bluuebunny/crossref_metadata_2025_split huggingface dataset:
Deep Learning in the prediction of Angiography-proven Severe Coronary Stenosis in Patients with Apparently Normal Electrocardiograms
Authors: Xue Zhengkai , Geng Shijia , Guo Shaohua , Mu Guanyu , Yu Bo , Wang Peng , Hu Sutao , Xu Weilun , Liu Yanhong , Yang Lei , Tao Huayue , Chen Kangyin , Hong Shenda 
Published: November 2022

AbstractPatients with severe coronary artery stenosis may have apparently normal electrocardiograms (ECGs), making it difficult to detect the adverse health conditions during screening or physical examinations, resulting in them missing the optimal window of treatment. The goal of this study was to develop an artificial intelligence-based ECG model which can distinguish severe coronary stenosis (≥ 90%) from no or mild coronary stenosis (< 50%) in patients with apparently normal ECGs. Deep learning (DL) models trained from scratch with pre-trained parameters (transfer learning) were tested on ECG alone as well as on ECG along with clinical information (age, sex, hypertension, diabetes, dyslipidemia and smoking status). We also compared the performance of logistic regression for clinical information only and found that DL models trained from scratch with ECG alone can achieve a specificity of 0.746; however, they have low sensitivity, which is comparable to the performance of logistic regression with clinical data. Although adding clinical information to the ECG DL model trained from scratch can improve the sensitivity, it can reduce the specificity. Combining clinical information with the ECG transfer learning model provides the best performance, with a 0.847 AUC, 0.848 sensitivity, and 0.704 specificity.
222 nm Far-UVC from filtered Krypton-Chloride excimer lamps does not cause eye irritation when deployed in a simulated office environment
Authors: Kousha Obaid , O’Mahoney Paul , Hammond Robert , Wood Kenneth , Eadie Ewan 
Published: November 2022

AbstractFar-UVC, in the form of filtered Krypton-Chloride lamps, is a promising technology for reducing airborne transmission of disease. Whilst significant research has been undertaken to investigate skin safety of these lamps, less work has been undertaken on eye safety. In particular, there is very limited data on human eye safety or discomfort from the deployment of this germicidal technology. In this pilot study, immediate and delayed eye discomfort were assessed in a simulated office environment with deployment of Krypton-Chloride lamps. The discomfort was assessed immediately post-exposure to the Far-UVC and several days after exposure using the validated, standardized Standard Patient Evaluation Eye Dryness (SPEED) and Ocular Surface Disease Index (OSDI) questionnaires. Our results show that there was no significant eye discomfort or adverse effects from the deployment of Far-UVC in this simulated office environment, even when the lamps were operated continuously. In addition, through collection of bacteria and fungi on agar plates, with this non-optimised lamp arrangement a statistically significant reduction in pathogens of 52% was observed. Far-UVC in this simulated office environment did not cause any clinically significant eye discomfort and was effective at reducing pathogens in the room. These results contribute an important step to further investigation of the interaction of Far-UVC with the human eye.
Frontal tDCS reduces alcohol relapse rates by increasing connections from left dorsolateral prefrontal cortex to addiction networks
Authors: Camchong Jazmin , Fiecas Mark , Gilmore Casey S. , Kushner Matt , Kummerfeld Erich , Mueller Bryon A. , Roediger Donovan , Lim Kelvin O. 
Published: November 2022

