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Abstract The mouse genome has a high degree of homology with the human genome, and its physiological, biochemical, and developmental regulation mechanisms are similar to those of humans; therefore, mice are widely used as experimental animals. However, it is undeniable that interspecies differences between humans and mice can lead to experimental errors. The differences in the immune system have become an important factor limiting current immunological research. The application of immunodeficient mice provides a possible solution to these problems. By transplanting human immune cells or tissues, such as peripheral blood mononuclear cells or hematopoietic stem cells, into immunodeficient mice, a human immune system can be reconstituted in the mouse body, and the engrafted immune cells can elicit human‐specific immune responses. Researchers have been actively exploring the development and differentiation conditions of host recipient animals and grafts in order to achieve better immune reconstitution. Through genetic engineering methods, immunodeficient mice can be further modified to provide a favorable developmental and differentiation microenvironment for the grafts. From initially only being able to reconstruct single T lymphocyte lineages, it is now possible to reconstruct lymphoid and myeloid cells, providing important research tools for immunology‐related studies. In this review, we compare the differences in immune systems of humans and mice, describe the development history of human immune reconstitution from the perspectives of immunodeficient mice and grafts, and discuss the latest advances in enhancing the efficiency of human immune cell reconstitution, aiming to provide important references for immunological related researches.

The nabla fractional-order uncertain difference equation with Caputo-type was analyzed in this article. To begin, the existence and uniqueness theorem of solutions for nabla Caputo uncertain difference equations with almost surely bounded uncertain variables was presented. Furthermore, the uncertainty distributions of the solutions for the proposed equations were obtained by establishing a connection between the solutions of equations and their α-paths based on new comparison theorems. Finally, an application of the uncertain difference equations in a logistic population model involving Allee effect was provided and examples were performed to demonstrate the validity of the theoretical results presented.

Abstract Objective Breast cancer has become the most prevalent malignant tumor in women, and the occurrence of distant metastasis signifies a poor prognosis. Utilizing predictive models to forecast distant metastasis in breast cancer presents a novel approach. This study aims to utilize readily available clinical data and advanced machine learning algorithms to establish an accurate clinical prediction model. The overall objective is to provide effective decision support for clinicians. Methods Data from 239 patients from two centers were analyzed, focusing on clinical blood biomarkers (tumor markers, liver and kidney function, lipid profile, cardiovascular markers). Spearman correlation and the least absolute shrinkage and selection operator regression were employed for feature dimension reduction. A predictive model was built using LightGBM and validated in training, testing, and external validation cohorts. Feature importance correlation analysis was conducted on the clinical model and the comprehensive model, followed by univariate and multivariate regression analysis of these features. Results Through internal and external validation, we constructed a LightGBM model to predict de novo bone metastasis in newly diagnosed breast cancer patients. The area under the receiver operating characteristic curve values of this model in the training, internal validation test, and external validation test1 cohorts were 0.945, 0.892, and 0.908, respectively. Our validation results indicate that the model exhibits high sensitivity, specificity, and accuracy, making it the most accurate model for predicting bone metastasis in breast cancer patients. Carcinoembryonic Antigen, creatine kinase, albumin-globulin ratio, Apolipoprotein B, and Cancer Antigen 153 (CA153) play crucial roles in the model’s predictions. Lipoprotein a, CA153, gamma-glutamyl transferase, α-Hydroxybutyrate dehydrogenase, alkaline phosphatase, and creatine kinase are positively correlated with breast cancer bone metastasis, while white blood cell ratio and total cholesterol are negatively correlated. Conclusion This study successfully utilized clinical blood biomarkers to construct an artificial intelligence model for predicting distant metastasis in breast cancer, demonstrating high accuracy. This suggests potential clinical utility in predicting and identifying distant metastasis in breast cancer. These findings underscore the potential prospect of developing economically efficient and readily accessible predictive tools in clinical oncology.

