Identification of N7-methylguanosine-related miRNAs as potential biomarkers for prognosis and drug response in breast cancer.

Objectives: The impact of N7-methylguanosine (m7G) on tumor progression and the regulatory role of microRNAs (miRNAs) in immune function significantly influence breast cancer (BC) prognosis. Investigating the interplay between m7G modification and miRNAs provides novel insights for assessing prognostics and drug responses in BC. Materials and methods: RNA sequences (miRNA and mRNA profiles) and clinical data for BC were acquired from the Cancer Genome Atlas (TCGA) database. A miRNA signature associated with 15 m7G in this cohort was identified using Cox regression and LASSO. The risk score model was evaluated using Kaplan-Meier and time-dependent ROC analysis, categorizing patients into high-risk and low-risk groups. Functional enrichment analyses were conducted to explore potential pathways. The immune system, including scores, cell infiltration, function, and drug sensitivity, was examined and compared between high-risk and low-risk groups. A nomogram that combines risk scores and clinical factors was developed and validated. Single-sample gene set enrichment analysis (ssGSEA) was employed to explore m7G-related miRNA signatures and immune cell relationships in the tumor microenvironment. Additionally, drug susceptibility was compared between risk groups. Results: Fifteen m7G-related miRNAs were independently correlated with overall survival (OS) in BC patients. Time-dependent ROC analysis yielded area under the curve (AUC) values of 0.742, 0.726, and 0.712 for predicting 3-, 5-, and 10-year survival rates, respectively. The Kaplan-Meier analysis revealed a significant disparity in OS between the high-risk and low-risk groups (p = 1.3e-6). Multiple regression identified the risk score as a significant independent prognostic factor. An excellent calibration nomogram with a C-index of 0.785 (95 % CI: 0.728-0.843) was constructed. In immune analysis, low-risk patients exhibited heightened immune function and increased responsiveness to immunotherapy and chemotherapy compared to high-risk patients. Conclusion: This study systematically analyzed m7G-related miRNAs and revealed their regulatory mechanisms concerning the tumor microenvironment (TME), pathology, and the prognosis of BC patient. Based on these miRNAs, a prognostic model and nomogram were developed for BC patients, facilitating prognostic assessments. These findings can also assist in predicting treatment responses and guiding medication selection.

Discovery of novel bromodomain-containing protein 4 (BRD4-BD1) inhibitors combined with 3d-QSAR, molecular docking and molecular dynamics in silico.

Bromine-containing domain protein 4 (BRD4) plays a crucial role in regulating transcription and genome stability. Selective inhibitors of BRD4-BD1 can specifically target specific bromine domains to affect cell proliferation, apoptosis, and differentiation. In this work, 43 selective benzoazepinone BRD4-BD1 inhibitors were studied using molecular simulations and three-dimensional quantitative conformation relationships (3D-QSAR). A reliable 3D-QSAR model was established based on COMFA (Q2 = 0.532, R2 = 0.981) and COMSIA (S + E + H (Q2 = 0.536, R2 = 0.979) two different analysis methods. Through 3D-QSAR model prediction and quantum chemical analysis, 15 small molecules with stronger inhibitory activity than the template compounds were constructed, and 5 new compounds with higher predictive activity and binding affinity were screened by molecular docking and ADMET methods. According to the molecular dynamics simulation, the key residues that can interact with BRD4-BD1 protein and molecular docking results are consistent, including ASN140, MET132, GLN85, MET105, ASN135 and TYR97. From the MD trajectory, we calculated and analyzed RMSD, RMSF, free binding energy, FECM, DCCM and PCA, the loop region formed by amino acids VAL45∼PRO62 showed α-helix, ß-folding and clustering towards the active center with the extension of simulation time. Further optimization of the structure of active candidate compounds A6, A11, A14, and A15 will provide the necessary theoretical basis for the synthesis and activity evaluation of novel BRD4-BD1 inhibitors.Communicated by Ramaswamy H. Sarma.