ABSTRACTBackgroundBrain-based interventions are needed to address persistent relapse in alcohol use disorder (AUD). Neuroimaging evidence suggests higher fronto-striatal connectivity as well as higher within-network connectivity of theoretically defined addiction networks is associated with reduced relapse rates and extended abstinence during follow-up periods.Objective/HypothesisA longitudinal randomized double-blind sham-controlled clinical trial investigated whether a non-invasive neuromodulation intervention delivered during early abstinence can (i) modulate connectivity of addiction networks supporting abstinence and (ii) improve relapse rates. Hypotheses: Active transcranial direct current stimulation (tDCS) will (i) increase connectivity of addiction networks known to support abstinence and (ii) reduce relapse rates.MethodsShort-term abstinent AUD participants (n=60) were assigned to 5 days of either active tDCS or sham during cognitive training. Causal discovery analysis (CDA) examined the directional influence from left dorsolateral prefrontal cortex (LDLPFC, stimulation site) to addiction networks that support abstinence.ResultsActive tDCS had an effect on the average strength of CDA-determined connectivity from LDLPFC to the incentive salience and negative emotionality addiction networks - increasing in the active tDCS group and decreasing in the sham group. Active tDCS had an effect on relapse rates 1-month following the intervention, with lower probability of relapse in the active tDCS vs. sham groups. Active tDCS showed an unexpected sex-dependent effect on relapse rates.ConclusionOur results suggest that LDLPFC stimulation delivered during early abstinence has an effect on addiction networks supporting abstinence and on relapse rates. The unexpected sex-dependent neuromodulation effects need to be further examined in larger clinical trials.
Input of Major Histocompatibility Complex class II immunostaining in idiopathic inflammatory myopathies
Authors: Lessard Lola E. R. , Robert Marie , Fenouil Tanguy , Landel Véréna , Carlesimo Marie , Hot Arnaud , Chazaud Bénédicte , Laumonier Thomas , Streichenberger Nathalie , Gallay Laure 
Published: November 2022

AbstractBackgroundIdiopathic inflammatory myopathies (IIMs) are a group of rare acquired muscular diseases. In healthy muscle, myofibers do not express major histocompatibility complex (MHC) class I and II. It was established that MHC-I positive immunostaining, although non-specific, is a marker for IIM diagnosis, while the significance of MHC-II immunostaining remains unclear. The present study investigates the expression of MCH-II in myofibers and capillaries of IIM muscles, taking into account the current IIM classification.Patients & MethodsA historical cohort was designed, including dermatomyositis (DM), inclusion body myositis (IBM), anti-synthetase syndrome (ASyS), immune-mediated necrotizing myopathy (IMNM), or overlap myositis (OM). MHC-II immunostaining was performed on patient muscle sections and was analyzed in a standardized and blind manner.ResultsMuscle sections from biopsies of 72 IIM patients were included: 23 DM, 17 IBM, 12 IMNM, 9 ASyS, and 11 OM. Overall, abnormal MHC-II immunostaining was found in myofibers and/or capillaries in 67 (93%) patients. Myofiber MHC-II immunostaining patterns differed according to the IIM subgroup: the immunostaining was diffuse in IBM (100%), negative in IMNM (75%), perifascicular in ASyS (67%), and either diffuse heterogeneous, clustered, or perifascicular in OM (27%, 27%, and 18%, respectively). MHC-II expression was found in 50% of DM (n=11/22).While all IIM subgroups presented quantitative and qualitative abnormalities of MHC-II immunostaining in capillaries, some subgroups displayed specificities. Most IBM and IMNM muscles presented frequent dilated capillaries (88% and 67%, respectively). DM, ASyS, and OM exhibited high frequencies of capillary lesions, including capillary dropout, leaky capillaries, and dilated capillaries.ConclusionWhile recent expert opinion (EURO-NMD pathology working group) recommended that MHC-II immunostaining of muscle biopsy remains optional, the present work demonstrates that the expression pattern of MHC-II allows to distinguish between several IIM subgroups. Our data argue for the inclusion of MHC-II immunostaining in the routine histological diagnosis for IIMs.
What can we learn from talented General Practitioners and Elderly Care Medicine Specialists? A protocol for a qualitative interview study
Authors: Khawar Abdullah , Visser Mechteld , Mak Marianne , Smalbrugge Martin , Slootweg Irene , van Dijk Nynke 
Published: November 2022

