bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Relationship Between Pregnancy Exposure to Antidepressant and Atypical Antipsychotic Medications and Placental Weight and Birth Weight Ratio
Authors: Frayne Jacqueline , Nguyen Thinh , Hauck Yvonne , Liira Helena , Keelan Jeffrey A. 
Published: October 2018

Abstract

Background
Psychotropic medication use in pregnancy has been associated with altered fetal growth. The aim of this study was to investigate the relationship between placental weight and placental weight–to–birth weight (PBW) ratio, as a potential marker of placental efficiency, and medication use in a cohort of women with severe mental illness in pregnancy.

Methods
A retrospective database analysis was carried out on a cohort of pregnant women with severe mental illness (242 singleton pregnancies) and grouped according to their psychotropic medication use. Demographic, obstetric, neonatal, and psychiatric variables were analyzed using t tests, χ2, analysis of variance, univariate, binary, and multiple regression adjusting for potential confounders.

Results
Multiple regression analysis demonstrated a mean adjusted increase in placental weight of 114 g (95% confidence interval [CI], 60.2–165.6 g) in women taking antidepressant medication and 113 g (CI, 65.1–162.8 g) in women taking combined antidepressant and atypical antipsychotic medication in pregnancy. There was also a significantly elevated PBW ratio in these 2 medication groups (B 0.02: CI, 0.006–0.034; and B 0.025: CI, 0.012–0.038). Binary regression, adjusted for sex and gestational age, showed a significant odds ratio of 4.57 (95% CI, 2.17–9.62) for PBW ratio of greater than 90% in those taking antidepressant medication, either alone or in combination, compared with unmedicated women.

Conclusions
The use of antidepressant medication, alone or in combination, has a significant effect on placental weight and PBW ratio after adjusting for confounding variables. Given that this may reflect adverse effects on intrauterine growth and have possible long-term implications for the fetus, further research is warranted to confirm these findings.
A Hermeneutic Interpretation of Nurses’ Experiences of Truth Telling and Harms in Cancer Care in Qatar
Authors: Alsaadi Wafa A. , Rankin Janet M. , Bylund Carma L. 
Published: October 2018

Background
Telling the truth to cancer patients remains under debate in the Middle East, where concealment about diagnosis and prognosis occurs in some cases. Concealment results in challenges for nurses providing care.

Objective
The aim of this study was to understand nurses’ lived experiences of caring for cancer patients whose cancer diagnosis or prognosis has been withheld from them.

Methods
Eight nurses from the national cancer center in Qatar were interviewed. The transcripts of the interview texts were interpreted using Gadamer’s hermeneutic approach.

Results
The interpretations are shaped by understandings of harm. Nurses assessed harm using empathy. Nurses’ empathy was permeated with fears that accompany a cancer diagnosis; the language of cancer is interpreted as a language of fear. It is ideas about harms and evoking patients’ fear that generates nurses’ experiences of complexity, ambiguity, and conflicting feelings regarding truth telling and concealment. The meanings nurses drew from their experiences rested on understandings about love, vulnerability, and opportunities to atone. We interpret nurses’ descriptions of being enmeshed in a web of lies through which multidimensional harms are experienced. The complexities of nurses’ experiences go well beyond the universal concepts of informed consent and patients’ rights.

Conclusions
Nurses’ experiences reveal insights that likely resonate across other jurisdictions in the Arabic Gulf and other Eastern cultures, where nurses deal with these sensitive issues case by case.

Implications for Practice
Leaders and health professionals in cancer care in such cultures must establish more nuanced and transparent interdisciplinary approaches to respond to the complexities of truth telling in cancer care.
Utilization of a Clinical Decision Support Tool to Reduce Child Tobacco Smoke Exposure in the Urgent Care Setting
Authors: Mahabee-Gittens Esther Melinda , Merianos Ashley L. , Dexheimer Judith W. , Meyers Gabe T. , Stone Lara , Tabangin Meredith , Khoury Jane C. , Gordon Judith S. 
Published: October 2018

Background
Clinical decision support systems (CDSS) may facilitate caregiver tobacco screening and counseling by pediatric urgent care (UC) nurses.

Objective
This study aimed to assess the feasibility of a CDSS to address caregivers' tobacco use and child tobacco smoke exposure (TSE).