Abstract Breast cancer metastasis significantly impacts women's health globally. This study aimed to construct predictive models using clinical blood markers and ultrasound data to predict distant metastasis in breast cancer patients, ensuring clinical applicability, cost-effectiveness, relative non-invasiveness, and accessibility of these models. Analysis was conducted on data from 416 patients across two centers, focusing on clinical blood markers (tumor markers, liver and kidney function indicators, blood lipid markers, cardiovascular biomarkers) and maximum lesion diameter from ultrasound. Feature reduction was performed using Spearman correlation and LASSO regression. Two models were built using LightGBM: a clinical model (using clinical blood markers) and a combined model (incorporating clinical blood markers and ultrasound features), validated in training, internal test, and external validation (test1) cohorts. Feature importance analysis was conducted for both models, followed by univariate and multivariate regression analyses of these features. The AUC values of the clinical model in the training, internal test, and external validation (test1) cohorts were 0.950, 0.795, and 0.883, respectively. The combined model showed AUC values of 0.955, 0.835, and 0.918 in the training, internal test, and external validation (test1) cohorts, respectively. Clinical utility curve analysis indicated the combined model's superior net benefit in identifying breast cancer with distant metastasis across all cohorts. This suggests the combined model's superior discriminatory ability and strong generalization performance. Creatine kinase isoenzyme (CK-MB), CEA, CA153, albumin, creatine kinase, and maximum lesion diameter from ultrasound played significant roles in model prediction. CA153, CK-MB, lipoprotein (a), and maximum lesion diameter from ultrasound positively correlated with breast cancer distant metastasis, while indirect bilirubin and magnesium ions showed negative correlations. This study successfully utilized clinical blood markers and ultrasound data to develop AI models for predicting distant metastasis in breast cancer. The combined model, incorporating clinical blood markers and ultrasound features, exhibited higher accuracy, suggesting its potential clinical utility in predicting and identifying breast cancer distant metastasis. These findings highlight the potential prospects of developing cost-effective and accessible predictive tools in clinical oncology.

Polymorphism is universal in energetic materials (EMs), which is originated from the differences of molecular conformers and stacking mode. The polymorphic transition may lead to the change of crystal structure and properties of EMs. In this work, β-Hx2TNBI·2H2O (β-1) was successfully synthesized through solvent-induced conformational transition of Hx2TNBI·2H2O. From the perspective of quantum chemistry and molecular dynamics, the crystal stacking changes caused by different molecular conformations are discussed in detail, which leads to the properties difference of EMs. The results show that β-1 featured wave-like crystal stacking, making it less sensitive to external mechanical stimuli than α-Hx2TNBI·2H2O (α-1) (α-1: IS > 6 J, FS > 288 N; β-1: IS > 20 J, FS > 360 N). α-1 featured better the aromaticity, which gives it higher thermal stability than β-1 (α-1: Td = 186 °C; β-1: Td = 146 °C). Simultaneously, compared with β-1, α-1 has higher crystal density and detonation performance. This work provides a new and effective way to change the safety of EMs.

This paper proposes an optimisation model for an urban rail transit line timetable considering headway coordination between the mainline and the depot during the transition period. The model accounts for the tracking operation scenario of trains inserted from the depot onto the mainline and related train operation constraints. The optimisation objectives are the number of trains inserted, maximum train capacity rate and average headway deviation. Second-generation non-dominated sorting genetic algorithm is designed to solve the model. A case study shows that optimisation achieves a total of 25 trains inserted, a maximum train capacity rate of 0.975 and an average headway deviation of 9.5 s, resulting in significant improvements in train operations and passenger satisfaction. Compared with the current train timetable before optimisation, the average dwell time and the maximum train capacity rate at various stations have been reduced after optimisation. The proposed model and approach can be used for train timetabling optimisation and managing the operations of urban rail transit lines.

Abstract Hepatocellular carcinoma (HCC) is a common primary liver cancer with a high incidence and mortality. Members of the growth-arresting-specific 2 (GAS2) family are involved in various biological processes in human malignancies. To date, there is only a limited amount of information available about the expression profile and clinical importance of GAS2 family in HCC. In this study, we found that GAS2L1 and GAS2L3 were distinctly upregulated in HCC specimens compared to non-tumor specimens. Pan-cancer assays indicated that GAS2L1 and GAS2L3 were highly expressed in most cancers. The Pearson’s correlation revealed that the expressions of GAS2, GAS2L1 and GAS2L2 were negatively associated with methylation levels. Survival assays indicated that GAS2L1 and GAS2L3 were independent prognostic factors for HCC patients. Immune cell infiltration analysis revealed that GAS2, GAS2L1 and GAS2L3 were associated with several immune cells. Finally, we confirmed that GAS2L1 was highly expressed in HCC cells and its knockdown suppressed the proliferation of HCC cells. Taken together, our findings suggested the expression patterns and prognostic values of GAS2 members in HCC, providing insights for further study of the GAS2 family as sensitive diagnostic and prognostic markers for HCC.