1.AbstractBackgroundSome postgraduate medical trainees in the General Practice (GP) and Elderly Care Medicine (ECM) training program, are considered being talented trainees (TTs) by their peers, teachers (at the institute), trainers (in clinical practice), and/or patients. We are currently unaware if these TTs are trained to meet their maximal potential or whether they are not fully stimulated during their learning process in the postgraduate training program. It is important to acknowledge them, because if we fail to acknowledge and suitably challenge TTs, it may lower their work ethos and satisfaction or cause loss of our TTs. As a first step, we will explore the way TTs make use of the learning possibilities provided during the postgraduate training program and what stimulates and hinders their learning in the clinical workplace.Knowledge on this subject is important for two reasons. First, knowing how TTs learn, can provide insights for improving learning in the workplace for all trainees. Secondly, with this knowledge, we can enrich the development of TTs. Thirdly, enhancement of workplace-based learning might lead to higher quality of learning, resulting in health care professionals trained to their full potential.Methods and analysisWe will address these questions in an open approach, conducting explorative qualitative semi-structured interview study with GPs and ECM specialists, who during their postgraduate training were seen as talented by their third year supervisors and completed their postgraduate medical training two or less than two years ago. We will perform the interviews in Dutch. We expect that the interview will not take longer than 45-60 minutes per interview. We plan to start at the beginning of 2023 and will continue until data sufficiency is reached. All interviews will be audio-recorded and transcribed verbatim. We will use thematic analysis to analyse the transcripts of the interviews. This is an iterative process of familiarizing yourself with the data, generating initial codes, searching for themes, reviewing themes, defining and naming themes. We will use MAXQDA 2022 software.Ethical considerationsThe Ethics committee of the Dutch Association of Medical Education (NVMO) gave ethical approval for this work (NERB dossier number: 2022.7.3).Data availability statementAll data produced in the present study are available upon reasonable request to the authors.Funding statementThis study is funded by ZonMw (project number 839130008).
A lightweight, automated neural network-based stage-specific malaria detection software using dimension reduction: The malaria stage classifier
Authors: Katharina Preißinger , István Kézsmárki , János Török 
Published: November 2022

AbstractDue to climate change and the COVID-19 pandemic, the number of malaria cases and deaths increased between 2019 and 2020 [1]. Reversing this trend and eliminating malaria worldwide requires improvements in malaria diagnosis, in which artificial intelligence (AI) has recently been demonstrated to have a great potential. Here, we describe an AI-based approach that boosts the performance of light (LM), atomic force (AFM) and fluorescence microscopy (FM)-based malaria diagnosis. As the main challenge, the stage-specific recognition of infected red blood cells (RBCs) usually requires large sets of microscopy images for training a neural network, which is difficult to obtain. Our tool, the Malaria Stage Classifier, provides a fast, high-accuracy recognition that works even with limited training sets due to a smart reduction of data dimension. Individual RBCs are extracted from an image, reduced to characteristic one-dimensional cross-sections, and classified. We show that our method is applicable to images recorded by various microscopy techniques. It is available as a software package athttps://github.com/KatharinaPreissinger/Malaria_stage_classifierand can be used within a python environment. Technical support is provided by the corresponding author (katharina.preissinger@physik.uni-augsburg.de).Author summaryThe Malaria Stage Classifier is a software helping the user to detect and stage RBCs infected with malaria. Accurate recognition of malaria infected RBCs still imposes a challenge in endemic regions, as it is time-consuming and subjective. These deficiencies can be overcome by autonomous computer assisted recognition using neural networks (NNs). The Malaria Stage Classifier offers a user-friendly interface for the stage-specific classification of malaria infected RBCs into four categories—healthy ones and three classes of infected ones according to the parasite age. The use of data reduction, which forms the central element of the Malaria Stage Classifier, allows for a fast and accurate classification of RBCs. It is applicable for light, atomic force, and fluorescence microscopy images and allows for retraining the implemented NN with new images. Our simple concept further has the potential to be generalised for the classification of other cells or objects.
Adverse events connected to breast cancer treatment and their associated decrease in quality of life scores
Authors: Diernberger Katharina , Gray Ewan , Atter Marek , Bullen Alistair , Hall Peter 
Published: November 2022

