bluuebunny/crossref_metadata_2025_split huggingface dataset:
Identification of an ANCA-Associated Vasculitis Cohort Using Deep Learning and Electronic Health Records
Authors: Wang Liqin , Novoa-Laurentiev John , Cook Claire , Srivatsan Shruthi , Hua Yining , Yang Jie , Miloslavsky Eli , Choi Hyon K. , Zhou Li , Wallace Zachary S. 
Published: June 2024

ABSTRACTBackgroundANCA-associated vasculitis (AAV) is a rare but serious disease. Traditional case-identification methods using claims data can be time-intensive and may miss important subgroups. We hypothesized that a deep learning model analyzing electronic health records (EHR) can more accurately identify AAV cases.MethodsWe examined the Mass General Brigham (MGB) repository of clinical documentation from 12/1/1979 to 5/11/2021, using expert-curated keywords and ICD codes to identify a large cohort of potential AAV cases. Three labeled datasets (I, II, III) were created, each containing note sections. We trained and evaluated a range of machine learning and deep learning algorithms for note-level classification, using metrics like positive predictive value (PPV), sensitivity, F-score, area under the receiver operating characteristic curve (AUROC), and area under the precision and recall curve (AUPRC). The deep learning model was further evaluated for its ability to classify AAV cases at the patient-level, compared with rule-based algorithms in 2,000 randomly chosen samples.ResultsDatasets I, II, and III comprised 6,000, 3,008, and 7,500 note sections, respectively. Deep learning achieved the highest AUROC in all three datasets, with scores of 0.983, 0.991, and 0.991. The deep learning approach also had among the highest PPVs across the three datasets (0.941, 0.954, and 0.800, respectively). In a test cohort of 2,000 cases, the deep learning model achieved a PPV of 0.262 and an estimated sensitivity of 0.975. Compared to the best rule-based algorithm, the deep learning model identified six additional AAV cases, representing 13% of the total.ConclusionThe deep learning model effectively classifies clinical note sections for AAV diagnosis. Its application to EHR notes can potentially uncover additional cases missed by traditional rule-based methods.SIGNIFICANCE AND INNOVATIONS–Traditional approaches to identifying AAV cases for research have relied on registries assembled through clinical care and/or on billing codes which may miss important subgroups.–Unstructured data entered as free text by clinicians document a patient’s diagnosis, symptoms, manifestations, and other features of their condition which may be useful for identifying AAV cases–We found that a deep learning approach can classify notes as being indicative of AAV and, when applied at the case level, identifies more cases with AAV than rule-based algorithms.
Incidence and Predictors of Recurrent Pericarditis in Systemic Lupus Erythematosus
Authors: Kim Yoo Jin , Lovell Jana , Diab Alaa , Magder Laurence S. , Goldman Daniel , Petri Michelle , Fava Andrea , Adamo Luigi 
Published: June 2024

ABSTRACTIMPORTANCEPericarditis is the most common cardiac manifestation of Systemic Lupus Erythematosus (SLE) and known to recur among patients. Yet, the prevalence and risk factors of recurrent pericarditis in SLE patients are unknown.OBJECTIVEDetermine the frequency and risk factors for the recurrence of pericarditis in patients with SLE.DESIGNRetrospective analysis of a well-characterized, prospective cohort of SLE patients enrolled between 1988 and 2023.SETTINGA single-center cohort study of a diverse group of SLE patients treated at a tertiary medical center.PARTICIPANTSPatients diagnosed with pericarditis (n=590) among those enrolled in the Hopkins Lupus Cohort (n=2931).MAIN OUTCOMERe-occurrence of pericarditis. The SELENA revision of the SLE Disease Activity Index (SLEDAI) was used to define pericarditis. Clinical information was examined for all follow-up encounters after the first episode of pericarditis. Pericarditis that occurred at least six weeks after the first recorded episode was defined as “Recurrent”.RESULTSOf 2931 patients within the cohort, 590 had a history of pericarditis. In 3.4% of patients, the diagnosis of pericarditis was confirmed via electrocardiogram (EKG) or dedicated imaging, with 100% concordance between clinical and data-based diagnoses. During a median follow-up of 7 years (IQR: 3 – 14), 20% (n=120) of patients experienced recurrent pericarditis (recurrence rate ≈ 0.05 per person-year of follow-up). Most patients (51%) experienced only one recurrence, whereas 49% had ≥2 recurrences. In multivariate analysis, predictors of recurrence included younger age (≥60 years vs. <40, RR 0.11 (0.04, 0.32),P<.001), treatment with prednisone (≥20 mg vs. 0, RR 1.99 (1.17, 3.40),P= 0.012), active SLE disease (SLEDAI ≥3 vs. 0, RR 1.55 (1.21, 2.00),P<.001), and time since initial episode (3-10 years vs. <1, RR 0.32 (0.20, 0.52),P<.001).CONCLUSION AND RELEVANCERecurrence is more likely to occur within one year of the onset of pericarditis, and younger patients and those with uncontrolled disease are at greater risk of recurrence. As in the general population, oral prednisone therapy is associated with a higher chance of recurrence in SLE patients, with a dose-dependent effect. These findings set the basis for future studies to define optimal treatment for recurrent pericarditis in SLE patients and suggest that oral corticosteroids should be avoided when treating pericarditis.
Can we Predict Training Performance with Shooting Heart Rate in Archers? – A Machine Learning Approach
Authors: Guru Chandra Sekara , Mahajan Uma , Krishnan Anup , Datta Karuna , Sharma Deep 
Published: June 2024