Abstract Background Assessing mechanical properties of the respiratory system (Cst) during mechanical ventilation necessitates an end-inspiration flow of zero, which requires an end-inspiratory occlusion maneuver. This lung model study aimed to observe the effect of airflow obstruction on the accuracy of respiratory mechanical properties during pressure-controlled ventilation (PCV) by analyzing dynamic signals. Methods A Hamilton C3 ventilator was attached to a lung simulator that mimics lung mechanics in healthy, acute respiratory distress syndrome (ARDS) and chronic obstructive pulmonary disease (COPD) models. PCV and volume-controlled ventilation (VCV) were applied with tidal volume (VT) values of 5.0, 7.0, and 10.0 ml/kg. Performance characteristics and respiratory mechanics were assessed and were calibrated by virtual extrapolation using expiratory time constant (RCexp). Results During PCV ventilation, drive pressure (DP) was significantly increased in the ARDS model. Peak inspiratory flow (PIF) and peak expiratory flow (PEF) gradually declined with increasing severity of airflow obstruction, while DP, end-inspiration flow (EIF), and inspiratory cycling ratio (EIF/PIF%) increased. Similar estimated values of Crs and airway resistance (Raw) during PCV and VCV ventilation were obtained in healthy adult and mild obstructive models, and the calculated errors did not exceed 5%. An underestimation of Crs and an overestimation of Raw were observed in the severe obstruction model. Conclusion Using the modified dynamic signal analysis approach, respiratory system properties (Crs and Raw) could be accurately estimated in patients with non-severe airflow obstruction in the PCV mode.

Abstract Purpose To investigate whether the mixed approach is a safe and advantageous way to operate laparoscopic right hemicolectomy. Methods A retrospective study was performed on 316 patients who underwent laparoscopic right hemicolectomy in our center. They were assigned to the middle approach group (n = 158) and the mixed approach group (n = 158) according to the surgical approaches. The baseline data like gender、age and body mass index as well as the intraoperative and postoperative conditions including operation time, blood loss, postoperative hospital stay and complications were analyzed. Results There were no significant differences in age, sex, BMI, ASA grade and tumor characteristics between the two groups. Compared with the middle approach group, the mixed approach group was significantly lower in terms of operation time (217.61 min vs 154.31 min, p

Abstract Microbial acetate metabolism is an important part of marine carbon cycling. We present a comprehensive study to constrain microbial acetate metabolism and its regulation in surface seawater of the northwest Pacific Ocean. We found that acetate oxidation (rate constant k: 0.016–0.506 day−1) accounted for 77.6%–99.4% of the total microbial acetate uptake, suggesting that acetate was predominantly used as a microbial energy source. Acetate also served as a significant biomass carbon source, as reflected by the elevated contribution of acetate assimilation to bacterial carbon production. Acetate turnover was largely influenced by water mass mixing and nutrient conditions. Atmospheric deposition was a source of acetate in surface water and this process can also impact the microbial acetate uptake. Microbial utilization of acetate could account for up to 25.9% of the bacterial carbon demand, suggesting the significant role of acetate metabolism in microbial carbon cycling in the open ocean.

Ferroptosis is a type of programmed cell death driven by iron-dependent lipid peroxidation. The TNF-mediated biosynthesis of glutathione has been shown to protect synovial fibroblasts from ferroptosis in the hyperplastic synovium. Ferroptosis induction provides a novel therapeutic approach for rheumatoid arthritis (RA) by reducing the population of synovial fibroblasts. The beginning and maintenance of synovitis in RA are significantly influenced by macrophages, as they generate cytokines that promote inflammation and contribute to the destruction of cartilage and bone. However, the vulnerability of macrophages to ferroptosis in RA remains unclear. In this study, we found that M2 macrophages are more vulnerable to ferroptosis than M1 macrophages in the environment of the arthritis synovium with a high level of iron, leading to an imbalance in the M1/M2 ratio. During ferroptosis, HMGB1 released by M2 macrophages interacts with TLR4 on M1 macrophages, which in turn triggers the activation of STAT3 signaling in M1 macrophages and contributes to the inflammatory response. Knockdown of TLR4 decreased the level of cytokines induced by HMGB1 in M1 macrophages. The ferroptosis inhibitor liproxstatin-1 (Lip-1) started at the presymptomatic stage in collagen-induced arthritis (CIA) model mice, and GPX4 overexpression in M2 macrophages at the onset of collagen antibody-induced arthritis (CAIA) protected M2 macrophages from ferroptotic cell death and significantly prevented the development of joint inflammation and destruction. Thus, our study demonstrated that M2 macrophages are vulnerable to ferroptosis in the microenvironment of the hyperplastic synovium and revealed that the HMGB1/TLR4/STAT3 axis is critical for the ability of ferroptotic M2 macrophages to contribute to the exacerbation of synovial inflammation in RA. Our findings provide novel insight into the progression and treatment of RA.

