MRI-based deep learning and radiomics for prediction of occult cervical lymph node metastasis and prognosis in early-stage oral and oropharyngeal squamous cell carcinoma: a diagnostic study.

INTRODUCTION: The incidence of occult cervical lymph node metastases (OCLNM) is reported to be 20%-30% in early-stage oral cancer and oropharyngeal cancer. There is a lack of an accurate diagnostic method to predict occult lymph node metastasis and to help surgeons make precise treatment decisions. AIM: To construct and evaluate a preoperative diagnostic method to predict occult lymph node metastasis (OCLNM) in early-stage oral and oropharyngeal squamous cell carcinoma (OC and OP SCC) based on deep learning features (DLFs) and radiomics features. METHODS: A total of 319 patients diagnosed with early-stage OC or OP SCC were retrospectively enrolled and divided into training, test and external validation sets. Traditional radiomics features and DLFs were extracted from their MRI images. The least absolute shrinkage and selection operator (LASSO) analysis was employed to identify the most valuable features. Prediction models for OCLNM were developed using radiomics features and DLFs. The effectiveness of the models and their clinical applicability were evaluated using the area under the curve (AUC), decision curve analysis (DCA) and survival analysis. RESULTS: Seventeen prediction models were constructed. The Resnet50 deep learning (DL) model based on the combination of radiomics and DL features achieves the optimal performance, with AUC values of 0.928 (95% CI: 0.881-0.975), 0.878 (95% CI: 0.766-0.990), 0.796 (95% CI: 0.666-0.927) and 0.834 (95% CI: 0.721-0.947) in the training, test, external validation set1 and external validation set2, respectively. Moreover, the Resnet50 model has great prediction value of prognosis in patients with early-stage OC and OP SCC. CONCLUSION: The proposed MRI-based Resnet50 deep learning model demonstrated high capability in diagnosis of OCLNM and prognosis prediction in the early-stage OC and OP SCC. The Resnet50 model could help refine the clinical diagnosis and treatment of the early-stage OC and OP SCC.

b-MAR: bidirectional artifact representations learning framework for metal artifact reduction in dental CBCT.

OBJECTIVE: Metal artifacts in computed tomography (CT) images hinder diagnosis and treatment significantly. Specifically, dental cone-beam computed tomography (Dental CBCT) images are seriously contaminated by metal artifacts due to the widespread use of low tube voltages and the presence of various high-attenuation materials in dental structures. Existing supervised metal artifact reduction (MAR) methods mainly learn the mapping of artifact-affected images to clean images, while ignoring the modeling of the metal artifact generation process. Therefore, we propose the bidirectional artifact representations learning framework to adaptively encode metal artifacts caused by various dental implants and model the generation and elimination of metal artifacts, thereby improving MAR performance. Approach. we introduce an efficient artifact encoder to extract multi-scale representations of metal artifacts from artifact-affected images. These extracted metal artifact representations are then bidirectionally embedded into both the metal artifact generator and the metal artifact eliminator, which can simultaneously improve the performance of artifact removal and artifact generation. The artifact eliminator learns artifact removal in a supervised manner, while the artifact generator learns artifact generation in an adversarial manner. To further improve the performance of the bidirectional task networks, we propose artifact consistency loss to align the consistency of images generated by the eliminator and the generator with or without embedding artifact representations. Main results. To validate the effectiveness of our algorithm, experiments are conducted on simulated and clinical datasets containing various dental metal morphologies. Quantitative metrics are calculated to evaluate the results of the simulation tests,which demonstrate b-MAR improvements of > 1.4131 dB in PSNR, > 0.3473 HU decrements in RMSE, and > 0.0025 promotion in SSIM over the current state-of-the-art MAR methods. All results indicate that the proposed b-MAR method can remove artifacts caused by various metal morphologies and restore the structural integrity of dental tissues effectively. Significance. The proposed b-MAR method strengthens the joint learning of the artifact removal process and the artifact generation process by bidirectionally embedding artifact representations, thereby improving the model's artifact removal performance. Compared with other comparison methods, b-MAR can robustly and effectively correct metal artifacts in dental CBCT images caused by different dental metals.

