Determining Risk Factors Associated with Depression and Anxiety in Young Lung Cancer Patients: A Novel Optimization Algorithm

Automatic Segmentation of Periapical Radiograph Using Color Histogram and Machine Learning for Osteoporosis Detection

Osteoporosis leads to the loss of cortical thickness, a decrease in bone mineral density (BMD), deterioration in the size of trabeculae, and an increased risk of fractures. Changes in trabecular bone due to osteoporosis can be observed on periapical radiographs, which are widely used in dental practice. This study proposes an automatic trabecular bone segmentation method for detecting osteoporosis using a color histogram and machine learning (ML), based on 120 regions of interest (ROI) on periapical radiographs, and divided into 60 training and 42 testing datasets. The diagnosis of osteoporosis is based on BMD as evaluated by dual X-ray absorptiometry. The proposed method comprises five stages: the obtaining of ROI images, conversion to grayscale, color histogram segmentation, extraction of pixel distribution, and performance evaluation of the ML classifier. For trabecular bone segmentation, we compare K-means and Fuzzy C-means. The distribution of pixels obtained from the K-means and Fuzzy C-means segmentation was used to detect osteoporosis using three ML methods: decision tree, naive Bayes, and multilayer perceptron. The testing dataset was used to obtain the results in this study. Based on the performance evaluation of the K-means and Fuzzy C-means segmentation methods combined with 3 ML, the osteoporosis detection method with the best diagnostic performance was K-means segmentation combined with a multilayer perceptron classifier, with accuracy, specificity, and sensitivity of 90.48%, 90.90%, and 90.00%, respectively. The high accuracy of this study indicates that the proposed method provides a significant contribution to the detection of osteoporosis in the field of medical and dental image analysis.

Association of sleep behaviour and pattern with the risk of glaucoma: a prospective cohort study in the UK Biobank

OBJECTIVES

Given the role of intraocular pressure in glaucoma, the patient's sleeping pattern might contribute to the development and progression of glaucoma. We performed a study to understand the association between sleep behaviours and glaucoma.

DESIGN

Our study was a prospective cohort study.

SETTING

This was a prospective cohort study in the UK Biobank. Self-reported data on five sleep behaviours were collected using a questionnaire at baseline. We identified four sleep patterns based on a cluster analysis of the sleep behaviours.

PARTICIPANTS

In the UK Biobank, 409 053 participants were recruited between 2006 and 2010 and followed for a diagnosis of glaucoma. We identified glaucoma as any hospital admission with a diagnosis of glaucoma, based on UK Biobank inpatient hospital data. Individuals who withdrew from the UK Biobank, or were diagnosed with glaucoma before recruitment, or had self-reported surgery or laser treatment for glaucoma, or had no information on sleep behaviors were excluded.

PRIMARY AND SECONDARY OUTCOME MEASURES

We estimated hazard ratios (HRs) with 95% confidence intervals (CI) using Cox proportional hazards models to estimate the associations of different sleep behaviors, as well as identified sleep patterns, with the risk of glaucoma, adjusting for multiple confounders.

RESULTS

Compared with individuals who had a healthy sleep pattern, an excess risk of any glaucoma was observed among individuals with snoring and daytime sleepiness (HR 1.11, 95% CI 1.03 to 1.19) or insomnia and short/long sleep duration (HR 1.13, 95% CI 1.06 to 1.20), but not late chronotype sleep pattern (HR 0.98, 95% CI 0.93 to 1.03).

CONCLUSION

Snoring, daytime sleepiness, insomnia, and short/long duration, individually or jointly, were all associated with the risk of glaucoma. These findings underscore the need for sleep intervention for individuals at high risk of glaucoma as well as potential ophthalmologic screening among individuals with chronic sleep problems for glaucoma prevention.

Influence of Psychological Nursing Procedure on Negative Emotion, Stress State, Quality of Life and Nursing Satisfaction in Patients with Lung Cancer Radical Operation

Objective

To discuss the influence of psychological nursing procedure on negative emotion, stress state, quality of life and nursing satisfaction in patients with lung cancer radical operation.

Methods

106 patients with lung cancer who underwent radical resection in our hospital from September 2019 to September 2021 were selected. According to the intervention time, patients were divided into Group A and Group B, with 53 cases in each group. Group A received routine nursing, Group B used psychological nursing procedure on the basis of Group A. The negative emotions, stress state, quality of life and nursing satisfaction of patient were observed.

Results

Self-rating anxiety scale and self-rating depression scale scores of Group B were lower than Group A (P < 0.05). The levels of norepinephrine, epinephrine and cortisol in Group B were lower than Group A (P < 0.05). Generic quality of life inventory-74 scores of Group B were higher than Group A (P < 0.05). The nursing satisfaction of Group B (88.68%) was higher than Group A (73.58%) (P < 0.05).

Conclusion

Psychological nursing procedure is conducive to reducing the negative emotion, relieving stress reaction, improving the quality of life, increasing nursing satisfaction of patients with lung cancer radical operation.