Engineering novel scaffolds for specific HDAC11 inhibitors against metabolic diseases exploiting deep learning, virtual screening, and molecular dynamics simulations.

The prevalence of metabolic diseases is increasing at a frightening rate year by year. The burgeoning development of deep learning enables drug design to be more efficient, selective, and structurally novel. The critical relevance of Histone deacetylase 11 (HDAC11) to the pathogenesis of several metabolic diseases makes it a promising drug target for curbing metabolic disorders. The present study aims to design new specific HDAC11 inhibitors for the treatment of metabolic diseases. Deep learning was performed to learn the properties of existing HDAC11 inhibitors and yield a novel compound library containing 23,122 molecules. Subsequently, the compound library was screened by ADMET properties, Lipinski & Veber rules, traditional machine classification models, and molecular docking, and 10 compounds were screened as candidate HDAC11 inhibitors. The stability of the 10 new molecules was further evaluated by deploying RMSD, RMSF, MM/GBSA, free energy landscape mapping, and PCA analysis in molecular dynamics simulations. As a result, ten compounds, Cpd_17556, Cpd_2184, Cpd_8907, Cpd_7771, Cpd_14959, Cpd_7108, Cpd_12383, Cpd_13153, Cpd_14500and Cpd_21811, were characterized as good HDAC11 inhibitors and are expected to be promising drug candidates for metabolic disorders, and further in vitro, in vivo and clinical trials to demonstrate in the future.

Structural optimization of pyrrolopyrimidine BTK inhibitors based on molecular simulation.

CONTEXT: BTK is a critical regulator involved in the proliferation, differentiation, and apoptosis of B cells. BTK inhibitors can effectively alleviate various diseases such as tumors, leukemia, and asthma. During this study, a range of novel BTK inhibitors were designed using 3D-QSAR, molecular docking, and molecular dynamics (MD) simulation. METHODS: We selected 41 pyrrolopyrimidine derivatives as BTK inhibitors to structure a 3D-QSAR model. Comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA) were adopted to research the connection between the pharmacological activities and chemical structures of the compounds. The CoMFA model (q2 = 0.519, R2 = 0.971), CoMSIA model (q2 = 0.512, R2 = 0.990), and external validation demonstrated excellent predictive performance and reliability of the 3D-QSAR model. We designed eight novel molecules with higher inhibitory activities according to the three-dimensional equipotential fields and explored the interactions between the compounds and BTK by molecular docking, which showed that the novel molecules had higher binding affinities with BTK than the template molecule 18. Then, the results of molecular docking were further verified by MD simulation, which showed that amino acid residues such as Leu528, Val416, and Met477 played vital parts in the interaction, and the binding free energy analysis showed that the novel molecules had higher stability with BTK. Finally, the ADME/T properties were predicted for all of the novel compounds, and the results showed that the majority of them had favorable pharmacokinetic properties. Therefore, this study provides strong support for the development of novel BTK inhibitors.

Through-Holes Design for Ideal LiNbO3 A1 Resonators.

This paper proposes a method to realize ideal lithium niobate (LiNbO3) A1 resonators. By introducing subwavelength through-holes between the interdigital transducer (IDT) electrodes on the LiNbO3 surface, all unfavorable spurious modes of the resonators can be suppressed completely. It is convenient and valid for various IDT electrode parameters and different LiNbO3 thicknesses. Also, this method does not require additional device fabrication steps. At the same time, these through-holes can greatly reduce the suspended area of the LiNbO3 thin film, thus significantly improving the design flexibility, compactness, mechanical stability, temperature stability, and power tolerance of the resonators (and subsequent filters). It is expected to become an important means to promote the practical application of LiNbO3 A1 filters and even all Lamb waves filters.

Virtual screening identifies tipranavir as a SIRT1 inhibitor with anti-hepatocarcinoma effect.

