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3,748 results for “Pregnancy”
Unexplained Repeated Pregnancy Loss is Associated with Altered Perceptual and Brain Responses to Men’s Body-Odor
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Inter-Chemical Correlation results for the study: HHEARx2016-1534 (A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function)
Title: A Nested Case-Control Study of Prenatal Exposure to Phthalates and Psychosocial Stress: Adverse Pregnancy Outcomes and the Mediating Role of Placental Function <br>Species: Homo sapiens <br>Number of samples: 5789 <br>Number of named analytes: 17 <br>Datasource url: https://hheardatacenter.mssm.edu/PublicFile/ViewPublicFile?projectid=14 <br>
Data from: "Correlates of mid-winter pregnancy and early reproductive outcomes in a reintroduced elk (Cervus canadensis) population"
<p>Raw and processed datasets used for analysis in "Correlates of mid-winter pregnancy and early reproductive outcomes in a reintroduced elk (<em>Cervus canadensis</em>) population" by Hooven et al., published in <em>Mammalian Biology</em>. Datasets are as follows:</p> <p>Pregnancy.csv - Raw dataset detailing year and date of capture, individual identifier, and measured intrinsic variables, along with confirmed or predicted pregnancy/calf viability status.</p> <p>Pregnancy_final_mass.csv - Raw dataset after body mass estimation for individuals that were not weighed. </p> <p>all_confirmed_preg.csv - Subset of raw data for all individuals with confirmed pregnancy status (via lab PSPB assay).</p> <p>preg_ageclass.csv - Subset of all_confirmed_preg dataset including all individuals with general age classification (e.g., adult or subadult).</p> <p>preg_numeric.csv - Subset of all_confirmed_preg dataset including all individuals with numeric age value (from incisiform canine cementum annuli).</p> <p>all_fns.csv - Subset of dataset including all individuals with confirmed or predicted fetal/early neonatal survival ("offspring viability") status.</p> <p>fns_ageclass.csv - Subset of all_fns.csv including all individuals with general age classification.</p> <p>fns_numeric.csv - Subset of all_fns.csv including all individuals with numeric age values.</p> <p>parameter_est.csv - Parameter estimates from top-performing generalized linear mixed models for both pregnancy and offpsinrg viability, for plotting.</p>
Dataset of Impact of pre-breeding feeding practices on rabbit mammary gland development at mid-pregnancy
<p>The dataset includes search results used in article “Impact of pre-breeding feeding practices on rabbit mammary gland development at mid-pregnancy” biorXiv, 2022.01.17.476562, ver. 3 peer-reviewed and recommended by Peer Community in Animal Science. <a href="https://doi.org/10.1101/2022.01.17.476562">https://doi.org/10.1101/2022.01.17.476562</a></p> <p> </p> <p>Search Results Description: Please use Figure 1 from paper to trace the data made available and experimental group.</p> <p>The excel “raw data 2022-06-24” file contains the following data</p> <ol> <li>Body weight of each rabbit on a weekly basis</li> <li>Analysis of breeding parameters at mid-pregnancy</li> <li>Histological areas of each mammry tissue measured</li> <li>Optical density values obtained for biochemical leptin concentration determination</li> <li>Optical density values obtained for biochemical triglyceride concentration determination</li> <li>Optical density values obtained for biochemical glucose concentration determination</li> <li>Optical density values obtained for biochemical cholesterol concentration determination</li> <li>RT-qPCR results (Ct) from QuantStudio export for milk protein analysis</li> <li>RT-qPCR results (Ct) from QuantStudio export for lipid metabolism analysisen</li> </ol> <p>“Statistical analysis.doc” contained the description of the statistics used in Excel and the description of the linear mixed model analysis , the reference of the script available by the CRAN project is also include.The R scipt file for using the linear mixed model in R added with the "data-croissance-analysis" file.</p>
Inflammatory Responses in the Placenta upon SARS-CoV-2 Infection Late in Pregnancy - IHC data
<p>SARS-CoV-2 infection during pregnancy does not affect the large majority of neonates but presents an increased risk for adverse pregnancy outcome. The effects of SARS-CoV-2 of its recently identified variants on placental function are not well understood. In this study, we investigated the impact of late gestational SARS-CoV-2 infection on the placenta.</p> <p>This dataset of is comprised of 897 images of classic immunohistochemistry for 3 markers in placenta from COVID-19 patients and controls.</p> <p><strong>A full description of the tissues, markers, and donors is available in the metadata.csv file.</strong></p>
