bluuebunny/crossref_metadata_2025_split huggingface dataset:
Evaluating denoising strategies in resting-state fMRI in traumatic brain injury (EpiBioS4Rx)
Authors: Weiler Marina , Casseb Raphael F. , de Campos Brunno M. , Crone Julia S. , Lutkenhoff Evan S. , Vespa Paul M. , Monti Martin M. ,   for the EpiBioS4Rx Study Group
Published: December 2021

ABSTRACTObjectiveResting-state functional MRI is increasingly used in the clinical setting and is now included in some diagnostic guidelines for severe brain injury patients. However, to ensure high-quality data, one should mitigate fMRI-related noise typical of this population. Therefore, we aimed to evaluate the ability of different preprocessing strategies to mitigate noise-related signal (i.e., in-scanner movement and physiological noise) in functional connectivity of traumatic brain injury patients.MethodsWe applied nine commonly used denoising strategies, combined into 17 pipelines, to 88 traumatic brain injury patients from the Epilepsy Bioinformatics Study for Anti-epileptogenic Therapy clinical trial (EpiBioS4Rx). Pipelines were evaluated by three quality control metrics across three exclusion regimes based on the participant’s head movement profile.ResultsWhile no pipeline eliminated noise effects on functional connectivity, some pipelines exhibited relatively high effectiveness depending on the exclusion regime. Once high-motion participants were excluded, the choice of denoising pipeline becomes secondary - although this strategy leads to substantial data loss. Pipelines combining spike regression with physiological regressors were the best performers, whereas pipelines that used automated data driven methods performed comparatively worse.ConclusionIn this study, we report the first large-scale evaluation of denoising pipelines aimed at reducing noise-related functional connectivity in a clinical population known to be highly susceptible to in-scanner motion and significant anatomical abnormalities. If resting-state functional magnetic resonance is to be a successful clinical technique, it is crucial that procedures mitigating the effect of noise be systematically evaluated in the most challenging populations, such as traumatic brain injury datasets.
Discrete Protein Metric (DPM): A new image similarity metric to calculate accuracy of deep learning-generated cell focal adhesion predictions
Authors: Contreras Miguel , Bachman William , Long David S. 
Published: December 2021

AbstractUnderstanding cell behaviors can provide new knowledge on the development of different pathologies. Focal adhesion (FA) sites are important sub-cellular structures that are involved in these processes. To better facilitate the study of FA sites, deep learning (DL) can be used to predict FA site morphology based on limited datasets (e.g., cell membrane images). However, calculating the accuracy score of these predictions can be challenging due to the discrete/point pattern like nature of FA sites. In the present work, a new image similarity metric, discrete protein metric (DPM), was developed to calculate FA prediction accuracy. This metric measures differences in distribution (d), shape/size (s), and angle (a) of FA sites between the predicted image and its ground truth image. Performance of the DPM was evaluated by comparing it to three other commonly used image similarity metrics: Pearson correlation coefficient (PCC), feature similarity index (FSIM), and Intersection over Union (IoU). A sensitivity analysis was performed by comparing changes in each metric value due to quantifiable changes in FA site location, number, aspect ratio, area, or orientation. Furthermore, accuracy score of DL-generated predictions was calculated using all four metrics to compare their ability to capture variation across samples. Results showed better sensitivity and range of variation for DPM compared to the other metrics tested. Most importantly, DPM had the ability to determine which FA predictions were quantitatively more accurate and consistent with qualitative assessments. The proposed DPM hence provides a method to validate DL-generated FA predictions and can be extended to evaluating other predicted or segmented discrete structures of biomedical relevance.
Developing A Deep Learning Natural Language Processing Algorithm For Automated Reporting Of Adverse Drug Reactions
Authors: McMaster Christopher , Chan Julia , Liew David FL , Su Elizabeth , Frauman Albert G , Chapman Wendy W , Pires Douglas EV 
Published: December 2021

