Jefferson Digital Commons quarterly report: April-June 2019

This quarterly report includes: Articles CREATE Day Presentations Dissertations From the Archives Grand Rounds and Lectures House Staff Quality Improvement and Patient Safety Posters JCIPE Student Hotspotting Posters Journals and Newsletters MPH Capstone Presentations Posters Sigma Xi Research Day What People are Saying About the Jefferson Digital Common

Chapter Integrative Systems Biology Resources and Approaches in Disease Analytics

Currently, our analytical competences are struggling to keep-up the pace of in-deep analysis of all generated large-scale data resultant of high-throughput omics platforms. While, a substantial effort was spent on methods enhancement regarding technical aspects across many detection omics platforms, the development of integrative down-stream approaches is still challenging. Systems biology has an immense applicability in the biomedical and pharmacological areas since the main goal of those focuses in the translation of measured outputs into potential markers of a Human ailment and/or to provide new compound leads for drug discovery. This approach would become more straightforward and realistic to use in standard analysis workflows if the collation of all available information of every component of a biological system was ensured into a single database framework, instead of search and fetch a single component at time across a scatter of databases resources. Here, we will describe several database resources, standalone and web-based tools applied in disease analytics workflows based in data-driven integration of outputs of multi-omic detection platforms

SALMANTICOR study. Rationale and design of a population-based study to identify structural heart disease abnormalities: a spatial and machine learning analysis

[EN]Introduction: This study aims to obtain data on the prevalence and incidence of structural heart disease in a population setting and, to analyse and present those data on the application of spatial and machine learning methods that, although known to geography and statistics, need to become used for healthcare research and for political commitment to obtain resources and support effective public health programme implementation. Methods and analysis: We will perform a cross-sectional survey of randomly selected residents of Salamanca (Spain). 2400 individuals stratified by age and sex and by place of residence (rural and urban) will be studied. The variables to analyse will be obtained from the clinical history, different surveys including social status, Mediterranean diet, functional capacity, ECG, echocardiogram, VASERA and biochemical as well as genetic analysis. Ethics and dissemination: The study has been approved by the ethical committee of the healthcare community. All study participants will sign an informed consent for participation in the study. The results of this study will allow the understanding of the relationship between the different influencing factors and their relative importance weights in the development of structural heart disease

Rapid prediction of multidrug-resistant klebsiella pneumoniae through deep learning analysis of sers spectra

Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-To-Treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenemsensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE: This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings

Kaasaegsete kognitiivsete ja sotsiaalsete sekkumistehnikate loomine pediaatrilises neurorehabilitatsioonis ajukahjustusega lastel

Väitekirja elektrooniline versioon ei sisalda publikatsiooneOmandatud ajukahjustusega lapsed vajavad kaasuva kognitiivse ja sotsiaalse defitsiidi diagnostikat ja rehabilitatsiooni, mis on oluline lapse akadeemilise edukuse ja elukvaliteedi parandamisel. Neurorehabilitatsioon on plaanipärane sekkumine, mille eesmärk on kompenseerida või kergendada ajukahjustusest põhjustatud defitsiiti. Antud doktoritöö eesmärk oli uute arvutipõhiste meetodite kasutuselevõtmine omandatud ajukahjustusega laste kognitiivses ja sotsiaalses neurorehabilitatioonis. Treeningdisainid loodi tähelepanu, ruumitaju ja sotsiaalse kompetentsi häirete raviks. Uuringus osales 59 epilepsia, ajutrauma või tikkide diagnoosiga ja 47 tervet kontrollgrupi last vanuses 8–12 aastat. Patsiendid läbisid rehabilitatsiooni (10 treeningut) koos eelneva ja treeningujärgse testimisega. Tähelepanu ja ruumitaju treeningus kasutati arvutipõhist ForamenRehab lastele kohandatud programmi väljatöötatud treeningprotokollidega. Sotsiaalsete häirete raviks loodi esmalt struktureeritud neurorehabilitatsiooni mudel, mis koosnes sotsiaalse kompetentsi olulistest komponentidest, nende hindamismeetoditest ja rehabilitatsiooni vahenditest: puutetundlikud lauad Snowflake Multiteach Tabletop ja Diamond Touch Table, ning virtuaalreaalsuse keskkonnad. Tulemusena esines patsientidel treeningu eelselt väljendunud tähelepanu, ruumitaju ja sotsiaalse kompetentsi defitsiit. Arvutipõhised ja virtuaalreaalsuse programmid olid efektiivsed kognitiivsete häirete ravis. Patsientidel esines treeningute järgselt oluline paranemine kahes tähelepanu komponendis (tähelepanu jagamine ja seiramine) ja kolmes ruumitaju komponendis (visuaal-konstruktiivsed võimed, visuaalne tähelepanu ja nägemis-ruumitaju) ning raviefekt oli säilinud järeltestimisel 1,3 aastat hiljem. Sotsiaalse rehabilitatsiooni järgselt paranesid oluliselt patsientide vaimuteooria (Theory of Mind) ja emotsioonide äratundmine, kasutati rohkem koostööoskuseid, verbaalset ja mitteverbaalset kommunikatsiooni ning pragmaatika oskuseid. Uuringute tugevuseks oli sajaprotsendiline ravisoostumus ning positiivne tagasiside. Olulised on töö käigus välja töötatud teaduspõhised sekkumisprotokollid ja uued tehnoloogiapõhised rehabilitatsioonimeetodid häirunud funktsioonide spetsiifiliseks raviks lastel.Children with acquired brain injury (ABI) need diagnosis of accompanying cognitive and socio-emotional deficits and neurorehabilitation to enhance their future academic success and quality of life. Neurorehabilitation is a systematic intervention designed to compensate for or remediate the impairments caused by brain injury. The main aim of the thesis was implementing new computer-based programs, multitouch-multiuser tabletops and virtual reality in cognitive and social neurorehabilitation for children with ABI. Rehabilitation designs were developed for the treatment of attention, visuospatial, and social competence deficits. 59 children aged 8–12 years with ABI diagnosis (epilepsy, traumatic brain injury or tic disorder) and 47 healthy controls participated. Study group patients completed 10 training sessions guided by therapists. Pre-intervention assessments, and outcome assessments immediately and 1.31 years after the rehabilitation were carried out. ForamenRehab computer-programme was adapted to children and intervention protocols were created for attention and visuospatial function remediation. For social deficit remediation, the structured neurorehabilitation model was created, composed of the main components of social competence with evaluation and intervention tools: Snowflake Multiteach Tabletop, Diamond Touch Table and virtual reality programmes. Pre-intervention assessments showed that children with ABI had significant deficits in attention, visuospatial abilities and social competence functions. Computer-based and virtual reality programs were effective in the remediation of cognitive deficits in patients. After training, the patients had improved performance in two attention (complex attention and tracking) and three visuospatial components (visual organization, visual attention and visuospatial perception). The positive training effect had preserved after 1.3 years in follow-up assessments. Additionally, after social deficit rehabilitation, the patients showed improvements in Theory of Mind and emotion recognition, and they used more cooperation, communication, and pragmatic skills. The patients’ compliance was 100% and feedback was positive for all three interventions. In sum, the developed evidence-based intervention protocols and new technology-based rehabilitation methods are important in the remediation of specific cognitive deficits in children.https://www.ester.ee/record=b528718

Bioinformatics and Machine Learning for Cancer Biology

Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer