bluuebunny/crossref_metadata_2025_split huggingface dataset:
A blocking monoclonal antibody reveals dimerization of intracellular domains of ALK2 associated with genetic disorders
Authors: Katagiri Takenobu , Tsukamoto Sho , Kuratani Mai , Tsuji Shinnosuke , Nakamura Kensuke , Ohte Satoshi , Kawaguchi Yoshiro , Takaishi Kiyosumi 
Published: May 2023

AbstractMutations in activin receptor-like kinase 2 (ALK2) can cause the pathological osteogenic signaling seen in some patients with fibrodysplasia ossificans progressiva and other conditions such as diffuse intrinsic pontine glioma. Here, we report that intracellular domain of wild-type ALK2 readily dimerizes in response to BMP7 binding to drive osteogenic signaling. This osteogenic signaling is pathologically triggered by heterotetramers of type II receptor kinases and ALK2 mutant forms, which form intracellular domain dimers in response to activin A binding. We develop a blocking monoclonal antibody, Rm0443, that can suppress ALK2 signaling. We solve the crystal structure of the ALK2 extracellular domain complex with a Fab fragment of Rm0443 and show that Rm0443 induces dimerization of ALK2 extracellular domains in a back-to-back orientation on the cell membrane by binding the residues H64 and F63 on opposite faces of the ligand-binding site. Rm0443 could prevent heterotopic ossification in a mouse model of fibrodysplasia ossificans progressiva that carries the human R206H pathogenic mutant.
Establishment of gastrointestinal assembloids to study the interplay between epithelial crypts and their mesenchymal niche
Authors: Lin Manqiang , Hartl Kimberly , Heuberger Julian , Beccaceci Giulia , Berger Hilmar , Li Hao , Liu Lichao , Müllerke Stefanie , Conrad Thomas , Heymann Felix , Woehler Andrew , Tacke Frank , Rajewsky Nikolaus , Sigal Michael 
Published: May 2023

AbstractThe cellular organization of gastrointestinal crypts is orchestrated by different cells of the stromal niche but available in vitro models fail to fully recapitulate the interplay between epithelium and stroma. Here, we establish a colon assembloid system comprising the epithelium and diverse stromal cell subtypes. These assembloids recapitulate the development of mature crypts resembling in vivo cellular diversity and organization, including maintenance of a stem/progenitor cell compartment in the base and their maturation into secretory/absorptive cell types. This process is supported by self-organizing stromal cells around the crypts that resemble in vivo organization, with cell types that support stem cell turnover adjacent to the stem cell compartment. Assembloids that lack BMP receptors either in epithelial or stromal cells fail to undergo proper crypt formation. Our data highlight the crucial role of bidirectional signaling between epithelium and stroma, with BMP as a central determinant of compartmentalization along the crypt axis.
A neonatal mouse model characterizes transmissibility of SARS-CoV-2 variants and reveals a role for ORF8
Authors: Rodriguez-Rodriguez Bruno A. , Ciabattoni Grace O. , Duerr Ralf , Valero-Jimenez Ana M. , Yeung Stephen T. , Crosse Keaton M. , Schinlever Austin R. , Bernard-Raichon Lucie , Rodriguez Galvan Joaquin , McGrath Marisa E. , Vashee Sanjay , Xue Yong , Loomis Cynthia A. , Khanna Kamal M. , Cadwell Ken , Desvignes Ludovic , Frieman Matthew B. , Ortigoza Mila B. , Dittmann Meike 
Published: May 2023

