bluuebunny/crossref_metadata_2025_split huggingface dataset:
Pre operative fitness score accurately predicts uneventful post operative course in gastrointestinal and hepatobiliary surgery
Authors: Vasavada Bhavin , patel Hardik 
Published: April 2020

AbstractBackgroundAim of our study was to analyse if we can accurately predict uneventful post operative course pre operatively in gastrointestinal and HPB surgery patients.Material and MethodsWe retrospectively evaluated patients who have undergone gastrointestinal and hepatobiliary surgery at our institute in last 3 years and analysed 90 days mortality and morbidity rates among these patients. We described any 90 day morbidity and mortality as an “event”. We performed univariate and multivariate analyses for factors predicting an “event”. Then based on pre operative factors that predicted an “event” we formulated a score. Statistical analysis was done using SPSS version 23.ResultsTotal 264 patient operated for gastrointestinal and HPB surgeries between April 2016 to may 2019 were evaluated .Total 45 (17%) events occurred. On univariate analysis CDC grade, ASA score,Operative time,Blood products used, emergency surgeries and open surgeries predicted an event. We developed score based on pre operative factors like ASA score, CDC grade of surgery,open surgery and emergency surgeries included in the score. We proposed score grater than 2 was associated with 90 day event. This score had sensitivity of 77.78%, specificity of 81.65%. low positive predictive value of 46.67% but very high negative predictive value of 94.68%. AUROC showed AUROC of 0.797 (p < 0.0001, 95% confidence interval 0.721-0.874). Pre operative fitness score, Open Surgery and operative time independently predicted an “event” on multivariate analysis. (p =0.003 and 0.026 respectively.ConclusionsPre operative fitness score accurately predicts uneventful post operative course in gastrointestinal and hepatobiliary surgery.
Pre transplant malnutrition predicts post transplant respiratory complications in living donor liver transplantation for biliary atresia- single center retrospective analysis of 110 children
Authors: Vasavada BB 
Published: April 2020

Back GroundBiliary atresia is commonly associated with malnutrition and failure to thrive. Very few studies have been published on impact of preoperative malnutrition of post transplant outcome in these children.Material and Methods110 children underwent living donor liver transplantation between January 2003 to march 2013.Pre transplant malnutrition was defined according to z scores for weight for age and height for age as per who definition. Patients having both Z score of < −2 were compared with control group. Statistical analysis were done using SPSS version 21 (IBM).Results39 children out of 110 were having z score for weight for age < −2.There was no statistical difference between PELD score, graft weight, GRWR, intraoperative blood loss between to groups. 22 out of 39 patients in malnourished group developed clavein grade 3,garde 4 complications and 32 patients out of 71 in control group developed clavien grade 3 grade 4 complications. (p= 0.318). Over all mortality rate was 4.5% and mortality rates in malnourished vs control group was respectively 7.69% and 2.81% (p= 0.278). Total 14 patient developed postoperative pulmonary complications. Pulmonary complications were significantly high in malnourished group. p=0.003.ConclusionPreoperative malnutrition is associated with high postoperative pulmonary complication rate in liver transplantation for biliary atresia.
lncRNA DLEU2 acts as a miR-181a sponge regulated SEPP1 (may as a biomarker for sarcopenia) to inhibit skeletal muscle differentiation and regeneration
Authors: Wang Yao , Kang Xue-ran , Chen Yisheng 
Published: April 2020

