Lactic acid bacteria-malted vinegar: fermentation characteristics and anti-hyperlipidemic effect.

In this study, the fermentation characteristics and functional properties of lactic acid bacteria-malted vinegar (LAB-MV) were investigated during the fermentation period. Changes in the components (organic acids, free sugars, free amino acids, ß-glucan, and gamma-aminobutyric acid (GABA)) of MV (BWAF0d, BWAF10d, BWAF20d) and LAB-MV (LBWAF0d, LBWAF10d, LBWAF20d) were analyzed according to the fermentation time. The amounts of ß-glucan and GABA in LBWAF20d were greater than those in BWAF20d (122.00 µg/mL, 83.06 µg/mL and 531.00 µg/mL, 181.31 µg/mL, respectively). The ACE1 and HMG-CoA reductase inhibitory activities of LBWAF20d were 98.16% (1/20 dilution factor, DF) and 91.01% (1/25 DF), respectively. The lipid accumulation ratio and total cholesterol levels in HepG2 cells treated with LBWAF20d (1/200 DF) were reduced by 45.85% and 54.48%, respectively, compared to those in the untreated group. These results suggest that LAB-MV, which comprises barley wine manufactured from LAB and yeast, may improve hepatic lipid metabolism.

Usefulness of longitudinal nodule-matching algorithm in computer-aided diagnosis of new pulmonary metastases on cancer surveillance CT scans.

Background: Detecting new pulmonary metastases by comparing serial computed tomography (CT) scans is crucial, but a repetitive and time-consuming task that burdens the radiologists' workload. This study aimed to evaluate the usefulness of a nodule-matching algorithm with deep learning-based computer-aided detection (DL-CAD) in diagnosing new pulmonary metastases on cancer surveillance CT scans. Methods: Among patients who underwent pulmonary metastasectomy between 2014 and 2018, 65 new pulmonary metastases missed by interpreting radiologists on cancer surveillance CT (Time 2) were identified after a retrospective comparison with the previous CT (Time 1). First, DL-CAD detected nodules in Time 1 and Time 2 CT images. All nodules detected at Time 2 were initially considered metastasis candidates. Second, the nodule-matching algorithm was used to assess the correlation between the nodules from the two CT scans and to classify the nodules at Time 2 as "new" or "pre-existing". Pre-existing nodules were excluded from metastasis candidates. We evaluated the performance of DL-CAD with the nodule-matching algorithm, based on its sensitivity, false-metastasis candidates per scan, and positive predictive value (PPV). Results: A total of 475 lesions were detected by DL-CAD at Time 2. Following a radiologist review, the lesions were categorized as metastases (n=54), benign nodules (n=392), and non-nodules (n=29). Upon comparison of nodules at Time 1 and 2 using the nodule-matching algorithm, all metastases were classified as new nodules without any matching errors. Out of 421 benign lesions, 202 (48.0%) were identified as pre-existing and subsequently excluded from the pool of metastasis candidates through the nodule-matching algorithm. As a result, false-metastasis candidates per CT scan decreased by 47.9% (from 7.1 to 3.7, P<0.001) and the PPV increased from 11.4% to 19.8% (P<0.001), while maintaining sensitivity. Conclusions: The nodule-matching algorithm improves the diagnostic performance of DL-CAD for new pulmonary metastases, by lowering the number of false-metastasis candidates without compromising sensitivity.

Use of artificial intelligence in triaging of chest radiographs to reduce radiologists' workload.

