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Abstract In this study, the removal effect of a new MOF-on MOF adsorbent based on Cu–Co bimetallic organic frameworks on tetracycline antibiotics (TCs) in water system was studied. The adsorbent (Cu-MOF@Co-MOF) were synthesized by solvothermal and self-assembly method at different concentrations of Co2+/Cu2+. The characterization results of SEM, XRD, XPS, FTIR and BET indicated that the MOF-on MOF structure of Cu-MOF@Co-MOF exhibited the best recombination and physicochemical properties when the molar ratio of Co2+: Cu2+ is 5:1. In addition, the Cu-MOF@Co-MOF have a high specific surface area and bimetallic clusters, which can achieve multi-target synergistic adsorption of TCs. Based on above advantages, Cu-MOF@Co-MOF provided a strong affinity and could efficiently adsorb more than 80% of pollutants in just 5 to 15 min using only 10 mg of the adsorbent. The adsorption capacity of tetracycline and doxycycline was 434.78 and 476.19 mg/g, respectively, showing satisfactory adsorption performance. The fitting results of the experimental data were more consistent with the Langmuir isotherm model and pseudo-second-order kinetic model, indicating that the adsorption process of TC and DOX occurred at the homogeneous adsorption site and was mainly controlled by chemisorption. Thermodynamic experiments showed that Cu-MOF@Co-MOF was thermodynamically advantageous for the removal of TCs, and the whole process was spontaneous. The excellent adsorption capacity and rapid adsorption kinetics indicate the prepared MOF-on MOF adsorbent can adsorb TCs economically and quickly, and have satisfactory application prospects for removing TCs in practical environments. The results of the study pave a new way for preparing novel MOFs-based water treatment materials with great potential for efficient removal.

Abstract Aiming at the problem of "crystallization" on the surface of Yuan blue and white ceramics, which causes reflections and loss of image texture, an image processing method is proposed to repair the image texture information. A multi-scale Retinex pre-processing method is proposed to enhance the contrast between the pattern and the background. A color factor is introduced to prevent color distortion. A weighted average function is constructed to enhance image details and improve texture information. The Yuan blue and white pattern can be effectively segmented from the background using a combination of multi-peak thresholding for segmentation and other techniques. The experimental results demonstrate that, in comparison to other algorithms, the multi-scale Retinex and histogram multi-peak threshold coupled segmentation method proposed in this paper exhibits the highest F1-score of 0.03067 and an accuracy of 92.67% in cross-evaluation with other algorithms. This indicates that the overall performance of the algorithm is the best. The proposed method has the potential to inform the protection of cultural relics.

Abstract Background Tall wheatgrass is a perennial salt‐tolerant bunchgrass, which is a promising candidate for establishing a “Coastal Grass Belt” in China, particularly in the coastal saline–alkaline soils surrounding the Bohai Sea. Methods Seven harvesting treatments were performed to explore the optimal harvesting time and frequency for tall wheatgrass in coastal area. The dry matter yield (DMY) and forage nutritional values were investigated for each cut. The correlation between harvesting time and frequency thereof among the investigated traits was also determined. Results The results showed that the two‐cut on June 18 and October 29 produced the highest DMY. Another two‐cut on May 26 and October 29 produced a relatively high crude protein (CP) yield. The DMY, contents of neutral detergent fiber (NDF), acid detergent fiber (ADF), and crude cellulose (CC) as well as CP yield were positively correlated to plant height, while the CP content and the relative feed value (RFV) were negatively correlated to plant height. The accumulating growing degree days, accumulated precipitation, and sunshine duration were positively correlated with plant height, DMY, contents of NDF, ADF, and CC as well as CP yield, but negatively correlated with CP content and RFV for the first cut. Conclusions The two‐cut treatment at the end of May and October may be suitable for tall wheatgrass in the “Coastal Grass Belt” targeted area.

Abstract Background Cancer stem cells (CSCs) are a specific subpopulation of cancer cells with the ability of self‐renewal, infinite proliferation, multidifferentiation and tumorigenicity, and play critical roles in cancer progression and treatment resistance. CSCs are tightly regulated by the tumor microenvironment, such as hypoxia; however, how hypoxia regulates CSCs in non‐small cell lung cancer (NSCLC) remains unclear. Methods The proportion of ALDHhi cells was examined using the Aldefluor assay. Tankyrase inhibitor XAV939 and siRNA were used to inhibit β‐catenin while pcDNA3‐β‐catenin (S33Y) plasmid enhanced the expression of β‐catenin. Western blot was administered for protein detection. The mRNA expression was measured by quantitative real‐time PCR. Results We found that hypoxia led to an increase in the proportion of ALDHhi cells in lung squamous carcinoma (LUSC) H520 cells, while causing a decrease in the ALDHhi cell proportion in lung adenocarcinoma (LUAD) A549 cells. Similarly, β‐catenin expression was upregulated in H520 cells but downregulated in A549 cells upon exposure to hypoxia. Mechanically, the proportion of ALDHhi cells in both cell lines was decreased by β‐catenin inhibitor or siRNA knockdown, whereas increased after β‐catenin overexpression. Furthermore, hypoxia treatment suppressed E‐cadherin expression in H520 cells and enhanced N‐cadherin and β‐catenin expression, while this effect was completely opposite in A549 cells. Conclusion The hypoxia‐EMT‐β‐catenin axis functions as an important regulator for the proportion of CSCs in NSCLC and could potentially be explored as therapeutic targets in the future.