ABSTRACTPurposeHeart rate (HR) values during different phases of shooting can be used for performance analysis. Machine learning (ML) methods are used in predicting performance. We aimed to develop ML model to predict performance scores using shooting HR values and also to predict the importance of these parameters in an archer.Methods32 archers (15 elite & 17 non-elite) shot two sessions of 30 arrows each indoor wearing heartrate chest monitor and were videographed. When each arrow was shot, 11 HR values were identified at different shooting phases. Other parameters with 35 linear variables and second-degree polynomial HR values were used to build ML models in Python V3.11.4. Session 1 and 2 total scores were used to train and test respectively. Root Mean Squared Error (RMSE) was used to evaluate the model performance after fine-tuning.ResultsRMSE of all 12 ML models ranged from 6.262 – 9.612. The Cat Boost model with the lowest RMSE of 6.262 was used to predict the Session 2 score. SHapley Additive exPlanations (SHAP) values showed each variable’s importance in prediction.ConclusionsSports age, resting systolic blood pressure, previous competition score, right hand grip-strength, age, HR before 2sec of arrow release, waist-to-hip ratio, concentration disruption trait anxiety and HR after 5sec of release are top parameters to predict score.Practical ApplicationsML model with shooting HR provides a better prediction of archery score of an individual archer.
Seroprevalence of SARS-CoV-2 and risk factors for infection among children in Uganda: a serial cross-sectional study
Authors: Bagala Irene , Namuganga Jane Frances , Nayebare Patience , Cuu Gloria , Katairo Thomas , Nabende Isaiah , Gonahasa Samuel , Nassali Martha , Tukwasibwe Stephen , Dorsey Grant , Nankabirwa Joaniter , Kitaka Sabrina Bakeera , Kiguli Sarah , Greenhouse Bryan , Ssewanyana Isaac , Kamya Moses R , Briggs Jessica 
Published: June 2024

ABSTRACTBackgroundUnderstanding COVID-19’s impact on children is vital for public health policy, yet age-specific data is scarce, especially in Uganda. This study examines SARS-CoV-2 seroprevalence and risk factors among Ugandan children at two timepoints, along with COVID-19-related knowledge and practices in households, including adult vaccination status.MethodsBaseline surveys were conducted in 12 communities from April to May 2021 (post-Alpha wave) and follow-up surveys in 32 communities from November 2021 to March 2022 (Omicron wave). Household questionnaires and blood samples were collected to test for malaria by microscopy and for SARS-CoV-2 using a Luminex assay. Seroprevalence was estimated at both the survey and community level. Mixed-effects logistic regression models assessed the association between individual and household factors and SARS-CoV-2 seropositivity in children, adjusting for household clustering.ResultsMore households reported disruptions in daily life at baseline compared to follow-up, though economic impacts lingered. By the follow-up survey, 52.7% of adults had received at least one COVID-19 vaccine dose. Overall seroprevalence in children was higher at follow-up compared to baseline (71.6% versus 19.2%, p < 0.001). Seroprevalence in children ranged across communities from 6-37% at baseline and 50-90% at follow-up. At baseline, children from the poorest households were more likely to be infected. Increasing age remained the only consistent risk factor for SARS-CoV-2 seroconversion at both timepoints.ConclusionsResults indicate that a larger number of children were infected by the Delta and Omicron waves of COVID-19 compared to the Alpha wave. This study is the largest seroprevalence survey in children in Uganda, providing evidence that most children were infected with SARS-CoV-2 before the vaccine was widely available to pediatric populations. Pediatric infections were vastly underreported by case counts, highlighting the importance of seroprevalence surveys in assessing disease burden when testing and reporting rates are limited and many cases are mild or asymptomatic.
Evaluating survey techniques in wastewater-based epidemiology for accurate COVID-19 incidence estimation
Authors: Murakami Michio , Ando Hiroki , Yamaguchi Ryo , Kitajima Masaaki 
Published: June 2024