Effects of polyethylene microplastics occurrence on estrogens degradation in soil.

Growing focus has been drawn to the continuous detection of high estrogens levels in the soil environment. Additionally, microplastics (MPs) are also of growing concern worldwide, which may affect the environmental behavior of estrogens. However, little is known about effects of MPs occurrence on estrogens degradation in soil. In this study, polyethylene microplastics (PE-MPs) were chosen to examine the influence on six common estrogens (estrone (E1), 17α-estradiol (17α-E2), 17ß-estradiol (17ß-E2), estriol (E3), diethylstilbestrol (DES), and 17α-ethinylestradiol (17α-EE2)) degradation. The results indicated that PE-MPs had little effect on the degradation of E3 and DES, and slightly affected the degradation of 17α-E2, however, significantly inhibited the degradation of E1, 17α-EE2, and 17ß-E2. It was explained that (i) obvious oxidation reaction occurred on the surface of PE-MPs, indicating that PE-MPs might compete with estrogens for oxidation sites, such as redox and biological oxidation; (ii) PE-MPs significantly changed the bacterial community in soil, resulting in a decline in the abundance of some bacterial communities that biodegraded estrogens. Moreover, the rough surface of PE-MPs facilitated the estrogen-degrading bacterial species (especially for E1, E2, and EE2) to adhere, which decreased their reaction to estrogens. These findings are expected to deepen the understanding of the environmental behavior of typical estrogens in the coexisting system of MPs.

DDT-Net: Dose-Agnostic Dual-Task Transfer Network for Simultaneous Low-Dose CT Denoising and Simulation.

Deep learning (DL) algorithms have achieved unprecedented success in low-dose CT (LDCT) imaging and are expected to be a new generation of CT reconstruction technology. However, most DL-based denoising models often lack the ability to generalize to unseen dose data. And they only learn the posterior distribution of latent normal-dose CT (NDCT) images conditioned on observed LDCT images in the traditional maximum a posteriori (MAP) framework, while ignoring the noise generation process of LDCT images. Moreover, most simulation tools for LDCT typically operate on proprietary projection data, which is generally not accessible without an established collaboration with CT manufacturers. To alleviate these issues, in this work, we propose a dose-agnostic dual-task transfer network, termed DDT-Net, for simultaneous LDCT denoising and simulation. Concretely, the dual-task learning module is constructed to integrate the LDCT denoising and simulation tasks into a unified optimization framework by learning the joint distribution of LDCT and NDCT data. We approximate the joint distribution of continuous dose level data by training DDT-Net with discrete dose data, which can be generalized to denoising and simulation of unseen dose data. In particular, the mixed-dose training strategy adopted by DDT-Net can promote the denoising performance of lower-dose data. The paired dataset simulated by DDT-Net can be used for data augmentation to further restore the tissue texture of LDCT images. Experimental results on synthetic data and clinical data show that the proposed DDT-Net outperforms competing methods in terms of denoising and generalization performance at unseen dose data, and it also provides a simulation tool that can quickly simulate realistic LDCT images at arbitrary dose levels.

Metal-Organic Frameworks Possessing Suitable Pores for Xe/Kr Separation.

Adsorption separation of the Xe/Kr mixture remains a tough issue since Xe and Kr have an inert nature and similar sizes. Here we present a chlorinated metal-organic framework (MOF) [JXNU-19(Cl)] and its nonchlorinated analogue (JXNU-19) for Xe/Kr separation. The two isostructural MOFs constructed from the heptanuclear cobalt-hydroxyl clusters bridged by organic ligands are three-dimensional structures. Detailed contrast of the Xe/Kr adsorption separation properties of the MOF shows that significantly enhanced Xe uptakes and Xe/Kr adsorption selectivity (17.1) are observed for JXNU-19 as compared to JXNU-19(Cl). The main binding sites for Xe in the MOF revealed by computational simulations are far away from the chlorine sites, suggesting that the introduction of the chlorine groups results in the unfavorable Xe adsorption for JXNU-19(Cl). The optimal pores, high surface area, and multiple strong Xe-framework interactions facilitate the effective Xe/Kr separation for JXNU-19.

