The combination of breast cancer PDO and mini-PDX platform for drug screening and individualized treatment.

The majority of advanced breast cancers exhibit strong aggressiveness, heterogeneity, and drug resistance, and currently, the lack of effective treatment strategies is one of the main challenges that cancer research must face. Therefore, developing a feasible preclinical model to explore tailored treatments for refractory breast cancer is urgently needed. We established organoid biobanks from 17 patients with breast cancer and characterized them by immunohistochemistry (IHC) and next generation sequencing (NGS). In addition, we in the first combination of patient-derived organoids (PDOs) with mini-patient-derived xenografts (Mini-PDXs) for the rapid and precise screening of drug sensitivity. We confirmed that breast cancer organoids are a high-fidelity three-dimension (3D) model in vitro that recapitulates the original tumour's histological and genetic features. In addition, for a heavily pretreated patient with advanced drug-resistant breast cancer, we combined PDO and Mini-PDX models to identify potentially effective combinations of therapeutic agents for this patient who were alpelisib + fulvestrant. In the drug sensitivity experiment of organoids, we observed changes in the PI3K/AKT/mTOR signalling axis and oestrogen receptor (ER) protein expression levels, which further verified the reliability of the screening results. Our study demonstrates that the PDO combined with mini-PDX model offers a rapid and precise drug screening platform that holds promise for personalized medicine, improving patient outcomes and addressing the urgent need for effective therapies in advanced breast cancer.

Epigenetic silencing of LDHB promotes hepatocellular carcinoma by remodeling the tumor microenvironment.

Lactate dehydrogenase B (LDHB) reversibly catalyzes the conversion of pyruvate to lactate or lactate to pyruvate and expressed in various malignancies. However, the role of LDHB in modulating immune responses against hepatocellular carcinoma (HCC) remains largely unknown. Here, we found that down-regulation of lactate dehydrogenase B (LDHB) was coupled with the promoter hypermethylation and knocking down the DNA methyltransferase 3A (DNMT 3A) restored LDHB expression levels in HCC cell lines. Bioinformatics analysis of the HCC cohort from The Cancer Genome Atlas revealed a significant positive correlation between LDHB expression and immune regulatory signaling pathways and immune cell infiltrations. Moreover, immune checkpoint inhibitors (ICIs) have shown considerable promise for HCC treatment and patients with higher LDHB expression responded better to ICIs. Finally, we found that overexpression of LDHB suppressed HCC growth in immunocompetent but not in immunodeficient mice, suggesting that the host immune system was involved in the LDHB-medicated tumor suppression. Our findings indicate that DNMT3A-mediated epigenetic silencing of LDHB may contribute to HCC progression through remodeling the tumor immune microenvironment, and LDHB may become a potential prognostic biomarker and therapeutic target for HCC immunotherapy.

Deep learning pan-specific model for interpretable MHC-I peptide binding prediction with improved attention mechanism.

Accurate prediction of peptide binding affinity to the major histocompatibility complex (MHC) proteins has the potential to design better therapeutic vaccines. Previous work has shown that pan-specific prediction algorithms can achieve better prediction performance than other approaches. However, most of the top algorithms are neural networks based black box models. Here, we propose DeepAttentionPan, an improved pan-specific model, based on convolutional neural networks and attention mechanisms for more flexible, stable and interpretable MHC-I binding prediction. With the attention mechanism, our ensemble model consisting of 20 trained networks achieves high and more stabilized prediction performance. Extensive tests on IEDB's weekly benchmark dataset show that our method achieves state-of-the-art prediction performance on 21 test allele datasets. Analysis of the peptide positional attention weights learned by our model demonstrates its capability to capture critical binding positions of the peptides, which leads to mechanistic understanding of MHC-peptide binding with high alignment with experimentally verified results. Furthermore, we show that with transfer learning, our pan model can be fine-tuned for alleles with few samples to achieve additional performance improvement. DeepAttentionPan is freely available as an open-source software at https://github.com/jjin49/DeepAttentionPan.

Dual effects of targeting S100A11 on suppressing cellular metastatic properties and sensitizing drug response in gastric cancer.

