CKIP-1-Loaded Cartilage-Affinitive Nanoliposomes Reverse Osteoarthritis by Restoring Chondrocyte Homeostasis.

Osteoarthritis (OA) is a chronic joint disease characterized by cartilage imbalance and disruption of cartilage extracellular matrix secretion. Identifying key genes that regulate cartilage differentiation and developing effective therapeutic strategies to restore their expression is crucial. In a previous study, we observed a significant correlation between the expression of the gene encoding casein kinase-2 interacting protein-1 (CKIP-1) in the cartilage of OA patients and OA severity scores, suggesting its potential involvement in OA development. To test this hypothesis, we synthesized a chondrocyte affinity plasmid, liposomes CKIP-1, to enhance CKIP-1 expression in chondrocytes. Our results demonstrated that injection of CAP-Lipos-CKIP-1 plasmid significantly improved OA joint destruction and restored joint motor function by enhancing cartilage extracellular matrix (ECM) secretion. Histological and cytological analyses confirmed that CKIP-1 maintains altered the phosphorylation of the signal transduction molecule SMAD2/3 of the transforming growth factor-ß (TGF-ß) pathway by promoting the phosphorylation of the 8T, 416S sit. Taken together, this work highlights a novel approach for the precise modulation of chondrocyte phenotype from an inflammatory to a noninflammatory state for the treatment of OA and may be broadly applicable to patients suffering from other arthritic diseases.

A novel subtype based on driver methylation-transcription in lung adenocarcinoma.

AIMS: To identify driver methylation genes and a novel subtype of lung adenocarcinoma (LUAD) by multi-omics and elucidate its molecular features and clinical significance. METHODS: We collected LUAD patients from public databases, and identified driver methylation genes (DMGs) by MethSig and MethylMix algrothms. And novel driver methylation multi-omics subtypes were identified by similarity network fusion (SNF). Furthermore, the prognosis, tumor microenvironment (TME), molecular features and therapy efficiency among subtypes were comprehensively evaluated. RESULTS: 147 overlapped driver methylation were identified and validated. By integrating the mRNA expression and methylation of DMGs using SNF, four distinct patterns, termed as S1-S4, were characterized by differences in prognosis, biological features, and TME. The S2 subtype showed unfavorable prognosis. By comparing the characteristics of the DMGs subtypes with the traditional subtypes, S3 was concentrated in proximal-inflammatory (PI) subtype, and S4 was consisted of terminal respiratory unit (TRU) subtype and PI subtype. By analyzing TME and epithelial mesenchymal transition (EMT) features, increased immune infiltration and higher expression of immune checkpoint genes were found in S3 and S4. While S4 showed higher EMT score and expression of EMT associated genes, indicating S4 may not be as immunosensitive as the S3. Additionally, S3 had lower TIDE and higher IPS score, indicating its increased sensitivity to immunotherapy. CONCLUSION: The driver methylation-related subtypes of LUAD demonstrate prognostic predictive ability that could help inform treatment response and provide complementary information to the existing subtypes.

Extracellular Vesicles Derived from Neutrophils Accelerate Bone Regeneration by Promoting Osteogenic Differentiation of BMSCs.

The reconstruction of bone defects has been associated with severe challenges worldwide. Nowadays, bone marrow mesenchymal stem cell (BMSC)-based cell sheets have rendered this approach a promising way to facilitate osteogenic regeneration in vivo. Extracellular vesicles (EVs) play an essential role in intercellular communication and execution of various biological functions and are often employed as an ideal natural endogenous nanomedicine for restoring the structure and functions of damaged tissues. The perception of polymorphonuclear leukocytes (neutrophils, PMNs) as indiscriminate killer cells is gradually changing, with new evidence suggesting a role for these cells in tissue repair and regeneration, particularly in the context of bone healing. However, the role of EVs derived from PMNs (PMN-EVs) in bone regeneration remains largely unknown, with limited research being conducted on this aspect. In the current study, we investigated the effects of PMN-EVs on BMSCs and the underlying molecular mechanisms as well as the potential application of PMN-EVs in bone regeneration. Toward this end, BMSC-based cell sheets with integrated PMN-EVs (BS@PMN-EVs) were developed for bone defect regeneration. PMN-EVs were found to significantly enhance the proliferation and osteogenic differentiation of BMSCs in vitro. Furthermore, BS@PMN-EVs were found to significantly accelerate bone regeneration in vivo by enhancing the maturation of the newly formed bone in rat calvarial defects; this is likely attributable to the effect of PMN-EVs in promoting the expression of key osteogenic proteins such as SOD2 and GJA1 in BMSCs. In conclusion, our findings demonstrate the crucial role of PMN-EVs in promoting the osteogenic differentiation of BMSCs during bone regeneration. Furthermore, this study proposes a novel strategy for enhancing bone repair and regeneration via the integration of PMN-EVs with BMSC-based cell sheets.

