bluuebunny/crossref_metadata_2025_split huggingface dataset:
“Acquired” Type Castellvi-IIIa Lumbarization Transformed From Castellvi-IIa Following Discectomy and Fusion at Lumbosacral Level
Authors: Hou Lisheng , Bai Xuedong , Li Haifeng , Cheng Shi , Wen Tianyong , He Qing , Ruan Dike 
Published: May 2018

Study Design.
A retrospective case report.

Objective.
To report a case that transformed from type Castellvi-IIa sacralization to type Castellvi-IIIa after decompression and fusion surgery at transitional disc (TD).

Summary of Background Data.
Traditionally, lumbosacral transitional vertebra has been regarded as a congenital anomaly. No literature has ever reported that transformation from one type lumbosacral transitional vertebra to another would happen after birth.

Methods.
A 60-year-old man presented to our department with the complaint of low back pain and left sciatic pain, and was diagnosed of lumbar disc herniation at L4–5 and L5-S1 levels. Lumbar digital radiography revealed an anomalous articulation formed between sacrum and enlarged right L5 transverse process, exhibiting a typical Castellvi-IIa sacralization. Dynamic lateral radiographs showed mobilization existed at L5-S1 disc which was TD. The patient received posterior lumbar interbody fusion (PLIF) surgery at L4–5 and L5-S1 levels. Postoperative computed tomography examinations were taken to identify the progress of solid fusion at the operated segments.

Results.
After surgery, the patient's symptoms alleviated obviously. As solid fusion developed at intended interbody regions with time, we found that bony bridge passed through the patient's anomalous articulation region gradually, and finally, complete osseous fusion of the right L5 transverse process to the sacrum developed 1 year after surgery.

Conclusion.
Following PLIF surgery at TD segment on a Castellvi-IIa Sacralization case, “acquired” transformation to Castellvi-IIIa might develop.

Level of Evidence: 5
What do clinicians consider when assessing chronic low back pain? A content analysis of multidisciplinary pain centre team assessments of functioning, disability, and health
Authors: Bagraith Karl S. , Strong Jenny , Meredith Pamela J. , McPhail Steven M. 
Published: May 2018

Abstract
Beyond expert suggestions as to the appropriate subject matter for chronic pain assessments, little is known about the actual content of multidisciplinary pain centre (MPC) clinical assessments. The International Classification of Functioning, Disability and Health Low Back Pain Core Set (ICF LBP-CS) provides a universal language to support the consistent description of LBP-related assessments across disciplines within multidisciplinary teams (MDTs). This study sought to map the content of MPC clinical assessments to the ICF to: (1) identify and compare the content of clinical MDT assessments using a cross-disciplinary framework and (2) examine the content validity of the LBP-CS. A qualitative examination of MPC team clinical assessments of chronic low back pain was undertaken. Multidisciplinary team (pain medicine, psychiatry, nursing, physiotherapy, occupational therapy, and psychology) assessments were audio-recorded and transcribed. Concepts were extracted from transcripts using a meaning condensation procedure and then linked to the ICF. Across 7 MDT assessments, comprised 42 discipline-specific assessments and 241,209 transcribed words, 8596 concepts were extracted. Contextual factors (ie, the person and environment), except for physiotherapy, accounted for almost half of each discipline's assessments (range: 49%-58%). Concepts spanned 113 second-level ICF categories, including 73/78 LBP-CS categories. Overall, the findings revealed novel insights into the content of MPC clinical assessments that can be used to improve health care delivery. International Classification of Functioning, Disability and Health–based assessment profiles demonstrated unique contributions from each discipline to chronic low back pain assessment. Finally, users of the LBP-CS can be confident that the tool exhibits sound content validity from the perceptive of MDT assessments of functioning, disability, and health.
Amount and Socio-Ecological Correlates of Exercise in Men and Women at Cardiac Rehabilitation Completion
Authors: Taherzadeh Golnoush , Reid Robert D. , Prince Stephanie A. , Blanchard Chris M. , Chessex Caroline , Harris Jennifer , Pipe Andrew L. , Grace Sherry L. 
Published: May 2018

Objective
The aim of the study was to describe (1) the amount of physical activity (PA) in cardiac rehabilitation (CR) graduates by sex, and (2) the correlates of their PA.