Methods
We conducted a 3-month prospective study on caregivers screened using a CDSS. Nurses used the CDSS to advise, assess, and assist caregivers to quit. We assessed caregiver sociodemographics, smoking habits, and child TSE.

Results
We screened 185 caregivers whose children were exposed to TSE for study inclusion; 155 (84%) met the eligibility criteria, and 149 (80.5%) were included in the study. Study nurses advised 35.2% of the caregivers to quit, assessed 35.9% for readiness to quit, and assisted 32.4%. Of the 149 participants, 83.1% were female; 47.0% were white and 45.6% African American; 84.6% had public insurance or were self-pay; 71.1% were highly nicotine dependent; 50.0% and 50.7% allowed smoking in the home and car, respectively; and 81.3% of children were biochemically confirmed to be exposed to tobacco smoke. At follow-up (86.6% retention), 58.9% reported quit attempts at 3 months. There was a significant decrease in nicotine dependence and a significant increase in motivation to quit. Self-reported quit rate was 7.8% at 3 months.

Conclusions
An electronic health record–embedded CDSS was feasible to incorporate into busy UC nurses' workloads and was associated with encouraging changes in the smoking behavior of caregivers. More research on the use of CDSS to screen and counsel caregivers who smoke in the UC and other acute care settings is warranted.
Comparison of Anorectal Manometry Testing in Left Lateral and Lithotomy Positions
Authors: Kadam-Halani Priyanka K. , Pahwa Avita K. , Koelper Nathanael C. , Arya Lily A. , Sammel Mary D. , Andy Uduak U. 
Published: October 2018

Objectives
Anorectal manometry (ARM) is typically performed in left lateral position, but many practitioners are more familiar with the lithotomy position. We aimed to evaluate agreement between ARM performed in left lateral and lithotomy positions and patient preference for testing position.

Methods
We performed a prospective comparison study of left lateral versus lithotomy position for women undergoing ARM for the evaluation of fecal incontinence. Women were randomly assigned to undergo testing in either left lateral position first followed by lithotomy position, or vice versa. Women then completed a survey assessing preference of position. We performed Bland-Altman analysis to measure the level of agreement between anorectal measurements obtained in the 2 positions.

Results
Twenty-one women were enrolled (mean age, 65 ± 2.2 years). We noted an acceptable level of agreement between anal pressure values obtained in left lateral versus lithotomy positions: anal resting pressure (mean difference, 0.9 mm Hg; 95% limits of agreement, 30.2 and −28.5) and anal squeeze pressure (mean difference, 1.8 mm Hg; 95% limits of agreement, 54.3 and −50.7). The level of agreement for sensory values was outside the predetermined clinical acceptability range. Most women (17/21 [81%]) reported a “good” or “very good” experience in both positions.

Conclusions
Anorectal manometry testing in the 2 positions can be used interchangeably for anal resting and squeeze pressures, but not for anorectal sensation. This modification can be introduced into clinical practice to accommodate the preference of women and practitioners who favor lithotomy position.
Genome sequence of the corn leaf aphid (Rhopalosiphum maidis Fitch)
Authors: Chen Wenbo , Shakir Sara , Bigham Mahdiyeh , Fei Zhangjun , Jander Georg 
Published: October 2018

AbstractBackgroundThe corn leaf aphid (Rhopalosiphum maidis Fitch) is the most economically damaging aphid pest on maize (Zea mays), one of the world’s most important grain crops. In addition to causing direct damage due to the removal of photoassimilates, R. maidis transmits several destructive maize viruses, including Maize yellow dwarf virus, Barley yellow dwarf virus, Sugarcane mosaic virus, and Cucumber mosaic virus.FindingsA 326-Mb genome assembly of BTI-1, a parthenogenetically reproducing R. maidis clone, was generated with a combination of PacBio (208-fold coverage) and Illumina sequencing (80-fold coverage), which contains a total of 689 contigs with an N50 size of 9.0 Mb. The contigs were further clustered into four scaffolds using the Phase Genomics Hi-C interaction maps, consistent with the commonly observed 2n = 8 karyotype of R. maidis. Most of the assembled contigs (473 spanning 321 Mb) were successfully orientated in the four scaffolds. The R. maidis genome assembly captured the full length of 95.8% of the core eukaryotic genes, suggesting that it is highly complete. Repetitive sequences accounted for 21.2% of the assembly, and a total of 17,647 protein-coding genes were predicted in the R. maidis genome with integrated evidence from ab initio and homology-based gene predictions and transcriptome sequences generated with both PacBio and Illumina. An analysis of likely horizontally transferred genes identified two from bacteria, seven from fungi, two from protozoa, and nine from algae.ConclusionsA high-quality R. maidis genome was assembled at the chromosome level. This genome sequence will enable further research related to ecological interactions, virus transmission, pesticide resistance, and other aspects of R. maidis biology. It also serves as a valuable resource for comparative investigation of other aphid species.
Comparative validation of breast cancer risk prediction models and projections for future risk stratification
Authors: Pal Choudhury Parichoy , Wilcox Amber N. , Brook Mark N. , Zhang Yan , Ahearn Thomas , Orr Nick , Coulson Penny , Schoemaker Minouk J. , Jones Michael E. , Gail Mitchell H. , Swerdlow Anthony J. , Chatterjee Nilanjan , Garcia-Closas Montserrat 
Published: October 2018