BACKGROUND: S100A11 is a member of the S100 family of proteins containing two EF-hand calcium-binding motifs. The dysregulated expression of the S100A11 gene has been implicated in tumour metastasis. However, the role of S100A11 protein in tumour cell response to chemotherapeutic drugs has not been characterised. METHODS: Transcript levels of S100A11 in gastric cancer were evaluated using an in-house patient cohort. Protein expression of S100A11 in gastric cancer was estimated by immunohistochemistry of a tissue microarray. The stable gastric cancer cell lines were established using lentiviral shRNA vectors. The knockdown of S100A11 was validated by qRT-PCR, PCR, and Western blot. The cellular function of S100A11 was estimated by assays of cell adhesion, migration, and invasion. The cell cytotoxic assay was performed to investigate the response to chemotherapeutic drugs. An unsupervised hierarchical clustering and principal component analysis (HCPC) was applied to unveil the dimensional role of S100A11 among all S100 family members in gastric cancer. RESULTS: High expression of S100A11 is associated with poor survival of gastric cancer patients (p < 0.001, HR = 1.85) and is an independent prognostic factor of gastric cancer. We demonstrate that S100A11 plays its role as a tumour promoter through regulating the MMP activity and the epithelial-mesenchymal transition (EMT) process. The stable knockdown of S100A11 suppresses the metastatic properties of gastric cancer cells, which include enhancing cell adhesion, but decelerating cell migration and invasion. Furthermore, the knockdown of S100A11 gene expression dramatically induces the cellular response of gastric cancer cells to the first-line chemotherapeutic drugs fluoropyrimidine 5-fluorouracil (5-FU) and cisplatin. CONCLUSION: The present study identifies S100A11 as a tumour promoter in gastric cancer. More importantly, the S100A11-specific targeting potentially presents dual therapeutic benefits by not only controlling tumour progression but also sensitising chemotherapeutic cytotoxic response.

Predicting drug-drug interactions using multi-modal deep auto-encoders based network embedding and positive-unlabeled learning.

Drug-drug interactions (DDIs) are crucial for public health and patient safety, which has aroused widespread concern in academia and industry. The existing computational DDI prediction methods are mainly divided into four categories: literature extraction-based, similarity-based, matrix operations-based and network-based. A number of recent studies have revealed that integrating heterogeneous drug features is of significant importance for developing high-accuracy prediction models. Meanwhile, drugs that lack certain features could utilize other features to learn representations. However, it also brings some new challenges such as incomplete data, non-linear relations and heterogeneous properties. In this paper, we propose a multi-modal deep auto-encoders based drug representation learning method named DDI-MDAE, to predict DDIs from large-scale, noisy and sparse data. Our method aims to learn unified drug representations from multiple drug feature networks simultaneously using multi-modal deep auto-encoders. Then, we apply four operators on the learned drug embeddings to represent drug-drug pairs and adopt the random forest classifier to train models for predicting DDIs. The experimental results demonstrate the effectiveness of our proposed method for DDI prediction and significant improvement compared to other state-of-the-art benchmark methods. Moreover, we apply a specialized random forest classifier in the positive-unlabeled (PU) learning setting to enhance the prediction accuracy. Experimental results reveal that the model improved by PU learning outperforms the original method DDI-MDAE by 7.1% and 6.2% improvement in AUPR metric respectively on 3-fold cross-validation (3-CV) and 5-fold cross-validation (5-CV). And in F-measure metric, the improved model gains 10.4% and 8.4% improvement over DDI-MDAE respectively on 3-CV and 5-CV. The usefulness of DDI-MDAE is further demonstrated by case studies.

Heterologous expression of AHL lactonase AiiK by Lactobacillus casei MCJΔ1 with great quorum quenching ability against Aeromonas hydrophila AH-1 and AH-4.