AbstractBackgroundBreast cancer is the most commonly diagnosed form of cancer in the UK, with over 55,000 newly diagnosed cases annually. Fortunately, many patients are cured, with a five-year survival rate of about 80%. Adjuvant chemotherapy in early breast cancer is common and has been shown to increase survival but frequently comes with several adverse events. These can impact patients’ quality of life (QoL) and influence health care costs. Relatively little is known about the magnitude of effects on the QoL of specific toxicities and toxicity profiles.MethodsAdverse event and QoL data (using EQ-VAS and EQ5D) from sub-studies embedded in two different randomized controlled trials (RCTs) of standard adjuvant chemotherapy regimens were used in the analysis. Adverse events were grouped into 20 main toxicity categories. QoL data were reported at baseline and following phases of chemotherapy treatment. Correlations between toxicity groups were explored. Univariate and multivariate analyses investigated the association between individual adverse events and reported QoL. To predict the impact of specific adverse events, a regression model specification was developed based on data from one trial using a backwards selection procedure and assessed for validity using data from the other trial.ResultsThe most frequently reported toxicities in both trials were Alopecia, Lethargy-Depression-Anxiety, Nausea-Vomiting and Stomatitis. The univariate analysis showed a clear decrease in patients’ QoL measured through the visual analogue scale (EQ-VAS). Results based on EQ5D measurements did not show a clear direction of toxicities’ influence on patients’ QoL. Multivariate results demonstrated a significant change in QoL for Lethargy-Depression-Anxiety, Diarrhoea, Skin-disorders, Infection, Dyspnoea-Respiratory and specified pain.ConclusionsOnly a small part of the change in patients’ QoL is induced by the different adverse events the patients faced. Results based on VAS showed a much bigger influence of certain adverse events on patients’ QoL than those derived from EQ5D, leading to the question of whether EQ5D’s domains are capturing what is of importance to patients during chemotherapy treatment.
Wearable devices can identify Parkinson’s disease up to 7 years before clinical diagnosis
Authors: Schalkamp Ann-Kathrin , Peall Kathryn J , Harrison Neil A , Sandor Cynthia 
Published: November 2022

AbstractParkinson’s disease (PD) is a progressive neurodegenerative movement disorder with a latent phase and no currently existing disease-modifying treatments. Reliable predictive biomarkers that could transform efforts to develop neuroprotective treatments remain to be identified. Using UK Biobank, we investigated the predictive value of accelerometry in identifying prodromal PD in the general population and compared this digital biomarker to models based on genetics, lifestyle, blood biochemistry, and prodromal symptoms data. Machine learning models trained using accelerometry data achieved better test performance in distinguishing both clinically diagnosed PD (N = 153) (area under precision recall curve (AUPRC): 0.14+ 0.04) and prodromal PD (N = 113) up to seven years pre-diagnosis (AUPRC: 0.07+ 0.03) from the general population (N = 33009) than all other modalities tested. Accelerometry is a potentially important, low-cost screening tool for determining people at risk of developing PD and identifying subjects for clinical trials of neuroprotective treatments.
Characterization of secretory phospholipase A2 inhibitory activity inTragia hispidaas potential therapeutic agent for treatment of dengue
Authors: Hemalika D. V. Dayangi , Chandrika U.G. , Abeysekera Ajith M. , Samarakoon Sameera , Wijewickrama Ananda , Malavige Gathsaurie Neelika 
Published: November 2022