Background: Hepatic lipoprotein receptor-related protein 1 (LRP-1) plays a central role in peripheral amyloid beta (Aβ) clearance, but its importance in Alzheimer's disease (AD) pathology is understudied. Our previous work showed that intragastric alcohol feeding to C57BL/6 J mice reduced hepatic LRP-1 expression which correlated with significant AD-relevant brain changes. Herein, we examined the role of hepatic LRP-1 in AD pathogenesis in APP/PS1 AD mice using two approaches to modulate hepatic LRP-1, intragastric alcohol feeding to model chronic heavy drinking shown by us to reduce hepatic LRP-1, and hepato-specific LRP-1 silencing. Methods: Eight-month-old male APP/PS1 mice were fed ethanol or control diet intragastrically for 5 weeks (n = 7–11/group). Brain and liver Aβ were assessed using immunoassays. Three important mechanisms of brain amyloidosis were investigated: hepatic LRP-1 (major peripheral Aβ regulator), blood-brain barrier (BBB) function (vascular Aβ regulator), and microglia (major brain Aβ regulator) using immunoassays. Spatial LRP-1 gene expression in the periportal versus pericentral hepatic regions was confirmed using NanoString GeoMx Digital Spatial Profiler. Further, hepatic LRP-1 was silenced by injecting LRP-1 microRNA delivered by the adeno-associated virus 8 (AAV8) and the hepato-specific thyroxine-binding globulin (TBG) promoter to 4-month-old male APP/PS1 mice (n = 6). Control male APP/PS1 mice received control AAV8 (n = 6). Spatial memory and locomotion were assessed 12 weeks after LRP-1 silencing using Y-maze and open-field test, respectively, and brain and liver Aβ were measured. Results: Alcohol feeding reduced plaque-associated microglia in APP/PS1 mice brains and increased aggregated Aβ (p

Multi-functional coupling evaluation of land use is the basis of rural land use coordination and allocation, and it is the key to determine the inherent function combination pattern of urban land use and the dynamic trade-offs between functions, which is of great theoretical and practical significance, but a set of feasible coupling methodology has not been constructed for a long time. In this study, we constructed a functional classification system for rural ecological-production-living space (PLES), systematically integrated the accounting function groups of spatial functional values, optimize the ecological sources (ESs) and form an ecological pattern in line with the rural human settlement environment. The topologized network is extracted by using linkage mapper, and the network priority is evaluated by combining with sDNA plug-in. The results of the study demonstrate that:1) Four types of patches, namely, low coupling, antagonistic, friction, and coordinated coupling, with areas of 497.03, 93.38, 434.91, and 287.61 km2, respectively, were identified, which provided a theoretical basis for the spatial survival of PLES coupling and the spatial selection of rural source sites. 2) The comprehensive evaluation yielded 20 source sites accounting for 11.05 % of the total area. The overall pattern of ESs is “more at the edge and less in the center”. Compared with the selection based on the dPC results and the minimum area threshold, the combination of the coupling surface results is more adaptable to the development needs of the rural areas under the multiple scenarios. Ecological corridors (ECs) are 46 in number, with a total length of 426.13 km, which are evenly distributed in the region. The construction of ECs in combination with rivers is more in line with the habitat characteristics of Jiangnan water towns, and is conducive to the symbiosis of blue-green ecological space. 3) The priority of the ecological corridor network evaluates 7 preferred corridors, based on the structural optimization perspective, which provides an effective path based on low-impact exploitation for the rapid construction of the habitat network in rural areas. 4) A multi-level composite ecological pattern of “one belt, three corridors and three wedges” was integrated. This study constructed a multi-functional coupled refined evaluation system. It explored the ecological pattern in line with the ecological context of the combination of multi-river and hilly habitats in the rural areas of Jiangnan, and had some innovations in technical methods in the links such as prioritization evaluation. Overall, it created a replicable ecological security pattern construction and optimization path in the same type of area.

