bluuebunny/crossref_metadata_2025_split huggingface dataset:
Higher self-perceived stress reactivity is associated with increased chronic pain risk
Authors: Boring Brandon L. , Richter Alison , Mathur Vani A. 
Published: March 2023

Abstract

Introduction:
Experiencing stress can contribute to unfavorable pain experiences, but outcomes vary across individuals. Evidence suggests that a person's specific reactivity to stressful events may influence pain responses. Previous studies measuring physiological stress reactivity have found associations with pain both clinically and in the laboratory. However, the time and cost required for testing physiological stress reactivity may limit clinical application.

Objective:
Self-reported perception of one's own stress reactivity has been shown to correlate with physiological stress reactivity in relation to health outcomes and may represent a valuable tool in clinical pain assessment.

Methods:
Using data from the Midlife in the US survey, we selected participants who did not have chronic pain at baseline (n = 1512) and who had data at follow-up 9 years later. Stress reactivity was assessed using a subscale of the Multidimensional Personality Questionnaire. We conducted a binary logistic regression to determine the odds of developing chronic pain, controlling for demographics and other health-related variables.

Results:
Results indicate that higher reported stress reactivity at baseline increased the odds of developing chronic pain at follow-up (odds ratio (OR) = 1.085, 95% confidence interval (CI) (1.021, 1.153), P = 0.008), with the only other significant predictor being the number of chronic conditions (OR = 1.118, 95% CI (1.045, 1.197), P = 0.001).

Conclusion:
Findings provide evidence for the predictive criterion validity of self-reported stress reactivity in the context of chronic pain risk. More generally, with increased need for virtual assessment and care, self-reported stress reactivity may be a useful, time-efficient, and cost-efficient tool for predicting pain outcomes in research and clinical contexts.
DIAGNOSIS AND OUTCOMES OF 225 CONSECUTIVE CASES OF CRPS OF THE HAND.
Authors: del PIÑAL Francisco 
Published: March 2023

Background:
The lack of specific tests and potential diagnostic inaccuracy may be behind the stunning figures of complex regional pain syndrome (CRPS) cases. The author tested the hypothesis that patients “diagnosed with” CRPS at referral could be assigned to recognized conditions and treated accordingly.

Methods:
From January 2018 to April 2021, 225 consecutive patients attended the author’s office having been diagnosed with and treated for CRPS for an average of 16±26 months. There were 180 females and 45 males - no patient was excluded.

Results:
All patients could be allocated in named conditions: 79 had a wrong diagnosis; 7 had a true causalgia; 16 were dystonic-psychogenic hands; 20 presented a flare reaction; and 90 had an “irritative” carpal tunnel syndrome. The remaining 13 patients had a miscellany of symptoms within a substandard management setting. Surgery was offered to 175 with a correctable cause; 50 (20 of whom had a tangible cause responsible for their pain) declined and their outcome is unknown. The remaining 125 were operated and tracked for an average of 20±9 months. In the operated group, pain dropped 7.5±2.2 points (p<0.0001) on a numerical rating scale of 0 to 10. Disabilities of the Arm, Shoulder and Hand (DASH) scoring fell from 80 to 16 (p<0.0001). Operated patients rated their satisfaction on a scale of 0 to 10 as 8.9±1.9.

Conclusions
. Unlike CRPS, all patients in this series had a true, diagnosable condition explaining their clinical picture. Most patients who accepted to be operated had a favorable outcome.
Standardization of suspension and imaging mass cytometry readouts for clinical decision making
Authors: Casanova Ruben , Xu Shuhan , Sivapatham Sujana , Jacobs Andrea , Engler Stefanie ,   Tumor Profiler Consortium, Levesque Mitchell P. , Dummer Reinhard , Bodenmiller Bernd , Chevrier Stéphane 
Published: March 2023

