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Plasmon-mediated chemical reactions (PMCRs) constitute a vibrant research field, advancing such goals as using sunlight to convert abundant precursors such as CO 2 and water to useful fuels and chemicals. A key question in this burgeoning field which has not, as yet, been fully resolved, relates to the precise mechanism through which the energy absorbed through plasmonic excitation, ultimately drives such reactions. Among the multiple processes proposed, two have risen to the forefront: plasmon-increased temperature and generation of energetic charge carriers. However, it is still a great challenge to confidently separate these two effects and quantify their relative contribution to chemical reactions. Here, we describe a strategy based on the construction of a plasmonic electrode coupled with photoelectrochemistry, to quantitatively disentangle increased temperature from energetic charge carriers effects. A clear separation of the two effects facilitates the rational design of plasmonic nanostructures for efficient photochemical applications and solar energy utilization. Confidently separating the photothermal effect from the generation of energetic charge carriers and quantifying their relative contribution to chemical reactions remain a great challenge in plasmon-mediated chemical reactions. Here, authors describe a strategy based on the construction of a plasmonic electrode coupled with photoelectrochemistry to quantitatively disentangle these two effects.

FAK is a nonreceptor intracellular tyrosine kinase which plays an important biological function. Many studies have found that FAK is overexpressed in many human cancer cell lines, which promotes tumor cell growth by controlling cell adhesion, migration, proliferation, and survival. Therefore, targeting FAK is considered to be a promising cancer therapy with small molecules. Many FAK inhibitors have been reported as anticancer agents with various mechanisms. Currently, six FAK inhibitors, including GSK-2256098 (Phase I), VS-6063 (Phase II), CEP-37440 (Phase I), VS-6062 (Phase I), VS-4718 (Phase I), and BI-853520 (Phase I) are undergoing clinical trials in different phases. Up to now, there have been many novel FAK inhibitors with anticancer activity reported by different research groups. In addition, FAK degraders have been successfully developed through “proteolysis targeting chimera” (PROTAC) technology, opening up a new way for FAK-targeted therapy. In this paper, the structure and biological function of FAK are reviewed, and we summarize the design, chemical types, and activity of FAK inhibitors according to the development of FAK drugs, which provided the reference for the discovery of new anticancer agents.

Carbon–hydrogen (C–H) bond functionalization catalysed by potassium tert-butoxide, which is abundant and inexpensive, offers a direct route to the silylation of aromatic heterocycles that both obviates the need for precious metal catalysts and overcomes the limitations of previous methods. C–Si bonds from C–H This paper describes a new general method for converting ubiquitous carbon–hydrogen bonds into carbon–silicon bonds that both obviates the need for precious metal catalysts and overcomes several of the limitations of previous methods. The method uses inexpensive and readily available potassium tert-butoxide to catalyse the acceptorless cross-dehydrogenative coupling of aromatic heterocycles with hydrosilanes to produce synthetically versatile heteroarylsilanes in a single step. Silicon-containing molecules are of importance in drug discovery, as medical diagnostic agents and as precursors for advanced electronics materials. Heteroaromatic compounds containing carbon–silicon (C–Si) bonds are of great interest in the fields of organic electronics and photonics 1 , drug discovery 2 , nuclear medicine 3 and complex molecule synthesis 4 , 5 , 6 , because these compounds have very useful physicochemical properties. Many of the methods now used to construct heteroaromatic C–Si bonds involve stoichiometric reactions between heteroaryl organometallic species and silicon electrophiles 6 , 7 or direct, transition-metal-catalysed intermolecular carbon–hydrogen (C–H) silylation using rhodium or iridium complexes in the presence of excess hydrogen acceptors 8 , 9 . Both approaches are useful, but their limitations include functional group incompatibility, narrow scope of application, high cost and low availability of the catalysts, and unproven scalability. For this reason, a new and general catalytic approach to heteroaromatic C–Si bond construction that avoids such limitations is highly desirable. Here we report an example of cross-dehydrogenative heteroaromatic C–H functionalization catalysed by an Earth-abundant alkali metal species. We found that readily available and inexpensive potassium tert-butoxide catalyses the direct silylation of aromatic heterocycles with hydrosilanes, furnishing heteroarylsilanes in a single step. The silylation proceeds under mild conditions, in the absence of hydrogen acceptors, ligands or additives, and is scalable to greater than 100 grams under optionally solvent-free conditions. Substrate classes that are difficult to activate with precious metal catalysts are silylated in good yield and with excellent regioselectivity. The derived heteroarylsilane products readily engage in versatile transformations enabling new synthetic strategies for heteroaromatic elaboration, and are useful in their own right in pharmaceutical and materials science applications.