Semi-supervised iterative adaptive network for low-dose CT sinogram recovery.

Background.Concern has been expressed regarding the risk of carcinogenesis from medical computed tomography (CT) radiation. Lowering radiation in CT without appropriate modifications often leads to severe noise-induced artifacts in the images. The utilization of deep learning (DL) techniques has achieved promising reconstruction performance in low-dose CT (LDCT) imaging. However, most DL-based algorithms require the pre-collection of a large set of image pairs (low-dose/standard-dose) and the training of networks in an end-to-end supervised manner. Meanwhile, securing such a large volume of paired, well-registered training data in clinical practice is challenging. Moreover, these algorithms often overlook the potential to utilize the abundant information in a large collection of LDCT-only images/sinograms.Methods.In this paper, we introduce a semi-supervised iterative adaptive network (SIA-Net) for LDCT imaging, utilizing both labeled and unlabeled sinograms in a cohesive network framework, integrating supervised and unsupervised learning processes. Specifically, the supervised process captures critical features (i.e. noise distribution and tissue characteristics) latent in the paired sinograms, while the unsupervised process effectively learns these features in the unlabeled low-dose sinograms, employing a conventional weighted least-squares model with a regularization term. Furthermore, the SIA-Net method is designed to adaptively transfer the learned feature distribution from the supervised to the unsupervised process, thereby obtaining a high-fidelity sinogram through iterative adaptive learning. Finally, high-quality CT images can be reconstructed from the refined sinogram using the filtered back-projection algorithm.Results.Experimental results on two clinical datasets indicate that the proposed SIA-Net method achieves competitive performance in terms of noise reduction and structure preservation in LDCT imaging, when compared to traditional supervised learning methods.

Targeted Screening of Fiber Degrading Bacteria with Probiotic Function in Herbivore Feces.

Cellulolytic bacteria with probiotic functions play a crucial role in promoting the intestinal health in herbivores. In this study, we aimed to correlate the 16S rRNA gene amplicon sequencing and fiber-degrading enzyme activity data from six different herbivore feces samples. By utilizing the separation and screening steps of probiotics, we targeted and screened high-efficiency fiber-degrading bacteria with probiotic functions. The animals included Maiwa Yak (MY), Holstein cow (CC), Tibetan sheep (TS), Southern Sichuan black goat (SG), Sichuan white rex rabbit (CR), and New Zealand white rabbit (ZR). The results showed that the enzymes associated with fiber degradation were higher in goat and sheep feces compared to cattle and rabbit's feces. Correlation analysis revealed that Bacillus and Fibrobacter were positively correlated with five types of fiber-degrading related enzymes. Notably, the relative abundance of Bacillus in the feces of Tibetan sheep was significantly higher than that of other five herbivores. A strain TS5 with good cellulose decomposition ability from the feces of Tibetan sheep by Congored staining, filter paper decomposition test, and enzyme activity determination was isolated. The strain was identified as Bacillus velezensis by biological characteristics, biochemical analysis, and 16S rRNA gene sequencing. To test the probiotic properties of Bacillus velezensis TS5, we evaluated its tolerance to acid and bile salt, production of digestive enzymes, antioxidants, antibacterial activity, and adhesion ability. The results showed that the strain had good tolerance to pH 2.0 and 0.3% bile salts, as well as good potential to produce cellulase, protease, amylase, and lipase. This strain also had good antioxidant capacity and the ability to antagonistic Staphylococcus aureus BJ216, Salmonella SC06, Enterotoxigenic Escherichia coli CVCC196, and Escherichia coli ATCC25922. More importantly, the strain had good self-aggregation and Caco-2 cell adhesion rate. In addition, we tested the safety of Bacillus velezensis TS5 by hemolysis test, antimicrobial susceptibility test, and acute toxicity test in mice. The results showed that the strain had no hemolytic phenotype, did not develop resistance to 19 commonly used antibiotics, had no cytotoxicity to Caco-2, and did not have acute toxic harm to mice. In summary, this study targeted isolated and screened a strain of Bacillus velezensis TS5 with high fiber-degrading ability and probiotic potency. This strain can be used as a potential probiotic for feeding microbial preparations for ruminants.

