bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Affordable Care Act and Its Impact on Plastic and Gender-Affirmation Surgery
Authors: Wiegmann Aaron L. , Young Ezra I. , Baker Kellan E. , Khalid Syed I. , Seu Michelle , Shenaq Deana S. , Dorafshar Amir H. , Schechter Loren S. 
Published: December 2020

Summary:
The Affordable Care Act’s provisions have affected and will continue to affect plastic surgeons and their patients, and an understanding of its influence on the current American health care system is essential. The law’s impact on pediatric plastic surgery, craniofacial surgery, and breast reconstruction is well documented. In addition, gender-affirmation surgery has seen exponential growth, largely because of expanded insurance coverage through the protections afforded to transgender individuals by the Affordable Care Act. As gender-affirming surgery continues to grow, plastic surgeons have the opportunity to adapt and diversify their practices.
Significant Dysfunction of Kidney Grafts Exposed to Prolonged Warm Ischemia Is Minimized Through Normothermic Ex Vivo Kidney Perfusion
Authors: Urbanellis Peter , Kollmann Dagmar , Linares Ivan , Ganesh Sujani , Oquendo Fabiola , Mazilescu Laura , Goto Toru , Noguchi Yuki , John Rohan , Konvalinka Ana , Mucsi Istvan , Ghanekar Anand , Bagli Darius , Selzner Markus , Robinson Lisa A. 
Published: December 2020

Background.
Normothermic ex vivo kidney perfusion (NEVKP) is an emerging technique for renal graft preservation. We investigated whether NEVKP could improve early function of severely injured grafts and reduce the incidence of significant renal dysfunction (SRD) similar to delayed graft function in a model of donation after circulatory death.

Methods.
Kidneys from 30-kg Yorkshire pigs were removed following 120 minutes of warm ischemia (WI). These grafts were then preserved in static cold storage (SCS, n = 6) or subjected to NEVKP (n = 5) for 8 hours before heterotopic autotransplantation. SRD was defined as postoperative day (POD) 4 oliguria <500 mL/24 h with serum K +> 6.0 mmol/L.

Results.
All 4 surviving animals with 120 minutes WI grafts stored with SCS developed SRD, compared with 1/5 in the NEVKP group (P = 0.02). The NEVKP group, when compared with SCS, also demonstrated significantly decreased serum creatinine peak values (1118.51 ± 206.90 µmol/L versus 1675.56 ± 98.15 µmol/L; P = 0.002) and higher creatinine clearance (POD4: 9.05 ± 6.97 mL/min versus 0.89 ± 0.56 mL/min; P = 0.05). By POD7, serum creatinine was not significantly different than baseline in the NEVKP (431.49 ± 492.50 µmol/L versus 90.19 ± 14.15 µmol/L, respectively; P = 0.20) but remained elevated following SCS (1189.25 ± 309.47 µmol/L versus 97.26 ± 29.18 µmol/L, respectively; P < 0.01). Histology demonstrated significantly decreased tubular injury scores compared with SCS grafts (P = 0.03).

Conclusions.
Kidney grafts subjected to 120 minutes WI before retrieval showed significant improvement in function, prevention of SRD, and decreased injury following 8 hours of NEVKP.
Urinary Cell Transcriptome Profiling and Identification of ITM2A, SLAMF6, and IKZF3 as Biomarkers of Acute Rejection in Human Kidney Allografts
Authors: Dooley Bryan J. , Verma Akanksha , Ding Ruchuang , Yang Hua , Muthukumar Thangamani , Lubetzky Michele , Shankaranarayanan Divya , Elemento Olivier , Suthanthiran Manikkam 
Published: December 2020

Background.
Identification of a shared gene expression pattern between T cell–mediated rejection (TCMR) and antibody-mediated rejection (AMR) in human kidney allografts may help prioritize targets for the treatment of both types of acute rejection.

Methods.
We performed RNA sequencing and bioinformatics of genome-wide transcriptome profiles of urinary cells to identify novel mRNAs shared between TCMR and AMR and of mechanistic relevance. Customized RT-QPCR assays were then used to validate their abundance in urinary cells. Urinary cell transcriptome profiles and mRNA abundance were assessed in 22 urine samples matched to 22 TCMR biopsies, 7 samples matched to 7 AMR biopsies, and 24 samples matched to 24 No Rejection (NR) biopsies and correlated with biopsy diagnosis.