BACKGROUND: Nowadays, microbial infections have caused increasing economic losses in aquaculture industry and deteriorated worldwide environments. Many of these infections are caused by opportunistic pathogens through cell-density mediated quorum sensing (QS). The disruption of QS, known as quorum quenching (QQ), is an effective and promising way to prevent and control pathogens, driving it be the potential bio-control agents. In our previous studies, AHL lactonase AiiK was identified with many characteristics, and constitutive expression vector pELX1 was constructed to express heterologous proteins in Lactobacillus casei MCJΔ1 (L. casei MCJΔ1). In this study, recombinant strain pELCW-aiiK/L. casei MCJΔ1 (LcAiiK) and wild-type Aeromonas hydrophila (A. hydrophila) were co-cultured to test the QQ ability of LcAiiK against A. hydrophila. RESULTS: A cell wall-associated expression vector pELCW for L. casei MCJΔ1 was constructed. Localization assays revealed that the expressed AiiK was anchored at the surface layer of LcAiiK via vector pELCW-aiiK. LcAiiK (OD600 = 0.5) degraded 24.13 µM of C6-HSL at 2 h, 40.99 µM of C6-HSL at 12 h, and 46.63 µM of C6-HSL at 24 h. Over 50% LcAiiK cells maintained the pELCW-aiiK plasmid after 15 generations of cultivation without erythromycin. Furthermore, LcAiiK inhibited the swimming motility, extracellular proteolytic activity, haemolytic activity and biofilm formation of A. hydrophila AH-1 and AH-4. CONCLUSION: The AHL lactonase AiiK is firstly and constitutively expressed at the surface layer of L. casei MCJΔ1. LcAiiK displayed considerable AHL lactonase activity and great QQ abilities against A. hydrophila AH-1 and AH-4 by attenuating their QS processes instead of killing them. Therefore, the LcAiiK can be exploited as an anti-pathogenic drug or a bio-control agent to control the AHL-mediated QS of pathogenic bacteria.

Tim-3 promotes cell aggressiveness and paclitaxel resistance through NF-κB/STAT3 signalling pathway in breast cancer cells.

OBJECTIVE: Although T-cell immunoglobulin and mucin-domain containing molecule-3 (Tim-3) has been recognized as a promising target for cancer immunotherapy, its exact role in breast cancer has not been fully elucidated. METHODS: Tim-3 gene expression in breast cancer and its prognostic significance were analyzed. Associated mechanisms were then explored in vitro by establishing Tim-3-overexpressing breast cancer cells. RESULTS: In a pooled analysis of The Cancer Genome Atlas (TCGA) database, Tim-3 gene expression levels were significantly higher (P<0.001) in breast cancer tissue, compared with normal tissues. Tim-3 was a prognosis indicator in breast cancer patients [relapse-free survival (RFS), P=0.004; overall survival (OS), P=0.099]. Tim-3 overexpression in Tim-3low breast cancer cells promoted aggressiveness of breast cancer cells, as evidenced by enhanced proliferation, migration, invasion, tight junction deterioration and tumor-associated tubal formation. Tim-3 also enhanced cellular resistance to paclitaxel. Furthermore, Tim-3 exerted its function by activating the NF-κB/STAT3 signalling pathway and by regulating gene expression [cyclin D1 (CCND1), C-Myc, matrix metalloproteinase-1(MMP1), TWIST, vascular endothelial growth factor (VEGF) upregulation, concomitant with E-cadherin downregulation). Lastly, Tim-3 downregulated tight junction-associated molecules zona occludens (ZO)-2, ZO-1 and occludin, which may further facilitate tumor progression. CONCLUSIONS: Tim-3 plays an oncogenic role in breast cancer and may represent a potential target for antitumor therapy.

MicroRNA-7 suppresses the homing and migration potential of human endothelial cells to highly metastatic human breast cancer cells.

