Intraoral Neuromodulation to Treat Swallowing Disorder and Obstructive Sleep Apnea, Based on Electrical Characterization of the Tongue and Soft Palate

Intraoral functions are results of complex sensorimotor loop operations, and therefore vulnerable to the small functional or neural defects. To secure the vital intraoral functions, it is important to find a way to favorably intervene the intraoral sensorimotor loop operations. The tongue and the soft palate are heavily associated with several sensorimotor loops for intraoral functions, with their dense neural innervations and occupancy of intraoral space. Electrical neuromodulation onto the tongue and the soft palate have a great potential to solve the problems in intraoral functions, such as swallowing, breathing, and talking. However, both the tongue and the soft palate have not been characterized well yet for electrical neuromodulation. In this study, we characterized electrical impedance between electrodes across the tongue and the soft palate, measured stimulation thresholds for perception, and identified type of perception evoked by the stimulation. For impedance characterization, we selected R-R-C model, which is typically used for skin impedance characterization. We found the equivalent series resistance, parallel resistance, and parallel capacitance values for R-R-C model, as 1.837 kΩ, 5.741 kΩ, and 30.148 nF, respectively. We also found that the perception thresholds for the tongue tip, lateral-inferior side of the tongue, and the soft palate as 0.16, 0.34, and 1.47 mA, respectively. As the amplitude of stimulation increases, subjects felt more natural pressure-like sensation than electrical tingling, in all three locations. Subjects could not distinguish the temporal difference of perception between 25 and 100 Hz well. The discomfort at the highest amplitude of stimulation was described as stabbing on the soft palate and stiffness on the tongue. Based on the electrical characterization of the tongue and the soft palate, we found out the effect of electrical neuromodulation, onto the tongue and the soft palate, on the pharyngeal phase of swallowing and obstructive sleep apnea, which is one of the most important intraoral sensorimotor loop operations

The Medical Segmentation Decathlon

International challenges have become the de facto standard for comparative assessment of image analysis algorithms given a specific task. Segmentation is so far the most widely investigated medical image processing task, but the various segmentation challenges have typically been organized in isolation, such that algorithm development was driven by the need to tackle a single specific clinical problem. We hypothesized that a method capable of performing well on multiple tasks will generalize well to a previously unseen task and potentially outperform a custom-designed solution. To investigate the hypothesis, we organized the Medical Segmentation Decathlon (MSD) - a biomedical image analysis challenge, in which algorithms compete in a multitude of both tasks and modalities. The underlying data set was designed to explore the axis of difficulties typically encountered when dealing with medical images, such as small data sets, unbalanced labels, multi-site data and small objects. The MSD challenge confirmed that algorithms with a consistent good performance on a set of tasks preserved their good average performance on a different set of previously unseen tasks. Moreover, by monitoring the MSD winner for two years, we found that this algorithm continued generalizing well to a wide range of other clinical problems, further confirming our hypothesis. Three main conclusions can be drawn from this study: (1) state-of-the-art image segmentation algorithms are mature, accurate, and generalize well when retrained on unseen tasks; (2) consistent algorithmic performance across multiple tasks is a strong surrogate of algorithmic generalizability; (3) the training of accurate AI segmentation models is now commoditized to non AI experts

Intraoral Neuromodulation to Treat Swallowing Disorder and Obstructive Sleep Apnea, Based on Electrical Characterization of the Tongue and Soft Palate

Intraoral functions are results of complex sensorimotor loop operations, and therefore vulnerable to the small functional or neural defects. To secure the vital intraoral functions, it is important to find a way to favorably intervene the intraoral sensorimotor loop operations. The tongue and the soft palate are heavily associated with several sensorimotor loops for intraoral functions, with their dense neural innervations and occupancy of intraoral space. Electrical neuromodulation onto the tongue and the soft palate have a great potential to solve the problems in intraoral functions, such as swallowing, breathing, and talking. However, both the tongue and the soft palate have not been characterized well yet for electrical neuromodulation. In this study, we characterized electrical impedance between electrodes across the tongue and the soft palate, measured stimulation thresholds for perception, and identified type of perception evoked by the stimulation. For impedance characterization, we selected R-R-C model, which is typically used for skin impedance characterization. We found the equivalent series resistance, parallel resistance, and parallel capacitance values for R-R-C model, as 1.837 kΩ, 5.741 kΩ, and 30.148 nF, respectively. We also found that the perception thresholds for the tongue tip, lateral-inferior side of the tongue, and the soft palate as 0.16, 0.34, and 1.47 mA, respectively. As the amplitude of stimulation increases, subjects felt more natural pressure-like sensation than electrical tingling, in all three locations. Subjects could not distinguish the temporal difference of perception between 25 and 100 Hz well. The discomfort at the highest amplitude of stimulation was described as stabbing on the soft palate and stiffness on the tongue. Based on the electrical characterization of the tongue and the soft palate, we found out the effect of electrical neuromodulation, onto the tongue and the soft palate, on the pharyngeal phase of swallowing and obstructive sleep apnea, which is one of the most important intraoral sensorimotor loop operations

The Cut-off Value of Blood Mercury Concentration in Relation to Insulin Resistance