SummarySuspension and imaging mass cytometry are single-cell, proteomic-based methods used to characterize tissue composition and structure. Data assessing the consistency of these methods over an extended period of time are still sparse and are needed if mass cytometry-based methods are to be used clinically. Here, we present experimental and computational pipelines developed within the Tumor Profiler clinical study, an observational clinical trial assessing the relevance of cutting-edge technologies in guiding treatment decisions for advanced cancer patients. By using aliquots of frozen antibody panels, batch effects between independent experiments performed within a time frame of one year were minimized. The inclusion of well-characterized reference samples allowed us to assess and correct for batch effects. A systematic evaluation of a test tumor sample analyzed in each run showed that our batch correction approach consistently reduced signal variations. We provide an exemplary analysis of a representative patient sample including an overview of data provided to clinicians and potential treatment suggestions. This study demonstrates that standardized suspension and imaging mass cytometry measurements generate robust data that meet clinical requirements for reproducibility and provide oncologists with valuable insights on the biology of patient tumors.
High performanceLegionella pneumophilasource attribution using genomics-based machine learning classification
Authors: Buultjens Andrew H. , Vandelannoote Koen , Mercoulia Karolina , Ballard Susan , Sloggett Clare , Howden Benjamin P. , Seemann Torsten , Stinear Timothy P. 
Published: March 2023

ABSTRACTFundamental to effective Legionnaires’ disease outbreak control is the ability to rapidly identify the environmental source(s) of the causative agent,Legionella pneumophila. Genomics has revolutionised pathogen surveillance butL. pneumophilahas a complex ecology and population structure that can limit source inference based on standard core genome phylogenetics. Here we present a powerful machine learning approach that assigns the geographical source of Legionnaires’ disease outbreaks more accurately than current core genome comparisons. Models were developed upon 534L. pneumophilagenome sequences, including 149 genomes linked to 20 previously reported Legionnaires’ disease outbreaks through detailed case investigations. Our classification models were developed in a cross-validation framework using only environmentalL. pneumophilagenomes. Assignments of clinical isolate geographic origins demonstrated high predictive sensitivity and specificity of the models, with no false positives or false negatives for 13 out of 20 outbreak groups, despite the presence of within-outbreak polyclonal population structure. Analysis of the same 534-genome panel with a conventional phylogenomic tree and a core genome multi-locus sequence type allelic distance-based classification approach revealed that our machine learning method had the highest overall classification performance – agreement with epidemiological information. Our multivariate statistical learning approach maximises use of genomic variation data and is thus well-suited for supporting Legionnaires’ disease outbreak investigations.
SpaceWalker: Interactive Gradient Exploration for Spatial Transcriptomics Data
Authors: Li Chang , Thijssen Julian , Abdelaal Tamim , Höllt Thomas , Lelieveldt Boudewijn 
Published: March 2023

SummarySpatial transcriptomics (ST) enables profiling the expression of hundreds of genes in tissue sections, down to the level of single cells in their tissue environment. The gradient structure of ST data is particularly interesting for tissue biology, since spatial gene expression gradients often represent tissue compartment edges, whereas in the single-cell transcriptomic domain, gene expression gradients may represent cell type differences and smooth phenotypic transitions. Various computational approaches have been developed to extract information from either the spatial domain or gene expression domain individually. However, integrative biological interpretation of expression gradients in single cell and ST data spaces remains challenging. Many prior spatial transcriptomics analysis pipelines are script-based, lack interactive exploration facilities, and do not have specific facilities for automatic identification of localized expression gradients. Here, we present SpaceWalker, a visual analytics tool for exploring the local gradient structure of ST data. The user is guided by the local intrinsic dimensionality of the high-dimensional data to define seed locations, from which a flood-fill algorithm approximates k-nearest neighbor subgraph topology on the fly. In several use cases, we demonstrate that the spatial projection of these local subgraphs highlights tissue architectural features, and that interactive retrieval of gene expression gradients in the spatial and transcriptomic domains confirms known biology, and provides additional insights into the tissue architecture. We also show that SpaceWalker generalizes to several different ST protocols, and scales well to large, multi-slice, whole-brain ST data, while maintaining real-time interaction performance.
Deep learning for rapid analysis of cell divisionsin vivoduring epithelial morphogenesis and repair
Authors: Turley Jake , Chenchiah Isaac V. , Martin Paul , Liverpool Tanniemola B. , Weavers Helen 
Published: March 2023