AbstractSmall animal models have been a challenge for the study of SARS-CoV-2 transmission, with most investigators using golden hamsters or ferrets. Mice have the advantages of low cost, wide availability, less regulatory and husbandry challenges, and the existence of a versatile reagent and genetic toolbox. However, adult mice do not robustly transmit SARS-CoV-2. Here we establish a model based on neonatal mice that allows for transmission of clinical SARS-CoV-2 isolates. We characterize tropism, respiratory tract replication and transmission of ancestral WA-1 compared to variants Alpha (B.1.1.7), Beta (B.1.351), Gamma (P.1), Delta (B.1.617.2), Omicron BA.1 and Omicron BQ.1.1. We identify inter-variant differences in timing and magnitude of infectious particle shedding from index mice, both of which shape transmission to contact mice. Furthermore, we characterize two recombinant SARS-CoV-2 lacking either the ORF6 or ORF8 host antagonists. The removal of ORF8 shifts viral replication towards the lower respiratory tract, resulting in significantly delayed and reduced transmission in our model. Our results demonstrate the potential of our neonatal mouse model to characterize viral and host determinants of SARS-CoV-2 transmission, while revealing a role for an accessory protein in this context.
Maximizing protein production by keeping cells at optimal secretory stress levels using real-time control approaches
Authors: Sosa-Carrillo Sebastián , Galez Henri , Napolitano Sara , Bertaux François , Batt Gregory 
Published: May 2023

AbstractOptimizing the production of recombinant proteins is a problem of major industrial and pharmaceutical importance. Secretion of the protein by the host cell considerably simplifies downstream purification processes. However, for many proteins, this is also the limiting production step. Current solutions involve extensive engineering of the chassis cell to facilitate protein trafficking and limit protein degradation triggered by excessive secretion-associated stress. Here, we propose instead a regulation-based strategy in which induction is dynamically adjusted to an optimal strength based on the current stress level of the cells. Using a small collection of hard-to-secrete proteins, a bioreactor-based platform with automated cytometry measurements, and a systematic assay to quantify secreted protein levels, we demonstrate that the secretion sweet spot is indicated by the appearance of a subpopulation of cells that accumulate high amounts of proteins, decrease growth, and face significant stress, that is, experience a secretion burnout. In these cells, adaptations capabilities are overwhelmed by a too strong production. Using these notions, we show for a single-chain antibody variable fragment that secretion levels can be improved by 70% by dynamically keeping the cell population at optimal stress levels using real-time closed-loop control.
Particular genomic and virulence traits associated with preterm infant-derived toxigenic Clostridium perfringens strains
Authors: Kiu Raymond , Shaw Alexander G. , Sim Kathleen , Acuna-Gonzalez Antia , Price Christopher A. , Bedwell Harley , Dreger Sally A. , Fowler Wesley J. , Cornwell Emma , Pickard Derek , Belteki Gusztav , Malsom Jennifer , Phillips Sarah , Young Gregory R. , Schofield Zoe , Alcon-Giner Cristina , Berrington Janet E. , Stewart Christopher J. , Dougan Gordon , Clarke Paul , Douce Gillian , Robinson Stephen D. , Kroll J. Simon , Hall Lindsay J. 
Published: May 2023

AbstractClostridium perfringensis an anaerobic toxin-producing bacterium associated with intestinal diseases, particularly in neonatal humans and animals. Infant gut microbiome studies have recently indicated a link betweenC. perfringensand the preterm infant disease necrotizing enterocolitis (NEC), with specific NEC cases associated with overabundantC. perfringenstermedC. perfringens-associated NEC (CPA-NEC). In the present study, we carried out whole-genome sequencing of 272C. perfringensisolates from 70 infants across 5 hospitals in the United Kingdom. In this retrospective analysis, we performed in-depth genomic analyses (virulence profiling, strain tracking and plasmid analysis) and experimentally characterized pathogenic traits of 31 strains, including 4 from CPA-NEC patients. We found that the gene encoding toxin perfringolysin O,pfoA, was largely deficient in a human-derived hypovirulent lineage, as well as certain colonization factors, in contrast to typicalpfoA-encoding virulent lineages. We determined that infant-associatedpfoA+strains caused significantly more cellular damage thanpfoA−strains in vitro, and further confirmed this virulence trait in vivo using an oral-challenge C57BL/6 murine model. These findings suggest both the importance ofpfoA+C. perfringensas a gut pathogen in preterm infants and areas for further investigation, including potential intervention and therapeutic strategies.
Synthesis of prebiotic organics from CO2 by catalysis with meteoritic and volcanic particles
Authors: Peters Sophia , Semenov Dmitry A. , Hochleitner Rupert , Trapp Oliver 
Published: May 2023