Background : Increased blood mercury concentration is associated with inflammation, and chronic inflammation can cause insulin resistance. We examined the cut-off value of blood mercury in relation to an increased score on the homeostasis model assessment for insulin resistance (HOMA-IR). Methods : We used data from the Korean National Health and Nutrition Examination Survey (2008–2010). Relevant data from 5,184 subjects (2,523 men and 2,661 women) were analyzed cross-sectionally. General linear analysis was performed to evaluate the relationship between HOMA-IR score and blood mercury concentration. In addition, we determined the cut-off value of blood mercury concentration in relation to increased HOMA-IR score (> 2.34) using an ROC curve. Results : The mean value of blood mercury concentration in men and women was 5.88 μg/L and 4.11 μg/L, respectively. In men, comparing to the first quartile, HOMA-IR score increased significantly in the third and fourth blood mercury quartiles. In women, however, the increase in HOMA-IR score was not significant. The cut-off value that best represented the association between increased HOMA-IR score and blood mercury concentration in men was found to be 4.71 μg/L. Conclusion : Blood mercury concentration was associated with increased HOMA-IR score in men, and the cut-off value of blood mercury concentration that was correlated with increased HOMA-IR score was around 4.71 μg/L

Dietary Calcium Intake May Contribute to the HOMA-IR Score in Korean Females with Vitamin D Deficiency (2008–2012 Korea National Health and Nutrition Examination Survey)

Background : Vitamin D and calcium are important factors involved in the regulation of blood glucose and insulin secretion. The Homeostatic Model Assessment of Insulin Resistance (HOMA-IR) score is a useful variable for evaluating insulin resistance, and therefore we cross-sectionally compared HOMA-IR scores according to serum vitamin D levels and dietary calcium intake. Methods : We selected data from healthy males (n=5,163) and females (n=7,506) analyzed over 5 years (2008–2012) via the Korea National Health and Nutrition Examination Survey (KNHANES). We calculated HOMA-IR scores and compared them according to serum 25-hydroxyvitamin D (25(OH)D) concentration classification (30 ng/mL) and dietary calcium quintile after adjustment for relevant variables using complex sample analysis. Comparisons were done after data weighting. Results : The mean dietary calcium intake in males and females was 558.1 mg/day and 445.9 mg/day, respectively. The mean serum 25(OH)D concentration in males and females was 19.4 ng/mL and 16.8 ng/mL, respectively. After adjustment for relevant variables, HOMA-IR score was significantly correlated with serum 25(OH)D concentration and dietary calcium intake in females, whereas it was only correlated with serum 25(OH)D concentration in males. HOMA-IR was significantly lower in the top quintile of dietary calcium intake (mean, 866 mg/day) within females with vitamin D deficiency (P=0.047). Conclusion : Adequate dietary calcium intake may be important for normal HOMA-IR in females with vitamin D deficiency

Prediction of Adverse Events in Stable Non-Variceal Gastrointestinal Bleeding Using Machine Learning

Clinical risk-scoring systems are important for identifying patients with upper gastrointestinal bleeding (UGIB) who are at a high risk of hemodynamic instability. We developed an algorithm that predicts adverse events in patients with initially stable non-variceal UGIB using machine learning (ML). Using prospective observational registry, 1439 out of 3363 consecutive patients were enrolled. Primary outcomes included adverse events such as mortality, hypotension, and rebleeding within 7 days. Four machine learning algorithms, namely, logistic regression with regularization (LR), random forest classifier (RF), gradient boosting classifier (GB), and voting classifier (VC), were compared with the Glasgow–Blatchford score (GBS) and Rockall scores. The RF model showed the highest accuracies and significant improvement over conventional methods for predicting mortality (area under the curve: RF 0.917 vs. GBS 0.710), but the performance of the VC model was best in hypotension (VC 0.757 vs. GBS 0.668) and rebleeding within 7 days (VC 0.733 vs. GBS 0.694). Clinically significant variables including blood urea nitrogen, albumin, hemoglobin, platelet, prothrombin time, age, and lactate were identified by the global feature importance analysis. These results suggest that ML models will be useful early predictive tools for identifying high-risk patients with initially stable non-variceal UGIB admitted at an emergency department

Vibrio sp. dhg as a platform for the biorefinery of brown macroalgae

Although brown macroalgae holds potential as an alternative feedstock, its utilization by conventional microbial platforms has been limited due to the inability to metabolize one of the principal sugars, alginate. Here, we isolate Vibrio sp. dhg, a fast-growing bacterium that can efficiently assimilate alginate. Based on systematic characterization of the genomic information of Vibrio sp. dhg, we establish a genetic toolbox for its engineering. We also demonstrate its ability to rapidly produce ethanol, 2,3-butanediol, and lycopene from brown macroalgae sugar mixture with high productivities and yields. Collectively, Vibrio sp. dhg can be used as a platform for the efficient conversion of brown macroalgae sugars into diverse value-added biochemicalsThis research was supported by the C1 Gas Refinery Program (NRF-2018M3D3A1A01055754), the Global Research Laboratory Program (NRF-2016K1A1A2912829), and the Bio & Medical Technology Development Program (NRF-2018M3A9H3020459) through the National Research Foundation of Korea (NRF) funded by the Ministry of Science and ICT. The Basic Science Research Program (NRF-2018R1A6A3A11045727) through the NRF funded by the Ministry of Education and Creative-Pioneering Researchers Program through Seoul National University (SNU) also supported this research