AbstractBackgroundWell-validated risk models are critical for risk stratified breast cancer prevention. We used the Individualized Coherent Absolute Risk Estimation (iCARE) tool for comparative model validation of five-year risk of invasive breast cancer in a prospective cohort, and to make projections for population risk stratification.MethodsPerformance of two recently developed models, iCARE-BPC3 and iCARE-Lit, were compared with two established models (BCRAT, IBIS) based on classical risk factors in a UK-based cohort of 64,874 women (863 cases) aged 35-74 years. Risk projections in US White non-Hispanic women aged 50-70 years were made to assess potential improvements in risk stratification by adding mammographic breast density (MD) and polygenic risk score (PRS).ResultsThe best calibrated models were iCARE-Lit (expected to observed number of cases (E/O)=0.98 (95% confidence interval [CI]=0.87 to 1.11)) for women younger than 50 years; and iCARE-BPC3 (E/O=1.00 (0.93 to 1.09)) for women 50 years or older. Risk projections using iCARE-BPC3 indicated classical risk factors can identify ~500,000 women at moderate to high risk (>3% five-year risk). Additional information on MD and a PRS based on 172 variants is expected to increase this to ~3.6 million, and among them, ~155,000 invasive breast cancer cases are expected within five years.ConclusionsiCARE models based on classical risk factors perform similarly or better than BCRAT or IBIS. Addition of MD and PRS can lead to substantial improvements in risk stratification. Independent prospective validation of integrated models is needed prior to clinical evaluation risk stratified breast cancer screening and prevention.
Evaluation of the NCCN guidelines using the RIGHT Statement and AGREE II instrument: a cross-sectional review
Authors: Wayant Cole , Cooper Craig , Turner D’Arcy , Vassar Matt 
Published: October 2018

AbstractIntroductionRobust, clearly reported clinical practice guidelines (CPGs) are essential for evidence-based clinical practice. The Reporting Items for practice Guidelines in HealThcare (RIGHT) statement and Appraisal of Guidelines for Research and Evaluation (AGREE) II instrument were published to improve the methodological and reporting quality in healthcare CPGs.MethodsWe applied the RIGHT statement checklist and AGREE II instrument to 48 National Comprehensive Cancer Network (NCCN) guidelines. Our primary objective was to assess the adherence to RIGHT and AGREE II items. Since neither RIGHT nor AGREE-II can judge the clinical usefulness of a guideline, our study is designed to only focus on the methodological and reporting quality of each guideline.ResultsThe NCCN guidelines demonstrated notable strengths and weaknesses. For example, RIGHT statement items 19 (conflicts of interest), 7b (description of subgroups), and 13a (clear, precise recommendations) were fully reported in all guidelines. However, the guidelines inconsistently incorporated patient values and preferences and cost, nor did they consistently describe the method for assessing the quality and certainty of evidence. Regarding the AGREE II instrument, the NCCN guidelines scored highly on the domains 4 (clear, precise recommendations) and 6 (handling of conflicts of interest), but lowest on domain 2 (inclusion of all relevant stakeholders).ConclusionsIn this investigation we found that NCCN CPGs demonstrate key strengths and weaknesses with respect to the reporting of key items essential to CPGs. We recommend the continued use of NCCN guidelines and adherence to the RIGHT and AGREE II items. Doing so serves to improve the evidence delivered to healthcare providers, thus potentially improving patient care.
A fast and general method to empirically estimate the complexity of brain responses to transcranial and intracranial stimulations
Authors: Comolatti Renzo , Pigorini Andrea , Casarotto Silvia , Fecchio Matteo , Faria Guilherme , Sarasso Simone , Rosanova Mario , Gosseries Olivia , Boly Mélanie , Bodart Olivier , Ledoux Didier , Brichant Jean-François , Nobili Lino , Laureys Steven , Tononi Giulio , Massimini Marcello , Casali Adenauer G. 
Published: October 2018