Results.
RNA sequencing data and bioinformatics identified 127 genes in urine to be shared between TCMR and AMR. We selected 3 novel mRNAs—ITM2A, SLAMF6, and IKZF3—for absolute quantification and validation by customized RT-QPCR assays. The abundance of all 3 mRNAs was significantly higher in urine matched to TCMR or AMR than in urine matched to NR biopsies. Receiver-operating-characteristic curve analysis showed that all 3 mRNAs distinguished TCMR or AMR from NR. Their abundance was similar in patients with TCMR and those with AMR.

Conclusions.
State-of-the-art antirejection therapies are mostly effective to treat TCMR but not AMR. Our identification of mRNAs shared between TCMR and AMR and contributing to T cell–B cell interactions may help prioritize therapeutic targets for the simultaneous treatment of TCMR and AMR.
A Light-weight Text Summarizer for Fast Access to Medical Evidence
Authors: Sarker Abeed , Yang Yuan-Chi , Al-Garadi Mohammed Ali 
Published: December 2020

AbstractThe performances of current medical text summarization systems rely on resource-heavy domain-specific knowledge sources, and preprocessing methods (e.g., classification or deep learning) for deriving semantic information. Consequently, these systems are often difficult to customize, extend or deploy in low-resource settings, and are operationally slow. We propose a fast summarization system that can aid practitioners at point-of-care, and, thus, improve evidence-based healthcare. At runtime, our system utilizes similarity measurements derived from pre-trained domain-specific word embeddings in addition to simple features, rather than clunky knowledge bases and resource-heavy preprocessing. Automatic evaluation on a public dataset for evidence-based medicine shows that our system’s performance, despite the simple implementation, is statistically comparable with the state-of-the-art.
A low-cost automized anaerobic chamber for long-term growth experiments and sample handling
Authors: Herrmann Achim J. , Gehringer Michelle M. 
Published: December 2020

1AbstractThe handling of oxygen sensitive samples and growth of obligate anaerobic organisms requires the stringent exclusion of oxygen, which is omnipresent in our normal atmospheric environment. Anaerobic workstations (aka. Glove boxes) enable the handling of oxygen sensitive samples during complex procedures, or the long-term incubation of anaerobic organisms. Depending on the application requirements, commercial workstations can cost up to 60.000 €. Here we present the complete build instructions for a highly adaptive, Arduino based, anaerobic workstation for microbial cultivation and sample handling, with features normally found only in high cost commercial solutions. This build can automatically regulate humidity, H2levels (as oxygen reductant), log the environmental data and purge the airlock. It is built as compact as possible to allow it to fit into regular growth chambers for full environmental control. In our experiments, oxygen levels during the continuous growth of oxygen producing cyanobacteria, stayed under 0.03 % for 21 days without needing user intervention. The modular Arduino controller allows for the easy incorporation of additional regulation parameters, such as CO2concentration or air pressure. This paper provides researchers with a low cost, entry level workstation for anaerobic sample handling with the flexibility to match their specific experimental needs.Specifications table[please fill in right-hand column of the table below]
COVID-19 Risk Stratification and Mortality Prediction in Hospitalized Indian Patients
Authors: Alle Shanmukh , Siddiqui Samreen , Kanakan Akshay , Garg Akshit , Karthikeyan Akshaya , Mishra Neha , Waghdhare Swati , Tyagi Akansha , Tarai Bansidhar , Hazarika Pranjal Pratim , Das Poonam , Budhiraja Sandeep , Nangia Vivek , Dewan Arun , Sethuraman Ramanathan , Subramanian C. , Srivastava Mashrin , Chakravarthi Avinash , Jacob Johnny , Namagiri Madhuri , Konala Varma , Dash Debasish , Jha Sujeet , Pandey Rajesh , Agrawal Anurag , Vinod P. K. , Priyakumar U. Deva 
Published: December 2020

