bluuebunny/crossref_metadata_2025_split huggingface dataset:
Accuracy of Dual-Energy Computed Tomography Techniques for Fat Quantification in Comparison With Magnetic Resonance Proton Density Fat Fraction and Single-Energy Computed Tomography in an Anthropomorphic Phantom Environment
Authors: Wu Zi Jun , Hippe Daniel S. , Zamora David A. , Briller Noah , Amin Kathan A. , Kolokythas Orpheus , Mileto Achille 
Published: September 2021

Objective
To investigate in an anthropomorphic phantom study the accuracy of dual-energy computed tomography (DECT) techniques for fat quantification in comparison with magnetic resonance (MR) proton density fat fraction (PDFF) and single-energy computed tomography (SECT), using known fat content as reference standard.

Methods
Between August 2018 and November 2020, organic material-based cylinders, composed of mixtures of lean and fat tissues mimics, iodine, and iron, were constructed to simulate varying fat content levels (0%, 10%, 15%, 25%, 50%, 75%, and 100%) in a parenchymal organ and were embedded into an anthropomorphic phantom simulating 3 patient sizes (circumference, 91, 126, and 161 cm). The phantom was imaged with multiecho MR, DECT, and SECT. Magnetic resonance PDFF, DECT fat fraction, and computed tomography (CT) numbers (SECT polychromatic and DECT monochromatic data, virtual unenhanced images) were estimated. Performances of MR PDFF and CT techniques to detect differences in fat content were measured using the area under the curve (AUC). Noninferiority of each CT technique relative to MR PDFF was tested using a noninferiority margin of −0.1.

Results
MR PDFF, DECT 140 keV monochromatic data, and fat fraction most closely correlated with known fat content (R
2 = 0.98, 0.98, and 0.96, respectively). Unlike SECT and all other DECT techniques, DECT fat fraction was not affected by presence of iodine (mean difference, 0.3%; 95% confidence interval [CI], −0.9% to 1.5%). Dual-energy computed tomography fat fraction showed noninferiority to MR PDFF in detecting differences of 5% in fat content in medium-sized phantoms (ΔAUC, −0.05; 95% CI, −0.08 to −0.01), and 7% in large (ΔAUC, −0.04; 95% CI, −0.0 to 0.00) or extralarge sized phantoms (ΔAUC, −0.02; 95% CI, −0.07 to 0.00).

Conclusions
Dual-energy computed tomography fat fraction shows linear correlation with true fat content in the range up to 50% fat fraction. Dual-energy computed tomography fat fraction has comparable estimation error and shows noninferiority to MR PDFF in detecting small differences in fat content across different body sizes.
Texture Analysis of Tumor and Peritumoral Tissues Based on 18F-Fluorodeoxyglucose Positron Emission Tomography/Computed Tomography Hybrid Imaging in Patients With Rectal Cancer
Authors: Orel Valerii E. , Ashykhmin Andrii , Golovko Tetiana , Rykhalskyi Oleksandr , Orel Valerii B. 
Published: September 2021

Objective
This study aimed to determine whether texture parameters could be used in differentiation between the tumor and the peritumoral tissues based on hybrid 18F-Fluorodeoxyglucose positron emission tomography/computed tomography imaging for patients with rectal cancer.

Methods
Seven parameters, including heterogeneity, entropy, energy, skewness, kurtosis, standard deviation, and average brightness, were extracted from positron emission tomography/computed tomography scans of 22 patients (12 male and 10 female; mean age, 61 ± 2 years).

Results
The peritumoral tissue had a significantly lower value of the heterogeneity parameter (23%) than the tumor. Tumor size (r = −0.48, P < 0.05) and extramural venous invasion scores (r = 0.64, P < 0.05) correlated with heterogeneity in the peritumoral tissue. There were significant differences (P < 0.05) in the correlation coefficients between men and women.