OBJECTIVES: To evaluate whether deep learning-based detection algorithms (DLD)-based triaging can reduce outpatient chest radiograph interpretation workload while maintaining noninferior sensitivity. METHODS: This retrospective study included patients who underwent initial chest radiography at the outpatient clinic between June 1 and June 30, 2017. Readers interpreted radiographs with/without a commercially available DLD that detects nine radiologic findings (atelectasis, calcification, cardiomegaly, consolidation, fibrosis, nodules, pneumothorax, pleural effusion, and pneumoperitoneum). The reading order was determined in a randomized, crossover manner. The radiographs were classified into negative and positive examinations. In a 50% worklist reduction scenario, radiographs were sorted in descending order of probability scores: the lower half was regarded as negative exams, while the remaining were read with DLD by radiologists. The primary analysis evaluated noninferiority in sensitivity between radiologists reading all radiographs and simulating a 50% worklist reduction, with the inferiority margin of 5%. The specificities were compared using McNemar's test. RESULTS: The study included 1964 patients (median age [interquartile range], 55 years [40-67 years]). The sensitivity was 82.6% (195 of 236; 95% CI: 77.5%, 87.3%) when readers interpreted all chest radiographs without DLD and 83.5% (197 of 236; 95% CI: 78.8%, 88.1%) in the 50% worklist reduction scenario. The difference in sensitivity was 0.8% (95% CI: - 3.8%, 5.5%), establishing noninferiority of 50% worklist reduction (p = 0.01). The specificity increased from 86.7% (1498 of 1728) to 90.4% (1562 of 1728) (p < 0.001) with DLD-based triage. CONCLUSION: Deep learning-based triaging may substantially reduce workload without lowering sensitivity while improving specificity. CLINICAL RELEVANCE STATEMENT: Substantial workload reduction without lowering sensitivity was feasible using deep learning-based triaging of outpatient chest radiograph; however, the legal responsibility for incorrect diagnoses based on AI-standalone interpretation remains an issue that should be defined before clinical implementation. KEY POINTS: • A 50% workload reduction simulation using deep learning-based detection algorithm maintained noninferior sensitivity while improving specificity. • The CT recommendation rate significantly decreased in the disease-negative patients, whereas it slightly increased in the disease-positive group without statistical significance. • In the exploratory analysis, the noninferiority of sensitivity was maintained until 70% of the workload was reduced; the difference in sensitivity was 0%.

Radiologic Extranodal Extension of Metastatic Lymph Nodes in Patients With Non-Small Cell Lung Cancer: Prognostic Utility and Diagnostic Performance.

BACKGROUND. Pathologic extranodal extension (ENE) in metastatic lymph nodes (LNs) has been associated with unfavorable prognosis in patients with non-small cell lung cancer (NSCLC). OBJECTIVE. The purpose of this article was to evaluate the prognostic utility of radiologic ENE and its diagnostic performance in predicting pathologic ENE in patients with NSCLC. METHODS. This retrospective study included 382 patients (mean age, 67 ± 10 [SD] years; 297 men, 85 women) diagnosed with NSCLC and clinical N1 or N2 disease between January 2010 and December 2016. Two thoracic radiologists reviewed staging chest CT examinations to record subjective overall impression for radiologic ENE (no ENE, possible/probable ENE, or unambiguous ENE), reviewing 30 examinations in consensus and the remaining examinations independently. Kaplan-Meier survival analysis and multivariable Cox proportional hazards model were used to evaluate the utility of radiologic ENE in predicting overall survival (OS). Prognostic utility of radiologic ENE was also assessed in patients with clinical N2a disease. In patients who underwent surgery, sensitivity and specificity were determined of radiologic unambiguous ENE in predicting pathologic ENE. RESULTS. The 5-year OS rates for no ENE, possible/probable ENE, and unambiguous ENE were 44.4%, 39.1%, and 20.9% for reader 1 and 45.7%, 36.6%, and 25.6% for reader 2, respectively. Unambiguous ENE was an independent prognostic factor for worse OS (reader 1: adjusted HR, 1.72, p = .008; reader 2: adjusted HR, 1.56, p = .03), whereas possible/probable ENE was not (reader 1: adjusted HR, 1.18, p = .33; reader 2: adjusted HR, 1.21, p = .25). In patients with clinical N2a disease, 5-year OS rate in patients with versus without unambiguous ENE for reader 1 was 22.2% versus 40.6% (p = .59) and for reader 2 was 27.6% versus 41.0% (p = .49). In 203 patients who underwent surgery (66 with pathologic ENE), sensitivity and specificity of radiologic unambiguous ENE for predicting pathologic ENE were 11% and 93% for reader 1 and 23% and 87% for reader 2. CONCLUSION. Radiologic unambiguous ENE was an independent predictor of worse OS in patients with NSCLC. The finding had low sensitivity but high specificity for pathologic ENE. CLINICAL IMPACT. Radiologic ENE may have a role in NSCLC staging workup and treatment selection.