Super Resolution Dual-Energy Cone-Beam CT Imaging With Dual-Layer Flat-Panel Detector.

In flat-panel detector (FPD) based cone-beam computed tomography (CBCT) imaging, the native receptor array is usually binned into a smaller matrix size. By doing so, the signal readout speed could be increased by 4-9 times at the expense of a spatial resolution loss of 50%-67%. Clearly, such manipulation poses a key bottleneck in generating high spatial and high temporal resolution CBCT images at the same time. In addition, the conventional FPD is also difficult in generating dual-energy CBCT images. In this paper, we propose an innovative super resolution dual-energy CBCT imaging method, named as suRi, based on dual-layer FPD (DL-FPD) to overcome these aforementioned difficulties at once. With suRi, specifically, a 1D or 2D sub-pixel (half pixel in this study) shifted binning is applied instead of the conventionally aligned binning to double the spatial sampling rate during the dual-energy data acquisition. As a result, the suRi approach provides a new strategy to enable high spatial resolution CBCT imaging while at high readout speed. Moreover, a penalized likelihood material decomposition algorithm is developed to directly reconstruct the high resolution bases from these dual-energy CBCT projections containing sub-pixel shifts. Numerical and physical experiments are performed to validate this newly developed suRi method with phantoms and biological specimen. Results demonstrate that suRi can significantly improve the spatial resolution of the CBCT image. We believe this developed suRi method would greatly enhance the imaging performance of the DL-FPD based dual-energy CBCT systems in future.

OSCNet: Orientation-Shared Convolutional Network for CT Metal Artifact Learning.

X-ray computed tomography (CT) has been broadly adopted in clinical applications for disease diagnosis and image-guided interventions. However, metals within patients always cause unfavorable artifacts in the recovered CT images. Albeit attaining promising reconstruction results for this metal artifact reduction (MAR) task, most of the existing deep-learning-based approaches have some limitations. The critical issue is that most of these methods have not fully exploited the important prior knowledge underlying this specific MAR task. Therefore, in this paper, we carefully investigate the inherent characteristics of metal artifacts which present rotationally symmetrical streaking patterns. Then we specifically propose an orientation-shared convolution representation mechanism to adapt such physical prior structures and utilize Fourier-series-expansion-based filter parametrization for modelling artifacts, which can finely separate metal artifacts from body tissues. By adopting the classical proximal gradient algorithm to solve the model and then utilizing the deep unfolding technique, we easily build the corresponding orientation-shared convolutional network, termed as OSCNet. Furthermore, considering that different sizes and types of metals would lead to different artifact patterns (e.g., intensity of the artifacts), to better improve the flexibility of artifact learning and fully exploit the reconstructed results at iterative stages for information propagation, we design a simple-yet-effective sub-network for the dynamic convolution representation of artifacts. By easily integrating the sub-network into the proposed OSCNet framework, we further construct a more flexible network structure, called OSCNet+, which improves the generalization performance. Through extensive experiments conducted on synthetic and clinical datasets, we comprehensively substantiate the effectiveness of our proposed methods. Code will be released at https://github.com/hongwang01/OSCNet.

Temporal and cellular analysis of granuloma development in mycobacterial infected adult zebrafish.

Because granulomas are a hallmark of tuberculosis pathogenesis, the study of the dynamic changes in their cellular composition and morphological character can facilitate our understanding of tuberculosis pathogenicity. Adult zebrafish infected with Mycobacterium marinum form granulomas that are similar to the granulomas in human patients with tuberculosis and therefore have been used to study host-mycobacterium interactions. Most studies of zebrafish granulomas, however, have focused on necrotic granulomas, while a systematic description of the different stages of granuloma formation in the zebrafish model is lacking. Here, we characterized the stages of granulomas in M. marinum-infected zebrafish, including early immune cell infiltration, nonnecrotizing granulomas, and necrotizing granulomas, using corresponding samples from patients with pulmonary tuberculosis as references. We combined hematoxylin and eosin staining and in situ hybridization to identify the different immune cell types and follow their spatial distribution in the different stages of granuloma development. The macrophages in zebrafish granulomas were shown to belong to distinct subtypes: epithelioid macrophages, foamy macrophages, and multinucleated giant cells. By defining the developmental stages of zebrafish granulomas and the spatial distribution of the different immune cells they contain, this work provides a reference for future studies of mycobacterial granulomas and their immune microenvironments.