Abstract Backgrounds Microfibril-associated protein 2 (MFAP2) is a protein presenting in the extracellular matrix that governs the activity of microfibrils through its interaction with fibrillin. While the involvement of MFAP2 in metabolic disorders has been documented, its expression and prognostic significance in triple-negative breast cancer (TNBC) remain unexplored. Methods We acquired datasets pertaining to breast cancer (BC) from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases. Next, a Venn diagram was used to identify the differentially expressed genes (DEGs). The DEGs were used to perform Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), protein–protein interaction (PPI), immune and survival analysis. The expressions of MFAP2, PD-1 and PD-L1 were examined by immunohistochemistry and western blot and their relationship with clinical pathological parameters were analyzed by clinical specimen samples from patients with TNBC. Tumor Immune Estimation Resource (TIMER, https://cistrome.shinyapps.io/timer/ ) was adopted to calculate the immune infiltration level of TNBC. The link between gene expression and tumor mutational burden (TMB) was described using Spearman’s correlation analysis. Results We identified 66 differentially expressed genes (DEGs) that were up-regulated. Among these DEGs, MFAP2 was found to be overexpressed in TNBC and was associated with a lower probability of survival. This finding was confirmed through the use of immunohistochemistry and western blot techniques. Additionally, MFAP2 was found to be related to various pathological parameters in TNBC patients. Mechanistically, gene set enrichment analysis (GSEA) revealed that MFAP2 primarily influenced cellular biological behavior in terms of epithelial mesenchymal transition, glycolysis, and apical junction. Notably, MFAP2 expression was positively correlated with the abundance of macrophages, while a negative correlation was observed with the abundance of B cells, CD4 + T cells, CD8 + T cells, neutrophils and dendritic cells through immune analysis. Furthermore, it was observed that MFAP2 displayed a negative correlation not only with tumor mutational burden (TMB), a recognized biomarker for PD-1/PD-L1 immunotherapy, but also with PD-L1 in samples of TNBC. Conclusion MFAP2 may be an important prognostic biomarker for TNBC, as well as a viable target for immunotherapy in this disease.

We de novo assembled and analysed the chloroplast genomes of Capsella bursa-pastoris, Descurainia sophia, and Lepidium apetalum of the plant family Brassicaceae. These chloroplast genomes exhibited a quadripartite structure characterised by inverted repeat regions (26,268–26,460 bp) separating large single copy (83,771–83,870 bp) and small single copy (17,570–18,014 bp) regions. Genetic analysis revealed 112 unique genes, comprising 78 protein-coding genes, 30 transfer RNA (tRNA) genes, and 4 ribosomal RNA (rRNA) genes. Notably, phylogenetic analysis of 21 species revealed close relationships between C. bursa-pastoris and C. orientalis, D. sophia and D. erodiifolia, and L. apetalum and L. cordatum, with 100% bootstrap support. In conclusion, the analysis of these genomes will contribute to the growing knowledge of chloroplast genomes in the Brassicaceae family and of the specific genera Capsella, Descurainia, and Lepidium.

Three-cycle minimum surface structures show excellent lightweight and energy absorption capabilities, and additive manufacturing techniques provide a unique degree of freedom for lattice structure design. Therefore, in this study, three-period minimum surface (TPMS) structures of Schwarz-P boron steel alloys were prepared by Selective Laser Melting (SLM) technique. By investigating the effect of lattice structure design, we have analyzed the deformation behavior, mechanical properties and energy absorption capacity under compression. In addition, tensile properties and fracture morphology were analyzed to gain insight into the performance characteristics of these structures, and the effect of structural design on hardness was explored. The results of the study show that the structural design has a small effect on the microstructure and hardness. Under compressive loading, the P (0.5 1) structure exhibits a uniform deformation behavior, while the other four structures show a crushing deformation pattern. In particular, the P (0.5 1) structure exhibits excellent compressive performance, with an energy absorption work per unit mass as high as 296.2 J/mm^3, and its stiffness reaches 1627 MPa. Meanwhile, the P (0.35 1) structure exhibits excellent tensile performance with the tensile strength of 687 MPa. The morphological analysis of the tensile fracture reveals that the fracture mainly occurs in the strut nodes, and it mainly exhibits ductile tensile fracture.

Highlights MXene/TiO2 hybrids are prepared by a simple calcination treatment, and their electromagnetic response is customized by in situ atomic reconstruction engineering. Based on the excellent electromagnetic response of MXene/TiO2 hybrids, a series of electromagnetic devices are constructed. Multi-spectrum stealth is realized covering visible-light, infrared radiation and GHz.

Abstract Atomically precise defect engineering is essential to manipulate the properties of emerging topological quantum materials for practical quantum applications. However, this remains challenging due to the obstacles in modifying the typically complex crystal lattice with atomic precision. Here, we report the atomically precise engineering of the vacancy-localized spin–orbit polarons in a kagome magnetic Weyl semimetal Co3Sn2S2, using scanning tunneling microscope. We achieve the step-by-step repair of the selected vacancies, leading to the formation of artificial sulfur vacancies with elaborate geometry. We find that that the bound states localized around these vacancies undergo a symmetry dependent energy shift towards Fermi level with increasing vacancy size. As the vacancy size increases, the localized magnetic moments of spin–orbit polarons become tunable and eventually become itinerantly negative due to spin–orbit coupling in the kagome flat band. These findings provide a platform for engineering atomic quantum states in topological quantum materials at the atomic scale.

Liver fibrosis, characterized by scar tissue formation, can ultimately result in liver failure. It's a major cause of morbidity and mortality globally, often associated with chronic liver diseases like hepatitis or alcoholic and non-alcoholic fatty liver diseases. However, current treatment options are limited, highlighting the urgent need for the development of new therapies. As a reversible regulatory mechanism, epigenetic modification is implicated in many biological processes, including liver fibrosis. Exploring the epigenetic mechanisms involved in liver fibrosis could provide valuable insights into developing new treatments for chronic liver diseases, although the current evidence is still controversial. This review provides a comprehensive summary of the regulatory mechanisms and critical targets of epigenetic modifications, including DNA methylation, histone modification, and RNA modification, in liver fibrotic diseases. The potential cooperation of different epigenetic modifications in promoting fibrogenesis was also highlighted. Finally, available agonists or inhibitors regulating these epigenetic mechanisms and their potential application in preventing liver fibrosis were discussed. In summary, elucidating specific druggable epigenetic targets and developing more selective and specific candidate medicines may represent a promising approach with bright prospects for the treatment of chronic liver diseases.