Data from: Genome-wide selection components analysis in a fish with male pregnancy
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Reproductive hormones mediate changes in the gut microbiome during pregnancy and lactation in Phayre's leaf monkeys
Studies in multiple host species have shown that gut microbial diversity and composition change during pregnancy and lactation. However, the specific mechanisms underlying these shifts are not well understood. Here, we use longitudinal data from wild Phayre's leaf monkeys to test the hypothesis that fluctuations in reproductive hormone concentrations contribute to gut microbial shifts during pregnancy. We described the microbial taxonomic composition of 91 fecal samples from 15 females (n=16 cycling, n=36 pregnant, n=39 lactating) using 16S rRNA gene amplicon sequencing and assessed whether the resulting data were better explained by overall reproductive stage or by fecal estrogen (fE) and progesterone (fP) concentrations. Our results indicate that while overall reproductive stage affected gut microbiome composition, the observed patterns were driven by reproductive hormones. Females had lower gut microbial diversity during pregnancy and fP concentration was negatively correlated with diversity. Additionally, fP concentration predicted both unweighted and weighted UniFrac distances, while reproductive state only predicted unweighted UniFrac distances. Seasonality (rainfall and periods of phytoprogestin consumption) additionally influenced gut microbial diversity and composition. Our results indicate that reproductive hormones, specifically progestagens, contribute to the shifts in the gut microbiome during pregnancy and lactation.
Pregnancy advertisement Japanese macaques, Data Set
<p>Data set used for the analyses of female Japanese macaques (<em>Macaca fuscata</em>) sexual signals of pregnancy (variations in behaviors, estrus calls and face color).</p> <p>Here are some of the variables tested: ecall=estrus calls, contactm=contact made, contactb=contact borken, apf=female approaches, apm=male approaches, rd=R/G ratio (redness), lum=luminance, pregmonth=period of interest with pcp:pre-conceptive, m1:1<sup>st</sup> month of pregnancy, m2: 2<sup>nd</sup> month of pregnancy.</p>
Different genes are recruited during convergent evolution of pregnancy and the placenta
<p>The repeated evolution of the same traits in distantly related groups (convergent evolution) raises a key question in evolutionary biology: do the same genes underpin convergent phenotypes? Here, we explore one such trait, viviparity (live birth), which, qualitative studies suggest, may indeed have evolved via genetic convergence. There are 150 independent origins of live birth in vertebrates, providing a uniquely powerful system to test the mechanisms underpinning convergence in morphology, physiology, and/or gene recruitment during pregnancy. We compared transcriptomic data from eight vertebrates (lizards, mammals, sharks) that gestate embryos within the uterus. Since many previous studies detected qualitative similarities in gene use during independent origins of pregnancy, we expected to find significant overlap in gene use in viviparous taxa. However, we found no more overlap in uterine gene expression associated with viviparity than we would expect by chance alone. Each viviparous lineage exhibits the same core set of uterine physiological functions. Yet, contrary to prevailing assumptions about this trait, we find that none of the same genes are differentially expressed in all viviparous lineages, or even in all viviparous amniote lineages. Therefore, across distantly related vertebrates, different genes have been recruited to support the morphological and physiological changes required for successful pregnancy. We conclude that redundancies in gene function have enabled the repeated evolution of viviparity through recruitment of different genes from genomic "toolboxes", which are uniquely constrained by the ancestries of each lineage.</p>
Fig. 4 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy
Fig. 4. Predicted mean and standard error body condition scores show associations with infection presence and season, with co-infected buffalo in much lower condition in the early wet season (Table S2). Coccidia infection status is represented with C– and C+; nematode infection status is represented with N– and N+.