Complete genome analysis of Bacillus velezensis TS5 and its potential as a probiotic strain in mice.

Introduction: In recent years, a large number of studies have shown that Bacillus velezensis has the potential as an animal feed additive, and its potential probiotic properties have been gradually explored. Methods: In this study, Illumina NovaSeq PE150 and Oxford Nanopore ONT sequencing platforms were used to sequence the genome of Bacillus velezensis TS5, a fiber-degrading strain isolated from Tibetan sheep. To further investigate the potential of B. velezensis TS5 as a probiotic strain, in vivo experiments were conducted using 40 five-week-old male specific pathogen-free C57BL/6J mice. The mice were randomly divided into four groups: high fiber diet control group (H group), high fiber diet probiotics group (HT group), low fiber diet control group (L group), and low fiber diet probiotics group (LT group). The H and HT groups were fed high-fiber diet (30%), while the L and LT groups were fed low-fiber diet (5%). The total bacteria amount in the vegetative forms of B. velezensis TS5 per mouse in the HT and LT groups was 1 × 109 CFU per day, mice in the H and L groups were given the same volume of sterile physiological saline daily by gavage, and the experiment period lasted for 8 weeks. Results: The complete genome sequencing results of B. velezensis TS5 showed that it contained 3,929,788 nucleotides with a GC content of 46.50%. The strain encoded 3,873 genes that partially related to stress resistance, adhesion, and antioxidants, as well as the production of secondary metabolites, digestive enzymes, and other beneficial nutrients. The genes of this bacterium were mainly involved in carbohydrate metabolism, amino acid metabolism, vitamin and cofactor metabolism, biological process, and molecular function, as revealed by KEGG and GO databases. The results of mouse tests showed that B. velezensis TS5 could improve intestinal digestive enzyme activity, liver antioxidant capacity, small intestine morphology, and cecum microbiota structure in mice. Conclusion: These findings confirmed the probiotic effects of B. velezensis TS5 isolated from Tibetan sheep feces and provided the theoretical basis for the clinical application and development of new feed additives.

Pathomorphological characteristics of tuberculous placenta and its clinical implication.

BACKGROUND: The study of pathologic diagnosis of placental TB is rare. The aim of this study is analyzing the pathomorphological characteristics of tuberculosis (TB) placenta during pregnancy and its clinical significance. METHODS: Nineteen cases of placental tissue specimens during pregnancy were collected from June 2015 to February 2022 at Shanghai Public Health Clinical Center, the only inpatient center for pregnant women with TB in Shanghai, China. Hematoxylin-eosin staining, acid-fast staining, and molecular testing were applied to analyze them comprehensively in combination with clinical information. RESULTS: Among the 19 cases, 7 cases caused intrauterine stillbirth, 3 cases received artificial abortion required by the pregnant woman, the other 9 cases received standard delivery and the infants survived, however, 3 of them were low-weight preterm infants, and another 1 case suffered mild intrauterine asphyxia. The 9 surviving infants were followed-up, of which 3 cases got congenital TB. For pathological characteristics of placental tissues under light microscopy, there were 3 cases of epithelioid granuloma formation, 13 cases of acute fetal membranitis, 4 cases of caseous necrosis, 7 cases of inflammatory necrosis, 10 cases of coagulative necrosis, and 6 cases with small focal calcifications. All placental tissues were positive for acid-fast staining and polymerase chain reaction (PCR). Molecular pathological diagnosis showed that 18 cases were positive for Mycobacterium tuberculosis, with 1 case not having received examination. CONCLUSIONS: Combining acid-fast staining and molecular pathological testing is helpful for accurately diagnosing placental TB.

Effect comparisons of different conditioners and microbial agents on the degradation of estrogens during dairy manure composting.

