bluuebunny/crossref_metadata_2025_split huggingface dataset:
In Utero Exposure to the Great Depression is Reflected in Late-Life Epigenetic Aging Signatures
Authors: Schmitz Lauren L. , Duque Valentina 
Published: May 2022

AbstractResearch on maternal-fetal epigenetic programming argues that adverse exposures to the intrauterine environment can have long-term effects on adult morbidity and mortality. However, causal research on epigenetic programming in humans at a population level is rare and is often unable to separate intrauterine effects from conditions in the postnatal period that may continue to impact child development. In this study, we used a quasi-natural experiment that leverages state-year variation in economic shocks during the Great Depression to examine the causal effect of environmental exposures in early life on late-life accelerated epigenetic aging for 832 participants in the U.S. Health and Retirement Study (HRS). HRS is the first population-representative study to collect epigenome-wide DNA methylation data that has the sample size and geographic variation necessary to exploit quasi-random variation in environments, which expands possibilities for causal research in epigenetics. Our findings suggest that exposure to changing economic conditions in the 1930s had lasting impacts on next-generation epigenetic aging signatures that were developed to predict mortality risk (GrimAge) and physiological decline (DunedinPoAm). We show that these effects are localized to the in utero period specifically as opposed to the pre-conception, postnatal, childhood, or early adolescent periods. After evaluating changes in mortality rates for Depression-era birth cohorts, we conclude that these effects likely represent lower bound estimates of the true impacts of the economic shock on long-term epigenetic aging.
Automatically Identifying Twitter Users for Interventions to Support Dementia Family Caregivers: Annotated Data Set and Benchmark Classification Models
Authors: Klein Ari Z. , Magge Arjun , O’Connor Karen , Gonzalez-Hernandez Graciela 
Published: May 2022

AbstractBackgroundMore than 6 million people in the United States have Alzheimer’s disease and related dementias, receiving help from more than 11 million family or other informal caregivers. A range of traditional interventions have been developed to support family caregivers; however, most of them have not been implemented in practice and remain largely inaccessible. While recent studies have shown that family caregivers of people with dementia use Twitter to discuss their experiences, methods have not been developed to enable the use of Twitter for interventions.ObjectiveThe objective of this study was to develop an annotated data set and benchmark classification models for automatically identifying a cohort of Twitter users who have a family member with dementia.MethodsBetween May 4, 2021 and May 20, 2021, we collected 10,733 tweets, posted by 8846 users, that mention a dementia-related keyword, a linguistic marker that potentially indicates a diagnosis, and a select familial relationship. Three annotators annotated one random tweet per user to distinguish those that indicate having a family member with dementia from those that do not. We used the annotated tweets to train and evaluate deep neural network classifiers based on pretrained transformer models. To assess the scalability of our approach, we, then, deployed automatic classification on tweets that were continuously collected between May 4, 2021 and March 9, 2022.ResultsInter-annotator agreement was 0.82 (Fleiss’ kappa). A classifier based on a BERT model pretrained on tweets achieved the highest F1-score of 0.962 (precision = 0.946, recall = 0.979) for the class of tweets indicating that the user has a family member with dementia. The classifier detected 128,838 tweets that indicate having a family member with dementia, posted by 74,290 users between May 4, 2021 and March 9, 2022—that is, approximately 7500 users per month.ConclusionsOur annotated data set can be used to automatically identify Twitter users who have a family member with dementia, enabling the use of Twitter on a large scale to not only explore family caregivers’ experiences, but also directly target interventions at these users.
Artificial intelligence and opioid use: a narrative review
Authors: Gadhia Seema , Richards Georgia C. , Marriott Tracey , Rose James 
Published: May 2022

