bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical Experience With 75-mg Dose of Erlotinib for Mutated Metastatic EGFR Non-small Cell Lung Cancer
Authors: Aren Osvaldo , Samtani Suraj , Frelinghuysen Micahel , Burotto Mauricio 
Published: September 2019

Background:
Non-small cell lung cancer (NSCLC) remains the leading cause of cancer death worldwide, although important advances in target therapy have been developed in the past few years. Erlotinib is a reversible epidermal growth factor receptor (EGFR) inhibitor, which was approved at its maximum tolerated dose of 150 mg/d determined from the initial phase I study. Studies suggest that the optimal biological dose of erlotinib should be lower and dependent on different variables.

Study Question:
We aimed to evaluate the response rates and toxicity with 75 mg/d dose of erlotinib in South American patients.

Method:
We performed a retrospective review of 18 patients with histologically proven (+) EGFR (+) mutation metastatic NSCLC (mNSCLC) treated with 75 mg/d erlotinib as starting dose.

Measures and Outcomes:
Clinical information, including toxicity grade 1–4, drug discontinuation, clinical evolution and radiological evaluation, and overall survival (OS), was revised.

Results:
Patients received 75 mg/d of erlotinib as starting dose. Sixteen (89%) patients were treated in first-line treatment and 2 (11%) in second-line treatment. Mean age was 62 years (range 36–89 years), and 50% patients were female. Sixteen percent of the patients had brain metastases at first diagnosis. All patients had mutation positive EGFR, 12 (66%) had Del19 and 6 (34%) exon 21 mutation. Median progression-free survival was 17 months and OS 23 months. The main grade 1–2 toxicities were rash (44%) and diarrhea (22%). No grade 3–4 toxicity and no cases of drug discontinuation were reported.

Conclusions:
In South American population with mutated mNSCLC, a dose of 75 mg/d of erlotinib was well tolerated. This dose resulted in comparable benefits in progression-free survival and OS when compared to those reported in the literature with the standard dose. More studies are needed to explore the use of adjusted doses of biological agents in different ethnic backgrounds.
Is autologous stem cell transplantation still relevant for multiple myeloma?
Authors: Choi Taewoong 
Published: September 2019

Purpose of review
Autologous stem cell transplantation has been the standard of care in myeloma treatment for many years, but the availability of newer antimyeloma drugs and the emerging data from chimeric antigen receptor (CAR) T-cell clinical studies make us question the relevance of it. The purpose of this review is to go over recent data and to reassess the current status of autologous stem cell transplantation as a standard of care.

Recent findings
Autologous stem cell transplantation can be safely performed for elderly patients and there is no absolute age limit. Recent data on BEAM (Carmustine, Etoposide, Cytarabine, and Melphalan), Busulfan/Melphalan, and Carmustine/Melphalan conditioning when compared with Melphalan showed favorable survival outcomes with manageable toxicities although we need to see data from randomized, multicenter studies. Posttransplant maintenance and consolidation can maximize the benefit of transplant by prolonging progression-free survival. Current B-cell maturation antigen CAR T-cell therapy showed remarkably high response rates, but didn’t seem to provide durable response yet.

Summary
Recent advances in myeloma therapy and autologous stem cell transplantation are described. Although we’ve seen many new developments including CAR T-cell therapies, autologous stem cell transplantation remains as the standard of care. However, it may be replaced by or combined with newer therapies in the future.
Milan-out Criteria and Worse Intention-to-Treat Outcome Postliver Transplantation
Authors: Herreras Julia , Di Maira Tommaso , Vinaixa Carmen , San Juan Fernando , Rubín Ángel , Berenguer Marina 
Published: September 2019

Background.
Milan criteria are widely used for liver transplantation selection in hepatocellular carcinoma but have been recognized to be too restrictive. Milan-out criteria are increasingly being adopted. Our aim was to analyze if liver transplantation waitlisted Milan-out hepatocellular carcinoma patients have different outcome than Milan patients.

Methods.
Retrospective study including all consecutive patients with hepatocellular carcinoma admitted in the waiting list for liver transplantation between January 2012 and January 2015. We included 177 patients, 146 of which eventually transplanted. Downstaging was achieved in the Milan-out cases (n = 29) before waitlisting.

