Exploring the degree of nicotine dependence and willingness to quit smoking in Chinese smoking patients with stroke: A cross-sectional survey.

ABSTRACT: Smoking is closely related to the occurrence of stroke. The degree of nicotine dependence and willingness to quit smoking are key factors that determine whether a smoker can successfully quit smoking. Few studies have been conducted on factors affecting the willingness to quit smoking among smokers who suffered from stroke.Although the smoking rate has declined, it is still the main changeable risk factor for cerebrovascular diseases. We aimed to investigate the current status of nicotine dependence and willingness to quit smoking among patients who suffered from stroke. We also clarified factors that affect the willingness to quit smoking among stroke patients and provide evidence for quitting smoking.Convenience sampling methods were used to conduct cross-sectional studies. A questionnaire survey was administered to 215 stroke patients who smoked. A general information questionnaire as well as patient smoking and cessation status questionnaire were used. Nicotine dependence test scale, smoking attitude factor score, and tobacco harm perception scale were applied for investigation. Logistic regression was used to analyze factors related to patients' willingness to quit smoking.Statistically significant differences in willingness to quit smoking were found among stroke patients with different ages, household registration status, education levels, marital status, occupation, smoking attitude, and nicotine dependence (P < .05). About 34.4%, 39.1%, and 26.5% of smoking patients had low, moderate, and severe nicotine dependence, respectively. Logistic analysis showed that compared with other occupations, workers, retirees, farmers, and freelancers compared with other people (0.050, 0.081, 0.053, 0.048) had a negative impact on the willingness to quit smoking among stroke patients. Compared with positive attitudes, negative and neutral smoking attitudes (0.190, 0.048) had a negative impact on the willingness to quit smoking among stroke patients. Compared with high nicotine dependence, low and moderate nicotine dependence (4.628, 2.596) had a positive impact on willingness to quit smoking.Smoking patients in neurology department are more willing to quit smoking (67.9%). Factors such as occupation, smoking attitude, and nicotine dependence should be considered when establishing smoking cessation interventions for stroke smokers.

TUSC8 enhances cisplatin sensitivity of NSCLC cells through regulating VEGFA.

PURPOSE: We aimed at studying LncRNA TUSC8 expression in non-small cell lung cancer (NSCLC) cells and its sensitivity to cisplatin chemotherapy, and explore its role in the occurrence, development and treatment of NSCLC. METHODS: NSCLC tissues and adjacent normal ones were randomly selected from 45 patients in our hospital who were pathologically diagnosed as NSCLC. Then H358 and H1299 cells were treated with cisplatin at different concentrations (0 µM, 2 µM, 4 µM, 8 µM, 16 µM) for 24 hours. RESULTS: Our data showed that long non-coding RNA (LncRNA) TUSC8 mRNA expression in NSCLC tissue specimens was remarkably lower than that in adjacent ones. A great link was found between LncRNA TUSC8 and tumor size, TNM stage and overall survival rates of patients with Lung cancer (LCa). The proliferation of NSCLC cells remarkably reduced after overexpression of LncRNA TUSC8 compared with the control group pcDNA3.1-NC, while cell apoptosis indicated an opposite trend. A binding relationship between LncRNA TUSC8 and its downstream target gene VEGFA was verified by luciferase assay. The proliferation rate of NSCLC cells decreased with the increase of cisplatin concentration, and the inhibition rate of LncRNA TUSC8 overexpression group was higher than that of the control group pcDNA3.1-NC under different concentrations of cisplatin. CONCLUSIONS: Lowly expressed LncRNA TUSC8 in NSCLC is related to pathological parameters and prognosis of NSCLC patients. It may negatively regulate VEGFA by targeting its 3'UTR, thereby increasing the sensitivity of NSCLC cell lines to cisplatin, inhibiting the proliferation of NSCLC cells and promoting their apoptosis.

End-to-End Automated Lane-Change Maneuvering Considering Driving Style Using a Deep Deterministic Policy Gradient Algorithm.

Changing lanes while driving requires coordinating the lateral and longitudinal controls of a vehicle, considering its running state and the surrounding environment. Although the existing rule-based automated lane-changing method is simple, it is unsuitable for unpredictable scenarios encountered in practice. Therefore, using a deep deterministic policy gradient (DDPG) algorithm, we propose an end-to-end method for automated lane changing based on lidar data. The distance state information of the lane boundary and the surrounding vehicles obtained by the agent in a simulation environment is denoted as the state space for an automated lane-change problem based on reinforcement learning. The steering wheel angle and longitudinal acceleration are used as the action space, and both the state and action spaces are continuous. In terms of the reward function, avoiding collision and setting different expected lane-changing distances that represent different driving styles are considered for security, and the angular velocity of the steering wheel and jerk are considered for comfort. The minimum speed limit for lane changing and the control of the agent for a quick lane change are considered for efficiency. For a one-way two-lane road, a visual simulation environment scene is constructed using Pyglet. By comparing the lane-changing process tracks of two driving styles in a simplified traffic flow scene, we study the influence of driving style on the lane-changing process and lane-changing time. Through the training and adjustment of the combined lateral and longitudinal control of autonomous vehicles with different driving styles in complex traffic scenes, the vehicles could complete a series of driving tasks while considering driving-style differences. The experimental results show that autonomous vehicles can reflect the differences in the driving styles at the time of lane change at the same speed. Under the combined lateral and longitudinal control, the autonomous vehicles exhibit good robustness to different speeds and traffic density in different road sections. Thus, autonomous vehicles trained using the proposed method can learn an automated lane-changing policy while considering safety, comfort, and efficiency.