BACKGROUND: MicroRNA-7 (miR-7) has been observed as a potent tumour suppressor in multiple cancer types including breast cancer. The aim of this study was to investigate the response sensitivities of metastatic breast cancer cells to miR-7 and the roles of miR-7 in the interaction of endothelial cells and metastatic cancer cells. METHODS: Expression profile of miRNAs in a breast cancer specimen cohort and breast cancer cells were determined using real-time quantitative miRNA assays. Effect of the altering expression of miR-7 on migration, invasion, proliferation, interaction and underlying molecular mechanism of breast cancer cells and endothelial cells was investigated after treatment with the synthesised mimic of miR-7. Luciferase activity analysis was performed to validate Wave-3 as a novel target of miR-7. RESULTS: miR-7 expression was negatively correlated with the stage, grade and survival of the breast cancer patients. There was also differential expression of miRNAs including miR-7 in the breast cancer cells. The synthesised mimic of miR-7 inhibits the motility and wound healing potential of breast cancer cells. The highly metastatic MDA-MB-231 cells are more sensitive to the miR-7 treatment than the poorly invasive MCF-7 cells. Treatment with miR-7 downregulated the expression of EGFR, IGF1R and Wave3 in MDA-MB-231 cells but not in MCF-7 cells. In addition, we further demonstrated that miR-7 inhibited the proliferation, migration and invasion of endothelial cells. And more importantly, miR-7 suppressed the homing and migration of endothelial cells to more aggressive tumour cell conditions. CONCLUSIONS: Given the dual inhibitory effect of miR-7 on metastatic breast cancer cells alone and the interaction of endothelial cells with the tumour-conditioned microenvironment, we suggest miR-7 may be a new therapeutic candidate for its capacity not only to prevent breast cancer cell spreading but also to inhibit tumour-associated angiogenesis in the metastatic breast cancer.

[The correlations between corneal sensation, tear meniscus volume, and tear film osmolarity after femtosecond laser-assisted LASIK].

OBJECTIVE: To investigate the correlations between corneal sensation, tear meniscus volume, and tear film osmolarity after femtosecond laser-assisted LASIK (FS-LASIK) surgery. METHODS: In this prospective clinical study, 31 patients undergoing FS-LASIK for myopia were recruited. The upper and lower tear meniscus volumes (UTMV and LTMV) were measured by customized anterior segment optical coherence tomography, tear film osmolarity was measured by a TearLab Osmolarity test device, central corneal sensation was measured by a Cochet-Bonner esthesiometer preoperatively, at 1 week, 1 and 3 months postoperatively. Repeated measures analysis of variance was used to evaluate whether the tear film osmolarity, tear meniscus volume, and corneal sensation were changed after surgery. The correlations between these variables were analyzed by the Pearson correlation analysis. RESULTS: The tear film osmolarity was (310.03 ± 16.48) mOsms/L preoperatively, (323.51 ± 15.92) mOsms/L at 1 week, (319.93 ± 14.27) mOsms/L at 1 month, and (314.97±12.91) mOsms/L at 3 months. The UTMV was (0.42±0.15), (0.25± 0.09), (0.30±0.11), and (0.35±0.09) µL, respectively; the LTMV was (0.60±0.21),(0.37±0.08), (0.44± 0.14), and (0.52±0.17) µL, respectively. The tear film osmolarity was significantly higher at 1 week and 1 month postoperatively compared with the baseline (P=0.001, 0.004), and reduced to the preoperative level at 3 months (P=0.573). The UTMV, LTMV, and corneal sensation values presented significant decreases at all postoperative time points (all P<0.05). The Pearson correlation analysis showed the postoperative UTMV had a weak relationship with corneal sensation at 1 week after surgery (r=0.356,P=0.005). There were significant correlations between the preoperative LTMV and corneal sensation at 1 week, 1 and 3 months (respectively, r=0.422, 0.366, 0.352;P=0.001, 0.004, 0.006). No significant correlations were found between the tear film osmolarity, tear meniscus volume, and corneal sensation after surgery (all P>0.05). CONCLUSION: The tear film osmolarity, tear meniscus volume, and corneal sensation became aggravated due to the FS-LASIK surgery procedures. There were significant correlations between the preoperative tear meniscus volume and recovery of corneal sensation early after surgery. A higher tear meniscus volume before surgery may contribute to a faster corneal sensation recovery.

Adoptive cellular immunotherapy for the treatment of patients with breast cancer: a meta-analysis.