Aim: To identify novel inhibitors of SIRT1 and to understand their mechanism of action in hepatocellular carcinoma. Materials & methods: Molecular docking and dynamic simulations were conducted to identify potential SIRT1 inhibitors. The in vitro efficacy of the inhibitors was evaluated by methyl thiazolyl tetrazolium assays, flow cytometry and western blot analysis. Additionally, the in vivo antitumor activity of the inhibitor was evaluated. Results: Tipranavir, a US FDA-approved anti-HIV-1 medication, was found to possess potential as a SIRT1 inhibitor. Tipranavir selectively inhibited HepG2 cell proliferation without causing toxicity to normal human hepatic cells. Additionally, tipranavir treatment resulted in a reduction of SIRT1 expression and induction of apoptosis in HepG2 cells. Furthermore, tipranavir was found to suppress tumorigenesis in a xenograft mouse model and decreased the expression of SIRT1 in vivo. Conclusion: Tipranavir holds desirable potential as a promising therapeutic agent against hepatoma.

3D-QSAR, molecular docking, and molecular dynamics analysis of dihydrodiazaindolone derivatives as PARP-1 inhibitors.

CONTEXT: PARP-1 plays an important role in DNA repair and apoptosis, and PARP-1 inhibitors have shown to be effective in the treatment of several malignancies. To evaluate the function of new PARP-1 inhibitors as anticancer adjuvant medicines, 3D-QSAR, molecular docking, and molecular dynamics (MD) simulations of a sequence of dihydrodiazepinoindolone derivatives PARP-1 inhibitors were undertaken in this study. METHODS: In this paper, 43 PARP-1 inhibitors were studied in a three-dimensional quantitative structure-activity relationship (3D-QSAR) using comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). CoMFA with q2 of 0.675 and r2 of 0.981 was achieved, as was CoMSIA with q2 of 0.755 and r2 of 0.992. The changed areas of these compounds are shown by steric, electrostatic, hydrophobic, and hydrogen-bonded acceptor field contour maps. Subsequently, molecular docking, and molecular dynamics simulations further confirmed that key residues Gly863 and Ser904 of PARP-1 are vital residues for protein interactions and their binding affinity. The effects of 3D-QSAR, molecular docking and molecular dynamics simulations supply a new route for the search of new PARP-1 inhibitors. Finally, we designed eight new compounds with exact activity and ADME/T properties.

Virtual Screening and Multi-targets Investigation of Novel Diazine Derivatives as Potential Xanthine Oxidase Inhibitors Based on QSAR, Molecular Docking, ADMET Properties, Dynamics Simulation and Network Pharmacology.

BACKGROUND: Hyperuricemia is closely related to the occurrence of gout, hypertension, diabetes, hyperlipidemia, cardiovascular disease, kidney disease, metabolic syndrome, etc. However, xanthine oxidase inhibitors (XOIs) can fundamentally solve the problem of excessive uric acid. Compared to single-target drugs, multi-target drugs are not prone to adverse reactions and exert a synergistic effect. Therefore, the discovery of new multi-target XOIs and their mechanism of therapeutic hyperuricemia are important to overcome adverse effects and resistance to currently available drugs. OBJECTIVE: The purpose of this paper is to obtain novel diazine derivatives as promising multi-target XOIs and discover the interaction mechanism for the better treatment of hyperuricemia. METHODS: Novel multi-target XOIs diazine derivatives, and their interaction mechanism have been obtained through QSAR, molecular docking, dynamics simulation, and network pharmacology. In addition, ADMET properties and synthetic accessibility of novel XOIs have been considered using ADMETLAB 2.0 and SwissADME. RESULTS: 24 novel diazine derivatives as potential multi-target XOIs lead compounds have been found through virtual screening of the PubChem database. Moreover, the most notable top five compounds are worthy of further developing as multi-target XOIs drugs. XDH, TBK1, DGAT1, MYC, CDKN1A, PPARD, PDE6C, and EIF4E are recommended as relevant targets of therapeutic hyperuricemia. CONCLUSION: Through the combination of different methods, we have discovered five novel promising diazine derivatives as potential multi-target XOIs drugs. Meanwhile, eight targets have been found to be helpful in the research on therapeutic hyperuricemia. We expect this investigation will offer clear insights into the production of efficient XOIs drugs.