Investigating the Impact of Gut Microbiota on Gout Through Mendelian Randomization.

Background: The relationship between gout and gut microbiota has attracted significant attention in current research. However, due to the diverse range of gut microbiota, the specific causal effect on gout remains unclear. This study utilizes Mendelian randomization (MR) to investigate the causal relationship between gut microbiota and gout, aiming to elucidate the underlying mechanism of microbiome-mediated gout and provide valuable guidance for clinical prevention and treatment. Materials and Methods: The largest genome-wide association study meta-analysis conducted by the MiBioGen Consortium (n=18,340) was utilized to perform a two-sample Mendelian randomization investigation on aggregate statistics of intestinal microbiota. Summary statistics for gout were utilized from the data released by EBI. Various methods, including inverse variance weighted, weighted median, weighted model, MR-Egger, and Simple-mode, were employed to assess the causal relationship between gut microbiota and gout. Reverse Mendelian randomization analysis revealed a causal association between bacteria and gout in forward Mendelian randomization analysis. Cochran's Q statistic was used to quantify instrumental variable heterogeneity. Results: The inverse variance weighted estimation revealed that Rikenellaceae exhibited a slight protective effect on gout, while the presence of Ruminococcaceae UCG_011 is associated with a marginal increase in the risk of gout. According to the reverse Mendelian Randomization results, no significant causal relationship between gout and gut microbiota was observed. No significant heterogeneity of instrumental variables or level pleiotropy was detected. Conclusion: Our MR analysis revealed a potential causal relationship between the development of gout and specific gut microbiota; however, the causal effect was not robust, and further research is warranted to elucidate its underlying mechanism in gout development. Considering the significant association between diet, gut microbiota, and gout, these findings undoubtedly shed light on the mechanisms of microbiota-mediated gout and provide new insights for translational research on managing and standardizing treatment for this condition.

Lipidomic Comparisons of Whole Cream Buttermilk Whey and Cheese Whey Cream Buttermilk of Caprine Milk.

Buttermilk is a potential material for the production of a milk fat globule membrane (MFGM) and can be mainly classified into two types: whole cream buttermilk and cheese whey cream buttermilk (WCB). Due to the high casein micelle content of whole cream buttermilk, the removal of casein micelles to improve the purity of MFGM materials is always required. This study investigated the effects of rennet and acid coagulation on the lipid profile of buttermilk rennet-coagulated whey (BRW) and buttermilk acid-coagulated whey (BAW) and compared them with WCB. BRW has significantly higher phospholipids (PLs) and ganglioside contents than BAW and WCB. The abundance of arachidonic acid (ARA)- and eicosapentaenoic acid (EPA)-structured PLs was higher in WCB, while docosahexaenoic acid (DHA)-structured PLs were higher in BRW, indicating that BRW and WCB intake might have a greater effect on improving cardiovascular conditions and neurodevelopment. WCB and BRW had a higher abundance of plasmanyl PL and plasmalogen PL, respectively. Phosphatidylcholine (PC) (28:1), LPE (20:5), and PC (26:0) are characteristic lipids among BRW, BAW, and WCB, and they can be used to distinguish MFGM-enriched whey from different sources.

The neural mechanism of communication between graduate students and advisers in different adviser-advisee relationships.

Communication is crucial in constructing the relationship between students and advisers, ultimately bridging interpersonal interactions. Only a few studies however explore the communication between postgraduate students and advisers. To fill the gaps in the empirical researches, this study uses functional near-infrared spectroscopy (FNIRS) techniques to explore the neurophysiology differences in brain activation of postgraduates with different adviser-advise relationships during simulated communication with their advisers. Results showed significant differences in the activation of the prefrontal cortex between high-quality and the low-quality students during simulating and when communicating with advisers, specifically in the Broca's areas, the frontal pole, and the orbitofrontal and dorsolateral prefrontal cortices. This further elucidated the complex cognitive process of communication between graduate students and advisers.