BACKGROUND: To evaluate the therapeutic efficacy of dendritic cells (DC) alone, cytokine-induced killer (CIK) cells alone and the combination of DC and CIK cells in the treatment of breast cancer, we performed a systemic review of the relevant published clinical studies, collectively referred to as DC-CIK cell therapy. METHODS: Six hundred thirty-three patients with breast cancer were assigned to cohorts, and a meta-analysis was conducted. RESULTS: The treatment of breast cancer with DC-CIK cells was associated with a significantly improved 1-year survival (P = 0.0001). The Karnofsky performance status scale of the patients treated with DC-CIK cells was significantly improved compared with that of the non-DC-CIK group (P < 0.0001). The percentage of T cells (CD3(+), CD4(+) and CD4(+)CD8(+)), CD16(+) monocytes, and CD3(+)CD56(+) natural killer T cells in the peripheral blood of cancer patients was significantly increased (P ≤ 0.05), whereas the percentage of CD4(+)CD25(+) regulatory T cells was not significantly decreased (P = 0.32) in the DC-CIK treatment group compared with the non-DC-CIK group. The levels of interleukin-2, interleukin-12, tumor necrosis factor-α, interferon-γ, and nucleolar organizer region protein in the peripheral blood of cancer patients, which reflect immune function, were significantly increased (P < 0.001) after DC-CIK cell treatment. Furthermore, after DC-CIK treatment, the average levels of the alpha-fetoprotein, cancer antigen embryonic antigen and carbohydrate antigen tumor markers were decreased (P < 0.00001). CONCLUSIONS: DC-CIK cell therapy markedly prolongs survival time, enhances immune function, and improves the efficacy of the treatment of breast cancer patients.

Resonance of fatty acid metabolism and immune infiltration in anti-PD-1 monotherapy for breast cancer.

The interaction between tumor fatty acid metabolism and immune microenvironment is a novel topic in oncology research, and the relationship of lipid-derived factors with immune editing in tumor is unclear. The breast cancer samples from the TCGA database were used as the training set, and samples from GSE42568 were employed as the validation set for constructing a model to identify a signature associated with fatty acid metabolism through Lasso Cox regression. And the changes in immune related signatures and risk score before and after anti-PD-1 monotherapy were caught by the differential analysis in GSE225078. A 14-gene prognostic risk scoring model identifying by fatty acid metabolism relevant signature was conducted, and the high risk group had shorter overall survival and progression free survival than low risk group. Many metabolism-related pathways were enriched in the high risk group, and many immune-related pathways were enriched in low risk group. The crucial differentially expressed genes between the high/low risk groups, CYP4F8 and CD52, were found to be strongly associated with SUCLA2 and ACOT4 of 14-gene model, and strongly related to immune infiltration. Immune related signatures, fatty acid metabolism-risk score and the expression level of ALDH1A1 (in 14-gene-model) changed after anti-PD-1 monotherapy. And the mice model results also showed anti-PD-1 mAb could significantly reduce the expression level of ALDH1A1 (p < 0.01). These results brought up the crosstalk between immune components and fatty acid metabolism in breast cancer microenvironment, which provided a new possibility of targeting fatty acid metabolism for combination therapy in breast cancer immunotherapy.

Total synthesis of (+)- and (-)-decursivine and (±)-serotobenine through a cascade Witkop photocyclization/elimination/addition sequence: scope and mechanistic insights.

In this article, the total syntheses of antimalarial compound decursivine and its biologically inactive sibling serotobenine are presented. The biomimetic synthesis of (±)-serotobenine was investigated first, but failed. During the subsequent investigation of other synthetic routes, we discovered a new cascade Witkop photocyclization/elimination/addition sequence, which enabled the expedient synthesis of not only racemic decursivine and serotobenine, but also enantiopure (+)- and (-)-decursivine and a variety of their analogues. The present syntheses represent the shortest pathway for the total synthesis of decursivine and serotobenine to date. Moreover, the newly developed cascade sequence for the total synthesis of decursivine does not need any protecting steps. The scope and the reaction mechanism of the cascade sequence were also studied. A rational mechanism for the cascade sequence is proposed, which is consistent with the previous studies and our current experimental results.

Direct transformation of methyl imines to α-iminonitriles under mild and transition-metal-free conditions.

