Development of a predictive model for hypoxia due to sedatives in gastrointestinal endoscopy: a prospective clinical study in Korea.

Background/Aims: Sedation has become a standard practice for patients undergoing gastrointestinal (GI) endoscopy. However, considering the serious cardiopulmonary adverse events associated with sedatives, it is important to identify patients at high risk. Machine learning can generate reasonable prediction for a wide range of medical conditions. This study aimed to evaluate the risk factors associated with sedation during GI endoscopy and develop a predictive model for hypoxia during endoscopy under sedation. Methods: This prospective observational study enrolled 446 patients who underwent sedative endoscopy at the Korea University Ansan Hospital. Clinical data were used as predictor variables to construct predictive models using the random forest method that is a machine learning algorithm. Results: Seventy-two of the 446 patients (16.1%) experienced life-threatening hypoxia requiring immediate medical intervention. Patients who developed hypoxia had higher body weight, body mass index (BMI), neck circumference, and Mallampati scores. Propofol alone and higher initial and total dose of propofol were significantly associated with hypoxia during sedative endoscopy. Among these variables, high BMI, neck circumference, and Mallampati score were independent risk factors for hypoxia. The area under the receiver operating characteristic curve for the random forest-based predictive model for hypoxia during sedative endoscopy was 0.82 (95% confidence interval, 0.79-0.86) and displayed a moderate discriminatory power. Conclusions: High BMI, neck circumference, and Mallampati score were independently associated with hypoxia during sedative endoscopy. We constructed a model with acceptable performance for predicting hypoxia during sedative endoscopy.

Differences in cortical structure between cognitively normal East Asian and Caucasian older adults: a surface-based morphometry study.

There is a growing literature on the impact of ethnicity on brain structure and function. Despite the regional heterogeneity in age-related changes and non-uniformity across brain morphometry measurements in the aging process, paucity of studies investigated the difference in cortical anatomy between the East Asian and Caucasian older adults. The present study aimed to compare cortical anatomy measurements, including cortical thickness, volume and surface area, between cognitively normal East Asian (n = 171) and Caucasian (n = 178) older adults, using surface-based morphometry and vertex-wise group analysis of high-dimensional structural magnetic resonance imaging (MRI) data. The East Asian group showed greater cortical thickness and larger cortical volume in the right superior temporal gyrus, postcentral gyrus, bilateral inferior temporal gyrus, and inferior parietal cortex. The Caucasian group showed thicker and larger cortex in the left transverse temporal cortex, lingual gyrus, right lateral occipital cortex, and precentral gyrus. Additionally, the difference in surface area was discordant with that in cortical thickness. Differences in brain structure between the East Asian and Caucasian might reflect differences in language and information processing, but further studies using standardized methods for assessing racial characteristics are needed. The research results represent a further step towards developing a comprehensive understanding of differences in brain structure between ethnicities of older adults, and this would enrich clinical research on aging and neurodegenerative diseases.

Mutual regulation between OGT and XIAP to control colon cancer cell growth and invasion.

O-GlcNAc transferase (OGT) is an enzyme that catalyzes the O-GlcNAc modification of nucleocytoplasmic proteins and is highly expressed in many types of cancer. However, the mechanism regulating its expression in cancer cells is not well understood. This study shows that OGT is a substrate of the E3 ubiquitin ligase X-linked inhibitor of apoptosis (XIAP) which plays an important role in cancer pathogenesis. Although LSD2 histone demethylase has already been reported as an E3 ubiquitin ligase in lung cancer cells, we identified XIAP as the main E3 ubiquitin ligase in colon cancer cells. Interestingly, OGT catalyzes the O-GlcNAc modification of XIAP at serine 406 and this modification is required for the E3 ubiquitin ligase activity of XIAP toward specifically OGT. Moreover, O-GlcNAcylation of XIAP suppresses colon cancer cell growth and invasion by promoting the proteasomal degradation of OGT. Therefore, our findings regarding the reciprocal regulation of OGT and XIAP provide a novel molecular mechanism for controlling cancer growth and invasion regulated by OGT and O-GlcNAc modification.

O-GlcNAcylation of Mef2c regulates myoblast differentiation.