Abstract Minimal hepatic encephalopathy (MHE) has a substantial impact on the clinical outcomes and quality of life (QOL) of patients with cirrhosis. However, timely diagnosis and intervention are challenging due to sophisticated diagnostic methods. In this study, 673 healthy controls and 905 patients with cirrhosis were screened, and 660 healthy controls and 757 patients with cirrhosis, divided into the test (292 patients) and validation (465 patients) cohort, were analyzed after screening. A diagnostic model of the Stroop test (Stroop‐CN) was constructed by multivariate linear regression based on the results of healthy controls. The prevalence of MHE and the comparison results with psychometric hepatic encephalopathy score through the Stroop‐CN model were stable in the test and validation cohorts. Moreover, the prevalence of MHE remained significantly higher in patients with worse disease conditions marked as high Child–Pugh grades and the Model for End‐stage Liver Disease and Sodium (MELD‐Na) scores in the test and validation cohort. The EuroQol 5‐D questionnaire revealed that patients with MHE had a worse QOL than those without MHE both in the test and validation cohort. In conclusion, an easy and practical Stroop‐CN model for MHE diagnosis based on the EncephalApp is established. It is found that a considerable number of Chinese patients with cirrhosis experience MHE, which significantly impacts their QOL.

IntroductionThis study explores the influence of artificial intelligence (A.I.) applications on the job performance of healthcare providers, based on data from standardised-trained residents in the First People’s Hospital of Yunnan Province in China.MethodsThe ordinary least squares model is employed to examine the relationship between A.I. applications and job performance. To address potential endogeneity and missing variables, we utilise the propensity score matching method and alternative regression models.ResultsThe findings indicate that the job performance of standardised-trained residents positively correlates with A.I. applications. This relationship remains robust after addressing endogenous and missing variables. Further discussion reveals that patients’ support mediates the relationship between A.I. and job performance. Under identical conditions, the job performance of female residents empowered by A.I. is found to be significantly better than that of their male counterparts. Conversely, no heterogeneity is observed regarding the impact of A.I. on the job performance of medical practitioners and clinical medical technicians.DiscussionThis study underscores the positive role of A.I. applications in enhancing the job performance of standardised-trained residents. The results highlight the mediating role of patient support and suggest gender-based differences in the efficacy of A.I. empowerment.

Cochlear implant (CI) is a neural prosthesis that can restore hearing for patients with severe to profound hearing loss. Observed variability in auditory rehabilitation outcomes following cochlear implantation may be due to cerebral reorganization. Electroencephalography (EEG), favored for its CI compatibility and non-invasiveness, has become a staple in clinical objective assessments of cerebral plasticity post-implantation. However, the electrical activity of CI distorts neural responses, and EEG susceptibility to these artifacts presents significant challenges in obtaining reliable neural responses. Despite the use of various artifact removal techniques in previous studies, the automatic identification and reduction of CI artifacts while minimizing information loss or damage remains a pressing issue in objectively assessing advanced auditory functions in CI recipients. To address this problem, we propose an approach that combines machine learning algorithms—specifically, Support Vector Machines (SVM)—along with Independent Component Analysis (ICA) and Ensemble Empirical Mode Decomposition (EEMD) to automatically detect and minimize electrical artifacts in EEG data. The innovation of this research is the automatic detection of CI artifacts using the temporal properties of EEG signals. By applying EEMD and ICA, we can process and remove the identified CI artifacts from the affected EEG channels, yielding a refined signal. Comparative analysis in the temporal, frequency, and spatial domains suggests that the corrected EEG recordings of CI recipients closely align with those of peers with normal hearing, signifying the restoration of reliable neural responses across the entire scalp while eliminating CI artifacts.

Based on delay-Doppler maps (DDMs) in raw counts generated by spaceborne global navigation satellite system reflectometry (GNSS-R) receivers, retrieving ocean surface wind speeds is feasible, so several spaceborne GNSS-R missions have been carried out. However, it is currently troubled by global data latency of several hours or even more due to the bottleneck in the satellite downlink. Consequently, this article, for the first time, presents an algorithm for spaceborne GNSS-R receivers to conduct the DDM calibration in orbit and then to retrieve ocean surface wind speeds in real time, which contributes to not only lightening the burden on downloading a wealth of scientific data but also broadcasting real-time ocean surface wind speeds to users. Since there is a power correlation between direct and reflected signals from the same GNSS satellite with respect to the GNSS-R receiver, this algorithm calibrates direct signal power first, and then it estimates the real-time GNSS transmitter effective isotropic radiated power at the reflected signal according to the normalized antenna pattern of the corresponding GNSS satellite. Afterward, DDMs in raw counts are calibrated. Finally, ocean surface wind speeds are computed using pretrained geophysical model functions. Exploiting the scientific data from the GNOS-II onboard China's FY-3E satellite, this algorithm is validated carefully, and final retrieved ocean surface wind speeds against collocated European Centre for Medium-Range Weather Forecasts wind speeds have an overall root-mean-square error of 1.68 m/s and 1.50 m/s for GPS-R and BDS-R, respectively.

To solve the problems of noise coverage defect and low contrast between the defect and the background of ZrO2 ceramic bearing balls, a surface defect extraction algorithm based on shearlet transform image enhancement for ZrO2 ceramic bearing balls is proposed. According to the shape characteristics of ceramic bearing balls, the surface defect image acquisition platform is built to collect and analyze surface defect images. Gaussian filtering weakens the scatter-particle noise in the image, and the threshold corrects the coefficient generated by the shearlet transform. After shearlet transform, the relatively low-frequency and high-frequency parts appear. The low-frequency part reflects the edge information of defects, and the high-frequency part reflects the edge and texture information of defects. Thus, the integrity of the defect is ensured, and an enhanced surface defect image is obtained. The gray histogram of the enhanced image is observed. The optimal threshold is selected by the histogram threshold segmentation method, and the process of defects being completely extracted from the background is realized. Experimental results showed that the extraction rates of pits, scratches, and cracks in ZrO2 ceramic bearing balls’ surface images are 95.00%, 92.50%, and 92.50%, respectively.

Background: Influenza and COVID-19 patients share similar features and outcomes amongst adults. However, the difference between these diseases is not explored in paediatric age group especially in terms of inflammatory markers, coagulation profile and outcomes. Hence, we did this review to compare the inflammatory, coagulation features and outcomes between influenza and COVID-19 infected children. Methods: Literature search was done in PubMed Central, Scopus, EMBASE, CINAHL, Cochrane library, Google Scholar & ScienceDirect from November 2019 to May 2022. Risk of bias assessment was done through Newcastle Ottawa scale. Meta-analysis was done using random-effects model and the final pooled estimate was reported as pooled odds ratio (OR) or standardized mean difference (SMD) along with 95 % confidence interval (CI) depending on the type of outcome. Results: About 16 studies were included with most studies having higher risk of bias. Influenza paediatric patients had significantly higher erythrocyte sedimentation rate (ESR) (pooled SMD = 0.60; 95%CI: 0.30–0.91; I2 = 0 %), lactate dehydrogenase (LDH) (pooled SMD = 2.01; 95%CI: 0.37–3.66; I2 = 98.4 %) and prothrombin time (PT) (pooled SMD = 2.12; 95%CI: 0.44–3.80; I2 = 98.3 %) when compared to paediatric COVID-19 patients. There was no significant difference in terms of features like CRP, procalcitonin, serum albumin, aPTT, mortality and need for mechanical ventilation. Conclusion: Inflammatory markers like ESR, LDH and PT was significantly higher in influenza patients when compared to COVID-19 in children, while rest of the markers and adverse clinical outcomes were similar between both the groups. Identification of these biomarkers has helped in understanding the distinctness of COVID-19 and influenza virus and develop better management strategies.

ABSTRACTThe importance of teaching the Luria-Delbrück experiment to biology students is increasingly recognized by educators, and improved pedagogical methods for teaching the classic experiment have been proposed and tested in the classroom. However, there are still obstacles that impede the proper teaching of the classic experiment. This note proposes two strategies to further improve the teaching of the classic experiment. The first strategy is to be frank with an inherent limitation of the classic experiment, and instructors should explain from a logical point of view why the classic experiment cannot be used to refute the possibility of directed mutation. The second strategy is to emphasize the pioneering work of Delbrück on developing the mutant distribution that enables researchers to estimate microbial mutation rates using data generated by fluctuation experiments, and instructors should shift their attention to the overlooked essential role of the mutant distribution.

Alpha rhythm slowing is an important electroencephalogram(EEG) feature associated with (AD). This study aims to understand the correlation between alpha band deceleration and molecular changes from the perspective of neural computing. Considering the effect of Aβ amyloid deposition on the inhibitory changes in the thalamic, a thalamic cortical model coupled with Aβ amyloid is established. The results show that Aβ amyloid deposition may induce neurotoxicity in thalamic reticular nucleus neurons, which results in inhibitory changes in the thalamus and slows the alpha rhythm of EEG output from the thalamus. In order to understand the pathogenesis more intuitively, some numerical simulations are provided to illustrate the obtained theories. This research is helpful to understand the pathogenesis of AD, so as to provide theoretical basis for the intervention and control of the disease.

Licorice is extensively utilized in the food industry, and glycyrrhetinic acid is among the most critical active constituents in licorice. This ingredient has garnered increasing attention in recent years. In a previous study, we identified a new series of Glycyrrhetinic acid derivatives containing piperazine skeleton, using virtual screening techniques based on docking. In this study, we conducted further optimization of the lead compound 4c, with a focus on enhancing the hydrophobic interactions in the binding site to improve agonist efficacy for peroxisome proliferator-activated receptor γ (PPARγ). The obtained compounds were characterized using 1H NMR, 13C NMR, HRMS, and HPLC. Furthermore, these compounds were evaluated strong antitumor activity. Some compounds with potent antitumor activities were tested for their PPARγ agonist activity. Especially, compound 4f showed the most potent antitumor activity with half maximal inhibitory concentration (IC50) values of 2.382 μM against the tested tumor cell lines Siha. Docking simulation was performed to position compound 4f into the PPARγ active site to determine the probable binding conformation. Consequently, we discovered a novel PPARγ agonist 4f that demonstrated high in vitro agonist activity.

C17H13F4NO4, monoclinic, P21/n (no. 14), a = 5.470(4) Å, b = 26.141(17) Å, c = 11.689(8) Å, β = 90.01°, V = 1671.3(19) Å3, Z = 4, Rgt${R}_{\mathit{gt}}$ (F) = 0.0612, wRref$w{R}_{\mathit{ref}}$ (F2) = 0.1650, T = 296(2) K.

Studying efficient and accurate soil heavy-metal detection technology is of great significance to establishing a modern system for monitoring soil pollution, early warning and risk assessment, which contributes to the continuous improvement of soil quality and the assurance of food safety. Laser-induced breakdown spectroscopy (LIBS) is considered to be an emerging and effective tool for heavy-metal detection, compared with traditional detection technologies. Limited by the soil matrix effect, the LIBS signal of target elements for soil heavy-metal detection is prone to interference, thereby compromising the accuracy of quantitative detection. Thus, a series of signal-enhancement methods are investigated. This study aims to explore the effect of conductive materials of NaCl and graphite on the quantitative detection of lead (Pb) in soil using LIBS, seeking to find a reliable signal-enhancement method of LIBS for the determination of soil heavy-metal elements. The impact of the addition amount of NaCl and graphite on spectral intensity and parameters, including the signal-to-background ratio (SBR), signal-to-noise ratio (SNR), and relative standard deviation (RSD), were investigated, and the mechanism of signal enhancement by NaCl and graphite based on the analysis of the three-dimensional profile data of ablation craters and plasma parameters (plasmatemperature and electron density) were explored. Univariate and multivariate quantitative analysis models including partial least-squares regression (PLSR), least-squares support vector machine (LS-SVM), and extreme learning machine (ELM) were developed for the quantitative detection of Pb in soil with the optimal amount of NaCl and graphite, and the performance of the models was further compared. The PLSR model with the optimal amount of graphite obtained the best prediction performance, with an Rp that reached 0.994. In addition, among the three spectral lines of Pb, the univariate model of Pb I 405.78 nm showed the best prediction performance, with an Rp of 0.984 and the lowest LOD of 26.142 mg/kg. The overall results indicated that the LIBS signal-enhancement method based on conductive materials combined with appropriate chemometric methods could be a potential tool for the accurate quantitative detection of Pb in soil and could provide a reference for environmental monitoring.

Irritable bowel syndrome (IBS) is a common gastrointestinal disorder with gut microbiota imbalance playing a significant role. There are increasing numbers of research studies exploring treatment options involving probiotics, prebiotics, synbiotics, and fecal microbiota transplantation (FMT), but it is still uncertain which treatment option is superior. The research was conducted on various databases and unpublished trial data (up to February 2023). Randomized controlled trials (RCTs) were screened for adult patients with IBS comparing interventions with placebo. Probiotics, prebiotics, synbiotics, and FMT were assessed for their impact using mean difference and Bayesian network meta-analysis. Out of 6528 articles, 54 were included for probiotics, 7 for prebiotics/synbiotics, and 6 for FMT. Probiotics showed improvement in IBS symptoms, particularly with Bifidobacterium and Lactobacillus strains. Prebiotics and synbiotics did not show significant improvement. Network meta-analysis indicated the favorable effects of probiotics (OR = 0.53, 95% CI, 0.48 to 0.59) and FMT (OR = 0.46, 95% CI, 0.33 to 0.64) on IBS, with no serious adverse events reported. In short, probiotics and FMT are effective for managing IBS, with Bifidobacterium and Lactobacillus being dominant strains. However, the most effective probiotic combination or strain remains unclear, while prebiotics and synbiotics did not show significant improvement.

Objective: The objective of this study was to evaluate the therapeutic efficacy of ursodeoxycholic acid (UDCA) and mesenchymal stem cells (MSCs) in patients with severe COVID-19. Methods: We included severe COVID-19 patients hospitalized at Shulan (Hangzhou) Hospital between December 2022 and June 2023. We used a logistic regression model to compare the use of UDCA and MSCs in the two distinct groups of improved and poor outcomes. It is noteworthy that the deterioration group encompassed instances of both death and abandonment of treatment. The receiver operating characteristic (ROC) curve was plotted to assess the performance of the model. The aim was to assess the therapeutic effect of UDCA and MSCs on the outcome of severe COVID-19 patients. Results: A total of 167 patients with severe COVID-19 were included in this study. The analysis revealed that out of 42 patients (25.1%), 17 patients (10.2%) had taken UDCA, and 17 patients (10.2%) had used MSCs. Following a multivariable logistic regression, the results indicated a negative association between UDCA treatment (OR = 0.38 (0.16–0.91), p = 0.029), MSCs treatment (OR = 0.21 (0.07–0.65), p = 0.007), and the risk of severe COVID-19 mortality. Additionally, age showed a positive association with the risk of mortality (OR = 1.03 (1.01–1.07), p = 0.025). Conclusions: UDCA and MSCs have shown potential in improving the prognosis of severe COVID-19 patients and could be considered as additional treatments for COVID-19 in the future.

Stout beer was selected as the research object to screen angiotensin-converting enzyme (ACE) inhibitory peptides. The peptide sequences of stout beer were identified using ultra-performance liquid chromatography-quadrupole-Orbitrap mass spectrometry with de novo, and 41 peptides were identified with high confidence. Peptide Ranker was used to score the biological activity and six peptides with a score ≥ 0.5 were screened to predict their potential ACE inhibitory (ACEI) activity. The toxicity, hydrophilicity, absorption, and excretion of these peptides were predicted. In addition, molecular docking between the peptides and ACE revealed a significant property of the peptide DLGGFFGFQR. Furthermore, molecular docking conformation and molecular dynamics simulation revealed that DLGGFFGFQR could be tightly bound to ACE through hydrogen bonding and hydrophobic interaction. Lastly, the ACEI activity of DLGGFFGFQR was confirmed using in vitro evaluation and the IC50 value was determined to be 24.45 μM.