AbstractBackgroundSarcopenia is a serious public health problem. The ceRNA network has been demonstrated vital in the development of skeletal muscle, but there is currently no effective method to assess the risk of sarcopenia. The purpose of this research is to create and authenticate a ceRNA pathway based on a predictive model of sarcopenia.MethodsA clinical prediction model for sarcopenia was established using the RNAs (validated by clinical data) that are co-differentially expressed in the database, and a ceRNA network was constructed. The correlation analysis of each element in the ceRNA network was performed according to the clinical samples and the GTEX database, and the possible key ceRNA pathways were screened. C2C12 mouse myoblast Cells experiments were used to verify these ceRNA pathways.FindingsBased on four molecular markers of SEPP1, SV2A, GOT1 and GFOD1, we developed a new model for predicting sarcopenia with well accuracy, and constructed a ceRNA network accordingly. Clinical sample showed that the expression levels of lncDLEU2, SEPP1, and miR-181a were closely related to the risk of sarcopenia. The C2C12 mouse myoblast cells were cultivated to verify that lncDLEU2 inhibits muscle proliferation and differentiation by acting as a miR-181a sponge regulated SEPP1.InterpretationOur research developed a highly accurate prediction tool for the risk of sarcopenia. These findings suggest that lncDLEU2-miR-181a-SEPP1 pathway inhibits muscle differentiation and regeneration. This pathway may uncover some new therapeutic targets for the treatment of sarcopenia caused by aging.
Clinically adaptable polymer enables simultaneous spatial analysis of colonic tissues and biofilms
Authors: Macedonia Mary C. , Drewes Julia L. , Markham Nicholas O. , Simmons Alan J. , Roland Joseph T. , Vega Paige N. , Scurrah Cherie’ R. , Coffey Robert J. , Shrubsole Martha J. , Sears Cynthia L. , Lau Ken S. 
Published: April 2020

AbstractMicrobial influences on host cells depend upon the identities of the microbes, their spatial localization, and the responses they invoke on specific host cell populations. Multi-modal analyses of both microbes and host cells in a spatially-resolved fashion would enable studies into these complex interactions in native tissue environments, potentially in clinical specimens. While techniques to preserve each of the microbial and host cell compartments have been used to examine tissues and microbes separately, we endeavored to develop approaches to simultaneously analyze both compartments. Herein, we established an original method for mucus preservation using Poloxamer 407 (also known as Pluronic F-127), a thermoreversible polymer with mucus-adhesive characteristics. We demonstrate that this approach can preserve spatially-defined compartments of the mucus bi-layer in the colon and the bacterial communities within, compared with their marked absence when tissues were processed with traditional formalin-fixed paraffin-embedded (FFPE) pipelines. Additionally, antigens for antibody staining of host cells were preserved and signal intensity for 16S rRNA fluorescencein situhybridization (FISH) was enhanced in Poloxamer-fixed samples. This in turn enabled us to integrate multi-modal analysis using a modified multiplex immunofluorescence (MxIF) protocol. Importantly, we have formulated Poloxamer 407 to polymerize and crosslink at room temperature for use in clinical workflows. These results suggest that the fixative formulation of Poloxamer 407 can be integrated into biospecimen collection pipelines for simultaneous analysis of microbes and host cells.
High content live profiling reveals concomitant gain and loss of function pathomechanisms in C9ORF72 amyotrophic lateral sclerosis
Authors: Pal Arun , Kretner Benedikt , Abo-Rady Masin , Glaß Hannes , Naumann Marcel , Japtok Julia , Kreiter Nicole , Böckers Tobias M. , Sterneckert Jared , Hermann Andreas 
Published: April 2020

AbstractIntronic hexanucleotide repeat expansions (HREs) in C9ORF72 are the most frequent genetic cause of amyotrophic lateral sclerosis (ALS), a devastating, incurable motoneuron (MN) disease. The mechanism by which HREs trigger pathogenesis remains elusive. The discovery of repeat-associated non-ATG (RAN) translation of dipeptide repeat proteins (DPRs) from HREs along with reduced exonic C9ORF72 expression suggests gain of toxic functions (GOF) through DPRs versus loss of C9ORF72 functions (LOF). Through multiparametric HC live profiling in spinal MNs from induced pluripotent stem cells (iPSCs) and comparison to mutant FUS and TDP43, we show that HRE C9ORF72 caused a distinct, later spatiotemporal appearance of mainly proximal axonal organelle motility deficits concomitant to augmented DNA strand breaks (DSBs), DPRs and apoptosis. We show that both GOF and LOF were necessary to yield the overall C9ORF72 pathology. Finally, C9ORF72 LOF was sufficient – albeit to a smaller extent – to induce proximal axonal trafficking deficits and increased DSBs.Single sentence summaryPathogenesis in C9ORF72 ALS shows a distinct spatiotemporal axonal organelle trafficking impairment caused by gain and loss of function mechanisms.
Computational synthesis of cortical dendritic morphologies
Authors: Kanari Lida , Dictus Hugo , Chalimourda Athanassia , Van Geit Werner , Coste Benoit , Shillcock Julian , Hess Kathryn , Markram Henry 
Published: April 2020