Conclusions
Therefore, we provided additional quantitative information to differentiate the tumor from the peritumoral tissue and indicated possible application for extramural venous invasion evaluation in rectal cancer.
Disparities in Alzheimer Disease and Mild Cognitive Impairment Among Native Hawaiians and Pacific Islanders
Authors: Smith Maiya , Van Nicholas , Roberts Alyssa , Hosaka Kalei R.J. , Choi So Yung , Viereck Jason , Carrazana Enrique , Borman Pat , Chen John J. , Liow Kore Kai 
Published: September 2021

Background:
Previous studies of racial differences in Alzheimer disease (AD) presentation have not included Native Hawaiians and Pacific Islanders (NHPI).

Objective:
To explore the presentation of AD and mild cognitive impairment (MCI) in NHPI.

Method:
We conducted a retrospective review of patient records from Hawaii with a diagnosis of unspecified AD or MCI from September 2000 to September 2019. Variables of interest included age at diagnosis, gender, race, marital status, insurance, comorbidities, and scores on the Mini-Mental State Examination (MMSE) or the Montreal Cognitive Assessment (MoCA).

Results:
We reviewed the medical records of 598 patients, including 224 Asians, 202 Whites, 87 NHPI, and 85 Other. AD was more dominant than MCI across all of the groups, with the highest percentage in NHPI. Among the mean ages of diagnosis, NHPI were the youngest. Across all groups, a higher proportion of women than men had AD, with the highest female prevalence among NHPI. Hypertension, hyperlipidemia, and type II diabetes were highest among NHPI compared with the other groups. Of individuals with MMSE/MoCA scores, there were significant variations in scores by racial group. The mean MMSE/MoCA score was highest among Whites and lowest among NHPI.

Conclusion:
Compared with other racial groups, NHPI have a higher proportion of AD than MCI at diagnosis, are diagnosed at a younger age, have a higher female prevalence, have more comorbidities that may contribute to AD/MCI onset, and present with lower MMSE scores.
Development of a women empowerment framework to promote voluntary medical male circumcision uptake for HIV prevention in Zimbabwe
Authors: Danda Grace , Mavundla Thandisizwe R , Mudokwenyu- Rawdon Christina 
Published: September 2021

ABSTRACTIntroductionThis study aimed to develop a women empowerment framework to promote Voluntary Medical Male Circumcision (VMMC) uptake for human immune-deficiency virus (HIV) prevention, guided by the following objectives: To explore relevant literature on the role of women in promoting male circumcision uptake; To develop a women empowerment framework to promote male circumcision uptake; To describe the women empowerment framework to promote male circumcision uptake.MethodsThe study used a theory-generation design to explore, describe and develop a women empowerment framework from a broad literature review.ResultsA three-phase approach suitable for the framework development emerged from the literature review. Phase one involved exploring relevant literature on the role of women in male circumcision uptake, using the Population/problem, Intervention, Comparison and Outcome (PICO) method to identify and define the key concepts. Phase 2 adopted and adapted the model by Maibvise and Mavundla in identifying the following concepts: change agents as the health care providers, recipients as the women to influence men eligible for male circumcision, promoting male circumcision uptake and success of the male circumcision program. Phase 3 provided a detailed description of the framework including three key steps of empowerment of women, influencing positive perceptions of men and utilisation of male circumcision services.ConclusionsThe identified concepts resulted in development of a women empowerment framework, which can be used as an education and advocacy tool in building the capacity of women in supporting the male circumcision programme to promote uptake. Further research is required to expand the utilisation of the framework in male circumcision healthcare services.
A Deep Transcriptome Meta-Analysis Reveals Sex-based Molecular Differences in Multiple Sclerosis
Authors: Català-Senent José Francisco , Andreu Zoraida , Hidalgo Marta R. , Roig Francisco José , Yanguas-Casás Natalia , Neva-Alejo Almudena , López-Cerdán Adolfo , Soler-Sáez Irene , de la Iglesia-Vayá María , García-García Francisco 
Published: September 2021

