Nanjing University

About: Nanjing University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 85961 authors who have published 105504 publications receiving 2289036 citations. The organization is also known as: NJU & Nanking University.

Treatment-Related Adverse Events of PD-1 and PD-L1 Inhibitors in Clinical Trials: A Systematic Review and Meta-analysis.

Abstract: Importance Programmed cell death (PD-1) and programmed cell death ligand 1 (PD-L1) inhibitors have been increasingly used in cancer therapy. Understanding the treatment-related adverse events of these drugs is critical for clinical practice. Objective To evaluate the incidences of treatment-related adverse events of PD-1 and PD-L1 inhibitors and the differences between different drugs and cancer types. Data Sources PubMed, Web of Science, Embase, and Scopus were searched from October 1, 2017, through December 15, 2018. Study Selection Published clinical trials on single-agent PD-1 and PD-L1 inhibitors with tabulated data on treatment-related adverse events were included. Data Extraction and Synthesis Trial name, phase, cancer type, PD-1 and PD-L1 inhibitor used, dose escalation, dosing schedule, number of patients, number of all adverse events, and criteria for adverse event reporting data were extracted from each included study, and bayesian multilevel regression models were applied for data analysis. Main Outcomes and Measures Incidences of treatment-related adverse events and differences between different drugs and cancer types. Results This systematic review and meta-analysis included 125 clinical trials involving 20 128 patients; 12 277 (66.0%) of 18 610 patients from 106 studies developed at least 1 adverse event of any grade (severity), and 2627 (14.0%) of 18 715 patients from 110 studies developed at least 1 adverse event of grade 3 or higher severity. The most common all-grade adverse events were fatigue (18.26%; 95% CI, 16.49%-20.11%), pruritus (10.61%; 95% CI, 9.46%-11.83%), and diarrhea (9.47%; 95% CI, 8.43%-10.58%). The most common grade 3 or higher adverse events were fatigue (0.89%; 95% CI, 0.69%-1.14%), anemia (0.78%; 95% CI, 0.59%-1.02%), and aspartate aminotransferase increase (0.75%; 95% CI, 0.56%-0.99%). Hypothyroidism (6.07%; 95% CI, 5.35%-6.85%) and hyperthyroidism (2.82%; 95% CI, 2.40%-3.29%) were the most frequent all-grade endocrine immune-related adverse events. Nivolumab was associated with higher mean incidences of all-grade adverse events compared with pembrolizumab (odds ratio [OR], 1.28; 95% CI, 0.97-1.79) and grade 3 or higher adverse events (OR, 1.30; 95% CI, 0.89-2.00). PD-1 inhibitors were associated with a higher mean incidence of grade 3 or higher adverse events compared with PD-L1 inhibitors (OR, 1.58; 95% CI, 1.00-2.54). Conclusions and Relevance Different PD-1 and PD-L1 inhibitors appear to have varying treatment-related adverse events; a comprehensive summary of the incidences of treatment-related adverse events in clinical trials provides an important guide for clinicians.

High electron mobility and quantum oscillations in non-encapsulated ultrathin semiconducting Bi2O2Se.

Abstract: Air-stable, non-encapsulated Bi2O2Se ultrathin films grown by chemical vapour deposition display high electron mobility and exceptional semiconducting transport properties, making the observation of quantum oscillations possible and suggesting potential applications in electronics.

Multi-Instance Multi-Label Learning with Application to Scene Classification

Abstract: In this paper, we formalize multi-instance multi-label learning, where each training example is associated with not only multiple instances but also multiple class labels Such a problem can occur in many real-world tasks, eg an image usually contains multiple patches each of which can be described by a feature vector, and the image can belong to multiple categories since its semantics can be recognized in different ways We analyze the relationship between multi-instance multi-label learning and the learning frameworks of traditional supervised learning, multi-instance learning and multi-label learning Then, we propose the MIMLBOOST and MIMLSVM algorithms which achieve good performance in an application to scene classification

Surface-Enhanced Raman Scattering Active Gold Nanoparticles with Enzyme-Mimicking Activities for Measuring Glucose and Lactate in Living Tissues

Abstract: Gold nanoparticles (AuNPs) with simultaneous plasmonic and biocatalytic properties provide a promising approach to developing versatile bioassays However, the combination of AuNPs’ intrinsic enzyme-mimicking properties with their surface-enhanced Raman scattering (SERS) activities has yet to be explored Here we designed a peroxidase-mimicking nanozyme by in situ growing AuNPs into a highly porous and thermally stable metal–organic framework called MIL-101 The obtained AuNPs@MIL-101 nanozymes acted as peroxidase mimics to oxidize Raman-inactive reporter leucomalachite green into the active malachite green (MG) with hydrogen peroxide and simultaneously as the SERS substrates to enhance the Raman signals of the as-produced MG We then assembled glucose oxidase (GOx) and lactate oxidase (LOx) onto AuNPs@MIL-101 to form AuNPs@MIL-101@GOx and AuNPs@MIL-101@LOx integrative nanozymes for in vitro detection of glucose and lactate via SERS Moreover, the integrative nanozymes were further explored for monitoring