AbstractNeuronal morphologies provide the foundation for the electrical behavior of neurons, the connectomes they form, and the dynamical properties of the brain. Comprehensive neuron models are essential for defining cell types, discerning their functional roles and investigating structural alterations associated with diseased brain states. Recently, we introduced a topological descriptor that reliably categorizes dendritic morphologies. We apply this descriptor to digitally synthesize dendrites to address the challenge of insufficient biological reconstructions. The synthesized cortical dendrites are statistically indistinguishable from the corresponding reconstructed dendrites in terms of morpho-electrical properties and connectivity. This topology-guided synthesis enables the rapid digital reconstruction of entire brain regions from relatively few reference cells, thereby allowing the investigation of links between neuronal morphologies and brain function across different spatio-temporal scales. We synthesized cortical networks based on structural alterations of dendrites associated with medical conditions and revealed principles linking branching properties to the structure of large-scale networks.Graphical abstractA topological model of neuronal shapes is used to investigate the link between the branching patterns of dendritic morphologies and the connectivity of the neuronal networks they form. Starting from reconstructed cells (in black) of cortical dendrites, we extract the topological barcode that is used to create a statistically similar synthesized pyramidal cell (in red), and respectively a group of pyramidal cells of the same morphological type. From reconstructed cells examples of all layers and morphological types we generate synthesized dendrites and build a synthesized cortical column (colors corresponds to cortical layers). The synthesized dedrites are statistically similar to the reconstructed dendrites in terms of morpho-electrical properties and the connectome of the synthesized column (colored connectome) is almost indistinguishable from the connectome of the reconstructed column (greyscale).
Classifying protein structures into folds by convolutional neural networks, distance maps, and persistent homology
Authors: Hong Yechan , Deng Yongyu , Cui Haofan , Segert Jan , Cheng Jianlin 
Published: April 2020

AbstractThe fold classification of a protein reveals valuable information about its shape and function. It is important to find a mapping between protein structures and their folds. There are numerous machine learning techniques to predict protein folds from 1-dimensional (1D) protein sequences, but there are few machine learning methods to directly class protein 3D (tertiary) structures into predefined folds (e.g. folds defined in the SCOP database). We develop a 2D-convolutional neural network to classify any protein structure into one of 1232 folds. We extract two classes of input features for each protein: residue-residue distance matrix and persistent homology images derived from 3D protein structures. Due to restrictions in computing resources, we sample every other point in the carbon alpha chain to generate a reduced distance map representation. We find that it does not lead to significant loss in accuracy. Using the distance matrix, we achieve an accuracy of 95.2% on the SCOP dataset. With persistence homology images of 100 × 100 resolution, we achieve an accuracy of 56% on SCOPe 2.07 dataset. Combining the two kinds of features further improves classification accuracy. The source code of our method (PRO3DCNN) is available athttps://github.com/jianlin-cheng/PRO3DCNN.
Deep Learning Improves Macromolecule Identification in 3D Cellular Cryo-Electron Tomograms
Authors: Moebel E. , Martinez-Sanchez A. , Lamm L. , Righetto R. , Wietrzynski W. , Albert S. , Larivière D. , Fourmentin E. , Pfeffer S. , Ortiz J. , Baumeister W. , Peng T. , Engel B.D. , Kervrann C. 
Published: April 2020

