bluuebunny/crossref_metadata_2025_split huggingface dataset:
Clinical phenotype classification to predict risk and optimize the management of patients with atrial fibrillation using the Atrial Fibrillation Better Care (ABC) pathway: a report from the COOL-AF registry
Authors: Krittayaphong R , Treewaree S , Wongtheptien W , Kaewkumdee P , Lip G Y H 
Published: October 2023

Summary

Background
Phenotypic classification is a method of grouping patients with similar phenotypes.

Aim
We aimed to use phenotype classification based on a clustering process for risk stratification of patients with non-valvular atrial fibrillation (AF) and second, to assess the benefit of the Atrial Fibrillation Better Care (ABC) pathway.

Methods
Patients with AF were prospectively enrolled from 27 hospitals in Thailand from 2014 to 2017, and followed up every 6 months for 3 years. Cluster analysis was performed from 46 variables using the hierarchical clustering using the Ward minimum variance method. Outcomes were a composite of all-cause death, ischemic stroke/systemic embolism, acute myocardial infarction and heart failure.

Results
A total of 3405 patients were enrolled (mean age 67.8 ± 11.3 years, 58.2% male). During the mean follow-up of 31.8 ± 8.7 months. Three clusters were identified: Cluster 1 had the highest risk followed by Cluster 3 and Cluster 2 with a hazard ratio (HR) and 95% confidence interval (CI) of composite outcomes of 2.78 (2.25, 3.43), P &lt; 0.001 for Cluster 1 and 1.99 (1.63, 2.42), P &lt; 0.001 for Cluster 3 compared with Cluster 2. Management according to the ABC pathway was associated with reductions in adverse clinical outcomes especially those who belonged to Clusters 1 and 3 with HR and 95%CI of the composite outcome of 0.54 (0.40, 073), P &lt; 0.001 for Cluster 1 and 0.49 (0.38, 0.63), P &lt; 0.001 for Cluster 3.

Conclusion
Phenotypic classification helps in risk stratification and prognostication. Compliance with the ABC pathway was associated with improved clinical outcomes.
Variable Phenotype of Congenital Corneal Opacities in Biallelic CYP1B1 Pathogenic Variants
Authors: Franco Elena , Gagrani Meghal , Scanga Hannah L. , Areaux Raymond G. , Chu Charleen T. , Nischal Ken K. 
Published: October 2023

Purpose:
The aim of this study is to describe the variable phenotype of congenital corneal opacities occurring in patients with biallelic CYP1B1 pathogenic variants.

Methods:
A retrospective chart review was conducted to identify patients with congenital corneal opacities and CYP1B1 pathogenic variants seen at UPMC Children's Hospital of Pittsburgh. Ophthalmic examination, high-frequency ultrasound, anterior segment optical coherence tomography, histopathologic images, and details of genetic testing were reviewed.

Results:
Three children were identified. All presented with raised intraocular pressure. Two patients showed bilateral limbus-to-limbus avascular corneal opacification that did not resolve with intraocular pressure control; 1 showed unilateral avascular corneal opacity with a crescent of clear cornea, iridocorneal adhesions, iridolenticular adhesions, and classical features of congenital glaucoma in the fellow eye (enlarged corneal diameter, Haab striae, and clearing of the corneal clouding with appropriate intraocular pressure control). The first 2 patients were visually rehabilitated with penetrating keratoplasty. Histopathology revealed distinct features: a variably keratinized epithelium; a thick but discontinuous Bowman-like layer with areas of disruption and abnormal cellularity; Descemet membrane, when observed, showed reduced endothelial cells; and no pathological changes of Haab striae were identified. Two patients had compound heterozygous pathogenic variants in CYP1B1 causing premature stop codons, whereas 1 was homozygous for a pathogenic missense variant.

