Research on visual question answering based on dynamic memory network model of multiple attention mechanisms.

Since the existing visual question answering model lacks long-term memory modules for answering complex questions, it is easy to cause the loss of effective information. In order to further improve the accuracy of the visual question answering model, this paper applies the multiple attention mechanism combining channel attention and spatial attention to memory networks for the first time and proposes a dynamic memory network model (DMN-MA) based on the multiple attention mechanism. The model uses the multiple attention mechanism in the situational memory module to obtain the most relevant visual vectors for answering questions based on continuous memory updating, storage and iterative inference of the questions, and effectively uses contextual information for answer inference. The experimental results show that the accuracy of the model in this paper reaches 64.57% and 67.18% on the large-scale public datasets COCO-QA and VQA2.0, respectively.

Ligand- and structure-based identification of novel CDK9 inhibitors for the potential treatment of leukemia.

Cyclin-dependent kinase 9 (CDK9) plays a vital role in controlling cell transcription and has been an attractive target for cancer treatment. Herein, ten predictive models derived from 1330 unique molecules against CDK9 were constructed based on molecular fingerprints and graphs using two conventional machine learning and four deep learning methods. The evaluation results showed that FP-GNN deep learning architecture performed best for CDK9 inhibitors prediction with the highest BA and F1 values of 0.681 and 0.912 for testing set. We then performed virtual screening to identify new CDK9 inhibitors by incorporating the optimal established predictive model and molecular docking. Five compounds were identified to show broad anticancer activity against various cancer cell lines through bioassays. For example, C9 exhibited antiproliferative activities against HeLa, MOLM-13 and MDA-MB-231 with IC50 values of 2.53, 3.92 and 11.65 µM. Kinase inhibition assay results demonstrated that these compounds displayed submicromolar (214 âˆ¼ 504 nM) inhibitory activities against CDK9. Further cellular mechanism evaluation revealed that C9 suppressed the activity of CDK9 and interfered with the expression of Mcl-1 and cleaved PARP in MOLM-13 cells, resulting in the induction of cellular apoptosis. In addition, C9 displayed a good stability in rat liver microsomes, artificial gastrointestinal fluid and plasm. An online platform (called DEEPCDK9Pred) was developed based on the FP-GNN models to predict or design new CDK9 inhibitors. Collectively, our findings demonstrated that FP-GNN algorithm can achieve accurate prediction of CDK9 inhibitors and the subsequent discovery of C9 as a new potential CDK9 inhibitor deserves further structural modification for the treatment of leukemia.

Corrigendum: Machine Learning Enables Accurate and Rapid Prediction of Active Molecules Against Breast Cancer Cells.

[This corrects the article DOI: 10.3389/fphar.2021.796534.].

Machine Learning Enables Accurate and Rapid Prediction of Active Molecules Against Breast Cancer Cells.

Breast cancer (BC) has surpassed lung cancer as the most frequently occurring cancer, and it is the leading cause of cancer-related death in women. Therefore, there is an urgent need to discover or design new drug candidates for BC treatment. In this study, we first collected a series of structurally diverse datasets consisting of 33,757 active and 21,152 inactive compounds for 13 breast cancer cell lines and one normal breast cell line commonly used in in vitro antiproliferative assays. Predictive models were then developed using five conventional machine learning algorithms, including naïve Bayesian, support vector machine, k-Nearest Neighbors, random forest, and extreme gradient boosting, as well as five deep learning algorithms, including deep neural networks, graph convolutional networks, graph attention network, message passing neural networks, and Attentive FP. A total of 476 single models and 112 fusion models were constructed based on three types of molecular representations including molecular descriptors, fingerprints, and graphs. The evaluation results demonstrate that the best model for each BC cell subtype can achieve high predictive accuracy for the test sets with AUC values of 0.689-0.993. Moreover, important structural fragments related to BC cell inhibition were identified and interpreted. To facilitate the use of the model, an online webserver called ChemBC (http://chembc.idruglab.cn/) and its local version software (https://github.com/idruglab/ChemBC) were developed to predict whether compounds have potential inhibitory activity against BC cells.

Dioscin Promotes Prostate Cancer Cell Apoptosis and Inhibits Cell Invasion by Increasing SHP1 Phosphorylation and Suppressing the Subsequent MAPK Signaling Pathway.

Dioscin possesses antioxidant effects and has anticancer ability in many solid tumors including prostate cancer (PCa). Nevertheless, its effect and mechanism of anti-PCa action remain unclear. The tyrosine protein phosphatase SHP1, which contains an oxidation-sensitive domain, has been confirmed as a target for multicancer treatment. Further studies are needed to determine whether dioscin inhibits PCa through SHP1. We performed in vitro studies using androgen-sensitive (LNCaP) and androgen-independent (LNCaP -C81) cells to investigate the anticancer effects and possible mechanisms of dioscin after administering interleukin-6 (IL-6) and dihydrotestosterone (DHT). Our results show that dioscin inhibited cell growth and invasion by increasing SHP1 phosphorylation [p-SHP1 (Y536)] and inhibiting the subsequent P38 mitogen-activated protein kinase signaling pathway. Further in vivo studies confirmed that dioscin promoted caspase-3 and Bad-related cell apoptosis in these two cell lines. Our research suggests that the anticancer effects of dioscin on PCa may occur through SHP1. Dioscin may be useful to treat androgen-sensitive and independent PCa in the future.

miR-223-5p targeting ERG inhibits prostate cancer cell proliferation and migration.