AbstractThe emergence of prebiotic organics was a mandatory step toward the origin of life. The significance of the exogenous delivery versus the in-situ synthesis from atmospheric gases is still under debate. We experimentally demonstrate that iron-rich meteoritic and volcanic particles activate and catalyse the fixation of CO2, yielding the key precursors of life-building blocks. This catalysis is robust and produces selectively aldehydes, alcohols, and hydrocarbons, independent of the redox state of the environment. It is facilitated by common minerals and tolerates a broad range of the early planetary conditions (150–300 °C, ≲ 10–50 bar, wet or dry climate). We find that up to 6 × 108 kg/year of prebiotic organics could have been synthesized by this planetary-scale process from the atmospheric CO2on Hadean Earth.
Distinguishing intentional from nonintentional actions through eeg and kinematic markers
Authors: Derchi C. C. , Mikulan E. , Mazza A. , Casarotto S. , Comanducci A. , Fecchio M. , Navarro J. , Devalle G. , Massimini M. , Sinigaglia C. 
Published: May 2023

AbstractHow can an intentional movement be distinguished from the same movement done nonintentionally? How can this distinction be drawn without asking the subject, or in patients who are unable to communicate? Here we address these questions, by focusing on blinking. This is one of the most frequent spontaneous actions in our daily life, but it can also be done intentionally. Furthermore, blinking is often spared in patients with severe brain injuries, and for some, it is the only way to report complex meanings. Using kinematic and EEG-based measures, we found that intentional and spontaneous blinking are preceded by different brain activities, even when they are indistinguishable. Unlike spontaneous ones, intentional blinks are characterized by a slow negative EEG drift, resembling the classic readiness potential. We investigated the theoretical implication of this finding in stochastic decision models as well as the practical significance of using brain-based signals to improve the discrimination between intentional and nonintentional actions. As proof of principle, we considered three brain-injured patients with rare neurological syndromes characterized by motor and communicative impairments. Although further research is needed, our results indicate that brain-based signals can offer a feasible way to infer intentionality even in absence of overt communication.
Hedgehog morphogen gradient is robust towards variations in tissue morphology in Drosophila
Authors: Pierini Giulia , Dahmann Christian 
Published: May 2023

AbstractDuring tissue development, gradients of secreted signaling molecules known as morphogens provide cells with positional information. The mechanisms underlying morphogen spreading have been widely studied, however, it remains largely unexplored whether the shape of morphogen gradients is influenced by tissue morphology. Here, we developed an analysis pipeline to quantify the distribution of proteins within a curved tissue. We applied it to the Hedgehog morphogen gradient in the Drosophila wing and eye-antennal imaginal discs, which are flat and curved tissues, respectively. Despite a different expression profile, the slope of the Hedgehog gradient was comparable between the two tissues. Moreover, inducing ectopic folds in wing imaginal discs did not affect the slope of the Hedgehog gradient. Suppressing curvature in the eye-antennal imaginal disc also did not alter the Hedgehog gradient slope but led to ectopic Hedgehog expression. In conclusion, through the development of an analysis pipeline that allows quantifying protein distribution in curved tissues, we show that the Hedgehog gradient is robust towards variations in tissue morphology.
Body height estimation from automated length measurements on standing long leg radiographs using artificial intelligence
Authors: Simon Sebastian , Fischer Barbara , Rinner Alexandra , Hummer Allan , Frank Bernhard J. H. , Mitterer Jennyfer A. , Huber Stephanie , Aichmair Alexander , Schwarz Gilbert M. , Hofstaetter Jochen G. 
Published: May 2023