Background: Idiopathic pulmonary fibrosis (IPF) is a type of interstitial lung disease characterized by unknown causes and a poor prognosis. Recent research indicates that age-related mechanisms, such as cellular senescence, may play a role in the development of this condition. However, the relationship between cellular senescence and clinical outcomes in IPF remains uncertain. Methods: Data from the GSE70867 database were meticulously analyzed in this study. The research employed differential expression analysis, as well as univariate and multivariate Cox regression analysis, to pinpoint senescence-related genes (SRGs) linked to prognosis and construct a prognostic risk model. The model’s clinical relevance and its connection to potential biological processes were systematically assessed in training and testing datasets. Additionally, the expression location of prognosis-related SRGs was identified through immunohistochemical staining, and the correlation between SRGs and immune cell infiltration was deduced using the GSE28221 dataset. Result: The prognostic risk model was constructed based on five SRGs (cellular communication network factor 1, CYR61, stratifin, SFN, megakaryocyte-associated tyrosine kinase, MATK, C-X-C motif chemokine ligand 1, CXCL1, LIM domain, and actin binding 1, LIMA1). Both Kaplan-Meier (KM) curves (p = 0.005) and time-dependent receiver operating characteristic (ROC) analysis affirmed the predictive accuracy of this model in testing datasets, with respective areas under the ROC curve at 1-, 2-, and 3-years being 0.721, 0.802, and 0.739. Furthermore, qRT-RCR analysis and immunohistochemical staining verify the differential expression of SRGs in IPF samples and controls. Moreover, patients in the high-risk group contained higher infiltration levels of neutrophils, eosinophils, and M1 macrophages in BALF, which appeared to be independent indicators of poor prognosis in IPF patients. Conclusion: Our research reveals the effectiveness of the 5 SRGs model in BALF for risk stratification and prognosis prediction in IPF patients, providing new insights into the immune infiltration of IPF progression.

The pericarp of Herpetospermum pedunculosum (HPP) has traditionally been used for treating jaundice and hepatitis. However, the specific hepatoprotective components and their safety/efficacy profiles remain unclear. This study aimed to characterize the total cucurbitacins (TCs) extracted from HPP and evaluate their hepatoprotective potential. As a reference, Hu-lu-su-pian (HLSP), a known hepatoprotective drug containing cucurbitacins, was used for comparison of chemical composition, effects, and safety. Molecular networking based on UHPLC-MS/MS identified cucurbitacin B, isocucurbitacin B, and cucurbitacin E as the major components in TCs, comprising 70.3%, 26.1%, and 3.6% as determined by RP-HPLC, respectively. TCs treatment significantly reversed CCl4-induced metabolic changes associated with liver damage in a dose-dependent manner, impacting pathways including energy metabolism, oxidative stress and phenylalanine metabolism, and showed superior efficacy to HLSP. Safety evaluation also showed that TCs were safe, with higher LD50 and no observable adverse effect level (NOAEL) values than HLSP. The median lethal dose (LD50) and NOAEL values of TCs were 36.21 and 15 mg/kg body weight (BW), respectively, while the LD50 of HLSP was 14 mg/kg BW. In summary, TCs extracted from HPP demonstrated promising potential as a natural hepatoprotective agent, warranting further investigation into synergistic effects of individual cucurbitacin components.

Summary: Background: With increasingly prevalent coexistence of chronic hepatitis B (CHB) and hepatic steatosis (HS), simple, non-invasive diagnostic methods to accurately assess the severity of hepatic inflammation are needed. We aimed to build a machine learning (ML) based model to detect hepatic inflammation in patients with CHB and concurrent HS. Methods: We conducted a multicenter, retrospective cohort study in China. Treatment-naive CHB patients with biopsy-proven HS between April 2004 and September 2022 were included. The optimal features for model development were selected by SHapley Additive explanations, and an ML algorithm with the best accuracy to diagnose moderate to severe hepatic inflammation (Scheuer's system ≥ G3) was determined and assessed by decision curve analysis (DCA) and calibration curve. This study is registered with ClinicalTrials.gov (NCT05766449). Findings: From a pool of 1,787 treatment-naive patients with CHB and HS across eleven hospitals, 689 patients from nine of these hospitals were chosen for the development of the diagnostic model. The remaining two hospitals contributed to two independent external validation cohorts, comprising 509 patients in validation cohort 1 and 589 in validation cohort 2. Eleven features regarding inflammation, hepatic and metabolic functions were identified. The gradient boosting classifier (GBC) model showed the best performance in predicting moderate to severe hepatic inflammation, with an area under the receiver operating characteristic curve (AUROC) of 0.86 (95% CI 0.83–0.88) in the training cohort, and 0.89 (95% CI 0.86–0.92), 0.76 (95% CI 0.73–0.80) in the first and second external validation cohorts, respectively. A publicly accessible web tool was generated for the model. Interpretation: Using simple parameters, the GBC model predicted hepatic inflammation in CHB patients with concurrent HS. It holds promise for guiding clinical management and improving patient outcomes. Funding: This research was supported by the National Natural Science Foundation of China (No. 82170609, 81970545), Natural Science Foundation of Shandong Province (Major Project) (No. ZR2020KH006), Natural Science Foundation of Jiangsu Province (No.BK20231118), Tianjin Key Medical Discipline (Specialty), Construction Project, TJYXZDXK-059B, Tianjin Health Science and Technology Project key discipline special, TJWJ2022XK034, and Research project of Chinese traditional medicine and Chinese traditional medicine combined with Western medicine of Tianjin municipal health and Family Planning Commission (2021022).