Fig. 5 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy
Fig. 5. Season and co-infection differences in nematode aggregation. (a) Aggregation patterns in calves (b) and non-calves. (c) In non-calves, the distribution of nematode parasites in the late wet season shows that k is not significantly different in coccidia positive vs. negative buffalo. (d) In the early wet season coccidia positive buffalo have a truncated distribution, resulting in significantly reduced aggregation. Arrows indicate nematode intensity values in the tail of the distribution of coccidia negative buffalo. Coccidia infection status is represented with C– and C+.
Fig. 3 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy
Fig. 3. Patterns of parasite egg/oocyst counts with co-infection for (a) nematodes and (b) coccidia. (c), the mean nematode intensity in calves is higher in early wet season than in the late wet season independent of co-infection with coccidia. (d) Co-infection with coccidia alters the seasonal patterns of nematode intensity in non-calf buffalo (>1 year, juvenile through senescent). Calf vs. non-calf division is based on model paramters (Table 1).
Fig. 2 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy
Fig. 2. Age specific patterns of parasite prevalence with co-infection. (a) Prevalence of nematodes is higher in buffalo co-infected with coccidia (C+) compared to coccidia negative buffalo (C–) in all age categories (N = 33, 318, 166, 272, 162 for calf, juvenile, subadult, adult and senescent C– buffalo; N = 58, 237, 55, 54, 20 for C+ buffalo). (b) Prevalence of coccidia is higher in buffalo co-infected with nematodes (N+) compared to nematode negative buffalo (N–) in calf, juvenile, subadult, and senescent buffalo but not adult buffalo (N = 13, 107, 92, 144, 56 for calf, juvenile, subadult, adult and senescent N– buffalo; N = 38, 448, 129, 208, 100 for N+ buffalo).
Fig. 1 in Nematode-coccidia parasite co-infections in African buffalo: Epidemiology and associations with host condition and pregnancy
Fig. 1. Age, sex and seasonal patterns of infection. Both parasites had the highest (a) prevalence (sample size for calf, juvenile, subadult, adult, and senescent respectively: N = 91, 555, 221, 326, 182) and (b) mean intensity in calves and juveniles (nematode N = 78, 448, 129, 208, 100; coccidia N = 58, 237, 55, 60, 14). (c) Males had lower estimated nematode prevalence and (d) higher estimated coccidia intensity compared to female buffalo. (e) The estimated nematode prevalence, coccidia prevalence, and (f) mean coccidia intensity were all increased in the early wet season compared to the late wet season. ‡Indicates significant differences at p <0.05.
Is the Gaze Behavior During Stair Walking Affected by Pregnancy?-Figure 2. Eye-tracking glasses image showing the gaze location during stair ascent
<p>At each data collection, participants walked the same U-shaped staircase descending a 22- treads (riser: 0.16 m, run: 0.33 m, and width: 1.15 m), making a short U-turn downstairs and ascending back the staircase, one tread at a time (Figure 1). Only the data of stair walking were taken for further analysis. The staircase was equipped with a handrail on one side but none of the participants used it. To monitor the gaze a SensoMotoric Instruments (SMI) eye-tracking glasses (ETG) system (SMI, Inc.) at a frequency of 60 frames per second and 1280x960 pixel picture was used. Calibration was performed using a matrix of 3 points placed on a board in different highs and different horizontal placement. Mean gaze vectors of the right eye (x, y, z) for stair descent and stair ascent were obtained for each data collection session. Gaze vector x, y, z starts at the eye and heads off in mediolateral, up and down, and anterior-posterior direction, respectively (Figure 2) (Haffegee, Alexandrov, & Barrow, 2007; Scheel, & Staadt, 2015).</p>
Is the Gaze Behavior During Stair Walking Affected by Pregnancy?-Figure 1. A simplified representation of the analysed staircase path
<p>At each data collection, participants walked the same U-shaped staircase descending a 22- treads (riser: 0.16 m, run: 0.33 m, and width: 1.15 m), making a short U-turn downstairs and ascending back the staircase, one tread at a time (Figure 1). Only the data of stair walking were taken for further analysis. The staircase was equipped with a handrail on one side but none of the participants used it. To monitor the gaze a SensoMotoric Instruments (SMI) eye-tracking glasses (ETG) system (SMI, Inc.) at a frequency of 60 frames per second and 1280x960 pixel picture was used. Calibration was performed using a matrix of 3 points placed on a board in different highs and different horizontal placement. Mean gaze vectors of the right eye (x, y, z) for stair descent and stair ascent were obtained for each data collection session. Gaze vector x, y, z starts at the eye and heads off in mediolateral, up and down, and anterior-posterior direction, respectively (Figure 2) (Haffegee, Alexandrov, & Barrow, 2007; Scheel, & Staadt, 2015).</p>