To investigate the degradation efficiency of conditioners and commercial microbial agents on estrogens (E1, 17α-E2, 17ß-E2, E3, EE2, and DES) in the composting process of dairy manure, seven different treatments (RHB-BF, OSP-BF, SD-BF, MR-BF, MR-FS, MR-EM, and MR-CK) under forced ventilation conditions were composted and monitored regularly for 30 days. The results indicated that the removal rates of estrogens in seven treatments ranged from 95.35% to 99.63%, meanwhile the degradation effect of the composting process on 17ß-Estradiol equivalent (EEQ) was evaluated, and the removal rate of ΣEEQ ranged from 96.42% to 99.72%. With the combined addition of rice husk biochar (RHB) or oyster shell powder (OSP) and bio-bacterial fertilizer starter cultures (BF), namely RHB-BF and OSP-BF obviously promoted the rapid degradation of estrogens. 17ß-E2 was completely degraded on the fifth day of composting in OSP-BF. Microbial agents have some promotional effect and enhances the microbial degradation of synthetic estrogen (EE2, DES). According to the results of RDA, pH and EC were the main environmental factors affecting on the composition and succession of estrogen-related degrading bacteria in composting system. As predominant estrogens-degrading genera, Acinetobacter, Bacillus, and Pseudomonas effected obviously on the change of estrogens contents. The research results provide a practical reference for effective composting of dairy manure to enhancing estrogens removal and decreasing ecological risk.

Cucurbituril-Shaped Cd18(triazolate)12 Unit-Based Metal-Organic Framework Exhibiting an C2H2/CO2 Separation Ability.

Herein, a metal-organic framework (MOF), {[(Me2NH2)4][Cd(H2O)6][Cd18(TrZ)12(TPD)15(DMF)6]}n (denoted as JXNU-18, TrZ = triazolate), constructed from the unique cucurbituril-shaped Cd18(TrZ)12 secondary building units bridged by 2,5-thiophenedicarboxylic (TPD2-) ligands, is presented. The formation of the cucurbituril-shaped Cd18(TrZ)12 unit is unprecedented, demonstrating the geometric compatibility of the organic linkers and the coordination configurations of the cadmium atoms. Each Cd18(TrZ)12 unit is connected to eight neighboring Cd18(TrZ)12 units through 30 TPD2- linkers, affording the three-dimensional structure of JXNU-18. More interesting is that JXNU-18 displays an efficient C2H2/CO2 separation ability, as revealed by the gas adsorption experiments and dynamic gas breakthrough experiments, which afford insights into the potential applications of JXNU-18 in gas separation. The tubular pores composed of two Cd18(TrZ)12 units bridged by six 2,5-thiophenedicarboxylic linkers provide the suitable pore space for C2H2 trapping, as unveiled by computational simulations.

Cross-Domain Unpaired Learning for Low-Dose CT Imaging.

Supervised deep-learning techniques with paired training datasets have been widely studied for low-dose computed tomography (LDCT) imaging with excellent performance. However, the paired training datasets are usually difficult to obtain in clinical routine, which restricts the wide adoption of supervised deep-learning techniques in clinical practices. To address this issue, a general idea is to construct a pseudo paired training dataset based on the widely available unpaired data, after which, supervised deep-learning techniques can be adopted for improving the LDCT imaging performance by training on the pseudo paired training dataset. However, due to the complexity of noise properties in CT imaging, the LDCT data are difficult to generate in order to construct the pseudo paired training dataset. In this article, we propose a simple yet effective cross-domain unpaired learning framework for pseudo LDCT data generation and LDCT image reconstruction, which is denoted as CrossDuL. Specifically, a dedicated pseudo LDCT sinogram generative module is constructed based on a data-dependent noise model in the sinogram domain, and then instead of in the sinogram domain, a pseudo paired dataset is constructed in the image domain to train an LDCT image restoration module. To validate the effectiveness of the proposed framework, clinical datasets are adopted. Experimental results demonstrate that the CrossDuL framework can obtain promising LDCT imaging performance in both quantitative and qualitative measurements.

The panda-derived Lactiplantibacillus plantarum BSG201683 improves LPS-induced intestinal inflammation and epithelial barrier disruption in vitro.