AbstractBackgroundMultiple sclerosis (MS), a chronic auto-immune, inflammatory, and degenerative disease of the central nervous system, affects both males and females; however, females suffer from a higher risk of developing MS (2-3:1 ratio compared to males). Current knowledge does not allow a precise definition of the sex-based factors influencing MS. Here, we explore the role of sex in MS to identify potential molecular mechanisms underlying sex-based differences that may guide novel therapeutic approaches tailored for males or females.MethodsWe performed a rigorous and systematic review of whole transcriptome studies of MS that included patient information regarding sex in Gene Expression Omnibus and ArrayExpress databases following PRISMA statement guidelines. We analyzed differential gene expression for each selected study and addressed 3 meta-analyses based on genes to evaluate common features and sex bias: the first meta-analysis of 4 nervous tissue studies, a second in 5 blood studies, and a third integrating 9 studies from both tissues. Finally, we performed a gene set analysis on the meta-analyzed differential transcriptomic profiles of the nervous system to study sex-based differences in biological pathways and phenotypes (physiological and pathological states).ResultsAfter screening 122 publications, the systematic review provided a selection of 9 studies (5 in blood and 4 in nervous tissue) with a total of 474 samples (189 MS females and 109 control females; 82 MS males and 94 control males). The tissue-specific meta-analysis identified the overexpression of KIR2DL3 in blood in females and 13 genes with a sex-based differential expression pattern in the nervous system (7 overexpressed in females: ARL17B, CECR7, CEP78, STMP1, TRAF3IP2-AS1, ZNF117 and ZNF488; and 6 overexpressed in males: IFFO2, LOC401127, NUDT18, RNF10, SLC17A5, and UBXN2B). The two-tissue meta-analysis detected a single gene overexpressed in females (LOC102723701). Functional analyses revealed different altered immune scenarios in females and males. A pro-inflammatory environment and innate immune responses related to myeloid linage predominate in females, while in males, adaptative responses associated with the lymphocyte linage. Additionally, MS females displayed alterations in mitochondrial respiratory chain complexes, purine, and glutamate metabolism, while MS males displayed alterations in stress response to metal ion, amine, and amino acid transport.ConclusionWe found transcriptomic and functional differences between MS males and females (especially in the immune system), which may support the development of sex-specific treatments. Our study highlights the importance of understanding sex as a variable in MS.
High-Throughput Adaptable SARS-CoV-2 Screening for Rapid Identification of Dominant and Emerging Regional Variants
Authors: Hubler Zita , Song Xiao , Norris Cameron , Jani Mehul , Alouani David , Atchley Maureen , Stempak Lisa , Cherian Sarah , Schmotzer Christine , Sadri Navid 
Published: September 2021

ABSTRACTObjectivesEmerging SARS-CoV-2 variant strains can be associated with increased transmissibility, more severe disease, and reduced effectiveness of treatments. To improve the availability of regional variant surveillance, we describe a variant genotyping system that is rapid, accurate, adaptable, and able to detect new low-level variants built with existing hospital infrastructure.MethodsWe use a tiered high-throughput SARS-CoV-2 screening program to characterizes variants in a supra-regional health system over 76 days. Combining targeted qPCR and selective sequencing, we screen positive SARS-CoV-2 samples from all hospitals within our health care system for genotyping dominant and emerging variants.ResultsThe median turnaround for genotyping was two days using the high-throughput qPCR-based screen, allowing us to rapidly characterize the emerging Delta variant. In our population, the Delta variant is associated with a lower CT value, lower age at infection, and increased vaccine breakthrough cases. Detection of low-level and potentially emerging variants highlights the utility of a tiered approach.ConclusionsThese findings underscore the need for fast, low-cost, high-throughput monitoring of regional viral sequences as the pandemic unfolds and the emergence of SARS-CoV-2 variants increases. Combing qPCR-based screening with selective sequencing allows for rapid genotyping of variants and dynamic system improvement.Key messagesA tiered approach that uses qPCR-based screening to identify dominant variants and sequencing for unique variants maximizes throughput, turnaround time, and information gleaned from each sample.In our population, the Delta variant became dominant in less than a month and is associated with lower CT, lower age at infection, and increased breakthrough cases.We identified low-level variants, including the variant of interest B.1.621 and a Delta variant with an E484K mutation in our population using existing hospital infrastructure.
Integrative genetic analysis of the amyotrophic lateral sclerosis spinal cord implicates glial activation and suggests new risk genes
Authors: Humphrey Jack , Venkatesh Sanan , Hasan Rahat , Herb Jake T. , de Paiva Lopes Katia , Küçükali Fahri , Byrska-Bishop Marta , Evani Uday S. , Narzisi Giuseppe , Fagegaltier Delphine ,   NYGC ALS Consortium, Sleegers Kristel , Phatnani Hemali , Knowles David A. , Fratta Pietro , Raj Towfique 
Published: September 2021

