elongation, and robust RNA polymerase II (Pol II) binding. Instead, canonical promoter nucleosome organization correlates with the presence of histone H3 lysine 4 trimethylation (H3K4me3) and affects future transcriptional activation. These findings reveal that genome activation is central to the organization of nucleosome arrays during early embryogenesis.

Clinically Approved Ferric Maltol: A Potent Nanozyme with Added Effect for High-Efficient Catalytic Disinfection.

Nanozyme has been proven to be an attractive and promising candidate to alleviate the current pressing medical problems. However, the unknown clinical safety and limited function beyond the catalysis of the most reported nanozymes cannot promise an ideal therapeutic outcome in further clinical application. Herein, we find that ferric maltol (FM), a clinically approved iron supplement synthesized through a facile scalable method, exhibits excellent peroxidase-like activity than natural horseradish peroxidase-like (HRP) and commonly reported Fe-based nanozymes, and also shows high antibacterial performance for methicillin-resistant Staphylococcus aureus (MRSA) elimination (100%) and wound disinfection. In addition, with added effects inherited from contained maltol, FM can accelerate skin barrier recovery. Therefore, the exploration of FM as a safe and desired nanozyme provides a timely alternative to current antibiotic therapy against drug-resistant bacteria.

Metal-phenolic networks derived CN-FeC hollow nanozyme with robust peroxidase-like activity for total antioxidant capacity detection.

A tannate-iron network-derived peroxidase-like catalyst loaded with Fe ions on carbon nitride (C3N4) was reported for detection of total antioxidant capacity (TAC) in food in this study. Metal-phenolic networks (MPNs) was employed to form a low coordination compound on C3N4, and calcined catalyst formed hollow structure with abundant and uniform Fe sites and surface folds. CN-FeC exhibited significant peroxidase-like activity and high substrate affinity. The homogeneous distribution of amorphous Fe elements on the C3N4 substrate provides more active sites, resulting in provided excellent catalytic activity to activate H2O2 to ·OH, 1O2 and O2·-. The established CN-FeC/TMB/H2O2 colorimetric system can detect AA in the concentration range of 5-40 µM, with the detection limits of 1.40 µM, respectively. It has good accuracy for the detection of vitamin C tablets, beverages. Taken together, this work shows that metal-phenolic networks can be an effective way to achieve efficient utilization of metal atoms and provides a promising idea for metal-phenolic networks in nanoparticle enzyme performance enhancement.

Microenvironment-triggered cascade metal-polyphenolic nanozyme for ROS/NO synergistic hyperglycemic wound healing.

Wound infection of hyperglycemic patient often has extended healing period and increased probability due to the high glucose level. However, achieving precise and safe therapy of the hyperglycemic wound with specific wound microenvironment (WME) remains a major challenge. Herein, a WME-activated smart L-Arg/GOx@TA-Fe (LGTF) nanozymatic system composed of generally recognized as safe (GRAS) compound is engineered. The nanozymatic system combining metal-polyphenol nanozyme (tannic acid-Fe3+, TA-Fe) and natural enzyme (glucose oxidase, GOx) can consume the high-concentration glucose, generating reactive oxygen species (ROS) and nitric oxide (NO) in situ to synergistically disinfect hyperglycemia wound. In addition, glucose consumption and gluconic acid generation can lower glucose level to promote wound healing and reduce the pH of WME to enhance the catalytic activities of the LGTF nanozymatic system. Thereby, low-dose LGTF can perform remarkable synergistic disinfection and healing effect towards hyperglycemic wound. The superior biosafety, high catalytic antibacterial and beneficial WME regulating capacity demonstrate this benign GRAS nanozymatic system is a promising therapeutic agent for hyperglycemic wound.

Impacts of Adaptive Statistical Iterative Reconstruction-V and Deep Learning Image Reconstruction Algorithms on Robustness of CT Radiomics Features: Opportunity for Minimizing Radiomics Variability Among Scans of Different Dose Levels.