Conclusions:
Congenital corneal opacities seen in biallelic CYP1B1 pathogenic variants have a variable phenotype. One is that commonly termed as Peters anomaly type 1 (with iridocorneal adhesions, with or without iridolenticular adhesions) and the other is a limbus-to-limbus opacity, termed CYP1B1 cytopathy. Clinicians should be aware of this phenotypic variability.
Sphingobium agri sp. nov., isolated from rhizospheric soil of eggplant
Authors: Huq Md. Amdadul , Lee Sun-Young , Moon BoKyung , Ma Juncai , Siddiqi Muhammad Zubair , Srinivasan Sathiyaraj , Rahman Md. Shahedur , Akter Shahina 
Published: October 2023

A Gram-stain-negative, aerobic, short rod-shaped and motile bacterial strain, designated MAH-33T, was isolated from rhizospheric soil of eggplant. The colonies were observed to be yellow-coloured, smooth, spherical and 0.1–0.3 mm in diameter when grown on TSA agar medium for 2 days. Strain MAH-33T was found to be able to grow at 10–40 °C, at pH 5.0–10.0 and at 0–3.0 % NaCl (w/v). The strain was found to be positive for both oxidase and catalase tests. The strain was positive for hydrolysis of tyrosine and aesculin. According to the 16S rRNA gene sequence comparisons, the isolate was identified as a member of the genus

Sphingobium

and to be closely related to

Sphingobium quisquiliarum

P25T (98.4 % similarity),

Sphingobium mellinum

WI4T (97.8 %),

Sphingobium fuliginis

TKPT (97.3 %) and

Sphingobium herbicidovorans

NBRC 16415T (96.9 %). The novel strain MAH-33T has a draft genome size of 3 908 768 bp (28 contigs), annotated with 3689 protein-coding genes, 45 tRNA and three rRNA genes. The average nucleotide identity and digital DNA–DNA hybridization values between strain MAH-33T and closely related type strains were in the range of 79.8–81.6 % and 23.2–24.5 %, respectively. The genomic DNA G+C content was determined to be 62.2 %. The predominant isoprenoid quinone was ubiquinone 10. The major fatty acids were identified as C16 : 0 and summed feature 8 (C18 : 1
ω7c and/or C18 : 1
ω6c). The polar lipids identified in strain MAH-33T were phosphatidylethanolamine, diphosphatidylglycerol, phosphatidylglycerol, sphingoglycolipid, phosphatidylcholine; one unknown phospholipid and one unknown lipid. On the basis of digital DNA–DNA hybridization, ANI value, genotypic analysis, chemotaxonomic and physiological data, strain MAH-33T represents a novel species within the genus

Sphingobium

, for which the name Sphingobium agri sp. nov. is proposed, with MAH-33T (=KACC 19973T = CGMCC 1.16609T) as the type strain.
Enhanced disease susceptible variant identification via short identity by descent segments
Authors: Wang Chonghao , Veldsman Werner Pieter , Zhang Lu 
Published: October 2023

AbstractRare diseases affect millions of individuals worldwide, yet diagnostic yields for them still remain low. Among variant identification approaches, identity by descent (IBD) mapping is used to identify disease susceptible variants originating from a recent common ancestor among affected individuals, but existing IBD detection models struggle to identify these variants in short IBD segments. Here, we introduce SILO, a novel model to detect disease susceptible variants in both short and long IBD segments. SILO employs a two-stage procedure to detect IBD segments. In the first stage, SILO identifies long IBD segments based on common variants. In the second stage, SILO utilizes rare variants to detect short IBD segments using a seed-and-extend algorithm. We evaluated SILO in simulated data and real data from the 1000 Genomes Project. Our results demonstrate that SILO outperforms existing models in detecting disease susceptible variants within short IBD segments, and show comparable performance in longer IBD segments. These findings highlight the potential of SILO to increase diagnostic yields for rare diseases by enhancing the identification of previously overlooked disease susceptible variants in short IBD segments.
Serotonin 1A receptor distribution in treatment-resistant depression during psychopharmacotherapy compared to healthy controls
Authors: Murgaš Matej , Milz Christian , Stöhrmann Peter , Godbersen Godber M. , Unterholzner Jakob , Nics Lukas , Kranz Georg S. , Hahn Andreas , Hacker Marcus , Kasper Siegfried , Lanzenberger Rupert 
Published: October 2023