ABSTRACTBackgroundOpioids are strong pain medications that can be essential for acute pain. However, opioids are also commonly used for chronic conditions and illicitly where there are well recognised concerns about the balance of their benefits and harms. Technologies using artificial intelligence (AI) are being developed to examine and optimise the use of opioids. Yet, this research has not been synthesised to determine the types of AI models being developed and the application of these models.MethodsWe aimed to synthesise studies exploring the use of AI in people taking opioids. We searched three databases: the Cochrane Database of Systematic Reviews, EMBASE, and Medline on 4 January 2021. Studies were included if they were published after 2010, conducted in a real-life community setting involving humans, and used AI to understand opioid use. Data on the types and applications of AI models were extracted and descriptively analysed.ResultsEighty-one articles were included in our review, representing over 5.3 million participants and 14.6 million social media posts. Most (93%) studies were conducted in the USA. The types of AI technologies included natural language processing (46%) and a range of machine learning algorithms, the most common being random forest algorithms (36%). AI was predominately applied for the surveillance and monitoring of opioids (46%), followed by risk prediction (42%), pain management (10%), and patient support (2%). Few of the AI models were ready for adoption, with most (62%) being in preliminary stages.ConclusionsMany AI models are being developed and applied to understand opioid use. However, there is a need for these AI technologies to be externally validated and robustly evaluated to determine whether they can improve the use and safety of opioids.SUMMARY BOXKey PointsAcross the landscape of opioid research, natural language processing models (46%) and ensemble algorithms, particularly random forest algorithms (36%), were the most common types of AI technologies studied.There were four domains to which AI was applied to assess the use of opioids, including surveillance and monitoring (46%), risk prediction (42%), pain management (10%), and patient support (2%).The AI technologies were at various stages of development, validation, and deployment, with most (62%) models in preliminary stages, 11% required external validation, and few models were openly available to access (6%).
LONG-LASTING INSECTICIDE NETS OWNERSHIP AND MALARIA MORBIDITY IN KRACHI EAST MUNICIPALITY, GHANA
Authors: Wuresah Israel , Elmi Siman , Adjuiky Martin 
Published: May 2022

ABSTRACTBackgroundMalaria-related morbidity and mortality are issues of great concern to public health globally though, a higher proportion of cases reside within Sub-Saharan Africa. The situation in Ghana though not new, is very disturbing, as millions of people especially children and pregnant women suffer severely from malaria. Seasonal chemoprevention and indoor residual spraying are among many measures deployed in the northern parts of Ghana with nationwide outreach and point distribution of LLINs across the country but reports from OPDs indicate millions of malaria cases annually.ObjectiveTo identify the levels of ownership and usage of the treated bed nets, and describe the relationship between ownership of LLINs and malaria morbidity.MethodsThe 30-cluster sampling method was deployed. Using both a modified WHO EPI survey method for more rural areas and a random walk sampling for more urban areas, each community had a listed starting point where the use of a spun pen determined the direction to conduct the surveys within the specified cluster. Selected households’ heads/representatives (any adult aged 18 years and above, in a household where the head is absent) participated voluntarily. STATA version 16.0 was used to run the statistical analysis and the results were presented in tables and figures.ResultsFindings revealed high levels of ownership of LLINs (73.4%) but moderately low usage levels (49.5%). Some other uses of LLINs (22.9%) aside from sleeping under them were identified. Malaria morbidity (59.6%) was also determined. Multivariate analysis results revealed statistically significant association between some socio-demographic characteristics and LLINs ownership including female sex (AOR = 2.1 (95% CI: 1.15, 3.87) p=0.016), being married (AOR = 3.4 (95% CI: 1.76, 6.74) p<0.001), cohabiting (AOR = 6.1 (95% CI: 2.15, 17.02) p=0.001) and being separated or divorced (AOR = 9.4 (95% CI: 1.09, 81.27) p=0.041). A positive correlation was identified between ownership of LLINs and their usage.ConclusionDespite high levels of ownership of LLINs, usage is minimal with a consequential effect on malaria morbidity. The study recommended service points and periodic household and/or community sensitization on LLINs usage as measures to increase usage levels.
Brain morphometric changes in congenitally blind subjects: a 7 Tesla MRI study
Authors: Kupers Ron , Zhan Minye , Paré Samuel , Dricot Laurence , Vaessen Maarten , de Gelder Beatrice 
Published: May 2022