AbstractAmyotrophic lateral sclerosis (ALS) is a progressively fatal neurodegenerative disease affecting motor neurons in the brain and spinal cord. We used 380 post-mortem tissue RNA-seq transcriptomes from 154 ALS cases and 49 control individuals from cervical, thoracic, and lumbar spinal cord segments to investigate the gene expression response to ALS. We observed an increase in microglia and astrocyte expression, accompanied by a decrease in oligodendrocytes. By creating a gene co-expression network in the ALS samples, we identify several activated microglia modules that negatively correlate with retrospective disease duration.We map molecular quantitative trait loci and find several potential ALS risk loci that may act through gene expression or splicing in the spinal cord and assign putative cell-types for FNBP1, ACSL5, SH3RF1 and NFASC. Finally, we outline how repeat expansions that alter splicing of C9orf72 are tagged by common variants, and use this to suggest ATXN3 as a putative risk gene.
Predictive Classification of IBS-subtype: Performance of a 250-gene RNA expression panel vs. Complete Blood Count (CBC) profiles under a Random Forest model
Authors: Robinson Jeffrey 
Published: September 2021

AbstractIn this experiment, an R-script was developed to select the best performing machine learning (ML) predictive classification algorithm for IBS-subtype, and compare the performance of two datasets from the same clinical cohort – 1) The Complete Blood Count (CBC) results, and 2) A 250-gene Nanostring expression panel run on RNA from the “Buffy Coat” fraction. This publicly available data was compiled from open-source repositories and previously published supplementary data. Column labels were reformatted according to “tidy-data” standards. NA values in the data were imputed based on the mean value of the data column. Subject groups included Control (ie. healthy), IBS-D (diarrhea predominant), and IBS-C (constipation predominant) subtypes. These groups had unequal numbers in the original study, and so random re-sampling was used to make the group numbers equal for downstream linear regression-based analyses. The data was randomly split into training and validation subsets, and 5 classification algorithms were tested. Random Forest was clearly the best performing algorithm for both CBC and gene expression panel data, generally with >95% predictive accuracy, without additional tuning. The 250-gene RNA expression panel performed somewhat better than the CBC profile under a Random Forest model, however the CBC profiles had only 13 predictor variables vs. the 250 of the RNA expression panel. Some artifacts may result from the duplication of IBS-D and IBS-C rows from to the group-size balancing method, and so larger and more comprehensive datasets will be obtained for a follow-up analysis. The R-script and reformatted data are published as supplementary material here, and as a component of the ‘AnalyzeBloodworkv1.2’ GitHub repository.
Optimal COVID-19 testing strategy on limited resources
Authors: Tatsuki Onishi , Naoki Honda , Igarashi Yasunobu 
Published: September 2021