AbstractWastewater-based epidemiology (WBE) requires high-quality survey methods to determine the incidence of infections in catchment areas. In this study, the wastewater survey methods necessary for comprehending the incidence of infection by WBE are clarified. This clarification is based on the correlation with the number of confirmed coronavirus disease 2019 (COVID-19) cases, considering factors such as handling non-detect data, calculation method for representative values, analytical sensitivity, analytical reproducibility, sampling frequency, and survey duration. Data collected from 15 samples per week for two and a half years using a highly accurate analysis method were regarded as gold standard data, and the correlation between severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA concentrations in wastewater and confirmed COVID-19 cases was analyzed by Monte Carlo simulation under the hypothetical situation where the quality of the wastewater survey method was reduced. Regarding data handling, it was appropriate to replace non-detect data with estimates based on distribution, and to use geometric means to calculate representative values. For the analysis of SARS-CoV-2 RNA in samples, using a highly sensitive and reproducible method (non-detect rates of < 40%; ≤ 0.4 standard deviation) and surveying at least three samples, preferably five samples, per week were considered desirable. Furthermore, conducting the survey over a period of time that included at least 50 weeks was necessary. A WBE that meets these survey criteria is sufficient for the determination of the COVID-19 infection incidence in the catchment area. Furthermore, WBE can offer additional insights into infection rates in the catchment area, such as the estimated 48% decrease in confirmed COVID-19 cases visiting a clinic following a COVID-19 legal reclassification in Japan.
Sex-biased gene expression across tissues reveals unexpected differentiation in the gills of the threespine stickleback
Authors: Sylvestre Florent , Aubin-Horth Nadia , Bernatchez Louis 
Published: June 2024

AbstractSexual dimorphism can evolve through sex-specific regulation of the same gene set. However, sex chromosomes can also facilitate this by directly linking gene expression to sex. Moreover, differences in gene content between heteromorphic sex chromosomes contributes to sexual dimorphism. Understanding patterns of sex-biased gene expression across organisms is important for gaining insight about the evolution of sexual dimorphism and sex chromosomes. Moreover, studying gene expression in species with recently established sex chromosomes can help understand the evolutionary dynamics of gene loss and dosage compensation. The three-spined stickleback is known for its strong sexual dimorphism, especially during the reproductive period. Sex is determined by a young XY sex chromosome pair with a non-recombining region divided in three strata, which have started to degenerate. Using the high multiplexing capability of 3′ QuantSeq to sequence the sex-biased transcriptome of liver, gills and brain, we provide the first characterization of sex-specific transcriptomes from ∼80 stickleback (40 males and 40 females) collected from a natural population during the reproductive period. We find that the liver is extremely differentiated between sexes (36% of autosomal genes) and reflects ongoing reproduction, while the brain shows very low levels of differentiation (0.78%) with no functional enrichment. Finally, the gills exhibit high levels of differentiation (5%), suggesting that sex should be considered in physiological and ecotoxicological studies of gill responses in fishes. We also find that sex-biased gene expression in hemizygous genes is mainly driven by a lack of dosage compensation. However, sex-biased expression of genes that have conserved copies on both sex chromosomes is likely driven by the degeneration of Y allele expression and a down-regulation of male-beneficial mutations on the X chromosome.
Predicting the alternative conformation of a known protein structure based on the distance map of AlphaFold2
Authors: Li Jiaxuan , Zhu Zefeng , Song Chen 
Published: June 2024