AbstractThe clinical course of coronavirus disease 2019 (COVID-19) infection is highly variable with the vast majority recovering uneventfully but a small fraction progressing to severe disease and death. Appropriate and timely supportive care can reduce mortality and it is critical to evolve better patient risk stratification based on simple clinical data, so as to perform effective triage during strains on the healthcare infrastructure. This study presents risk stratification and mortality prediction models based on usual clinical data from 544 COVID-19 patients from New Delhi, India using machine learning methods. An XGboost classifier yielded the best performance on risk stratification (F1 score of 0.81). A logistic regression model yielded the best performance on mortality prediction (F1 score of 0.71). Significant biomarkers for predicting risk and mortality were identified. Examination of the data in comparison to a similar dataset with a Wuhan cohort of 375 patients was undertaken to understand the much lower mortality rates in India and the possible reasons thereof. The comparison indicated higher survival rate in the Delhi cohort even when patients had similar parameters as the Wuhan patients who died. Steroid administration was very frequent in Delhi patients, especially in surviving patients whose biomarkers indicated severe disease. This study helps in identifying the high-risk patient population and suggests treatment protocols that may be useful in countries with high mortality rates.
Association Between the Swallowing Reflex and the Development of Aspiration Pneumonia in Patients with Dysphagia Admitted to Long-term Care Wards -A Prospective Cohort Study of 60 Days-
Authors: Omura Tomoya , Matsuyama Miwa , Nishioka Shota , Sagawa Shomu , Seto Masaya , Naoe Mitsugu 
Published: December 2020

AbstractObjectiveTo investigate the association between the simple swallowing provocation test (SSPT) and development of aspiration pneumonia in patients with dysphagia in long-term care (LTC) wards.DesignThe study design was a prospective cohort study. Subjects were followed for 60 days from admission.SettingLTC wards.ParticipantsStudy subjects were patients with dysphagia aged 65 years or older who were admitted to LTC wards between August 2018 and August 2019. In total, 39 subjects were included in the analysis (20 males, 19 females; mean age 83.8 ± 8.5 years). Subjects were divided into two groups based on SSPT results: normal swallowing reflex (SSPT normal group) and abnormal swallowing reflex (SSPT abnormal group). The covariates were age and sex, primary disease, history of cerebrovascular disease, Glasgow coma scale, body mass index, geriatric nutritional risk index, the mann assessment of swallowing ability, food intake level scale, functional independence measure, and oral health assessment tool.InterventionsNot applicable.Main Outcome MeasureThe outcome was the incidence of aspiration pneumonia during the first 60 days of hospitalization, and the predictive factor was SSPT: 0.4 ml.ResultsThe incidence of aspiration pneumonia was 33.3% in the SSPT normal group and 76.2% in the SSPT abnormal group. The phi coefficient was −0.43, the risk ratio was 2.29, and the 95% confidence interval (95%CI) was 1.14 to 4.58. The predictive factor for aspiration pneumonia was SSPT: 0.4 ml (95% CI: 1.57–26.03).ConclusionsOur findings suggest that the SSPT provides a valid index for the development of aspiration pneumonia in older patients with dysphagia admitted to LTC wards.
An atlas connecting shared genetic architecture of human diseases and molecular phenotypes provides insight into COVID-19 susceptibility
Authors: Wang Liuyang , Balmat Thomas J. , Antonia Alejandro L. , Constantine Florica J. , Henao Ricardo , Burke Thomas W. , Ingham Andy , McClain Micah T. , Tsalik Ephraim L. , Ko Emily R. , Ginsburg Geoffrey S. , DeLong Mark R. , Shen Xiling , Woods Christopher W. , Hauser Elizabeth R. , Ko Dennis C. 
Published: December 2020

AbstractWhile genome-wide associations studies (GWAS) have successfully elucidated the genetic architecture of complex human traits and diseases, understanding mechanisms that lead from genetic variation to pathophysiology remains an important challenge. Methods are needed to systematically bridge this crucial gap to facilitate experimental testing of hypotheses and translation to clinical utility. Here, we leveraged cross-phenotype associations to identify traits with shared genetic architecture, using linkage disequilibrium (LD) information to accurately capture shared SNPs by proxy, and calculate significance of enrichment. This shared genetic architecture was examined across differing biological scales through incorporating data from catalogs of clinical, cellular, and molecular GWAS. We have created an interactive web database (interactive Cross-Phenotype Analysis of GWAS database (iCPAGdb);http://cpag.oit.duke.edu) to facilitate exploration and allow rapid analysis of user-uploaded GWAS summary statistics. This database revealed well-known relationships among phenotypes, as well as the generation of novel hypotheses to explain the pathophysiology of common diseases. Application of iCPAGdb to a recent GWAS of severe COVID-19 demonstrated unexpected overlap of GWAS signals between COVID-19 and human diseases, including with idiopathic pulmonary fibrosis driven by theDPP9locus. Transcriptomics from peripheral blood of COVID-19 patients demonstrated thatDPP9was induced in SARS-CoV-2 compared to healthy controls or those with bacterial infection. Further investigation of cross-phenotype SNPs with severe COVID-19 demonstrated colocalization of the GWAS signal of theABOlocus with plasma protein levels of a reported receptor of SARS-CoV-2, CD209 (DC-SIGN), pointing to a possible mechanism whereby glycosylation of CD209 byABOmay regulate COVID-19 disease severity. Thus, connecting genetically related traits across phenotypic scales links human diseases to molecular and cellular measurements that can reveal mechanisms and lead to novel biomarkers and therapeutic approaches.
Design, Implementation and Feasibility of an Informatics Infrastructure for Standardized Data Acquisition, Transfer, Storage and Export in Psychiatric Clinical Routine
Authors: Blitz R. , Storck M. , Baune B.T. , Dugas M. , Opel N. 
Published: December 2020

