Miracle-dz/UniFAR-FTUs huggingface dataset:
Suitability of Deep Weakly Supervised Learning to detect Acute Ischemic Stroke and Hemorrhagic Infarction Lesions Using Diffusion-weighted Imaging

Objectives: The automatic detection of acute ischemic stroke (AIS) and hemorrhagic infarction (HI) based on deep learning could avoid missed diagnosis. The fully supervised learning requires the amount of time and the expertise to manually outline lesions, which limits its applicability. The weakly supervised learning has the potential to reduce the labeling workload. The purpose of this study was to evaluate a weakly supervised method in detection of AIS and HI location using DWI. Methods: We proposed to adopt weakly supervised learning to spatially-locate AIS lesions by residual neural network (ResNet) and visual geometry group (VGG) network. On an AIS dataset, the area under the receiver operating characteristic (ROC) curve (AUC), sensitivity and precision were calculated. Next, ResNet, which presented superior performance on the AIS dataset, was further applied to an HI dataset. Results: In the AIS dataset, the AUCs of ResNet and VGG on identifying image slices with AIS were 0.97 and 0.94, respectively. On spatially-locating the AIS lesions, ResNet provided higher sensitivity and a lower missed diagnosis rate than VGG, especially for pontine AIS lesions. In the HI dataset, the sensitivity of ResNet was 87.73% for AIS detection, and 86.20% for HI detection, respectively. Conclusions: Weakly supervised learning can effectively detect the location of AIS and HI lesions in DWI, which is of paramount importance in avoiding misdiagnosis in clinical scenario.
Does TB Vaccination Reduce COVID-19 Infection?: No Evidence from a Regression Discontinuity Analysis

In the middle of the global COVID-19 pandemic, the BCG hypothesis, the prevalence and severity of the COVID-19 outbreak seems to be correlated with whether a country has a universal coverage of Bacillus-Calmette-Guerin (BCG), a vaccine for tuberculosis disease (TB), has emerged and attracted the attention of scientific community and media outlets. However, all existing claims are based on cross-country correlations that do not exclude the possibility of spurious correlation. We merged country-age-level case statistics with the start/termination years of BCG vaccination policy and conducted a regression discontinuity and difference-in-difference analysis. The results do not support the BCG hypothesis.
Highly tweeted endodontic articles: a network analysis and machine learning perspective

Introduction: we aimed to analyze highly tweeted endodontic articles to find active journals, authors and hot topics. Moreover, we planned to examine correlation and relativeness between number of Tweeter mentions and various bibliometric factors such as citations. Methods: In December 23, 2019 the Altmetric database was searched using the titles of 11 endodontic journals. Bibliometric data of top 5% endodontic articles with the highest Twitter mentions extracted from PubMed and analyzed using the VOSviewer. To find influential factors on Twitter mentions, the random forest model (a machine learning algorithm) was used. Results: 3,918 Tweeter mentions (from 3,881 individual posts) related to endodontic articles from seven journals found, which were mostly from the U.S. @endogarrido with 560 mentions related to endodontic articles was the most active Twitter account, followed by @autismepi with 218 mentions and @Dddent2) with 159 mentions. 5% endodontic articles with the highest Twitter mentions involved 177 articles. Systematic review and apical periodontitis were the most popular keywords. Petel S and Dummer MPH had the highest number of popular articles in Twittersphere. Number of Twitter mentions were not correlated with number of mendeley readers (r= 0.082) and citations (r= 0.007). Random forest model showed journal title, author's affiliation and funding source were not important factored regarding number of Twitter mentions. Discussion: Journal of Endodontics received the most Tweeter mentions. This journal which related to the American Association of Endodontists , were quite active in Twittersphere via @savingyourteeth (4,748 Tweea ts and 3,224 Followers). Yet, The International Endodontic Journal which is the Official Journal of the European Society of Endodontology has lower level of activity via @EuSocEndo (474 Tweets and 239 Followers). This journal had first rank among all dental journals according to SCImago 2017 report (SJR: 2.146 and H Index:107) and could be more active in Twittersphere. Keywords: Twitter, endodontics, altmetrics, citation, network analysis, machine learning
Which Measures are Effective in Containing COVID-19?Empirical Research Based on Prevention and Control Cases in China