Numerical methods are widely used to calculate the secure key rate of many quantum key distribution protocols in practice, but they consume many computing resources and are too time-consuming. In this work, we take the homodyne detection discrete-modulated continuous-variable quantum key distribution (CV-QKD) as an example, and construct a neural network that can quickly predict the secure key rate based on the experimental parameters and experimental results. Compared to traditional numerical methods, the speed of the neural network is improved by several orders of magnitude. Importantly, the predicted key rates are not only highly accurate but also highly likely to be secure. This allows the secure key rate of discrete-modulated CV-QKD to be extracted in real time on a low-power platform. Furthermore, our method is versatile and can be extended to quickly calculate the complex secure key rates of various other unstructured quantum key distribution protocols.

Background Breast cancer is the leading cause of female mortality worldwide. (SET And MYND Domain Containing 4) SMYD4 has been reported to be a tumour suppressor. However, the molecular mechanism of SMYD4 remains unclear. Methods The expression level of SMYD4 in breast cancer cells was detected by qRT-PCR and western blot. The effect of SMYD4 was verified in vitro and in vivo. The interaction between SMYD4 and MYH9 was investigated by co‑IP assay. The regulation of SMYD4 on WNT signaling pathway was detected by luciferase reporter assay and ChIP analysis. Results This study found that SMYD4 downregulation was associated with poor prognosis. SMYD4 was performed as a tumor suppressor both in vitro and in vivo. SMYD4 was found to interact with the downstream protein MYH9 and impede WNT signaling pathway. Further studies revealed that SMYD4 impeded the binding of MYH9 to the CTNNB1 promoter region by promoting lysine monomethylation and ubiquitination degradation of MYH9. Conclusions These findings reveal the emerging character of SMYD4 in Wnt/β‑catenin signaling and bring new sights of gene interaction. The discovery of this SMYD4/MYH9/CTNNB1/WNT/β-Catenin signalling pathway axis suggests that SMYD4 is a potential therapeutic target for breast cancer.

Carcinoma-associated fibroblasts (CAFs) are the major components of mesenchymal cells in the inflammatory tumor microenvironment. They are involved in epithelial-mesenchymal transition (EMT) and chemotherapy resistance by directly contacting with cancer cells or secretory cytokines. In the present study, we examined the role of CAFs in the induction of EMT in ovarian cancer. Primary ovarian cancer cells, CAFs and normal fibroblasts (NFs) were isolated from fresh cancer tissue and cultured for immunohistochemistry studies. Enzyme-linked immunosorbent assay (ELISA) was used to detect the expression of IL-6 in the culture supernatants of these cells. The expression of IL-6 at the mRNA level was examined by RT-PCR. The expression of IL-6 at the protein level in ovarian cancer tissues was determined using an immunofluorescence assay in both tissue sections and cell lobes. OVCAR3 cells were treated with the culture supernatants collected from CAFs and NFs. IL-6 monoclonal antibody (mAb) was employed to neutralize IL-6. The expression of phosphorylated STAT3 was assessed. Changes in EMT, proliferation, invasion and proapoptotic protein expression were also examined. Flow cytometry was performed to detect the changes in apoptosis resistance of OVCAR3 cells. The JAK2/STAT3 pathway-specific inhibitor AG490 was used to block this pathway and the β-TGF inhibitor was used to inhibit EMT. The clinical data of patients treated in our hospital were collected between January 1st, 2009 and June 30th, 2013. The expression of interstitial IL-6 in paraffin-embedded tissues was detected by immunohistochemistry. The relationship between the expression of interstitial IL-6 and the treatment response was examined by linear regression and multiple linear regression analyses. We found that CAFs were the main source of IL-6 in ovarian cancer tissue. CAFs promoted the phosphorylation of STAT3 in ovarian cancer and enhanced the proliferation, invasion and EMT. Enhanced EMT may lead to apoptosis resistance, inhibitory expression of pro-apoptotic proteins and paclitaxel resistance. A total of 255 patients were enrolled in this retrospective study. Univariate and multivariate analyses revealed that age, CA125, interstitial IL-6 expression and cytoreduction satisfaction were closely related to the sensitivity of the TP (docetaxel plus cisplatin or carbopatin) regimen in ovarian cancer (P<0.05). These results demonstrated that CAFs highly secreted IL-6 and promoted β-TGF-mediated EMT in ovarian cancer via the JAK2/STAT3 pathway, leading to inhibited apoptosis and subsequent paclitaxel resistance. Therefore, CAFs may be a new therapeutic target for the treatment of ovarian cancer.