Three-dimensional quantitative structural-activity relationship and molecular dynamics study of multivariate substituted 4-oxyquinazoline HDAC6 inhibitors.

3D-QSAR models were established by collecting 46 multivariate-substituted 4-oxyquinazoline HDAC6 inhibitors. The relationship of molecular structure and inhibitory activity was studied by comparative molecular field analysis (CoMFA) and comparative molecular similarity index analysis (CoMSIA). The results showed the models established by CoMFA (q2 = 0.590, r2 = 0.965) and CoMSIA (q2 = 0.594, r2 = 0.931) had good prediction ability. At the same time, 3D-QSAR models met the internal verification, external verification and AD test. Ten new compounds were designed based on CoMFA and CoMSIA contour maps and their pharmacokinetic/toxic properties (ADME/T) were evaluated. It was found that most compounds have well safety profile and pharmacokinetic property. Then, we explored the interaction between HDAC6 and compounds by molecular docking. The results showed that the binding mode of the new compounds with HDAC6 was the same as the template compound 46, and the hydrogen bond and hydrophobic bond played a vital role in the binding process. Molecular dynamics simulation results showed that residues Ser531, His574 and Tyr745 played key roles in the binding process. All newly designed compounds had lower energy gap and binding energy than compound 46 according to DFT analysis and free energy analysis. This study provided a theoretical reference for designing compounds of higher activity and a new idea for the development of novel HDAC6 inhibitors.

Transcriptome analysis of tea (Camellia sinensis) leaves in response to ammonium starvation and recovery.

The tea plant is a kind of ammonium-preferring crop, but the mechanism whereby ammonium (NH4 +) regulate its growth is not well understood. The current study focused on the effects of NH4 + on tea plants. Transcriptomic analysis was performed to investigate the early- and late-stage NH4 + deprivation and resupply in tea plants shoots. Through short- and long-term NH4 + deficiency, the dynamic response to NH4 + stress was investigated. The most significant effects of NH4 + deficiency were found to be on photosynthesis and gene ontology (GO) enrichment varied with the length of NH4 + deprivation. Enriched KEGG pathways were also different when NH4 + was resupplied at different concentrations which may indicate reasons for tolerance of high NH4 + concentration. Using weighted gene co-expression network analysis (WGCNA), modules related to significant tea components, tea polyphenols and free amino acids, were identified. Hence, NH4 + could be regarded as a signaling molecule with the response of catechins shown to be higher than that of amino acids. The current work represents a comprehensive transcriptomic analysis of plant responses to NH4 + and reveals many potential genes regulated by NH4 + in tea plants. Such findings may lead to improvements in nitrogen efficiency of tea plants.

Combining virtual screening and in vitro evaluation for the discovery of potential CYP11B2 inhibitors.

Aim: To search for highly bioactive hits for CYP11B2 inhibitors by virtual screening and in vitro evaluation. Materials & methods: Virtual screening of potential CYP11B2 inhibitors was performed by molecular docking and molecular dynamics simulation. Compound activity was determined by in vitro evaluation using MTT and ELISA assays. Results & conclusion: Based on the results of molecular docking and molecular dynamics simulation, nine lead hits were selected for in vitro biochemical testing. All hits in in vitro experiments had lower inhibitory effects on cell proliferation and certain inhibitory effects on aldosterone secretion. These hits may be excellent candidates for CYP11B2 inhibitors.

Automated Magnetic Resonance Image Segmentation of Spinal Structures at the L4-5 Level with Deep Learning: 3D Reconstruction of Lumbar Intervertebral Foramen.

