bluuebunny/crossref_metadata_2025_split huggingface dataset:
Tradition Versus Value: Is There Utility in Protocolized Postoperative Laboratory Testing After Elective Colorectal Surgery?
Authors: McKenna Nicholas P. , Habermann Elizabeth B. , Glasgow Amy E. , Cima Robert R. 
Published: December 2019

Objective:
Determine if routine ordering of postoperative day 1 (POD 1) serum laboratory tests after elective colorectal surgery are clinically warranted and valuable given the associated costs of these lab tests.

Summary of Background Data:
Routine postoperative serum laboratory tests are a part of many colorectal surgery order sets. Whether these protocolized lab tests represent cost-effective care is unknown.

Methods:
Patients undergoing elective colorectal surgery between January 1, 2015 and December 31, 2017 at our institution were identified. The protocolized POD 1 lab tests obtained as part of the postoperative order set were reviewed to determine the rate of abnormal values and any intervention in response. Costs associated with protocolized laboratory testing were calculated using dollar amounts representing 2017 outpatient Medicare reimbursement.

Results:
A total of 2252 patients were identified with 8205 total lab test values. Of these, only 4% were abnormal (3% of hemoglobin values, 6% of creatinine values, 3% of potassium of values, and 3% of glucose values), and only 1% were actively intervened upon. The total aggregate cost of the protocolized POD 1 laboratory tests in these years was $64,000 based on Medicare outpatient reimbursement dollars.

Conclusions:
Routine POD 1 lab tests after elective colorectal surgery are rarely abnormal, and they even less frequently require active intervention beyond rechecking. This results in increased resource utilization and cost of care without appreciable impact on clinical care, and is not cost-effective. Protocolized POD 1 laboratory testing should be replaced with clinically-based criteria to trigger serum laboratory investigations.
Hexamaps for Visualizing Age-Period-Cohort Data Trends
Authors: Jalal Hawre , Burke Donald S. 
Published: November 2019

AbstractAge-period-cohort (APC) analyses often reveal important insights into patterns of disease incidence and mortality such as cancer. Both visual and analytical APC tools are available to reveal patterns by age, period and cohort. While developing new analytical methods of APC data is an active area of research, the choices of visual tools specific to APC data has been limited. In this study, we propose a “hexamap” as a new APC specific data visualization tool. This tool uses hexagons to address the unique challenge of APC data visualization, that is cohort= period - age. The approach is further illustrated using alcohol related mortality. Flexible Open-Source functions for implementation in R is also provided.
Compromised intestinal barrier underlies gut microbiota dysbiosis of intestinal diseases
Authors: Jiang Puzi , Lai Senying , Wu Sicheng , Yin Xiushan , Zhang Jingjing , Zhao Xing-Ming , Xu Shun-Qing , Chen Wei-Hua 
Published: November 2019