AbstractBackgroundThe Perturbational Complexity Index (PCI) was recently introduced to assess the capacity of thalamocortical circuits to engage in complex patterns of causal interactions. While showing high accuracy in detecting consciousness in brain injured patients, PCI depends on elaborate experimental setups and offline processing and has restricted applicability to other types of brain signals beyond transcranial magnetic stimulation and high-density EEG (TMS/hd-EEG) recordings.ObjectiveWe aim to address these limitations by introducing PCIST, a fast method for estimating perturbational complexity of any given brain response signal.MethodsPCISTis based on dimensionality reduction and state transitions (ST) quantification of evoked potentials. The index was validated on a large dataset of TMS/hd-EEG recordings obtained from 108 healthy subjects and 108 brain injured patients, and tested on sparse intracranial recordings (SEEG) of 9 patients undergoing intra-cerebral single-pulse electrical stimulation (SPES).ResultsWhen calculated on TMS/hd-EEG potentials, PCISTperformed with the same accuracy as the original PCI, while improving on the previous method by being computed in less than a second and requiring a simpler set-up. In SPES/SEEG signals, the index was able to quantify a systematic reduction of intracerebral complexity during sleep, confirming the occurrence of state-dependent changes in the effective connectivity of thalamocortical circuits, as originally assessed through TMS/hd-EEG.ConclusionsPCISTrepresents a fundamental advancement towards the implementation of a reliable and fast clinical tool for the bedside assessment of consciousness as well as a general measure to explore the neuronal mechanisms of loss/recovery of brain complexity across scales and models.
MicroED data collection with SerialEM
Authors: Jason de la Cruz M. , Martynowycz Michael W. , Hattne Johan , Gonen Tamir 
Published: October 2018

AbstractWe developed a procedure for the cryoEM method MicroED using SerialEM. With this approach, SerialEM coordinates stage rotation, microscope operation, and camera functions for automated continuous-rotation MicroED data collection. More than 300 datasets can be collected overnight in this way, facilitating high-throughput MicroED data collection for large-scale data analyses.
Causal Association between Birth Weight and Adult Diseases: Evidence from a Mendelian Randomisation Analysis
Authors: Zeng Ping , Zhou Xiang 
Published: October 2018

AbstractBackgroundIt has long been hypothesized that birth weight has a profound long-term impact on individual predisposition to various diseases at adulthood: a hypothesis commonly referred to as the fetal origins of adult diseases. However, it is not fully clear to what extent the fetal origins of adult diseases hypothesis holds and it is also not completely known what types of adult diseases are causally affected by birth weight. Determining the causal impact of birth weight on various adult diseases through traditional randomised intervention studies is a challenging task.MethodsMendelian randomisation was employed and multiple genetic variants associated with birth weight were used as instruments to explore the relationship between 21 adult diseases and 38 other complex traits from 37 large-scale genome-wide association studies up to 340,000 individuals of European ancestry. Causal effects of birth weight were estimated using inverse-variance weighted methods. The identified causal relationships between birth weight and adult diseases were further validated through extensive sensitivity analyses and simulations.ResultsAmong the 21 adult diseases, three were identified to be inversely causally affected by birth weight with a statistical significance level passing the Bonferroni corrected significance threshold. The measurement unit of birth weight was defined as its standard deviation (i.e. 488 grams), and one unit lower birth weight was causally related to an increased risk of coronary artery disease (CAD), myocardial infarction (MI), type 2 diabetes (T2D) and BMI-adjusted T2D, with the estimated odds ratios of 1.34 [95% confidence interval (CI) 1.17 - 1.53,p= 1.54E-5], 1.30 (95% CI 1.13 - 1.51,p= 3.31E-4), 1.41 (95% CI 1.15 - 1.73,p= 1.11E-3) and 1.54 (95% CI 1.25 - 1.89,p= 6.07E-5), respectively. All these identified causal associations were robust across various sensitivity analyses that guard against various confounding due to pleiotropy or maternal effects as well as inverse causation. In addition, analysis on 38 additional complex traits found that the inverse causal association between birth weight and CAD/MI/T2D was not likely to be mediated by other risk factors such as blood-pressure related traits and adult weight.ConclusionsThe results suggest that lower birth weight is causally associated with an increased risk of CAD, MI and T2D in later life, supporting the fetal origins of adult diseases hypothesis.
Genomic and transcriptomic determinants of therapy resistance and immune landscape evolution during anti-EGFR treatment in colorectal cancer
Authors: Woolston Andrew , Khan Khurum , Spain Georgia , Barber Louise J , Griffiths Beatrice , Exposito Reyes Gonzalez , Patil Yatish , Mansukhani Sonia , Davies Matthew N , Furness Andrew , Sclafani Francesco , Peckitt Clare , Kouvelakis Kyriakos , Ranftl Romana , Begum Ruwaida , Rana Isma , Thomas Janet , Bryant Annette , Quezada Sergio , Wotherspoon Andrew , Khan Nasir , Fotiadis Nikolaos , Marafioti Teresa , Powles Thomas , Calvo Fernando , Guettler Sebastian , von Loga Katharina , Rao Sheela , Watkins David , Starling Naureen , Chau Ian , Sadanandam Anguraj , Cunningham David , Gerlinger Marco 
Published: October 2018

