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.

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.

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.

[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.

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.

Free-Standing Air Cathodes Based on 3D Hierarchically Porous Carbon Membranes: Kinetic Overpotential of Continuous Macropores in Li-O2 Batteries.

Free-standing macroporous air electrodes with enhanced interfacial contact, rapid mass transport, and tailored deposition space for large amounts of Li2 O2 are essential for improving the rate performance of Li-O2 batteries. An ordered mesoporous carbon membrane with continuous macroporous channels was prepared by inversely topological transformation from ZnO nanorod array. Utilized as a free-standing air cathode for Li-O2 battery, the hierarchically porous carbon membrane shows superior rate performance. However, the increased cross-sectional area of the continuous macropores on the cathode surface leads to a kinetic overpotential with large voltage hysteresis and linear voltage variation against Butler-Volmer behavior. The kinetics were investigated based on the rate-determining step of second electron transfer accompanied by migration of Li+ in solid or quasi-solid intermediates. These discoveries shed light on the design of the air cathode for Li-O2 batteries with high-rate performance.

Different effects of neuropeptide Y on proliferation of vascular smooth muscle cells via regulation of Geminin.

The proliferation-promoting effect of neuropeptide Y (NPY) always functions in low-serum-cultured vascular smooth muscle cells (VSMCs), and the phenotypic switch of VSMCs is regulated by concentrations of serum. Whether the property of the NPY proliferative effect in VSMCs relies on phenotype of VSMCs is unclear. We aimed to explore the role of NPY on proliferation of different VSMC phenotypes in the pathogenesis of atherosclerosis. By stimulating A10 cells with 200 nM NPY in 0.5 or 10% serum, 3H-thymidine and 5-ethynyl-2'-deoxyuridine (EdU) and CCK8 measurements were used to detect VSMC proliferation. RT-PCR and Flow cytometry were performed to detect the factors involved in different properties of the NPY proliferative effect in VSMCs. Instead of facilitating proliferation, NPY had no significant effect on the growth of VSMCs when cultured in 10% serum (VSMCs stayed at synthetic states). The underlying mechanism may be involved in down-regulation of Y1 receptor (P < 0.05 vs. Vehicle) and up-regulation of Geminin (P < 0.05 vs. Vehicle) in 10% serum-cultured VSMCs co-incubated with 200 nM NPY. Besides, modulation of Geminin was effectively blocked by the Y1 receptor antagonist. The stimulation of NPY on proliferation of VSMCs could be a double-edged sword in the development of atherosclerosis and thus provides new knowledge for therapy of atherosclerosis.

Toward Lower Overpotential through Improved Electron Transport Property: Hierarchically Porous CoN Nanorods Prepared by Nitridation for Lithium-Oxygen Batteries.

To lower the overpotential of a lithium-oxygen battery, electron transport at the solid-to-solid interface between the discharge product Li2O2 and the cathode catalyst is of great significance. Here we propose a strategy to enhance electron transport property of the cathode catalyst by the replace of oxygen atoms in the generally used metal oxide-based catalysts with nitrogen atoms to improve electron density at Fermi energy after nitridation. Hierarchically porous CoN nanorods were obtained by thermal treatment of Co3O4 nanorods under ammonia atmosphere at 350 °C. Compared with that of the pristine Co3O4 precursor before nitridation, the overpotential of the obtained CoN cathode was significantly decreased. Moreover, specific capacity and cycling stability of the CoN nanorods were enhanced. It is assumed that the discharged products with different morphologies for Co3O4 and CoN cathodes might be closely associated with the variation in the electronic density induced by occupancy of nitrogen atoms into interstitial sites of metal lattice after nitridation. The nitridation strategy for improved electron density proposed in this work is proved to be a simple but efficient way to improve the electrochemical performance of metal oxide based cathodes for lithium-oxygen batteries.