ABSTRACTMajor depressive disorder (MDD) is associated with a high lifetime prevalence and is a major cause of disability. An additional burden on patients is that up to 60% of the first antidepressant (AD) trials do not provide adequate symptom relief and after two subsequent AD trials, a patient is referred to as treatment-resistant. The serotonin 1A receptor subtype (5-HT1A) has commonly been used to study pathophysiological alteration in MDD. However, PET data on treatment-resistant depression (TRD) populations is still limited.In this cross-sectional study, 5-HT1Areceptor binding was assessed in 20 TRD patients (9 female, mean age ± SD, 29.0 ± 5.2) and 20 healthy controls (HC) (10 female, mean age ± SD, 33.2 ± 8.2). Positron emission tomography (PET) scans with the radiotracer [carbonyl-11C]WAY-100635 were acquired and 5-HT1Anon-displaceable binding potential (BPND) was quantified using the multilinear reference tissue model 2, with the cerebellar white matter as reference region. Mean regional BPNDin five regions of interest (amygdala, anterior cingulate cortex, hippocampus, insula and orbitofrontal cortex) was compared in a repeated measures analysis of covariance (rmANCOVA) with age, sex and group as covariates.Estimated marginal means showed slightly lower BPNDin TRD group (mean ± SD = 5.464 ± 0.247) than in the HC group (mean ± SD = 5.938 ± 0.245). However, the rmANCOVA showed no significant group difference (p = 0.659).Studies on 5-HT1Abinding in MDD show heterogeneous results, where the directionality of difference as well as the significance of findings strongly depend on specific outcome measures (BPND, BPFor BPP), reference region or quantification method. Here we showed no significant effect of TRD on BPND, similar to other studies applying the same methodology for MDD cohorts.
Hormonal plasticity to food restriction is heritable
Authors: Ouyang Jenny Q , Lendvai Ádám Z 
Published: October 2023

AbstractTheoretical and empirical studies agree that populations harbor extensive among-individual variation in phenotypic plasticity, but the mechanisms generating and maintaining this variation are often unknown. Endocrine systems that exhibit plastic changes in response to environmental variation may be subject to natural selection, but their evolution requires heritable variation. It is currently unknown if endocrine plasticity to environmental challenges is heritable. We tested whether glucocorticoid responsiveness to food restriction is heritable in house sparrows,Passer domesticus, by subjecting individuals to a standardized dietary restriction and selecting individuals according to their hormonal responsiveness to the treatments: into high plastic, low plastic, and control groups and let them reproduce. Using a cross-foster design, we compared the parental and the F1 generation to partition the heritability of glucocorticoid responsiveness into genetic and environmental sources of variation. We found moderate heritability (h2>20%) of glucocorticoid plasticity in response to food availability in both restricted and adequate food conditions. Environmental variance played a larger role under restricted than adequate food conditions, whereas residual variance was much higher under adequate food conditions. Our findings provide empirical evidence for the existence of heritable individual variation in glucocorticoid plasticity that selection can act upon, especially in rapidly changing environments.
Quantifying myelin density in the feline auditory cortex
Authors: Robertson Austin , Miller Daniel J. , Hull Adam , Butler Blake E. 
Published: October 2023

AbstractThe cerebral cortex comprises many distinct regions that differ in structure, function, and patterns of connectivity. Current approaches to parcellating these regions often take advantage of functional neuroimaging approaches that can identify regions involved in a particular process with reasonable spatial resolution. However neuroanatomical biomarkers are also very useful in identifying distinct cortical regions either in addition to, or in place of functional measures. For example, differences in myelin density are thought to relate to functional differences between regions, are sensitive to individual patterns of experience, and have been shown to vary across functional hierarchies in a predictable manner. Accordingly, the current study provides quantitative stereological estimates of myelin density for each of the 13 regions that make up the feline auditory cortex. We demonstrate that significant differences can be observed between auditory cortical regions, with the highest myelin density observed in the regions that comprise the auditory core (i.e., the primary auditory cortex and anterior auditory field). Moreover, our myeloarchitectonic map suggests that myelin density varies in a hierarchical fashion that conforms to the traditional model of spatial organization in auditory cortex. Taken together, these results establish myelin as a useful biomarker for parcellating auditory cortical regions, and provide detailed estimates against which other, less invasive methods of quantifying cortical myelination may be compared.
Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data
Authors: Kapralov Nikolai , Idaji Mina Jamshidi , Stephani Tilman , Studenova Alina , Vidaurre Carmen , Ros Tomas , Villringer Arno , Nikulin Vadim 
Published: October 2023