AbstractAnti-epidermal growth factor receptor (EGFR) antibodies (anti-EGFR-Ab) are effective in a subgroup of patients with metastatic colorectal cancer (CRC). We applied genomic and transcriptomic analyses to biopsies from 35 RAS wild-type CRCs treated with the anti-EGFR-Ab cetuximab in a prospective trial to interrogate the molecular resistance landscape. This validated transcriptomic CRC-subtypes as predictors of cetuximab benefit; identified novel associations of NF1-inactivation and non-canonical RAS/RAF-aberrations with primary progression; and of FGF10- and non-canonical BRAF-aberrations with AR. No genetic resistance drivers were detected in 64% of AR biopsies. The majority of these had switched from the cetuximab-sensitive CMS2-subtype pretreatment to the fibroblast- and growth factor-rich CMS4-subtype at progression. Fibroblast supernatant conferred cetuximab resistance in vitro, together supporting subtype-switching as a novel mechanism of AR. Cytotoxic immune infiltrates and immune-checkpoint expression increased following cetuximab responses, potentially providing opportunities to treat CRCs with molecularly heterogeneous AR with immunotherapy.
The First Implication of Image Processing Techniques on Influenza A Virus Sub-Typing Based on HA/NA Protein Sequences, using Convolutional Deep Neural Network
Authors: Ahsan Reza , Ebrahimi Mansour 
Published: October 2018

AbstractIncrease in influenza A virus host range throughout its evolution has given rise to major concerns worldwide. Although the increasing host range mechanism of the virus is largely unknown; persistent genetic mutations have been blamed as a key factor in the re-organization of the host response and the host range. To uncover the underlying core bases of the two important antigenic proteins of influenza virus (HA and NA), functional data mining and image processing analysis of over 8000 protein sequences of different HA and NA subtypes were performed. Each amino acid sequence in HA or NA proteins sat as a feature or variable and two polynomial datasets were created and subjected into conventional prediction models. The average accuracies of these predictive models for HA subtype classifications ranged from 38.9% for SVM to 87.2% for Decision Tree models. NA subtype classification with conventional prediction models varied from 81.3% to 99.87% for SVM and KNN models, respectively. Then amino acid sequence datasets were converted to binary images; subtypes feature sat as target variable, and target label determined by image processing convolution neural network. The performances of Image processing models (convolutional neural network) on binary images for both HA and NA datasets reached to 100%; and the application of Gabor2 filter decreased the time for the predicting model to reach the best performance for HA subtype; while it increased the epochs time for NA subtype classification.For the first time ever, converting influenza virus’ HA and NA amino acid sequences into the binary image datasets and their classifications by convolution neural network increased the prediction accuracies and performances to the highest possible point. The finding of this paper paves new avenues for virus classification based on antigenic HA and NA amino acid profiles, and easily classifying and predicting the possible future emerging strains of pandemic influenza.