Preventive and (Neo)Adjuvant Therapeutic Effects of Metformin on Cancer

Metformin, the first-line antidiabetic drug, has become an attractive candidate in cancer therapy since retrospective clinical investigations reported that patients with type 2 diabetes receiving metformin had lower incidence of cancer than those with other glucose lowering drugs. In line with this, preclinical studies have demonstrated that the antitumor activity of metformin could proceed through several mechanisms. Thus far, metformin has been used in cancer prevention with reduced risk as consequence and treatment of various cancers as an adjuvant or neoadjuvant drug. Thus, existing data support the beneficial effects of metformin on many types of cancers such as reducing metastasis and mortality and improving pathological responses and survival rates. However, some reports do not support this and even show adverse effects. The discrepancy may be attributed to expression levels of its transporters or genetic background. Hence, this chapter briefly reviews information on the mechanism of metformin action and summarizes both completed and ongoing clinical trials in an attempt to evaluate the value of metformin in prevention and treatment of various cancer types

YATO: Yet Another deep learning based Text analysis Open toolkit

We introduce YATO, an open-source, easy-to-use toolkit for text analysis with deep learning. Different from existing heavily engineered toolkits and platforms, YATO is lightweight and user-friendly for researchers from cross-disciplinary areas. Designed in a hierarchical structure, YATO supports free combinations of three types of widely used features including 1) traditional neural networks (CNN, RNN, etc.); 2) pre-trained language models (BERT, RoBERTa, ELECTRA, etc.); and 3) user-customized neural features via a simple configurable file. Benefiting from the advantages of flexibility and ease of use, YATO can facilitate fast reproduction and refinement of state-of-the-art NLP models, and promote the cross-disciplinary applications of NLP techniques. The code, examples, and documentation are publicly available at https://github.com/jiesutd/YATO. A demo video is also available at https://youtu.be/tSjjf5BzfQg

On-Site Quantification and Infection Risk Assessment of Airborne SARS-CoV-2 Virus Via a Nanoplasmonic Bioaerosol Sensing System in Healthcare Settings

On-site quantification and early-stage infection risk assessment of airborne severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) with high spatiotemporal resolution is a promising approach for mitigating the spread of coronavirus disease 2019 (COVID-19) pandemic and informing life-saving decisions. Here, a condensation (hygroscopic growth)-assisted bioaerosol collection and plasmonic photothermal sensing (CAPS) system for on-site quantitative risk analysis of SARS-CoV-2 virus-laden aerosols is presented. The CAPS system provided rapid thermoplasmonic biosensing results after an aerosol-to-hydrosol sampling process in COVID-19-related environments including a hospital and a nursing home. The detection limit reached 0.25 copies/µL in the complex aerosol background without further purification. More importantly, the CAPS system enabled direct measurement of the SARS-CoV-2 virus exposures with high spatiotemporal resolution. Measurement and feedback of the results to healthcare workers and patients via a QR-code are completed within two hours. Based on a dose-responseµ model, it is used the plasmonic biosensing signal to calculate probabilities of SARS-CoV-2 infection risk and estimate maximum exposure durations to an acceptable risk threshold in different environmental settings

Super-resolution imaging and tracking of protein–protein interactions in sub-diffraction cellular space

Imaging the location and dynamics of individual interacting protein pairs is essential but often difficult because of the fluorescent background from other paired and non-paired molecules, particularly in the sub-diffraction cellular space. Here we develop a new method combining bimolecular fluorescence complementation and photoactivated localization microscopy for super-resolution imaging and single-molecule tracking of specific protein–protein interactions. The method is used to study the interaction of two abundant proteins, MreB and EF-Tu, in Escherichia coli cells. The super-resolution imaging shows interesting distribution and domain sizes of interacting MreB–EF-Tu pairs as a subpopulation of total EF-Tu. The single-molecule tracking of MreB, EF-Tu and MreB–EF-Tu pairs reveals intriguing localization-dependent heterogonous dynamics and provides valuable insights to understanding the roles of MreB–EF-Tu interactions

Synthesis of metallasiloxanes of group 13-15 and their application in catalysis

973 Program [2012CB821704]; National Nature Science Foundation of China [91027014, 20972129]; National Key Lab Foundation for PCOSS [20923004]; Innovative Research Team Program [IRT1036]Herein we report on the synthesis, characterization and catalytic application of metallasiloxanes of group 13-15. Reactions of R(Me)Si(OH)(2) (R = N(SiMe3)-2,6-iPr(2)C(6)H(3)) (A) with Bi(NEt2)(3), Sb(NEt2)(3), Ge[N(SiMe3)(2)](2) and AlMe3 afforded [R(Me)SiO2BiNEt2](2) (1), [R(Me)SiO2SbOSi(OH)(Me)R](2) (2), [R(Me)SiO2](3)(GeH)(2) (3), and [R(Me)SiO2AlMe(THF)](2) (4), respectively. Reactions of RSi(OH)(3) (B) with Bi(NEt2)(3) and AlMe3 produced complexes (RSiO3Bi)(4) (5) and (RSiO3)(2)[AlMe(THF)](3) (6). Compounds 1-6 have been characterized by IR and NMR spectroscopy, single crystal X-ray structure and elemental analysis. Each of the compounds 1, 2 and 4 features an eight-membered ring of composition Si2O4Bi2, Si2O4Sb2 and Si2O4Al2, while 3 and 6 exhibit a bicyclic structure with the respective skeletons of Si3O6Ge2 and Si2O6Al3. Compound 5 has a cubic core of Si4O12Bi4. Compounds 1-6 exhibit very good catalytic activity in the addition reaction of trimethylsilyl cyanide (TMSCN) with benzaldehyde. Compound 5 was found to be the best catalyst and its activity was probed in the reactions of TMSCN with a number of aldehydes and ketones