Summary: Background: The protection of fourth dose mRNA vaccination against SARS-CoV-2 is relevant to current global policy decisions regarding ongoing booster roll-out. We aimed to estimate the effect of fourth dose vaccination, prior infection, and duration of PCR positivity in a highly-vaccinated and largely prior-COVID-19 infected cohort of UK healthcare workers. Methods: Participants underwent fortnightly PCR and regular antibody testing for SARS-CoV-2 and completed symptoms questionnaires. A multi-state model was used to estimate vaccine effectiveness (VE) against infection from a fourth dose compared to a waned third dose, with protection from prior infection and duration of PCR positivity jointly estimated. Findings: 1298 infections were detected among 9560 individuals under active follow-up between September 2022 and March 2023. Compared to a waned third dose, fourth dose VE was 13.1% (95% CI 0.9 to 23.8) overall; 24.0% (95% CI 8.5 to 36.8) in the first 2 months post-vaccination, reducing to 10.3% (95% CI −11.4 to 27.8) and 1.7% (95% CI −17.0 to 17.4) at 2–4 and 4–6 months, respectively. Relative to an infection >2 years ago and controlling for vaccination, 63.6% (95% CI 46.9 to 75.0) and 29.1% (95% CI 3.8 to 43.1) greater protection against infection was estimated for an infection within the past 0–6, and 6–12 months, respectively. A fourth dose was associated with greater protection against asymptomatic infection than symptomatic infection, whilst prior infection independently provided more protection against symptomatic infection, particularly if the infection had occurred within the previous 6 months. Duration of PCR positivity was significantly lower for asymptomatic compared to symptomatic infection. Interpretation: Despite rapid waning of protection, vaccine boosters remain an important tool in responding to the dynamic COVID-19 landscape; boosting population immunity in advance of periods of anticipated pressure, such as surging infection rates or emerging variants of concern. Funding: UK Health Security Agency, Medical Research Council, NIHR HPRU Oxford, Bristol, and others.

Phytophthora sojae is a type of pathogenic oomycete that causes Phytophthora root stem rot (PRSR), which can seriously affect the soybean yield and quality. To subvert immunity, P. sojae secretes a large quantity of effectors. However, the molecular mechanisms regulated by most P. sojae effectors, and their host targets remain unexplored. Previous studies have shown that the expression of PsAvh113, an effector secreted by Phytophthora sojae, enhances viral RNA accumulations and symptoms in Nicotiana benthamiana via VIVE assay. In this study, we analyzed RNA-sequencing data based on disease symptoms in N. benthamiana leaves that were either mocked or infiltrated with PVX carrying the empty vector (EV) and PsAvh113. We identified 1769 differentially expressed genes (DEGs) dependent on PsAvh113. Using stricter criteria screening and Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) analysis of DEGs, we found that 38 genes were closely enriched in response to PsAvh113 expression. We selected three genes of N. benthamiana (NbNAC86, NbMyb4, and NbERF114) and found their transcriptional levels significantly upregulated in N. benthamiana infected with PVX carrying PsAvh113. Furthermore, individual silencing of these three genes promoted P. capsici infection, while their overexpression increased resistance to P. capsici in N. benthamiana. Our results show that PsAvh113 interacts with transcription factors NbMyb4 and NbERF114 in vivo. Collectively, these data may help us understand the pathogenic mechanism of effectors and manage PRSR in soybeans.

Lignin is insoluble in water, thereby limiting its use in the synthesis of adhesives. Therefore, in this study, an aminated lignin compound was prepared through a lignin amination reaction to increase the amount of raw lignin material that can be used in the synthesis of adhesives; moreover, structural analysis was conducted. The main result of this was the introduction of amino groups into phenolic hydroxyl groups in the hydrolyzing lignin from the raw lignin materials, thus generating the product of aminated lignin. The resulting particle sizes were about 100 nm, the average molecular weight was 57,627 g/mol, and the water solubility of the aminated lignin was about 0.45 g/100 mL. Therefore, the water solubility of raw lignin was greatly improved. The proposed reaction mechanism of phenolic hydroxyl groups and carboxylic acid groups in lignin is a reaction with ammonia molecules; thus, the successful introduction of amino groups generated the aminated lignin compounds. Hence, this article enriches the scientific theory of lignin reactions and provides a reference for the widespread application of raw lignin materials in the field of adhesives.

Cholangiopathies lack effective medicines and can progress into end-stage liver diseases. Mining natural product transcriptome databases for bioactive ingredients, which can reverse disease-associated transcriptomic phenotypes, holds promise as an effective approach for drug discovery. To identify disease-associated transcriptomic changes, we performed RNA-sequencing on bile duct ligation (BDL)-induced cholestatic liver fibrosis mice, as well as PBC and PSC patients, and found that PANoptosis and activation of type-I interferon (IFN) signaling were observed in BDL mice and patients with PBC and PSC. We then established a transcriptotype-driven screening system based on HERB and ITCM databases. Among 283 natural ingredients screened, apigenin (Api), which is widely distributed in varieties of food and medicinal plants, was screened out by our screen system since it reversed the expression pattern of key genes associated with PANoptosis and type-I IFN responses. In BDL, Abcb4−/−, and DDC-fed mice, Api effectively ameliorated liver injuries, inflammation, and fibrosis. It also protected cholangiocytes from bile acid-stimulated PANoptosis, thus alleviating damage-associated molecular pattern-mediated activation of TBK1-NF-κB in macrophages. Additionally, Api directly inhibited type-I IFN-induced downstream inflammatory responses. Our study demonstrated the pathogenic roles of PANoptosis and type-I IFN signaling in cholestatic liver fibrosis and verified the feasibility of transcriptotype-based drug screening. Furthermore, this study revealed a novel anti-inflammatory mechanism of Api and identified it as a promising candidate for the treatment of cholestatic liver fibrosis.

Ruthenium is required to separate from high-level liquid waste (HLLW) because Ru is a valuable resource and is negatively influential on the vitrification process of HLLW. However, the separation of Ru is very challenging due to its complicated complexation properties. In this study, the adsorption and desorption characteristics of ruthenium on a synthesized SiPyR-N3 (weak-base anion exchange resin with pyridine functional groups) composite were investigated in nitric acid and nitrite–nitric acid systems, respectively, and the adsorption mechanism was explored. The experimental results showed that SiPyR-N3 has a significantly better adsorption effect on Ru in the nitrite–nitric acid system than in the nitric acid system, with an increase in the adsorption capacity of approximately three times. The maximum adsorption capacity of Ru is 45.6 mg/g in the nitrite–nitric acid system. The SiPyR-N3 possesses good adsorption selectivity (SFRu/other metal ions is around 100) in 0.1 M NO2−–0.1 M HNO3 solution. The adsorption processes of Ru in the two different systems are fitted with the pseudo-second-order kinetic model and Langmuir model for uptake kinetics and adsorption isotherms, respectively. The results obtained from the FT-IR, XPS, and UV absorption spectrometry indicate that NO2− was involved in the adsorption process either as a complexing species with the metal ions or as free NO2− from the solution. A 0.1 M HNO3 + 1 M thiourea mixed solution shows effective desorption performance, and the desorption efficiency can reach 92% at 328 K.