AbstractCell division is fundamental to all healthy tissue growth, as well as being rate-limiting in the tissue repair response to wounding and during cancer progression. However, the role that cell divisions play in tissue growth is a collective one, requiring the integration of many individual cell division events. It is particularly difficult to accurately detect and quantify multiple features of large numbers of cell divisions (including their spatio-temporal synchronicity and orientation) over extended periods of time. It would thus be advantageous to perform such analyses in an automated fashion, which can naturally be enabled using Deep Learning. Hence, we develop a pipeline of Deep Learning Models that accurately identify dividing cells in time-lapse movies of epithelial tissuesin vivo. Our pipeline also determines their axis of division orientation, as well as their shape changes before and after division. This strategy enables us to analyse the dynamic profile of cell divisions within theDrosophilapupal wing epithelium, both as it undergoes developmental morphogenesis and as it repairs following laser wounding. We show that the division axis is biased according to lines of tissue tension and that wounding triggers a synchronised (but not oriented) burst of cell divisions back from the leading edge.Highlights□Accurate and efficient detection of epithelial cell divisions can be automated by deep learning of dynamic time-lapse imaging data□Optimal division detection is achieved using multiple timepoints and dual channels for visualisation of nuclei and cell boundaries□Epithelial cell divisions are orientated according to lines of global tissue tension after post-division shuffling□Spatio-temporal cell division analyses following wounding reveal spatial synchronicity that scales with wound size□Additional deep learning tools enable rapid analysis of cell division orientation
Scalable parallel and distributed simulation of an epidemic on a graph
Authors: Dou Guohao 
Published: March 2023

AbstractWe propose an algorithm to simulate Markovian SIS epidemics with homogeneous rates and pairwise interactions on a fixed undirected graph, assuming a distributed memory model of parallel programming and limited bandwidth. We offer an implementation of the algorithm in the form of pseudocode in the Appendix. Also, we analyze its algorithmic complexity and its induced dynamical system. Finally, we design experiments to show its scalability and faithfulness. We believe this algorithm offers a way of scaling out, allowing researchers to run simulation tasks at a scale that was not accessible before. Furthermore, we believe this algorithm lays a solid foundation for extensions to simulating more advanced epidemic processes and graph dynamics in other fields.Author summaryModeling and simulation are two essential components in many decision-making processes. Many real-world phenomena can be modeled by a spreading process on a graph, such as the gossip protocol in distributed computing, the word-of-mouth effect in marketing, and a contagious disease that spreads among a population. It is not always possible to study these problems analytically, making computer-based simulations the only tool to make predictions about the system under study. Depending on the scale of the system, such simulations can be computationally expensive, especially when a large range of parameters are to be tested. We propose in this article an algorithm to leverage parallel or distributed computing hardware for discrete event simulations and use a simple susceptible-infected-susceptible epidemic to illustrate the key idea of the algorithm. This algorithm allows one to make trade-offs between scalability and accuracy of the simulation. We believe that this algorithm will find wide applications in simulating graph dynamics.
Growth-inhibiting effects of the unconventional plant APYRASE 7 ofArabidopsis thalianainfluences the LRX1/FER/RALF growth regulatory module
Authors: Gupta Shibu , Herger Aline , Guérin Amandine , Hou Xiaoyu , Schaufelberger Myriam , Diet Anouck , Roffler Stefan , Wicker Thomas , Ringli Christoph 
Published: March 2023

