Iodine-Promoted Reductive Sulfenylation Using Ketones as Hydride Donors.

Herein, an iodine-promoted reductive sulfenylation reaction of ketones with disulfides has been developed. This method provides an approach for synthesizing unsymmetrical alkyl-alkyl and alkyl-aryl sulfides in a single step. Investigation of the reaction mechanism revealed that ketones play a dual role in this process. They react with disulfides to produce vinyl thioethers and act as effective organic hydride donors, reducing the number of vinyl thioethers that are formed in situ. This study expands the range of applications of ketones in chemical synthesis.

Iodine-promoted transfer of dihydrogen from ketones to alkenes, triphenylmethyl, and diphenylmethyl derivatives.

Herein, a novel class of transfer hydrogenation agent, cycloheptanone, was successfully employed in metal-free hydrogenation facilitated by iodine. A series of alkenes, triphenylmethyl derivatives, and diphenylmethyl derivatives were reduced to the desired compounds in moderate to excellent yields. The transfer hydrodeuteration of alkenes using α-deuterated cyclododecanone exhibited high regioselectivity. Preliminary mechanism studies confirmed the origins of the two hydrogen atoms involved in the reduction of alkenes. The current study paves the way for the use of ketones as unique transfer hydrogenation agents in chemical synthesis.

Cellular uptake, intracellular behavior, and acute/sub-acute cytotoxicity of a PEG-modified quantum dot with promising in-vivo biomedical applications.

Quantum Dots (QDs) modified with branched Polyethylene Glycol-amine (6- or 8-arm PEG-amine) coupled with methoxy PEG (mPEG) hold great promise for in vivo biomedical applications due to a long half-life in blood and negligible toxicity. However, the potential risks regarding their concomitant prolonged co-incubation with cardiovascular and blood cells remains inconclusive. In the present study, the feasible, effective and convenient proliferating-restricted cell line models representing the circulatory system were established to investigate the cellular internalization followed by intracellular outcomes and resulting acute/sub-acute cytotoxicity of the 6-arm PEG-amine/mPEG QDs. We found a dose-, time- and cell type-dependent cellular uptake of the 6-arm PEG-amine/mPEG QDs, which was ten-fold lower compared to the traditional linear PEG-modified counterpart. The QDs entered cells via multiple endocytic pathways and were mostly preserved in Golgi apparatus for at least one week instead of degradation in lysosomes, resulting in a minimal acute cytotoxicity, which is much lower than other types of PEG-modified QDs previously reported. However, a sub-acute cytotoxicity of QDs were observed several days post exposure using the concentrations eliciting no-significant acute cytotoxic effects, which was associated with elevated ROS generation caused by QDs remained inside cells. Finally, a non-cytotoxic concentration of the QDs was identified at the sub-acute cytotoxic level. Our study provided important information for clinical translation of branched PEG-amine/mPEG QDs by elucidating the QDs-cell interactions and toxicity mechanism using the proliferation-restricted cell models representing circulatory system. What's more, we emphasized the indispensability of sub-acute cytotoxic effects in the whole biosafety evaluation process of nanomaterials like QDs.

Interpretable Multi-modal Image Registration Network Based on Disentangled Convolutional Sparse Coding.

Multi-modal image registration aims to spatially align two images from different modalities to make their feature points match with each other. Captured by different sensors, the images from different modalities often contain many distinct features, which makes it challenging to find their accurate correspondences. With the success of deep learning, many deep networks have been proposed to align multi-modal images, however, they are mostly lack of interpretability. In this paper, we first model the multi-modal image registration problem as a disentangled convolutional sparse coding (DCSC) model. In this model, the multi-modal features that are responsible for alignment (RA features) are well separated from the features that are not responsible for alignment (nRA features). By only allowing the RA features to participate in the deformation field prediction, we can eliminate the interference of the nRA features to improve the registration accuracy and efficiency. The optimization process of the DCSC model to separate the RA and nRA features is then turned into a deep network, namely Interpretable Multi-modal Image Registration Network (InMIR-Net). To ensure the accurate separation of RA and nRA features, we further design an accompanying guidance network (AG-Net) to supervise the extraction of RA features in InMIR-Net. The advantage of InMIR-Net is that it provides a universal framework to tackle both rigid and non-rigid multi-modal image registration tasks. Extensive experimental results verify the effectiveness of our method on both rigid and non-rigid registrations on various multi-modal image datasets, including RGB/depth images, RGB/near-infrared (NIR) images, RGB/multi-spectral images, T1/T2 weighted magnetic resonance (MR) images and computed tomography (CT)/MR images. The codes are available at https://github.com/lep990816/Interpretable-Multi-modal-Image-Registration.