PFN2, a novel marker of unfavorable prognosis, is a potential therapeutic target involved in esophageal squamous cell carcinoma.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is one of the most aggressively malignant tumors with dismal prognosis. Profilin 2 (PFN2) is an actin-binding protein that regulates the dynamics of actin polymerization and plays a key role in cell motility. Recently, PFN2 have emerged as significant regulators of cancer processes. However, the clinical significance and biological function of PFN2 in ESCC remain unclear. METHODS: PFN2 protein expression was validated by immunohistochemistry (IHC) on tissue microarray from Chinese Han and Kazakh populations with ESCC. The associations among PFN2 expression, clinicopathological features, and prognosis of ESCC were analyzed. The effects on cell proliferation, invasion and migration were examined using MTT and Transwell assays. Markers of epithelial-mesenchymal transition (EMT) were detected by Western blot analysis. RESULTS: Compared with normal esophageal epithelium (NEE), PFN2 protein expression was markedly increased in low-grade intraepithelial neoplasia (LGIN), high-grade intraepithelial neoplasia (HGIN), and ESCC, increased gradually from LGIN to ESCC, and finally reached high grade in HGIN in the Han population. Similarly, PFN2 protein was more overexpressed in ESCC than in NEE in the Kazakh population. The results of Western blot analysis also showed that PFN2 expression was significantly higher in the ESCC tissue than in a matched adjacent non-cancerous tissue. PFN2 expression was positively correlated with invasion depth and lymph node metastasis. High PFN2 expression was significantly correlated with short overall survival (OS) (P = 0.023). Cox regression analysis revealed that PFN2 expression was an independent prognostic factor for poor OS in ESCC. Downregulation of PFN2 inhibited, rather than proliferated, cell invasion and migration, as well as induced an EMT phenotype, including increased expression of epithelial marker E-cadherin, decreased mesenchymal marker Vimentin, Snail, Slug and ZEB1, and morphological changes in ESCC cells in vitro. CONCLUSIONS: Our findings demonstrate that PFN2 has a novel role in promoting ESCC progression and metastasis and portending a poor prognosis, indicating that PFN2 could act as an early biomarker of high-risk population. Targeting PFN2 may offer a promising therapeutic strategy for ESCC treatment.

SLC39A6: a potential target for diagnosis and therapy of esophageal carcinoma.

BACKGROUND: Esophageal squamous cell carcinoma (ESCC) is a highly lethal cancer, and its underlying molecular mechanisms are poorly understood. Recent large-scale genome-wide association studies in Chinese Han populations have identified an ESCC susceptibility locus within the SLC39A6 gene. Here, we sought to explore the expression and biological function of SLC39A6 in ESCC. METHODS: Multiethnic validation of SLC39A6 protein expression was performed in different cohorts of patients from Chinese Han and Kazakh populations in the Xinjiang region by immunohistochemistry. The associations among SLC39A6 expression, clinicopathological parameters, and prognosis outcomes of ESCC were analyzed. And the effects of SLC39A6 silencing by siRNA on cell proliferation, apoptosis, and invasiveness, as well as the proteins involved in epithelial-to-mesenchymal transition (EMT) of esophageal cancer cells, were studied. RESULTS: SLC39A6 protein expression increased progressively from normal esophageal epithelium (NEE) to low-grade intraepithelial neoplasia to ESCC, and finally reached the highest in high-grade intraepithelial neoplasia from Han ethnic. Similarly, SLC39A6 protein was significantly overexpressed in Kazakh ethnic ESCC compared with that in NEE. Increased expression of SLC39A6 was found to be closely correlated with histological grade and early Tumor-Node-Metastasis stage I/II. High tumorous SLC39A6 expression was significantly correlated with shorter overall survival (OS). Cox regression analysis confirmed that SLC39A6 expression was an independent prognostic factor for poor OS in ESCC. Experimentally, the suppression of SLC39A6 expression promoted ESCC cell apoptosis but abrogated proliferation and invasion, and induced an EMT phenotype that included enhanced expression of E-cadherin, loss of vimentin, and morphological changes in ESCC cells in vitro. CONCLUSIONS: Combined, our findings highlight a tumor-promoting role for SLC39A6 in ESCC, suggesting that SLC39A6 could serve as an early detector of high-risk subjects and prognostic biomarker. The targeting of SLC39A6 might be a potential therapeutic strategy for blocking ESCC.