AbstractObjectiveServing as a channel for communication with locked-in patients or control of prostheses, sensorimotor brain-computer interfaces (BCIs) decode imaginary movements from the recorded activity of the user’s brain. However, many individuals remain unable to control the BCI, and the underlying mechanisms are unclear. The user’s BCI performance was previously shown to correlate with the resting-state signal-to-noise ratio (SNR) of the mu rhythm and the phase synchronization (PS) of the mu rhythm between sensorimotor areas. Yet, these predictors of performance were primarily evaluated in a single BCI session, while the longitudinal aspect remains rather uninvestigated. In addition, different analysis pipelines were used to estimate PS in source space, potentially hindering the reproducibility of the results.ApproachTo systematically address these issues, we performed an extensive validation of the relationship between pre-stimulus SNR, PS, and session-wise BCI performance using a publicly available dataset of 62 human participants performing up to 11 sessions of BCI training. We performed the analysis in sensor space using the surface Laplacian and in source space by combining 24 processing pipelines in a multiverse analysis. This way, we could investigate how robust the observed effects were to the selection of the pipeline.Main resultsOur results show that SNR had both between- and within-subject effects on BCI performance for the majority of the pipelines. In contrast, the effect of PS on BCI performance was less robust to the selection of the pipeline and became non-significant after controlling for SNR.SignificanceTaken together, our results demonstrate that changes in neuronal connectivity within the sensorimotor system are not critical for learning to control a BCI, and interventions that increase the SNR of the mu rhythm might lead to improvements in the user’s BCI performance.
In vitrocultivation techniques for modeling liver organogenesis, building assembloids, and designing synthetic tissues
Authors: Kumar Simran , Patel Helly , Parashurama Natesh 
Published: October 2023

ABSTRACTChronic liver disease has reached epidemic proportions, affecting over 800 million people globally. The current treatment, orthotopic liver transplantation, has several limitations. Promising solutions have emerged in the field of liver regenerative medicine, with liver organogenesis holding significant potential. Early liver organogenesis, occurring between E8.5 and 11.5, involves the formation of epithelial-mesenchymal interactions leading to morphogenesis, hepatic cord formation, and collective migration. However, there is a lack of methods forin vitromodeling of this process. In this study, a detailed series of methods are presented enabling the modeling of various stages and aspects of liver organogenesis. In one method series, assembloid technology with hepatic and mesenchymal spheroids is utilized, replicating early structures found in liver organogenesis, modeling early morphogenesis, and demonstrating interstitial cell migration as seenin vivo. These innovative assembloid systems help identify factors influencing assembloid formation and migration. Hepatic spheroid cultivation systems were also employed to model collective migration and branching morphogenesis. Fibroblast-conditioned media (MES-CM) plays a significant role in initiating dose-dependent branching migration. Future work will involve high temporal and spatial resolution imaging of hepatic and mesenchymal interactions to determine the cascade of cellular and molecular events involved in tissue formation, morphogenesis, and migration.SUMMARYOrganoids revolutionize personalized tissue modeling for organ development, drug discovery, and disease research. Organoid engineering advances into creating intricate synthetic tissues. The aim is to integrate morphogenesis, assembloid technology, and biomatrices to advance tissue engineering. The presented methods aid in modeling liver organogenesis and establishing guidelines for synthetic tissue construction.
A consistent map in the medial entorhinal cortex supports spatial memory
Authors: Malone Taylor J. , Tien Nai-Wen , Ma Yan , Cui Lian , Lyu Shangru , Wang Garret , Nguyen Duc , Zhang Kai , Myroshnychenko Maxym V. , Tyan Jean , Gordon Joshua A. , Kupferschmidt David A. , Gu Yi 
Published: October 2023