Use of MRI Radiomics Models in Evaluating the Low HER2 Expression in Breast Cancer.

OBJECTIVE: To investigate the magnetic resonance imaging (MRI) radiomics models in evaluating the human epidermal growth factor receptor 2(HER2) expression in breast cancer.

Materials and Methods: The MRI data of 161 patients with invasive ductal carcinoma (non-special type) of breast cancer were retrospectively collected, and the MRI radiomics models were established based on the MRI imaging features of the fat suppression T2 weighted image (T2WI) sequence, dynamic contrast-enhanced (DCE)-T1WIsequence and joint sequences. The T-test and the least absolute shrinkage and selection operator (LASSO) algorithm were used for feature dimensionality reduction and screening, respectively, and the random forest (RF) algorithm was used to construct the classification model.

Results: The model established by the LASSO-RF algorithm was used in the ROC curve analysis. In predicting the low expression state of HER2 in breast cancer, the radiomics models of the fat suppression T2WI sequence, DCE-T1WI sequence, and the combination of the two sequences showed better predictive efficiency. In the receiver operating characteristic (ROC) curve analysis for the verification set of low, negative, and positive HER2 expression, the area under the ROC curve (AUC) value was 0.81, 0.72, and 0.62 for the DCE-T1WI sequence model, 0.79, 0.65 and 0.77 for the T2WI sequence model, and 0.84, 0.73 and 0.66 for the joint sequence model, respectively. The joint sequence model had the highest AUC value.

Conclusions: The MRI radiomics models can be used to effectively predict the HER2 expression in breast cancer and provide a non-invasive and early assistant method for clinicians to formulate individualized and accurate treatment plans.

Improving the classification of multiple sclerosis and cerebral small vessel disease with interpretable transfer attention neural network.

As an autoimmune-mediated inflammatory demyelinating disease of the central nervous system, multiple sclerosis (MS) is often confused with cerebral small vessel disease (cSVD), which is a regional pathological change in brain tissue with unknown pathogenesis. This is due to their similar clinical presentations and imaging manifestations. That misdiagnosis can significantly increase the occurrence of adverse events. Delayed or incorrect treatment is one of the most important causes of MS progression. Therefore, the development of a practical diagnostic imaging aid could significantly reduce the risk of misdiagnosis and improve patient prognosis. We propose an interpretable deep learning (DL) model that differentiates MS and cSVD using T2-weighted fluid-attenuated inversion recovery (FLAIR) images. Transfer learning (TL) was utilized to extract features from the ImageNet dataset. This pioneering model marks the first of its kind in neuroimaging, showing great potential in enhancing differential diagnostic capabilities within the field of neurological disorders. Our model extracts the texture features of the images and achieves more robust feature learning through two attention modules. The attention maps provided by the attention modules provide model interpretation to validate model learning and reveal more information to physicians. Finally, the proposed model is trained end-to-end using focal loss to reduce the influence of class imbalance. The model was validated using clinically diagnosed MS (n=112) and cSVD (n=321) patients from the Beijing Tiantan Hospital. The performance of the proposed model was better than that of two commonly used DL approaches, with a mean balanced accuracy of 86.06 % and a mean area under the receiver operating characteristic curve of 98.78 %. Moreover, the generated attention heat maps showed that the proposed model could focus on the lesion signatures in the image. The proposed model provides a practical diagnostic imaging aid for the use of routinely available imaging techniques such as magnetic resonance imaging to classify MS and cSVD by linking DL to human brain disease. We anticipate a substantial improvement in accurately distinguishing between various neurological conditions through this novel model.

Network pharmacology, bioinformatics, and experimental validation to identify the role of Hedyotis diffusa willd against gastric cancer through the activation of the endoplasmic reticulum stress.

