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Estimating Somatotype from a Single-camera 3D Body Scanning System.

Eur J Sport Sci 2021 May 4:1-25. Epub 2021 May 4.

Sports Engineering Research Group, Sheffield Hallam University, Sheffield, UK.

Somatotype is an approach to quantify body physique (shape and body composition). Somatotyping by manual measurement (the anthropometric method) or visual rating (the photoscopic method) needs technical expertise to minimize intra- and inter-observer errors. This study aims to develop machine learning models which enable automatic estimation of Heath-Carter somatotypes using a single-camera 3D scanning system. Read More

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Intraoperative and postoperative complications in colorectal procedures: the role of continuous updating in medicine.

Minerva Surg 2021 May 4. Epub 2021 May 4.

IRCAD, Research Institute against Digestive Cancer, Strasbourg, France.

Accepting surgical complications, especially those related to the learning curve, as unavoidable events in colorectal procedures, is like accepting to fly onboard an aircraft with a 10 to 20% chance of not arriving at final destination. Under this condition, it is very likely that the aviation industry and the concurrent reshaping of the world and of our lives would have not been possible in the absence of high reliability and reproducibility of safe flights. It's hard to imagine surgery without any intraoperative and/or postoperative complications. Read More

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Investigating Factors of Active Aging among Chinese Older Adults: A Machine Learning Approach.

Gerontologist 2021 May 4. Epub 2021 May 4.

Sociology Department, Case Western Reserve University, Cleveland, Ohio, USA.

Background And Objectives: With the extension of healthy life expectancy, promoting active aging has become a policy response to rapid population aging in China. Yet, it has been inconclusive about the relative importance of the determinants of active aging. By applying a machine learning approach, this study aims to identify the most important determinants of active aging in three domains, i. Read More

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Machine learning applied to near-infrared spectra for clinical pleural effusion classification.

Sci Rep 2021 May 3;11(1):9411. Epub 2021 May 3.

Cancer Hospital of the University of Chinese Academy of Sciences, Chinese Academy of Sciences, Banshandong Road#1, Hangzhou, 310000, Zhejiang Province, China.

Lung cancer patients with malignant pleural effusions (MPE) have a particular poor prognosis. It is crucial to distinguish MPE from benign pleural effusion (BPE). The present study aims to develop a rapid, convenient and economical diagnostic method based on FTIR near-infrared spectroscopy (NIRS) combined with machine learning strategy for clinical pleural effusion classification. Read More

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Using machine learning to develop a novel COVID-19 Vulnerability Index (C19VI).

Sci Total Environ 2021 Jun 5;773:145650. Epub 2021 Feb 5.

Department of Microbiology and Immunology, UIC, Chicago, IL, USA.

COVID-19 is now one of the most leading causes of death in the United States (US). Systemic health, social and economic disparities have put the minorities and economically poor communities at a higher risk than others. There is an immediate requirement to develop a reliable measure of county-level vulnerabilities that can capture the heterogeneity of vulnerable communities. Read More

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Lessons to learn from tumor-educated platelets.

Blood 2021 May 3. Epub 2021 May 3.

Harvard Institute of Medicine, United States.

Platelets have long been known to play important roles beyond hemostasis and thrombosis. Now recognized as a bonafide mediator of malignant disease, platelets influence various aspects of cancer progression, most notably tumor cell metastasis. Interestingly, platelets isolated from cancer patients often display distinct RNA and protein profiles, with no clear alterations in hemostatic activity. Read More

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A data-driven framework for spatiotemporal characteristics, complexity dynamics, and environmental risk evaluation of river water quality.

Sci Total Environ 2021 Apr 16;785:147134. Epub 2021 Apr 16.

College of Water Sciences, Beijing Normal University, Beijing 100875, China; Chinese Research Academy of Environmental Sciences, Beijing 100012, China.