AbstractBackgroundDespite recent efforts, a single factor underlying the gut microbiota dysbiosis in intestinal diseases is not identified. We hypothesized that compromised intestinal barrier (CIB) could lead to increased host-derived contents including human cells in the gut, change its physio-metabolic properties, and globally alter gut microbiota and their metabolic capacities.ResultsConsistently, we found human DNA contents (HDCs), calculated as the percentage of metagenomic sequencing reads mapped to the human genome, were significantly elevated in colorectal cancer (CRC) patients; HDC correlated with microbial- and metabolic-pathway-biomarkers of CRC, and was the most important contributor to patient stratification. We found similar results in Crohn’s disease (CD); additionally, patients treated with diet and drug intervention showed reduced HDC levels over time, and were accompanied by reversing changes of many CD-signature species.ConclusionsOur results suggested that host-derived contents may have greater impact on gut microbiota than previously anticipated, and CIB could be an ideal treatment target that could reverse dysbiosis globally and precisely.
Population based targeted sequencing of 54 candidate genes identifies PALB2 as a susceptibility gene for high grade serous ovarian cancer
Authors: Song Honglin , Dicks Ed , Tyrer Jonathan P. , Intermaggio Maria , Chenevix-Trench Georgia , Bowtell David D , Traficante Nadia ,   AOCS Group, Brenton James D. , Goranova Teodora , Hosking Karen , Piskorz Anna , Van Oudenhove Elke , Doherty Jennifer Anne , Harris Holly R. , Rossing Mary Anne , Dürst Matthias , Dörk Thilo , Bogdanova Natalia V. , Modugno Francesmary , Moysich Kirsten B. , Odunsi Kunle , Ness Roberta B. , Karlan Beth Y. , Lester Jenny , Jensen Allan , Kjaer Susanne K. , Høgdall Estrid , Campbell Ian , Lazaro Conxi , Pujana Miquel Angel , Cunningham Julie M. , Vierkant Robert A. , Winham Stacey J. , Hildebrandt Michelle A.T. , Huff Chad , Li Donghui , Wu Xifeng , Yu Yao , Permuth Jennifer B. , Levine Douglas A. , Schildkraut Joellen M. , Riggan Marjorie J. , Berchuck Andrew , Webb Penelope M. ,   OPAL Study Group, Cybulski Cezary , Gronwald Jacek , Jakubowska Anna , Lubiński Jan , Alsop Jennifer , Harrington Patricia A. , Chan Isaac , Menon Usha , Pearce Celeste L. , Wu Anna H. , de Fazio Anna , Kennedy Catherine J. , Goode Ellen L. , Ramus Susan J. , Gayther Simon A. , Pharoah Paul D.P. 
Published: November 2019

AbstractPurposeThe known EOC susceptibility genes account for less than 50% of the heritable risk of ovarian cancer suggesting other susceptibility genes exist. The aim of this study was to evaluate the contribution to ovarian cancer susceptibility of rare deleterious germline variants in a set of candidate genes.MethodsWe sequenced the coding region of 54 candidate genes in 6385 invasive EOC cases and 6115 controls of broad European ancestry. Genes with an increased frequency of putative deleterious variants in cases verses controls were further examined in an independent set of 14,146 EOC cases and 28,661 controls from the ovarian cancer association consortium and the UK Biobank. For each gene, we estimated the EOC risks and evaluated associations between germline variant status and clinical characteristics.ResultsThe odds ratios (OR) associated for high-grade serous ovarian cancer were 3.01 for PALB2 (95% CI 1.59 – 5.68; P = 0.00068), 1.99 for POLK (95% CI 1.15 – 3.43; P = 0.014), and 4.07 for SLX4 (95% CI 1.34-12.4; P = 0.013). Deleterious mutations in FBXO10 were associated with a reduced risk of disease (OR 0.27, 95% CI 0.07 −1.00, P=0.049). However, based on the Bayes false discovery probability, only the association for PALB2 in high-grade serous ovarian cancer is likely to represent a true positive.ConclusionsWe have found strong evidence that carriers of PALB2 deleterious mutations are at increased risk of high-grade serous ovarian cancer. Whether the magnitude of risk is sufficiently high to warrant the inclusion of PALB2 in cancer gene panels for ovarian cancer risk testing is unclear; much larger sample sizes will be needed to provide sufficiently precise estimates for clinical counselling.
Asthma Exacerbation Prediction and Interpretation based on Time-sensitive Attentive Neural Network: A Retrospective Cohort Study
Authors: Xiang Yang , Ji Hangyu , Zhou Yujia , Li Fang , Du Jingcheng , Rasmy Laila , Wu Stephen , Zheng Wenjin.Jim , Xu Hua , Zhi Degui , Zhang Yaoyun , Tao Cui 
Published: November 2019