Ectopic expression of miR-223-5p, the lagging strand of miR-223 duplex, has been reported acting as anti-tumor miRNA in many cancers. How miR-223-5p influencing prostate cancer (PCa) remains obscure and worth of experimental investigation. In this study, the expressions of miR-223-5p and ERG in common PCa cell lines were detected and compared to RWPE-1, respectively. Then luciferase reporter assay was performed to verify whether miR-223-5p could specifically target and regulate ERG. Further discovery ERG's role in the PCa oncogenesis was also conducted by up or down regulating miR-223-3p expression. We found miR-223-5p was significantly down-regulated in DU145, while it was only up-regulated in LNCaP. Similarly, ERG expression remarkably decreased in both PC-3 and DU145 than that in RWPE-1, but significantly increasing in LNCaP. Luciferase assay demonstrated slightly decreased ERG expression after miR-223-5p-mimics but significantly increased ERG expression after miR-223-5p-inhibtor. Using gene interference, we further confirmed that both ERG mRNA and protein expressions were decreased in all PCa lines transfected ERG siRNA, but increasing in both DU145 and LNCaP cells with miR-223-5p antisense oligonucleotides. MTT assay, Transwell invasion and migration assay supported the function of ERG in PCa oncogenesis. We revealed tumor suppressive abilities of miR-223-5p in PCa by negatively targeting ERG gene. It could serve as a fundamental supplement and extension of our previous study about miR-223-3p in PCa, revealing the coordinative regulation between miR-223-5p and miR-223-3p in PCa cell biological behaviors. Exploration of miR-233-duplex orientated pathway networks may help us develop novel potential therapeutic options for PCa.

lncRNA DGCR5 Up-Regulates TGF-ß1, Increases Cancer Cell Stemness and Predicts Survival of Prostate Cancer Patients.

BACKGROUND: Long non-coding RNA (lncRNA) DiGeorge syndrome critical region gene 5 (DGCR5) plays different roles in different types of human cancer, but its role in prostate cancer (PC) has not been reported. METHODS: DGCR5 and TGF-ß1 expression in paired tumor and adjacent healthy tissues from 64 PC patients was analyzed by performing RT-qPCR. A 5-year follow-up study was performed to analyze the prognostic value of DGCR5 for PC. The interaction between DGCR5 and TGF-ß1 was analyzed by overexpression experiments. Cell stemness was analyzed by cell stemness assay. RESULTS: In our study, we found that DGCR5 was down-regulated in tumor tissues than in adjacent healthy tissues of PC patients, but TGF-ß1 was up-regulated in the tumor tissues. DGCR5 expression was not affected by clinical stages, but low DGCR5 level in the tumor was correlated with poor survival. DGCR5 and TGF-ß1 were inversely correlated in tumor tissues but not in adjacent healthy tissues. DGCR5 over-expression resulted in down-regulation of TGF-ß1, while TGF-ß1 treatment did not significantly affect DGCR5 expression. DGCR5 over-expression led to decreased stemness of PC cells, but TGF-ß1 treatment played a reverse role and attenuated the effects of DGCR5 over-expression. DGCR5 may decrease the stemness of PC cells by down-regulating TGF-ß1.

[DMSO arrested hybridoma cells for enhanced antibody production].

Dimethyl sulfoxide (DMSO), a well-known differentiation inducer in several myeloid cells, induces G1 phase arrest in many cell lines. In this study, we investigated the possibility of using DMSO to arrest H18 hybridoma cells to the G1 phase and monitor whether the arrest improves antibody production. We showed that DMSO in concentration ranging between 0.3% and 0.6% efficiently arrested H18 hybridoma cells in G1 phase. In our experiment, > 80% of cells grown for 36h in presence of the 0.6% DMSO were arrested in G1. Furthermore, expression levels of P27 were up-regulated tow fold during the G1 phase. Higher concentration of DMSO at 0.9% leads to cytotoxicity. Herein we show a simple way, a two-stage process for antibody production, which consists of a proliferation phase leading to the desired cell density, followed by an extended production phase during which the cells remain at G1 phase. Our observation that the addition of DMSO results in increase antibody production is of significance in further use of hybridoma cells in high density large scale cell culture.

[The role of vagal innervation in the variability of heart beat].

To determine the role of vagi in heart rate variability, conscious rabbits were employed and electrocardiogram was recorded under conditions of bilateral vagi intact, unilateral vagotomy, and bilateral vagotomy. The variability of RR intervals (RRI) was analyzed using power spectrum and approximate entropy (ApEn). The results showed that the values of high frequency power (HF) component, low frequency power (LF) component and ApEn in animals with bilateral vagi intact were the highest, but the LF/HF ratio was the lowest; unilateral vagotomy decreased ApEn, right vagotomy increased LF/HF ratio but left vagotomy did not; the LF/HF ratio increased while ApEn decreased significantly in animals with bilateral vagotomy. It is suggested that the variability of RRI is mainly regulated by the vagi and the role of right vagi is more important than that of the left. When measuring heart rate variability, the results obtained with conventional method are consistent with those with nonlinear method.