Unlike other types of glycosylation, O-GlcNAcylation is a single glycosylation which occurs exclusively in the nucleus and cytosol. O-GlcNAcylation underlie metabolic diseases, including diabetes and obesity. Furthermore, O-GlcNAcylation affects different oncogenic processes such as osteoblast differentiation, adipogenesis and hematopoiesis. Emerging evidence suggests that skeletal muscle differentiation is also regulated by O-GlcNAcylation, but the detailed molecular mechanism has not been fully elucidated. In this study, we showed that hyper-O-GlcNAcylation reduced the expression of myogenin, a transcription factor critical for terminal muscle development, in C2C12 myoblasts differentiation by O-GlcNAcylation on Thr9 of myocyte-specific enhancer factor 2c. Furthermore, we showed that O-GlcNAcylation on Mef2c inhibited its DNA binding affinity to myogenin promoter. Taken together, we demonstrated that hyper-O-GlcNAcylation attenuates skeletal muscle differentiation by increased O-GlcNAcylation on Mef2c, which downregulates its DNA binding affinity.

O-Linked N-Acetylglucosamine Modification of Mitochondrial Antiviral Signaling Protein Regulates Antiviral Signaling by Modulating Its Activity.

Post-translational modifications, including O-GlcNAcylation, play fundamental roles in modulating cellular events, including transcription, signal transduction, and immune signaling. Several molecular targets of O-GlcNAcylation associated with pathogen-induced innate immune responses have been identified; however, the direct regulatory mechanisms linking O-GlcNAcylation with antiviral RIG-I-like receptor signaling are not fully understood. In this study, we found that cellular levels of O-GlcNAcylation decline in response to infection with Sendai virus. We identified a heavily O-GlcNAcylated serine-rich region between amino acids 249-257 of the mitochondrial antiviral signaling protein (MAVS); modification at this site disrupts MAVS aggregation and prevents MAVS-mediated activation and signaling. O-GlcNAcylation of the serine-rich region of MAVS also suppresses its interaction with TRAF3; this prevents IRF3 activation and production of interferon-ß. Taken together, these results suggest that O-GlcNAcylation of MAVS may be a master regulatory event that promotes host defense against RNA viruses.

Synthesizing diffusion tensor imaging from functional MRI using fully convolutional networks.

PURPOSE: Medical image synthesis can simulate a target modality of interest based on existing modalities and has the potential to save scanning time while contributing to efficient data collection. This study proposed a three-dimensional (3D) deep learning architecture based on a fully convolutional network (FCN) to synthesize diffusion-tensor imaging (DTI) from resting-state functional magnetic resonance imaging (fMRI). METHODS: fMRI signals derived from white matter (WM) exist and can be used for assessing WM alterations. We constructed an initial functional correlation tensor image using the correlation patterns of adjacent fMRI voxels as one input to the FCN. We considered T1-weighted images as an additional input to provide an algorithm with the structural information needed to synthesize DTI. Our architecture was trained and tested using a large-scale open database dataset (training n = 648; testing n = 293). RESULTS: The average correlation value between synthesized and actual diffusion tensors for 38 WM regions was 0.808, which significantly improves upon an existing study (r = 0.480). We also validated our approach using two open databases. Our proposed method showed a higher correlation with the actual diffusion tensor than the conventional machine-learning method for many WM regions. CONCLUSIONS: Our method synthesized DTI images from fMRI images using a 3D FCN architecture. We hope to expand our method of synthesizing various other imaging modalities from a single image source.

Binary code DOE optimization for speckle suppression in a laser display.

Using a laser light source in imaging devices provides a wide color gamut, high brightness, resolution, and efficiency. At the same time, it creates a speckle pattern that deteriorates the image quality. This paper is related to the application of the moving binary code diffractive optical element (DOE) for speckle suppression in a laser display. Analytical optimization of DOE parameters is made in this paper. The optimal DOE pitch is found analytically. Limitation of the DOE code length is shown, and the highest possible code length is estimated. Application of the compound Barker code for the DOE design is suggested. As an alternative, the M-sequence and minimum peak side-lobe codes are considered. The expected residual speckle contrast ratio after application of those codes is estimated for the number of codes. A comparison of the different codes is done, and recommendations for speckle suppression DOE design are provided.

Fabrication-Method-Dependent Excited State Dynamics in CH3NH3PbI3 Perovskite Films.