OBJECTIVE: 3D reconstruction of lumbar intervertebral foramen (LIVF) has been beneficial in evaluating surgical trajectory. Still, the current methods of reconstructing the 3D LIVF model are mainly based on manual segmentation, which is laborious and time-consuming. This study aims to explore the feasibility of automatically segmenting lumbar spinal structures and increasing the speed and accuracy of 3D lumbar intervertebral foramen (LIVF) reconstruction on magnetic resonance image (MRI) at the L4-5 level. METHODS: A total of 100 participants (mean age: 42.2 ± 14.0 years; 52 males and 48 females; mean body mass index, 22.7 ± 3.2 kg/m2 ), were enrolled in this prospective study between March and July 2020. All participants were scanned on L4-5 level with a 3T MR unit using 3D T2-weighted sampling perfection with application-optimized contrast with various flip-angle evolutions (SPACE) sequences. The lumbar spine's vertebra bone structures (VBS) and intervertebral discs (IVD) were manually segmented by skilled surgeons according to their anatomical outlines from MRI. Then all manual segmentation were saved and used for training. An automated segmentation method based on a 3D U-shaped architecture network (3D-UNet) was introduced for the automated segmentation of lumbar spinal structures. A number of quantitative metrics, including dice similarity coefficient (DSC), precision, and recall, were used to evaluate the performance of the automated segmentation method on MRI. Wilcoxon signed-rank test was applied to compare morphometric parameters, including foraminal area, height and width of 3D LIVF models between automatic and manual segmentation. The intra-class correlation coefficient was used to assess the test-retest reliability and inter-observer reliability of multiple measurements for these morphometric parameters of 3D LIVF models. RESULTS: The automatic segmentation performance of all spinal structures (VBS and IVD) was found to be 0.918 (healthy levels: 0.922; unhealthy levels: 0.916) for the mean DSC, 0.922 (healthy levels: 0.927; unhealthy levels: 0.920) for the mean precision, and 0.917 (healthy levels: 0.918; unhealthy levels: 0.917) for the mean recall in the test dataset. It took approximately 2.5 s to achieve each automated segmentation, far less than the 240 min for manual segmentation. Furthermore, no significant differences were observed in the foraminal area, height and width of the 3D LIVF models between manual and automatic segmentation images (P > 0.05). CONCLUSION: A method of automated MRI segmentation based on deep learning algorithms was capable of rapidly generating accurate segmentation of spinal structures and can be used to construct 3D LIVF models from MRI at the L4-5 level.

Automatic Grading of Disc Herniation, Central Canal Stenosis and Nerve Roots Compression in Lumbar Magnetic Resonance Image Diagnosis.

Aim: Accurate severity grading of lumbar spine disease by magnetic resonance images (MRIs) plays an important role in selecting appropriate treatment for the disease. However, interpreting these complex MRIs is a repetitive and time-consuming workload for clinicians, especially radiologists. Here, we aim to develop a multi-task classification model based on artificial intelligence for automated grading of lumbar disc herniation (LDH), lumbar central canal stenosis (LCCS) and lumbar nerve roots compression (LNRC) at lumbar axial MRIs. Methods: Total 15254 lumbar axial T2W MRIs as the internal dataset obtained from the Fifth Affiliated Hospital of Sun Yat-sen University from January 2015 to May 2019 and 1273 axial T2W MRIs as the external test dataset obtained from the Third Affiliated Hospital of Southern Medical University from June 2016 to December 2017 were analyzed in this retrospective study. Two clinicians annotated and graded all MRIs using the three international classification systems. In agreement, these results served as the reference standard; In disagreement, outcomes were adjudicated by an expert surgeon to establish the reference standard. The internal dataset was randomly split into an internal training set (70%), validation set (15%) and test set (15%). The multi-task classification model based on ResNet-50 consists of a backbone network for feature extraction and three fully-connected (FC) networks for classification and performs the classification tasks of LDH, LCCS, and LNRC at lumbar MRIs. Precision, accuracy, sensitivity, specificity, F1 scores, confusion matrices, receiver-operating characteristics and interrater agreement (Gwet k) were utilized to assess the model's performance on the internal test dataset and external test datasets. Results: A total of 1115 patients, including 1015 patients from the internal dataset and 100 patients from the external test dataset [mean age, 49 years ± 15 (standard deviation); 543 women], were evaluated in this study. The overall accuracies of grading for LDH, LCCS and LNRC were 84.17% (74.16%), 86.99% (79.65%) and 81.21% (74.16%) respectively on the internal (external) test dataset. Internal and external testing of three spinal diseases showed substantial to the almost perfect agreement (k, 0.67 - 0.85) for the multi-task classification model. Conclusion: The multi-task classification model has achieved promising performance in the automated grading of LDH, LCCS and LNRC at lumbar axial T2W MRIs.

