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General General

Subjective and objective quality assessment of gastrointestinal endoscopy images: From manual operation to artificial intelligence.

In Frontiers in neuroscience ; h5-index 72.0

Gastrointestinal endoscopy has been identified as an important tool for cancer diagnosis and therapy, particularly for treating patients with early gastric cancer (EGC). It is well known that the quality of gastroscope images is a prerequisite for achieving a high detection rate of gastrointestinal lesions. Owing to manual operation of gastroscope detection, in practice, it possibly introduces motion blur and produces low-quality gastroscope images during the imaging process. Hence, the quality assessment of gastroscope images is the key process in the detection of gastrointestinal endoscopy. In this study, we first present a novel gastroscope image motion blur (GIMB) database that includes 1,050 images generated by imposing 15 distortion levels of motion blur on 70 lossless images and the associated subjective scores produced with the manual operation of 15 viewers. Then, we design a new artificial intelligence (AI)-based gastroscope image quality evaluator (GIQE) that leverages the newly proposed semi-full combination subspace to learn multiple kinds of human visual system (HVS) inspired features for providing objective quality scores. The results of experiments conducted on the GIMB database confirm that the proposed GIQE showed more effective performance compared with its state-of-the-art peers.

Yuan Peng, Bai Ruxue, Yan Yan, Li Shijie, Wang Jing, Cao Changqi, Wu Qi

2022

gastroscope images, human visual system, motion blur, semi-full combination subspace, subjective and objective quality assessment

General General

Structural Characterization of the Chlorophyllide a Oxygenase (CAO) Enzyme Through an In Silico Approach.

In Journal of molecular evolution

Chlorophyllide a oxygenase (CAO) is responsible for converting chlorophyll a to chlorophyll b in a two-step oxygenation reaction. CAO belongs to the family of Rieske-mononuclear iron oxygenases. Although the structure and reaction mechanism of other Rieske monooxygenases have been described, a member of plant Rieske non-heme iron-dependent monooxygenase has not been structurally characterized. The enzymes in this family usually form a trimeric structure and electrons are transferred between the non-heme iron site and the Rieske center of the adjoining subunits. CAO is supposed to form a similar structural arrangement. However, in Mamiellales such as Micromonas and Ostreococcus, CAO is encoded by two genes where non-heme iron site and Rieske cluster localize on the distinct polypeptides. It is not clear if they can form a similar structural organization to achieve the enzymatic activity. In this study, the tertiary structures of CAO from the model plant Arabidopsis thaliana and the Prasinophyte Micromonas pusilla were predicted by deep learning-based methods, followed by energy minimization and subsequent stereochemical quality assessment of the predicted models. Furthermore, the chlorophyll a binding cavity and the interaction of ferredoxin, which is the electron donor, on the surface of Micromonas CAO were predicted. The electron transfer pathway was predicted in Micromonas CAO and the overall structure of the CAO active site was conserved even though it forms a heterodimeric complex. The structures presented in this study will serve as a basis for understanding the reaction mechanism and regulation of the plant monooxygenase family to which CAO belongs.

Dey Debayan, Tanaka Ryouichi, Ito Hisashi

2023-Mar-03

Chlorophyll b biosynthesis, Chlorophyllide a oxygenase, Computational prediction, Micromonas pusilla, Molecular docking

General General

Potential sensitive period effects of maltreatment on amygdala, hippocampal and cortical response to threat.

In Molecular psychiatry ; h5-index 103.0

Childhood maltreatment is a leading risk factor for psychopathology, though it is unclear why some develop risk averse disorders, such as anxiety and depression, and others risk-taking disorders including substance abuse. A critical question is whether the consequences of maltreatment depend on the number of different types of maltreatment experienced at any time during childhood or whether there are sensitive periods when exposure to particular types of maltreatment at specific ages exert maximal effects. Retrospective information on severity of exposure to ten types of maltreatment during each year of childhood was collected using the Maltreatment and Abuse Chronology of Exposure scale. Artificial Intelligence predictive analytics were used to delineate the most important type/time risk factors. BOLD activation fMRI response to threatening versus neutral facial images was assessed in key components of the threat detection system (i.e., amygdala, hippocampus, anterior cingulate, inferior frontal gyrus and ventromedial and dorsomedial prefrontal cortices) in 202 healthy, unmedicated, participants (84 M/118 F, 23.2 ± 1.7 years old). Emotional maltreatment during teenage years was associated with hyperactive response to threat whereas early childhood exposure, primarily to witnessing violence and peer physical bullying, was associated with an opposite pattern of greater activation to neutral than fearful faces in all regions. These findings strongly suggest that corticolimbic regions have two different sensitive period windows of enhanced plasticity when maltreatment can exert opposite effects on function. Maltreatment needs to be viewed from a developmental perspective in order to fully comprehend its enduring neurobiological and clinical consequences.