ABSTRACTThe medial entorhinal cortex (MEC) is hypothesized to function as a cognitive map for memory-guided navigation. How this map develops during learning and influences memory remains unclear. By imaging MEC calcium dynamics while mice successfully learned a novel virtual environment over ten days, we discovered that the dynamics gradually became more spatially consistent and then stabilized. Additionally, grid cells in the MEC not only exhibited improved spatial tuning consistency, but also maintained stable phase relationships, suggesting a network mechanism involving synaptic plasticity and rigid recurrent connectivity to shape grid cell activity during learning. Increased c-Fos expression in the MEC in novel environments further supports the induction of synaptic plasticity. Unsuccessful learning lacked these activity features, indicating that a consistent map is specific for effective spatial memory. Finally, optogenetically disrupting spatial consistency of the map impaired memory-guided navigation in a well-learned environment. Thus, we demonstrate that the establishment of a spatially consistent MEC map across learning both correlates with, and is necessary for, successful spatial memory.
Gut Microbiome Wellness Index 2 for Enhanced Health Status Prediction from Gut Microbiome Taxonomic Profiles
Authors: Chang Daniel , Gupta Vinod K. , Hur Benjamin , Cobo-López Sergio , Cunningham Kevin Y. , Han Nam Soo , Lee Insuk , Kronzer Vanessa L. , Teigen Levi M. , Karnatovskaia Lioudmila V. , Longbrake Erin E. , Davis John M. , Nelson Heidi , Sung Jaeyun 
Published: October 2023

AbstractRecent advancements in human gut microbiome research have revealed its crucial role in shaping innovative predictive healthcare applications. We introduce Gut Microbiome Wellness Index 2 (GMWI2), an advanced iteration of our original GMWI prototype, designed as a robust, disease-agnostic health status indicator based on gut microbiome taxonomic profiles. Our analysis involved pooling existing 8069 stool shotgun metagenome data across a global demographic landscape to effectively capture biological signals linking gut taxonomies to health. GMWI2 achieves a cross-validation balanced accuracy of 80% in distinguishing healthy (no disease) from non-healthy (diseased) individuals and surpasses 90% accuracy for samples with higher confidence (i.e., outside the “reject option”). The enhanced classification accuracy of GMWI2 outperforms both the original GMWI model and traditional species-level α-diversity indices, suggesting a more reliable tool for differentiating between healthy and non-healthy phenotypes using gut microbiome data. Furthermore, by reevaluating and reinterpreting previously published data, GMWI2 provides fresh insights into the established understanding of how diet, antibiotic exposure, and fecal microbiota transplantation influence gut health. Looking ahead, GMWI2 represents a timely pivotal tool for evaluating health based on an individual’s unique gut microbial composition, paving the way for the early screening of adverse gut health shifts. GMWI2 is offered as an open-source command-line tool, ensuring it is both accessible to and adaptable for researchers interested in the translational applications of human gut microbiome science.
Self-supervised segmentation and characterization of fiber bundles in anatomic tracing data
Authors: Sundaresan Vaanathi , Lehman Julia F. , Maffei Chiara , Haber Suzanne N. , Yendiki Anastasia 
Published: October 2023

AbstractAnatomic tracing is the gold standard tool for delineating brain connections and for validating more recently developed imaging approaches such as diffusion MRI tractography. A key step in the analysis of data from tracer experiments is the careful, manual charting of fiber trajectories on histological sections. This is a very time-consuming process, which limits the amount of annotated tracer data that are available for validation studies. Thus, there is a need to accelerate this process by developing a method for computer-assisted segmentation. Such a method must be robust to the common artifacts in tracer data, including variations in the intensity of stained axons and background, as well as spatial distortions introduced by sectioning and mounting the tissue. The method should also achieve satisfactory performance using limited manually charted data for training. Here we propose the first deep-learning method, with a self-supervised loss function, for segmentation of fiber bundles on histological sections from macaque brains that have received tracer injections. We address the limited availability of manual labels with a semi-supervised training technique that takes advantage of unlabeled data to improve performance. We also introduce anatomic and across-section continuity constraints to improve accuracy. We show that our method can be trained on manually charted sections from a single case and segment unseen sections from different cases, with a true positive rate of ~0.80. We further demonstrate the utility of our method by quantifying the density of fiber bundles as they travel through different white-matter pathways. We show that fiber bundles originating in the same injection site have different levels of density when they travel through different pathways, a finding that can have implications for microstructure-informed tractography methods. The code for our method is available athttps://github.com/v-sundaresan/fiberbundle_seg_tracing.