Design, synthesis and biological evaluation of novel 9-N-substituted-13-alkylberberine derivatives from Chinese medicine as anti-hepatocellular carcinoma agents.

A series of novel 9-N-substituted-13-alkylberberine derivatives from Chinese medicine were designed and synthesized with improved anti-hepatocellular carcinoma (HCC) activities. The optimal compound 4d showed strong activities against HepG2, Sk-Hep-1, Huh-7 and Hep3B cells with IC50 values of 0.58-1.15 µM, which were superior to positive reference cisplatin. Interestingly, 4d exhibited over 40-fold more potent activity against cisplatin-resistant HepG2/DPP cells while showing lower cytotoxicity in normal LX-2 cells. The mechanism studies revealed 4d greatly stabilized G-quadruplex DNA leading to intracellular c-MYC expression downregulation, blocked G2/M-phase cell cycle by affecting related p-cdc25c, cdc2 and cyclin B1 expressions, and induced apoptosis by a ROS-promoted PI3K/Akt-mitochondrial pathway. Furthermore, 4d possessed good pharmacokinetic properties and significantly inhibited the tumor growth in the H22 liver cancer xenograft mouse model without obvious toxicity. Altogether, the remarkably biological profiles of 4d both in vitro and in vivo would make it a promising candidate for HCC therapy.

Development of novel 9-O-substituted-13-octylberberine derivatives as potential anti-hepatocellular carcinoma agents.

A series of novel 9-O-substituted-13-octylberberine derivatives were designed, synthesised and evaluated for their anti-hepatocellular carcinoma (HCC) activities. Compound 6k showed the strongest activity against three human hepatoma cells including HepG2, Sk-Hep-1 and Huh-7 cells with IC50 values from 0.62 to 1.69 µM, which were much superior to berberine (IC50 >50 µM). More importantly, 6k exhibited lower cytotoxicity against normal hepatocytes L-02 with good lipid-water partition properties. The mechanism studies revealed that 6k caused G2/M phase arrest of the cell cycle, stabilised G-quadruplex DNA, and induced apoptosis via a mitochondrial apoptotic pathway. Finally, the in vivo anti-HCC activity of 6k was validated in the H22 liver cancer xenograft mouse model. Collectively, the current study would provide a new insight into the discovery of novel, safe and effective anti-HCC agents.

Discovery of a Novel Vascular Disrupting Agent Inhibiting Tubulin Polymerization and HDACs with Potent Antitumor Effects.

Most vascular disrupting agents (VDAs) fail to prevent the regrowth of blood vessels at the edge of tumors, causing tumor rebound and relapse. Herein, a series of novel multifunctional vascular disrupting agents (VDAs) capable of inhibiting microtubule polymerization and histone deacetylases (HDACs) were designed and synthesized using the tubulin polymerization inhibitor TH-0 as the lead compound. Among them, compound TH-6 exhibited the most potent antiproliferative activity (IC50 = 18-30 nM) against a panel of cancer cell lines. As expected, TH-6 inhibited tubulin assembly and increased the acetylation level of HDAC substrate proteins in HepG2 cells. Further in vivo antitumor assay displayed that TH-6 effectively inhibited tumor growth with no apparent toxicity. More importantly, TH-6 disrupted both the internal and peripheral tumor vasculatures, which contributed to the persistent tumor inhibitory effects after drug withdrawal. Altogether, TH-6 deserves to be further investigated for the new approach to clinical cancer therapy.

Design and synthesis of NAD(P)H: Quinone oxidoreductase (NQO1)-activated prodrugs of 23-hydroxybetulinic acid with enhanced antitumor properties.

A series of NQO1 selectively activated prodrugs were designed and synthesized by introducing indolequinone moiety to the C-3, C-23 or C-28 position of 23-hydroxybetulinic acid (23-HBA) and its analogues. Among them, the representative compound 32j exhibited significant antiproliferative activities against NQO1-overexpressing HT-29 cells and A549 cells, with IC50 values of 1.87 and 2.36 µM, respectively, which were 20-30-fold more potent than those of parent compound 23-HBA. More importantly, it was demonstrated in the in vivo antitumor experiment that 32j effectively suppressed the tumor volume and largely reduced tumor weight by 72.69% with no apparent toxicity, which was more potent than the positive control 5-fluorouracil. This is the first breakthrough in the improvement of in vivo antitumor activities of 23-HBA derivatives. The further molecular mechanism study revealed that 32j blocked cell cycle arrest at G2/M phase, induced cell apoptosis, depolarized mitochondria and elevated the intracellular ROS levels in a dose-dependent manner. Western blot analysis indicated that 32j induced cell apoptosis by interfering with the expression of apoptosis-related proteins. These findings suggest that compound 32j could be considered as a potent antitumor prodrug candidate which deserves to be further investigated for personalized cancer therapy.

