Successful In Situ Growth of Conductive MOFs on 2D Cobalt-Based Compounds and Their Electrochemical Performance.

Conductive metal-organic frameworks (cMOFs), as a kind of porous material, are considered to be highly promising materials in the field of electrochemistry due to their excellent conductivity. However, due to the low specific capacitance of pure cMOFs, their application in supercapacitors is limited. By virtue of the high theoretical capacity and excellent chemical stability of Co-based compounds, in this work, cMOFs' M-HHTP (M = Ni, Co, NiCo, HHTP = 2,3,6,7,10,11-hexahydroxytriphenylene) are grown in situ on Co(OH)2, CoP, and Co3O4 nanosheets, resulting in a series of electroactive compounds as electrode materials used in supercapacitors. Among all of the compounds, Ni-HHTP@Co(OH)2 shows the most excellent energy storage performance and outstanding cyclic stability in the application of aqueous asymmetric supercapacitors.

Conductive Metal-Organic Framework Grown on the Nickel-Based Hydroxide to Realize High-Performance Electrochemical Glucose Sensing.

Glucose holds significant importance in disease diagnosis as well as beverage quality monitoring. The high-efficiency electrochemical sensor plays a crucial role in the electrochemical conversion technology. Ni(OH)2 nanosheets are provided with high specific surface area and redox activity that are widely used in electrochemistry. Conductive metal-organic frameworks (cMOFs) perfectly combine the structural controllability of organic materials with the long-range ordering of inorganic materials that possess the characteristic of high electron mobility. Based on the above considerations, the combination of Ni(OH)2 and Ni-HHTP (HHTP=2,3,6,7,10,11-hexahydroxytriphenylene) as an electrode modification material is designed to enhance electrochemical performance. In this work, to improve glucose detection, a sequence of Ni(OH)2@NiCo-HHTP and NiM-LDH@Ni-HHTP (M=Co2+, Mn2+, Cu2+, LDH=layered double hydroxide) are successfully synthesised by doping metals into Ni-HHTP and Ni(OH)2, respectively. As a result, NiCu-LDH@Ni-HHTP showed the best excellent glucose detection sensitivity.

Advances of Artificial Intelligence in Anti-Cancer Drug Design: A Review of the Past Decade.

Anti-cancer drug design has been acknowledged as a complicated, expensive, time-consuming, and challenging task. How to reduce the research costs and speed up the development process of anti-cancer drug designs has become a challenging and urgent question for the pharmaceutical industry. Computer-aided drug design methods have played a major role in the development of cancer treatments for over three decades. Recently, artificial intelligence has emerged as a powerful and promising technology for faster, cheaper, and more effective anti-cancer drug designs. This study is a narrative review that reviews a wide range of applications of artificial intelligence-based methods in anti-cancer drug design. We further clarify the fundamental principles of these methods, along with their advantages and disadvantages. Furthermore, we collate a large number of databases, including the omics database, the epigenomics database, the chemical compound database, and drug databases. Other researchers can consider them and adapt them to their own requirements.

Biologically Interpretable Deep Learning To Predict Response to Immunotherapy In Advanced Melanoma Using Mutations and Copy Number Variations.

Only 30-40% of advanced melanoma patients respond effectively to immunotherapy in clinical practice, so it is necessary to accurately identify the response of patients to immunotherapy pre-clinically. Here, we develop KP-NET, a deep learning model that is sparse on KEGG pathways, and combine it with transfer- learning to accurately predict the response of advanced melanomas to immunotherapy using KEGG pathway-level information enriched from gene mutation and copy number variation data. The KP-NET demonstrates best performance with AUROC of 0.886 on testing set and 0.803 on an unseen evaluation set when predicting responders (CR/PR/SD with PFS ≥6 mo) versus non-responders (PD/SD with PFS <6 mo) in anti-CTLA-4 treated melanoma patients. The model also achieves an AUROC of 0.917 and 0.833 in predicting CR/PR versus PD, respectively. Meanwhile, the AUROC is 0.913 when predicting responders versus non-responders in anti-PD-1/PD-L1 melanomas. Moreover, the KP-NET reveals some genes and pathways associated with response to anti-CTLA-4 treatment, such as genes PIK3CA, AOX1 and CBLB, and ErbB signaling pathway, T cell receptor signaling pathway, et al. In conclusion, the KP-NET can accurately predict the response of melanomas to immunotherapy and screen related biomarkers pre-clinically, which can contribute to precision medicine of melanoma.

Directional Growth of Conductive Metal-Organic Framework Nanoarrays along [001] on Metal Hydroxides for Aqueous Asymmetric Supercapacitors.

Metal-organic frameworks (MOFs) are promising electrochemical materials that possess large specific surface areas, high porosities, good adjustability, and high activities. However, many conventional MOFs exhibit poor conductivity, which hinders their application in electrochemistry. In recent years, conductive MOFs (cMOFs) have attracted a considerable attention. As an important transition metal hydroxide, Ni(OH)2 nanosheets exhibit a high theoretical specific capacitance and a high energy density but a poor electrical conductivity. In this study, we combined a typical cMOF(Ni-HHTP, HHTP = 2,3,6,7,10,11-hexahydroxybenzene) with Ni(OH)2 nanosheets and synthesized a series of Ni-HHTP@Ni(OH)2 nanoarrays. The composite materials exhibit a high electrical conductivity and ionic transfer efficiency and a good stability. Most importantly, our study reveals the chemical interaction between cMOFs and metal hydroxide composites and the relationship between facet exposure and the growth orientation of cMOFs. When Ni-HHTP@Ni(OH)2-2 was assembled as a positive electrode material in an aqueous asymmetric supercapacitor, 98% of the initial capacitance was maintained after 5000 cycles at a high current density of 3 A g-1. The findings of this study will provide meaningful insights into the design of cMOF composites combining other metal hydroxides.