Abstract Deep convolutional neural networks have made significant strides in the field of medical image segmentation. Although existing convolutional structures enhance performance by leveraging local image information, they often lose the interdependence information between contexts. Therefore, the article utilizes the multi-attention mechanism of the Transformer structure to more comprehensively express relationships between contexts and introduced the Transformer network architecture into the field of medical image segmentation. Most models based on this Transformer structure typically require large datasets for training. However, in the medical field, the limited size of datasets makes training models with the Transformer structure challenging. To address this, the article propose a Weighted Medical Transformer (WMT) model that imposes low requirements on dataset quantity. The weighting mechanism in the WMT model aims to improve the issue of inaccurate relative positional coding when dealing with small medical datasets. Additionally, a coarse-grained and fine-grained segmentation mechanism is introduced, focusing on both the detailed aspects within image blocks and the boundary information connecting blocks. Experimental results on a liver dataset demonstrate that the model achieves F1 and IoU scores of 88.48% and 79.41%, respectively. Results on the MoNuSeg dataset show comparable high F1 and IoU scores of 79.58% and 66.19%, respectively. The model’s accuracy surpasses that of U-Net++ and U-Net models. Compared to other models, this approach is applicable to scenarios with limited datasets, exhibiting high execution efficiency and accuracy.

Abstract Rheumatoid arthritis (RA) is a chronic autoimmune inflammatory disease, and the role of HOXA transcript at the distal tip (HOTTIP) in its pathogenesis remains underexplored. This study investigates the mechanism by which HOTTIP influences apoptosis and the inflammatory response of fibroblast‐like synoviocytes (FLS). An RA mouse model was established, and clinical scores were analyzed. Pathological changes in synovial tissues, bone mineral density (BMD) of the paws, serum tartrate‐resistant acid phosphatase (TRAP) activity, and TNF‐α and IL‐1β levels were assessed. FLS were transfected, and cell proliferation and apoptosis were examined. The RNA‐pull‐down assay determined HOTTIP's interaction with mixed‐lineage leukemia 1 (MLL1), while RNA immunoprecipitation assay measured HOTTIP expression pulled down by MLL1. The levels of MLL1 and toll‐like receptor 4 (TLR4) after MLL1 overexpression based on HOTTIP silencing were determined. Chromatin immunoprecipitation (ChIP) was performed with H3K4me3 as an antibody, followed by the evaluation of TLR4 expression. HOTTIP expression was elevated in RA mouse synovial tissues. Inhibition of HOTTIP led to reduced clinical scores, inflammatory infiltration, synovial hyperplasia, TRAP activity, and TNF‐α and IL‐1β levels, along with increased BMD. In vitro Interference with HOTTIP suppressed RA‐FLS apoptosis and inflammation. HOTTIP upregulated TLR4 expression by recruiting MLL1 to facilitate TLR4 promoter methylation. MLL1 overexpression reversed HOTTIP silencing‐mediated repression of RA‐FLS apoptosis. Activation of H3K4 methylation counteracted HOTTIP knockout, ameliorating the inflammatory response. HOTTIP regulates TLR4 expression by recruiting MLL1, leading to TLR4 promoter methylation, thereby suppressing RA‐FLS proliferation and inducing cell apoptosis and inflammatory response in RA.

AIM: To investigate the types of bacteria in patients with eye infections in Suzhou and their drug resistance to commonly used antibacterial drugs. METHODS: The clinical data of 155 patients were retrospectively collected in this study, and the pathogenic bacteria species and drug resistance of each pathogenic bacteria were analyzed. RESULTS: Among the 155 patients (age from 12 to 87 years old, with an average age of 57, 99 males and 56 females) with eye infections (160 eyes: 74 in the left eye, 76 in the right eye and 5 in both eyes, all of which were exogenous), 71 (45.81%) strains were gram-positive bacteria, 23 (14.84%) strains were gram-negative bacteria and 61 (39.35%) strains were fungi. Gram-positive bacteria were highly resistant to penicillin and erythromycin (78.87% and 46.48% respectively), but least resistant to vancomycin at 0. Gram-negative bacteria were highly resistant to cefoxitin and compound sulfamethoxazole (100% and 95.65% respectively), but least resistant to meropenem at 0. Comparison of the resistance of gram-positive and gram-negative bacteria to some drugs revealed statistically significant differences (P