Effective suppression of triple negative breast cancer by paclitaxel nanoparticles conjugated with transmembrane TNF-α monoclonal antibody.

Transmembrane TNF-α (tmTNF), a transmembrane form of TNF-α, was reported overexpressed in approximately 84% of triple-negative breast cancer (TNBC) patients and has emerged as a valid candidate biomarker for targeting TNBC. Paclitaxel is a first-line chemotherapeutic agent for the treatment of triple-negative breast cancer, but suffers from low water solubility, resulting in its low bioavailability. To achieve site-specific delivery of the anticancer chemotherapeutic drug (paclitaxel) on TNBC, we developed tmTNF-α monoclonal antibody (mAb)-conjugated paclitaxel (PTX) nanoparticles (NPs) (tmTNF-α mAb-PTX NPs) as potential nanocarriers. This targeted delivery-therapy nanocarriers was conducted by using an emulsification-evaporation method. tmTNF-α mAb-PTX NPs displayed favorable physicochemical properties. Compared with the control groups, tumor growth in human MDA-MB-231 xenograft mice was suppressed significantly by tmTNF-α mAb-PTX NPs. TmTNF-α mAb-PTX NPs exerts anti-tumor effects via promoting apoptosis and regulating mitogen-activated protein kinase (MAPK), phosphatidylinositol 3-kinase (PI3K) / protein kinase B (AKT)/ mammalian target of rapamycin (mTOR) cascade, as well as AMP-activated protein kinase (AMPK) and nuclear factor Kappa-B (NF-κB) pathways. Moreover, tmTNF-α mAb-PTX NPs can inhibit the process of epithelial-mesenchymal transition (EMT) in TNBC to suppress tumor progression and metastasis. Together, the novel tmTNF-α mAb-PTX NPs based targeted drug delivery system is a potentially highly effective approach for treating TNBC.

fMRI Brain Decoding and Its Applications in Brain-Computer Interface: A Survey.

Brain neural activity decoding is an important branch of neuroscience research and a key technology for the brain-computer interface (BCI). Researchers initially developed simple linear models and machine learning algorithms to classify and recognize brain activities. With the great success of deep learning on image recognition and generation, deep neural networks (DNN) have been engaged in reconstructing visual stimuli from human brain activity via functional magnetic resonance imaging (fMRI). In this paper, we reviewed the brain activity decoding models based on machine learning and deep learning algorithms. Specifically, we focused on current brain activity decoding models with high attention: variational auto-encoder (VAE), generative confrontation network (GAN), and the graph convolutional network (GCN). Furthermore, brain neural-activity-decoding-enabled fMRI-based BCI applications in mental and psychological disease treatment are presented to illustrate the positive correlation between brain decoding and BCI. Finally, existing challenges and future research directions are addressed.

Semantic Perceptual Image Compression With a Laplacian Pyramid of Convolutional Networks.

The existing image compression methods usually choose or optimize low-level representation manually. Actually, these methods struggle for the texture restoration at low bit rates. Recently, deep neural network (DNN)-based image compression methods have achieved impressive results. To achieve better perceptual quality, generative models are widely used, especially generative adversarial networks (GAN). However, training GAN is intractable, especially for high-resolution images, with the challenges of unconvincing reconstructions and unstable training. To overcome these problems, we propose a novel DNN-based image compression framework in this paper. The key point is decomposing an image into multi-scale sub-images using the proposed Laplacian pyramid based multi-scale networks. For each pyramid scale, we train a specific DNN to exploit the compressive representation. Meanwhile, each scale is optimized with different aspects, including pixel, semantics, distribution and entropy, for a good "rate-distortion-perception" trade-off. By independently optimizing each pyramid scale, we make each stage manageable and make each sub-image plausible. Experimental results demonstrate that our method achieves state-of-the-art performance, with advantages over existing methods in providing improved visual quality. Additionally, a better performance in the down-stream visual analysis tasks which are conducted on the reconstructed images, validates the excellent semantics-preserving ability of the proposed method.

Deep Coupled Feedback Network for Joint Exposure Fusion and Image Super-Resolution.