Background: Globally, gastric cancer (GC) is recognized as the third leading cause of cancer-related deaths and the fifth most prevalent malignant disease. Multiple studies have indicated that Hedyotis diffusa Willd, in pinyin, called Bai Hua She Cao (BHSSC), a traditional Chinese medicine (TCM) is an herbal remedy for cancer treatment. However, the specific mechanisms underlying its anti-tumor properties and mode of action are still unclear. Methods: To determine the role of BHSSC in GC, candidate target genes were selected from The Encyclopedia of Traditional Chinese Medicine (ETCM) and analyzed using network pharmacology, bioinformatics, and experimental validation. Differentially expressed genes (DEGs) associated with gastric cancer were obtained from RNA sequencing (RNA-seq) data sourced from The Cancer Genome Atlas-Stomach adenocarcinoma (TCGA-STAD). The Reactome Pathway was examined using Analysis Tools, while KEGG pathways were analyzed using KOBAS. Gene Ontology (GO) evaluations were performed using WebGestalt and DAVID. The relationships between proteins were investigated using the STRING database. Furthermore, cell viability, colony formation, and cell migration ability were conducted in gastric cancer cells, BGC-823 and MGC-803. Results: Network pharmacology and bioinformatics analyses revealed a significant association between BHSSC and metabolic pathways. In vitro experiments demonstrated that BHSSC effectively suppressed gastric cancer cell proliferation and colony formation, inhibited cell migration, and activated the endoplasmic reticulum (ER) stress. Furthermore, it was found that enhancement of the expression of IRE1α and BIP is the mechanism by which BHSSC activates ER stress. Conclusions: The findings suggest that BHSSC exerts its effects through modulation of metabolic pathways, leading to the suppression of cell proliferation, inhibition of cell migration, and activation of the endoplasmic reticulum. These results provide valuable insights into the mechanisms underlying the therapeutic effects of BHSSC in GC and support its potential as a novel treatment option.

Electric-field modulated energy transfer in phosphorescent material- and fluorescent material-codoped polymer light-emitting diodes.

The excited-state energy transfer widely exists in mixed-material systems and devices. The modulation of an electric field on the energy transfer in photoluminescence has been demonstrated. However, to date, no studies on the electric-field modulation of the excited-state energy transfer in organic optoelectronic devices have been reported. Herein, we investigate the effect of an electric field on the energy transfer in the poly(N-vinylcarbazole) (PVK) thin films doped with iridium(iii)[bis(4,6-difluorophenyl)pyridinato-N,C2']-tetrakis(1-pyrazolyl)borate (Fir6) and 5,6,11,12-tetraphenylnaphthacene (rubrene) (PVK:Fir6:rubrene) and the corresponding light-emitting diodes. Combined with the Onsager model describing electric-field enhanced exciton dissociation, we find that the electric field increases the rate of Dexter energy transfer from Fir6 to rubrene in the films and the diodes. The voltage-dependent color shift in the PVK:Fir6:rubrene light-emitting diodes can be explained by the electric-field enhanced Dexter energy transfer from Fir6 to rubrene. Our findings are important for the control of energy transfer process in organic optoelectronic devices by an electric field for desirable applications.

Adaptively leverage multiple real-world data sources for treatment effect estimation based on similarity.

The incorporation of real-world data (RWD) into medical product development and evaluation has exhibited consistent growth. However, there is no universally adopted method of how much information to borrow from external data. This paper proposes a study design methodology called Tree-based Monte Carlo (TMC) that dynamically integrates patients from various RWD sources to calculate the treatment effect based on the similarity between clinical trial and RWD. Initially, a propensity score is developed to gauge the resemblance between clinical trial data and each real-world dataset. Utilizing this similarity metric, we construct a hierarchical clustering tree that delineates varying degrees of similarity between each RWD source and the clinical trial data. Ultimately, a Gaussian process methodology is employed across this hierarchical clustering framework to synthesize the projected treatment effects of the external group. Simulation result shows that our clustering tree could successfully identify similarity. Data sources exhibiting greater similarity with clinical trial are accorded higher weights in treatment estimation process, while less congruent sources receive comparatively lower emphasis. Compared with another Bayesian method, meta-analytic predictive prior (MAP), our proposed method's estimator is closer to the true value and has smaller bias.

Comparative transcriptome and metabolome profiles of the leaf and fruits of a Xianjinfeng litchi budding mutant and its mother plant.