AbstractBackgroundEmpirically driven personalized diagnostic and treatment is widely perceived as a major hallmark in psychiatry. However, databased personalized decision making requires standardized data acquisition and data access, which is currently absent in psychiatric clinical routine.ObjectiveHere we describe the informatics infrastructure implemented at the psychiatric university hospital Münster allowing for standardized acquisition, transfer, storage and export of clinical data for future real-time predictive modelling in psychiatric routine.MethodsWe designed and implemented a technical architecture that includes an extension of the EHR via scalable standardized data collection, data transfer between EHR and research databases thus allowing to pool EHR and research data in a unified database and technical solutions for the visual presentation of collected data and analyses results in the EHR. The Single-source Metadata ARchitecture Transformation (SMA:T) was used as the software architecture. SMA:T is an extension of the EHR system and uses Module Driven Software Development to generate standardized applications and interfaces. The Operational Data Model (ODM) was used as the standard. Standardized data was entered on iPads via the Mobile Patient Survey (MoPat) and the web application Mopat@home, the standardized transmission, processing, display and export of data was realized via SMA:T.ResultsThe technical feasibility was demonstrated in the course of the study. 19 standardized documentation forms with 241 items were created. In 317 patients, 6,451 instances were automatically transferred to the EHR system without errors. 96,323 instances were automatically transferred from the EHR system to the research database for further analyses.ConclusionsWith the present study, we present the successful implementation of the informatics infrastructure enabling standardized data acquisition, and data access for future real-time predictive modelling in clinical routine in psychiatry. The technical solution presented here might guide similar initiatives at other sites and thus help to pave the way towards future application of predictive models in psychiatric clinical routine.
Identification of SARS-CoV-2-specific immune alterations in acutely ill patients
Authors: Rébillard Rose-Marie , Charabati Marc , Grasmuck Camille , Filali-Mouhim Abdelali , Tastet Olivier , Brassard Nathalie , Daigneault Audrey , Bourbonnière Lyne , Balthazard Renaud , Moratalla Ana Carmena , Solorio Yves Carpentier , Farzam-kia Negar , Fournier Antoine Philippe , Gowing Elizabeth , Jamann Hélène , Lemaître Florent , Mamane Victoria Hannah , Thai Karine , Cailhier Jean-François , Chomont Nicolas , Finzi Andrés , Chassé Michaël , Durand Madeleine , Arbour Nathalie , Kaufmann Daniel E. , Prat Alexandre , Larochelle Catherine 
Published: December 2020