AbstractThe detection of adverse drug reactions (ADRs) is critical to our understanding of the safety and risk-benefit profile of medications. With an incidence that has not changed over the last 30 years, ADRs are a significant source of patient morbidity, responsible for 5-10% of acute care hospital admissions worldwide. Spontaneous reporting of ADRs has long been the standard method of reporting, however this approach is known to have high rates of under-reporting, a problem that limits pharmacovigilance efforts. Automated ADR reporting presents an alternative pathway to increase reporting rates, although this may be limited by over-reporting of other drug-related adverse events.We developed a deep learning natural language processing algorithm to identify ADRs in discharge summaries at a single academic hospital centre. Our model was developed in two stages: first, a pre-trained model (DeBERTa) was further pre-trained on 1.1 million unlabelled clinical documents; secondly, this model was fine-tuned to detect ADR mentions in a corpus of 861 annotated discharge summaries. This model was compared to a version without the pre-training step, and a model finetuned from the ClinicalBERT model, which has demonstrated state-of-the-art performance on other pharmacovigilance tasks. To ensure that our algorithm could differentiate ADRs from other drug-related adverse events, the annotated corpus was enriched for both validated ADR reports and confounding drug-related adverse events using. The final model demonstrated good performance with a ROC-AUC of 0.955 (95% CI 0.946 - 0.963) for the task of identifying discharge summaries containing ADR mentions, significantly outperforming the two comparator models.
Machine learning based preoperative analytics for the prediction of anastomotic insufficiency in colorectal surgery: a single-centre pilot study
Authors: Taha-Mehlitz Stephanie , Wentzler Larissa , Angehrn Fiorenzo , Hendie Ahmad , Ochs Vincent , Staartjes Victor E. , Flüe Markus von , Taha Anas , Steinemann Daniel 
Published: December 2021

AbstractIntroductionAnastomotic insufficiency (AI) is a relatively common but grave complication after colorectal surgery. This study aims to determine whether AI can be predicted from simple preoperative data using machine learning (ML) algorithms.Methods and analysisIn this retrospective analysis, patients undergoing colorectal surgery with creation of a bowel anastomosis from the University Hospital of Basel were included. Data was split into a training set (80%) and a test set (20%). The group of patients with AI was oversampled to a ratio of 50:50 in the training set and missing values were imputed. Known predictors of AI were included as inputs: age, BMI, smoking status, the Charlson Comorbidity Index, the American Society of Anesthesiologists score, type of operation, indication, haemoglobin and albumin levels, and renal function.ResultsOf the 593 included patients, 88 experienced AI. At internal validation on unseen patients from the test set, area under the curve (AUC) was 0.61 (95% confidence interval [CI]: 0.44-0.79), calibration slope was 0.16 (95% CI: −0.06-0.39) and calibration intercept was 0.06 (95% CI: 0.02-0.11). We observed a specificity of 0.67 (95% CI: 0.58-0.76), sensitivity of 0.36 (95% CI: 0.08-0.67), and accuracy of 0.64 (95% CI: 0.55-0.72).ConclusionBy using 10 patient-related risk factors associated with AI, we demonstrate the feasibility of ML-based prediction of AI after colorectal surgery. Nevertheless, it is crucial to include multicenter data and higher sample sizes to develop a robust and generalisable model, which will subsequently allow for deployment of the algorithm in a web-based application.Strengths and limitations of this studyTo the best of our knowledge, this is the first study to establish a risk prediction model for anastomotic insufficiency in a perioperative setting in colon surgery.Data from all patients that underwent colon surgery within 8 years at University Hospital Basel were included.We evaluated the feasibility of developing a machine learning model that predicts the outcome by using well-known risk factors for anastomotic insufficiency.Although our model showed promising results, it is crucial to validate our findings externally before clinical practice implications are possible.
Reliability of transcranial magnetic stimulation evoked potentials to detect the effects of theta-burst stimulation of the prefrontal cortex
Authors: Moffa Adriano H. , Nikolin Stevan , Martin Donel , Loo Colleen , Boonstra Tjeerd W. 
Published: December 2021