ABSTRACTPlant cell growth involves coordination of numerous processes and signaling cascades among the different cellular compartments to concomitantly enlarge the protoplast and the surrounding cell wall. InArabidopsis thaliana, the cell wall integrity-sensing process involves the extracellular LRX (LRR-Extensin) proteins that bind RALF (Rapid ALkalinization Factor) peptide hormones and, in vegetative tissues, interact with the transmembrane receptor kinase FERONIA (FER). This LRX/RALF/FER signaling module influences cell wall composition and regulates cell growth. The numerous proteins involved in or influenced by this module are beginning to be characterized. In a genetic screen, mutations inApyrase 7(APY7) were identified to suppress growth defects observed inlrx1andfermutants.APY7encodes a Golgi-localized NTP-diphosphohydrolase, but opposed to other apyrases of Arabidopsis, APY7 revealed to be a negative regulator of cell growth. APY7 modulates the growth-inhibiting effect of RALF1, influences the cell wall architecture, and alters the pH of the extracellular matrix, all of which affect cell growth. Together, this study reveals a function of APY7 in cell wall formation and cell growth that is connected to growth processes influenced by the LRX/FER/RALF signaling module.
B cell-intrinsic STAT3-mediated support of latency and interferon suppression during murine gammaherpesvirus 68 infection revealed through anin vivocompetition model
Authors: Hogan Chad H. , Owens Shana M. , Reynoso Glennys V. , Kirillov Varvara , Meyer Thomas J. , Zelazowska Monika A. , Liu Bin , Li Xiaofan , Chikhalya Aniska , Dong Qiwen , Khairallah Camille , Reich Nancy C. , Sheridan Brian , McBride Kevin M. , Hearing Patrick , Hickman Heather D. , Forrest J. Craig , Krug Laurie T. 
Published: March 2023

ABSTRACTCancers associated with the oncogenic gammaherpesviruses, Epstein-Barr virus and Kaposi sarcoma herpesvirus, are notable for their constitutive activation of the transcription factor STAT3. To better understand the role of STAT3 during gammaherpesvirus latency and immune control, we utilized murine gammaherpesvirus 68 (MHV68) infection. Genetic deletion of STAT3 in B cells ofCD19cre/+Stat3f/fmice reduced peak latency approximately 7-fold. However, infectedCD19cre/+Stat3f/fmice exhibited disordered germinal centers and heightened virus-specific CD8 T cell responses compared to WT littermates. To circumvent the systemic immune alterations observed in the B cell-STAT3 knockout mice and more directly evaluate intrinsic roles for STAT3, we generated mixed bone marrow chimeras consisting of WT and STAT3-knockout B cells. Using a competitive model of infection, we discovered a dramatic reduction in latency in STAT3-knockout B cells compared to their WT B cell counterparts in the same lymphoid organ. RNA sequencing of sorted germinal center B cells revealed that STAT3 promotes proliferation and B cell processes of the germinal center but does not directly regulate viral gene expression. Last, this analysis uncovered a STAT3-dependent role for dampening type I IFN responses in newly infected B cells. Together, our data provide mechanistic insight into the role of STAT3 as a latency determinant in B cells for oncogenic gammaherpesviruses.IMPORTANCEThere are no directed therapies to the latency program of the gammaherpesviruses, Epstein-Barr virus and Kaposi sarcoma herpesvirus. Activated host factor STAT3 is a hallmark of cancers caused by these viruses. We applied the murine gammaherpesvirus pathogen system to explore STAT3 function upon primary B cell infection in the host. Since STAT3 deletion in all CD19+ B cells of infected mice led to altered B and T cell responses, we generated chimeric mice with both normal and STAT3-deleted B cells. B cells lacking STAT3 failed to support virus latency compared to normal B cells from the same infected animal. Loss of STAT3 impaired B cell proliferation and differentiation and led to a striking upregulation of interferon-stimulated genes. These findings expand our understanding of STAT3-dependent processes key to its function as a pro-viral latency determinant for oncogenic gammaherpesviruses in B cells and may provide novel therapeutic targets.
TheC. elegansproteome response to two protectivePseudomonasmutualists
Authors: Pees Barbara , Peters Lena , Treitz Christian , Hamerich Inga K. , Kissoyan Kohar A. B. , Tholey Andreas , Dierking Katja 
Published: March 2023

