bluuebunny/crossref_metadata_2025_split huggingface dataset:
The Diagnostic Efficacy of an App-based Diagnostic Health Care Application in the Emergency Room: eRadaR-Trial. A prospective, Double-blinded, Observational Study
Authors: Faqar-Uz-Zaman Sara F. , Anantharajah Luxia , Baumartz Philipp , Sobotta Paula , Filmann Natalie , Zmuc Dora , von Wagner Michael , Detemble Charlotte , Sliwinski Svenja , Marschall Ursula , Bechstein Wolf O. , Schnitzbauer Andreas A. 
Published: August 2022

Objective:
To evaluate the diagnostic accuracy of the app-based diagnostic tool Ada and the impact on patient outcome in the emergency room (ER).

Background:
Artificial intelligence-based diagnostic tools can improve targeted processes in health care delivery by integrating patient information with a medical knowledge base and a machine learning system, providing clinicians with differential diagnoses and recommendations.

Methods:
Patients presenting to the ER with abdominal pain self-assessed their symptoms using the Ada-App under supervision and were subsequently assessed by the ER physician. Diagnostic accuracy was evaluated by comparing the App-diagnoses with the final discharge diagnoses. Timing of diagnosis and time to treatment were correlated with complications, overall survival, and length of hospital stay.

Results:
In this prospective, double-blinded study, 450 patients were enrolled and followed up until day 90. Ada suggested the final discharge diagnosis in 52.0% (95% CI [0.47, 0.57]) of patients compared with the classic doctor-patient interaction, which was significantly superior with 80.9% (95% CI [0.77, 0.84], P<0.001). However, when diagnostic accuracy of both were assessed together, Ada significantly increased the accuracy rate (87.3%, P<0.001), when compared with the ER physician alone. Patients with an early time point of diagnosis and rapid treatment allocation exhibited significantly reduced complications (P<0.001) and length of hospital stay (P<0.001).

Conclusion:
Currently, the classic patient-physician interaction is superior to an AI-based diagnostic tool applied by patients. However, AI tools have the potential to additionally benefit the diagnostic efficacy of clinicians and improve quality of care.
Palliative care delivery changes during COVID-19 and enduring implications in oncology nursing: a rapid review
Authors: Levoy Kristin , Foxwell Anessa , Rosa William E. 
Published: August 2022

Purpose of review
A rapid review was conducted to synthesize evidence of palliative care delivery changes during the COVID-19 pandemic. Changes were synthesized according to the eight domains of high-quality palliative care and enduring implications for oncology nurses beyond the pandemic discussed.

Recent findings
The most significant changes occurred in the structure and processes of palliative care (Domain 1), where increased utilization of telehealth was critical in circumventing barriers imposed by COVID-19 mitigation. The suboptimal availability of community-based psychosocial supports for patients and caregivers and inadequate health system-based psychosocial supports for healthcare providers were highlighted (Domains 3–5). The pandemic also ushered in an increased emphasis on the need for advance care planning (ACP), where integrating its delivery earlier in the outpatient setting and shifting policy to promote subsequent virtual documentation (Domain 8) were essential to ensure care preferences were clarified and accessible before health crises occurred.

Summary
Continuing to embrace and sustain systems-level changes with respect to telehealth, psychosocial supports, and ACP are critical to bridging gaps in palliative care delivery underscored by the pandemic. Oncology nurses are well positioned to fill these gaps in care beyond the pandemic by providing evidence-based, palliative care throughout the cancer continuum.
Graph Theory Analysis of Semantic Fluency in Russian–English Bilinguals
Authors: Sinha Vidushi , Lissemore Frances , Lerner Alan J. 
Published: August 2022

Background:
Semantic category fluency is a widely used task involving language, memory, and executive function. Previous studies of bilingual semantic fluency have shown only small differences between languages. Graph theory analyzes complex relationships in networks, including node and edge number, clustering coefficient, average path length, average number of direct neighbors, and scale-free and small-world properties.

Objective:
To shed light on whether the underlying neural processes involved in semantic category fluency testing yield substantially different networks in different languages.