A novel transformation of methyl imines into α-iminonitriles under mild and transition-metal-free conditions is described. Three C sp 3-H bonds are cleaved in a radical pathway at room temperature under air. Simple bromide salts are employed to assist this radical process (see scheme; FG=functional group, PIDA = iodobenzene diacetate, TMS = trimethylsilyl).

Transplantation of human pericyte progenitor cells improves the repair of infarcted heart through activation of an angiogenic program involving micro-RNA-132.

RATIONALE: Pericytes are key regulators of vascular maturation, but their value for cardiac repair remains unknown. OBJECTIVE: We investigated the therapeutic activity and mechanistic targets of saphenous vein-derived pericyte progenitor cells (SVPs) in a mouse myocardial infarction (MI) model. METHODS AND RESULTS: SVPs have a low immunogenic profile and are resistant to hypoxia/starvation (H/S). Transplantation of SVPs into the peri-infarct zone of immunodeficient CD1/Foxn-1(nu/nu) or immunocompetent CD1 mice attenuated left ventricular dilatation and improved ejection fraction compared to vehicle. Moreover, SVPs reduced myocardial scar, cardiomyocyte apoptosis and interstitial fibrosis, improved myocardial blood flow and neovascularization, and attenuated vascular permeability. SVPs secrete vascular endothelial growth factor A, angiopoietin-1, and chemokines and induce an endogenous angiocrine response by the host, through recruitment of vascular endothelial growth factor B expressing monocytes. The association of donor- and recipient-derived stimuli activates the proangiogenic and prosurvival Akt/eNOS/Bcl-2 signaling pathway. Moreover, microRNA-132 (miR-132) was constitutively expressed and secreted by SVPs and remarkably upregulated, together with its transcriptional activator cyclic AMP response element-binding protein, on stimulation by H/S or vascular endothelial growth factor B. We next investigated if SVP-secreted miR-132 acts as a paracrine activator of cardiac healing. In vitro studies showed that SVP conditioned medium stimulates endothelial tube formation and reduces myofibroblast differentiation, through inhibition of Ras-GTPase activating protein and methyl-CpG-binding protein 2, which are validated miR-132 targets. Furthermore, miR-132 inhibition by antimiR-132 decreased SVP capacity to improve contractility, reparative angiogenesis, and interstitial fibrosis in infarcted hearts. CONCLUSION: SVP transplantation produces long-term improvement of cardiac function through a novel paracrine mechanism involving the secretion of miR-132 and inhibition of its target genes.

Waste-biomass-derived activated carbon supported Co-Cu-P nanocatalysts for hydrolytic dehydrogenation of ammonia borane.

Hydrolytic dehydrogenation of ammonia borane is a significant and promising approach for on-site hydrogen production at ambient conditions, and developing highly efficient and low-cost catalysts has attracted considerable attention. Herein, waste-biomass-derived activated carbon (AC) was prepared by hydrothermal carbonization and alkali-assisted activation, and non-precious bimetal phosphides (Co-Cu-P) nanocatalysts with a series of different Co/Cu ratios were synthesized on the AC surface through in situ phosphidation method. Owing to the synergetic effects, the optimal Co0.8Cu0.2P/AC presents an outstanding turnover frequency of 26.5 min-1 (25 °C), which is much higher than that of many reported catalysts. The reaction activation energy was measured to be 34.6 kJ mol-1. Benefiting from the ferromagnetic nature of the phosphides, the Co0.8Cu0.2P/AC can be magnetically separated and reused again. After recycling six times, the catalyst still retains 72% of the initial activity, thus indicating great potential for practical applications.

Influence of Analytic Processing on Divergent and Convergent Thinking Tasks: The Role of Rational and Experiential Thinking Styles.