AbstractBackgroundAsthma exacerbation is an acute or sub-acute episode of progressive worsening of asthma symptoms and can have significant impacts on patients’ daily life. In 2016, 12.4 million current asthmatics (46.9%) in the U.S. had at least one asthma exacerbation in the previous year.ObjectiveThe objectives of this study were to predict the risk of asthma exacerbations and to explore potential risk factors involved in progressive asthma.MethodsWe proposed a time-sensitive attentive neural network to predict asthma exacerbation using clinical variables from electronic health records (EHRs). The clinical variables were collected from the Cerner Health Facts® database between 1992 and 2015 including 31,433 asthmatic adult patients. Interpretations on both the patient level and the cohort level were investigated based on the model parameters.ResultsThe proposed model obtains an AUC value of 0.7003 through 5-fold cross-validation, which outperforms the baseline methods. The results also demonstrate that the addition of elapsed time embeddings considerably improves the performance on this dataset. Through further analysis, it was witnessed that risk factors behaved distinctly along the timeline and across patients. We also found supporting evidence from peer-reviewed literature for some possible cohort-level risk factors such as respiratory syndromes and esophageal reflux.ConclusionsThe proposed time-sensitive attentive neural network is superior to traditional machine learning methods and performs better than state-of-the-art deep learning methods in realizing effective predictive models for the prediction of asthma exacerbation. We believe that the interpretation and visualization of risk factors can help the clinical community to better understand the underlying mechanisms of the disease progression.
Does the metabolic syndrome exist? The identification of cardio-metabolic profiles in a South Asian population study
Authors: Harber-Aschan Lisa , Bakolis Ioannis , Glozier Nicholas , Ismail Khalida , Jayaweera Kaushalya , Pannala Gayani , Pariante Carmine , Rijsdijk Fruhling , Siribaddana Sisira , Sumathipala Athula , Zavos Helena M.S. , Zunszain Patricia , Hotopf Matthew 
Published: November 2019

AbstractOBJECTIVEResearch testing the validity of the metabolic syndrome (MetS) as a clinical construct associated with cardiovascular disease risk has produced inconsistent results. This study tested the existence of the MetS, explored alternative cardiometabolic risk characterisations, and examined the relative influence of genetic and environmental factors in a South Asian sample.RESEARCH DESIGN AND METHODSData came from the Colombo Twin and Singleton follow-up Study, CoTaSS-2 (N=3969). Latent class analysis tested the clustering of MetS indicators (waist circumference, high-density-lipoprotein cholesterol (HDL-C), triglycerides (TG), blood pressure, fasting plasma glucose (FPG), medications and diabetes). Regression analyses tested cross-sectional associations between identified latent classes and covariates. Structural equation modelling estimated genetic and environmental influences on these classes. Analyses were stratified by gender (n=1681 men, n=2288 women).RESULTSThree classes were identified in men: 1) “Healthy” (52.3%), 2) “Central obesity, high TG, high FPG” (40.2%), and 3) “Central obesity, high TG, diabetes” (7.6%). Four classes were identified in women: 1) “Healthy” (53.2%), 2) “Very high central obesity, low HDL-C, raised FPG” (32.8%), 3) “Very high central obesity, diabetes” (7.2%) and 4) “Central obesity, hypertension, raised FPG” (6.8%). Older age in men and women, and high socioeconomic status in men, was associated with cardiometabolic risk categories, compared to the “Healthy” classes. In men, individual differences in cardiometabolic class membership were due to environmental effects. In females, genetic differences significantly predicted class membership.CONCLUSIONSThe findings did not support the MetS construct. Instead, distinct clinical profiles were identified in men and women, suggesting different aetiological pathways.
CAPICE: a computational method for Consequence-Agnostic Pathogenicity Interpretation of Clinical Exome variations
Authors: Li Shuang , van der Velde K. Joeri , de Ridder Dick , van Dijk Aalt D.J. , Soudis Dimitrios , Zwerwer Leslie R. , Deelen Patrick , Hendriksen Dennis , Charbon Bart , van Gijn Marielle , Abbott Kristin M. , Sikkema-Raddatz B. , van Diemen Cleo C. , Kerstjens-Frederikse Wilhelmina S. , Sinke Richard J. , Swertz Morris A. 
Published: November 2019