The MILD combustion is a combustion mode that occurs in a moderate or extremely low oxygen environment, combining the advantages of high combustion heat utilization and very low NOx emissions, and is considered by the international combustion field to be one of the cleaner combustion technologies with great potential for application. Since the relevant research began around 1990, the technology for achieving a MILD combustion of gas fuels has been relatively mature, while the research on the MILD combustion mechanism and implementation conditions of solid fuels such as coal powder and biomass is still relatively lacking. Based on the high momentum oxidizer jet, internal recirculation is achieved, eliminating the need for external high-temperature preheating to establish MILD combustion, greatly expanding the application range of MILD combustion. This paper provides an overview of the basic characteristics of MILD powder fuel combustion from the aspects of particle dispersion, heating, ignition, combustion, and pollutants. Due to the uneven dispersion and reaction of particles, the MILD combustion process of carbon-based solid fuels such as coal powder is more complex than that of gas fuels. High speed jet MILD combustion not only increases ignition delay but also expands ignition and reaction zones, requiring systematic research on the characteristics and mechanisms of each combustion stage. This paper introduces the research achievements in the theoretical design and equipment development of solid fuel MILD combustion. It is recommended to improve the existing coal-fired boiler burner, adjust the process parameters to match the MILD combustion mode, increase the residence time of coke particles to improve combustion efficiency, improve combustion stability, and suppress various pollutant emissions, including fine particulate matter, through high-precision numerical simulation. Based on the overall approach of interconnected energy systems, the coupling research between MILD combustion and various new combustion technologies should be promoted, especially strengthening the research on the co-combustion characteristics of coal powder, biomass and hydrogen, ammonia and other combustible gases, and assisting in energy transformation. Exploring the characteristics of turbulent two-phase flow, turbulent interphase heat transfer, and turbulent chemical coupling in the powder MILD combustion is crucial for deepening the understanding of powder MILD combustion. It involves multivariate analysis and high-precision simulation, which will be the focus and difficulty of future research.

Protein post-translational modifications (PTMs) are at the heart status of cellular signaling events and broadly involved in tumor progression. CD147 is a tumor biomarker with various PTMs, promoting tumor metastasis and metabolism reprogramming. Nevertheless, the relationship between the PTMs of CD147 and apoptosis has not been reported. In our study, we produced a specific anti-CD147-K71 di-methylation (CD147-K71me2) antibody by immunizing with a di-methylated peptide and observed that the level of CD147-K71me2 in non-small cell lung cancer (NSCLC) tissues were lower than that in NSCLC adjacent tissues. SETDB1 was identified as the methyltransferase catalyzing CD147 to generate CD147-K71me2. RNA-seq showed that FOSB was the most significant differentially expressed gene (DEG) between wild-type CD147 (CD147-WT) and K71-mutant CD147 (CD147-K71R) groups. Subsequently, we found that CD147-K71me2 promoted the expression of FOSB by enhancing the phosphorylation of p38, leading to tumor cell apoptosis. In vivo experiments showed that CD147-K71me2 significantly inhibited tumor progression by promoting cell apoptosis. Taken together, our findings indicate the inhibitory role of CD147-K71me2 in tumor progression from the perspective of post-translational modification, which is distinct from the pro-cancer function of CD147 itself, broadening our perspective on tumor-associated antigen CD147.

The main acne-causing bacteria are Propionibacterium acnes, Staphylococcus aureus and Staphylococcus epidermidis. Quorum sensing, an important factor for bacterial survival, is closely associated with expression of bacterial virulence factors and biofilm formation. Although the quorum sensing types of Staphylococcus aureus and Staphylococcus epidermidis have been identified, the quorum sensing types of Propionibacterium acnes are still controversial. Plant-orginated quorum sensing inhibitors with few side effects have a greater potential in the treatment of acne. This article reviews the progress on the mechanisms by which plant-originated quorum sensing inhibitors inhibit pathogenic bacteria of acne.