AbstractCryo-electron tomography (cryo-ET) visualizes the 3D spatial distribution of macromolecules at nanometer resolution inside native cells. While this label-free cryogenic imaging technology produces data containing rich structural information, automated identification of macromolecules inside cellular tomograms is challenged by noise and reconstruction artifacts, as well as the presence of many molecular species in the crowded volumes. Here, we present a computational procedure that uses artificial neural networks to simultaneously localize with a multi-class strategy several macromolecular species in cellular cryo-electron tomograms. Once trained, the inference stage of DeepFinder is significantly faster than template matching, and performs better than other competitive deep learning methods at identifying macromolecules of various sizes in both synthetic and experimental datasets. On cellular cryo-ET data, DeepFinder localized membrane-bound and cytosolic ribosomes (~3.2 MDa), Rubisco (~540 kDa soluble complex), and photosystem II (~550 kDa membrane complex) with comparable accuracy to expert-supervised ground truth annotations. Furthermore, we show that DeepFinder is flexible and can be combined with template matching to localize the missing macromolecules not found by one or the other method. The DeepFinder algorithm is therefore very promising for the semi-automated analysis of a wide range of molecular targets in cellular tomograms, including macromolecules with weights of 500-600 kDa and membrane proteins.
Uneven distribution of Purkinje cell injury in the cerebellar vermis of term neonates with hypoxic-ischemic encephalopathy
Authors: Annink K.V. , van Leeuwen I.C.E. , van der Aa N.E. , Alderliesten T. , Groenendaal F. , Jellema R.K. , Nijboer C.H.A. , Nikkels P.G.J. , Lammens M. , Benders M.J.N.L. , Hoebeek F.E. , Dudink J. 
Published: April 2020

ABSTRACTIntroductionIn term neonates with hypoxic-ischemic encephalopathy (HIE), cerebellar injury is becoming more and more acknowledged. Animal studies demonstrated that Purkinje cells (PCs) are especially vulnerable for hypoxic-ischemic injury. In neonates, however, the extent and pattern of PC injury has not been investigated. The aim of this study was to determine the distribution of PC injury in the cerebellar vermis of term born neonates with HIE.MethodsTerm born neonates with HIE that underwent post-mortem autopsy of the cerebellar vermis were included. Haematoxylin & Eosin (H&E) stained sections of the vermis were used to determine total PC count and morphology (normal, abnormal or non-classified) at the bases and crown of the folia and of the lobules in both the anterior and posterior lobes. Differences in PC count and PC morphology between the anterior and posterior lobe and between the bases and crown were calculated using the paired samples T-test or Wilcoxon-signed rank test.ResultsThe total number of PCs were significantly higher at the crown compared to the bases (p<0.001) irrespective of the precise location. Besides, PCs at the bases more often had an abnormal morphology. No significant difference between the total number of PCs in the anterior and posterior lobe was observed.ConclusionThe abnormal PC count and morphology in term neonates with HIE resembles supratentorial ulegyria.
Genetic variation reveals individual-level climate tracking across the annual cycle of a migratory bird
Authors: Bay Rachael A. , Karp Daniel S. , Saracco James F. , Anderegg William R.L. , Frishkoff Luke O. , Wiedenfeld David , Smith Thomas B. , Ruegg Kristen 
Published: April 2020

AbstractFor migratory species, seasonal movements complicate local climate adaptation, as it is unclear whether individuals track climate niches across the annual cycle. In the migratory songbird yellow warbler (Setophaga petechia), we find a correlation between individual-level wintering and breeding precipitation but not temperature. Birds wintering in the driest regions of the Neotropics breed in the driest regions of North America. Individuals from drier regions also possess distinct morphologies and population responses to varying rainfall. We find a positive association between bill size and breeding season precipitation which, given documented climate-associated genomic variation, might reflect adaptation to local precipitation regimes. Relative abundance in the breeding range is linked to interannual fluctuations in precipitation, but the directionality of this response varies across geography. Together, our results suggest that variation in climate optima may exist across the breeding range of yellow warblers and provide a mechanism for selection across the annual cycle.
Incremental language comprehension difficulty predicts activity in the language network but not the multiple demand network
Authors: Wehbe Leila , Blank Idan Asher , Shain Cory , Futrell Richard , Levy Roger , von der Malsburg Titus , Smith Nathaniel , Gibson Edward , Fedorenko Evelina 
Published: April 2020