Design
Secondary analysis of baseline data from a randomized trial was undertaken. Graduates were recruited from three CR programs. Participants completed a questionnaire, which assessed constructs from the socio-ecological model (i.e., individual-level, social- and physical-environmental levels). Physical activity was measured objectively using an ActiGraph GT3X accelerometer. Multilevel modeling was performed.

Results
Two hundred fifty-five patients consented, of which 200 (78.4%) completed the survey and provided valid accelerometer data. Participants self-reported engaging in a mean ± standard deviation of 184.51 ± 129.10 min of moderate-to-vigorous-intensity PA (MVPA) per week (with men engaging in more than women, P < 0.05). Accelerometer data revealed participants engaged in 169.65 ± 136.49 mins of MVPA per week, with 43 (25.1%) meeting recommendations. In the mixed models, the socio-ecological correlate significantly related to greater self-reported MVPA was self-regulation (P = 0.01); the correlate of accelerometer-derived MVPA was neighborhood aesthetics (P = 0.02).

Conclusions
Approximately one-quarter of CR program completers are achieving MVPA recommendations, although two-thirds perceive they are. The CR programs should exploit accelerometry and promote self-regulation skills, namely, self-monitoring, goal-setting, positive reinforcement, time management, and relapse prevention. Patients should be encouraged to exercise in pleasing locations.
Vertical Spacing of Perforators in Deep Inferior Epigastric Perforator Flap Breast Reconstruction Can Affect the Outcomes
Authors: Lee Kyeong-Tae , Eom Yeseul , Jeon Byung-Joon , Mun Goo-Hyun 
Published: May 2018

Background:
The present study aimed to evaluate the influence of vertical location and spacing of perforators within flaps on the outcomes of deep inferior epigastric perforator (DIEP) flap breast reconstruction.

Methods:
Patients who underwent unilateral breast reconstruction with unipedicle DIEP flaps were identified. They were categorized into cohorts based on the entry of perforators in the middle third (cohort 1), upper third (cohort 2), and multiple third parts (cohort 3) of the flaps. Perfusion-related complications including fat necrosis diagnosed with ultrasound and donor-site morbidity were compared between the cohorts.

Results:
A total of 287 patients were analyzed, including 51 in cohort 1, 75 in cohort 2, and 161 in cohort 3. The cohorts were well matched, except for a greater number of perforators and more frequent harvest of both medial and lateral row perforators in cohort 3. Rates of overall perfusion-related complications and fat necrosis differed significantly between cohorts, with the highest rates in cohort 2. Fat necrosis occurred predominantly in the caudal portion of the flap in cohort 2, whereas it was relatively evenly distributed in the cephalic and caudal portions in cohorts 1 and 3. Rates of donor-site complications were similar across the cohorts. Multivariate analyses demonstrated that vertical spacing of perforators had an independent influence on developing perfusion-related complications and fat necrosis, showing that cohort 2 had significantly higher odds compared with cohorts 1 and 3, respectively.

Conclusion:
Vertical spacing of perforators might affect the risk of perfusion-related complications in DIEP flap breast reconstruction.

CLINICAL QUESTION/LEVEL OF EVIDENCE:
Therapeutic, III.
Transforming Pain With Prosocial Meaning: A Functional Magnetic Resonance Imaging Study
Authors: López-Solà Marina , Koban Leonie , Wager Tor D. 
Published: May 2018

ABSTRACT

Objective
Contextual factors can transform how we experience pain, particularly if pain is associated with other positive outcomes. Here, we test a novel meaning-based intervention. Participants were given the opportunity to choose to receive pain on behalf of their romantic partners, situating pain experience in a positive, prosocial meaning context. We predicted that the ventromedial prefrontal cortex (vmPFC), a key structure for pain regulation and generation of affective meaning, would mediate the transformation of pain experience by this prosocial interpersonal context.

Methods
We studied fMRI activity and behavioral responses in 29 heterosexual female participants during (1) a baseline pain challenge and (2) a task in which participants decided to accept a self-selected number of additional pain trials to reduce pain in their male romantic partners (“accept-partner-pain” condition).

