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Protein & Cell May 2018Microbes appear in every corner of human life, and microbes affect every aspect of human life. The human oral cavity contains a number of different habitats. Synergy and... (Review)
Review
Microbes appear in every corner of human life, and microbes affect every aspect of human life. The human oral cavity contains a number of different habitats. Synergy and interaction of variable oral microorganisms help human body against invasion of undesirable stimulation outside. However, imbalance of microbial flora contributes to oral diseases and systemic diseases. Oral microbiomes play an important role in the human microbial community and human health. The use of recently developed molecular methods has greatly expanded our knowledge of the composition and function of the oral microbiome in health and disease. Studies in oral microbiomes and their interactions with microbiomes in variable body sites and variable health condition are critical in our cognition of our body and how to make effect on human health improvement.
Topics: Human Body; Humans; Microbiota; Mouth; Mouth Diseases
PubMed: 29736705
DOI: 10.1007/s13238-018-0548-1 -
Journal of Digital Imaging Aug 2023This study demonstrates the high performance of deep learning in identification of body regions covering the entire human body from magnetic resonance (MR) and computed...
This study demonstrates the high performance of deep learning in identification of body regions covering the entire human body from magnetic resonance (MR) and computed tomography (CT) axial images across diverse acquisition protocols and modality manufacturers. Pixel-based analysis of anatomy contained in image sets can provide accurate anatomic labeling. For this purpose, a convolutional neural network (CNN)-based classifier was developed to identify body regions in CT and MRI studies. Seventeen CT (18 MRI) body regions covering the entire human body were defined for the classification task. Three retrospective datasets were built for the AI model training, validation, and testing, with a balanced distribution of studies per body region. The test datasets originated from a different healthcare network than the train and validation datasets. Sensitivity and specificity of the classifier was evaluated for patient age, patient sex, institution, scanner manufacturer, contrast, slice thickness, MRI sequence, and CT kernel. The data included a retrospective cohort of 2891 anonymized CT cases (training, 1804 studies; validation, 602 studies; test, 485 studies) and 3339 anonymized MRI cases (training, 1911 studies; validation, 636 studies; test, 792 studies). Twenty-seven institutions from primary care hospitals, community hospitals, and imaging centers contributed to the test datasets. The data included cases of all sexes in equal proportions and subjects aged from 18 years old to + 90 years old. Image-level weighted sensitivity of 92.5% (92.1-92.8) for CT and 92.3% (92.0-92.5) for MRI and weighted specificity of 99.4% (99.4-99.5) for CT and 99.2% (99.1-99.2) for MRI were achieved. Deep learning models can classify CT and MR images by body region including lower and upper extremities with high accuracy.
Topics: Humans; Adolescent; Deep Learning; Image Processing, Computer-Assisted; Retrospective Studies; Human Body; Tomography, X-Ray Computed; Magnetic Resonance Imaging
PubMed: 36894697
DOI: 10.1007/s10278-022-00767-9 -
Neuroscience Apr 2023We accurately sense locations of objects touching various points on the body and, if they are irritants, make accurate rapid movements to remove them. Such movements...
We accurately sense locations of objects touching various points on the body and, if they are irritants, make accurate rapid movements to remove them. Such movements require accurate proprioception of orientation and motion of the reaching limb and of the target. However, it is unknown whether acuity of these sensations is similar for different points on the body. We investigated accuracy of comfortable speed reaching movements of the right index-tip by 10 subjects (five females) to touch 12 different body locations with and without vision with the body part stationary in different locations and moving in different directions. Reaching movements to points on the face/head and trunk had mean errors averaging less than 0.2 cm greater than under vision conditions. Mean errors for reaches to touch points on the left arm and digits were less accurate (p < 0.05), but average less than 1 cm relative to vision conditions. Mean errors for reaches to touch points on the left lower limb were least accurate (p < 0.05), with mean errors averaging 1.5-3.1 cm relative to movements made with vision. We conclude that there is high proprioceptive acuity for locations of points on axial structures and the left upper limb including the digits, which contrasts with previous reports of greatly distorted proprioceptive maps of the face/head and hand. Apparently low proprioceptive acuity for points on the leg may be task sensitive as many lower limb motor tasks can be performed accurately without vision.
Topics: Female; Humans; Human Body; Psychomotor Performance; Proprioception; Movement; Hand
PubMed: 36841275
DOI: 10.1016/j.neuroscience.2023.02.015 -
Science (New York, N.Y.) Sep 2022A human-occupied indoor space shares many similarities with Earth and its atmosphere.
A human-occupied indoor space shares many similarities with Earth and its atmosphere.
Topics: Air Pollution, Indoor; Human Body; Humans
PubMed: 36048941
DOI: 10.1126/science.add8461 -
Attention, Perception & Psychophysics Nov 2022Evidence from multisensory body illusions suggests that body representations may be malleable, for instance, by embodying external objects. However, adjusting body...
Evidence from multisensory body illusions suggests that body representations may be malleable, for instance, by embodying external objects. However, adjusting body representations to current task demands also implies that external objects become disembodied from the body representation if they are no longer required. In the current web-based study, we induced the embodiment of a two-dimensional (2D) virtual hand that could be controlled by active movements of a computer mouse or on a touchpad. Following initial embodiment, we probed for disembodiment by comparing two conditions: Participants either continued moving the virtual hand or they stopped moving and kept the hand still. Based on theoretical accounts that conceptualize body representations as a set of multisensory bindings, we expected gradual disembodiment of the virtual hand if the body representations are no longer updated through correlated visuomotor signals. In contrast to our prediction, the virtual hand was instantly disembodied as soon as participants stopped moving it. This result was replicated in two follow-up experiments. The observed instantaneous disembodiment might suggest that humans are sensitive to the rapid changes that characterize action and body in virtual environments, and hence adjust corresponding body representations particularly swiftly.