Results.
From diagnosis to last follow-up, 29% patients died. Survival at 1 and 5 years from diagnosis was 93% and 75%, respectively in the within Milan group compared with 91% and 61% in the Milan-out group (P = 0.03). Treatment failure occurred in 20% of cases due to tumor progression in the waiting list (44%), death on the waiting list (20%), and hepatocellular carcinoma recurrence postliver transplantation (9%). Milan-out criteria was the only variable predictive of treatment failure remaining in the multivariate analysis with a hazard ratio (HR) of 1.7 (HR, 1.7; 95% confidence interval, 1.34-4.55; P = 0.010) and HR of 1.43 (1.23-6.5) in the hepatocellular carcinoma recurrence.

Conclusions.
Milan-out criteria are associated with a higher intention-to-treat liver transplantation failure from time of inclusion in the waiting list. However, survival rates are still >50% at 5 years of follow-up.
Outcomes of Liver Transplantation in Patients on Renal Replacement Therapy: Considerations for Simultaneous Liver Kidney Transplantation Versus Safety Net
Authors: Pita Alejandro , Kaur Navpreet , Emamaullee Juliet , Lo Mary , Nguyen Brian , Sabour Andrew , Tristan Vincent , Nadim Mitra , Genyk Yuri , Sher Linda 
Published: September 2019

Background.
As the liver transplant (LT) waiting list continues to outpace organ availability, many patients require renal replacement therapy (RRT) before LT. It is unclear which patients will benefit from simultaneous liver kidney (SLK) transplant as opposed to awaiting a Safety Net kidney transplant (KT) post-LT.

Methods.
In this study, a retrospective analysis of the United Network for Organ Sharing dataset was performed to identify risk factors associated with poor outcome for patients on RRT before LT who were listed for SLK and received either SLK vs LT alone (LTA).

Results.
Between January 2003 and December 2016, 8971 adult LT recipients were on RRT at the time of LT. 5359 were listed for and received LTA (Group 1). Of 3612 patients listed for SLK, 3414 (38.1%) received SLK (Group 2) and 198 (2.2%) received LTA (Group 3). Overall, Group 3 had lower graft and patient survival post-LT when compared with Groups 1 and 2 (P < 0.001). Serum creatinine at 1 year post-LT and cumulative incidence for KT at 3 years post-LT were higher for Group 3 (P < 0.001). On multivariate analysis, pre-LT diabetes (P = 0.002), Model of End-Stage Liver Disease score (P = 0.01), and donor kidney donor profile index (P = 0.025) were significant in Group 2. Recipient age >60 (P < 0.001) and RRT pre-LT (>90 days; P = 0.001) were associated with lower patient survival in Group 3.

Conclusions.
Among LT recipients on RRT before LT who were listed for SLK, RRT >90 days, and age >60 were associated with poor outcome following LTA. This suggests that programs should carefully weigh the decision to proceed with LTA vs waiting for SLK in this patient population. Future access to Safety Net KT will be an important consideration for these patients moving forward.
Paired Associative Stimulation Fails to Induce Plasticity in Freely Behaving Intact Rats
Authors: Ting Windsor Kwan-Chun , Huot-Lavoie Maxime , Ethier Christian 
Published: September 2019

AbstractPaired Associative Stimulation (PAS) has been explored in humans as a non-invasive tool to drive plasticity and promote recovery after neurological insult. A more thorough understanding of the phenomenon of PAS-induced plasticity is needed to fully harness it as a clinical tool. Here, we tested a PAS model in awake rats in order to study the principles of associative plasticity. Using chronically implanted electrodes in motor cortex and forelimb, we explored PAS parameters to effectively change corticomotor excitability, assessed using closed loop, EMG-controlled cortical stimulation. We tested eleven stimulation intervals, chosen to force the coincidence of neuronal activity in the rats’ motor cortex and spinal cord, with timings relevant to the principles of Hebbian spike-timing-dependent plasticity. However, despite a relatively large number of stimulus pairings (300), none of the tested intervals reliably changed corticospinal excitability relative to control conditions. Our results question PAS effectiveness under these conditions.
Direct and High-Throughput Assays for Human Cell Killing through Trogocytosis byEntamoeba histolytica
Authors: Bettadapur Akhila , Ralston Katherine S. 
Published: September 2019