AbstractArtificial-intelligence (AI) allows large-scale analyses of long-leg-radiographs (LLRs). We used this technology to derive an update for the classical regression formulae by Trotter and Gleser, which are frequently used to infer stature based on long-bone measurements. We analyzed calibrated, standing LLRs from 4200 participants taken between 2015 and 2020. Automated landmark placement was conducted using the AI-algorithm LAMA™ and the measurements were used to determine femoral, tibial and total leg-length. Linear regression equations were subsequently derived for stature estimation. The estimated regression equations have a shallower slope and larger intercept in males and females (Femur-male: slope = 2.08, intercept = 77.49; Femur-female: slope = 1.9, intercept = 79.81) compared to the formulae previously derived by Trotter and Gleser 1952 (Femur-male: slope = 2.38, intercept = 61.41; Femur-female: slope = 2.47, intercept = 54.13) and Trotter and Gleser 1958 (Femur-male: slope = 2.32, intercept = 65.53). All long-bone measurements showed a high correlation (r ≥ 0.76) with stature. The linear equations we derived tended to overestimate stature in short persons and underestimate stature in tall persons. The differences in slopes and intercepts from those published by Trotter and Gleser (1952, 1958) may result from an ongoing secular increase in stature. Our study illustrates that AI-algorithms are a promising new tool enabling large-scale measurements.
Stratification of responses to tDCS intervention in a healthy pediatric population based on resting-state EEG profiles
Authors: Dagnino Paulina Clara , Braboszcz Claire , Kroupi Eleni , Splittgerber Maike , Brauer Hannah , Dempfle Astrid , Breitling-Ziegler Carolin , Prehn-Kristensen Alexander , Krauel Kerstin , Siniatchkin Michael , Moliadze Vera , Soria-Frisch Aureli 
Published: May 2023

AbstractTranscranial Direct Current Stimulation (tDCS) is a non-invasive neuromodulation technique with a wide variety of clinical and research applications. As increasingly acknowledged, its effectiveness is subject dependent, which may lead to time consuming and cost ineffective treatment development phases. We propose the combination of electroencephalography (EEG) and unsupervised learning for the stratification and prediction of individual responses to tDCS. A randomized, sham-controlled, double-blind crossover study design was conducted within a clinical trial for the development of pediatric treatments based on tDCS. The tDCS stimulation (sham and active) was applied either in the left dorsolateral prefrontal cortex or in the right inferior frontal gyrus. Following the stimulation session, participants performed 3 cognitive tasks to assess the response to the intervention: the Flanker Task, N-Back Task and Continuous Performance Test (CPT). We used data from 56 healthy children and adolescents to implement an unsupervised clustering approach that stratify participants based on their resting-state EEG spectral features before the tDCS intervention. We then applied a correlational analysis to characterize the clusters of EEG profiles in terms of participant’s difference in the behavioral outcome (accuracy and response time) of the cognitive tasks when performed after a tDCS-sham or a tDCS-active session. Better behavioral performance following the active tDCS session compared to the sham tDCS session is considered a positive intervention response, whilst the reverse is considered a negative one. Optimal results in terms of validity measures was obtained for 4 clusters. These results show that specific EEG-based digital phenotypes can be associated to particular responses. While one cluster presents neurotypical EEG activity, the remaining clusters present non-typical EEG characteristics, which seem to be associated with a positive response. Findings suggest that unsupervised machine learning can be successfully used to stratify and eventually predict responses of individuals to a tDCS treatment.
Optical forces in heat-assisted magnetic recording head-disk interface
Authors: Tom Roshan Mathew , Smith Robert , Ruiz Oscar , Dai Qing , Bogy David B. 
Published: May 2023

