bluuebunny/crossref_metadata_2025_split huggingface dataset:
Cardiometabolic syndrome in HIV-positive and HIV-negative patients at Zewditu Memorial Hospital, Addis Ababa, Ethiopia: a comparative cohort study
Authors: Woldu Minyahil , Minzi Omary , Shibeshi Workineh , Shewaamare Aster , Engidawork Ephrem 
Published: December 2022

Background
Cardiometabolic syndrome (CMetS) has recently emerged as a serious public health concern, particularly for individuals living with chronic conditions. This study aimed to determine the incidence and prevalence of CMetS, as well as the risk factors linked with it, in HIV-positive and HIV-negative adult patients.

Methods
A comparative cohort study was designed. The National Cholesterol Education Program (NCEP) and the International Diabetes Federation (IDF) tools were used to determine the outcome variables. Association studies were done using logistic regression.

Result
CMetS was found to have a greater point and period prevalence, and incidence estimation in HIV-negative than HIV+ patients using both the NCEP and the IDF tools. Using the NCEP tool, the risk of obesity was 44.1% [odds ratio (OR) = 0.559, 95% confidence interval (CI), (0.380–0.824); P = 0.003] lower in HIV+ than in HIV-negative participants. By contrast, no apparent difference was noted using the IDF tool. Similarly, hyperglycemia [OR = 0.651, 95% CI (0.457–0.926); P = 0.017], and hypertension [OR = 0.391, 95% CI (0.271–0.563); P < 0.001] were shown to be lower in HIV+ patients than HIV-negative patients by 34.9% and 60.9%, respectively. The study revealed significant variation in all biomarkers across the follow-up period in both HIV+ and HIV-negative participants, except for SBP.

Conclusions
CMetS caused more overall disruption in HIV-negative people with chronic diseases than in HIV-positive people. All of the indicators used to assess the increased risk of CMetS were equally meaningful in HIV+ and HIV-negative subjects.
Walking humans and running mice: perception and neural encoding of optic flow during self-motion
Authors: Horrocks Edward A. B. , Mareschal Isabelle , Saleem Aman B. 
Published: December 2022

Locomotion produces full-field optic flow that often dominates the visual motion inputs to an observer. The perception of optic flow is in turn important for animals to guide their heading and interact with moving objects. Understanding how locomotion influences optic flow processing and perception is therefore essential to understand how animals successfully interact with their environment. Here, we review research investigating how perception and neural encoding of optic flow are altered during self-motion, focusing on locomotion. Self-motion has been found to influence estimation and sensitivity for optic flow speed and direction. Nonvisual self-motion signals also increase compensation for self-driven optic flow when parsing the visual motion of moving objects. The integration of visual and nonvisual self-motion signals largely follows principles of Bayesian inference and can improve the precision and accuracy of self-motion perception. The calibration of visual and nonvisual self-motion signals is dynamic, reflecting the changing visuomotor contingencies across different environmental contexts. Throughout this review, we consider experimental research using humans, non-human primates and mice. We highlight experimental challenges and opportunities afforded by each of these species and draw parallels between experimental findings. These findings reveal a profound influence of locomotion on optic flow processing and perception across species.
This article is part of a discussion meeting issue ‘New approaches to 3D vision’.
Do you hear what I see? How do early blind individuals experience object motion?
Authors: Fine Ione , Park Woon Ju 
Published: December 2022

One of the most important tasks for 3D vision is tracking the movement of objects in space. The ability of early blind individuals to understand motion in the environment from noisy and unreliable auditory information is an impressive example of cortical adaptation that is only just beginning to be understood. Here, we compare visual and auditory motion processing, and discuss the effect of early blindness on the perception of auditory motion. Blindness leads to cross-modal recruitment of the visual motion area hMT+ for auditory motion processing. Meanwhile, the planum temporale, associated with auditory motion in sighted individuals, shows reduced selectivity for auditory motion. We discuss how this dramatic shift in the cortical basis of motion processing might influence the perceptual experience of motion in early blind individuals.
This article is part of a discussion meeting issue ‘New approaches to 3D vision’.
The role of binocular vision in the control and development of visually guided upper limb movements
Authors: Niechwiej-Szwedo Ewa , Colpa Linda , Wong Agnes 
Published: December 2022