Understanding the excited-state dynamics in perovskite photovoltaics is necessary for progress in these materials, but changes in dynamics depending on the fabrication processes used for perovskite photoactive layers remain poorly characterised. Here we report a comparative study on femtosecond transient absorption (TA) in CH3NH3PbI3 perovskite films fabricated by various solution-processing methods. The grain sizes and the number of voids between grains on each film varied according to the film synthesis method. At the low excitation fluence of 0.37 µJ cm-2, fast signal drops in TA dyanmics within 1.5 ps were observed in all perovskite films, but the signal drop magnitudes differed becuase of the variations in charge migration to trap states and band gap renormalisation. For high excitation fluences, the buil-up time of the TA signal was increased by the activated hot-phonon bottleneck, while the signal decay rate was accelerated by fluence-dependent high-order charge recombination. These fluence-dependent dynamics changed for different perovskite fabrication methords, indicating that the dynamics were affected by morphological features such as grain sizes and defects.

Structural and functional connectional fingerprints in mild cognitive impairment and Alzheimer's disease patients.

Regional volume atrophy and functional degeneration are key imaging hallmarks of Alzheimer's disease (AD) in structural and functional magnetic resonance imaging (MRI), respectively. We jointly explored regional volume atrophy and functional connectivity to better characterize neuroimaging data of AD and mild cognitive impairment (MCI). All data were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. We compared regional volume atrophy and functional connectivity in 10 subcortical regions using structural MRI and resting-state functional MRI (rs-fMRI). Neuroimaging data of normal controls (NC) (n = 35), MCI (n = 40), and AD (n = 30) were compared. Significant differences of regional volumes and functional connectivity measures between groups were assessed using permutation tests in 10 regions. The regional volume atrophy and functional connectivity of identified regions were used as features for the random forest classifier to distinguish among three groups. The features of the identified regions were also regarded as connectional fingerprints that could distinctively separate a given group from the others. We identified a few regions with distinctive regional atrophy and functional connectivity patterns for NC, MCI, and AD groups. A three label classifier using the information of regional volume atrophy and functional connectivity of identified regions achieved classification accuracy of 53.33% to distinguish among NC, MCI, and AD. We identified distinctive regional atrophy and functional connectivity patterns that could be regarded as a connectional fingerprint.

Imaging analysis of Parkinson's disease patients using SPECT and tractography.

Parkinson's disease (PD) is a degenerative disorder that affects the central nervous system. PD-related alterations in structural and functional neuroimaging have not been fully explored. This study explored multi-modal PD neuroimaging and its application for predicting clinical scores on the Movement Disorder Society-sponsored Unified Parkinson's Disease Rating Scale (MDS-UPDRS). Multi-modal imaging that combined 123I-Ioflupane single-photon emission computed tomography (SPECT) and diffusion tensor imaging (DTI) were adopted to incorporate complementary brain imaging information. SPECT and DTI images of normal controls (NC; n = 45) and PD patients (n = 45) were obtained from a database. The specific binding ratio (SBR) was calculated from SPECT. Tractography was performed using DTI. Group-wise differences between NC and PD patients were quantified using SBR of SPECT and structural connectivity of DTI for regions of interest (ROIs) related to PD. MDS-UPDRS scores were predicted using multi-modal imaging features in a partial least-squares regression framework. Three regions and four connections within the cortico-basal ganglia thalamocortical circuit were identified using SBR and DTI, respectively. Predicted MDS-UPDRS scores using identified regions and connections and actual MDS-UPDRS scores showed a meaningful correlation (r = 0.6854, p < 0.001). Our study provided insight on regions and connections that are instrumental in PD.

Connectivity analysis of normal and mild cognitive impairment patients based on FDG and PiB-PET images.

Connectivity analysis allows researchers to explore interregional correlations, and thus is well suited for analysis of complex networks such as the brain. We applied whole brain connectivity analysis to assess the progression of Alzheimer's disease (AD). To detect early AD progression, we focused on distinguishing between normal control (NC) subjects and subjects with mild cognitive impairment (MCI). Fludeoxyglucose (FDG) and Pittsburgh compound B (PiB)-positron emission tomography (PET) were acquired for 75 participants. A graph network was implemented using correlation matrices. Correlation matrices of FDG and PiB-PET were combined into one matrix using a novel method. Group-wise differences between NC and MCI patients were assessed using clustering coefficients, characteristic path lengths, and betweenness centrality using various correlation matrices. Using connectivity analysis, this study identified important regions differentially affected by AD progression.