Background: Litchi (Litchi chinensis) is an important sub-tropical fruit in the horticulture market in China. Breeding for improved fruit characteristics is needed for satisfying consumer demands. Budding is a sustainable method for its propagation. During our ongoing breeding program, we observed a litchi mutant with flat leaves and sharp fruit peel cracking in comparison to the curled leaves and blunt fruit peel cracking fruits of the mother plant. Methods: To understand the possible molecular pathways involved, we performed a combined metabolome and transcriptome analysis. Results: We identified 1,060 metabolites in litchi leaves and fruits, of which 106 and 101 were differentially accumulated between the leaves and fruits, respectively. The mutant leaves were richer in carbohydrates, nucleotides, and phenolic acids, while the mother plant was rich in most of the amino acids and derivatives, flavonoids, lipids and organic acids and derivatives, and vitamins. Contrastingly, mutant fruits had higher levels of amino acids and derivatives, carbohydrates and derivatives, and organic acids and derivatives. However, the mother plant's fruits contained higher levels of flavonoids, scopoletin, amines, some amino acids and derivatives, benzamidine, carbohydrates and derivatives, and some organic acids and derivatives. The number of differentially expressed genes was consistent with the metabolome profiles. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway-enriched gene expressions showed consistent profiles as of metabolome analysis. Conclusion: These results provide the groundwork for breeding litchi for fruit and leaf traits that are useful for its taste and yield.

Thermal evaporation-driven fabrication of Ru/RuO2 nanoparticles onto nickel foam for efficient overall water splitting.

Developing high-performance bifunctional electrocatalysts towards the hydrogen evolution reaction/oxygen evolution reaction (HER/OER) holds great significance for efficient water splitting. This work presents a two-stage metal-organic thermal evaporation strategy for the fabrication of Ru-based catalysts (Ru/NF) through growing ruthenium (Ru)/ruthenium dioxide (RuO2) nanoparticles (NPs) on nickel foam (NF). The optimal Ru/NF shows remarkable performance in both the HER (26.1 mV) and the OER (235.4 mV) at 10 mA cm-2 in an alkaline medium. The superior OER performance can be attributed to the synergistic interaction between Ru and RuO2, facilitating fast alkaline water splitting. Density functional theory studies reveal that the resulting Ru/RuO2 with the (110) crystal surface reinforces the adsorption of oxygen on RuO2, while metallic Ru improves water dissociation in alkaline electrolytes. Besides, Ru/NF requires only 1.50 V at 10 mA cm-2 for overall water splitting, surpassing 20 wt% Pt/C/NF||RuO2/NF. This work demonstrates the promising potential of a thermal evaporation approach for designing stable Ru-based nanomaterials loaded onto conductive substrates for high performance overall water splitting.

Prediction of cytochrome P450-mediated bioactivation using machine learning models and in vitro validation.

Cytochrome P450 (P450)-mediated bioactivation, which can lead to the hepatotoxicity through the formation of reactive metabolites (RMs), has been regarded as the major problem of drug failures. Herein, we purposed to establish machine learning models to predict the bioactivation of P450. On the basis of the literature-derived bioactivation dataset, models for Benzene ring, Nitrogen heterocycle and Sulfur heterocycle were developed with machine learning methods, i.e., Random Forest, Random Subspace, SVM and Naïve Bayes. The models were assessed by metrics like "Precision", "Recall", "F-Measure", "AUC" (Area Under the Curve), etc. Random Forest algorithms illustrated the best predictability, with nice AUC values of 0.949, 0.973 and 0.958 for the test sets of Benzene ring, Nitrogen heterocycle and Sulfur heterocycle models, respectively. 2D descriptors like topological indices, 2D autocorrelations and Burden eigenvalues, etc. contributed most to the models. Furthermore, the models were applied to predict the occurrence of bioactivation of an external verification set. Drugs like selpercatinib, glafenine, encorafenib, etc. were predicted to undergo bioactivation into toxic RMs. In vitro, IC50 shift experiment was performed to assess the potential of bioactivation to validate the prediction. Encorafenib and tirbanibulin were observed of bioactivation potential with shifts of 3-6 folds or so. Overall, this study provided a reliable and robust strategy to predict the P450-mediated bioactivation, which will be helpful to the assessment of adverse drug reactions (ADRs) in clinic and the design of new candidates with lower toxicities.

Molybdenum, tungsten doped cobalt phosphides as efficient catalysts for coproduction of hydrogen and formate by glycerol electrolysis.

