The asymptotic and estimate costs and sizes of our IBI/IBS schemes and the mCFS-Stern scheme.

<p>The asymptotic and estimate costs and sizes of our IBI/IBS schemes and the mCFS-Stern scheme.</p

Graph Transformer with Convolution Parallel Networks for Predicting Single and Binary Component Adsorption Performance of Metal–Organic Frameworks

Metal–organic frameworks (MOFs) are considered one of the most important materials for carbon capture and storage (CCS) due to the advantages of porosity, multifunction, diverse structure, and controllable chemical composition. With the continuous development of artificial intelligence (AI) technology, more and more machine learning models are used to identify MOFs with high performance within a massive search space. However, current works have yet to form a model that uses graph-structured data only, which can predict the adsorption properties of single and binary components. In this work, we proposed and developed a graph transformer, combined with convolution parallel networks, called GC-Trans. The model can accurately and efficiently predict the adsorption performance of MOFs under the single- and binary-component adsorption conditions using only the features of the crystal diagram as inputs. By extracting and fusing local and global feature information, the model has stronger expression and generalization abilities. Thus, we used it to screen the ARC-MOF database and analyze the MOF structures that meet the target requirements. Additionally, to demonstrate the transferability of the model, we applied transfer learning methods to predict the CO2/CH4 separations and CH4 uptake, both of which showed good predictive performance

Graph Transformer with Convolution Parallel Networks for Predicting Single and Binary Component Adsorption Performance of Metal–Organic Frameworks

Metal–organic frameworks (MOFs) are considered one of the most important materials for carbon capture and storage (CCS) due to the advantages of porosity, multifunction, diverse structure, and controllable chemical composition. With the continuous development of artificial intelligence (AI) technology, more and more machine learning models are used to identify MOFs with high performance within a massive search space. However, current works have yet to form a model that uses graph-structured data only, which can predict the adsorption properties of single and binary components. In this work, we proposed and developed a graph transformer, combined with convolution parallel networks, called GC-Trans. The model can accurately and efficiently predict the adsorption performance of MOFs under the single- and binary-component adsorption conditions using only the features of the crystal diagram as inputs. By extracting and fusing local and global feature information, the model has stronger expression and generalization abilities. Thus, we used it to screen the ARC-MOF database and analyze the MOF structures that meet the target requirements. Additionally, to demonstrate the transferability of the model, we applied transfer learning methods to predict the CO2/CH4 separations and CH4 uptake, both of which showed good predictive performance

Graph Transformer with Convolution Parallel Networks for Predicting Single and Binary Component Adsorption Performance of Metal–Organic Frameworks

Metal–organic frameworks (MOFs) are considered one of the most important materials for carbon capture and storage (CCS) due to the advantages of porosity, multifunction, diverse structure, and controllable chemical composition. With the continuous development of artificial intelligence (AI) technology, more and more machine learning models are used to identify MOFs with high performance within a massive search space. However, current works have yet to form a model that uses graph-structured data only, which can predict the adsorption properties of single and binary components. In this work, we proposed and developed a graph transformer, combined with convolution parallel networks, called GC-Trans. The model can accurately and efficiently predict the adsorption performance of MOFs under the single- and binary-component adsorption conditions using only the features of the crystal diagram as inputs. By extracting and fusing local and global feature information, the model has stronger expression and generalization abilities. Thus, we used it to screen the ARC-MOF database and analyze the MOF structures that meet the target requirements. Additionally, to demonstrate the transferability of the model, we applied transfer learning methods to predict the CO2/CH4 separations and CH4 uptake, both of which showed good predictive performance

High-Temperature Luminescence Quenching of Colloidal Quantum Dots

Thermal quenching of quantum dot (QD) luminescence is important for application in luminescent devices. Systematic studies of the quenching behavior above 300 K are, however, lacking. Here, high-temperature (300–500 K) luminescence studies are reported for highly efficient CdSe core–shell quantum dots (QDs), aimed at obtaining insight into temperature quenching of QD emission. Through thermal cycling (yoyo) experiments for QDs in polymer matrices, reversible and irreversible luminescence quenching processes can be distinguished. For a variety of core–shell systems, reversible quenching is observed in a similar temperature range, between 100 and 180 °C. The irreversible quenching behavior varies between different systems. Mechanisms for thermal quenching are discussed

DataSheet1_Detection of pediatric drug-induced kidney injury signals using a hospital electronic medical record database.docx