Results
Enduring extra pain for the benefit of the romantic partner reduced pain-related unpleasantness (t = −2.54, p = .016) but not intensity, and increased positive thoughts (t = 3.60, p = .001) and pleasant feelings (t = 5.39, p < .0005). Greater willingness to accept the pain of one's partner predicted greater unpleasantness reductions (t = 3.94, p = .001) and increases in positive thoughts (r = .457, p = .013). The vmPFC showed significant increases (q < .05 FDR-corrected) in activation during accept-partner-pain, especially for women with greater willingness to relieve their partner's pain (t = 2.63, p = .014). Reductions in brain regions processing pain and aversive emotion significantly mediated reductions in pain unpleasantness (q < .05 FDR-corrected).

Conclusions
The vmPFC has a key role in transforming the meaning of pain, which is associated with a cascade of positive psychological and brain effects, including changes in affective meaning, value, and pain-specific neural circuits.
Exercise is Associated With Increased Small HDL Particle Concentration and Decreased Vascular Stiffness in Rheumatoid Arthritis
Authors: Byram Kevin W. , Oeser Annette M. , Linton MacRae F. , Fazio Sergio , Stein C. Michael , Ormseth Michelle J. 
Published: May 2018

Objective
Patients with rheumatoid arthritis (RA) have increased cardiovascular (CV) risk. In the general population, exercise improves several CV risk factors. In a cross-sectional study, we examined the hypothesis that more exercise is associated with protective traditional and non-traditional CV risk factor profile in patients with RA.

Methods
Patient-reported exercise outside of daily activities was quantified by time and metabolic equivalents per week (METmin/week) and CV risk factors including blood pressure, standard lipid profiles, lipoprotein particle concentrations (NMR spectroscopy), and vascular indices were measured in 165 patients with RA. The relationship between exercise and CV risk factors was assessed according to whether patients exercised or not, and after adjustment for age, race and sex.

Results
Over half (54%) of RA patients did not exercise. Among those who did exercise, median value for exercise duration was 113 min/week [IQR: 60, 210], and exercise metabolic equivalent expenditure was 484 METmin/week [IQR: 258, 990]. Disease activity (measured by DAS28 score), C-reactive protein, waist-hip ratio, and prevalence of hypertension were lower in patients who exercised compared to those who did not (all p-values < 0.05) but standard lipid profile and body mass index were not significantly different. Patients who exercised had significantly higher concentrations of HDL particles (p = 0.004) and lower vascular stiffness as measured by pulse wave velocity (p = 0.005).

Conclusions
More self-reported exercise in patients with RA was associated with a protective CV risk factor profile including lower waist-hip ratio, higher HDL particle concentration, lower vascular stiffness, and a lower prevalence of hypertension.
AI-MHC: an allele-integrated deep learning framework for improving Class I & Class II HLA-binding predictions
Authors: Sidhom John-William , Pardoll Drew , Baras Alexander 
Published: May 2018

AbstractMotivationThe immune system has potential to present a wide variety of peptides to itself as a means of surveillance for pathogenic invaders. This means of surveillances allows the immune system to detect peptides derives from bacterial, viral, and even oncologic sources. However, given the breadth of the epitope repertoire, in order to study immune responses to these epitopes, investigators have relied on in-silico prediction algorithms to help narrow down the list of candidate epitopes, and current methods still have much in the way of improvement.ResultsWe present Allele-Integrated MHC (AI-MHC), a deep learning architecture with improved performance over the current state-of-the-art algorithms in human Class I and Class II MHC binding prediction. Our architecture utilizes a convolutional neural network that improves prediction accuracy by 1) allowing one neural network to be trained on all peptides for all alleles of a given class of MHC molecules by making the allele an input to the net and 2) introducing a global max pooling operation with an optimized kernel size that allows the architecture to achieve translational invariance in MHC-peptide binding analysis, making it suitable for sequence analytics where a frame of interest needs to be learned in a longer, variable length sequence. We assess AI-MHC against internal independent test sets and compare against all algorithms in the IEDB automated server benchmarks, demonstrating our algorithm achieves state-of-the-art for both Class I and Class II prediction.Availability and ImplementationAI-MHC can be used via web interface at baras.pathology.jhu.edu/AI-MHCContactjsidhom1@jhmi.edu
Epigenetic selection and the DNA methylation signatures of adverse prenatal environments
Authors: Tobi Elmar W , van den Heuvel Joost , Zwaan Bas J. , Lumey L.H. , Heijmans Bastiaan T. , Uller Tobias 
Published: May 2018

