Background As a pandemic, a most-common pattern resembled organizing pneumonia (OP) has been identified by CT findings in novel coronavirus disease (COVID-19). We aimed to delineate the evolution of CT findings and outcome in OP of COVID-19.

Background Constructing a medical image feature database according to the category of disease can achieve a quick retrieval of images with similar pathological features. Therefore, this approach has important application values in the fields such as auxiliary diagnosis, teaching, research, and telemedicine.

Objectives The present study aims to elucidate the underlying mechanism how PFKP is regulated by BRCA1 and the clinical significance of PFKP in breast cancer.

Security vulnerabilities play a vital role in network security system. Fuzzing technology is widely used as a vulnerability discovery technology to reduce damage in advance. However, traditional fuzz testing faces many challenges, such as how to mutate input seed files, how to increase code coverage, and how to bypass the format verification effectively. Therefore machine learning techniques have been introduced as a new method into fuzz testing to alleviate these challenges. This paper reviews the research progress of using machine learning techniques for fuzz testing in recent years, analyzes how machine learning improves the fuzzing process and results, and sheds light on future work in fuzzing. Firstly, this paper discusses the reasons why machine learning techniques can be used for fuzzing scenarios and identifies five different stages in which machine learning has been used. Then this paper systematically studies machine learning-based fuzzing models from five dimensions of selection of machine learning algorithms, pre-processing methods, datasets, evaluation metrics, and hyperparameters setting. Secondly, this paper assesses the performance of the machine learning techniques in existing research for fuzz testing. The results of the evaluation prove that machine learning techniques have an acceptable capability of prediction for fuzzing. Finally, the capability of discovering vulnerabilities both traditional fuzzers and machine learning-based fuzzers is analyzed. The results depict that the introduction of machine learning techniques can improve the performance of fuzzing. We hope to provide researchers with a systematic and more in-depth understanding of fuzzing based on machine learning techniques and provide some references for this field through analysis and summarization of multiple dimensions.

Controlled mechanical ventilation (CMV) can cause diaphragmatic motionlessness to induce diaphragmatic dysfunction. Partial maintenance of spontaneous breathing (SB) can reduce ventilation-induced diaphragmatic dysfunction (VIDD). However, to what extent SB is maintained in CMV can attenuate or even prevent VIDD has been rarely reported. The current study aimed to investigate the relationship between SB intensity and VIDD and to identify what intensity of SB maintained in CMV can effectively avoid VIDD. Adult rats were randomly divided according to different SB intensities: SB (0% pressure controlled ventilation (PCV)), high-intensity SB (20% PCV), medium-intensity SB (40% PCV), medium-low intensity SB (60% PCV), low-intensity SB (80% PCV), and PCV (100% PCV). The animals underwent 24-h controlled mechanical ventilation (CMV). The transdiaphragmatic pressure (Pdi), the maximal Pdi (Pdi max) when phrenic nerves were stimulated, Pdi/Pdi max, and the diaphragmatic tonus under different frequencies of electric stimulations were determined. Calpain and caspase-3 were detected using ELISA and the cross-section areas (CSAs) of different types of muscle fibers were measured. The Pdi showed a significant decrease from 20% PCV and the Pdi max showed a significant decrease from 40% PCV (P<0.05). In vivo and vitro diaphragmatic tonus exhibited a significant decrease from 40% PCV and 20% PCV, respectively (P<0.05). From 20% PCV, the CSAs of types I, IIa, and IIb/x muscle fibers showed significant differences, which reached the lowest levels at 100% PCV. SB intensity is negatively associated with the development of VIDD. Maintenance of SB at an intensity of 60%-80% may effectively prevent the occurrence of VIDD.