To evaluate the evolution of river water quality in a changing environment, measuring the objective water quality is critical for understanding the rules of river water pollution. Based on the sample entropy theory and a nonlinear statistical method, this study aims to identify the spatiotemporal dynamics of water quality and its complexity in the Yangtze River basin using time series data, to separate the contributions of human activity and climate change to water quality, and to establish a data-driven risk assessment framework for the spatial (potential risk) and temporal (direct risk) aspects of water pollution. The results demonstrate that the spatiotemporal dynamics of water quality and sample entropy in each monitoring section are closely related to the characteristics of the corresponding location. Read More

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Discrepancies in Stroke Distribution and Dataset Origin in Machine Learning for Stroke.

J Stroke Cerebrovasc Dis 2021 Apr 30;30(7):105832. Epub 2021 Apr 30.

Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA. Electronic address:

Background: Machine learning algorithms depend on accurate and representative datasets for training in order to become valuable clinical tools that are widely generalizable to a varied population. We aim to conduct a review of machine learning uses in stroke literature to assess the geographic distribution of datasets and patient cohorts used to train these models and compare them to stroke distribution to evaluate for disparities.

Aims: 582 studies were identified on initial searching of the PubMed database. Read More

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The Effect of Sensor Placement and Number on Physical Activity Recognition and Energy Expenditure Estimation in Older Adults: Validation Study.

JMIR Mhealth Uhealth 2021 May 3;9(5):e23681. Epub 2021 May 3.

Department of Aging and Geriatric Research, University of Florida, Gainesville, FL, United States.

Background: Research has shown the feasibility of human activity recognition using wearable accelerometer devices. Different studies have used varying numbers and placements for data collection using sensors.

Objective: This study aims to compare accuracy performance between multiple and variable placements of accelerometer devices in categorizing the type of physical activity and corresponding energy expenditure in older adults. Read More

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Developing an Automatic System for Classifying Chatter About Health Services on Twitter: Case Study for Medicaid.

J Med Internet Res 2021 May 3;23(5):e26616. Epub 2021 May 3.

Department of Biomedical Informatics, School of Medicine, Emory University, Atlanta, GA, United States.

Background: The wide adoption of social media in daily life renders it a rich and effective resource for conducting near real-time assessments of consumers' perceptions of health services. However, its use in these assessments can be challenging because of the vast amount of data and the diversity of content in social media chatter.

Objective: This study aims to develop and evaluate an automatic system involving natural language processing and machine learning to automatically characterize user-posted Twitter data about health services using Medicaid, the single largest source of health coverage in the United States, as an example. Read More

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Craniofacial features of 3q29 deletion syndrome: Application of next-generation phenotyping technology.

Am J Med Genet A 2021 May 3. Epub 2021 May 3.

Department of Human Genetics, Emory University School, of Medicine, Atlanta, Georgia, USA.

3q29 deletion syndrome (3q29del) is a recurrent deletion syndrome associated with neuropsychiatric disorders and congenital anomalies. Dysmorphic facial features have been described but not systematically characterized. This study aims to detail the 3q29del craniofacial phenotype and use a machine learning approach to categorize individuals with 3q29del through analysis of 2D photos. Read More

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Cervical pessary plus vaginal progesterone in a singleton pregnancy with a short cervix: an experience-based analysis of cervical pessary's efficacy.

J Matern Fetal Neonatal Med 2021 May 2:1-11. Epub 2021 May 2.

Screening and Prevention of Preterm Birth Sector, Fetal Medicine Discipline, Obstetrics Department, Paulista School of Medicine, Federal University of São Paulo, São Paulo-SP, Brazil.

Background: Several studies were published about cervical pessary, with controversial results. These studies demonstrated that the patient follow-up after pessary insertion is very different between the study centers and the number of pessary insertions per center was often <30 cases. This study aims to determine cervical pessary performance in singleton pregnancies with a short cervix based on a single center learning curve. Read More

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Real-time deep learning-based colorectal polyp localization on clinical video footage achievable with a wide array of hardware configurations.

Endosc Int Open 2021 May 22;9(5):E741-E748. Epub 2021 Apr 22.