AbstractMaternal adversity is associated with long-term physiological changes in offspring. These are believed to be mediated through epigenetic mechanisms, including DNA methylation (DNAm). Changes in DNAm are often interpreted as damage or as part of plastic responses of the embryo. We propose that selection on stochastic DNAm differences generated during epigenetic reprogramming after fertilization contributes to the effects of maternal adversity on DNAm. Using a mathematical model of epigenetic reprogramming in the early embryo we predict that this “epigenetic selection” will generate a characteristic reduction in variance of DNAm at selected loci in populations exposed to maternal adversity. We tested this prediction using DNAm data from a human cohort prenatally exposed to the Dutch Famine and confirmed the reduction in DNAm variance, suggesting that epigenetic selection may have occurred. Epigenetic selection should be considered as a possible mechanism linking adversity in pregnancy to offspring health and may have implications for the likely effectiveness of intervention strategies.
Relevance of coral geometry in the outcomes of the coral-algal benthic war
Authors: George Emma E. , Mullinix James , Meng Fanwei , Bailey Barbara , Edwards Clinton , Felts Ben , Haas Andreas , Hartmann Aaron C. , Mueller Benjamin , Nulton Jim , Roach Ty N.F. , Salamon Peter , Silveira Cynthia B. , Vermeij Mark J.A. , Rohwer Forest L. , Luque Antoni 
Published: May 2018

AbstractCorals have built reefs on the benthos for millennia, becoming an essential element in marine ecosystems. Climate change and human impact, however, are favoring the invasion of non-calcifying benthic algae and reducing coral coverage. Corals rely on energy derived from photosynthesis and heterotrophic feeding, which depends on their surface area, to defend their outer perimeter. But the relation between geometric properties of corals and the outcome of competitive coral-algal interactions is not well known. To address this, 50 coral colonies interacting with algae were sampled in the Caribbean island of Curaçao. 3D and 2D digital models of corals were reconstructed to measure their surface area, perimeter, and polyp sizes. A box counting algorithm was applied to calculate their fractal dimension. The perimeter and surface dimensions were statistically non-fractal, but differences in the mean surface fractal dimension captured relevant features in the structure of corals. The mean fractal dimension and surface area were negatively correlated with the percentage of losing perimeter and positively correlated with the percentage of winning perimeter. The combination of coral perimeter, mean surface fractal dimension, and coral species explained 19% of the variability of losing regions, while the surface area, perimeter, and perimeter-to-surface area ratio explained 27% of the variability of winning regions. Corals with surface fractal dimensions smaller than two and small perimeters displayed the highest percentage of losing perimeter, while corals with large surface areas and low perimeter-to-surface ratios displayed the largest percentage of winning perimeter. This study confirms the importance of fractal surface dimension, surface area, and perimeter of corals in coral-algal interactions. In combination with non-geometrical measurements such as microbial composition, this approach could facilitate environmental conservation and restoration efforts on coral reefs.
GaKCo: a Fast Gappedk-mer string Kernel using Counting
Authors: Singh Ritambhara , Sekhon Arshdeep , Lanchantin Jack , Kowsari Kamran , Wang Beilun , Qi Yanjun 
Published: May 2018