Method:
We compared languages and methods using both network analysis and conventional analysis of word production. We administered the animal naming task to 51 Russian–English bilinguals in each language. We constructed network graphs using three methods: (a) simple association of unique co-occurring neighbors, (b) corrected associations between consecutive words occurring beyond chance, and (c) a network community approach using planar maximally filtered graphs. We compared the resultant network analytics as well as their scale-free and small-world properties.

Results:
Participants produced more words in Russian than in English. Small-worldness metrics were variable between Russian and English but were consistent across the three graph theory analytical methods.

Conclusion:
The networks had similar graph theory properties in both languages. The optimal methodology for creating networks from semantic category fluency remains to be determined.
Genome scan of landrace populations of the self-fertilizing crop species rice, collected across time, revealed climate changes’ selective footprints in the genes network regulating flowering time
Authors: Ahmadi Nour , Barry Mamadou Billo , Frouin Julien , de Navascués Miguel , Toure Mamadou Aminata 
Published: August 2022

AbstractAnalysis of the genetic bases of adaptation to climate changes are often conducted on natural populations. We report here on a study based on diachronic sampling (1980 & 2010) of the self-fertilising crop species, Oryza sativa (Asian rice) and Oryza glaberrima (African rice), in the tropical forest and the Sudanian savannah of West Africa. First, using historical meteorological data we confirmed changes in temperatures (+1°C on average) and rainfall regime (less predictable and reduced amount) in the target area. Second, phenotyping the populations for phenology, we observed significantly earlier heading time (up to 10 days) in the 2010 samples. Third, we implemented two genome-scan methods, one of which specially developed for selfing species, and detected 31 independent selection footprints. These loci showed significant enrichment in genes involved in reproductive processes and bore known heading time QTLs and genes, including OsGI, Hd1 and OsphyB. This rapid adaptive evolution, originated from subtle changes in the standing variation in genetic network regulating heading time, did not translate into predominance of multilocus genotypes, as it is often the case in selfing plants, and into notable selective sweeps. We argue that this high adaptive potential results from the multiline genetic structure of the rice landraces, and the rather large and imbricated genetic diversity of the rice meta-population at the farm, the village and the region levels, that hosted the adaptive variants in multiple genetic backgrounds well before the advent of the environmental selective pressure. The complex selection footprints observed in this empirical study calls for further model development on genetic bases of plant adaptation to environmental changes.
Genetically encoded photocatalytic protein labeling enables spatially-resolved profiling of intracellular proteome
Authors: Zheng Fu , Yu Chenxin , Zhou Xinyue , Zou Peng 
Published: August 2022

ABSTRACTMapping the subcellular organization of proteins is crucial for understanding their biological functions. Herein, we report a reactive oxygen species induced protein labeling and identification (RinID) method for profiling subcellular proteome in the context of living cells. Our method capitalizes on a genetically encoded photocatalyst, miniSOG, to locally generate singlet oxygen that reacts with proximal proteins. Labeled proteins are conjugated in situ with an exogenously supplied nucleophilic probe, which serves as a functional handle for subsequent affinity enrichment and mass spectrometry-based protein identification. From a panel of nucleophilic compounds, we identify biotin-conjugated aniline and propargyl amine as highly reactive probes. As a demonstration of the spatial specificity and depth of coverage in mammalian cells, we apply RinID in the mitochondrial matrix, capturing 394 mitochondrial proteins with 97% specificity. We further demonstrate the broad applicability of RinID in various subcellular compartments, including the nucleus and the endoplasmic reticulum.
Addictions may be driven by competition-induced microbiome dysbiosis
Authors: Lewin-Epstein Ohad , Jaques Yanabah , Feldman Marcus W , Kaufer Daniela , Hadany Lilach 
Published: August 2022