Genome-wide identification, characterization, and expression analysis of the ammonium transporter gene family in tea plants (Camellia sinensis L.).

As a preferred nitrogen form, ammonium (NH4 + ) transport via specific transporters is particularly important for the growth and development of tea plants (Camellia sinensis L.). However, our understanding of the functions of the AMT family in tea plants is limited. We identified and named 16 putative AMT genes according to phylogenetic analysis. All CsAMT genes were divided into three groups, distributed on 12 chromosomes with only one segmental duplication repetition. The CsAMT genes showed different expression levels in different organs, and most of them were expressed mainly in the apical buds and roots. Complementation analysis of yeast mutants showed that CsAMTs restored the uptake of NH4 + . This study provides insights into the genome-wide distribution and spatial expression of AMT genes in tea plants.

Brønsted Acid-Catalyzed Carbonyl-Olefin Metathesis: Synthesis of Phenanthrenes via Phosphomolybdic Acid as a Catalyst.

Compared with the impressive achievements of catalytic carbonyl-olefin metathesis (CCOM) mediated by Lewis acid catalysts, exploration of the CCOM through Brønsted acid-catalyzed approaches remains quite challenging. Herein, we disclose a synthetic protocol for the construction of a valuable polycycle scaffold through the CCOM with the inexpensive, nontoxic phosphomolybdic acid as a catalyst. The current annulations could realize carbonyl-olefin, carbonyl-alcohol, and acetal-alcohol in situ CCOM reactions and feature mild reaction conditions, simple manipulation, and scalability, making this strategy a promising alternative to the Lewis acid-catalyzed COM reaction.

[Application and progress of data mining in study of compatibility law of traditional Chinese medicine].

Data mining is an important method to obtain the key information from a large amount of data, and it is widely applied in the research on the modernization of traditional Chinese medicine(TCM). The compatibility law of herbs is a key issue in the research of TCM prescriptions. This reflects the flexibility and effectiveness of TCM prescriptions, and it is also a crucial link to the development of TCM modernization. Therefore, it is the core purpose of the research on TCM prescriptions to find the compatibility law of herbs and clarify the scientific connotation. Data mining, as an effective method and an important approach, has formed a standardized system in the research of compatibility law of herbs, which can reveal the relationship between different Chinese herbs and summarize the internal rules in compatibility. Two hundred and twenty two effective papers were sorted out and categorized in this article. The results showed that data mining was mainly applied in finding the core Chinese herb pairs, summarizing the utility and attributes of TCM prescriptions, revealing the relationship between prescriptions, Chinese herbs and syndromes, finding the optimal dose of Chinese herbs, and producing the new prescriptions. The problems of data mining in research of herbs compatibility rules were summarized, and its development and trend in current researches were discussed in this article to provide useful references for the in-depth study of data mining in the compatibility law of Chinese herbs.

Solidly Mounted Longitudinally Excited Shear Wave Resonator (YBAR) Based on Lithium Niobate Thin-Film.