Local retention efficacy of steroid-loaded PLGA microspheres in epidural injection.

Long-term effects of epidural steroid injections for pain management require novel drug formulations that increase tissue retention time. Present study aimed to investigate the local retention of steroid-loaded poly(lactic-co-glycolic acid) (PLGA) microspheres in epidural injection using a rabbit model. Twenty rabbits were randomly assigned to a PLGA group (n = 10) and a triamcinolone acetonide (TA) group (n = 10). Each animal was injected with either TA-loaded PLGA microspheres or conventional TA suspension into the lumbar epidural space. The lumbar segments were then harvested from the sacrificed rabbits on day 1, week 1, 2, and 4 after the injection. On day 1, the residual steroid concentration (RSC) was lower in the PLGA group than in the TA group (5.03 ppm vs. 13.01 ppm). However, after a week, more steroids remained in the PLGA group (3.29 ppm vs. 0.58 ppm). After 2 weeks, fewer steroids remained in the PLGA group than in the TA group, although both contained less than 10% of the initial retention dose. This study shows that steroid-loaded PLGA tended to have higher steroid retention in tissue than the steroid itself at the first week after epidural injection. However, most of the steroids disappeared after 2 weeks in both groups.

Pansori today: aesthetic demands vs. vocal health / Pansori hoy: demandas estéticas vs. salud vocal

Introduction:Pansori is a traditional Korean dramatic art form, which likely ap-peared in the mid-eighteenth century in the southern region of Korea. In pansorithere is a strong inclination toward preserving tradition, especially in regard to train-ing, which is generally considered particularly demanding in terms of risks to vo-cal health. Nevertheless ­as highlighted by recent studies­ some innovations took place in pansori characteristics and performances in the last few decades.Objective: We hypothesize that these innovations have impacted the attitudes of singers and teachers towards pansori training and vocal health issues, and that a new approach to voice training in pansori might be recommended.Method: Starting with recent evolutions of pansori and considering previous studies, we discuss how these changes might produce innovations ­or at least a demand for innovation­ in pansori's training. We also try to capture the viewpoint of pansori stu-dents and performers, through an anonymous survey.Results: Although further investigation is required, the results suggest that a new approach in teaching pansori is emerging and it is increasingly requested by the train-ee performers, despite some criticisms from traditionalists.Conclusion: Unlike previously thought, perhaps a more scientific and health-con-scious approach to pansori voice training will be something from which many pansorisingers can benefit.