This study aims to investigate the influence of adaptive statistical iterative reconstruction-V (ASIR-V) and deep learning image reconstruction (DLIR) on CT radiomics feature robustness. A standardized phantom was scanned under single-energy CT (SECT) and dual-energy CT (DECT) modes at standard and low (20 and 10 mGy) dose levels. Images of SECT 120 kVp and corresponding DECT 120 kVp-like virtual monochromatic images were generated with filtered back-projection (FBP), ASIR-V at 40% (AV-40) and 100% (AV-100) blending levels, and DLIR algorithm at low (DLIR-L), medium (DLIR-M), and high (DLIR-H) strength levels. Ninety-four features were extracted via Pyradiomics. Reproducibility of features was calculated between standard and low dose levels, between reconstruction algorithms in reference to FBP images, and within scan mode, using intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC). The average percentage of features with ICC > 0.90 and CCC > 0.90 between the two dose levels was 21.28% and 20.75% in AV-40 images, and 39.90% and 35.11% in AV-100 images, respectively, and increased from 15.43 to 45.22% and from 15.43 to 44.15% with an increasing strength level of DLIR. The average percentage of features with ICC > 0.90 and CCC > 0.90 in reference to FBP images was 26.07% and 25.80% in AV-40 images, and 18.88% and 18.62% in AV-100 images, respectively, and decreased from 27.93 to 17.82% and from 27.66 to 17.29% with an increasing strength level of DLIR. DLIR and ASIR-V algorithms showed low reproducibility in reference to FBP images, while the high-strength DLIR algorithm provides an opportunity for minimizing radiomics variability due to dose reduction.

DiNuP: a systematic approach to identify regions of differential nucleosome positioning.

MOTIVATION: With the rapid development of high-throughput sequencing technologies, the genome-wide profiling of nucleosome positioning has become increasingly affordable. Many future studies will investigate the dynamic behaviour of nucleosome positioning in cells that have different states or that are exposed to different conditions. However, a robust method to effectively identify the regions of differential nucleosome positioning (RDNPs) has not been previously available. RESULTS: We describe a novel computational approach, DiNuP, that compares nucleosome profiles generated by high-throughput sequencing under various conditions. DiNuP provides a statistical P-value for each identified RDNP based on the difference of read distributions. DiNuP also empirically estimates the false discovery rate as a cutoff when two samples have different sequencing depths and differentiate reliable RDNPs from the background noise. Evaluation of DiNuP showed it to be both sensitive and specific for the detection of changes in nucleosome location, occupancy and fuzziness. RDNPs that were identified using publicly available datasets revealed that nucleosome positioning dynamics are closely related to the epigenetic regulation of transcription. AVAILABILITY AND IMPLEMENTATION: DiNuP is implemented in Python and is freely available at http://www.tongji.edu.cn/~zhanglab/DiNuP. CONTACT: yzhang@tongji.edu.cn SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

GFOLD: a generalized fold change for ranking differentially expressed genes from RNA-seq data.

MOTIVATION: RNA-seq has been widely used in transcriptome analysis to effectively measure gene expression levels. Although sequencing costs are rapidly decreasing, almost 70% of all the human RNA-seq samples in the gene expression omnibus do not have biological replicates and more unreplicated RNA-seq data were published than replicated RNA-seq data in 2011. Despite the large amount of single replicate studies, there is currently no satisfactory method for detecting differentially expressed genes when only a single biological replicate is available. RESULTS: We present the GFOLD (generalized fold change) algorithm to produce biologically meaningful rankings of differentially expressed genes from RNA-seq data. GFOLD assigns reliable statistics for expression changes based on the posterior distribution of log fold change. In this way, GFOLD overcomes the shortcomings of P-value and fold change calculated by existing RNA-seq analysis methods and gives more stable and biological meaningful gene rankings when only a single biological replicate is available. AVAILABILITY: The open source C/C++ program is available at http://www.tongji.edu.cn/∼zhanglab/GFOLD/index.html

Robustness of radiomics features of virtual unenhanced and virtual monoenergetic images in dual-energy CT among different imaging platforms and potential role of CT number variability.