Various epidemic prevention and control measures aimed at reducing person-to-person contact has paid a certain cost while controlling the epidemic. So accurate evaluation of these measures helps to maximize the effectiveness of prevention and control while minimizing social costs. In this paper, we develop the model in Dirk Brockmann and Dirk Helbing (2013) to theoretically explain the impact mechanism of traffic control and social distancing measures on the spread of the epidemic, and empirically tests the effect of the two measures in China at the present stage using econometric approach. We found that both traffic control and social distancing measures have played a very good role in controlling the development of the epidemic. Nationally, social distancing measures are better than traffic control measures; the two measures are complementary and their combined action will play a better epidemic prevention effect; Traffic control and social distancing do not work everywhere. Traffic control only works in cities with higher GDP per capita and population size, while fails in cities with lower GDP per capita and population size. In cities with lower population size, social distancing becomes inoperative; the rapid and accurate transmission of information, a higher protection awareness of the public, and a stronger confidence of residents in epidemic prevention can promote the realization of the measure effects. The findings above verify the effectiveness and correctness of the measures implemented in China at present, at the same time, we propose that it is necessary to fully consider the respective characteristics of the two measures, cooperating and complementing each other; what's more, measures should be formulated according to the city's own situation, achieving precise epidemic prevention; Finally, we should increase the transparency of information, improve protection awareness of the public, guide emotions of the public in a proper way, enhancing public confidence.
Landscape of Gene Mutation in Synovium of Patients with Rheumatoid Arthritis

Objective To investigate accumulating single nucleotide variants (SNVs) in synovium of patients with early, pre-treatment rheumatoid arthritis (RA). Methods I leveraged RNA-seq dataset derived from patients with RA deposited in database. To capture synovium-specific mutations, I applied standardized SNVs-calling method to paired synovium and blood samples per individual. Results De novo mutational signatures constructed by non-negative matrix factorization reflected continuous relationship of synovial histology, namely lymphoid-myeloid (Lymphoid), diffuse myeloid (Myeloid) and pauci-immune fibroid (Fibroid). Using COSMIC signature, defective DNA damage repair-associated mutational signature was associated with the level of inflammation in synovitis. Further, pathway analysis based on specific genes harboring SNVs in each histological group represent different types of synovitis, with differing pathogenic process and inflammatory environment. Conclusion This data will promote our understandings about pathogenic status of synovitis, and suggests evidence that stratified treatments would be optimal according to histological subgroup.
Associations between dietary patterns and gene expression pattern in peripheral blood mononuclear cells: a cross-sectional study

Background: Diet may alter gene expression in immune cells involved in cardio-metabolic disease susceptibility. However, we still lack a robust understanding of the association between diet and immune cell-related gene expression in humans. Objective: Our objective was to examine the associations between dietary patterns (DPs) and gene expression profiles in peripheral blood mononuclear cells (PBMCs) in a population of healthy, Norwegian adults. Methods: We used factor analysis to define a posteriori DPs from food frequency questionnaire-based dietary assessment data. In addition, we derived interpretable features from microarray-based gene expression data (13 967 transcripts) using two algorithms: CIBERSORT for estimation of cell subtype proportions, and weighted gene co-expression network analysis (WGCNA) for cluster discovery. Finally, we associated DPs with either CIBERSORT-predicted PBMC leukocyte distribution or WGCNA gene clusters using linear regression models. All analyses were gender-stratified (n = 130 women and 105 men). Results: We detected three DPs that broadly reflected Western, Vegetarian, and Low carb diets. CIBERSORT-predicted percentage of monocytes associated strongly and negatively with the Vegetarian DP in both women and men. For women, the Vegetarian DP associated most strongly with a large gene cluster consisting of 600 genes mainly involved in regulation of DNA transcription. For men, the Western DP inversely associated most strongly with a smaller cluster of 36 genes mainly involved in regulation of metabolic and inflammatory processes. In subsequent protein-protein interaction network analysis, the most important driver genes within these WGCNA gene clusters seemed to physically interact in biological networks. Conclusions: DPs may affect percentage monocytes and regulation of key biological processes within the PBMC pool. Although the present findings are exploratory, our analysis pipeline serves a useful framework for studying the association between diet and gene expression.
Physical interventions to interrupt or reduce the spread of respiratory viruses. Part 2 - Hand hygiene and other hygiene measures: systematic review and meta-analysis.