Vision provides a key sensory input for the performance of fine motor skills, which are fundamentally important to daily life activities, as well as skilled occupational and recreational performance. Binocular visual function is a crucial aspect of vision that requires the ability to combine inputs from both eyes into a unified percept. Summation and fusion are two aspects of binocular processing associated with performance advantages, including more efficient visuomotor control of upper limb movements. This paper uses the multiple processes model of limb control to explore how binocular viewing could facilitate the planning and execution of prehension movements in adults and typically developing children. Insight into the contribution of binocularity to visuomotor control also comes from examining motor performance in individuals with amblyopia, a condition characterized by reduced visual acuity and poor binocular function. Overall, research in this field has advanced our understanding of the role of binocular vision in the development and performance of visuomotor skills, the first step towards developing assessment tools and targeted rehabilitation for children with neurodevelopment disorders at risk of poor visuomotor outcomes.
This article is part of a discussion meeting issue ‘New approaches to 3D vision’.
Variability in the spatiotemporal propagation of interictal spikes on magnetoencephalography in temporal lobe epilepsy
Authors: Zhou Daniel J. , Gumenyuk Valentina , Taraschenko Olga , Grobelny Bartosz T. , Stufflebeam Steven M. , Peled Noam 
Published: December 2022

AbstractMagnetoencephalography (MEG) is clinically used to help localize interictal spikes in discrete brain areas through the equivalent current dipole (ECD) method for patients with refractory epilepsy. The propagation of interictal spikes in adjacent regions was shown to be correlated with worse surgical outcomes in patients with temporal lobe epilepsy (TLE). Yet, the ECD method does not account for the temporal dynamics of spike activity, making it challenging to define the margins of the interictal network. Furthermore, the ECD results can be affected by states of arousal or sleep that alter background MEG activity.In this study, we developed a module in the custom-built Multi-Modal Analysis and Visualization Tool (MMVT) that analyzes the spatiotemporal dynamics of interictal spikes and removes normal variations associated with sleep. We analyzed MEG data from seven TLE patients and characterized their interictal spatiotemporal dynamics. In all patients, the onset of interictal spike activity appeared near the determined site of the epileptogenic zone based on corresponding neuroimaging and EEG data. The propagated source activity by 10 ms ranged from near the region of onset to spreading to extratemporal regions, such as the frontal lobe or insula.We demonstrated the feasibility of a novel method to assess the spatiotemporal properties of interictal spikes in patients with medically refractory TLE and to better delineate their onset and propagation patterns on MEG analysis. In doing so, we illustrate how variable the spike propagation distribution and speed may be within a 10 ms window. This visualization of the time component of the interictal networks offers an advantage over the currently used ECD method in characterizing the epileptic network and may help better guide surgical planning and provide for improved interventional outcomes in patients with TLE.
Amelioration of non-alcoholic fatty liver disease by targeting G protein-coupled receptor 110: A preclinical study
Authors: Wu Mengyao , Lo Tak-Ho , Li Liping , Sun Jia , Deng Chujun , Chan Ka-Ying , Li Xiang , Yeh Steve Ting-Yuan , Lee Jimmy Tsz Hang , Lui Pauline Po Yee , Xu Aimin , Wong Chi-Ming 
Published: December 2022