AbstractBackgroundTranscranial magnetic stimulation (TMS) with simultaneous electroencephalography (EEG) is a novel method for assessing cortical properties outside the motor region. Theta burst stimulation (TBS), a form of repetitive TMS, can non-invasively modulate cortical excitability and has been increasingly used to treat psychiatric disorders by targetting the dorsolateral prefrontal cortex (DLPFC). The TMS-evoked potentials (TEPs) analysis has been used to evaluate cortical excitability changes after TBS. However, it remains unclear whether TEPs can detect the neuromodulatory effects of TBS.ObjectivesTo confirm the reliability of TEP components within and between sessions and to measure changes in neural excitability induced by intermittent (iTBS) and continuous TBS (cTBS) applied to the left DLPFC.MethodsTest-retest reliability of TEPs and TBS-induced changes in cortical excitability were assessed in twenty-four healthy participants by stimulating the DLPFC in five separate sessions, once with sham and twice with iTBS and cTBS. EEG responses were recorded of 100 single TMS pulses before and after TBS, and the reproducibility measures were quantified with the concordance correlation coefficient (CCC).ResultsThe N100 and P200 components presented substantial reliability within the baseline block (CCCs>0.8) and moderate concordance between sessions (CCCmax≈0.7). Both N40 and P60 TEP amplitudes showed little concordance between sessions. Changes in TEP amplitudes after iTBS were marginally reliable for N100 (CCCmax=0.52), P200 (CCCmax=0.47) and P60 (CCCmax=0.40), presenting only fair levels of concordance at specific time points.ConclusionsThe present findings show that only the N100 and P200 components had good concordance between sessions. The reliability of earlier components may have been affected by TMS-evoked artefacts. The poor reliability to detect changes in neural excitability induced by TBS indicates that TEPs do not provide a precise estimate of the changes in excitability in the DLPFC or, alternatively, that TBS did not induce consistent changes in neural excitability.
Prolonged XPO1 inhibition is essential for optimal anti-leukemic activity in NPM1-mutated AML
Authors: Pianigiani Giulia , Gagliardi Andrea , Mezzasoma Federica , Rocchio Francesca , Tini Valentina , Bigerna Barbara , Sportoletti Paolo , Caruso Simona , Marra Andrea , Spinozzi Giulio , Shacham Sharon , Landesman Yosef , Quintarelli Concetta , Locatelli Franco , Martelli Maria Paola , Falini Brunangelo , Brunetti Lorenzo 
Published: December 2021

AbstractNPM1 encodes for a nucleolar multifunctional protein and is the most frequently mutated gene in adult acute myeloid leukemia (AML). NPM1 mutations cause the aberrant accumulation of mutant NPM1 (NPM1c) in the cytoplasm of leukemic cells, that is mediated by the nuclear exporter Exportin-1 (XPO1). Recent work has demonstrated that the interaction between NPM1c and XPO1 promotes high homeobox (HOX) genes expression, which is critical for maintaining the leukemic state of NPM1-mutated cells. However, the XPO1 inhibitor Selinexor administered once or twice/week in early-phase clinical trials did not translate into clinical benefit for NPM1-mutated AML patients. Here, we demonstrate that this dosing strategy results in only temporary disruption of the XPO1-NPM1c interaction and transient HOX genes downregulation, limiting the efficacy of Selinexor in the context of NPM1-mutated AML. Since second-generation XPO1 inhibitors can be administered more frequently, we compared intermittent (twice/week) versus prolonged (5 days/week) XPO1 inhibition in NPM1-mutated AML models. Integrating in vitro and in vivo data, we show that only prolonged XPO1 inhibition results in stable HOX downregulation, cell differentiation and remarkable anti-leukemic activity. This study lays the groundwork for the accurate design of clinical trials with second-generation XPO1 inhibitors in NPM1-mutated AML.
Peptide Aggregation Induced Immunogenic Rupture (PAIIR)
Authors: Gunay Gokhan , Hamsici Seren , Lang Gillian A. , Lang Mark L. , Kovats Susan , Acar Handan 
Published: December 2021

AbstractUnder the influence of stress and membrane damage, cells undergo immunogenic cell death (ICD), which involves the release of damage associated molecular patterns (DAMPs), natural adjuvants for enhancing an immune response. In the presence of an antigen, released DAMPs can determine the type and magnitude of the immune response, and therefore the longevity and efficacy of an antigen-specific immunity. In the last decade, the immune response effect of ICD has been shown, yet there is no tool that can induce controlled ICD with predictable results, regardless of the cell type. We designed a peptide-based tool, called [II], for controlled damage to cell membrane to induce ICD and DAMPs release. Herein we describe a series of experiments that determine that the mechanism of action of [II] includes a caspase-dependent ICD and subsequent release of immune stimulating DAMPs, on various cell types. Moreover, we tested the hypothesis that controlled DAMP release via [II] in vivo was associated with enhancement of antigen-specific adaptive immunity with influenza hemagglutinin (HA) subunit vaccine. HA and [II] showed significantly higher HA specific IgG1 and IgG2a antibodies, compared to HA-only immunized mice, while the peptide itself did not elicit antibodies. In this paper, we demonstrate the first peptide-aggregation induced immunogenic rupture (PAIIR) approach as vaccine adjuvants for increasing both humoral and cellular immunity. In consideration of its ability to enhance IgG2a responses that are associated with heterosubtypic influenza virus protection, PAIIR is a promising adjuvant to promote universal protection upon influenza HA vaccination.
Sensitizing solid tumors to CAR-mediated cytotoxicity using synthetic antigens
Authors: Gamboa Lena , Zamat Ali H. , Vanover Daryll , Thiveaud Chloé A. , Peck Hannah E. , Phuengkham Hathaichanok , Sivakumar Anirudh , Harris Adrian M. , Dahotre Shreyas N. , Su Fang-Yi , Santangelo Philip J. , Kwong Gabriel A. 
Published: December 2021