ABSTRACTExome sequencing is now mainstream in clinical practice, however, identification of pathogenic Mendelian variants remains time consuming, partly because limited accuracy of current computational prediction methods leaves much manual classification. Here we introduce CAPICE, a new machine-learning based method for prioritizing pathogenic variants, including SNVs and short InDels, that outperforms best general (CADD, GAVIN) and consequence-type-specific (REVEL, ClinPred) computational prediction methods, for both rare and ultra-rare variants. CAPICE is easily integrated into diagnostic pipelines and is available as free and open source command-line software, file of pre-computed scores, and as a web application with web service API.
The Project for Objective Measures Using Computational Psychiatry Technology (PROMPT): Rationale, Design, and Methodology
Authors: Kishimoto Taishiro , Takamiya Akihiro , Liang Kuo-ching , Funaki Kei , Fujita Takanori , Kitazawa Momoko , Yoshimura Michitaka , Tazawa Yuki , Horigome Toshiro , Eguchi Yoko , Kikuchi Toshiaki , Tomita Masayuki , Bun Shogyoku , Murakami Junichi , Sumali Brian , Warnita Tifani , Kishi Aiko , Yotsui Mizuki , Toyoshiba Hiroyoshi , Mitsukura Yasue , Shinoda Koichi , Sakakibara Yasubumi , Mimura Masaru 
Published: November 2019

AbstractBackgroundDepressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. Overcoming these disorders is an extremely important public health problem today. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. Due to advances in technology, it has become possible to quantify important features that clinicians perceive as reflective of disorder severity. Such features include facial expressions, phonic/speech information, body motion, daily activity, and sleep. The overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders.MethodsThis is a multi-center prospective study. DSM-5 criteria for major depressive disorder, bipolar disorder, and major and minor neurocognitive disorders are inclusion criteria for the depressive and neurocognitive disorder samples. Healthy samples are confirmed to have no history of psychiatric disorders by Mini-International Neuropsychiatric Interview, and have no current cognitive decline based on the Mini Mental State Examination. Participants go through approximately 10-minute interviews with a psychiatrist/psychologist, where participants talk about non-specific topics such as everyday living, symptoms of disease, hobbies, etc. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. The interviews take place ≤10 times within up to five years of follow-up. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms.DiscussionThe PROMPT goal is to develop objective digital biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity.Trial RegistrationUMIN000021396, University Hospital Medical Information Network (UMIN)
Polygenic background modifies penetrance of monogenic variants conferring risk for coronary artery disease, breast cancer, or colorectal cancer
Authors: Fahed Akl C. , Wang Minxian , Homburger Julian R. , Patel Aniruddh P. , Bick Alexander G. , Neben Cynthia L. , Lai Carmen , Brockman Deanna , Philippakis Anthony , Ellinor Patrick T. , Cassa Christopher A. , Lebo Matthew , Ng Kenney , Lander Eric S. , Zhou Alicia Y. , Kathiresan Sekar , Khera Amit V. 
Published: November 2019

ABSTRACTBackgroundGenetic variation can predispose to disease both through (i) monogenic risk variants in specific genes that disrupt a specific physiologic pathway and have a large effect on disease risk and (ii) polygenic risk that involves large numbers of variants of small effect that affect many different pathways. Few studies have explored the interaction between monogenic risk variants and polygenic risk.MethodsWe identified monogenic risk variants and calculated polygenic scores for three diseases, coronary artery disease, breast cancer, and colorectal cancer, in three study populations — case-control cohorts for coronary artery disease (UK Biobank; N=12,879) and breast cancer (Color Genomics; N=19,264), and an independent cohort of 49,738 additional UK Biobank participants.ResultsIn the coronary artery disease case-control cohort, increased risk for carriers of a monogenic variant ranged from 1.3-fold for those in the lowest polygenic score quintile to 12.6-fold for those in the highest. For breast cancer, increased risk ranged from 2.4 to 6.9-fold across polygenic score quintiles. Among the 49,738 UK Biobank participants who carried a monogenic risk variant, the probability of disease at age 75 years was strongly modified by polygenic risk. Across individuals in the lowest to highest percentiles of polygenic risk, the probability of disease ranged from 17% to 78% for coronary artery disease; 13% to 76% for breast cancer; and 11% to 80% for colon cancer.ConclusionsFor three important genomic conditions, polygenic risk powerfully modifies the risk conferred by monogenic risk variants.
Metagenomic analysis of common intestinal diseases reveals relationships among microbial signatures and powers multi-disease diagnostic models
Authors: Jiang Puzi , Wu Sicheng , Luo Qibin , Zhao Xing-ming , Chen Wei-Hua 
Published: November 2019