AbstractThe last three years have been spent combating COVID-19, and governments have been seeking optimal solutions to minimize the negative impacts on societies. Although two types of testing have been performed for this—follow-up testing for those who had close contact with infected individuals and mass-testing of those with symptoms—the allocation of resources has been controversial. Mathematical models such as the susceptible, infectious, exposed, recovered, and dead (SEIRD) model have been developed to predict the spread of infection. However, these models do not consider the effects of testing characteristics and resource limitations. To determine the optimal testing strategy, we developed a testing-SEIRD model that depends on testing characteristics and limited resources. In this model, people who test positive are admitted to the hospital based on capacity and medical resources. Using this model, we examined the infection spread depending on the ratio of follow-up and mass-testing. The simulations demonstrated that the infection dynamics exhibit an all-or-none response as infection expands or extinguishes. Optimal and worst follow-up and mass-testing combinations were determined depending on the total resources and cost ratio of the two types of testing. Furthermore, we demonstrated that the cumulative deaths varied significantly by hundreds to thousands of times depending on the testing strategy, which is encouraging for policymakers. Therefore, our model might provide guidelines for testing strategies in the cases of recently emerging infectious diseases.
Cell types and clonal relations in the mouse brain revealed by single-cell and spatial transcriptomics
Authors: Ratz Michael , von Berlin Leonie , Larsson Ludvig , Martin Marcel , Westholm Jakub Orzechowski , La Manno Gioele , Lundeberg Joakim , Frisén Jonas 
Published: September 2021

SummaryThe mammalian brain contains a large number of specialized cells that develop from a thin sheet of neuroepithelial progenitor cells1,2. Recently, high throughput single-cell technologies have been used to define the molecular diversity of hundreds of cell types in the nervous system3,4. However, the lineage relationships between mature brain cells and progenitor cells are not well understood, because transcriptomic studies do not allow insights into clonal relationships and classical fate-mapping techniques are not scalable5,6. Here we showin vivobarcoding of early progenitor cells that enables simultaneous profiling of cell phenotypes and clonal relations in the mouse brain using single-cell and spatial transcriptomics. We reconstructed thousands of clones to uncover the existence of fate-restricted progenitor cells in the mouse hippocampal neuroepithelium and show that microglia are derived from few primitive myeloid precursors that massively expand to generate widely dispersed progeny. By coupling spatial transcriptomics with clonal barcoding, we disentangle migration patterns of clonally related cells in densely labelled tissue sections. Compared to classical fate mapping, our approach enables high-throughput dense reconstruction of cell phenotypes and clonal relations at the single-cell and tissue level in individual animals and provides an integrated approach for understanding tissue architecture.
Activity-dependent organization of prefrontal hub-networks for associative learning and signal transformation
Authors: Agetsuma Masakazu , Sato Issei , Tanaka Yasuhiro R , Carrillo-Reid Luis , Kasai Atsushi , Arai Yoshiyuki , Yoshitomo Miki , Inagaki Takashi , Hashimoto Hitoshi , Nabekura Junichi , Nagai Takeharu 
Published: September 2021

AbstractAssociative learning is crucial for adapting to environmental changes. The encoding of associative learning involves the dorso-medial prefrontal cortex (dmPFC), and is underpinned by interactions within the resident neuronal population. However, the nature of this population coding is poorly understood. Here we developed a pipeline for computational dissection and longitudinal two-photon imaging of neural population activities in the mouse dmPFC during fear-conditioning procedures, enabling us to detect learning-dependent changes in the dmPFC topology. Through regularized regression methods and graphical modeling, we found fear conditioning organized neuronal ensembles encoding conditioned responses (CR), with enhancing their coactivity, functional connectivity, and association with conditioned stimuli (CS). This suggests that fear conditioning drives dmPFC reorganization to generate novel associative circuits for CS-to-CR transformation. Importantly, neurons strongly responding to unconditioned stimuli (US) during conditioning anterogradely became a hub of the CR ensemble. Altogether, we demonstrate learning-dependent dynamic modulation of population coding structured on an activity-dependent hub-network formation within the dmPFC.TeaserOptical and computational dissection uncovered how prefrontal cortical networks are rewired to encode new associative memorySignificance statementAnimals learn to adapt to changing environments. Associative learning is one of the simplest types of learning that has been intensively studied over the past century. Recent development in molecular, genetic, and optogenetic methods has enabled the identification of a neural population encoding the associative memory in the brain. However, it remains unclear how information is stored and processed by the neural population to encode and retrieve the associative memory. To investigate the nature of this population coding, we developed an optical and computational dissection method, demonstrating how associative learning drives reorganization of the neural network in the dorso-medial prefrontal cortex and generates novel circuits for associative memory and signal transformation.