Department of Gastroenterology with IBD Unit, Voivodship Hospital No 2 in Rzeszow, Rzeszów, Poland.

 Several computer-assisted polyp detection systems have been proposed, but they have various limitations, from utilizing outdated neural network architectures to a requirement for multi-graphics processing unit (GPU) processing, to validating on small or non-robust datasets. To address these problems, we developed a system based on a state-of-the-art convolutional neural network architecture able to detect polyps in real time on a single GPU and tested on both public datasets and full clinical examination recordings.  The study comprised 165 colonoscopy procedure recordings and 2678 still photos gathered retrospectively. Read More

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Farmers' livelihood strategies and perceived constraints from poor and non-poor households: A dataset from a field survey in Nghe An, Vietnam.

Data Brief 2021 Jun 31;36:106991. Epub 2021 Mar 31.

Faculty of Economics and Business, Phenikaa University, Hanoi 12116, Vietnam.

The first Sustainable Development Goals (SDGs) of The United Nations aims to "end poverty in all its forms everywhere". Its seven associated targets aim, among others, to eradicate extreme poverty for all people everywhere. In Vietnam, poverty eradication in ethnic minorities and mountainous areas are among the top priorities. Read More

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Detection of COVID-19 from CT Lung Scans Using Transfer Learning.

Comput Intell Neurosci 2021 8;2021:5527923. Epub 2021 Apr 8.

School of Mathematics, Statistics and Computer Science University of KwaZulu-Natal, Durban, South Africa.

This paper aims to investigate the use of transfer learning architectures in the detection of COVID-19 from CT lung scans. The study evaluates the performances of various transfer learning architectures, as well as the effects of the standard Histogram Equalization and Contrast Limited Adaptive Histogram Equalization. The findings of this study suggest that transfer learning-based frameworks are an alternative to the contemporary methods used to detect the presence of the virus in patients. Read More

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Effects of 7.5% Carbon Dioxide and Nicotine Administration on Latent Inhibition.

Front Psychiatry 2021 16;12:582745. Epub 2021 Apr 16.

Cambridge Cognition, Cambridge, United Kingdom.

Stratified medicine approaches have potential to improve the efficacy of drug development for schizophrenia and other psychiatric conditions, as they have for oncology. Latent inhibition is a candidate biomarker as it demonstrates differential sensitivity to key symptoms and neurobiological abnormalities associated with schizophrenia. The aims of this research were to evaluate whether a novel latent inhibition task that is not confounded by alternative learning effects such as learned irrelevance, is sensitive to (1) an in-direct model relevant to psychosis [using 7. Read More

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A Novel Ensemble-based Classifier for Detecting the COVID-19 Disease for Infected Patients.

Inf Syst Front 2021 Apr 25:1-17. Epub 2021 Apr 25.

Department of Computer Science, Government Bikram College of Commerce, Punjabi University, Patiala, Punjab India.

The recently discovered coronavirus, SARS-CoV-2, which was detected in Wuhan, China, has spread worldwide and is still being studied at the end of 2019. Detection of COVID-19 at an early stage is essential to provide adequate healthcare to affected patients and protect the uninfected community. This paper aims to design and develop a novel ensemble-based classifier to predict COVID-19 cases at a very early stage so that appropriate action can be taken by patients, doctors, health organizations, and the government. Read More

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Examining university students' behavioural intention to use e-learning during the COVID-19 pandemic: An extended TAM model.

Educ Inf Technol (Dordr) 2021 Apr 28:1-21. Epub 2021 Apr 28.

Realistic Mathematics Education Research Centre, Universitas Syiah Kuala, 23111 Banda Aceh, Indonesia.

This present study aims to investigate factors that impact behavioural intention of university students on e-learning use during the COVID-19 pandemic. An online questionnaire was utilised to gather data from 109 students enrolled in one of the universities in Indonesia. The Technology Acceptance Model (TAM) was the primary framework employed for analysis, in which system quality and e-learning experience were included as external constructs to seek out a much better model to improve the understanding of students' intention to adopt e-learning. Read More

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Clinical phenogroups are more effective than left ventricular ejection fraction categories in stratifying heart failure outcomes.

