BackgroundThe relationship between smoking and depression remains controversial. This study aimed to investigate the association between smoking and depression from three aspects: smoking status, smoking volume, and smoking cessation.MethodsData from adults aged ≥20 who participated in the National Health and Nutrition Examination Survey (NHANES) between 2005 and 2018 were collected. The study gathered information about the participants' smoking status (never smokers, previous smokers, occasional smokers, daily smokers), smoking quantity per day, and smoking cessation. Depressive symptoms were assessed using the Patient Health Questionnaire (PHQ-9), with a score ≥10 indicating the presence of clinically relevant symptoms. Multivariable logistic regression was conducted to evaluate the association of smoking status, daily smoking volume, and smoking cessation duration with depression.ResultsPrevious smokers [odds ratio (OR) = 1.25, 95% confidence interval (CI): 1.05–1.48] and occasional smokers (OR = 1.84, 95% CI: 1.39–2.45) were associated with a higher risk of depression compared with never smokers. Daily smokers had the highest risk of depression (OR = 2.37, 95% CI: 2.05–2.75). In addition, a tendency toward a positive correlation was observed between daily smoking volume and depression (OR = 1.65, 95% CI: 1.24–2.19) (P for trend < 0.05). Furthermore, the longer the smoking cessation duration, the lower the risk of depression (OR = 0.55, 95% CI: 0.39–0.79) (P for trend < 0.05).ConclusionsSmoking is a behavior that increases the risk of depression. The higher the smoking frequency and smoking volume, the higher the risk of depression, whereas smoking cessation is associated with decreased risk of depression, and the longer the smoking cessation duration, the lower the risk of depression.

Pneumonia is one of the hazardous diseases that lead to life insecurity. It needs to be diagnosed at the initial stages to prevent a person from more damage and help them save their lives. Various techniques are used to identify pneumonia, including chest X-ray, blood culture, sputum culture, fluid sample, bronchoscopy, and pulse oximetry. Chest X-ray is the most widely used method to diagnose pneumonia and is considered one of the most reliable approaches. To analyse chest X-ray images accurately, an expert radiologist needs expertise and experience in the desired domain. However, humanassisted approaches have some drawbacks: expert availability, treatment cost, availability of diagnostic tools, etc. Hence, the need for an intelligent and automated system comes into place that operates on chest X-ray images and diagnoses pneumonia. The primary purpose of technology is to develop algorithms and tools that assist humans and make their lives easier. This study proposes a scalable and interpretable deep convolutional neural network (DCNN) to identify pneumonia using chest X-ray images. The proposed modified DCNN model first extracts useful features from the images and then classifies them into normal and pneumonia classes. The proposed system has been trained and tested on chest X-ray images dataset. Various performance metrics have been utilized to inspect the stability and efficacy of the proposed model. The experimental result shows that the proposed model's performance is greater compared to the other state-of-the-art methodologies used to identify pneumonia.