AbstractBackgroundAs secretory phospholipase A2 (sPLA2) was shown to be elevated in patients who progress to severe dengue, it would be important to evaluate the usefulness of therapeutics that inhibit sPLA2 enzymes to prevent progression to severe dengue.ObjectivesTo explore the presence of sPLA2 inhibitors in plant extracts used in traditional medicine for treatment of fever in Sri Lanka.Study designAqueous and butanol extracts ofTragia hispida, Justicia adathodaand tubers ofCyperus rotunduswere screened for the presence of potential sPLA2 inhibitors using a commercial assay measuring sPLA2 activity.ResultsBoth the aqueous (THA) and butanol extracts (THB) ofTragia hispidahad sPLA2 inhibition levels comparable to the levels seen with the commercial sPLA2 inhibitor CAY10590. THB at concentrations of 0.1 µg/µL and 0.2 µg/µL, significantly reduced the sPLA2 activity (p<0.0001) in the sera of dengue patients and the inhibitory activity was significantly higher (p<0.0001) than of CAY10590. Thin layer chromatography of THB showed that it was likely to contain a mixture of flavonoid and phenolic compounds. HPLC displayed peaks at 3.207 min (λmax 222 nm, 272 nm) and 7.972 min (λmax 224 nm, 272 nm) were most likely to represent phenolics and that the peaks at 11.883 min (λmax 276 nm, 366 nm) and 16.898 min (λmax 254 nm, 370 nm) most likely to represent flavonoids.ConclusionsT. hispidaaqueous and butanol soluble fraction had potent sPLA2 inhibitory activities, which should be further explored for their potential to be used for treatment of dengue.
ethoscopy & ethoscope-lab: a framework for behavioural analysis to lower entrance barrier and aid reproducibility
Authors: Blackhurst Laurence , Gilestro Giorgio F. 
Published: November 2022

AbstractSummaryHigh-throughput analysis of behaviour is a pivotal instrument in modern neuroscience, allowing researchers to combine modern genetics breakthrough to unbiased, objective, reproducible experimental approaches. To this extent, we recently created an open-source hardware platform (ethoscope (Geissmannet al., 2017)) that allows for inexpensive, accessible, high-throughput analysis of behaviour inDrosophilaor other animal models. Here we equip ethoscopes with a Python framework for data analysis, ethoscopy, designed to be a user-friendly yet powerful platform, meeting the requirements of researchers with limited coding expertise as well as experienced data scientists. Ethoscopy is best consumed in a prebaked Jupyter-based docker container, ethoscope-lab, to improve accessibility and to encourage the use of notebooks as a natural platform to share post-publication data analysis.Availability and implementationEthoscopy is a Python package available on GitHub and PyPi. Ethoscope-lab is a docker container available on DockerHub. A landing page aggregating all the code and documentation is available athttps://lab.gilest.ro/ethoscopy.
Performance modulations phase-locked to action depend on internal state
Authors: Tosato Tommaso , Rohenkohl Gustavo , Fries Pascal 
Published: November 2022

AbstractSeveral studies have probed perceptual performance at different times after a self-paced motor action and found frequency-specific modulations of perceptual performance phase-locked to the action. Such action-related modulation has been reported for various frequencies and modulation strengths. In an attempt to establish a basic effect at the population level, we had a relatively large number of participants (n=50) perform a self-paced button press followed by a detection task at threshold, and we applied both fixed- and random-effects tests. The combined data of all trials and participants surprisingly did not show any significant action-related modulation. However, based on previous studies, we explored the possibility that such modulation depends on the participant’s internal state. Indeed, when we split trials based on performance in neighboring trials, then trials in periods of low performance showed an action-related modulation at ≈17 Hz. When we split trials based on the performance in the preceding trial, we found that trials following a “miss” showed an action-related modulation at ≈17 Hz. Finally, when we split participants based on their false-alarm rate, we found that participants with no false alarms showed an action-related modulation at ≈17 Hz. All these effects were significant in random-effects tests, supporting an inference on the population. Together, these findings indicate that action-related modulations are not always detectable. However, the results suggest that specific internal states such as lower attentional engagement and/or higher decision criterion are characterized by a modulation in the beta-frequency range.