ESC Heart Fail 2021 May 2. Epub 2021 May 2.

ICES, McMaster University, Hamilton, Ontario, Canada.

Aims: Heart failure (HF) guidelines place patients into 3 discrete groups according to left ventricular ejection fraction (LVEF): reduced (<40%), mid-range (40-49%), and preserved LVEF (≥50%). We assessed whether clinical phenogroups offer better prognostication than LVEF.

Methods And Results: This was a sub-study of the Patient-Centered Care Transitions in HF trial. Read More

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Crossing hands behind your back reduces recall of manual action sentences and alters brain dynamics.

Cortex 2021 Mar 31;140:51-65. Epub 2021 Mar 31.

Université Gustave Eiffel, Université de Paris, LaPEA, Versailles, France.

The embodied meaning approach posits that understanding action-related language recruits motor processes in the brain. However, the functional impact of these motor processes on cognition has been questioned. The present study aims to provide new electrophysiological (EEG) evidence concerning the role of motor processes in the comprehension and memory of action language. Read More

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Machine learning approach to support taxonomic species discrimination based on helminth collections data.

Parasit Vectors 2021 May 1;14(1):230. Epub 2021 May 1.

Laboratório de Biologia de Tripanosomatídeos-LABTRIP, Instituto Oswaldo Cruz, IOC-FIOCRUZ, Rio de Janeiro, RJ, Brazil.

Background: There are more than 300 species of capillariids that parasitize various vertebrate groups worldwide. Species identification is hindered because of the few taxonomically informative structures available, making the task laborious and genus definition controversial. Thus, its taxonomy is one of the most complex among Nematoda. Read More

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The perceived global impact of the COVID-19 pandemic on doctors' medical and surgical training: an international survey.

Int J Clin Pract 2021 May 1:e14314. Epub 2021 May 1.

School of Medicine, University of Central Lancashire, Preston, Lancashire, United Kingdom.

Introduction: The COVID-19 pandemic has resulted in a significant burden on healthcare systems causing disruption to medical and surgical training of doctors globally.

Aims And Objectives: This is the first international survey assessing the perceived impact of the COVID-19 pandemic on training of doctors of all grades and specialties.

Methods: An online global survey was disseminated using Survey Monkey® between 4 August 2020 and 17 November 2020. Read More

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Representation, Interaction, Interpretation. Making sense of the context in clinical reasoning.

Med Educ 2021 May 1. Epub 2021 May 1.

Department of Learning, Informatics, Management and Ethics, Karolinska Institutet, Stockholm, Sweden.

Background: All thinking occurs in some sort of context, rendering the relation between context and clinical reasoning a matter of significant interest. Context however has a notoriously vague and contested meaning and there is a profound disagreement between different research traditions studying clinical reasoning in how context is understood. Empirical evidence examining the impact (or not) of context on clinical reasoning cannot be interpreted without reference to the meaning ascribed to context. Read More

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A Review on Prediction Models for Pesticide Use, Transmission, and Its Impacts.

Rev Environ Contam Toxicol 2021 May 2. Epub 2021 May 2.

Department of Mechatronics Engineering, Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu, India.

The lure of increased productivity and crop yield has caused the imprudent use of pesticides in great quantity that has unfavorably affected environmental health. Pesticides are chemicals intended for avoiding, eliminating, and mitigating any pests that affect the crop. Lack of awareness, improper management, and negligent disposal of pesticide containers have led to the permeation of pesticide residues into the food chain and other environmental pathways, leading to environmental degradation. Read More

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Improving surgical site infection prevention in Asia-Pacific through appropriate surveillance programs: Challenges and recommendation.

Infect Dis Health 2021 Apr 27. Epub 2021 Apr 27.