AbstractDysregulated immune profiles have been described in symptomatic SARS-CoV-2-infected patients. Whether the reported immune alterations are specific to SARS-CoV-2 infection or also triggered by other acute illnesses remains unclear. We performed flow cytometry analysis on fresh peripheral blood from a consecutive cohort of i) patients hospitalized with acute SARS-CoV-2 infection; ii) patients of comparable age/sex hospitalized for other acute disease (SARS-CoV-2 negative); and iii) healthy controls. Using both data-driven and hypothesis-driven analyses, we found several dysregulations in immune cell subsets (e.g. decreased proportion of T cells) that are similarly associated with acute SARS-CoV-2 infection and non-COVID-19 related acute illnesses. In contrast, we identified specific differences in myeloid and lymphocyte subsets that are associated with SARS-CoV-2 status (e.g. elevated proportion of ICAM-1+mature/activated neutrophils, ALCAM+monocytes, and CD38+CD8+T cells). A subset of SARS-CoV-2-specific immune alterations correlated with disease severity, disease outcome at 30 days and mortality. Our data provides novel understanding of the immune dysregulation that are specifically associated with SARS-CoV-2 infection among acute care hospitalized patients. Our study lays the foundation for the development of specific biomarkers to stratify SARS-CoV-2+patients at risk of unfavorable outcome and uncover novel candidate molecules to investigate from a therapeutic perspective.
Identifying Sequential Complication and Mortality Patterns in Diabetes Mellitus: Comparisons of Machine Learning Methodologies
Authors: Zhou Jiandong , Lee Sharen , Wong Wing Tak , Liu Tong , Roever Leonardo , Jeevaratnam Kamalan , Wu William KK , Wong Ian Chi Kei , Tse Gary , Zhang Qingpeng 
Published: December 2020

AbstractBackgroundDiabetes mellitus-related complications adversely affect the quality of life. Better risk-stratified care through mining of sequential complication patterns is needed to enable early detection and prevention.MethodsUnivariable and multivariate logistic regression was used to identify significant variables that can predict mortality. A sequence analysis method termed Prefixspan was applied to identify the most common couple, triple, quadruple, quintuple and sextuple sequential complication patterns in the directed comorbidity pathology network. A knowledge enhanced CPT+ (KCPT+) sequence prediction model is developed to predict the next possible outcome along the progression trajectories of diabetes-related complications.FindingsA total of 14,144 diabetic patients (51% males) were included. Acute myocardial infarction (AMI) without known ischaemic heart disease (IHD) (odds ratio [OR]: 2.8, 95% CI: [2.3, 3.4]), peripheral vascular disease (OR: 2.3, 95% CI: [1.9, 2.8]), dementia (OR: 2.1, 95% CI: [1.8, 2.4]), and IHD with AMI (OR: 2.4, 95% CI: [2.1, 2.6]) are the most important multivariate predictors of mortality. KCPT+ shows high accuracy in predicting mortality (F1 score 0.90, ACU 0.88), osteoporosis (F1 score 0.86, AUC 0.82), ophthalmological complications (F1 score 0.82, AUC 0.82), IHD with AMI (F1 score 0.81, AUC 0.85) and neurological complications (F1 score 0.81, AUC 0.83) with a particular prior complication sequence.InterpretationSequence analysis identifies the most common pattern characteristics of disease-related complications efficiently. The proposed sequence prediction model is accurate and enables clinicians to diagnose the next complication earlier, provide better risk-stratified care, and devise efficient treatment strategies for diabetes mellitus patients.
Pre-treatment clinical behavioral and blood leukocyte gene expression patterns predict rate of change in response to early intervention in autism
Authors: Lombardo Michael V. , Busuoli Elena Maria , Schreibman Laura , Stahmer Aubyn C. , Pramparo Tiziano , Landi Isotta , Mandelli Veronica , Bertelsen Natasha , Barnes Cynthia Carter , Gazestani Vahid , Lopez Linda , Bacon Elizabeth C. , Courchesne Eric , Pierce Karen 
Published: December 2020

AbstractEarly detection and intervention are believed to be key to facilitating better outcomes in children with autism, yet the impact of age at treatment start on outcome is poorly understood. While clinical traits such as language ability have been shown to predict treatment outcome, whether or not and how information at the genomic level can predict treatment outcome is unknown. Leveraging a cohort of toddlers with autism who all received the same standardized intervention at a very young age and provided a blood sample, here we find that very early treatment engagement (i.e., < 24 months) leads to greater gains while controlling for time in treatment. Pre-treatment clinical behavioral measures predicts 21% of the variance in the rate of skill growth during early intervention. Pre-treatment blood leukocyte gene expression patterns also predicts rate of skill growth, accounting for 13% of the variance treatment slopes. Results indicated that 295 genes can be prioritized as driving this effect. These treatment-relevant genes highly interact at the protein level, are enriched for differentially histone acetylated genes in autism post-mortem cortical tissue, and are normatively highly expressed in variety of subcortical and cortical areas important for social-communication and language development. This work indicates for the first time that gene expression can predict the rate of early intervention response and that a key biological factor linked to treatment outcome could be the susceptibility for epigenetic change via mechanisms such as histone acetylation.