Introducción: Pansori es una forma de arte dramático tradicional coreano que pro-bablemente apareció a mediados del siglo XVIII en la región sur de Corea. En pansorihay una fuerte inclinación a preservar la tradición, especialmente en lo que respecta al entrenamiento, que generalmente se considera particularmente exigente en térmi-nos de riesgos para la salud vocal. Sin embargo, como destacan estudios recientes, se produjeron algunas innovaciones en las características y actuaciones del pansori en las últimas décadas.Objetivo: Hipotetizamos que estas innovaciones han impactado las actitudes de can-tantes y profesores hacia la formación del pansori y los problemas de salud vocal, y que podría recomendarse un nuevo enfoque para el entrenamiento de la voz en pansori.Método: Comenzando con las evoluciones recientes de pansori y considerando es-tudios previos, discutimos cómo estos cambios pueden producir innovaciones, o al menos una demanda de innovación, en la formación de pansori. También tratamos de captar el punto de vista de los estudiantes e intérpretes de pansori, a través de una encuesta anónima.Resultados: Aunque se requiere más investigación, los resultados sugieren que está surgiendo un nuevo enfoque en la enseñanza del pansori y es cada vez más solicitado por los artistas en formación, a pesar de algunas críticas de los tradicionalistas.Conclusión: A diferencia de lo que se pensaba anteriormente, quizás un enfoque más científico y consciente de la salud para el entrenamiento de la voz en pansori será algo de lo que muchos cantantes de pansori puedan beneficiarse

Troponin-Positive Non-Obstructive Coronary Arteries and Myocardial Infarction with Non-Obstructive Coronary Arteries: Definition, Etiologies, and Role of CT and MR Imaging.

In approximately 10% of patients with acute myocardial infarction (MI), angiography does not reveal an obstructive coronary stenosis. This is known as myocardial infarction with non-obstructive coronary arteries (MINOCA), which has complex and multifactorial causes. However, this term can be confusing and open to dual interpretation, because MINOCA is also used to describe patients with acute myocardial injury caused by ischemia-related myocardial necrosis. Therefore, with regards to this specific context of MINOCA, the generic term for MINOCA should be replaced with troponin-positive with non-obstructive coronary arteries (TpNOCA). The causes of TpNOCA can be subcategorized into epicardial coronary (causes of MINOCA), myocardial, and extracardiac disorders. Cardiac magnetic resonance imaging can confirm MI and differentiate various myocardial causes, while cardiac computed tomography is useful to diagnose the extracardiac causes.

Deep Learning-based Automatic Detection Algorithm for Reducing Overlooked Lung Cancers on Chest Radiographs.

Background It is uncertain whether a deep learning-based automatic detection algorithm (DLAD) for identifying malignant nodules on chest radiographs will help diagnose lung cancers. Purpose To evaluate the efficacy of using a DLAD in observer performance for the detection of lung cancers on chest radiographs. Materials and Methods Among patients diagnosed with lung cancers between January 2010 and December 2014, 117 patients (median age, 69 years; interquartile range [IQR], 64-74 years; 57 women) were retrospectively identified in whom lung cancers were visible on previous chest radiographs. For the healthy control group, 234 patients (median age, 58 years; IQR, 48-68 years; 123 women) with normal chest radiographs were randomly selected. Nine observers reviewed each chest radiograph, with and without a DLAD. They detected potential lung cancers and determined whether they would recommend chest CT for follow-up. Observer performance was compared with use of the area under the alternative free-response receiver operating characteristic curve (AUC), sensitivity, and rates of chest CT recommendation. Results In total, 105 of the 117 patients had lung cancers that were overlooked on their original radiographs. The average AUC for all observers significantly rose from 0.67 (95% confidence interval [CI]: 0.62, 0.72) without a DLAD to 0.76 (95% CI: 0.71, 0.81) with a DLAD (P < .001). With a DLAD, observers detected more overlooked lung cancers (average sensitivity, 53% [56 of 105 patients] with a DLAD vs 40% [42 of 105 patients] without a DLAD) (P < .001) and recommended chest CT for more patients (62% [66 of 105 patients] with a DLAD vs 47% [49 of 105 patients] without a DLAD) (P < .001). In the healthy control group, no difference existed in the rate of chest CT recommendation (10% [23 of 234 patients] without a DLAD and 8% [20 of 234 patients] with a DLAD) (P = .13). Conclusion Using a deep learning-based automatic detection algorithm may help observers reduce the number of overlooked lung cancers on chest radiographs, without a proportional increase in the number of follow-up chest CT examinations. © RSNA, 2020 Online supplemental material is available for this article.