AbstractEntamoeba histolyticais a microbial eukaryote and causative agent of the diarrheal disease amoebiasis. Pathogenesis is associated with profound damage to human tissues, and treatment options are limited. We discovered that amoebae attack and kill human cells through a cell-nibbling process that we named trogocytosis (trogo-:nibble). Trogocytosis is likely to underlie tissue damage during infection and it represents a potential target for therapeutic intervention, although the mechanism is still unknown. Assays in current use to analyze trogocytosis by amoebae have not been amenable to studying different types of human cells, or to high-throughput analysis. Here, we developed two complementary assays to measure trogocytosis by quantifying human cell viability, both of which can be used for suspension and adherent cells. The first assay uses CellTiterGlo, a luminescent readout for cellular ATP levels, as a proxy for cell viability. We found that the CellTiterGlo signal is proportional to the quantity of viable cells, and can be used to detect death of human cells after co-incubation with amoebae. We established a second assay that is microscopy-based and uses two fluorescent stains to directly differentiate live and dead human cells. Both assays are simple and inexpensive, can be used with suspension and adherent human cell types, and are amenable to high-throughput approaches. These new assays are tools to improve understanding of amoebiasis pathogenesis.
Deep learning-based auto-segmentation of swallowing and chewing structures
Authors: Iyer Aditi , Thor Maria , Haq Rabia , Deasy Joseph O. , Apte Aditya P. 
Published: September 2019

AbstractPurposeDelineating the swallowing and chewing structures in Head and Neck (H&N) CT scans is necessary for radiotherapy treatment (RT) planning to reduce the incidence of radiation-induced dysphagia, trismus, and speech dysfunction. Automating this process would decrease the manual input required and yield reproducible segmentations, but generating accurate segmentations is challenging due to the complex morphology of swallowing and chewing structures and limited soft tissue contrast in CT images.MethodsWe trained deep learning models using 194 H&N CT scans from our institution to segment the masseters (left and right), medial pterygoids (left and right), larynx, and pharyngeal constrictor muscle using DeepLabV3+ with the resnet-101 backbone. Models were trained in a sequential manner to guide the localization of each structure group based on prior segmentations. Additionally, an ensemble of models was developed using contextual information from three different views (axial, coronal, and sagittal), for robustness to occasional failures of the individual models. Output probability maps were averaged, and voxels were assigned labels corresponding to the class with the highest combined probability.ResultsThe median dice similarity coefficients (DSC) computed on a hold-out set of 24 CT scans were 0.87±0.02 for the masseters, 0.80±0.03 for the medial pterygoids, 0.81±0.04 for the larynx, and 0.69±0.07for the constrictor muscle. The corresponding 95th percentile Hausdorff distances were 0.32±0.08cm (masseters), 0.42±0.2cm (medial pterygoids), 0.53±0.3cm (larynx), and 0.36±0.15cm (constrictor muscle). Dose-volume histogram (DVH) metrics previously found to correlate with each toxicity were extracted from manual and auto-generated contours and compared between the two sets of contours to assess clinical utility. Differences in DVH metrics were not found to be statistically significant (p>0.05) for any of the structures. Further, inter-observer variability in contouring was studied in 10 CT scans. Automated segmentations were found to agree better with each of the observers as compared to inter-observer agreement, measured in terms of DSC.ConclusionsWe developed deep learning-based auto-segmentation models for swallowing and chewing structures in CT. The resulting segmentations can be included in treatment planning to limit complications following RT for H&N cancer. The segmentation models developed in this work are distributed for research use through the open-source platform CERR, accessible at https://github.com/cerr/CERR.
Personalized Cancer Therapy Prioritization Based on Driver Alteration Co-occurrence Patterns
Authors: Mateo Lidia , Duran-Frigola Miquel , Gris-Oliver Albert , Palafox Marta , Scaltriti Maurizio , Razavi Pedram , Chandarlapaty Sarat , Arribas Joaquin , Bellet Meritxell , Serra Violeta , Aloy Patrick 
Published: September 2019