This paper proposed a solid-mounted (SM) longitudinally excited shear wave resonator (i.e., YBAR). By adopting a 200 nm x-cut LiNbO3 film, top (aluminum) and bottom (platinum) electrodes in 50 nm thickness and 500 nm width, this resonator simultaneously achieves an operating frequency over 5 GHz with an electromechanical coupling coefficient exceeding 50%. Compared with previously proposed YBAR with suspended structure, the proposed SM-YBAR can effectively suppress unwanted spurious modes with only a slight loss of the electromechanical coupling coefficient. The SM-YABR also provides better device stability, possible low-temperature drift coefficient, and a more convenient and mature device processing. It has the potential to meet the multiple requirements for the next generation signal processing devices in terms of high frequency, large bandwidth, stability, and low cost, etc.

The complete chloroplast genome of Damnacanthus indicus C.F.Gaertn. (Rubiaceae).

Damnacanthus indicus C.F.Gaertn. is an understorey shrub widely distributed in East Asia. In this study, the complete chloroplast genome of D. indicus was assembled and annotated. The chloroplast genome is 153,997 bp in total length, consisting of a large single-copy region (LSC 85,159 bp), a small single-copy region (SSC 17,584 bp) and two inverted repeat regions (25,627 bp for IRA and IRB,respectively). It contains 134 genes, including 89 protein-coding genes, 37 tRNA genes, and 8 rRNA genes. The phylogenetic analysis indicated that D. indicus is sister to Mitchella repens, suggesting a close relationship of the two genera.

ADSC-Exos containing MALAT1 promotes wound healing by targeting miR-124 through activating Wnt/ß-catenin pathway.

Cutaneous wound is a soft tissue injury that is difficult to heal during aging. It has been demonstrated that adipose-derived stem cells (ADSCs) and its secreted exosomes exert crucial functions in cutaneous wound healing. The present study aimed to elucidate the mechanism of exosomes derived from ADSCs (ADSC-Exos) containing MALAT1 in wound healing. ADSCs were isolated from human normal subcutaneous adipose tissues and identified by flow cytometry analysis. Exosomes were extracted from ADSC supernatants and MALAT1 expression was determined using qRT-PCR analysis. HaCaT and HDF cells were exposed to hydrogen peroxide (H2O2) for simulating the skin lesion model. Subsequently, CCK-8, flow cytometry, wound healing and transwell assays were employed to validate the role of ADSC-Exos containing MALAT1 in the skin lesion model. Besides, cells were transfected with sh-MALAT1 to verify the protective role of MALAT1 in wound healing. The binding relationship between MALAT1 and miR-124 were measured by dual-luciferase reporter assay. ADSC-Exos promoted cell proliferation, migration, and inhibited cell apoptosis of HaCaT and HDF cells impaired by H2O2. However, the depletion of MALAT1 in ADSC-Exos lose these protective effects on HaCaT and HDF cells. Moreover, miR-124 was identified to be a target of MALAT1. Furthermore, ADSC-Exos containing MALAT1 could mediate H2O2-induced wound healing by targeting miR-124 and activating Wnt/ß-catenin pathway. ADSC-Exos containing MALAT1 play a positive role in cutaneous wound healing possibly via targeting miR-124 through activating the Wnt/ß-catenin pathway, which may provide novel insights into the therapeutic target for cutaneous wound healing.

AuCl3 -Catalyzed Ring-Closing Carbonyl-Olefin Metathesis.

Compared with the ripeness of olefin metathesis, exploration of the construction of carbon-carbon double bonds through the catalytic carbonyl-olefin metathesis reaction remains stagnant and has received scant attention. Herein, a highly efficient AuCl3 -catalyzed intramolecular ring-closing carbonyl-olefin metathesis reaction is described. This method features easily accessible starting materials, simple operation, good functional-group tolerance and short reaction times, and provides the target cyclopentenes, polycycles, benzocarbocycles, and N-heterocycle derivatives in good to excellent yields.