AbstractWhat role do domain-general executive functions play in human language comprehension? To address this question, we examine the relationship between behavioral measures of comprehension and neural activity in the domain-general “multiple demand” (MD) network, which has been linked to constructs like attention, working memory, inhibitory control, and selection, and implicated in diverse goal-directed behaviors. Specifically, fMRI data collected during naturalistic story listening are compared to theory-neutral measures of online comprehension difficulty and incremental processing load (reading times and eye-fixation durations). Critically, to ensure that variance in these measures is driven by features of the linguistic stimulus rather than reflecting participant-or trial-level variability, the neuroimaging and behavioral datasets were collected in non-overlapping samples. We find no behavioral-neural link in functionally localized MD regions; instead, this link is found in the domain-specific, fronto-temporal “core language network”, in both left hemispheric areas and their right hemispheric homologues. These results argue against strong involvement of domain-general executive circuits in language comprehension.
Evaluating rehabilitation progress using motion features identified by machine learning
Authors: Lu Lei , Tan Ying , Klaic Marlena , Galea Mary P. , Khan Fary , Oliver Annie , Mareels Iven , Oetomo Denny , Zhao Erying 
Published: April 2020

AbstractEvaluating progress throughout a patient’s rehabilitation episode is critical for determining effectiveness of the selected treatments and contributing to the evidence-based practice. The evaluation process is complex due to the inherent large human variations in motor recovery and the limitations of commonly used clinical measurement tools. Information recorded during a robot-assisted rehabilitation process can provide an effective means to continuously quantitatively assess movement performance and rehabilitation progress. However, selecting appropriate motion features for rehabilitation evaluation has always been challenging. This paper exploits unsupervised feature learning techniques to reduce the complexity of building the evaluation model of patients’ progress. A new feature learning technique is developed to select the most significant features from a large amount of kinematic features measured from robotics, providing clinically useful information to health practitioners with reduction of modeling complexity. A novel indicator that can reflect monotonicity and trendability is proposed to evaluate the suitability of kinematic features, which are derived from the collected data of a population of stroke patients participating in robot-aided rehabilitation. The selected kinematic features allow for human variations across a population of patients as well as over the sequence of rehabilitation sessions. The study is based on data records pertaining to 41 stroke patients using three different robot assisted exercises for upper limb rehabilitation. Consistent with the literature, the results indicate that features based on movement smoothness are the best measures among 17 kinematic features used to evaluate rehabilitation progress.
Toward a single dose cure for Buruli ulcer
Authors: Thomas Sangeeta S , Kalia Nitin Pal , Ruf Marie-Thérèse , Pluschke Gerd , Pethe Kevin 
Published: April 2020

AbstractA single dose of TELACEBEC (Q203), a phase 2 clinical candidate for tuberculosis, eradicatesMycobacterium ulceransin a mouse model of Buruli ulcer infection without relapse up to 19 weeks post treatment. Clinical use of Q203 could dramatically simplify the clinical management of Buruli ulcer, a neglected mycobacterial disease.
Stability ofβ-lactam antibiotics in bacterial growth media
Authors: Brouwers Rebecca , Vass Hugh , Dawson Angela , Squires Tracey , Tavaddod Sharareh , Allen Rosalind J. 
Published: April 2020

AbstractLaboratory assays such as MIC tests assume that antibiotic molecules are stable in the chosen growth medium - but rapid degradation has been observed for antibiotics includingβ-lactams under some conditions in aqueous solution. Degradation rates in bacterial growth medium are less well known. Here, we develop a ‘delay time bioassay’ that provides a simple way to estimate antibiotic stability in bacterial growth media. We use the bioassay to measure degradation half-lives of theβ-lactam antibiotics mecillinam, aztreonam and cefotaxime in widely-used bacterial growth media based on MOPS and Luria-Bertani (LB) broth. We find that mecillinam degradation can occur rapidly, with a half-life as short as 2 hours in MOPS medium at 37°C and pH 7.4, and 4-5 hours in LB, but that adjusting the pH and temperature can increase its stability to a half-life around 6 hours without excessively perturbing growth. Aztreonam and cefotaxime were found to have half-lives longer than 6 hours in MOPS medium at 37°C and pH 7.4, but still shorter than the timescale of a typical minimum inhibitory concentration (MIC) assay. Taken together, our results suggest that care is needed in interpreting MIC tests and other laboratory growth assays forβ-lactam antibiotics, since there may be significant degradation of the antibiotic during the assay.