AbstractBackgroundRecent research has shown that the G protein-coupled receptor 110 (GPR110) is an oncogene. The evidence mainly based on high expression of GPR110 in numerous cancer types; and knockdown GPR110 can reduced the cell migration, invasion, and proliferation. GPR110 is, however, mostly expressed in the liver of healthy individuals. The function of GPR110 in liver has not been revealed. Interestingly, expression level of hepatic GPR110 is dramatically decreased in obese subjects. Here, we examined whether GPR110 has a role in liver metabolism.MethodsWe used recombinant adeno-associated virus-mediated gene delivery system and antisense oligonucleotide to manipulate the hepatic GPR110 expression level in diet-induced obese mice to investigate the role of GPR110 in hepatic steatosis. The clinical relevance was examined using transcriptome profiling and archived biopsy specimens of liver tissues from non-alcoholic fatty liver disease (NAFLD) patients with different degree of fatty liver.ResultsThe expression of GPR110 in the liver was directly correlated to fat content in the livers of both obese mice and NAFLD patients. Stearoyl-coA desaturase 1 (SCD1), a crucial enzyme in hepatic de novo lipogenesis, was identified as a downstream target of GPR110 by RNA-sequencing analysis. Treatment with the liver-specific SCD1 inhibitor MK8245 and specific shRNAs against SCD1 in primary hepatocytes improved the hepatic steatosis of GPR110-overexpressing mice and lipid profile of hepatocytes, respectively.ConclusionsThese results indicate GPR110 regulates hepatic lipid metabolism through controlling the expression of SCD1. Down-regulation of GPR110 expression can potentially serve as a protective mechanism to stop the over-accumulation of fat in the liver in obese subjects. Overall, our findings not only reveal a new mechanism regulation the progression of NALFD, but also proposed a novel therapeutic approach to combat NAFLD by targeting GPR110.FundingsThis work was supported in part by National Natural Science Foundation of China 81870586 (CMW), 82270941 and 81974117 (JS), Area of Excellence AoE/M-707/18 (AX and CMW), and General Research Fund 15101520 (CMW).HighlightsGPR110 regulates hepatic lipid metabolism.High level of hepatic GPR110 aggravates the progression of NAFLD by inducing SCD1 expression.Reduction in hepatic GPR110 is required to alleviate the progression of NAFLD.Targeting hepatic GPR110 improves hepatic steatosis.
FDA approved Artificial Intelligence and Machine Learning (AI/ML)-Enabled Medical Devices: An updated landscape
Authors: Joshi Geeta , Jain Aditi , Araveeti Shalini Reddy , Adhikari Sabina , Garg Harshit , Bhandari Mukund 
Published: December 2022

AbstractAs artificial intelligence (AI) has been highly advancing in the last decade, machine learning (ML) enabled medical devices are increasingly used in healthcare. In this article, we performed comprehensive analysis of FDA approved Artificial Intelligence and Machine Learning (AI/ML)- Enabled Medical Devices and offer an in-depth analysis of clearance pathways, approval timeline, regulation type, medical specialty, decision type, recall history etc. We found a significant surge in approvals since 2018, with clear dominance of radiology specialty in the application of machine learning tools, attributed to the abundant data from routine clinical data. The study also reveals a reliance on the 510(k)-clearance pathway, emphasizing its basis on substantial equivalence and often bypassing the need for new clinical trials. Also, it notes an underrepresentation of pediatric- focused devices and trials, suggesting an opportunity for expansion in this demographic. Moreover, the geographical limitation of clinical trials, primarily within the United States, points to a need for more globally inclusive trails to encompass diverse patient demographics. This analysis not only maps the current landscape of AI/ML-Enabled Medical Devices but also pinpoints trends, potential gaps, and areas for future exploration, clinical trial practices, and regulatory approaches.
Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases
Authors: Alsentzer Emily , Li Michelle M. , Kobren Shilpa N. , Noori Ayush ,   Undiagnosed Diseases Network, Kohane Isaac S. , Zitnik Marinka 
Published: December 2022