H2 and formate are important energy carriers in fuel-cells and feedstocks in chemical industry. The hydrogen evolution reaction (HER) coupling with electro-oxidative cleavage of thermodynamically favorable polyols is a promising way to coproduce H2 and formate via electrochemical means, highly active catalysts for HER and electrooxidative cleavage of polycols are the key to achieve such a goal. Herein, molybdenum (Mo), tungsten (W) doped cobalt phosphides (Co2P) deposited onto nickel foam (NF) substrate, denoted as Mo-Co2P/NF and W-Co2P/NF, respectively, were investigated as catalytic electrodes for HER and electrochemical glycerol oxidation reaction (GOR) to yield H2 and formate. The W-Co2P/NF electrode exhibited low overpotential (η) of 113 mV to attain a current density (J) of -100 mA cm-2 for HER, while the Mo-Co2P/NF electrode demonstrated high GOR efficiency for selective production of formate. In situ Raman and infrared spectroscopic characterizations revealed that the evolved CoO2 from Co2P is the genuine catalytic sites for GOR. The asymmetric electrolyzer based on W-Co2P/NF cathode and Mo-Co2P/NF anode delivered a J = 100 mA cm-2 at 1.8 V voltage for glycerol electrolysis, which led to 18.2 % reduced electricity consumption relative to water electrolysis. This work highlights the potential of heteroelement doped phosphide in catalytic performances for HER and GOR, and opens up new avenue to coproduce more widespread commodity chemicals via gentle and sustainable electrocatalytic means.

A bidirectional interpretable compound-protein interaction prediction framework based on cross attention.

The identification of compound-protein interactions (CPIs) plays a vital role in drug discovery. However, the huge cost and labor-intensive nature in vitro and vivo experiments make it urgent for researchers to develop novel CPI prediction methods. Despite emerging deep learning methods have achieved promising performance in CPI prediction, they also face ongoing challenges: (i) providing bidirectional interpretability from both the chemical and biological perspective for the prediction results; (ii) comprehensively evaluating model generalization performance; (iii) demonstrating the practical applicability of these models. To overcome the challenges posed by current deep learning methods, we propose a cross multi-head attention oriented bidirectional interpretable CPI prediction model (CmhAttCPI). First, CmhAttCPI takes molecular graphs and protein sequences as inputs, utilizing the GCW module to learn atom features and the CNN module to learn residue features, respectively. Second, the model applies cross multi-head attention module to compute attention weights for atoms and residues. Finally, CmhAttCPI employs a fully connected neural network to predict scores for CPIs. We evaluated the performance of CmhAttCPI on balanced datasets and imbalanced datasets. The results consistently show that CmhAttCPI outperforms multiple state-of-the-art methods. We constructed three scenarios based on compound and protein clustering and comprehensively evaluated the model generalization ability within these scenarios. The results demonstrate that the generalization ability of CmhAttCPI surpasses that of other models. Besides, the visualizations of attention weights reveal that CmhAttCPI provides chemical and biological interpretation for CPI prediction. Moreover, case studies confirm the practical applicability of CmhAttCPI in discovering anticancer candidates.

The Construction of Three-Layered Biomimetic Arterial Graft Balances Biomechanics and Biocompatibility for Dynamic Biological Reconstruction.

The process of reconstructing an arterial graft is a complex and dynamic process that is subject to the influence of various mechanical factors, including tissue regeneration and blood pressure. The attainment of favorable remodeling outcomes is contingent upon the biocompatibility and biomechanical properties of the arterial graft. A promising strategy involves the emulation of the three-layer structure of the native artery, wherein the inner layer is composed of polycaprolactone (PCL) fibers aligned with blood flow, exhibiting excellent biocompatibility that fosters endothelial cell growth and effectively prevents platelet adhesion. The middle layer, consisting of PCL and polyurethane (PU), offers mechanical support and stability by forming a contractile smooth muscle ring and antiexpansion PU network. The outer layer, composed of PCL fibers with an irregular arrangement, promotes the growth of nerves and pericytes for long-term vascular function. Prioritizing the reconstruction of the inner and outer layers establishes a stable environment for intermediate smooth muscle growth. Our three-layer arterial graft is designed to provide the blood vessel with mechanical support and stability through nondegradable PU, while the incorporation of degradable PCL generates potential spaces for tissue ingrowth, thereby transforming our graft into a living implant.

EUS-guided bridging technique for treatment of biliary-enteric anastomotic stenosis after pancreaticoduodenectomy.