AbstractWe used ultra-high field (7 Tesla) magnetic resonance imaging (MRI) at submillimeter resolution to assess structural brain changes in congenitally blind (CB) compared to matched normal sighted control (SC) subject groups. Region-of-interest analysis revealed grey matter (GM) volumetric reductions in the CB group in left cuneus and occipital pole, right posterior collateral sulcus and right occipito-temporal medial lingual sulcus. Non-visual areas with GM reductions in CB included the left central, postcentral and superior frontal gyri, and the right subcallosal gyrus. In contrast, there were no significant group differences in cortical thickness when using stringent statistical criteria. Regional differences in white matter (WM) showed an overall pattern similar as that of GM changes, characterized by volume reductions in occipital, parietal and temporal areas, but with additional reductions in precuneus and medial orbitofrontal cortex. Differences in cortical curvature were mostly situated in the occipital cortex and bore a close relationship with areas showing GM alterations; they may be indicative of increased cortico-cortical connectivity of the visually-deprived occipital cortex. The CB group had GM reductions in the basal ganglia, i.e., caudate nucleus, putamen, nucleus accumbens, globus pallidus, and thalamus. Within the cerebellum, GM and WM volumes were also reduced in the CB. Segmentation of the thalamus, hippocampus and amygdala into anatomic divisions revealed GM reductions in a number of thalamic nuclei, a few hippocampal regions, but not within amygdala. There were no findings of increased volume or cortical thickness in the CB group. Together, these data reveal a multitude of GM and WM reductions in CB, comprising not only the occipital cortex, but also temporal, parietal, and prefrontal cortices, as well as the basal ganglia and cerebellum. These findings in the CB may seem at odds with the large literature showing that the visually-deprived occipital cortex becomes a multimodal cortex responding to diverse non-visual sensory and cognitive inputs. The seeming mismatch between morphological atrophy and enhanced multimodality of occipital areas combined with superior performance by the CB in various non-visual tasks poses a challenge for our understanding of brain plasticity.
Fusome topology and inheritance during insect gametogenesis
Authors: Diegmiller Rocky , Alsous Jasmin Imran , Li Duojia , Yamashita Yukiko M. , Shvartsman Stanislav Y. 
Published: May 2022

AbstractFrom insects to mammals, oocytes ad sperm develop within germline cysts comprising cells connected by intercellular bridges (ICBs). In numerous insects, formation of the cyst is accompanied by growth of the fusome – a membranous that permeates the cyst. Fusome composition and function are best understood in Drosophila melanogaster: during oogenesis, the fusome dictates cyst topology and size and facilitates oocyte selection, while during spermatogenesis, the fusome synchronizes the cyst’s response to DNA damage. Despite its myriad and sex-specific roles during insect gametogenesis, fusome growth and inheritance in females and its structure and connectivity in males have remained challenging to investigate. Here, we take advantage of advances in high resolution confocal microscopy and computational image processing tools to reconstruct the topology, growth, and distribution of the fusome in both sexes. Our findings inform a theoretical model for fusome assembly and inheritance during oogenesis, shedding light on symmetry-breaking processes that lead to oocyte selection. In males, we find that cell divisions can deviate from the maximally branched pattern observed in females, leading to greater topological variability. Our work consolidates existing disjoint experimental observations and contributes a readily generalizable computational approach for quantitative studies of gametogenesis within and across species.Author summaryThe ubiquity of germline cysts across animals and accelerating advances in microscopy call for quantitative and highly resolved studies of their developmental dynamics. Here we use Drosophila melanogaster gametogenesis as a model system, alongside a supervised learning algorithm to study a shared organelle that arises during sperm and oocyte development – the fusome. The fusome is a highly specialized membranous organelle that permeates the cyst in both sexes. Our three-dimensional (3D) reconstructions of the fusome and quantitative measurements at successive stages of cyst development during oogenesis shed light on the evolution of cell fate asymmetry within the germline cyst in females, where the cyst gives rise to a single oocyte. In males, where each cell of the cyst goes on to form sperm, the fusome fragments and exhbits topologies that deviate from the stereotypic maximally branched topology found in females. Our findings can be interpreted in the context of the divergent outcomes of gametogenesis in both sexes and highlight the centrality of quantitative measurements in evaluating hypotheses in biological sciences.
A “cooperative collaboration” between HAND2 and MYCN shapes neuroblastoma cell identity
Authors: Xu Man , Sun Ming , Zhang Xiyuan , Nguyen Rosa , Lei Haiyan , Shern Jack F. , Thiele Carol J. , Liu Zhihui 
Published: May 2022