OBJECTIVES: To evaluate robustness of dual-energy CT (DECT) radiomics features of virtual unenhanced (VUE) image and virtual monoenergetic image (VMI) among different imaging platforms. METHODS: A phantom with sixteen clinical-relevant densities was scanned on ten DECT platforms with comparable scan parameters. Ninety-four radiomic features were extracted via Pyradiomics from VUE images and VMIs at energy level of 70 keV (VMI70keV). Test-retest repeatability was assessed by Bland-Altman analysis. Inter-platform reproducibility of VUE images and VMI70keV was evaluated by coefficient of variation (CV) and quartile coefficient of dispersion (QCD) among platforms, and by intraclass correlation coefficient (ICC) and concordance correlation coefficient (CCC) between platform pairs. The correlation between variability of CT number radiomics reproducibility was estimated. RESULTS: 92.02% and 92.87% of features were repeatable between scan-rescans for VUE images and VMI70keV, respectively. Among platforms, 11.30% and 28.39% features of VUE images, and 15.16% and 28.99% features of VMI70keV were with CV < 10% and QCD < 10%. The average percentages of radiomics features with ICC > 0.90 and CCC > 0.90 between platform pairs were 10.00% and 9.86% in VUE images and 11.23% and 11.23% in VMI70keV. The CT number inter-platform reproducibility using CV and QCD showed negative correlations with percentage of the first-order radiomics features with CV < 10% and QCD < 10%, in both VUE images and VMI70keV (r2 0.3870-0.6178, all p < 0.001). CONCLUSIONS: The majority of DECT radiomics features were non-reproducible. The differences in CT number were considered as an indicator of inter-platform DECT radiomics variation. Critical relevance statement: The majority of radiomics features extracted from the VUE images and the VMI70keV were non-reproducible among platforms, while synchronizing energy levels of VMI to reduce the CT number value variability may be a potential way to mitigate radiomics instability.

Transition Metal High-Entropy Nanozyme: Multi-Site Orbital Coupling Modulated High-Efficiency Peroxidase Mimics.

Strong substrate affinity and high catalytic efficiency are persistently pursued to generate high-performance nanozymes. Herein, with unique surface atomic configurations and distinct d-orbital coupling features of different metal components, a class of highly efficient MnFeCoNiCu transition metal high-entropy nanozymes (HEzymes) is prepared for the first time. Density functional theory calculations demonstrate that improved d-orbital coupling between different metals increases the electron density near the Fermi energy level (EF ) and shifts the position of the overall d-band center with respect to EF , thereby boosting the efficiency of site-to-site electron transfer while also enhancing the adsorption of oxygen intermediates during catalysis. As such, the proposed HEzymes exhibit superior substrate affinities and catalytic efficiencies comparable to that of natural horseradish peroxidase (HRP). Finally, HEzymes with superb peroxidase (POD)-like activity are used in biosensing and antibacterial applications. These results suggest that HEzymes have great potential as new-generation nanozymes.

GeSICA: genome segmentation from intra-chromosomal associations.

BACKGROUND: Various aspects of genome organization have been explored based on data from distinct technologies, including histone modification ChIP-Seq, 3C, and its derivatives. Recently developed Hi-C techniques enable the genome wide mapping of DNA interactomes, thereby providing the opportunity to study genome organization in detail, but these methods also pose challenges in methodology development. RESULTS: We developed Genome Segmentation from Intra Chromosomal Associations, or GeSICA, to explore genome organization and applied the method to Hi-C data in human GM06990 and K562 cells. GeSICA calculates a simple logged ratio to efficiently segment the human genome into regions with two distinct states that correspond to rich and poor functional element states. Inside the rich regions, Markov Clustering was subsequently applied to segregate the regions into more detailed clusters. The binding sites of the insulator, cohesion, and transcription complexes are enriched in the boundaries between neighboring clusters, indicating that inferred clusters may have fine organizational features. CONCLUSIONS: Our study presents a novel analysis method, known as GeSICA, which gives insight into genome organization based on Hi-C data. GeSICA is open source and freely available at: http://web.tongji.edu.cn/~zhanglab/GeSICA/

Hydrogel loading 2D montmorillonite exfoliated by anti-inflammatory Lycium barbarum L. polysaccharides for advanced wound dressing.

Designing wound dressing materials with hemocompatibility, suitable mechanical properties, outstanding hemostatic effects and anti-inflammatory activity is of great practical significance for wound management. Herein, a hemostatic hydrogel loaded with Lycium barbarum L. polysaccharide (LBP)-functionalized ultrathin MMT nanosheets (L-MMT NSs) was fabricated for efficient hemostasis and wound healing. Loading the L-MMT NSs into polyvinyl alcohol (PVA), the obtained P-L-MMT hydrogel exhibited a 3D porous structure with good swelling properties, cytocompatibility, hemocompatibility, and anti-inflammatory activity. Importantly, in vivo investigations demonstrated that the P-L-MMT hydrogel exerts outstanding hemostasis activity in the hemorrhaging mouse liver model and reduces tissue damage caused by inflammation to shorten wound healing time. Altogether, the convenient exfoliation and functionalization of bulk MMT using LBPs make this inexpensive and rising nanostructure more attractive in the application of nanomedicine. Moreover, due to the synergy between hemostasis and anti-inflammation, this newly developed multifunctional P-L-MMT hydrogel represents a promising material in biomedical fields.