Introduction: Root dentin formation is an important process in tooth development. We tried to identify potential genes that regulate root dentin formation which could be potentially used for the regeneration and repair of defective or damaged dental roots.Methods: Tissues harvested from the labial and lingual sides of mouse incisors were used for microarray analysis. Gene ontology (GO) analysis of differentially expressed genes indicated the critical role of extracellular matrix in the discrepancy of dentin formation between root and crown, for which hemicentin-1 (Hmcn1) was selected as the target gene. Single-cell RNA sequencing analysis the expression pattern of Hmcn1 at different developmental stages in mouse molars. The spatiotemporal expression of HMCN1 in mouse incisors and molars was detected by immunohistochemical staining. The functions of HMCN1 in human dental pulp cells, including proliferation, differentiation and migration, were examined in vitro by CCK8 assay, BrdU assay, wound-healing assay, ALP staining and alizarin red staining, respectively.Results: It was showed that HMCN1 expression was more pronounced in papilla-pulp on the root than crown side in mouse incisors and molars. In vitro experiments presented inhibited dentinogenesis and migration after HMCN1-knockdown in human dental pulp cells, while there was no significant difference in proliferation between the HMCN1-knockdown group and control group.Discussion: These results indicated that HMCN1 plays an important role in dentinogenesis and migration of pulp cells, contributing to root dentin formation.

The relationship between phase structure and piezoelectric characteristics is demonstrated by producing a sequence of (1 − x)BiFeO3–xBaTiO3 ceramics. A morphotropic phase boundary between the R and PC phases in BF–xBT ceramics is observed within the 0.285 ≤ x ≤ 0.315 range. High piezoelectric properties with a piezoelectric constant (d33) of 174 pC/N and a planar electromechanical coupling coefficient of 0.33 are achieved in BF–xBT ceramics at 0.285 ≤ x ≤ 0.315 due to the coexistence of the R and PC phases. In addition, a slight variation (±15%) in Δd33/d33 over the temperature range of 25–400 °C is noted in BF–xBT ceramics at 0.285 ≤ x ≤ 0.315, indicating that BF–xBT ceramics hold promising potential for use in high-temperature applications.

ObjectiveThis study aims to develop an artificial intelligence model utilizing clinical blood markers, ultrasound data, and breast biopsy pathological information to predict the distant metastasis in breast cancer patients.MethodsData from two medical centers were utilized, Clinical blood markers, ultrasound data, and breast biopsy pathological information were separately extracted and selected. Feature dimensionality reduction was performed using Spearman correlation and LASSO regression. Predictive models were constructed using LR and LightGBM machine learning algorithms and validated on internal and external validation sets. Feature correlation analysis was conducted for both models.ResultsThe LR model achieved AUC values of 0.892, 0.816, and 0.817 for the training, internal validation, and external validation cohorts, respectively. The LightGBM model achieved AUC values of 0.971, 0.861, and 0.890 for the same cohorts, respectively. Clinical decision curve analysis showed a superior net benefit of the LightGBM model over the LR model in predicting distant metastasis in breast cancer. Key features identified included creatine kinase isoenzyme (CK-MB) and alpha-hydroxybutyrate dehydrogenase.ConclusionThis study developed an artificial intelligence model using clinical blood markers, ultrasound data, and pathological information to identify distant metastasis in breast cancer patients. The LightGBM model demonstrated superior predictive accuracy and clinical applicability, suggesting it as a promising tool for early diagnosis of distant metastasis in breast cancer.

In this paper, the finite-time projective synchronization (FTPS) problem of fractionalorder quaternion-valued fuzzy memristor neural networks (FOQVFMNNs) is studied. Through establishing a feedback controller with signed functions and an adaptive controller, sufficient conditions for FTPS for FOQVFMNNs are obtained. Furthermore, the synchronization establishment time is calculated. Finally, the practicability of the conclusions is verified by numerical simulations.

Abstract Arginase, a manganese (Mn)-dependent enzyme, is indispensable for urea generation and ammonia disposal in the liver. The potential role of fructose in Mn and ammonia metabolism is undefined. Here we demonstrate that fructose overconsumption impairs hepatic Mn homeostasis and ammonia disposal in male mice. Fructose overexposure reduces liver Mn content as well as its activity of arginase and Mn-SOD, and impairs the clearance of blood ammonia under liver dysfunction. Mechanistically, fructose activates the Mn exporter Slc30a10 gene transcription in the liver in a ChREBP-dependent manner. Hepatic overexpression of Slc30a10 can mimic the effect of fructose on liver Mn content and ammonia disposal. Hepatocyte-specific deletion of Slc30a10 or ChREBP increases liver Mn contents and arginase activity, and abolishes their responsiveness to fructose. Collectively, our data establish a role of fructose in hepatic Mn and ammonia metabolism through ChREBP/Slc30a10 pathway, and postulate fructose dietary restriction for the prevention and treatment of hyperammonemia.