Topics: Humans; Human Body; Illusions; Hand; Body Image; Movement; Visual Perception; Proprioception; Touch Perception
PubMed: 36045312
DOI: 10.3758/s13414-022-02544-w -
Clinical Anatomy (New York, N.Y.) Nov 2018
Topics: Human Body; Humans
PubMed: 30345561
DOI: 10.1002/ca.23305 -
The International Journal of... Oct 2015
Review
Topics: Child; Child Development; Human Body; Humans; Psychoanalytic Theory
PubMed: 26493168
DOI: 10.1111/1745-8315.12345 -
Molecular Biology of the Cell Aug 2018The adult human body is composed of nearly 37 trillion cells, each with potentially unique molecular characteristics. This Perspective describes some of the challenges... (Review)
Review
The adult human body is composed of nearly 37 trillion cells, each with potentially unique molecular characteristics. This Perspective describes some of the challenges and opportunities faced in mapping the molecular characteristics of these cells in specific regions of the body and highlights areas for international collaboration toward the broader goal of comprehensively mapping the human body with cellular resolution.
Topics: Cells; Genomics; Human Body; Humans; International Cooperation; Single-Cell Analysis
PubMed: 30058989
DOI: 10.1091/mbc.E18-04-0260 -
Revue de L'infirmiere 2020Consciousness and the body. Life is expressed in the body through physical, chemical and biological processes as well as through the emergence of immaterial dimensions...
Consciousness and the body. Life is expressed in the body through physical, chemical and biological processes as well as through the emergence of immaterial dimensions such as consciousness and subjectivity. These material and immaterial dimensions, connected and interdependent, form the basis of our humanity and should be considered together in the case of a global and personalised approach to the care practice.
Topics: Consciousness; Human Body; Humans
PubMed: 32993903
DOI: 10.1016/S1293-8505(20)30238-4 -
Medical Physics Oct 2020Automatic identification of consistently defined body regions in medical images is vital in many applications. In this paper, we describe a method to automatically...
PURPOSE
Automatic identification of consistently defined body regions in medical images is vital in many applications. In this paper, we describe a method to automatically demarcate the superior and inferior boundaries for neck, thorax, abdomen, and pelvis body regions in computed tomography (CT) images.
METHODS
For any three-dimensional (3D) CT image I, following precise anatomic definitions, we denote the superior and inferior axial boundary slices of the neck, thorax, abdomen, and pelvis body regions by NS(I), NI(I), TS(I), TI(I), AS(I), AI(I), PS(I), and PI(I), respectively. Of these, by definition, AI(I) = PS(I), and so the problem reduces to demarcating seven body region boundaries. Our method consists of a two-step approach. In the first step, a convolutional neural network (CNN) is trained to classify each axial slice in I into one of nine categories: the seven body region boundaries, plus legs (defined as all axial slices inferior to PI(I)), and the none-of-the-above category. This CNN uses a multichannel approach to exploit the interslice contrast, providing the neural network with additional visual context at the body region boundaries. In the second step, to improve the predictions for body region boundaries that are very subtle and that exhibit low contrast, a recurrent neural network (RNN) is trained on features extracted by CNN, limited to a flexible window about the predictions from the CNN.
RESULTS
The method is evaluated on low-dose CT images from 442 patient scans, divided into training and testing sets with a ratio of 70:30. Using only the CNN, overall absolute localization error for NS(I), NI(I), TS(I), TI(I), AS(I), AI(I), and PI(I) expressed in terms of number of slices (nS) is (mean ± SD): 0.61 ± 0.58, 1.05 ± 1.13, 0.31 ± 0.46, 1.85 ± 1.96, 0.57 ± 2.44, 3.42 ± 3.16, and 0.50 ± 0.50, respectively. Using the RNN to refine the CNN's predictions for select classes improved the accuracy of TI(I) and AI(I) to: 1.35 ± 1.71 and 2.83 ± 2.75, respectively. This model outperforms the results achieved in our previous work by 2.4, 1.7, 3.1, 1.1, and 2 slices, respectively for TS(I), TI(I), AS(I), AI(I) = PS(I), and PI(I) classes with statistical significance. The model trained on low-dose CT images was also tested on diagnostic CT images for NS(I), NI(I), and TS(I) classes; the resulting errors were: 1.48 ± 1.33, 2.56 ± 2.05, and 0.58 ± 0.71, respectively.
CONCLUSIONS
Standardized body region definitions are a prerequisite for effective implementation of quantitative radiology, but the literature is severely lacking in the precise identification of body regions. The method presented in this paper significantly outperforms earlier works by a large margin, and the deviations of our results from ground truth are comparable to variations observed in manual labeling by experts. The solution presented in this work is critical to the adoption and employment of the idea of standardized body regions, and clears the path for development of applications requiring accurate demarcations of body regions. The work is indispensable for automatic anatomy recognition, delineation, and contouring for radiation therapy planning, as it not only automates an essential part of the process, but also removes the dependency on experts for accurately demarcating body regions in a study.
Topics: Abdomen; Humans; Image Processing, Computer-Assisted; Neural Networks, Computer; Pelvis; Tomography, X-Ray Computed
PubMed: 32761899
DOI: 10.1002/mp.14439