The chalcone and quinoline scaffolds are frequently utilized to design novel anticancer agents. As the continuation of our work on effective anticancer agents, we assumed that linking chalcone fragment to the quinoline scaffold through the principle of molecular hybridization strategy could produce novel compounds with potential anticancer activity. Therefore, quinoline-chalcone derivatives were designed and synthesized, and we explored their antiproliferative activity against MGC-803, HCT-116, and MCF-7 cells. Among these compounds, compound 12e exhibited a most excellent inhibitory potency against MGC-803, HCT-116, and MCF-7 cells with IC50 values of 1.38, 5.34, and 5.21 µM, respectively. The structure–activity relationship of quinoline-chalcone derivatives was preliminarily explored in this report. Further mechanism studies suggested that compound 12e inhibited MGC-803 cells in a dose-dependent manner and the cell colony formation activity of MGC-803 cells, arrested MGC-803 cells at the G2/M phase and significantly upregulated the levels of apoptosis-related proteins (Caspase3/9 and cleaved-PARP) in MGC-803 cells. In addition, compound 12e could significantly induce ROS generation, and was dependent on ROS production to exert inhibitory effects on gastric cancer cells. Taken together, all the results suggested that directly linking chalcone fragment to the quinoline scaffold could produce novel anticancer molecules, and compound 12e might be a valuable lead compound for the development of anticancer agents.

Spiking neural network (SNN) is a new generation of artificial neural networks (ANNs), which is more analogous with the brain. It has been widely considered with neural computing and brain-like intelligence. SNN is a sparse trigger event-driven model, and it has the characteristics of hardware friendliness and energy saving. SNN is more suitable for hardware implementation and rapid information processing. SNN is also a powerful method for deep learning (DL) to study brain-like computing. In this paper, the common SNN learning and training methods in the field of image classification are reviewed. In detail, we examine the SNN algorithms based on synaptic plasticity, approximate backpropagation (BP), and ANN to SNN. This paper comprehensively introduces and tracks the latest progress of SNN. On this basis, we also analyze and discuss the challenges and opportunities it faces. Finally, this paper prospects for the future development of SNN in the aspects of the biological mechanism, network training and design, computing platform, and interdisciplinary communication. This review can provide a reference for the research of SNN to promote its application in complex tasks.

Breast cancer is the most common malignant tumor and the main cause of cancer-associated mortality in females worldwide. Long non-coding RNAs (lncRNAs) have been reported to play vital roles in breast cancer development and progression; however, our understanding of most lncRNAs in breast cancer is still limited. In this study, we demonstrated that small nucleolar RNA host gene 5 (SNHG5) promotes breast cancer cell proliferation both in vitro and in vivo, and depletion of SNHG5 significantly led to cell-cycle arrest at G1 phase. Accumulating evidence has shown that many lncRNA transcripts could function as competing endogenous RNAs (ceRNAs) by competitively binding common microRNAs (miRNAs). We found that SNHG5 acts as a sponge for miR-154-5p, reducing its ability to repress proliferating cell nuclear antigen (PCNA). SNHG5 promoted breast cancer proliferation and cell-cycle progression by upregulation of PCNA expression. Clinically, we observed an increased SNHG5 expression in breast cancer, whereas miR-154-5p was decreased in breast cancer tissues compared with the adjacent normal breast tissues. Furthermore, the SNHG5 expression was significantly negatively correlated with miR-154-5p expression. Taken together, our data uncover the SNHG5-miR-154-5p-PCNA axis and provide a novel mechanism to explain breast cancer proliferation. [Display omitted]

A label-free, rapid response colorimetric aptasensor for sensitive detection of chloramphenicol (CAP) was proposed, which was based on the strategy of ssDNA-modified gold nanoparticle (AuNP) aggregation assisted by lanthanum (La3+) ions. The AuNPs generated a color change that could be monitored in the red, green, and blue and analyzed by the smartphone imaging app. La3+, as a trigger agent, strongly combined with the phosphate groups of the surface of ssDNA-AuNPs probe, which helps create AuNP aggregation and the color change of AuNPs from red to blue. On the contrary, when mixing with CAP, the aptamer (Apt) bound to CAP to form a rigid structure of the Apt-CAP complex, and La3+ attached to the phosphate groups of the complex, which prevented the aptamer from binding to the surface of the AuNPs. As a result, the color of the AuNPs changed to violet-red. Finally, UV-vis absorption spectroscopy and the smartphone imaging app were employed to determine CAP with a lower detection limit of 7.65 nM and 5.88 nM, respectively. The proposed strategy featuring high selectivity and strong anti-interference ability for detection of CAP in practical samples was achieved. It is worth mentioning that the simple and portable colorimetric aptasensor will be used for facilitating on-site detection of food samples.