Nowadays, people are getting used to taking photos to record their daily life, however, the photos are actually not consistent with the real natural scenes. The two main differences are that the photos tend to have low dynamic range (LDR) and low resolution (LR), due to the inherent imaging limitations of cameras. The multi-exposure image fusion (MEF) and image super-resolution (SR) are two widely-used techniques to address these two issues. However, they are usually treated as independent researches. In this paper, we propose a deep Coupled Feedback Network (CF-Net) to achieve MEF and SR simultaneously. Given a pair of extremely over-exposed and under-exposed LDR images with low-resolution, our CF-Net is able to generate an image with both high dynamic range (HDR) and high-resolution. Specifically, the CF-Net is composed of two coupled recursive sub-networks, with LR over-exposed and under-exposed images as inputs, respectively. Each sub-network consists of one feature extraction block (FEB), one super-resolution block (SRB) and several coupled feedback blocks (CFB). The FEB and SRB are to extract high-level features from the input LDR image, which are required to be helpful for resolution enhancement. The CFB is arranged after SRB, and its role is to absorb the learned features from the SRBs of the two sub-networks, so that it can produce a high-resolution HDR image. We have a series of CFBs in order to progressively refine the fused high-resolution HDR image. Extensive experimental results show that our CF-Net drastically outperforms other state-of-the-art methods in terms of both SR accuracy and fusion performance. The software code is available here https://github.com/ytZhang99/CF-Net.

Saliency Prediction on Omnidirectional Image With Generative Adversarial Imitation Learning.

When watching omnidirectional images (ODIs), subjects can access different viewports by moving their heads. Therefore, it is necessary to predict subjects' head fixations on ODIs. Inspired by generative adversarial imitation learning (GAIL), this paper proposes a novel approach to predict saliency of head fixations on ODIs, named SalGAIL. First, we establish a dataset for attention on ODIs (AOI). In contrast to traditional datasets, our AOI dataset is large-scale, which contains the head fixations of 30 subjects viewing 600 ODIs. Next, we mine our AOI dataset and discover three findings: (1) the consistency of head fixations are consistent among subjects, and it grows alongside the increased subject number; (2) the head fixations exist with a front center bias (FCB); and (3) the magnitude of head movement is similar across the subjects. According to these findings, our SalGAIL approach applies deep reinforcement learning (DRL) to predict the head fixations of one subject, in which GAIL learns the reward of DRL, rather than the traditional human-designed reward. Then, multi-stream DRL is developed to yield the head fixations of different subjects, and the saliency map of an ODI is generated via convoluting predicted head fixations. Finally, experiments validate the effectiveness of our approach in predicting saliency maps of ODIs, significantly better than 11 state-of-the-art approaches. Our AOI dataset and code of SalGAIL are available online at https://github.com/yanglixiaoshen/SalGAIL.

Prognostic Role of S100A8 in Human Solid Cancers: A Systematic Review and Validation.

BACKGROUND: S100A8 plays a key role in many cellular processes and is highly expressed in various solid cancers. However, the prognostic role of S100A8 has not been well defined. Therefore, we conducted a quantitative meta-analysis to investigate whether or not S100A8 could be used as a prognostic biomarker in solid tumors. METHODS: PubMed, Web of Science, Embase, and Cochrane library were searched to acquire relevant studies that evaluated the association between expression of S100A8 and prognosis of cancer patients. Pooled hazard ratios (HRs) with their corresponding 95% confidence intervals (CIs) were extracted to evaluate the association between S100A8 overexpression and Overall Survival (OS), Disease-Free Survival (DFS), Recurrence-Free Survival (RFS), and Progression-Free Survival (PFS). The expression of S100A8 was also validated by Flow cytometry, immunohistochemistry (IHC), and western blot. RESULTS: A total of 2,817 patients from 13 independent studies, ranging from 43 to 1,117 patients in size, were statistically analyzed. Our results indicated that a high level of S100A8 expression was significantly associated with poor OS, poor DFS, and poor PFS/RFS. In term of clinical pathological characteristics, a high expression level of S100A8 was significantly associated with differentiation grades, lymphatic metastasis, ER statue, and PR statue. The validation studies showed that the expression of S100A8 was at high levels in MDA-MB-231 (79.7%), MDA-MB-453 (89.2%), HTB-9 (70.2%), and T24 (53.3%) cells and it was higher in breast cancer tissue and bladder cancer tissue than their corresponding para-carcinoma tissue. CONCLUSIONS: S100A8 overexpression was significantly associated with poor clinical prognosis in cancer patients. S100A8 is potential a prognostic biomarker in breast cancer and bladder cancer. More well-designed studies with adequate prognostic data are needed to confirm the prognostic role of S100A8 revealed in this study.