AbstractCAR T cell immunotherapy relies on CAR targeting of tumor-associated antigens, yet heterogenous antigen expression, interpatient variation, and off-tumor expression by healthy cells remain barriers. Here, we develop synthetic antigens to sensitize solid tumors for recognition and elimination by CAR T cells. Unlike tumor-associated antigens, we design synthetic antigens that are orthogonal to endogenous proteins to eliminate off-tumor targeting and that have a small genetic footprint to facilitate efficient tumor delivery to tumors by viral vectors. Using the RSV-F camelid single-domain antibody (VHH) as a synthetic antigen, we show that adoptive transfer of αVHH CAR T cells to mice bearing VHH expressing tumors reduced tumor burden in multiple syngeneic mouse models of cancer, improved survival, induced epitope spread, and protected against tumor rechallenge. Our work supportsin situdelivery of synthetic antigens to treat antigen low or negative tumors with CAR T cells.
Plasma neutralization properties of the SARS-CoV-2 Omicron variant
Authors: Schmidt Fabian , Muecksch Frauke , Weisblum Yiska , Silva Justin Da , Bednarski Eva , Cho Alice , Wang Zijun , Gaebler Christian , Caskey Marina , Nussenzweig Michel C. , Hatziioannou Theodora , Bieniasz Paul D. 
Published: December 2021

AbstractBACKGROUNDThe Omicron SARS-CoV-2 variant has spread internationally and is responsible for rapidly increasing case numbers. The emergence of divergent variants in the context of a heterogeneous and evolving neutralizing antibody response in host populations might compromise protection afforded by vaccines or prior infection.METHODSWe measured neutralizing antibody titers in 169 longitudinally collected plasma samples using pseudotypes bearing the Wuhan-hu-1 or the Omicron variant or a laboratory-designed neutralization-resistant SARS-CoV-2 spike (PMS20). Plasmas were obtained from convalescents who did or did not subsequently receive an mRNA vaccine, or naive individuals who received 3-doses of mRNA or 1-dose Ad26 vaccines. Samples were collected approximately 1, 5-6 and 12 months after initial vaccination or infection.RESULTSLike PMS20, the Omicron spike protein was substantially resistant to neutralization compared to Wuhan-hu-1. In convalescent plasma the median deficit in neutralizing activity against PMS20 or Omicron was 30- to 60-fold. Plasmas from recipients of 2 mRNA vaccine doses were 30- to 180- fold less potent against PMS20 and Omicron than Wuhan-hu-1. Notably, previously infected or two-mRNA dose vaccinated individuals who received additional mRNA vaccine dose(s) had 38 to 154-fold and 35 to 214-fold increases in neutralizing activity against Omicron and PMS20 respectively.CONCLUSIONSOmicron exhibits similar distribution of sequence changes and neutralization resistance as does a laboratory-designed neutralization-resistant spike protein, suggesting natural evolutionary pressure to evade the human antibody response. Currently available mRNA vaccine boosters, that may promote antibody affinity maturation, significantly ameliorate SARS-CoV-2 neutralizing antibody titers.
Landscape of Epithelial Mesenchymal Plasticity as an emergent property of coordinated teams in regulatory networks
Authors: Hari Kishore , Ullanat Varun , Balasubramanian Archana , Gopalan Aditi , Jolly Mohit Kumar 
Published: December 2021