Scientific interest in the relationship between analytic processing and creativity has increased in recent years. However, there is conflicting evidence on whether analytic processing reduces or enhances creativity. We hypothesize that differences in creativity measurement paradigms (divergent or convergent thinking tasks) and the research orientation of analytic processing (dispositional or situational) may explain the conflicting findings. The present study aims to investigate how priming analytic processing affects individuals' performance on divergent and convergent thinking tasks and the moderating role of thinking styles. In Study 1 (N = 155), participants were assigned to either an analytic processing group or a control group and performed convergent thinking (Remote Associates Task) and divergent thinking (Alternative Uses Test) tasks after priming. In Study 2 (N = 119), we conducted a priming paradigm of analytic processing that differed from Study 1, and a personal experiential-rational thinking style was introduced as a moderator. Results showed that priming analytic processing promoted convergent thinking performance but decreased fluency and flexibility scores on the divergent thinking task (Study 1). Notably, the effect of priming analytic processing on divergent thinking performance was significant only for participants with higher levels of rational thinking style (Study 2). These results suggest that thinking styles and dimensions of creativity should be considered in the relationship between analytic processing and creativity.

Dual-level clustering ensemble algorithm with three consensus strategies.

Clustering ensemble (CE), renowned for its robust and potent consensus capability, has garnered significant attention from scholars in recent years and has achieved numerous noteworthy breakthroughs. Nevertheless, three key issues persist: (1) the majority of CE selection strategies rely on preset parameters or empirical knowledge as a premise, lacking adaptive selectivity; (2) the construction of co-association matrix is excessively one-sided; (3) the CE method lacks a more macro perspective to reconcile the conflicts among different consensus results. To address these aforementioned problems, a dual-level clustering ensemble algorithm with three consensus strategies is proposed. Firstly, a backward clustering ensemble selection framework is devised, and its built-in selection strategy can adaptively eliminate redundant members. Then, at the base clustering consensus level, taking into account the interplay between actual spatial location information and the co-occurrence frequency, two modified relation matrices are reconstructed, resulting in the development of two consensus methods with different modes. Additionally, at the CE consensus level with a broader perspective, an adjustable Dempster-Shafer evidence theory is developed as the third consensus method in present algorithm to dynamically fuse multiple ensemble results. Experimental results demonstrate that compared to seven other state-of-the-art and typical CE algorithms, the proposed algorithm exhibits exceptional consensus ability and robustness.

Enhancing Drug-Drug Interaction Prediction Using Deep Attention Neural Networks.

Drug-drug interactions are one of the main concerns in drug discovery. Accurate prediction of drug-drug interactions plays a key role in increasing the efficiency of drug research and safety when multiple drugs are co-prescribed. With various data sources that describe the relationships and properties between drugs, the comprehensive approach that integrates multiple data sources would be considerably effective in making high-accuracy prediction. In this paper, we propose a Deep Attention Neural Network based Drug-Drug Interaction prediction framework, abbreviated as DANN-DDI, to predict unobserved drug-drug interactions. First, we construct multiple drug feature networks and learn drug representations from these networks using the graph embedding method; then, we concatenate the learned drug embeddings and design an attention neural network to learn representations of drug-drug pairs; finally, we adopt a deep neural network to accurately predict drug-drug interactions. The experimental results demonstrate that our model DANN-DDI has improved prediction performance compared with state-of-the-art methods. Moreover, the proposed model can predict novel drug-drug interactions and drug-drug interaction-associated events.

Mussel-inspired dynamic facet-selective capping approach to highly uniform α-calcium sulfate hemihydrate crystals.

We report herein a dynamic facet-selective capping (dFSC) strategy for α-calcium sulfate hemihydrate crystal growth from dihydrate gypsum in the presence of a catechol-derived PEI capping agent (DPA-PEI) with inspiration by the biomineralization process of mussel. The crystal shape is controllable and varies from long and pyramid-tipped prisms to thin hexagonal plate. The highly uniform truncated crystals have extremely high compression and bending strengths after hydration molding.

N-heterocyclic carbene-catalyzed homoenolate additions with N-aryl ketimines as electrophiles: efficient synthesis of spirocyclic γ-lactam oxindoles.

In pole position: A simple and efficient approach to spirocyclic γ-lactam oxindoles by the N-heterocyclic carbene catalyzed addition of homoenloate equivalents to N-aryl isatinimines has been developed (see scheme). The use of N-aryl isatinimines as electrophiles in the NHC-catalyzed umpolung reaction of α,ß-unsaturated aldehydes is demonstrated for the first time.