AbstractWith AlphaFold2 (AF2) becoming the top structural prediction tool, multiple studies have found that AF2 often favors one conformation state over others in high-precision structure predictions. Meanwhile, it has also been demonstrated that the prediction of multi-state structures from a given protein sequence is possible by subsampling multiple sequence alignment (MSA). In this work, we reveal that AF2 predictions contain information on multi-state structures even with the deepest MSA: protein distance maps extracted from AF2 often exhibit multi-peak signals in the distance probability distributions for residue pairs. By fitting and separating these multi-peak distributions of residue pairs, one can extract distinct distance information of two states, which can be incorporated into Rosetta as restraint energy functions to model large and complex conformational changes. Twenty protein systems with different types of conformational changes were selected for validation in modeling their alternative conformations. With our protocol, we successfully predicted the alternative conformations of 19 systems and achieved a template-based modeling score (TM-score) above 0.90 for the best-sampled models in nine cases. This work further expands the usage of AlphaFold2 in studying multi-state proteins.
An expanded metabolic pathway for androgen production by host-associated bacteria
Authors: Wang Taojun , Ahmad Saeed , Cruz-Lebrón Angélica , Ernst Sarah E. , Caicedo Kelly Yovani Olivos , Jeong Yoon , Binion Briawna , Mbuvi Pauline , Dutta Debapriya , Fernandez-Materan Francelys V. , Breister Adam M. , Lee Jae Won , Kang Jason D. , Harris Spencer C. , Ikegawa Shigeo , Gaskins H. Rex , Erdman John W. , Yang Glen , Cann Isaac , Daniel Steven L. , Hylemon Phillip B. , Anantharaman Karthik , Bernardi Rafael C. , Alves João M.P. , Sfanos Karen S. , Irudayaraj Joseph , Ridlon Jason M. 
Published: June 2024

AbstractA growing body of literature implicates host-associated microbiota in the modulation of circulating androgen levels in the host, which could have far-reaching implications for androgen-mediated diseases. However, the microbial genetic pathways involved in androgen production remain unknown. Here, we report the first host-associated microbial gene (desF) encoding an enzyme that catalyzes conversion of androstenedione to epitestosterone (epiT) in the gut bacterium,Clostridium scindens. Despite current dogma that epiT is a nuclear androgen-receptor (AR) antagonist, we demonstrate that epiT is a potent androgen, as assessed by its ability to promote prostate cancer cell growth and expression of prostate specific antigen (PSA). We then quantified thedesFgene in fecal samples collected from individuals with advanced prostate cancer (rising blood PSA) undergoing androgen deprivation therapy combined with abiraterone acetate and prednisone (AA/P). Strikingly, fecaldesFlevels were elevated in a subset of individuals progressing on AA/P versus samples taken during AA/P response (stable). Importantly, we observed that AA does not inhibit the bacterial desmolase enzyme that is analogous to the human drug target of AA. We then determined that bacterial isolates from urine or prostatectomy tissue are capable of androgen production. From these isolates we detected 17β-hydroxysteroid dehydrogenase (17β-HSDH) activity, which has not been previously reported in urinary tract bacteria, and discovered thedesGgene in urinary isolates encoding 17β-HSDH that catalyzed conversion of androstenedione to testosterone. Applying advanced artificial intelligence and molecular dynamics, we predict the structures and ligand binding to DesF and DesG. Using a novel bioengineered microencapsulation technique, we demonstrate that urinary androgen-producing bacterial strains can also promote prostate cancer cell growth through steroid metabolism. Taken together, our results are a significant advance for steroid microbiology in humans and suggest that these microbial biotransformations should be further studied in the context of androgen-mediated physiological processes and diseases.
Sex and stress interactions in fear synchrony of mouse dyads
Authors: Ito Wataru , Morozov Alexei 
Published: June 2024