AbstractRecent studies revealed mechanisms by which the microbiome affects its host’s brain, behavior and wellbeing, and that dysbiosis – persistent microbiome-imbalance – is associated with the onset and progress of various chronic diseases, including addictive behaviors. Yet, understanding of the ecological and evolutionary processes that shape the host-microbiome ecosystem and affect the host state, is still limited. Here we propose that competition dynamics within the microbiome, associated with host-microbiome mutual regulation, may promote dysbiosis and aggravate addictive behaviors. We constructed a mathematical framework, modeling the dynamics of the host-microbiome ecosystem in response to alterations. We find that when this ecosystem is exposed to substantial perturbations, the microbiome may shift towards a composition that reinforces the new host state. Such positive feedback loop augments post-perturbation imbalances, hindering attempts to return to the initial equilibrium, thus promoting relapse episodes and prolonged addictions. We also find that the initial microbiome composition is a key factor: a diverse microbiome enhances the ecosystem’s resilience, whereas lower microbiome diversity is more prone to reach dysbiosis, exacerbating addictions. This framework provides novel evolutionary and ecological perspectives on host-microbiome interactions and their implications for host behavior and health, while offering verifiable predictions with potential relevance to clinical treatments.
Interactions between culturable bacteria are predicted by individual species’ growth
Authors: Nestor Einat , Toledano Gal , Friedman Jonathan 
Published: August 2022

AbstractPredicting interspecies interactions is a key challenge in microbial ecology, as such interactions shape the composition and functioning of microbial communities. However, predicting microbial interactions is challenging since they can vary considerably depending on species’ metabolic capabilities and environmental conditions. Here, we employ machine learning models to predict pairwise interactions between culturable bacteria based on their phylogeny, monoculture growth capabilities, and interactions with other species. We trained our models on one of the largest available pairwise interactions dataset containing over 7500 interactions between 20 species from 2 taxonomic groups that were cocultured in 40 different carbon environments. Our models accurately predicted both the sign (accuracy of 88%) and the strength of effects (R2of 0.87) species had on each other’s growth. Encouragingly, predictions with comparable accuracy could be made even when not relying on information about interactions with other species, which are often hard to measure. However, species’ monoculture growth was essential to the model, as predictions based solely on species’ phylogeny and inferred metabolic capabilities were significantly less accurate. These results bring us a step closer to a predictive understanding of microbial communities, which is essential for engineering beneficial microbial consortia.
Elevated urine BMP phospholipids in LRRK2 and VPS35 mutation carriers with and without Parkinson’s disease
Authors: Gomes Sara , Garrido Alicia , Tonelli Francesca , Obiang Donina , Tolosa Eduardo , Marti Maria Jose , Ruiz-Martinez Javier , Aragon Ana Vinagre , Hernandez-Eguiazu Haizea , Croitoru Ioana , Marshall Vicky L , Koenig Theresa , Hotzy Christoph , Hsieh Frank , Sakalosh Marianna , Tengstrand Elizabeth , Padmanabhan Shalini , Merchant Kalpana , Bruecke Christof , Pirker Walter , Zimprich Alexander , Sammler Esther 
Published: August 2022

AbstractElevated urine bis(monoacylglycerol)phosphate (BMP) levels have been found in gain-of-kinase function LRRK2 G2019S mutation carriers. Here, we have expanded urine BMP analysis to other Parkinson’s disease (PD) associated mutations and found them to be consistently elevated in carriers of LRRK2 G2019S and R1441G/C as well as VPS35 D620N mutations. Urine BMP levels are promising biomarkers for patient stratification and potentially target engagement in clinical trials of emerging targeted PD therapies.
Ex vivo drug testing in an ultra-rare sarcoma reveals therapeutic vulnerability and resistance
Authors: Yi Chan Sharon Pei , Luo Baiwen , Chen Benjamin Jieming , Villanueva Andre , Xiu Sam Xin , Farah Benjamin Livingstone , Shannon Nicholas , Ong Chin-Ann Johnny , Chia Claramae Shulyn , Yong Ming-Hui , Kumar Krishan , Ooi London Lucien , Yong Tay Timothy Kwang , Woo Xing Yi , Toh Tan Boon , Chow Edward Kai-Hua , Yang Valerie Shiwen 
Published: August 2022