AbstractIdentification of actionable genomic vulnerabilities is the cornerstone of precision oncology. Based on a large-scale drug screening in patient derived-xenografts, we uncover connections between driver gene alterations, derive Driver Co-Occurrence (DCO) networks, and relate these to drug sensitivity. Our collection of 53 drug response predictors attained an average balanced accuracy of 58% in a cross-validation setting, which rose to a 66% for the subset of high-confidence predictions. Morevover, we experimentally validated 12 out of 14de novopredictions in mice. Finally, we adapted our strategy to obtain drug-response models from patients’ progression free survival data. By revealing unexpected links between oncogenic alterations, our strategy can increase the clinical impact of genomic profiling.
Wood capacitance is related to water content, wood density, and anatomy across 30 temperate tree species
Authors: Ziemińska Kasia , Rosa Emily , Gleason Sean M. , Holbrook N. Michele 
Published: September 2019

SUMMARYWater released from wood tissue during transpiration (capacitance) can meaningfully affect daily water use and drought response. To provide context for better understanding of capacitance mechanisms, we investigated links between capacitance and wood anatomy. On twig wood of 30 temperate angiosperm tree species, we measured capacitance, water content, wood density, and anatomical traits, i.e., vessel dimensions, tissue fractions, and vessel-tissue contact fractions (fraction of vessel circumference in contact with other tissues). Across all species, the strongest predictors of capacitance were wood density (WD) and predawn lumen volumetric water content (VWCL-pd,r2adj=0.44,P<0.0001). Vessel-tissue contact fractions explained an additional ∼10% of the variation in capacitance. Regression models were not improved by including predawn relative water content (RWCpd) or tissue lumen fractions. Among diffuse-porous species, VWCL-pdand vessel-ray contact fraction were the best predictors of capacitance, whereas among ring/semi-ring-porous species, VWCL-pd, WD and vessel-fibre contact fraction were the best predictors. Mean RWCpdwas 0.65±0.13 and uncorrelated with WD. VWCL-pdwas weakly negatively correlated with WD. Our findings imply that capacitance depends on the amount of stored water, tissue connectivity and the bulk wood properties arising from WD (e.g., elasticity), rather than the fraction of any particular tissue.
Autoencoder-based cluster ensembles for single-cell RNA-seq data analysis
Authors: Geddes Thomas A , Kim Taiyun , Nan Lihao , Burchfield James G , Yang Jean YH , Tao Dacheng , Yang Pengyi 
Published: September 2019

AbstractBackgroundSingle-cell RNA-sequencing (scRNA-seq) is a transformative technology, allowing global transcriptomes of individual cells to be profiled with high accuracy. An essential task in scRNA-seq data analysis is the identification of cell types from complex samples or tissues profiled in an experiment. To this end, clustering has become a key computational technique for grouping cells based on their transcriptome profiles, enabling subsequent cell type identification from each cluster of cells. Due to the high feature-dimensionality of the transcriptome (i.e. the large number of measured genes in each cell) and because only a small fraction of genes are cell type-specific and therefore informative for generating cell type-specific clusters, clustering directly on the original feature/gene dimension may lead to uninformative clusters and hinder correct cell type identification.ResultsHere, we propose an autoencoder-based cluster ensemble framework in which we first take random subspace projections from the data, then compress each random projection to a low-dimensional space using an autoencoder artificial neural network, and finally apply ensemble clustering across all encoded datasets for generating clusters of cells. We employ four evaluation metrics to benchmark clustering performance and our experiments demonstrate that the proposed autoencoder-based cluster ensemble can lead to substantially improved cell type-specific clusters when applied with both the standard k-means clustering algorithm and a state-of-the-art kernel-based clustering algorithm (SIMLR) designed specifically for scRNA-seq data. Compared to directly using these clustering algorithms on the original datasets, the performance improvement in some cases is up to 100%, depending on the evaluation metrics used.ConclusionsOur results suggest that the proposed framework can facilitate more accurate cell type identification as well as other downstream analyses. The code for creating the proposed autoencoder-based cluster ensemble framework is freely available from https://github.com/gedcom/autoencoder_cluster_ensemble
Astrovirus in Reunion Free-tailed Bat (Mormopterus francoismoutoui)
Authors: Joffrin Léa , Hoarau Axel O. G. , Lagadec Erwan , Köster Marie , Ramanantsalama Riana V. , Mavingui Patrick , Lebarbenchon Camille 
Published: September 2019