Captive pandas are suffering from intestinal infection due to intestinal microbiota characterized by a high abundance of Enterobacteriaceae induced by long-term captivity. Probiotic supplements showed improvement in intestinal barrier function and inflammation. However, the effects of panda-derived probiotics on the intestinal epithelium and inflammation have not been elucidated. In the present study, lipopolysaccharide (LPS) impaired Caco-2 and RAW264.7 inflammatory models were applied to assess the protection of Lactiplantibacillus plantarum BSG201683 (L. plantarum G83) on barrier disruption and inflammation. The results showed that treatment with L. plantarum G83 significantly decreased the paracellular permeability to fluorescein isothiocyanate conjugated dextran (MW 4000, FITC-D4) after LPS induction. Meanwhile, L. plantarum G83 alleviated the reduction in tight junction (TJ) proteins and downregulated proinflammatory cytokines caused by LPS in Caco-2 cells. L. plantarum G83 also significantly decreased the expression and secretion of pro-inflammatory cytokines in LPS-induced RAW264.7 cells. In addition, the IL-10 increased in both Caco-2 and RAW264.7 cells after L. plantarum G83 treatment. The phagocytosis activity of RAW264.7 cells was significantly increased after L. plantarum G83 treatment. Toll-like receptor 4/ nuclear factor kappa-B (TLR4/NF-κB) signaling pathways were significantly down-regulated after L. plantarum G83 intervention, and the phosphorylation of NF-κB/p65 was consistent with this result. Our findings suggest that L. plantarum G83 improves intestinal inflammation and epithelial barrier disruption in vitro.

Boosting the C2H2/CO2 Separation Performance of Metal-Organic Frameworks through Fluorine Substitution.

A pair of metal-organic frameworks (MOFs) of JXNU-15 (formulated as [Co6(µ3-OH)6(BTB)2(BPY)3]n, BTB3- = benzene-1,3,5-tribenzoate and BPY = 4,4'-bipyridine) and its fluorinated JXNU-15(F) ([Co6(µ3-OH)6(SFBTB)2(BPY)3]n) based on the fluorous 1,3,5-tri(3,5-bifluoro-4-carboxyphenyl)benzene (SFBTB3-) ligands were presented. The detailed comparisons of the acetylene/carbon dioxide (C2H2/CO2) separation abilities between the isostructural JXNU-15(F) and JXNU-15 were presented. In comparison with the parent JXNU-15, the higher C2H2 uptake, larger adsorption selectivity of the C2H2/CO2 (50/50) mixture, and enhanced C2H2/CO2 separation performance endow JXNU-15(F) with highly efficient C2H2/CO2 separation performance, which is demonstrated by singe-component gas adsorptions and dynamic gas mixture breakthrough experiments. The fluorine substituents exert the crucial effects on the enhanced C2H2/CO2 separation ability of JXNU-15(F) and play the dominant role in the C2H2-framework interactions, as uncovered by computational simulations. This work illustrates a powerful fluorine substitution strategy for boosting C2H2/CO2 separation ability for MOFs.

Fulvic acid enhanced peroxymonosulfate activation over Co-Fe binary metals for efficient degradation of emerging bisphenols.

Bisphenol F (BPF) and bisphenol S (BPS) are emerging bisphenols, which have become the main substitutes for bisphenol A (BPA) in industrial production and are also considered as new environmental pollution challenges. Thus, the necessity for an effective approach to remove BPF and BPS is essential. In this study, fulvic acid (FA) was used to modify Co-Fe binary metals (CFO) for peroxymonosulfate (PMS) activation. The characterization results demonstrated that CFO changed significantly in morphology after compounding with FA, with smaller particle size and 5.6 times larger specific surface area, greatly increasing the active sites of catalyst; Moreover, humic acid-like compounds increased the surface functional groups of CFO, especially phenolic hydroxyl, which could effectively prolong the PMS activation. The concentration of all reactive species, such as SO4•-, •OH, O2•-, and 1O2 increased in FA@CFO/PMS system. As a result, the degradation efficiency of CFO for both BPF and BPS was significantly improved after compounding FA, which also had a wide range of pH applications. The degradation pathways of both BPF and BPS were proposed based on liquid chromatography-mass spectrometry (LC-MS) analysis and the density functional theory (DFT) calculations. Our findings are expected to provide new strategies and methods for remediation of environmental pollution caused by emerging bisphenols.