AbstractA main challenge in Heat-Assisted Magnetic Recording technology is the build-up of contaminants called smear on the near field transducer. In this paper, we investigate the role of optical forces originating from the electric field gradient in the formation of smear. First, based on suitable theoretical approximations, we compare this force with air drag and the thermophoretic force in the head-disk interface for two smear nanoparticle shapes. Then, we evaluate the force field’s sensitivity to the relevant parameter space. We find that the smear nanoparticle’s refractive index, shape, and volume significantly impact the optical force. Further, our simulations reveal that the interface conditions, such as spacing and the presence of other contaminants, also influence the magnitude of the force.
Development and validation of a CT radiomics and clinical feature model to predict omental metastases for locally advanced gastric cancer
Authors: Wu Ahao , Wu Changlei , Zeng Qingwen , Cao Yi , Shu Xufeng , Luo Lianghua , Feng Zongfeng , Tu Yi , Jie Zhigang , Zhu Yanyan , Zhou Fuqing , Huang Ya , Li Zhengrong 
Published: May 2023

Abstract“”We employed radiomics and clinical features to develop and validate a preoperative prediction model to estimate the omental metastases status of locally advanced gastric cancer (LAGC). A total of 460 patients (training cohort, n = 250; test cohort, n = 106; validation cohort, n = 104) with LAGC who were confirmed T3/T4 stage by postoperative pathology were continuously collected retrospectively, including clinical data and preoperative arterial phase computed tomography images (APCT). Dedicated radiomics prototype software was used to segment the lesions and extract features from the preoperative APCT images. The least absolute shrinkage and selection operator (LASSO) regression was used to select the extracted radiomics features, and a radiomics score model was constructed. Finally, a prediction model of omental metastases status and a nomogram were constructed combining the radiomics scores and selected clinical features. An area under the curve (AUC) of the receiver operating characteristic curve (ROC) was used to validate the capability of the prediction model and nomogram in the training cohort. Calibration curves and decision curve analysis (DCA) were used to evaluate the prediction model and nomogram. The prediction model was internally validated by the test cohort. In addition, 104 patients from another hospital's clinical and imaging data were gathered for external validation. In the training cohort, the combined prediction (CP) model (AUC 0.871, 95% CI 0.798–0.945) of the radiomics scores combined with the clinical features, compared with clinical features prediction (CFP) model (AUC 0.795, 95% CI 0.710–0.879) and radiomics scores prediction (RSP) model (AUC 0.805, 95% CI 0.730–0.879), had the better predictive ability. The Hosmer–Lemeshow test of the CP model showed that the prediction model did not deviate from the perfect fitting (p = 0.893). In the DCA, the clinical net benefit of the CP model was higher than that of the CFP model and RSP model. In the test and validation cohorts, the AUC values of the CP model were 0.836 (95% CI 0.726–0.945) and 0.779 (95% CI 0.634–0.923), respectively. The preoperative APCT-based clinical-radiomics nomogram showed good performance in predicting omental metastases status in LAGC, which may contribute to clinical decision-making.
Automated screening of potential organ donors using a temporal machine learning model
Authors: Sauthier Nicolas , Bouchakri Rima , Carrier François Martin , Sauthier Michaël , Mullie Louis-Antoine , Cardinal Héloïse , Fortin Marie-Chantal , Lahrichi Nadia , Chassé Michaël 
Published: May 2023

AbstractOrgan donation is not meeting demand, and yet 30–60% of potential donors are potentially not identified. Current systems rely on manual identification and referral to an Organ Donation Organization (ODO). We hypothesized that developing an automated screening system based on machine learning could reduce the proportion of missed potentially eligible organ donors. Using routine clinical data and laboratory time-series, we retrospectively developed and tested a neural network model to automatically identify potential organ donors. We first trained a convolutive autoencoder that learned from the longitudinal changes of over 100 types of laboratory results. We then added a deep neural network classifier. This model was compared to a simpler logistic regression model. We observed an AUROC of 0.966 (CI 0.949–0.981) for the neural network and 0.940 (0.908–0.969) for the logistic regression model. At a prespecified cutoff, sensitivity and specificity were similar between both models at 84% and 93%. Accuracy of the neural network model was robust across donor subgroups and remained stable in a prospective simulation, while the logistic regression model performance declined when applied to rarer subgroups and in the prospective simulation. Our findings support using machine learning models to help with the identification of potential organ donors using routinely collected clinical and laboratory data.