Objectives: Thiamine is thought to modify sleeping patterns, while alcohol use diminishes internal thiamine levels. We investigated the association between thiamine intake and sleep duration and explored possible heterogeneity in the effect according to alcohol use. Methods: In total, 15 384 participants aged 19-64 were obtained from the Korea National Health and Nutrition Examination Survey 2012-2016. Nutrient intake, including thiamine, was measured using a food frequency questionnaire. Sleep duration was measured by a self-reported questionnaire. The highest thiamine intake quartile was set as the reference group. Participants were divided into 3 groups, with 7-8 hours of daily sleep as a reference group and those who slept more or less than that as “oversleeping” and “insufficient sleeping,” respectively. Multivariate logistic regression was used, adjusting for socioeconomic, medical, and nutritional factors. Additionally, participants were stratified according to high-risk alcohol use defined by the World Health Organization standards on alcohol use. Results: Low thiamine intake was associated with oversleeping (Q3: odds ratio [OR], 1.06; 95% confidence interval [CI], 0.86 to 1.32; Q2: OR, 1.24; 95% CI, 0.99 to 1.55; Q1: OR, 1.49; 95% CI, 1.16 to 1.91) and showed a significant trend for higher ORs at lower intake levels (p-trend<0.001). The effect was stronger in the high-risk alcohol use group (Q1: OR, 1.78; 95% CI, 1.28 to 2.49). Conclusions Low thiamine intake was associated with oversleeping, and alcohol use intensified that association. These results were found in a context where overt clinical symptoms due to thiamine deficiency are considered rare. More awareness of the potential relationship of thiamine intake with oversleeping and its related risks should be considered.

Coronary stenting for long complex lesion is effective but associated with complication. We compared the results of stenting between with multiple conventional stenting group (group A) and with single long stenting group (group B). Fifty patients were prospectively and randomly enrolled: 25 patients for each group. Each group showed no significant differences of clinical characteristics. One patient died of heart failure in each group, not associated with the procedure itself. One patients had cerebrovascular accident in each group. Five patients had major bleeding (2, group A; 3, group B). Angiographic success rate was 100% in each group and procedural success rate was 96% and 100% in group A and B, respectively. Angiographic and clinical restenosis rate at 6 months follow-up were 60%, 36% in group A and 65%, 44% in group B, respectively (p=S). Multivariate analysis showed that several factors affected the angiographic restenosis rate as follows; a) male gender (M:F=76.9%:25.0%, P<0.001), b) AMI (AMI:stable angina pectoris=72.7%:66.7%, P<0.001), c) lesion length d) residual stenosis. In conclusion, there were no statistical differences of restenosis and complication rate between the two groups. Our data support single long stenting is acceptable and economically more favorable for long diffuse lesion, compared to multiple conventional stenting.
Constriction, Pathologic ; Coronary Stenosis* ; Coronary Vessels* ; Follow-Up Studies ; Heart Failure ; Hemorrhage ; Humans ; Male ; Multivariate Analysis ; Prospective Studies ; Stents* ; Stroke

Nocardia species are aerobic, gram-positive pathogens found worldwide in soil. Nocardia is considered an opportunistic pathogen, and its infection mostly occurs in immunocompromised patients. We report a case of Nocardia farcinica induced mediastinitis and pneumonia that occurred in a 64-year-old male patient who had no significant medical history except for hypertension. He visited another hospital with a complaint of dyspnea and left chest wall pain. The symptoms arose 7 days ago without any trauma and they worsened. A mediastinal mass was found on computed tomography scan. After being transferred to our hospital for further evaluation, he was diagnosed with mediastinitis and pneumonia. As N. farcinica was found to be the causative organism by 16S rRNA sequencing, proper antibiotic therapy including trimethoprim/sulfamethoxazole was initiated immediately. After this, the patient improved and he was discharged. If an infection has a disseminating course, nocardiosis cannot be excluded even in immunocompetent patients. Once the diagnosis is established, prompt antibiotic therapy should be performed based on the severity.
Diagnosis ; Dyspnea ; Humans ; Hypertension ; Immunocompromised Host ; Male ; Mediastinitis* ; Middle Aged ; Nocardia Infections ; Nocardia* ; Pneumonia* ; Soil ; Thoracic Wall

Purpose: Chemotherapy-related cognitive impairment (CRCI) is a controversial concept not much explored on colorectal cancer patients. Materials and Methods: We identified 11 prospective studies: eight studies on 696 colorectal cancer patients who received chemotherapy and three studies on 346 rectal cancer patients who received neoadjuvant chemoradiotherapy. Standardized mean differences (SMDs) of neuropsychological test results and the cognitive quality-of-life scale were calculated using random effect models. A meta-regression was conducted to investigate the association between mean study population age and effect sizes. Results: The association between chemotherapy and cognitive impairment was not clear in colorectal cancer patients (SMD, 0.003; 95% confidence interval, ‒0.080 to 0.086). However, a meta-regression showed that older patients are more vulnerable to CRCI than younger patients (β=‒0.016, p < 0.001). Conclusion Chemotherapy has an overall positive negligible effect size on the cognitive function of colorectal patients. Age is a significant moderator of CRCI.

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: 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: 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: 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: 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.

Background: In recent years, implant-based breast reconstruction has been performed because of its simplicity, short operation time, and rapid recovery of patients. Several studies have reported treatment methods for implant surgery-related infection, which is a serious complication. The aim of this study was to introduce our strategy for salvaging infected implants and to evaluate its effectiveness.
Methods: The authors performed a retrospective study of 145 cases from 132 patients who underwent implant-based breast reconstruction from January 2012 to December 2018. Empirical antibiotics were immediately administered to patients with suspected infections. The patients then underwent salvage treatment including appropriate antibiotics, ultrasonography-guided aspiration, debridement, antibiotic lavage, and implant exchange through a multidisciplinary approach. Patient demographics, operative data, duration until drain removal, adjuvant treatment, and complications were analyzed.
Results: The total infection rate was 5.5% (8/145). A longer indwelling catheter period and adjuvant treatment were significantly associated with infection. The salvage treatment showed a success rate of 87.5% (7/8). Seven patients who received early aggressive salvage treatment recovered from infection. One patient with methicillin-resistant Staphylococcus aureus, who received salvage treatment 11 days after symptom onset, did not respond to drainage and antibiotic treatment. That patient subsequently underwent explantation.
Conclusions In implant-based breast reconstruction, prevention of infection is of the utmost importance. However, if an infection is suspected, proactive empirical antibiotic therapy and collaboration with the necessary departments are required. Through a multidisciplinary approach and proactive early management, swift and appropriate salvage should be performed.