Clinical Features of Non-Alcoholic Fatty Liver Disease in the Non-Lean Population.

INTRODUCTION: The prevalence of non-alcoholic fatty liver disease (NAFLD) in non-lean patients is significantly increased, and obesity significantly increases the risk of cirrhosis and HCC in NAFLD patients. However, whether there is a difference in clinical manifestations of NAFLD between overweight and obesity remains unclear. The objective of this study was to assess the clinical and histological features of NAFLD among a non-lean population. METHODS: Current study enrolled consecutive non-lean (body mass index [BMI] >23 kg/m2) patients with NAFLD and available liver biopsy results. Patients were stratified by BMI into two groups for the comparison of their clinical and histological variables, which included the overweight (BMI 23∼<28 kg/m2) and the obese (BMI ≥28 kg/m2). Risk factors for moderate to severe fibrosis (stage >1) were also analyzed through the logistic regression model. RESULTS: Among 184 non-lean patients with metabolic-associated fatty liver disease enrolled, 65 and 119 were overweight and obese, respectively. Patients in the obesity group had a significantly lower level of gamma-glutamyl transpeptidase, higher levels of platelet, glucose, prothrombin time, and more common of moderate to severe inflammatory activity when compared to those in the overweight group. However, a significant low frequency of moderate to severe fibrosis was found in the obesity group versus the overweight group (19.33% vs. 40.00%, p = 0.002). Binary logistics regression analysis of fibrosis found that aspartate transaminase (AST), BMI, alanine transaminase (ALT), and cholesterol (CHOL) were independent predictors for moderate to severe fibrosis in non-lean patients with NAFLD. Compared with the traditional fibrosis-4 (AUC = 0.77) and aminotransferase to platelet ratio index (AUC = 0.79) indexes, the combined index based on AST, BMI, ALT, and CHOL was more accurate in predicting moderate to severe fibrosis in non-lean patients with NAFLD (AUC = 0.87). CONCLUSIONS: Clinical and histological features differed between obesity and overweight patients with NAFLD. When compared to the traditional serum markers, the combination index including AST, BMI, ALT, and CHOL provided a better model to predict moderate to severe fibrosis in non-lean patients with NAFLD.

Prolonged fluoride exposure induces spatial-memory deficit and hippocampal dysfunction by inhibiting small heat shock protein 22 in mice.

Millions of residents in areas with high-fluoride drinking water supply ingest excessive levels of fluoride for long periods. This study investigated the mechanisms and impacts of lifelong exposure to naturally occurring moderate-high-fluoride drinking water on spatial-memory function by studying mice in controlled experiments. Spatial-memory deficits and disorders of hippocampal neuronal electrical activity were observed in mice exposed to 25-ppm or 50-ppm-fluoride drinking water for 56 weeks, but not in adult or old mice exposed to 50 ppm fluoride for 12 weeks. Ultrastructural analysis showed severely damaged hippocampal mitochondria, evidenced by reduced mitochondrial membrane potential and ATP content. Mitochondrial biogenesis was impaired in fluoride-exposed mice, manifesting as a significantly reduced mtDNA content, mtDNA-encoded subunits mtND6 and mtCO1, and respiratory complex activities. Fluoride reduced expression of Hsp22, a beneficial mediator of mitochondrial homeostasis, and decreased levels of signaling for the PGC-1α/TFAM pathway-which regulates mitochondrial biogenesis-and the NF-κß/STAT3 pathway-which regulates mitochondrial respiratory chain enzyme activity. Hippocampus-specific Hsp22-overexpression improved fluoride-induced spatial-memory deficits by activating the PGC-1α/TFAM and STAT3 signaling pathways, while Hsp22-silencing aggravated the deficits by inhibiting both pathways. Downregulation of Hsp22 plays a vital role in fluoride-induced spatial-memory deficits by impacting mtDNA-encoding subsets and mitochondrial respiratory chain enzyme activity.