Design, synthesis and biological evaluation of vinyl selenone derivatives as novel microtubule polymerization inhibitors.

A series of novel vinyl selenone derivatives were designed, synthesized and evaluated as the tubulin polymerization inhibitors using a bioisosteric strategy. Among them, the representative compound 11k exhibited satisfactory anti-proliferative activities with IC50 values ranging from 0.287 to 0.621 µM against a panel of cancer cell lines. Importantly, 11k displayed more potent in vivo antitumor activity than the positive control paclitaxel, CA-4 and parent compound 4 without apparent toxicity, which was presumably ascribed to the antiangiogenic, antiproliferative and selective effects of selenium, along with the unique physiological activity of indole skeleton, which were both introduced into the structure of target compounds. Further mechanism study demonstrated that compound 11k showed potent activity in tubulin polymerization inhibition with IC50 value of 1.82 µM. Moreover, cellular mechanism studies disclosed that 11k blocked cell cycle arrest at G2/M phase, induced cell apoptosis and depolarized mitochondria of K562 cells. Meanwhile, 11k reduced the cell migration and had potent vascular disrupting activity. In summary, 11k could serve as a promising lead for the development of more efficient microtubule polymerization inhibitors for cancer therapy.

Thermodynamic Simulation of Carbonate Cements-Water-Carbon Dioxide Equilibrium in Sandstone for Prediction of Precipitation/Dissolution of Carbonate Cements.

Carbonate cements, such as calcite, dolomite, ferrocalcite and ankerite, play important roles in the formation of pores in sandstones: precipitation of carbonate cements modifies pores and inhibits compaction, while dissolution creates secondary pores. This work proposed a precipitation-dissolution model for carbonate cements-CO2-H2O system by means of ion equilibrium concentration ([M2+], M = Ca, Mg, Fe or Mn) with different factors, such as temperature, depth, pH, [Formula: see text], variable rock composition and overpressure. Precipitation-dissolution reaction routes were also analyzed by minimization of the total Gibbs free energy (ΔG). Δ[M2+], the variation of [Ca2+], [Fe2+], [Mg2+] or [Mn2+] for every 100 m of burial depths, is used to predict precipitation or dissolution. The calculation results indicate that the increasing temperature results in decrease of equilibrium constant of reactions, while the increasing pressure results in a relatively smaller increase of equilibrium constant; As a result, with increasing burial depth, which brings about increase of both temperature and pressure, carbonate cements dissolve firstly and produces the maximal dissolved amounts, and then precipitation happens with further increasing depth; For example, calcite is dissolving from 0.0 km to 3.0 km with a maximal value of [Ca2+] at depth of 0.8 km, and then precipitates with depth deeper than 3.0 km. Meanwhile, with an increasing CO2 mole fraction in the gaseous phase from 0.1% to 10.0% in carbonate systems, the aqueous concentration of metal ions increases, e.g., dissolved amount of CaFe0.7Mg0.3(CO3)2 increases and reaches maximum of 1.78 mmol·L-1 and 8.26 mmol·L-1 at burial depth of 0.7 km with CO2 mole fraction of 0.1% and 10.0%, respectively. For the influence of overpressure in the calcite system, with overpressure ranging from 36 MPa to 83 MPa, pH reaches a minimum of 6.8 at overpressure of 51 MPa; meanwhile, Δ[Ca2+] increases slightly from -2.24 mmol·L-1 to -2.17 mmol·L-1 and remains negative, indicating it is also a precipitation process at burial depth of 3.9 km where overpressure generated. The method used in this study can be applied in assessing burial precipitation-dissolution processes and predicting possible pores in reservoirs with carbonate cement-water-carbon dioxide.

[Epidemiologic research on the clinical features of patients with cervical spondylosis].

OBJECTIVE: To investigate the clinical features of patients with cervical spondylosis. METHODS: Questionnaires were provided and X rays were examined in 1 009 people with different occupations, ages, and sexes. RESULTS: All the patients were diagnozed as cervical spondylosis. Of them, cadres occupied 78.83%, technologists made up 74.21%, and accountants 58.70%; nervous and long-time working people accounted for 59.75%; high and middle pillow-lovers occupied 80.03%. Imaging features: most of the degenerative changes of cervical spine were located between C5-6 (40.79%), C4-5 (26.29%), and C6-7 (18.20%). Patients with vertebral osteophyte were 65.75%, intervertebral space narrow 36.87%, intervertebral foramen narrow 29.19%, and physiological curve change 31.03%. CONCLUSION: This epidemiologic investigation is important, which can further understand the cause of cervical spondyiosis, and strengthen its prevention and treatment.