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Antibiotics and organic residues from Tetracycline (TC) and other pharmaceuticals administered to aquatic living organism have negative impacts on aquatic environment by killing-off non-target living organisms and developing antibiotic-resistant bacteria. In this study, Micro-Nano Bubble (MNB) system was used to remove TC residues. MNB system demonstrated good level of degradation efficiency, as resulted in experiment where in time of 100 min, the TC degraded at rate of 82.66% from its initial concentration of TC when the initial concentration was 1 mg/L. When the initial concentration was increased to 10 mg/L, MNB system degraded TC at 64.35% of their initial, this means MNB system demonstrated good level of efficiency for TC removal and indicated that it is more efficient in TC degradation under the conditions of low initial TC concentration and high availability of dissolved oxygen (DO). In the system as the temperature increased there was a significant decrease in DO saturation which was related to the TC complex structure that contain multiple function groups such as amino groups, hydroxyl and carboxyl which possess high strong affinity with oxygen that leads to their adsorption onto bubble surface. This study provides significant insights into the application of MNB system for the removal of organic residues within aquatic ecosystem and underscores the need for further exploration of MNB technology for environmental remediation., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Liu Yutao reports financial support was provided by Ministry of Science and Technology of the People's Republic of China. Liu Yutao reports financial support was provided by Jiangsu Provincial Agricultural Science and Technology Innovation Fund. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier Ltd.. All rights reserved.)

Immune checkpoint inhibitor (ICI) rechallenge in non-small cell lung cancer (NSCLC) is a promising therapeutic strategy. The situation for ICI rechallenge can be divided into three categories: adverse events (AEs); resistance to ICIs, and rechallenge becomes compulsive because of tumor relapse while the patients had completed a 2 year course of immunotherapy. However, these categories are still controversial and should be explored further. Through voting at the 6th Straits Summit Forum on Lung Cancer, in this study we summarize the consensus of 147 experts in ICI rechallenges. A total of 97.74% experts agreed to rechallenge; 48.87% experts rechallenge with the original drug, and the others rechallenge with a different drug; 40.3% agreed to rechallenge directly after progression; 88.06% experts agreed to ICI rechallenge with a combination regimen; and factors such as previous performance status score, PD-1 expression, and age should also be considered. Understanding the the clinical studies in ICI rechallenge could bring us one step closer to understanding the consensus. In patients with advanced NSCLC who have suffered recurrent or distant metastasis after immunotherapy, the option of rechallenge with ICIs is a promising treatment option., (© 2024 The Authors. Thoracic Cancer published by John Wiley & Sons Australia, Ltd.)

Skewed data are common in sample surveys. In probability proportional to size sampling, we propose two Bayesian model-based predictive methods for estimating finite population quantiles with skewed sample survey data. We assume the survey outcome to follow a skew-normal distribution given the probability of selection and model the location and scale parameters of the skew-normal distribution as functions of the probability of selection. To allow a flexible association between the survey outcome and the probability of selection, the first method models the location parameter with a penalized spline and the scale parameter with a polynomial function, while the second method models both the location and scale parameters with penalized splines. Using a fully Bayesian approach, we obtain the posterior predictive distributions of the nonsampled units in the population and thus the posterior distributions of the finite population quantiles. We show through simulations that our proposed methods are more efficient and yield shorter credible intervals with better coverage rates than the conventional weighted method in estimating finite population quantiles. We demonstrate the application of our proposed methods using data from the 2013 National Drug Abuse Treatment System Survey., (© The Author(s) 2019. Published by Oxford University Press on behalf of the American Association for Public Opinion Research. All rights reserved. For permissions, please email: journals.permissions@oup.com.)