Zhu Jianjun, Anderson Carl M, Ohashi Kyoko, Khan Alaptagin, Teicher Martin H

2023-Mar-03

Radiology Radiology

iBEAT V2.0: a multisite-applicable, deep learning-based pipeline for infant cerebral cortical surface reconstruction.

In Nature protocols

The human cerebral cortex undergoes dramatic and critical development during early postnatal stages. Benefiting from advances in neuroimaging, many infant brain magnetic resonance imaging (MRI) datasets have been collected from multiple imaging sites with different scanners and imaging protocols for the investigation of normal and abnormal early brain development. However, it is extremely challenging to precisely process and quantify infant brain development with these multisite imaging data because infant brain MRI scans exhibit (a) extremely low and dynamic tissue contrast caused by ongoing myelination and maturation and (b) inter-site data heterogeneity resulting from the use of diverse imaging protocols/scanners. Consequently, existing computational tools and pipelines typically perform poorly on infant MRI data. To address these challenges, we propose a robust, multisite-applicable, infant-tailored computational pipeline that leverages powerful deep learning techniques. The main functionality of the proposed pipeline includes preprocessing, brain skull stripping, tissue segmentation, topology correction, cortical surface reconstruction and measurement. Our pipeline can handle both T1w and T2w structural infant brain MR images well in a wide age range (from birth to 6 years of age) and is effective for different imaging protocols/scanners, despite being trained only on the data from the Baby Connectome Project. Extensive comparisons with existing methods on multisite, multimodal and multi-age datasets demonstrate superior effectiveness, accuracy and robustness of our pipeline. We have maintained a website, iBEAT Cloud, for users to process their images with our pipeline ( http://www.ibeat.cloud ), which has successfully processed over 16,000 infant MRI scans from more than 100 institutions with various imaging protocols/scanners.

Wang Li, Wu Zhengwang, Chen Liangjun, Sun Yue, Lin Weili, Li Gang

2023-Mar-03

General General

Bayesian Statistics for Medical Devices: Progress Since 2010.

In Therapeutic innovation & regulatory science

The use of Bayesian statistics to support regulatory evaluation of medical devices began in the late 1990s. We review the literature, focusing on recent developments of Bayesian methods, including hierarchical modeling of studies and subgroups, borrowing strength from prior data, effective sample size, Bayesian adaptive designs, pediatric extrapolation, benefit-risk decision analysis, use of real-world evidence, and diagnostic device evaluation. We illustrate how these developments were utilized in recent medical device evaluations. In Supplementary Material, we provide a list of medical devices for which Bayesian statistics were used to support approval by the US Food and Drug Administration (FDA), including those since 2010, the year the FDA published their guidance on Bayesian statistics for medical devices. We conclude with a discussion of current and future challenges and opportunities for Bayesian statistics, including artificial intelligence/machine learning (AI/ML) Bayesian modeling, uncertainty quantification, Bayesian approaches using propensity scores, and computational challenges for high dimensional data and models.

Campbell Gregory, Irony Telba, Pennello Gene, Thompson Laura

2023-Mar-03

Bayesian adaptive designs, Benefit-risk decision analysis, Diagnostic test accuracy, Hierarchical Bayesian modeling, Prior Information, Real-world evidence

General General

Reconstructing the infrared spectrum of a peptide from representative conformers of the full canonical ensemble.

In Communications chemistry

Leucine enkephalin (LeuEnk), a biologically active endogenous opioid pentapeptide, has been under intense investigation because it is small enough to allow efficient use of sophisticated computational methods and large enough to provide insights into low-lying minima of its conformational space. Here, we reproduce and interpret experimental infrared (IR) spectra of this model peptide in gas phase using a combination of replica-exchange molecular dynamics simulations, machine learning, and ab initio calculations. In particular, we evaluate the possibility of averaging representative structural contributions to obtain an accurate computed spectrum that accounts for the corresponding canonical ensemble of the real experimental situation. Representative conformers are identified by partitioning the conformational phase space into subensembles of similar conformers. The IR contribution of each representative conformer is calculated from ab initio and weighted according to the population of each cluster. Convergence of the averaged IR signal is rationalized by merging contributions in a hierarchical clustering and the comparison to IR multiple photon dissociation experiments. The improvements achieved by decomposing clusters containing similar conformations into even smaller subensembles is strong evidence that a thorough assessment of the conformational landscape and the associated hydrogen bonding is a prerequisite for deciphering important fingerprints in experimental spectroscopic data.

Kotobi Amir, Schwob Lucas, Vonbun-Feldbauer Gregor B, Rossi Mariana, Gasparotto Piero, Feiler Christian, Berden Giel, Oomens Jos, Oostenrijk Bart, Scuderi Debora, Bari Sadia, Meißner Robert H

2023-Mar-03