Deep Representation Learning of Physical Activity and Sleep Patterns During Pregnancy Identifies post-hoc Inferences Associated with Prematurity
<p><strong>Running title</strong>: series2signal gestational age "clock" for pregnancy monitoring</p> <p><strong>Summary</strong>: </p> <p>Preterm birth (PTB) is the leading cause of infant mortality globally. While research has focused on the development of predictive models for PTB, cost-effective interventions have remained understudied. Physical activity and sleep present unique opportunities for interventions in low- and middle-income populations. However, objective measurement of physical activity and sleep remains challenging and self-reported metrics suffer from low-resolution and accuracy that decays over time. In this study, we use physical activity data collected using a wearable device comprising over 181, 944 hours of data across N = 1, 083 patients. Using a new state-of-the art deep learning time-series classification architecture, we first develop a ”clock” of healthy dynamics in physical activity patterns during pregnancy by using gestational age (GA) as a surrogate for progression of pregnancy. We also developed a novel interpretability algorithm that integrates unsupervised clustering, model error analysis, feature attribution, and automated actigraphy analysis, allowing for model interpretation with respect to sleep, activity, and static clinical variables. Our model performs significantly better than 7 other machine learning and AI methods for modeling the progression of pregnancy based on measures of physical activity and sleep.</p> <p>Importantly, we found that deviations from this normal ”clock” of physical activity and sleep changes during pregnancy are strongly associated with pregnancy outcomes. When our model underestimates GA, there are 0.52 fewer preterm births than expected (P = 1.01e − 67) and when our model overestimates GA, there are 1.44 times (P = 2.82e − 39) more preterm births than expected. Model error is negatively correlated with interdaily stability (P = 0.043), indicating that our model assigns a more advanced GA when an individual’s daily rhythms are less precise. Supporting this, our model attributes higher importance to sleep periods in predicting higher-than-actual GA, relative to lower-than-actual GA (P = 1.01e − 21). Combining prediction with interpretability allows us to robustly signal when activity behaviors increase or decrease the likelihood of preterm birth and advocates for the future development of clinical decision support through passive monitoring and suggestions around exercise habits and sleep patterns, which are easily implemented in low- and middle-income countries (LMICs). Beyond this particular application, the presented pipeline can be used to analyze high-fidelity time-series data in other translational studies utilizing wearable devices.</p> <p> </p> <p><strong>Data description (brief)</strong>: the raw wearables data is available as .mtn files with the GA encoded in the filename after the underscore. The processed data with sleep annotations can be loaded using the pickle module for serialized objects in python. See https://github.com/nealgravindra/wearables for examples.</p>
Supplementary data to publication "Abomasal infusion of essential fatty acids and conjugated linoleic acid during late pregnancy and early lactation affects immunohematological and oxidative stress markers in dairy cows"
<p>Supplementary data to publication "Abomasal infusion of essential fatty acids and conjugated linoleic acid during late pregnancy and early lactation affects immunohematological and oxidative stress markers in dairy cows" in Journal of Dairy Science; DOI: <a href="https://doi.org/10.3168/jds.2022-22514">https://doi.org/10.3168/jds.2022-22514</a></p>
Evaluating the Response to Two Antiretroviral Medication Regimens in HIV-Infected Pregnant Women, Who Begin Antiretroviral Therapy Between 20 and 36 Weeks of Pregnancy, for the Prevention of Mother-to
ClinicalTrials.gov study NCT01618305. IPD Sharing: YES. Countries: 7. Publications: 1.
Happy Mother - Healthy Baby: Supplement Study on Biological Processes Underlying Anxiety During Pregnancy
ClinicalTrials.gov study NCT04566861. IPD Sharing: YES. Countries: 1. Publications: 4.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.