AbstractAstroviruses (AstVs) are RNA viruses infecting of a large diversity of avian and mammalian species, including bats, livestock, and humans. We investigated AstV infection in a free-tailed bat species, Mormopterus francoismoutoui, endemic to Reunion Island. A total of 380 guano samples were collected in a maternity colony during 38 different sampling sessions, from June 21st 2016 to September 4th 2018. Each sample was tested for the presence of the AstV RNA-dependent RNA-polymerase (RdRp) gene using a pan-AstV semi-nested polymerase chain reaction assay. In total, 27 guano samples (7.1%) tested positive, with high genetic diversity of the partial RdRp gene sequences among positive samples. A phylogenetic analysis further revealed that the detected viruses were genetically related to AstVs reported in rats, reptiles, dogs, and pigs, but did not cluster with AstVs commonly found in bats. Although more investigations need to be conducted to assess the prevalence of infected bats in the studied population, our findings show that Reunion free-tailed bats are exposed to AstVs, and suggest that cross-species transmission may occur with other hosts sharing the same habitat.
IL-22 induced cell extrusion and IL-18-induced pyroptosis prevent and cure rotavirus infection
Authors: Zhang Zhan , Zou Jun , Shi Zhenda , Zhang Benyue , Etienne-Mesmin Lucie , Chassaing Benoit , Gewirtz Andrew T. 
Published: September 2019

SummaryAdministration of bacterial flagellin elicits production of TLR5-mediated IL-22 and NLRC4-mediated IL-18 that act in concert to cure and prevent rotavirus (RV) infection. This study investigated the mechanism by which these cytokines act to impede this virus. Although IL-18 and IL-22 induce each other’s expression, we found that IL-18 and IL-22 both impeded RV independently of each other and did so by distinct mechanisms, in both cases via activation of their cognate receptors in intestinal epithelial cells (IEC). IL-22 drove IEC proliferation and migration toward villus tips, which resulted in increased extrusion of highly differentiated IEC that serve as the site of RV replication. In contrast, IL-18 induced pyroptotic death of RV-infected IEC thus directly interrupting the RV replication cycle, resulting in spewing of incompetent virus into the intestinal lumen and causing a rapid drop in levels of RV-infected IEC. Together, these actions resulted in rapid and complete expulsion of RV, even in hosts with severely compromised immune systems. These results suggest that IL-18/22 might be a means of treating viral infections that preferentially target short-lived epithelial cells.
The effects of data adequacy and calibration size on the accuracy of presence-only species distribution models
Authors: Santika Truly , Hutchinson Michael F. , Wilson Kerrie A. 
Published: September 2019

ABSTRACTPresence-only data used to develop species distribution models are often biased towards areas that are frequently surveyed. Furthermore, the size of calibration area with respect to the area covered by the species occurrences has been shown to affect model accuracy. However, existing assessments of the effect of data inadequacy and calibration size on model accuracy have predominately been conducted using empirical studies. These studies can give ambiguous results, since the data used to train and test the model can both be biased.These limitations were addressed by applying simulated data to assess how inadequate data coverage and the size of calibration area affect the accuracy of species distribution models generated by MaxEnt and BIOCLIM. The validity of four presence-only performance measures, Contrast Validation Index (CVI), Boyce index, AUC and AUCratio, was also assessed.CVI, AUC and AUCratio ranked the accuracy of univariate models correctly according to the true importance of their defining environmental variable, a desirable property of an accuracy measure. Contrastingly, Boyce index failed to rank the accuracy of univariate models correctly and a high percentage of irrelevant variables produced models with a high Boyce index.Inadequate data coverage and increased calibration area reduced model accuracy by reducing the correct identification of the dominant environmental determinant. BIOCLIM outperformed MaxEnt models in predicting the true distribution of simulated species with a symmetric dominant response. However, MaxEnt outperformed BIOCLIM in predicting the true distribution of simulated species with skew and linear dominant responses. Despite this, the standard performance measures consistently overestimated the performance of MaxEnt models and showed them as always having higher model accuracy than the BIOCLIM models.It has been acknowledged that research should be directed towards testing and improving species distribution modelling tools, particularly how to handle the inevitable bias and scarcity of species occurrence data. Simulated data, as demonstrated here, provides a powerful approach to comprehensively test the performance of modelling tools and to disentangle the effects of data properties and modelling options on model accuracy. This may be impossible to achieve using real-world data.