AbstractThere are more than 7,000 rare diseases, some affecting 3,500 or fewer patients in the US. Due to clinicians’ limited experience with such diseases and the heterogeneity of clinical presentations, approximately 70% of individuals seeking a diagnosis today remain undiagnosed. Deep learning has demonstrated success in aiding the diagnosis of common diseases. However, existing approaches require labeled datasets with thousands of diagnosed patients per disease. Here, we present SHEPHERD, a few shot learning approach for multi-faceted rare disease diagnosis. SHEPHERD performs deep learning over a biomedical knowledge graph enriched with rare disease information to perform phenotype-driven diagnosis. Once trained, we show that SHEPHERD can provide clinical insights about real-world patients. We evaluate SHEPHERD on a cohort of N = 465 patients representing 299 diseases (79% of genes and 83% of diseases are represented in only a single patient) in the Undiagnosed Diseases Network. SHEPHERD excels at several diagnostic facets: performing causal gene discovery (causal genes are predicted at rank = 3.56 on average), retrieving “patients-like-me” with the same causal gene or disease, and providing interpretable characterizations of novel disease presentations. We additionally examine SHEPHERD on two other real-world cohorts, MyGene2 (N = 146) and Deciphering Developmental Disorders Study (N = 1,431). SHEPHERD demonstrates the potential of deep learning to accelerate rare disease diagnosis and has implications for using deep learning on medical datasets with very few labels.
Meta-analysis of atopic dermatitis in 1,094,060 individuals identifies new risk loci, and sub-analysis characterizes the loci with disease severity and onset
Authors: Pasanen Anu , Sliz Eeva , Huilaja Laura , FinnGen  ,   Estonian Biobank Research Team, Reimann Ene , Mägi Reedik , Laisk Triin , Tasanen Kaisa , Kettunen Johannes 
Published: December 2022

AbstractAtopic dermatitis (AD) is a common inflammatory skin disease highly attributable to genetic factors. Here, we report results from a genome-wide meta-analysis of AD in 37,541 cases and 1,056,519 controls with data from the FinnGen project, the Estonian Biobank, the UK Biobank, the EAGLE Consortium, and the BioBank Japan. We detected 77 independent AD-associated loci of which 10 were novel. The associated loci showed enrichment in various immune regulatory processes. We further performed subgroup analyses of mild and severe AD, and of early and late-onset AD, with data from the FinnGen project. 55 of the 79 tested variants in the associated loci showed larger effect estimates for severe than mild AD as determined through administered treatment. The age of onset, as determined by the first hospital visit with AD diagnosis, was lower in patients with particular AD-risk alleles. Our findings add to the knowledge of the genetic background of AD and may underlay the development of new therapeutic strategies.
The effect of recognition on competition among clones in spatially structured microbial communities
Authors: Bingham Adrienna , Sur Aparajita , Shaw Leah B. , Murphy Helen A. 
Published: December 2022

AbstractIn spatially structured microbial communities, clonal growth of stationary cells passively generates clusters of related cells. This can lead to stable cooperation without the need for recognition mechanisms. However, recent research suggests that some biofilm-forming microbes may have mechanisms of kin recognition. To explore this unexpected observation, we studied the effects of different types of cooperation in a microbial colony using spatially explicit, agent-based simulations of two interacting strains. We found scenarios that favor a form of kin recognition in spatially structured microbial communities. In the presence of a “cheater” strain, strains with greenbeard cooperation was able to increase in frequency more than obligate cooperators. This effect was most noticeable in high density colonies and when the cooperators were not as abundant as the cheaters. We also studied whether a polychromatic greenbeard, in which cells only cooperate with their own type, could provide a numerical benefit beyond a simple, binary greenbeard. We found the greatest benefit to a polychromatic greenbeard when cooperation is highly effective. These results suggest that in some ecological scenarios, recognition mechanisms may be beneficial even in spatially structured communities.
Creating an Ignorance-Base: Exploring Known Unknowns in the Scientific Literature
Authors: Boguslav Mayla R. , Salem Nourah M. , White Elizabeth K. , Sullivan Katherine J. , Bada Michael , Hernandez Teri L. , Leach Sonia M. , Hunter Lawrence E. 
Published: December 2022