OBJECTIVE: To assess the effectiveness of hand hygiene, surface disinfecting, and other hygiene interventions in preventing or reducing the spread of illnesses from respiratory viruses. DESIGN: Update of a systematic review and meta-analysis focussing on randomised controlled trials (RCTs) and cluster-RCTs (c-RCTs) evidence only. DATA SOURCES: Eligible trials from the previous Cochrane review, search of the Cochrane Central Register of Controlled Trials, PubMed, Embase and CINAHL from 01 October 2010 to 01 April 2020, and forward and backward citation analysis of included studies. DATA SELECTION: RCTs and c-RCTs involving people of any age, testing the use of hand hygiene methods, surface disinfection or cleaning, and other miscellaneous barrier interventions. Face masks, eye protection, and person distancing are covered in Part 1 of our systematic review. Outcomes included acute respiratory illness (ARI), influenza-like illness (ILI) or laboratory-confirmed influenza (influenza) and/or related consequences (e.g. death, absenteeism from school or work). DATA EXTRACTION AND ANALYSIS: Six authors working in pairs independently assessed risk of bias using the Cochrane tool and extracted data. The generalised inverse variance method was used for pooling by using the random-effects model, and results reported with risk ratios (RR) and 95% confidence intervals (CIs). RESULTS: We identified 51 eligible trials. We included 25 randomised trials comparing hand hygiene interventions with a control; 15 of these could be included in meta-analyses. We pooled 8 trials for the outcome of ARI. Hand hygiene showed a 16% relative reduction in the number of participants with ARI (RR 0.84, 95% CI 0.82 to 0.86) in the intervention group. When we considered the more strictly defined outcomes of ILI and influenza, the RR for ILI was 0.98 (95% CI 0.85 to 1.14), and for influenza the RR was 0.91 (95% CI 0.61 to 1.34). Three trials measured absenteeism. We found a 36% relative reduction in absentee numbers in the hand hygiene group (RR 0.64, 95% CI 0.58 to 0.71). Comparison of different hand hygiene interventions did not favour one intervention type over another. We found no incremental effects of combining hand hygiene with using face masks or disinfecting surfaces or objects. CONCLUSIONS: Despite the lack of evidence for the impact of hand hygiene in reducing ILI and influenza, the modest evidence for reducing the burden of ARIs, and related absenteeism, justifies reinforcing the standard recommendation for hand hygiene measures to reduce the spread of respiratory viruses. Funding for relevant trials with an emphasis on adherence and compliance with such a measure is crucial to inform policy and global pandemic preparedness with confidence and precision.
Early Individual and Family Predictors of Weight Trajectories From Early Childhood to Adolescence: Results From the Millennium Cohort Study

Background: Early infancy and childhood are critical periods in the establishment of lifelong weight trajectories. Parents and early family environment have a strong effect on children's health behaviors that track into adolescence, influencing lifelong risk of obesity. Objective: We aimed to identify developmental trajectories of body mass index (BMI) from early childhood to adolescence and to assess their early individual and family predictors. Methods: This was a secondary analysis of the Millennium Cohort Study and included 17,165 children. Weight trajectories were estimated using growth mixture modeling based on age- and gender-specific BMI Z-scores, followed by a bias-adjusted regression analysis. Results: We found four BMI trajectories: Weight Loss (69%), Early Weight Gain (24%), Early Obesity (3.7%), and Late Weight Gain (3.3%). Weight trajectories were mainly settled by early adolescence. Lack of sleep and eating routines, low emotional self-regulation, child-parent conflict, and low child-parent closeness in early childhood were significantly associated with unhealthy weight trajectories, alongside poverty, low maternal education, maternal obesity, and prematurity. Conclusions: Unhealthy BMI trajectories were defined in early and middle-childhood, and disproportionally affected children from disadvantaged families. This study further points out that household routines, self-regulation, and child-parent relationship are possible areas for family-based obesity prevention interventions.
Clinically Applicable Deep Learning for Intracranial Aneurysm Detection in Computed Tomography Angiography Images: A Comprehensive Multicohort Study

Intracranial aneurysm is a common life-threatening disease. CTA is recommended as a standard diagnosis tool, while the interpretation is time-consuming and challenging. We presented a novel deep-learning-based framework trained on 1,177 DSA verified bone-removal CTA cases. The framework had excellent tolerance to the influence of occult cases of CTA-negative but DSA-positive aneurysms, image quality, and manufacturers. Simulated real-world studies were conducted in consecutive internal and external cohorts, achieving improved sensitivity and negative predictive value than radiologists. A specific cohort of suspected acute ischemic stroke was employed and found 96.8% predicted-negative cases can be trusted with high confidence, leading to reducing in human burden. A prospective study is warranted to determine whether the algorithm could improve patients' care in comparison to radiologists' assessment.
Use of artificial intelligence and neural network algorithms to predict arterial blood gas items in trauma victims