Estrogenic activity data extraction and in silico prediction show the endocrine disruption potential of bisphenol A replacement compounds.

Bisphenol A (BPA) replacement compounds are released to the environment and cause widespread human exposure. However, a lack of thorough safety evaluations on the BPA replacement compounds has raised public concerns. We assessed the endocrine disruption potential of BPA replacement compounds in the market to assist their safety evaluations. A literature search was conducted to ascertain the BPA replacement compounds in use. Available experimental estrogenic activity data of these compounds were extracted from the Estrogenic Activity Database (EADB) to assess their estrogenic potential. An in silico model was developed to predict the estrogenic activity of compounds lacking experimental data. Molecular dynamics (MD) simulations were performed to understand the mechanisms by which the estrogenic compounds bind to and activate the estrogen receptor (ER). Forty-five BPA replacement compounds were identified in the literature. Seven were more estrogenic and five less estrogenic than BPA, while six were nonestrogenic in EADB. A two-tier in silico model was developed based on molecular docking to predict the estrogenic activity of the 27 compounds lacking data. Eleven were predicted as ER binders and 16 as nonbinders. MD simulations revealed hydrophobic contacts and hydrogen bonds as the main interactions between ER and the estrogenic compounds.

Combined pharmacophore modeling, 3D-QSAR, homology modeling and docking studies on CYP11B1 inhibitors.

The mitochondrial cytochrome P450 enzymes inhibitor steroid 11ß-hydroxylase (CYP11B1) can decrease the production of cortisol. Therefore, these inhibitors have an effect in the treatment of Cushing's syndrome. A pharmacophore model generated by Genetic Algorithm with Linear Assignment for Hypermolecular Alignment of Datasets (GALAHAD) was used to align the compounds and perform comparative molecular field analysis (CoMFA) with Q2 = 0.658, R2 = 0.959. The pharmacophore model contained six hydrophobic regions and one acceptor atom, and electropositive and bulky substituents would be tolerated at the A and B sites, respectively. A three-dimensional quantitative structure-activity relationship (3D-QSAR) study based on the alignment with the atom root mean square (RMS) was applied using comparative molecular field analysis (CoMFA) with Q2 = 0.666, R2 = 0.978, and comparative molecular similarity indices analysis (CoMSIA) with Q2 = 0.721, R2 = 0.972. These results proved that all the models have good predictability of the bioactivities of inhibitors. Furthermore, the QSAR models indicated that a hydrogen bond acceptor substituent would be disfavored at the A and B groups, while hydrophobic groups would be favored at the B site. The three-dimensional (3D) model of the CYP11B1 was generated based on the crystal structure of the CYP11B2 (PDB code 4DVQ). In order to probe the ligand-binding modes, Surflex-dock was employed to dock CYP11B1 inhibitory compounds into the active site of the receptor. The docking result showed that the imidazolidine ring of CYP11B1 inhibitors form H bonds with the amino group of residue Arg155 and Arg519, which suggested that an electronegative substituent at these positions could enhance the activities of compounds. All the models generated by GALAHAD QSAR and Docking methods provide guidance about how to design novel and potential drugs for Cushing's syndrome treatment.

[Toxic effect of formaldehyde on mouse different brain regions].

