Safety and pharmacokinetics of novel selective vascular endothelial growth factor receptor-2 inhibitor YN968D1 in patients with advanced malignancies

<p>Abstract</p> <p>Background</p> <p>YN968D1 (Apatinib) selectively inhibits phosphorylation of VEGFR-2 and tumor angiogenesis in mice model. The study was conducted to determine the maximum tolerated dose (MTD), safety profile, pharmacokinetic variables, and antitumor activity in advanced solid malignancies.</p> <p>Methods</p> <p>This dose-escalation study was conducted according to the Chinese State Food and Drug Administration (SFDA) recommendations in patients with advanced solid tumors to determine the MTD for orally administered apatinib. Doses of continuously administered apatinib were escalated from 250 mg. Treatment continued after dose-escalation phase until withdrawal of consent, intolerable toxicities, disease progression or death.</p> <p>Results</p> <p>Forty-six patients were enrolled. Hypertension and hand-foot syndrome were the two dose-limiting toxicities noted at dose level of 1000 mg. MTD was determined to be 850 mg once daily. Pharmacokinetic analysis showed early absorption with a half-life of 9 hours. The mean half-life was constant over all dose groups. Steady-state conditions analysis suggested no accumulation during 56 days of once-daily administration. The most frequently observed drug-related adverse events were hypertension (69.5%, 29 grade 1-2 and 3 grade 3-4), proteinuria (47.8%, 16 grade 1-2 and 6 grade 3-4), and hand-foot syndrome (45.6%, 15 grade 1-2 and 6 grade 3-4). Among the thirty-seven evaluable patients, PR was noted in seven patients (18.9%), SD 24 (64.9%), with a disease control rate of 83.8% at 8 weeks.</p> <p>Conclusions</p> <p>The recommended dose of 750 mg once daily was well tolerated. Encouraging antitumor activity across a broad range of malignancies warrants further evaluation in selected populations.</p> <p>Trial registration</p> <p>ClinicalTrials.gov unique identifier: NCT00633490</p

International expert consensus on diagnosis and treatment of lung cancer complicated by chronic obstructive pulmonary disease

Background: Lung cancer combined by chronic obstructive pulmonary disease (LC-COPD) is a common comorbidity and their interaction with each other poses significant clinical challenges. However, there is a lack of well-established consensus on the diagnosis and treatment of LC-COPD. Methods: A panel of experts, comprising specialists in oncology, respiratory medicine, radiology, interventional medicine, and thoracic surgery, was convened. The panel was presented with a comprehensive review of the current evidence pertaining to LC-COPD. After thorough discussions, the panel reached a consensus on 17 recommendations with over 70% agreement in voting to enhance the management of LC-COPD and optimize the care of these patients. Results: The 17 statements focused on pathogenic mechanisms (n=2), general strategies (n=4), and clinical application in COPD (n=2) and lung cancer (n=9) were developed and modified. These statements provide guidance on early screening and treatment selection of LC-COPD, the interplay of lung cancer and COPD on treatment, and considerations during treatment. This consensus also emphasizes patient-centered and personalized treatment in the management of LC-COPD. Conclusions: The consensus highlights the need for concurrent treatment for both lung cancer and COPD in LC-COPD patients, while being mindful of the mutual influence of the two conditions on treatment and monitoring for adverse reactions

A High Spatiotemporal Enhancement Method of Forest Vegetation Leaf Area Index Based on Landsat8 OLI and GF-1 WFV Data

The leaf area index (LAI) is a crucial parameter for analyzing terrestrial ecosystem carbon cycles and global climate change. Obtaining high spatiotemporal resolution forest stand vegetation LAI products over large areas is essential for an accurate understanding of forest ecosystems. This study takes the northwestern part of the Inner Mongolia Autonomous Region (the northern section of the Greater Khingan Mountains) in northern China as the research area. It also generates the LAI time series product of the 8-day and 30 m forest stand vegetation growth period from 2013 to 2017 (from the 121st to the 305th day of each year). The Simulated Annealing-Back Propagation Neural Network (SA-BPNN) model was used to estimate LAI from Landsat8 OLI, and the multi-period GaoFen-1 WideField-View satellite images (GF-1 WFV) and the spatiotemporal adaptive reflectance fusion mode (STARFM) was used to predict high spatiotemporal resolution LAI by combining inversion LAI and Global LAnd Surface Satellite-derived vegetation LAI (GLASS LAI) products. The results showed the following: (1) The SA-BPNN estimation model has relatively high accuracy, with R2 = 0.75 and RMSE = 0.38 for the 2013 LAI estimation model, and R2 = 0.74 and RMSE = 0.17 for the 2016 LAI estimation model. (2) The fused 30 m LAI product has a good correlation with the LAI verification of the measured sample site (R2 = 0.8775) and a high similarity with the GLASS LAI product. (3) The fused 30 m LAI product has a high similarity with the GLASS LAI product, and compared with the GLASS LAI interannual trend line, it accords with the growth trend of plants in the seasons. This study provides a theoretical and technical reference for forest stand vegetation growth period LAI spatiotemporal fusion research based on high-score data, and has an important role in exploring vegetation primary productivity and carbon cycle changes in the future

Designing Alternative Representations of Confusion Matrices to Support Non-Expert Public Understanding of Algorithm Performance

Ensuring effective public understanding of algorithmic decisions that are powered by machine learning techniques has become an urgent task with the increasing deployment of AI systems into our society. In this work, we present a concrete step toward this goal by redesigning confusion matrices for binary classification to support non-experts in understanding the performance of machine learning models. Through interviews (n=7) and a survey (n=102), we mapped out two major sets of challenges lay people have in understanding standard confusion matrices: the general terminologies and the matrix design. We further identified three sub-challenges regarding the matrix design, namely, confusion about the direction of reading the data, layered relations and quantities involved. We then conducted an online experiment with 483 participants to evaluate how effective a series of alternative representations target each of those challenges in the context of an algorithm for making recidivism predictions. We developed three levels of questions to evaluate users' objective understanding. We assessed the effectiveness of our alternatives for accuracy in answering those questions, completion time, and subjective understanding. Our results suggest that (1) only by contextualizing terminologies can we significantly improve users' understanding and (2) flow charts, which help point out the direction of reading the data, were most useful in improving objective understanding. Our findings set the stage for developing more intuitive and generally understandable representations of the performance of machine learning models.</jats:p