ABSTRACTNoradrenergic neuroblastoma (NB) is characterized by a core transcriptional regulatory circuitry (CRC) comprised of transcription factors (TFs) such as PHOX2B, HAND2 and GATA3, which form a network with MYCN. Physiological levels of MYCN mainly binds to promoters but aberrantly upregulated MYCN in NB also binds to enhancers. How MYCN invades enhancers, and what role the CRC TFs play in this process is unknown. Here we find that HAND2 assists MYCN to invade enhancers through an “indirect cooperative” TF-DNA binding mechanism. HAND2 cooperates with MYCN to compete with nucleosomes and regulate global gene transcription. The treatment of NB with an Aurora A kinase inhibitor plus HDAC inhibitor potently downregulates both MYCN and the CRC TFs and suppresses MYCN-amplified NB tumor growth. Our study identifies a “cooperative collaboration” among MYCN and CRC TFs and the simultaneous targeting these TFs is an effective way to treat this aggressive pediatric tumor.
Single-cell Ca2+parameter inference reveals how transcriptional states inform dynamic cell responses
Authors: Wu Xiaojun , Wollman Roy , MacLean Adam L. 
Published: May 2022

AbstractSingle-cell genomic technologies offer vast new resources with which to study cells, but their potential to inform parameter inference of cell dynamics has yet to be fully realized. Here we develop methods for Bayesian parameter inference with data that jointly measure gene expression and Ca2+dynamics in single cells. We propose to share information between cells via transfer learning: for a sequence of cells, the posterior distribution of one cell is used to inform the prior distribution of the next. In application to intracellular Ca2+signaling dynamics, we fit the parameters of a dynamical model for thousands of cells with variable single-cell responses. We show that transfer learning accelerates inference with sequences of cells regardless of how the cells are ordered. However, only by ordering cells based on their transcriptional similarity can we distinguish Ca2+dynamic profiles and associated marker genes from the posterior distributions. Inference results reveal complex and competing sources of cell heterogeneity: parameter covariation can diverge between the intracellular and intercellular contexts. Overall, we discuss the extent to which single-cell parameter inference informed by transcriptional similarity can quantify relationships between gene expression states and signaling dynamics in single cells.
Real-World Effectiveness of Casirivimab and Imdevimab Among Patients Diagnosed With COVID-19 in the Ambulatory Setting: A Retrospective Cohort Study Using a Large Claims Database
Authors: Hussein Mohamed , Wei Wenhui , Mastey Vera , Sanchez Robert J. , Wang Degang , Murdock Dana J. , Hirshberg Boaz , Weinreich David M. , Jalbert Jessica J. 
Published: May 2022