AbstractString Kernel (SK) techniques, especially those using gappedk-mers as features (gk), have obtained great success in classifying sequences like DNA, protein, and text. However, the state-of-the-art gk-SK runs extremely slow when we increase the dictionary size (Σ) or allow more mismatches (M). This is because current gk-SK uses a trie-based algorithm to calculate co-occurrence of mismatched substrings resulting in a time cost proportional toO(ΣM). We propose afastalgorithm for calculatingGappedk-merKernel usingCounting (GaKCo). GaKCo uses associative arrays to calculate the co-occurrence of substrings using cumulative counting. This algorithm is fast, scalable to largerΣandM, and naturally parallelizable. We provide a rigorous asymptotic analysis that compares GaKCo with the state-of-the-art gk-SK. Theoretically, the time cost of GaKCo is independent of theΣMterm that slows down the trie-based approach. Experimentally, we observe that GaKCo achieves the same accuracy as the state-of-the-art and outperforms its speed by factors of 2, 100, and 4, on classifying sequences of DNA (5 datasets), protein (12 datasets), and character-based English text (2 datasets).1
The Charlson Index is insufficient to control for comorbidities in a national trauma registry
Authors: Renson Audrey , Bjurlin Marc A. 
Published: May 2018

AbstractBackgroundThe Charlson Comorbidity Index (CCI) is frequently used to control for confounding by comorbidities in observational studies, but its performance as such has not been studied. We evaluated the performance of CCI and an alternative summary method, logistic principal component analysis (LPCA), to adjust for comorbidities, using as an example the association between insurance and mortality.Materials and MethodsUsing all admissions in the National Trauma Data Bank 2010-2015, we extracted mortality, payment method, and 36 ICD-9-derived comorbidities. We estimated ORs for the association between uninsured status and mortality before and after adjusting for CCI, LPCA, and separate covariates. We also calculated standardized mean differences (SMDs) of comorbidity variables before and after weighting the sample using inverse probability of treatment weights (IPTW) for CCI, LPCA, and separate covariates.ResultsIn 4,936,880 admissions, most (68.3%) had at least one comorbidity. Considerable imbalance was observed in the unweighted sample (mean SMD=0.086, OR=1.17), which was almost entirely eliminated by IPTW on separate covariates (mean SMD=0.012, OR=1.36). The CCI performed similarly to the unweighted sample (mean SMD=0.080, OR=1.25), while 2 LPCA axes were better able to control for confounding (mean SMD=0.04, OR=1.31). Using covariate adjustment, the CCI accounted for 56.1% of observed confounding, whereas 2 LPCA axes accounted for 91.3%.ConclusionsThe use of the CCI to adjust for confounding may result in residual confounding, and alternative strategies should be considered. LPCA may be a viable alternative to adjusting for each comorbidity when samples are small or positivity assumptions are violated.
Controlling for contaminants in low biomass 16S rRNA gene sequencing experiments
Authors: Karstens Lisa , Asquith Mark , Davin Sean , Fair Damien , Gregory W. Thomas , Wolfe Alan J. , Braun Jonathan , McWeeney Shannon 
Published: May 2018

AbstractBackgroundMicrobial communities are commonly studied using culture-independent methods such as 16S rRNA gene sequencing. However, one challenge in accurately characterizing microbial communities is exogenous bacterial DNA contamination. This is particularly problematic for sites of low microbial biomass such as the urinary tract, placenta, and lower airway. Computational approaches have been proposed as a post-processing step to identify and remove potential contaminants, but their performance has not been independently evaluated.To identify the impact of decreasing microbial biomass on polymicrobial 16S rRNA gene sequencing experiments, we used a serial dilution of a mock microbial community. We evaluated two computational approaches to identify and remove contaminants: 1) identifying sequences that have an inverse correlation with DNA concentration implemented in Decontam and 2) predicting the proportion of experimental sample arising from defined contaminant sources implemented in SourceTracker.ResultsAs expected, the proportion of contaminant bacterial DNA increased with decreasing starting microbial biomass, with 79.12% of the most dilute sample arising from contaminant sequences. Inclusion of contaminant sequences in analyses leads to overinflated diversity estimates (up to 12 times greater than the expected values) and distorts microbiome composition. SourceTracker successfully removed over 98% of contaminants when the experimental environments are well defined. However, SourceTracker performed poorly when the experimental environment is unknown, failing to remove the majority of contaminants. Decontam successfully removed 74-91% of contaminants regardless of prior knowledge of the experimental environment.ConclusionsOur study indicates that computational methods can reduce the amount of contaminants in 16S rRNA gene sequencing experiments. The appropriate computational approach for removing contaminant sequences from an experiment depends on the prior knowledge about the microbial environment under investigation and can be evaluated with a dilution series of a mock microbial community.