Objective: Endoscopic surgery is gaining popularity as a minimally invasive option for adjacent segment degeneration (ASD). However, most of the previous literature has focused on transforaminal endoscopic techniques. This study aimed to assess the outcomes of decompressive laminectomy (unilateral laminectomy and bilateral decompression) with unilateral biportal endoscopic (UBE) in patients with lumbar ASD presenting with spinal stenosis. Methods: Thirty-nine ASD patients presenting with spinal stenosis who underwent UBE decompressive surgery between June 2018 and November 2022 at 4 different institutions were enrolled. The postoperative decompression amount and spinal instability were assessed using magnetic resonance images and dynamic radiographs. All patients were followed for at least 1 year, and clinical outcomes were assessed. Results: In the 39 patients, the cross-sectional area of the dural sac (0.55±0.21 cm2 to 1.11±0.41 cm2, p<0.001) indicated significant spinal decompression. Dynamic radiographs did not demonstrate significant occurrence of instability in operated segment. Three patients experienced complications, with one case each of hematoma, dural tear, and neurologic deficit. Visual analogue scale scores of the back (6.4±2.4 to 2.1±1.2, p<0.001) and leg (7.5±1.5 to 1.6±1.6, p<0.001), and Oswestry Disability Index scores (47.5±16.3 to 19.8±9.7, p<0.001) indicated significant clinical improvement that was sustained over 1 year. During the average follow-up period of 2 years, 2 patients underwent revision surgery due to failure and relapse. Conclusion UBE decompressive surgery had satisfactory outcomes in ASD patients, without significant failure or relapse.

Purpose: Deep learning–based image enhancement has significant potential in the field of ultrasound image processing, as it can accurately model complicated nonlinear artifacts and noise, such as ultrasonic speckle patterns. However, training deep learning networks to acquire reference images that are clean and free of noise presents significant challenges. This study introduces an unsupervised deep learning framework, termed speckle-to-speckle (S2S), designed for speckle and noise suppression. This framework can complete its training without the need for clean (speckle-free) reference images. Methods: The proposed network leverages statistical reasoning for the mutual training of two in vivo images, each with distinct speckle patterns and noise. It then infers speckle- and noise-free images without needing clean reference images. This approach significantly reduces the time, cost, and effort experts need to invest in annotating reference images manually. Results: The experimental results demonstrated that the proposed approach outperformed existing techniques in terms of the signal-to-noise ratio, contrast-to-noise ratio, structural similarity index, edge preservation index, and processing time (up to 86 times faster). It also performed excellently on images obtained from ultrasound scanners other than the ones used in this work. Conclusion S2S demonstrates the potential of employing an unsupervised learning-based technique in medical imaging applications, where acquiring a ground truth reference is challenging.

Purpose: Smoking is causally related to alcohol use disorder. Although polycyclic aromatic hydrocarbons (PAHs) are major neurotoxic pollutants in tobacco smoke, evidence is lacking on the role of PAHs in the relationship between smoking and alcohol use disorder. This study investigated the types of PAHs associated with smoking and whether exposure to those PAHs mediated the effect of smoking on alcohol use disorder. Materials and Methods: A total of 968 male firefighters were analyzed. Smoking history and cumulative pack-years were obtained using self-reported questionnaires. Alcohol use disorder was defined using the Alcohol Use Disorder Identification Test.PAH exposure was assessed by urinary metabolites. Regression analyses were performed between exposure (smoking), outcome (alcohol use disorder), and mediator (PAH metabolites) variables. A mediation analysis was performed to test the indirect effect of PAH metabolites on the association between smoking and alcohol use disorder. All analyses were repeated for 770 participants who were followed up after 2 years, while alcohol use disorder was redefined from follow-up data ensuring the temporal sequence of the variables. Results: Both 2-naphthol [β=0.78, 95% confidence interval (CI): 0.59–0.98] and 2-hydroxyfluorene (β=0.69, 95% CI: 0.56–0.82) were associated with smoking history. Furthermore, 2-naphthol and 2-hydroxyfluorene mediated the associations of smoking history (proportion mediated: 14.2%, 23.6% respectively) or cumulative pack-years (proportion mediated: 14.4%, 25.4% respectively) with alcohol use disorder. The results were consistent in longitudinal settings. Conclusion Exposure to PAHs mediated the association between tobacco smoking and alcohol use disorder. PAH exposure from tobacco may increase the risk of addictive disorders.