ABSTRACTSolitary fibrous tumors (SFTs) are rare soft tissue sarcomas for which therapeutic options are limited and ineffective. We successfully demonstrated how functional personalized treatment was implemented in the clinic for an ultra-rare sarcoma with otherwise limited options, through a combined strategy of patient-derived model development and computational drug analytics. Molecular profiling of tumours and patient-derived models uncovered potential biomarkers to predict responses to specific drugs.We generated patient-derived SFT cells (PDSC) and used a computational combinatorial drug screening analytics platform, Quadratic Phenotypic Optimization Platform (QPOP), to determine therapeutic vulnerability and resistance in an ultra-rare locally recurrent brain SFT and its distant liver metastasis. QPOP derived and ranked the efficacy of 531,441 drug combinations, revealing BETi-pazopanib synergy in the liver lesion that outperforms standard-of-care combination doxorubicin-ifosfamide, which was antagonistic. In tumour and PDSC from the pazopanib-resistant brain lesion, transcriptomic analyses identified the UGT1A family as potential biomarkers of pazopanib resistance. Eribulin sensitivity was predicted to be shared across both lesions. Our patient was therefore treated with eribulin and successfully gained clinically meaningful disease control.
Store-operated Ca2+ entry regulatory factor (SARAF) alters murine metabolic state age-dependent via hypothalamic pathways
Authors: Gataulin Diana , Kuperman Yael , Tsoory Michael , Biton Inbal E. , Karbat Izhar , Meshcheriakova Anna , Reuveny Eitan 
Published: August 2022

SummaryStore-operated Ca2+ entry (SOCE) is a vital process aimed at refilling cellular internal Ca2+ stores, and a primary cellular-signaling driver of transcription factors entry to the nucleus. SARAF (SOCE associated regulatory factor)/TMEM66 is an endoplasmic reticulum (ER) resident transmembrane protein that promotes SOCE inactivation and prevents Ca2+ overfilling of the cell. Here we demonstrate that mice deficient in SARAF develop age-dependent sarcopenic obesity with decreased energy expenditure, lean mass and locomotion without affecting food consumption. Moreover, SARAF ablation reduces hippocampal proliferation, modulates the activity of the hypothalamus-pituitary-adrenal (HPA) axis, and mediates changes in anxiety-related behaviors. Interestingly, selective SARAF ablation in the paraventricular nucleus (PVN) of the hypothalamus protects from old age-induced obesity and preserves locomotion, lean mass and energy expenditure, suggesting an opposing, site-specific role for SARAF. Lastly, SARAF ablation in hepatocytes leads to elevated SOCE, elevated vasopressin-induced Ca2+ oscillations, and an increased mitochondrial spare respiratory capacity, thus providing insights into the cellular mechanisms that may affect the global phenotypes. These effects may be mediated via the liver X receptor (LXR) and IL-1 signaling metabolic regulators explicitly altered in SARAF ablated cells. In short, our work supports both central and peripheral roles of SARAF in regulating metabolic, behavioral, and cellular responses.HighlightsLoss/absence of SARAF facilitates age-dependent obesity with decreased metabolic rate, lean mass, and locomotion, without affecting food consumption.Loss of SARAF leads to lipid droplet hypertrophy, BAT whitening and age-dependent hepatic steatosis.Mice lacking SARAF expression in the PVN have an increased metabolic rate, decreased BAT whitening, and are protected from sarcopenic obesity.SARAF ablation in hepatocytes increases SOCE, elevates Ca2+ oscillation in response to vasopressin, and increases the mitochondria’s spare respiratory capacity.Loss of SARAF leads to decreased hippocampal proliferation, sensitized HPA-axis and changes in anxiety-related behavior.
Rules for hardening influenza A virus liquid condensates
Authors: Etibor Temitope Akhigbe , Vale-Costa Sílvia , Sridharan Sindhuja , Brás Daniela , Becher Isabelle , Mello Victor Hugo , Ferreira Filipe , Alenquer Marta , Savitski Mikhail M , Amorim Maria João 
Published: August 2022