AbstractBackgroundScientific discovery progresses by exploring new and uncharted territory. More specifically, it advances by a process of transforming unknown unknowns first into known unknowns, and then into knowns. Over the last few decades, researchers have developed many knowledge bases to capture and connect the knowns, which has enabled topic exploration and contextualization of experimental results. But recognizing the unknowns is also critical for finding the most pertinent questions and their answers. Prior work on known unknowns has sought to understand them, annotate them, and automate their identification. However, no knowledge-bases yet exist to capture these unknowns, and little work has focused on how scientists might use them to trace a given topic or experimental result in search of open questions and new avenues for exploration. We show here that a knowledge base of unknowns can be connected to ontologically grounded biomedical knowledge to accelerate research in the field of prenatal nutrition.ResultsWe present the first ignorance-base, a knowledge-base created by combining classifiers to recognize ignorance statements (statements of missing or incomplete knowledge that imply a goal for knowledge) and biomedical concepts over the prenatal nutrition literature. This knowledge-base places biomedical concepts mentioned in the literature in context with the ignorance statements authors have made about them. Using our system, researchers interested in the topic of vitamin D and prenatal health were able to uncover three new avenues for exploration (immune system, respiratory system, and brain development), which were buried among the many standard enriched concepts, by searching for concepts enriched in ignorance statements. Additionally, we used the ignorance-base to enrich concepts connected to a gene list associated with vitamin D and spontaneous preterm birth and found an emerging topic of study (brain development) in an implied field (neuroscience). The researchers could look to the field of neuroscience for potential answers to the ignorance statements.ConclusionOur goal is to help students, researchers, funders, and publishers better understand the state of our collective scientific ignorance (known unknowns) in order to help accelerate research through the continued illumination of and focus on the known unknowns and their respective goals for scientific knowledge.Graphical AbstractHighlightsWe created the first ignorance-base (knowledge-base) to capture goals for scientific knowledgeOur exploration methods provide analyses, summaries, and visualizations based on a queryIgnorance enrichment provided fruitful avenues for future researchExploration by topic in vitamin D found three avenues to exploreExploration by experimental results for vitamin D and preterm birth found an emerging topic
HLApollo: A superior transformer model for pan-allelic peptide-MHC-I presentation prediction, with diverse negative coverage, deconvolution and protein language features
Authors: Thrift William John , Lounsbury Nicolas W. , Broadwell Quade , Heidersbach Amy , Freund Emily , Abdolazimi Yassan , Phung Qui T , Chen Jieming , Capietto Aude-Hélène , Tong Ann-Jay , Rose Christopher M. , Blanchette Craig , Lill Jennie R , Haley Benjamin , Delamarre Lélia , Bourgon Richard , Liu Kai , Jhunjhunwala Suchit 
Published: December 2022

AbstractAntigen presentation on MHC class I (MHC-I) is key to the adaptive immune response to cancerous cells. Computational prediction of peptide presentation by MHC-I has enabled individualized cancer immunotherapies. Here, we introduce HLApollo, a transformer-based approach with end-to-end modeling of MHC-I sequence, deconvolution, and flanking sequences. To achieve this, we develop a novel training strategy, negative set switching, which greatly reduces overfitting to falsely presumed negatives that are necessarily found in presentation datasets. HLApollo shows a meaningful improvement compared to recent MHC-I models on peptide presentation (20.19% average precision (AP)) and immunogenicity (4.1% AP). As expected, adding gene expression boosts the performance of HLApollo. More interestingly, we show that introduction of features from a protein language model, ESM 1b, remarkably recoups much of the benefits of gene expression in absence of true expression measurements. Finally, we demonstrate excellent pan-allelic generalization, and introduce a framework for estimating the expected accuracy of HLApollo for untrained alleles. This guides the use of HLApollo in a clinical setting, where rare alleles may be observed in some subjects, particularly for underrepresented minorities.