Purpose: Deep learning–based image enhancement has significant potential in the field of ultrasound image processing, as it can accurately model complicated nonlinear artifacts and noise, such as ultrasonic speckle patterns. However, training deep learning networks to acquire reference images that are clean and free of noise presents significant challenges. This study introduces an unsupervised deep learning framework, termed speckle-to-speckle (S2S), designed for speckle and noise suppression. This framework can complete its training without the need for clean (speckle-free) reference images. Methods: The proposed network leverages statistical reasoning for the mutual training of two in vivo images, each with distinct speckle patterns and noise. It then infers speckle- and noise-free images without needing clean reference images. This approach significantly reduces the time, cost, and effort experts need to invest in annotating reference images manually. Results: The experimental results demonstrated that the proposed approach outperformed existing techniques in terms of the signal-to-noise ratio, contrast-to-noise ratio, structural similarity index, edge preservation index, and processing time (up to 86 times faster). It also performed excellently on images obtained from ultrasound scanners other than the ones used in this work. Conclusion S2S demonstrates the potential of employing an unsupervised learning-based technique in medical imaging applications, where acquiring a ground truth reference is challenging.

Purpose: Smoking is causally related to alcohol use disorder. Although polycyclic aromatic hydrocarbons (PAHs) are major neurotoxic pollutants in tobacco smoke, evidence is lacking on the role of PAHs in the relationship between smoking and alcohol use disorder. This study investigated the types of PAHs associated with smoking and whether exposure to those PAHs mediated the effect of smoking on alcohol use disorder. Materials and Methods: A total of 968 male firefighters were analyzed. Smoking history and cumulative pack-years were obtained using self-reported questionnaires. Alcohol use disorder was defined using the Alcohol Use Disorder Identification Test.PAH exposure was assessed by urinary metabolites. Regression analyses were performed between exposure (smoking), outcome (alcohol use disorder), and mediator (PAH metabolites) variables. A mediation analysis was performed to test the indirect effect of PAH metabolites on the association between smoking and alcohol use disorder. All analyses were repeated for 770 participants who were followed up after 2 years, while alcohol use disorder was redefined from follow-up data ensuring the temporal sequence of the variables. Results: Both 2-naphthol [β=0.78, 95% confidence interval (CI): 0.59–0.98] and 2-hydroxyfluorene (β=0.69, 95% CI: 0.56–0.82) were associated with smoking history. Furthermore, 2-naphthol and 2-hydroxyfluorene mediated the associations of smoking history (proportion mediated: 14.2%, 23.6% respectively) or cumulative pack-years (proportion mediated: 14.4%, 25.4% respectively) with alcohol use disorder. The results were consistent in longitudinal settings. Conclusion Exposure to PAHs mediated the association between tobacco smoking and alcohol use disorder. PAH exposure from tobacco may increase the risk of addictive disorders.

Purpose: Deep learning–based image enhancement has significant potential in the field of ultrasound image processing, as it can accurately model complicated nonlinear artifacts and noise, such as ultrasonic speckle patterns. However, training deep learning networks to acquire reference images that are clean and free of noise presents significant challenges. This study introduces an unsupervised deep learning framework, termed speckle-to-speckle (S2S), designed for speckle and noise suppression. This framework can complete its training without the need for clean (speckle-free) reference images. Methods: The proposed network leverages statistical reasoning for the mutual training of two in vivo images, each with distinct speckle patterns and noise. It then infers speckle- and noise-free images without needing clean reference images. This approach significantly reduces the time, cost, and effort experts need to invest in annotating reference images manually. Results: The experimental results demonstrated that the proposed approach outperformed existing techniques in terms of the signal-to-noise ratio, contrast-to-noise ratio, structural similarity index, edge preservation index, and processing time (up to 86 times faster). It also performed excellently on images obtained from ultrasound scanners other than the ones used in this work. Conclusion S2S demonstrates the potential of employing an unsupervised learning-based technique in medical imaging applications, where acquiring a ground truth reference is challenging.

Purpose: Smoking is causally related to alcohol use disorder. Although polycyclic aromatic hydrocarbons (PAHs) are major neurotoxic pollutants in tobacco smoke, evidence is lacking on the role of PAHs in the relationship between smoking and alcohol use disorder. This study investigated the types of PAHs associated with smoking and whether exposure to those PAHs mediated the effect of smoking on alcohol use disorder. Materials and Methods: A total of 968 male firefighters were analyzed. Smoking history and cumulative pack-years were obtained using self-reported questionnaires. Alcohol use disorder was defined using the Alcohol Use Disorder Identification Test.PAH exposure was assessed by urinary metabolites. Regression analyses were performed between exposure (smoking), outcome (alcohol use disorder), and mediator (PAH metabolites) variables. A mediation analysis was performed to test the indirect effect of PAH metabolites on the association between smoking and alcohol use disorder. All analyses were repeated for 770 participants who were followed up after 2 years, while alcohol use disorder was redefined from follow-up data ensuring the temporal sequence of the variables. Results: Both 2-naphthol [β=0.78, 95% confidence interval (CI): 0.59–0.98] and 2-hydroxyfluorene (β=0.69, 95% CI: 0.56–0.82) were associated with smoking history. Furthermore, 2-naphthol and 2-hydroxyfluorene mediated the associations of smoking history (proportion mediated: 14.2%, 23.6% respectively) or cumulative pack-years (proportion mediated: 14.4%, 25.4% respectively) with alcohol use disorder. The results were consistent in longitudinal settings. Conclusion Exposure to PAHs mediated the association between tobacco smoking and alcohol use disorder. PAH exposure from tobacco may increase the risk of addictive disorders.