The aim of this study was to explore the mechanism of the nervous system lesions induced by formaldehyde (FA). Male Balb/c mice were exposed to gaseous formaldehyde for 7 days (8 h/d) with three different concentrations (0, 0.5 and 3.0 mg/m(3)). A group of animals injected with the nitric oxide synthase inhibitor L-NMMA (0.01 mL/g) was also set and exposed to 3.0 mg/m(3) FA. The concentrations of cAMP, cGMP, NO and the activity of NOS in cerebral cortex, hippocampus and brain stem were determined by corresponding assay kits. The results showed that, compared with the control (0 mg/m(3) FA) group, the cAMP contents in cerebral cortex and brain stem were significantly increased in 0.5 mg/m(3) FA group (P < 0.05), but decreased in 3.0 mg/m(3) FA group (P < 0.05); The concentration of cAMP in hippocampus was significantly decreased in 3.0 mg/m(3) FA group (P < 0.05). In comparison with the control group, L-NMMA group showed unchanged cAMP contents and NOS activities in different brain regions, but showed increased cGMP contents in hippocampus and NO contents in cerebral cortex (P < 0.05). In addition, compared with 3.0 mg/m(3) FA group, L-NMMA group showed increased contents of cAMP and reduced NOS activities in different brain regions, as well as significantly decreased cGMP contents in cerebral cortex and brain stem and NO content in brain stem. These results suggest that the toxicity of FA on mouse nervous system is related to NO/cGMP and cAMP signaling pathways.

Competitive molecular docking approach for predicting estrogen receptor subtype α agonists and antagonists.

BACKGROUND: Endocrine disrupting chemicals (EDCs) are exogenous compounds that interfere with the endocrine system of vertebrates, often through direct or indirect interactions with nuclear receptor proteins. Estrogen receptors (ERs) are particularly important protein targets and many EDCs are ER binders, capable of altering normal homeostatic transcription and signaling pathways. An estrogenic xenobiotic can bind ER as either an agonist or antagonist to increase or inhibit transcription, respectively. The receptor conformations in the complexes of ER bound with agonists and antagonists are different and dependent on interactions with co-regulator proteins that vary across tissue type. Assessment of chemical endocrine disruption potential depends not only on binding affinity to ERs, but also on changes that may alter the receptor conformation and its ability to subsequently bind DNA response elements and initiate transcription. Using both agonist and antagonist conformations of the ERα, we developed an in silico approach that can be used to differentiate agonist versus antagonist status of potential binders. METHODS: The approach combined separate molecular docking models for ER agonist and antagonist conformations. The ability of this approach to differentiate agonists and antagonists was first evaluated using true agonists and antagonists extracted from the crystal structures available in the protein data bank (PDB), and then further validated using a larger set of ligands from the literature. The usefulness of the approach was demonstrated with enrichment analysis in data sets with a large number of decoy ligands. RESULTS: The performance of individual agonist and antagonist docking models was found comparable to similar models in the literature. When combined in a competitive docking approach, they provided the ability to discriminate agonists from antagonists with good accuracy, as well as the ability to efficiently select true agonists and antagonists from decoys during enrichment analysis. CONCLUSION: This approach enables evaluation of potential ER biological function changes caused by chemicals bound to the receptor which, in turn, allows the assessment of a chemical's endocrine disrupting potential. The approach can be used not only by regulatory authorities to perform risk assessments on potential EDCs but also by the industry in drug discovery projects to screen for potential agonists and antagonists.

Whole genome sequencing of 35 individuals provides insights into the genetic architecture of Korean population.