AbstractSocially coordinated threat responses support the survival of animal groups. Given their distinct social roles, males and females must differ in such coordination. Here, we report such differences during the synchronization of auditory-conditioned freezing in mouse dyads. To study the interaction of emotional states with social cues underlying synchronization, we modulated emotional states with prior stress or modified the social cues by pairing unfamiliar or opposite-sex mice. In same-sex dyads, males exhibited more robust synchrony than females. Stress disrupted male synchrony in a prefrontal cortex-dependent manner but enhanced it in females. Unfamiliarity moderately reduced synchrony in males but not in females. In dyads with opposite-sex partners, fear synchrony was resilient to both stress and unfamiliarity. Decomposing the synchronization process in the same-sex dyads revealed sex-specific behavioral strategies correlated with synchrony magnitude: following partners’ state transitions in males and retroacting synchrony-breaking actions in females. Those were altered by stress and unfamiliarity. The opposite-sex dyads exhibited no synchrony-correlated strategy. These findings reveal sex-specific adaptations of socio-emotional integration defining coordinated behavior and suggest that sex-recognition circuits confer resilience to stress and unfamiliarity in opposite-sex dyads.
Barley variety Quest exhibits broad-spectrum, moderate quantitative resistance against diverseXanthomonas translucenspathovar translucens
Authors: Heiden Nathaniel , Burns Everett , Butchacas Jules , Roman-Reyna Veronica , Mikhail Kelly , Jacobs Jonathan M. 
Published: June 2024

AbstractBarley (Hordeum vulgaresubspp.vulgare) is the fourth most widely produced cereal today and is valuable for both animal and human consumption. Bacterial leaf streak (BLS) of barley is caused primarily byX. translucenspv. translucens (Xtt) and can lead to significant yield losses. There are currently no available control methods for BLS pathogens, but there are ongoing efforts to identify and characterize sources of resistance to Xtt in available barley germplasm. These screening projects require field trials which are time-consuming and challenged due to variables introduced by environmental and weather conditions, field conditions and other organisms that may be present. Reliable greenhouse phenotyping techniques are needed to accelerate screening of barley germplasm against more Xtt strains. In this study, we established a rapid greenhouse spray inoculation protocol for pathogen-plant phenotyping. This provides a framework for rapid germplasm screening before time-consuming and limited field nursery trials. Our method confirmed the moderate quantitative resistance phenotype discovered in field trials for the cultivars Quest and Tradition against the Xtt strain CIX95. One week old seedlings of the previously characterized resistant line PI329000 were not resistant relative to the susceptible check line MW14-5371-013. The cultivar Quest then demonstrated moderate quantitative resistance against a diverse panel of Xtt strains. Xtt subgroup did not influence BLS outcomes on tested barley lines. We found evidence to support the hypothesis that the virulence of modern strains is increasing, though this does not improve their ability to cause disease on Quest.
Cooperative dynamics of PARP-1 Zinc-finger domains in the detection of DNA single-strand breaks
Authors: Sarma Parvathy A. P. , Abbadie Corinne , Cleri Fabrizio 
Published: June 2024

ABSTRACTThe DNA single-strand break (SSB) repair pathway is initiated by the multifunctional enzyme PARP-1, which recognizes the broken DNA ends by its two zinc-finger domains, Zn1 and Zn2. Despite a number of experiments performed with different DNA configurations and reduced fragments of PARP-1, many details of this interaction that is crucial to the correct initiation of the repair chain are still unclear. We performed Molecular Dynamics (MD) computer simulations of the interaction between the Zn1/Zn2 domains of PARP-1 and a DNA hairpin including a missing nucleotide to simulate the presence of an SSB, a construct used in recent experiments. The role of Zn1 and Zn2 interacting with the SSB ends is studied in detail, both independently and cooperatively. We also explored, PARP-1 operating as a dimer, with the two Zn-fingers coming from two separate copies of the enzyme. By an extensive set of all-atom molecular simulations employing state-of-the art force fields, assisted by empirical docking and free-energy calculations, we conclude that the particular conformation of the DNA hairpin can indeed spontaneously open up by thermal fluctuations, up to extremely kinked deformations. However, such extreme localized deformations are rarely observed in free, long DNA fragments. Protein side-loops make contact with the DNA hairpin grooves, and help Zn2 to penetrate deep in the SSB gap. In this way, Zn2 can interact with the nucleotides opposite to the missing base. OVerall, Zn1 plays a secondary role: the crucial factor for the interaction is the relative arrangement of the Zn1/Zn2 couple, and their mutual orientation with respect to the 3′and 5′SSB end terminals. This helps to obtain an early interacting configuration, which ultimately leads to molecular PARP-1-DNA structures similar to those observed experimentally. Such findings represent an important step toward defining the detailed function of PARP-1 in the early stages of SSB recognition.