Background: Trauma is the third leading cause of death in the world and the first cause of death among people younger than 44 years. In traumatic patients, especially those who are injured early in the day, arterial blood gas (ABG) is considered a golden standard because it can provide physicians with important information such as detecting the extent of internal injury, especially in the lung. However, measuring these gases by laboratory methods is a time-consuming task in addition to the difficulty of sampling the patient. The equipment needed to measure these gases is also expensive, which is why most hospitals do not have this equipment. Therefore, estimating these gases without clinical trials can save the lives of traumatic patients and accelerate their recovery. Methods: In this study, a method based on artificial neural networks for the aim of estimation and prediction of arterial blood gas is presented by collecting information about 2280 traumatic patients. In the proposed method, by training a feed-forward backpropagation neural network (FBPNN), the neural network can only predict the amount of these gases from the patient's initial information. The proposed method has been implemented in MATLAB software, and the collected data have tested its accuracy, and its results are presented. Results: The results show 87.92% accuracy in predicting arterial blood gas. The predicted arterial blood gases included PH, PCO2, and HCO3, which reported accuracy of 99.06%, 80.27%, and 84.43%, respectively. Therefore, the proposed method has relatively good accuracy in predicting arterial blood gas. Conclusions: Given that this is the first study to predict arterial blood gas using initial patient information(systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse rate (PR), respiratory rate (RR), and age), and based on the results, the proposed method could be a useful tool in assisting hospital and laboratory specialists, to be used. Keywords: Arterial Blood Gases, Trauma, Neural Network, Prediction.
Accelerometry-assessed sleep duration and timing in late childhood and adolescence in Scottish schoolchildren: A feasibility study

Children and adolescents commonly suffer from sleep and circadian rhythm disturbances, which may contribute to poorer mental health and wellbeing during this critical developmental phase. Many studies however rely on self-reported sleep measures. This study assessed whether accelerometry data collection was feasible within the school setting as a method for investigating the extent of sleep and circadian disruption, and associations with subjective wellbeing, in Scotland. Fourteen days of wrist-worn accelerometry data were collected from 69 pupils, aged 10-14 years. Objective measures of sleep timing, sleep duration and circadian rest-activity patterns were derived. Questionnaires assessed subjective sleep timing, depressive symptoms, and experiences of wearing the accelerometer. Pupils slept on average less than 8 hours per night, failing to meet standard age-specific recommendations. Sleep timing was later and duration longer on weekends compared to weekdays (B = 0.87, 95% confidence interval (CI) 0.70, 1.04; B = 0.49, 95% CI 0.29, 0.69), indicating social jetlag. Lower daytime activity was correlated with higher depressive symptoms (r = -0.84, p = 0.008). Compared to primary school pupils, secondary pupils had shorter sleep window duration and lower circadian relative amplitude. Over half of participants reported some discomfort/inconvenience wearing the accelerometer. These data highlight that inadequate sleep is prevalent in this sample of schoolchildren. Future, larger scale investigations will examine in more detail the associations between sleep, circadian function and physical activity with mental health and wellbeing.
Regulation of flagellin glycosylation genes in Campylobacter jejuni: influence of NssR, the nitrosative stress response regulator.

Campylobacter jejuni is one of the leading causes of food borne illness worldwide; the main cause of infection is the consumption of undercooked, contaminated poultry. Motility, mediated by single polar flagella, is necessary for Campylobacter virulence. The flagellin proteins are extensively glycosylated in C. jejuni with pseudaminic acid and related derivatives; glycosylation is required for flagella filament formation. The flagellin glycosylation locus in C. jejuni NCTC11168 comprises 50 genes, 50% are potentially involved in glycan biosynthesis and others are hypothetical; the regulatory mechanism(s) of flagellin glycosylation remain unclear. The response to nitric oxide stress in C. jejuni is regulated by NssR, which also regulates a small number of genes in the flagellin glycosylation locus; an nssR mutant has defective motility. This work aims to elucidate the mechanisms by which NssR regulates these genes and investigate the consequences of nssR mutation. Transcriptional organisation of the affected genes was deduced and chromatin immunoprecipitation (ChIP) used to map NssR binding to six potential locations in the region. Methods to measure bacterial motility were assessed and used to document the defective motility of the nssR mutant, which was found to be due to truncated, sometimes asymmetric, flagella. Finally, mass spectrometry was used to examine flagellin glycosylation in all strains.
Novel Deep Learning Methodology for Automated Classification of Adamantinomatous Craniopharyngioma Using a Small Radiographic Dataset

Modern Deep Learning (DL) networks routinely achieve classification accuracy superior to human experts, leveraging scenarios with vast amounts of training data. Community focus has now seen a shift towards the design of accurate classifiers for scenarios with limited training data. Such an example is the uncommon pediatric brain tumor, Adamantinomatous Craniopharyngioma (ACP). Recent work has demonstrated the efficacy of Transfer Learning (TL) and novel loss functions for the training of DL networks in limited data scenarios. This work describes a DL approach utilizing TL and a state-of-the-art custom loss function for predicting ACP diagnosis from radiographic data, achieving performance (CT AUPR=0.99+/-0.01, MRI AUPR=0.99+/-0.02) superior to reported human performance (0.87).