Department of Surgery, Medical College of Wisconsin, Wisconsin, 53226, USA. Electronic address:

Background: Surgical site infections (SSIs) represent a substantial clinical and economic burden on patients and the healthcare system. The prevention of SSIs entails surveillance activities which lead to effective mitigation strategies, which are lacking across Asia Pacific (APAC). This manuscript aims to document gaps and challenges across APAC that affect the undertaking of a successful SSI surveillance activities and to provide recommendations on overcoming such challenges. Read More

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Neurologists' attitudes and options for anticoagulation therapy in central China.

Int J Clin Pract 2021 Apr 30:e14305. Epub 2021 Apr 30.

Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.

Aims: We aim to find out the factors affecting the use of anticoagulants and the intensity of their choices, and to establish a basis for improving neurologists' effective implementation of the guidelines.

Methods: A cross-sectional study is conducted in Hubei province in central China. Each neurologist completes a standard-structured anonymous questionnaire through face-to-face interviews. Read More

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Deep Learning-Based Automated Abdominal Organ Segmentation in the UK Biobank and German National Cohort Magnetic Resonance Imaging Studies.

Invest Radiol 2021 Jun;56(6):401-408

From the Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, UK.

Purpose: The aims of this study were to train and evaluate deep learning models for automated segmentation of abdominal organs in whole-body magnetic resonance (MR) images from the UK Biobank (UKBB) and German National Cohort (GNC) MR imaging studies and to make these models available to the scientific community for analysis of these data sets.

Methods: A total of 200 T1-weighted MR image data sets of healthy volunteers each from UKBB and GNC (400 data sets in total) were available in this study. Liver, spleen, left and right kidney, and pancreas were segmented manually on all 400 data sets, providing labeled ground truth data for training of a previously described U-Net-based deep learning framework for automated medical image segmentation (nnU-Net). Read More

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Artificial Intelligence and Radiomics in Head and Neck Cancer Care: Opportunities, Mechanics, and Challenges.

Am Soc Clin Oncol Educ Book 2021 Mar;41:1-11

Department of Radiation Oncology, MD Anderson Cancer Center, Houston, TX.

The advent of large-scale high-performance computing has allowed the development of machine-learning techniques in oncologic applications. Among these, there has been substantial growth in radiomics (machine-learning texture analysis of images) and artificial intelligence (which uses deep-learning techniques for "learning algorithms"); however, clinical implementation has yet to be realized at scale. To improve implementation, opportunities, mechanics, and challenges, models of imaging-enabled artificial intelligence approaches need to be understood by clinicians who make the treatment decisions. Read More

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Using an Information Package to Reduce Patients' Risk of Renal Damage: Protocol for a Randomized Feasibility Trial.

JMIR Res Protoc 2021 Apr 30;10(4):e29161. Epub 2021 Apr 30.

Department of General Practice and Rural Health, Otago Medical School - Dunedin Campus, University of Otago, Dunedin, New Zealand.

Background: Non-steroidal anti-inflammatory drugs (NSAIDs) are a common cause of renal damage, especially when taken together with angiotensin-converting enzyme inhibitors (ACE-i) or angiotensin II receptor blockers (ARBs) plus a diuretic - a combination known as the "triple whammy." New Zealand patients are at high risk of the "triple whammy" because they can easily purchase NSAIDs without a prescription and in nonpharmacy retail settings (eg, the supermarket), there is no legal requirement to include patient information sheets with medication, and direct-to-consumer drug advertising is permitted. A patient information package has been developed for those at greatest risk of the "triple whammy," consisting of a printable PDF and an interactive online learning activity. Read More

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Enabling Guidelines for the Adoption of eHealth Solutions: Scoping Review.

JMIR Form Res 2021 Apr 30;5(4):e21357. Epub 2021 Apr 30.

Centre for Connected Care, Oslo University Hospital, Oslo, Norway.

Background: Globally, public health care is under increasing pressure, an economic burden currently amplified by the COVID-19 outbreak. With the recognition that universal health coverage improves the health of a population and reduces health inequalities, universal health coverage has been acknowledged as a priority goal. To meet the global needs in a population with increased chronic illness and longer life expectancy, the health care system is in dire need of new, emerging technologies. Read More

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