BACKGROUND: Due to a significant decline in the costs associated with next-generation sequencing, it has become possible to decipher the genetic architecture of a population by sequencing a large number of individuals to a deep coverage. The Korean Personal Genomes Project (KPGP) recently sequenced 35 Korean genomes at high coverage using the Illumina Hiseq platform and made the deep sequencing data publicly available, providing the scientific community opportunities to decipher the genetic architecture of the Korean population. METHODS: In this study, we used two single nucleotide variant (SNV) calling pipelines: mapping the raw reads obtained from whole genome sequencing of 35 Korean individuals in KPGP using BWA and SOAP2 followed by SNV calling using SAMtools and SOAPsnp, respectively. The consensus SNVs obtained from the two SNV pipelines were used to represent the SNVs of the Korean population. We compared these SNVs to those from 17 other populations provided by the HapMap consortium and the 1000 Genomes Project (1KGP) and identified SNVs that were only present in the Korean population. We studied the mutation spectrum and analyzed the genes of non-synonymous SNVs only detected in the Korean population. RESULTS: We detected a total of 8,555,726 SNVs in the 35 Korean individuals and identified 1,213,613 SNVs detected in at least one Korean individual (SNV-1) and 12,640 in all of 35 Korean individuals (SNV-35) but not in 17 other populations. In contrast with the SNVs common to other populations in HapMap and 1KGP, the Korean only SNVs had high percentages of non-silent variants, emphasizing the unique roles of these Korean only SNVs in the Korean population. Specifically, we identified 8,361 non-synonymous Korean only SNVs, of which 58 SNVs existed in all 35 Korean individuals. The 5,754 genes of non-synonymous Korean only SNVs were highly enriched in some metabolic pathways. We found adhesion is the top disease term associated with SNV-1 and Nelson syndrome is the only disease term associated with SNV-35. We found that a significant number of Korean only SNVs are in genes that are associated with the drug term of adenosine. CONCLUSION: We identified the SNVs that were found in the Korean population but not seen in other populations, and explored the corresponding genes and pathways as well as the associated disease terms and drug terms. The results expand our knowledge of the genetic architecture of the Korean population, which will benefit the implementation of personalized medicine for the Korean population.

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.

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.

Secondary rhinoplasty for unilateral cleft lip nasal deformity using the complex of autologous costal cartilage and fascia grafts.

BACKGROUND: Secondary unilateral cleft lip nasal deformity is a prevalent condition in the world, and surgical repair remains a formidable challenge. The objective of the study is to investigate functionally and aesthetically outcomes of a new technique using a complex of autologous costal cartilage and fascia grafts to repair the secondary unilateral cleft lip nasal deformity. METHODS: First, the autologous costal cartilage and fascia were harvested from a total of 34 patients with secondary unilateral cleft lip nasal deformity, who were undergoing treatments at the First Affiliated Hospital of Xi'an Jiaotong University from April 2020 to June 2023. Then, the cartilage was sub-divided into 6 pieces and placed on the depressed alar base, nasal columella, lower lateral cartilage, and nasal tip. At the same time, the fascia was trimmed to cover the nasal tip cap graft and augment the dorsum of the nose. Patient outcomes were assessed through subjective evaluation and objective anthropometric measurements. In addition, associated complications were also examined. RESULTS: All were primary healing incisions, and no patients were encountered with any clinical complications. We found that the overall mean score of the Independent Rhinoplasty Outcome Score (IROS) was very good during the subjective assessment. Also, the objective measurements indicated an excellent nasal symmetry. Furthermore, both functionally and aesthetically satisfactory outcomes were obtained in all the patients during long-time follow-up. CONCLUSIONS: Secondary rhinoplasty using the complex of autologous costal cartilage and fascia grafts is a safe and effective approach for unilateral cleft lip nasal deformity repair.

Combined QSAR and molecule docking studies on predicting P-glycoprotein inhibitors.

P-glycoprotein (P-gp) is an ATP-binding cassette multidrug transporter. The over expression of P-gp leads to the development of multidrug resistance (MDR), which is a major obstacle to effective treatment of cancer. Thus, designing effective P-gp inhibitors has an extremely important role in the overcoming MDR. In this paper, both ligand-based quantitative structure-activity relationship (QSAR) and receptor-based molecular docking are used to predict P-gp inhibitors. The results show that each method achieves good prediction performance. According to the results of tenfold cross-validation, an optimal linear SVM model with only three descriptors is established on 857 training samples, of which the overall accuracy (Acc), sensitivity, specificity, and Matthews correlation coefficient are 0.840, 0.873, 0.813, and 0.683, respectively. The SVM model is further validated by 418 test samples with the overall Acc of 0.868. Based on a homology model of human P-gp established, Surflex-dock is also performed to give binding free energy-based evaluations with the overall accuracies of 0.823 for the test set. Furthermore, a consensus evaluation is also performed by using these two methods. Both QSAR and molecular docking studies indicate that molecular volume, hydrophobicity and aromaticity are three dominant factors influencing the inhibitory activities.