Gentiana straminea Maxim. polysaccharide decolored via high-throughput graphene-based column and its anti-inflammatory activity.

Gentiana straminea Maxim. exhibits various biological activities. However, the purification and functions of polysaccharides in Gentiana straminea Maxim. have never been reported. Herein, by proposing a flexible 3D graphene-based decoloration column (3DD column), Gentiana straminea Maxim. polysaccharide (GMP) was high-throughput obtained and its anti-inflammatory activity was investigated. Benefiting from the large macroporous network of 3D NH2-graphene oxide hydrogel with selective adsorption towards pigments, the 3DD column exhibits high decoloration ratio (96.41%). In addition, the 3DD column provides superior practical functionality as compared to the traditional approaches, which are time-consuming and need toxic solvents, and exhibiting widespread-application for the purification of polysaccharide from other common plant species. More importantly, the decolored GMP as a natural product has promising anti-inflammatory activity on RAW264.7 cells without negative impact on cell viability. Overall, this work reveals a new functional polysaccharides and provides a flexible approach for polysaccharide decoloration, exhibiting a promising prospect for natural polysaccharides in practical application of pharmaceutical.

Photothermal-boosted effect of binary CuFe bimetallic magnetic MOF heterojunction for high-performance photo-Fenton degradation of organic pollutants.

Overcoming the relatively low catalytic activity and strict acid pH condition of common photo-Fenton reaction is the key to alleviate the serious global burden caused by common organic pollutants. Herein, a binary homologous bimetallic heterojunction of magnetic CuFe2O4@MIL-100(Fe, Cu) metal-organic frameworks (MCuFe MOF) with photothermal-boosted photo-Fenton activity is constructed as an ideal practical photo-Fenton catalyst for the degradation of organic pollutants. Through an in-situ derivation strategy, the formed homologous bimetallic heterojunction with binary redox couples can simultaneously improve the visible light harvesting capacity and expedite the separation and transfer of photogenerated electrons/holes pairs, leading to the continuous and rapid circulation of both FeIII/FeII and CuII/CuI redox couples. Notably, the heterojunction shows intrinsic photo-thermal conversion effect, which is found to be beneficial to boost the photo-Fenton activity. Impressively, MCuFe MOF shows remarkable catalytic performance towards the degradation of various organic pollutants by comprehensively increasing H2O2 decomposition efficiency and decreasing the required dosage of MCuFe MOF (0.05 g L-1) with a wide pH range (3.0-10.0). As such, a photo-Fenton catalyst consisting of binary homologous bimetallic heterojunction is first disclosed, as well as its photothermal-enhanced effect, which is expected to drive great advance in the degradation of organic pollutants for practical applications.

Lycium Barbarum Polysaccharide-Iron (III) Chelate as Peroxidase Mimics for Total Antioxidant Capacity Assay of Fruit and Vegetable Food.

Quantitative evaluation of the antioxidant capacity of foods is of great significance for estimating food's nutritional value and preventing oxidative changes in food. Herein, we demonstrated an easy and selective colorimetric method for the total antioxidant capacity (TAC) assay based on 3,3',5,5'-tetramethyl-benzidine (TMB), hydrogen peroxide (H2O2) and synthetic Lycium barbarum polysaccharide-iron (III) chelate (LBPIC) with high peroxidase (POD)-like activity. The results of steady-state kinetics study showed that the Km values of LBPIC toward H2O2 and TMB were 5.54 mM and 0.16 mM, respectively. The detection parameters were optimized, and the linear interval and limit of detection (LOD) were determined to be 2-100 µM and 1.51 µM, respectively. Additionally, a subsequent study of the determination of TAC in six commercial fruit and vegetable beverages using the established method was successfully carried out. The results implied an expanded application of polysaccharide-iron (III) chelates with enzymatic activity in food antioxidant analysis and other biosensing fields.