Background: Drug-induced kidney injury (DIKI) is one of the most common complications in clinical practice. Detection signals through post-marketing approaches are of great value in preventing DIKI in pediatric patients. This study aimed to propose a quantitative algorithm to detect DIKI signals in children using an electronic health record (EHR) database.Methods: In this study, 12 years of medical data collected from a constructed data warehouse were analyzed, which contained 575,965 records of inpatients from 1 January 2009 to 31 December 2020. Eligible participants included inpatients aged 28 days to 18 years old. A two-stage procedure was adopted to detect DIKI signals: 1) stage 1: the suspected drugs potentially associated with DIKI were screened by calculating the crude incidence of DIKI events; and 2) stage 2: the associations between suspected drugs and DIKI were identified in the propensity score-matched retrospective cohorts. Unconditional logistic regression was used to analyze the difference in the incidence of DIKI events and to estimate the odds ratio (OR) and 95% confidence interval (CI). Potentially new signals were distinguished from already known associations concerning DIKI by manually reviewing the published literature and drug instructions.Results: Nine suspected drugs were initially screened from a total of 652 drugs. Six drugs, including diazepam (OR = 1.61, 95%CI: 1.43–1.80), omeprazole (OR = 1.35, 95%CI: 1.17–1.54), ondansetron (OR = 1.49, 95%CI: 1.36–1.63), methotrexate (OR = 1.36, 95%CI: 1.25–1.47), creatine phosphate sodium (OR = 1.13, 95%CI: 1.05–1.22), and cytarabine (OR = 1.17, 95%CI: 1.06–1.28), were demonstrated to be associated with DIKI as positive signals. The remaining three drugs, including vitamin K1 (OR = 1.06, 95%CI: 0.89–1.27), cefamandole (OR = 1.07, 95%CI: 0.94–1.21), and ibuprofen (OR = 1.01, 95%CI: 0.94–1.09), were found not to be associated with DIKI. Of these, creatine phosphate sodium was considered to be a possible new DIKI signal as it had not been reported in both adults and children previously. Moreover, three other drugs, namely, diazepam, omeprazole, and ondansetron, were shown to be new potential signals in pediatrics.Conclusion: A two-step quantitative procedure to actively explore DIKI signals using real-world data (RWD) was developed. Our findings highlight the potential of EHRs to complement traditional spontaneous reporting systems (SRS) for drug safety signal detection in a pediatric setting.</p

Phosphino-Triazole Ligands for Palladium-Catalysed Cross-Coupling

<div>Twelve 1,5-disubtituted and fourteen 5-substituted 1,2,3-triazole derivatives bearing diaryl or dialkyl phosphines at the 5-position were synthesised and used as ligands for palladium-catalysed Suzuki-Miyaura cross-coupling reactions. Bulky substrates were tested, and lead-like product formation was demonstrated. The online tool SambVca 2.0 was used to assess steric parameters of ligands and preliminary buried volume determination using XRD obtained data in a small number of cases proved to be informative. Two modelling approaches were compared for the determination of</div><div>the buried volume of ligands where XRD data was not available. An approach with imposed steric restrictions was found to be superior in leading to buried volume determinations that closely correlate with observed reaction conversions. The online tool LLAMA was used to determine lead-likeness of potential Suzuki-Miyaura cross-coupling products, from which ten of the most lead-like were successfully synthesised. Thus, confirming these readily accessible triazole-containing phosphines as highly suitable ligands for reaction screening and optimisation in drug discovery campaigns.</div

Additional file 2: Figure S1. of A novel monoclonal antibody against the von Willebrand Factor A2 domain reduces its cleavage by ADAMTS13

Characterization of mAb SZ-179. (A) Quantification of ELISA analyses detecting SZ-179 binding to IgG1, IgG2a, IgG2a, IgG3, or IgM. (B) Quantification of ELISA analyses for SZ-179 or murine IgG1 binding to VWFα5. Dose–response curves are shown. (C) Quantification of ELISA analyses for SZ-179 or murine IgG1 binding to plasma-derived VWF. Dose–response curves are shown. Data are mean ± SD of four independent experiments. (DOCX 974 kb

Additional file 3: Figure S2. of A novel monoclonal antibody against the von Willebrand Factor A2 domain reduces its cleavage by ADAMTS13

SZ-179 inhibits cleavage of VWF by ADAMTS13 in plasma under denaturing conditions. (A, B) Pooled normal human plasma was pre-incubated with SZ-179 or isotype control IgG1 for 2 h at 37°C, and then incubated with 1.5M urea for 16 h. The proteolytic products were separated by electrophoresis in a 1.3% agarose gel and detected by anti-VWF. (C) Dose–response curve for inhibition of plasma ADAMTS13-mediated cleavage of plasma-VWF. (D) Dose–response curve for inhibition of rADAMTS13-mediated GST-VWF73-H cleavage. Results represented as mean ± SD of four independent experiments. (DOCX 1519 kb

Near-Infrared Quantum Dot and ⁸⁹Zr Dual-Labeled Nanoparticles for <i>in Vivo</i> Cerenkov Imaging

Cerenkov luminescence (CL) is an emerging imaging modality that utilizes the light generated during the radioactive decay of many clinical used isotopes. Although it is increasingly used for background-free imaging and deep tissue photodynamic therapy, <i>in vivo</i> applications of CL suffer from limited tissue penetration. Here, we propose to use quantum dots (QDs) as spectral converters that can transfer the CL UV-blue emissions to near-infrared light that is less scattered or absorbed <i>in vivo</i>. Experiments on tissue phantoms showed enhanced penetration depth and increased transmitted intensity for CL in the presence of near-infrared (NIR) QDs. To realize this concept for <i>in vivo</i> imaging applications, we developed three types of NIR QDs and ⁸⁹Zr dual-labeled nanoparticles based on lipid micelles, nanoemulsions, and polymeric nanoplatforms, which enable codelivery of the radionuclide and the QDs for maximized spectral conversion efficiency. We finally demonstrated the application of these self-illuminating nanoparticles for imaging of lymph nodes and tumors in a prostate cancer mouse model