AbstractTheC. elegansnatural microbiota isolatesPseudomonas luridaMYb11 andPseudomonas fluorescensMYb115 protect the host against pathogens through distinct mechanisms. WhileP. luridaproduces an antimicrobial compound and directly inhibits pathogen growth,P. fluorescensMYb115 protects the host without affecting pathogen growth. It is unknown how these two protective microbes affect host biological processes. We used a proteomics approach to elucidate theC. elegansresponse to MYb11 and MYb115. We found that bothPseudomonasisolates increase vitellogenin protein production in young adults, which confirms previous findings on the effect of microbiota onC. elegansreproductive timing. Moreover, theC. elegansresponses to MYb11 and MYb115 exhibit common signatures with the response to other vitamin B12-producing bacteria, emphasizing the importance of vitamin B12inC. elegans-microbe metabolic interactions. We further analyzed signatures in theC. elegansresponse specific to MYb11 or MYb115. We provide evidence for distinct modification in lipid metabolism by both mutualistic microbes. We could identify activation of host pathogen defense responses as MYb11-specific proteome signature and provide evidence that the intermediate filament protein IFB-2 is required for MYb115-mediated protection. These results indicate that MYb11 not only produces an antimicrobial compound, but also activates host antimicrobial defenses, which together might increase resistance to infection. In contrast, MYb115 affects host processes such as lipid metabolism and cytoskeleton dynamics, which might increase host tolerance to infection. Overall, this study pinpoints proteins of interest that form the basis for additional exploration into the mechanisms underlyingC. elegansmicrobiota-mediated protection from pathogen infection and other microbiota-mediated traits.
Emerging Antifungal Resistance in Falco Species: A Novel Model for Human Medicine
Authors: Das Sibi , Christu Sethi Das , Silvanose Christudas , Binoy Ambili , Azmanis Panagiotis , Somma Antonio Di , Gladston Jibin V 
Published: March 2023

AbstractAntifungal resistance is a growing concern in the medical community, as fungal infections are becoming increasingly difficult to treat. In this study, Falco species were used as novel models for studying antifungal resistance since Aspergillosis, a fungal disease is common in falcons. The most isolated fungi in this study wereA. fumigatus, A. flavus, A. niger, andA. terreus, all of which can cause aspergillosis in falcons. Isavuconazole, posaconazole, and voriconazole had the lowest MICs among the drugs tested, suggesting that they may be effective treatment options. However, this study showed that 34% of the isolates were resistant to itraconazole, which is an increase from 21% in 2006. There is no resistance to voriconazole found in 2006 and 2011, but a 9% resistance rate was noted in 2022. Similarly, there is no resistance to posaconazole and isavuconazole was noticed in 2011, but resistance of 4.7% and 5.8%, respectively was noticed in 2022. Amphotericin B, which showed a 51% resistance rate in 2006, became even more resistant with an 80% rate in 2011, leading to its discontinuation from the treatment of falcons against aspergillosis. This study highlights a significant rise in antifungal resistance, which is a challenging problem in both falcon and human medicine.
Interneuron diversity in the human dorsal striatum
Authors: Garma Leonardo D. , Harder Lisbeth , Barba-Reyes Juan M. , Díez-Salguero Mónica , Serrano-Pozo Alberto , Hyman Bradley T. , Muñoz-Manchado Ana B. 
Published: March 2023

AbstractDeciphering the striatal interneuron diversity is key to understanding the basal ganglia circuit and to untangle the complex neurological and psychiatric diseases affecting this brain structure. We performed single-nucleus RNA-sequencing (snRNA-seq) of postmortem human caudate nucleus (CN) and putamen (Pu) samples to elucidate the diversity and abundance of interneuron populations and their transcriptional structure in the human dorsal striatum. We propose a new taxonomy of striatal interneurons with eight main classes. We provide specific markers for all subclasses and validated some of them with quantitativein situfluorescence hybridization, such as a novel PTHLH-expressing population that exhibits different abundance and gene expression between CN and Pu. For the most abundant interneuron populations in human striatum, PTHLH and TAC3, we found matching known mouse interneuron populations based on key functional genes such as ion channels and synaptic receptors. Remarkably, human TAC3 and mouse Th populations share important similarities including the expression of the neuropeptide tachykinin 3. Finally, we were able to integrate our dataset with several prior smaller human striatal snRNA-seq studies, thus supporting the generalizability of this new harmonized taxonomy.