Purpose: Deep learning–based image enhancement has significant potential in the field of ultrasound image processing, as it can accurately model complicated nonlinear artifacts and noise, such as ultrasonic speckle patterns. However, training deep learning networks to acquire reference images that are clean and free of noise presents significant challenges. This study introduces an unsupervised deep learning framework, termed speckle-to-speckle (S2S), designed for speckle and noise suppression. This framework can complete its training without the need for clean (speckle-free) reference images. Methods: The proposed network leverages statistical reasoning for the mutual training of two in vivo images, each with distinct speckle patterns and noise. It then infers speckle- and noise-free images without needing clean reference images. This approach significantly reduces the time, cost, and effort experts need to invest in annotating reference images manually. Results: The experimental results demonstrated that the proposed approach outperformed existing techniques in terms of the signal-to-noise ratio, contrast-to-noise ratio, structural similarity index, edge preservation index, and processing time (up to 86 times faster). It also performed excellently on images obtained from ultrasound scanners other than the ones used in this work. Conclusion S2S demonstrates the potential of employing an unsupervised learning-based technique in medical imaging applications, where acquiring a ground truth reference is challenging.

Purpose: Smoking is causally related to alcohol use disorder. Although polycyclic aromatic hydrocarbons (PAHs) are major neurotoxic pollutants in tobacco smoke, evidence is lacking on the role of PAHs in the relationship between smoking and alcohol use disorder. This study investigated the types of PAHs associated with smoking and whether exposure to those PAHs mediated the effect of smoking on alcohol use disorder. Materials and Methods: A total of 968 male firefighters were analyzed. Smoking history and cumulative pack-years were obtained using self-reported questionnaires. Alcohol use disorder was defined using the Alcohol Use Disorder Identification Test.PAH exposure was assessed by urinary metabolites. Regression analyses were performed between exposure (smoking), outcome (alcohol use disorder), and mediator (PAH metabolites) variables. A mediation analysis was performed to test the indirect effect of PAH metabolites on the association between smoking and alcohol use disorder. All analyses were repeated for 770 participants who were followed up after 2 years, while alcohol use disorder was redefined from follow-up data ensuring the temporal sequence of the variables. Results: Both 2-naphthol [β=0.78, 95% confidence interval (CI): 0.59–0.98] and 2-hydroxyfluorene (β=0.69, 95% CI: 0.56–0.82) were associated with smoking history. Furthermore, 2-naphthol and 2-hydroxyfluorene mediated the associations of smoking history (proportion mediated: 14.2%, 23.6% respectively) or cumulative pack-years (proportion mediated: 14.4%, 25.4% respectively) with alcohol use disorder. The results were consistent in longitudinal settings. Conclusion Exposure to PAHs mediated the association between tobacco smoking and alcohol use disorder. PAH exposure from tobacco may increase the risk of addictive disorders.

Purpose: Deep learning–based image enhancement has significant potential in the field of ultrasound image processing, as it can accurately model complicated nonlinear artifacts and noise, such as ultrasonic speckle patterns. However, training deep learning networks to acquire reference images that are clean and free of noise presents significant challenges. This study introduces an unsupervised deep learning framework, termed speckle-to-speckle (S2S), designed for speckle and noise suppression. This framework can complete its training without the need for clean (speckle-free) reference images. Methods: The proposed network leverages statistical reasoning for the mutual training of two in vivo images, each with distinct speckle patterns and noise. It then infers speckle- and noise-free images without needing clean reference images. This approach significantly reduces the time, cost, and effort experts need to invest in annotating reference images manually. Results: The experimental results demonstrated that the proposed approach outperformed existing techniques in terms of the signal-to-noise ratio, contrast-to-noise ratio, structural similarity index, edge preservation index, and processing time (up to 86 times faster). It also performed excellently on images obtained from ultrasound scanners other than the ones used in this work. Conclusion S2S demonstrates the potential of employing an unsupervised learning-based technique in medical imaging applications, where acquiring a ground truth reference is challenging.