SummaryMultiple viral infections form biomolecular condensates in the host cell to compartmentalize viral reactions. Accumulating evidence indicates that these viral condensates may be hardened, a strategy with potential for exploitation as novel antiviral therapy, given that viral reactions rely on specific material properties for function. However, there is no molecular understanding on how to specifically and efficiently modify the material properties of viral condensates, a pre-requisite for overcoming off-target effects by rational drug design.In vitro, the material properties of biological condensates are modified by different thermodynamic parameters, including free energy, concentration, and type/strength of interactions. Here, we used influenza A virus liquid cytosolic condensates, A.K.A viral inclusions, to provide a proof of concept that modulating the type/strength of transient interactions among the interactome in IAV inclusions is more efficient at hardening these structures than varying the temperature or concentration, both inin vitroand inin vivomodels. This stabilization can be achieved by a known pharmacological sticker that can specifically change the material properties of viral inclusions without affecting host proteome abundance nor solubility. Our work supports the development of antivirals targeting the material properties of biomolecular condensates in viral infections. It also provides a framework for the selection of compounds with this activity for general application and thus provides an advance in disease therapy.
An end-to-end pipeline based on open source deep learning tools for reliable analysis of complex 3D images of Medaka ovaries
Authors: Lesage Manon , Bugeon Jérôme , Thomas Manon , Pécot Thierry , Thermes Violette 
Published: August 2022

ABSTRACTComputational analysis of bio-images by deep learning (DL) algorithms has made exceptional progress in recent years and has become much more accessible to non-specialists with the development of ready-to-use tools. The study of oogenesis mechanisms and female reproductive success in fish has also recently benefited from the development of efficient three-dimensional (3D) imaging protocols on entire ovaries. Such large datasets have a great potential for the generation of new quantitative data on oogenesis but are, however, complex to analyze due to imperfect fluorescent signals and the lack of efficient image analysis workflows. Here, we applied two open-source DL tools, Noise2Void and Cellpose, to analyze the oocyte content of medaka ovaries at larvae and adult stages. These tools were integrated into end-to-end analysis pipelines that include image pre-processing, cell segmentation, and image post-processing to filter and combine labels. Our pipelines thus provide effective solutions to accurately segment complex 3D images of entire ovaries with either irregular fluorescent staining or low autofluorescence signal. In the future, these pipelines will be applicable to extensive cellular phenotyping in fish for developmental or toxicology studies.Summary statementAn accessible image analysis method for biologists, which includes easy-to-use deep learning algorithms, designed for accurate quantitative measurement of ovarian content from complex 3D fluorescent images.
Double-dose mRNA vaccination to SARS-CoV-2 progressively increases recognition of variants-of-concern by Spike RBD-specific memory B cells
Authors: Hartley Gemma E. , Edwards Emily S.J. , Varese Nirupama , Boo Irene , Aui Pei M. , Bornheimer Scott J. , Hogarth P. Mark , Drummer Heidi E. , O’Hehir Robyn E. , van Zelm Menno C. 
Published: August 2022

ABSTRACTBackgroundSARS-CoV-2 vaccination with BNT162b2 (Pfizer BioNTech) has been shown to be 95% effective.1 Double-dose vaccination generates high levels of spike-specific antibodies, memory B cells (Bmem) and T cells. However, variants of concern (VoC) with mutations in the spike Receptor Binding Domain (RBD) can evade antibody responses. Booster vaccinations improve antibody recognition of VoC, but it is unclear if this is due to higher total antibodies or their capacity to bind VoC. We here addressed the capacity of surface Ig on single Wuhan-specific Bmem after first and second dose BNT162b2 vaccination to recognize variant RBD.MethodsSamples were collected from 30 healthy COVID-19 naive individuals pre-BNT162b2 vaccination, 3 weeks post-dose 1 and 4-weeks post-dose 2. Plasma antibodies and Bmem were evaluated using recombinant RBD proteins of the Wuhan, Gamma and Delta strains.ResultsAll individuals generated a robust antibody response to BNT162b2 vaccination with all participants producing neutralizing antibodies following dose 2. IgM+ and IgG+ RBD-specific Bmem were generated after one vaccine dose, and those expressing IgG1 increased in absolute number after dose 2. The majority of RBD-specific Bmem bound the Gamma and/or Delta variants, and this proportion significantly increased after the second dose.ConclusionThe second dose of BNT162b2 increases the number of circulating Ig-class switched RBD-specific Bmem. Importantly, the second dose of vaccination is required for a high frequency of RBD-specific Bmem to recognize Gamma and Delta variants. This suggests that dose 2 not only increases the number of RBD-specific Bmem but also the affinity of the Bmem to overcome the point mutations in VoC.