Biliary-enteric anastomotic stenosis is one of the main long-term complications after pancreaticoduodenectomy, with an incidence of 2%-8%. Although the relevant reports and studies are relatively few, the consequences such as biliary obstruction and refractory cholangitis seriously affect the quality of life of patients. In this case, the patient is not willing to receive conventional surgery again. This paper provides a bridge technique of EUS-guided Biliary Drainage (EUS-BD) to treat biliary-enteric anastomotic stenosis and solve the problem of obstructive jaundice in the patient.

NOTCH3 inhibits transcription factor ZEB1 expression and metastasis of breast cancer cells via transcriptionally upregulating miR-223.

Background: NOTCH receptor 3 (NOTCH3) and zinc finger E-box binding protein 1 (ZEB1) play important roles in breast cancer respectively. NOTCH3 maintains the luminal phenotype and inhibits epithelial-mesenchymal transition (EMT) in breast cancer, while ZEB1 and NOTCH3 have the opposite effects. Methods: Public databases were used to predict the expression of NOTCH3 and ZEB1 in breast cancer cell lines. The regulatory effect of NOTCH3 on ZEB1 expression was verified by western blot and RT-PCR. MiRNAs regulating ZEB1 expression were identified by using multiple databases and confirmed by reporter gene experiments. Cellular function experiments were conducted to evaluate the role of NOTCH3/miR-223/ZEB1 in the proliferation and invasion of triple-negative breast cancer (TNBC). Results: NOTCH3 and ZEB1 have opposite expression pattern in MCF-7 cells that over-express LncATB or were incubated in TGF-ß to induce EMT. Western blotting and RT-PCR showed that NOTCH3 could regulate expression of ZEB1. MiR-223 inhibited the proliferation and invasion of breast cancer cells via down-regulating the expression of ZEB1. NOTCH3 inhibited the proliferation and invasion of breast cancer cells via up-regulating the expression of miR-223. Clinically, high expression of NOTCH3, miR-223 or low expression of ZEB1 were related to good prognosis of breast cancer patients. Conclusion: The current study reports a novel NOTCH3/miR-223/ZEB1 axis, which can inhibit the proliferation and invasion of breast cancer cells, and may serve as a potential biomarker for the prognosis of breast cancer.

Maxillofacial growth changes after maxillary protraction therapy in children with class III malocclusion: a dual control group retrospective study.

PURPOSE: To investigate the balance between post-treatment effect and continued nature growth after maxillary protraction treatment in patients with skeletal class III malocclusion. METHODS: 31 patients aged 8.79 ± 1.65 years with skeletal Class III malocclusion had been treated with maxillary protraction and the treatment lasted an average of 1.16 years. The average observation duration after treatment in the maxillary protraction group was 2.05 ± 0.39 years. In the control groups, a sample of 22 patients (9.64 ± 2.53 years) with untreated skeletal class III malocclusion and 24 patients (9.28 ± 0.96 years) with skeletal class I malocclusion were matched to the treatment group according to age, sex and observation period. The mean observation interval of the control groups was 2.39 ± 1.29 years in the class III group and 1.97 ± 0.49 years in the class I group. RESULTS: The active orthopedic treatment effect showed a opposite trend to the natural craniomaxillofacial growth effect after treatment in many aspects. In the observation duration of treatment group, decrease in ANB, Wits appraisal and BAr-AAr were statistically significant compared to class I control group (p < 0.001), and there was a significant increase in NA-FH (P < 0.001) which was contrary to class III control group. Treatment group presented a significant increase in Gn-Co (P < 0.01) and Co-Go (P < 0.001), except for changes in the extent of the mandibular base (Pog-Go, P = 0.149) compared to class I control group. The vertical maxillomandibular skeletal variables (Gonial; MP-SN; MP-FH; Y-axis) in treatment group decreased significantly compared to those in class III control group (P < 0.01). U1-SN and L1-MP showed a significant increase, which was similar to the class I group (P > 0.05), and overjet decreased significantly relative to both of the two control groups (P < 0.05). CONCLUSION: Maxillary protraction therapy led to stable outcomes in approximately 77.42% of children with Class III malocclusion approximately 2 years after treatment. Unfavorable skeletal changes were mainly due to the greater protrusion of the mandible but maxillary protraction did have a certain degree of postimpact on the mandibular base. Protraction therapy does not fundamentally change the mode of maxillary growth in Class III subjects except for the advancement of the maxilla. Craniomaxillofacial region tend to restabilize after treatment and lead to skeletal growth rotation and more dentoalveolar compensation.