ABSTRACTImportanceData on real-world effectiveness of casirivimab and imdevimab (CAS+IMD) for treatment of coronavirus 2019 (COVID-19) are limited, especially with regard to variants of concern such as Delta.ObjectiveTo assess effectiveness of CAS+IMD versus no COVID-19 antibody treatment among patients diagnosed with COVID-19 in the ambulatory setting overall and among subgroups, including patients diagnosed during the Delta-dominant period prior to Omicron emergence.DesignRetrospective cohort study.SettingKomodo Health closed claims database.ParticipantsPatients diagnosed with COVID-19 in ambulatory settings from December 2020 through September 2021 treated with CAS+IMD or untreated but treatment-eligible under the Emergency Use Authorization (EUA). Each treated patient was exact- and propensity score-matched without replacement to up to 5 untreated EUA-eligible patients.ExposureCAS+IMD treatment.Main Outcomes and MeasuresComposite endpoint of 30-day all-cause mortality or COVID-19-related hospitalization. Kaplan-Meier estimators were used to calculate risk of outcome overall and across subgroups defined by age groups, COVID-19 vaccination status, immunocompromised, and timing of COVID-19 diagnosis (December 2020 to June 2021, and July 2021 to September 2021). Cox proportional-hazards models were used to estimate adjusted hazard ratios (aHR) and 95% confidence intervals (95% CI).ResultsAmong 75 159 patients treated with CAS+IMD and 1 670 338 EUA-eligible untreated patients, 73 759 treated patients were matched to 310 688 untreated patients; matched patients had an average age ∼50 years, approximately 60% were women and were generally well-balanced across risk factors. The 30-day risk of the composite outcome was 2.1% and 5.2% in the CAS+IMD -treated and untreated patients, respectively; CAS+IMD treatment was associated with a 60% lower risk of the outcome (aHR 0.40; 95% CI, 0.38-0.42). The effect of CAS+IMD treatment was consistent across subgroups, including those who received a COVID-19 vaccine (aHR 0.48, 95% CI, 0.41-0.56), and those diagnosed during the Delta-dominant period (aHR 0.40, 95% CI, 0.38-0.42).Conclusions and RelevanceThe real-world effectiveness of CAS+IMD is consistent with the efficacy for reducing all-cause mortality or COVID-19-related hospitalization reported in clinical trials. Effectiveness is maintained across patient subgroups, including those who may be prone to breakthrough infections, and was effective against susceptible variants including Delta.□
Metagenomics Combined with Activity-Based Proteomics Point to Gut Bacterial Enzymes that Reactivate Mycophenolate
Authors: Simpson Joshua B. , Sekela Josh J. , Graboski Amanda L. , Borlandelli Valentina B. , Barker Natalie K. , Sorgen Alicia A. , Bivins Marissa M. , Mordant Angie L. , Johnson Rebecca L. , Bhatt Aadra P. , Fodor Anthony A. , Herring Laura E. , Overkleeft Hermen , Lee John R. , Redinbo Matthew. R. 
Published: May 2022

AbstractMycophenolate Mofetil (MMF) is an important immunosuppressant prodrug prescribed to prevent organ transplant rejection and to treat autoimmune diseases. MMF usage, however, is limited by severe gastrointestinal toxicity that is observed in approximately 45% of MMF recipients. The active form of the drug, mycophenolic acid (MPA), undergoes extensive enterohepatic recirculation by bacterial β-glucuronidase (GUS) enzymes, which reactivate MPA from mycophenolate glucuronide (MPAG) within the gastrointestinal tract. GUS enzymes demonstrate distinct substrate preferences based on their structural features, and gut microbial GUS enzymes that reactivate MPA have not been identified. Here, we compare the fecal microbiomes of transplant recipients receiving MMF to healthy individuals using shotgun metagenomic sequencing. We find that neither microbial composition nor the presence of specific structural classes of GUS genes are sufficient to explain the differences in MPA reactivation measured between fecal samples from the two cohorts. We next employed a GUS-specific activity-based chemical probe and targeted metaproteomics to identify and quantify the GUS proteins present in the human fecal samples. The identification of specific GUS enzymes was improved by using the metagenomics data collected from the fecal samples. We found that the presence of GUS enzymes that bind the flavin mononucleotide (FMN) is significantly correlated with efficient MPA reactivation. Furthermore, structural analysis identified motifs unique to these FMN-binding GUS enzymes that provide molecular support for their ability to process this drug glucuronide. These results indicate that FMN-binding GUS enzymes may be responsible for reactivation of MPA and could be a driving force behind MPA-induced GI toxicity.