AbstractElucidating the design principles of regulatory networks driving cellular decision-making has fundamental implications in mapping and eventually controlling cell-fate decisions. Despite being complex, these regulatory networks often only give rise to a few phenotypes. Previously, we identified two “teams” of nodes in a small cell lung cancer regulatory network that constrained the phenotypic repertoire and aligned strongly with the dominant phenotypes obtained from network simulations (Chauhan et al., 2021). However, it remained elusive whether these “teams” exist in other networks, and how do they shape the phenotypic landscape. Here, we demonstrate that five different networks of varying sizes governing epithelial-mesenchymal plasticity comprised of two “teams” of players – one comprised of canonical drivers of epithelial phenotype and the other containing the mesenchymal inducers. These “teams” are specific to the topology of these regulatory networks and orchestrate a bimodal phenotypic landscape with the epithelial and mesenchymal phenotypes being more frequent and dynamically robust to perturbations, relative to the intermediary/hybrid epithelial/ mesenchymal ones. Our analysis reveals that network topology alone can contain information about corresponding phenotypic distributions, thus obviating the need to simulate them. We propose “teams” of nodes as a network design principle that can drive cell-fate canalization in diverse decision-making processes.
Characterization of individual HIV-1 budding event using ultra-fast atomic force microscopy reveals a multiplexed role for VPS4
Authors: Harel Shimon , Altaras Yarin , Nachmias Dikla , Rotem-Dai Noa , Segal Inbar , Elia Natalie , Rousso Itay 
Published: December 2021

AbstractThe assembly and budding of newly formed human immunodeficiency virus-1 (HIV-1) particles occur at the plasma membrane of infected cells. Whereas the molecular basis for viral budding has been studied extensively, investigation of its spatiotemporal characteristics has been limited by the small dimensions (< 100 nm) of HIV particles and the fast kinetics of the process (a few minutes from bud formation to virion release). Here we applied ultra-fast atomic force microscopy to achieve real-time visualization of individual HIV-1 budding events from wildtype (WT) cell lines as well as from mutated lines lacking vacuolar protein sorting-4 (VPS4) or visceral adipose tissue-1 protein (VTA1). Using single-particle analysis, we show that HIV-1 bud formation follows two kinetic pathways (fast and slow) with each composed of three distinct phases (growth, stationary, decay). Notably, approximately 30% of events did not result in viral release and were characterized by the formation of short (rather than tall) particles that slowly decayed back into the cell membrane. These non-productive events became more abundant in VPS4 knockout cell lines. Strikingly, the absence of VPS4B, rather than VPS4A, increased the production of short viral particles, suggesting a role for VPS4B in earlier stages of HIV-1 budding than traditionally thought.
NFE2L1-mediated proteasome function protects from ferroptosis
Authors: Kotschi Stefan , Jung Anna , Willemsen Nienke , Ofoghi Anahita , Proneth Bettina , Conrad Marcus , Bartelt Alexander 
Published: December 2021

AbstractObjectiveFerroptosis continues to emerge as a novel modality of cell death with important therapeutic implications for a variety of diseases, most notably cancer and degenerative diseases. While susceptibility, initiation, and execution of ferroptosis have been linked to reprogramming of cellular lipid metabolism, imbalances in iron-redox homeostasis, and aberrant mitochondrial respiration, the detailed mechanisms of ferroptosis are still insufficiently well understood.Methods and ResultsHere we show that diminished proteasome function is a new mechanistic feature of ferroptosis. The transcription factor nuclear factor erythroid-2, like-1 (NFE2L1) protects from ferroptosis by sustaining proteasomal activity. In cellular systems, loss of NFE2L1 reduced cellular viability after the induction of both chemically and genetically induced ferroptosis, which was linked to the regulation of proteasomal activity under these conditions. Importantly, this was reproduced in a Sedaghatian-type Spondylometaphyseal Dysplasia (SSMD) patient-derived cell line carrying mutated glutathione peroxidase-4 (GPX4), a critical regulator of ferroptosis. Also, reduced proteasomal activity was associated with ferroptosis in Gpx4-deficient mice. In a mouse model for genetic Nfe2l1 deficiency, we observed brown adipose tissue (BAT) involution, hyperubiquitination of ferroptosis regulators, including the GPX4 pathway, and other hallmarks of ferroptosis.ConclusionOur data highlight the relevance of the NFE2L1-proteasome pathway in ferroptosis. Manipulation of NFE2L1 activity might enhance ferroptosis-inducing cancer therapies as well as protect from aberrant ferroptosis in neurodegeneration, general metabolism, and beyond.Graphical abstractHighlightsProteasome function is diminished during ferroptosisNFE2L1-mediated proteasomal activity protects from ferroptosisThe ubiquitination of the GPX4-glutathione pathway is implicated in Nfe2l1 deficiencyNFE2L1 deficiency in brown fat is associated with hallmarks of ferroptosis