The importance of teaching the Luria-Delbrück experiment to biology students is increasingly recognized by educators, and improved pedagogical methods for teaching the classic experiment have been proposed and tested in the classroom. However, there are still obstacles that impede the proper teaching of the classic experiment. This note proposes two strategies to further improve the teaching of the classic experiment. The first strategy is to be frank with an inherent limitation of the classic experiment, and instructors should explain from a logical point of view why the classic experiment cannot be used to refute the possibility of directed mutation. The second strategy is to emphasize the pioneering work of Delbrück on developing the mutant distribution that enables researchers to estimate microbial mutation rates using data generated by fluctuation experiments, and instructors should shift their attention to the overlooked essential role of the mutant distribution.

Second language (L2) speakers often experience difficulties in learning words with L2-specific phonemes due to the unfaithful lexical encoding predicted by the fuzzy lexical representations hypothesis. Currently, there is limited understanding of how allophonic variation in the first language (L1) influences L2 phonological and lexical encoding. We report how the Mandarin Chinese L1 phonemic inventory and allophonic variation subject to phonotactic constraints predict phonological encoding problems for novel L2 English words with the /v/-/w/ contrast. L1 English and L1 Chinese participants speaking two varieties of Mandarin Chinese differing as to the presence of [[voiced labiodental approximant]]-[w] allophonic variation for the /w/ phoneme participated in a vocabulary learning task. The novel L2 words with the /v/-/w/ contrast were systematically less robustly encoded than the control words on the day of training and 24 hours later. The degree of fuzziness in lexical representations was jointly predicted by L1 allophonic variation subject to phonotactic constraints and L2 phonological categorization.

Although academic resilience is of great concern to contemporary educational practitioners, there is no consensus on its measurement. Furthermore, protective factors characterized by East Asian societal contexts remain ambiguous. This systematic review aims to offer an overview of the operational definitions, statistical methodology, and protective factors of academic resilience identified in East Asian countries/economies. With a focus on large-scale assessment (LSA) research, three databases (i.e., Web of Science, CNKI, and AiritiLibary) were searched and returned 31 peer-reviewed studies over the last decade. Results indicated that the definition-driven method was commonly adopted in international LSA studies (e.g., Programme for the International Student Assessment) to measure academic resilience, and the research conducted in national/regional LSA (e.g., China Education Panel Survey) tended to use the process-driven approach. Logistic regression was the most frequently data analysis technique utilized in the definition-driven approach, while structural equation modeling and mediation/moderation analyses accounted for the largest proportion of the process-driven methods. Our study shed light on the methodological issues of academic resilience in LSA. Additionally, it highlights the aspiration of educational researchers to identify Asian-specific protective factors from the social-ecological perspective to propose appropriate interventions fostering academic resilience.

Collaborative problem-solving competence (CPSC) is a core skill in the twenty-first century. As an innovative literacy, CPSC was assessed for the first time in the Program for International Student Assessment (PISA) 2015. Our study aims to identify the effects of student's perceptions toward interpersonal relationships on CPSC in PISA 2015. Multiple linear regression was used to examine these effects across Hong Kong, Macao, Taiwan, and the four affluent communities in China. After controlling student's gender and socio-economic status, the results indicated that: (a) Student's external perceptions such as teacher unfairness and disciplinary climate, and student's internal perceptions such as valuing relationships and valuing teamwork significantly predicted CPSC; (b) Disciplinary climate was the strongest predictor among all the external perceptions analyzed; (c) Regarding the internal perceptions, valuing relationships positively predicted CPSC, while valuing teamwork negatively predicted CPSC. These findings enlighten educational practitioners to help students develop their CPSC from the perspective of interpersonal relationships.

Several international large-scale assessments were conducted at the turn of the new century, and during the past two decades the Programme for International Student Assessment (PISA) completed seven cycles of assessment to facilitate practitioners' policy debates and governance. This study reviews PISA-related articles published in English and Chinese. Three literature databases were searched, with a focus on SSCI, CSSCI, and TSSCI journal publications. The frequency of publication was analyzed according to the author's country affiliation, type of journal, and research categories/themes. Findings indicate that research on student-, school- and system-level indicators with a focus on students' learning processes and outcomes as well as critiques of technical matters on PISA were frequent topics in the literature during the past two decades. Issues of equality and equity examined in the publications have implications for enhancing practitioners' capabilities in terms of policy making and governance.

With the rapid growth of education data in large-scale assessment, machine learning techniques are crucial to the interdisciplinary development of education and information. Although data mining tools are increasingly used to predict overall student performance, resilient students in the digital world remain unstudied. Our study aims to comprehensively identify key features in the classification of academically resilient students (ARS) and non-academically resilient students (NRS) in digital reading. With a sample of 11,496 disadvantaged students from seven high-performing Asian economies, data drawn from the Programme for International Student Assessment (PISA) 2018 were analyzed through Support Vector Machine (SVM). Results indicated that 20 key features were selected from 105 contextual features at the individual, home, and school levels, which demonstrated a high predictive ability of the model. Personal experience, especially the use of metacognitive strategies in digital reading and reading enjoyment were predominant features. Interestingly, information and communication technology (ICT) resources and usage showed mixed effects on resilient students. This study provides significant implications for cultivating resilient students in online learning environments.