ABSTRACTCommon intestinal diseases such as Crohn’s disease (CD), ulcerative colitis (UC) and colorectal cancer (CRC), share clinical symptoms and altered gut microbes, necessitating cross-disease comparisons and the use of multi-disease models. Here, we performed meta-analyses on thirteen fecal metagenome datasets of the three diseases. We identified 87 species and 65 pathway markers that were consistently changed in multiple datasets of the same diseases. According to their overall trends, we grouped the disease-enriched marker species into disease-specific and -common clusters, and revealed their distinct phylogenetic relationships: species in CD-specific cluster are phylogenetically related, while those in CRC-specific cluster are more distant; strikingly, UC-specific species are phylogenetically closer to CRC, likely because UC-patients have higher risk of CRC. Consistent to their phylogenetic relationships, marker species had similar within-cluster and different between-cluster metabolic preferences. There were part of marker species and pathways correlated with an indicator of leaky gut, suggesting a link between gut dysbiosis and human derived contents. Marker species showed more coordinated changes and tighter inner-connections in cases than the controls, suggesting that the diseased gut may represent a stressed environment and pose stronger selection to gut microbes. With the marker species and pathways, we constructed four high-performance (including multi-disease) models with AUROC of 0.87 and true positive rates up to 90%, and explained their putative clinical applications. We identified consistent microbial alterations in common intestinal diseases, revealed metabolic capacities and the relationships among marker bacteria in distinct states, and supported the feasibility of metagenome-derived multi-disease diagnosis.ImportanceGut microbes have been identified as potential markers in distinguishing patients from controls in colorectal cancer, ulcerative colitis and Crohn’s disease individually, whereas there lacks a systematic analysis to investigate the exclusive microbial shifts of these enteropathies with similar clinical symptoms. Our meta-analysis and cross-disease comparisons identified consistent microbial alterations in each enteropathy, revealed microbial ecosystems among marker bacteria in distinct states, and demonstrated the necessity and feasibility of metagenome-based multi-disease classifications. To the best of our knowledge, this is the first study that constructed multi-class models in these common intestinal diseases.
Quality of life in cancer patients treated with mistletoe: a systematic review and meta-analysis
Authors: Loef Martin , Walach Harald 
Published: November 2019

AbstractBackgroundMistletoe extracts are used as an adjunct therapy for cancer patients, but there is dissent as to whether this therapy has a positive impact on quality of life (QoL).MethodsWe conducted a systematic review searching in several databases (Medline, Embase, CENTRAL, CINAHL, PsycInfo, Science Citation Index, clinicaltrials.gov, opengrey.org) by combining terms that cover the fields of “neoplasm”, “quality of life” and “mistletoe”. We included prospective controlled trials that compared mistletoe extracts with a control in cancer patients and reported QoL or related dimensions. The quality of the studies was assessed with the Cochrane Risk of Bias tool version 2.We conducted a quantitative meta-analysis.ResultsWe included 26 publications with 30 data sets. The studies were heterogeneous. The pooled standardized mean difference (random effects model) for global QoL after treatment with mistletoe extracts vs. control was d = 0.61 (95% CI 0.41-0.81; p<0,00001). The effect was stronger for younger patients, with longer treatment, in studies with lower risk of bias, in randomized and blinded studies. Sensitivity analyses support the validity of the finding. 50% of the QoL subdomains (e.g. pain, nausea) show a significant improvement after mistletoe treatment. Most studies have a high risk of bias or at least raise some concern.ConclusionMistletoe extracts produce a significant, medium-sized effect on QoL in cancer. Risk of bias in the analyzed studies is likely due to the specific type of treatment, which is difficult to blind; yet this risk is unlikely to affect the outcome.PROSPERO registration numberCRD42019137704