Comprehensive miscarriage dataset for an early miscarriage prediction.
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ABSTRACT:
SUBMITTER: Asri H
PROVIDER: S-EPMC5992995 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:PurposeFirst trimester miscarriage is a major concern in IVF-ET treatments, accounting for one out of nine clinical pregnancies and for up to one out of three recognized pregnancies. To develop a machine learning classifier for predicting the risk of cleavage-stage embryos to undergo first trimester miscarriage based on time-lapse images of preimplantation development.MethodsRetrospective study of a 4-year multi-center cohort of 391 women undergoing intra-cytoplasmatic sperm injection (ICSI) and fresh single or double embryo transfers. The study included embryos with positive indication of clinical implantation based on gestational sac visualization either with first trimester miscarriage or live-birth outcome. Miscarriage was determined based on negative fetal heartbeat indication during the first trimester. Data were recorded and obtained in hospital setting and research was performed in university setting.ResultsA minimal subset of six non-redundant morphodynamic features were screened that maintained high prediction capacity. Features that account for the distribution of the nucleolus precursor bodies within the small pronucleus and pronuclei dynamics were highly predictive of miscarriage outcome as evaluated using the SHapley Additive exPlanations (SHAP) methodology. Using this feature subset, XGBoost and random forest models were trained following a 100-fold Monte-Carlo cross validation scheme. Miscarriage was predicted with AUC 0.68 to 0.69.ConclusionWe report the development of a decision-support tool for identifying the embryos with high risk of miscarriage. Prioritizing embryos for transfer based on their predicted risk of miscarriage in combination with their predicted implantation potential is expected to improve live-birth rates and shorten time-to-pregnancy.
| S-EPMC9935804 | biostudies-literature
Project description:Fragaria vesca, a diploid woodland strawberry with a small and sequenced genome, is an excellent model for studying fruit development. The strawberry fruit is unique in that the edible flesh is actually enlarged receptacle tissue. The true fruit are the numerous dry achenes dotting the receptacleM-^Rs surface. Auxin produced from the achene is essential for the receptacle fruit set, a paradigm for studying crosstalk between hormone signaling and development. To investigate the molecular mechanism underlying strawberry fruit set, next-generation sequencing was employed to profile early-stage fruit development with five fruit tissue types and five developmental stages from floral anthesis to enlarged fruits. This two-dimensional data set provides a systems-level view of molecular events with precise spatial and temporal resolution.
2013-10-01 | E-MTAB-1902 | biostudies-arrayexpress
Project description:Plant diseases pose a significant obstacle to global agricultural productivity, impacting crop quality yield and causing substantial economic losses for farmers. Watermelon, a commonly cultivated succulent vine plant, is rich in hydration and essential nutrients. However, it is susceptible to various diseases due to unfavorable environmental conditions and external factors, leading to compromised quality and substantial financial setbacks. Swift identification and management of crop diseases are imperative to minimize losses, enhance yield, reduce costs, and bolster agricultural output. Conventional disease diagnosis methods are often labor-intensive, time-consuming, ineffective, and prone to subjectivity. As a result, there is a critical need to advance research into machine-based models for disease detection in watermelons. This paper presents a large dataset of watermelons that can be used to train a machine vision-based illness detection model. Images of healthy and diseased watermelons from the Mosaic Virus, Anthracnose, and Downy Mildew Disease are included in the dataset's five separate classifications. Images were painstakingly collected on June 25, 2023, in close cooperation with agricultural experts from the highly regarded Regional Horticulture Research Station in Lebukhali, Patuakhali.
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Project description: Not available
| S-EPMC10863102 | biostudies-literature
Project description:Background. Miscarriage is a very common complication during early pregnancy. So far, clinical therapies have limitation in preventing the early pregnancy loss. Chinese Medicine, regarded as gentle, effective, and safe, has become popular and common as a complementary and alternative treatment for miscarriages. However, the evidence to support its therapeutic efficacy and safety is still very limited. Objectives and Methods. To summarize the clinical application of Chinese Medicine for pregnancy and provide scientific evidence on the efficacy and safety of Chinese medicines for miscarriage, we located all the relevant pieces of literature on the clinical applications of Chinese Medicine for miscarriage and worked out this systematic review. Results. 339,792 pieces of literature were identified, but no placebo was included and only few studies were selected for systematic review and conducted for meta-analysis. A combination of Chinese medicines and Western medicines was more effective than Chinese medicines alone. No specific safety problem was reported, but potential adverse events by certain medicines were identified. Conclusions. Studies vary considerably in design, interventions, and outcome measures; therefore conclusive results remain elusive. Large scales of randomized controlled trials and more scientific evidences are still necessary to confirm the efficacy and safety of Chinese medicines during early pregnancy.
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Project description:Machine learning (ML) methods for the analysis of electrocardiography (ECG) data are gaining importance, substantially supported by the release of large public datasets. However, these current datasets miss important derived descriptors such as ECG features that have been devised in the past hundred years and still form the basis of most automatic ECG analysis algorithms and are critical for cardiologists' decision processes. ECG features are available from sophisticated commercial software but are not accessible to the general public. To alleviate this issue, we add ECG features from two leading commercial algorithms and an open-source implementation supplemented by a set of automatic diagnostic statements from a commercial ECG analysis software in preprocessed format. This allows the comparison of ML models trained on clinically versus automatically generated label sets. We provide an extensive technical validation of features and diagnostic statements for ML applications. We believe this release crucially enhances the usability of the PTB-XL dataset as a reference dataset for ML methods in the context of ECG data.
| S-EPMC10183020 | biostudies-literature
Project description:Zoonotic spillover of sarbecoviruses (SarbeCoVs) from non-human animals to humans under natural conditions has led to two large-scale pandemics, the severe acute respiratory syndrome (SARS) pandemic in 2003 and the ongoing COVID-19 pandemic. Knowledge of the genetic diversity, geographical distribution, and host specificity of SarbeCoVs is therefore of interest for pandemic surveillance and origin tracing of SARS-CoV and SARS-CoV-2. This study presents a comprehensive repository of publicly available animal-associated SarbeCoVs, covering 1,535 viruses identified from 63 animal species distributed in 43 countries worldwide (as of February 14,2023). Relevant meta-information, such as host species, sampling time and location, was manually curated and included in the dataset to facilitate further research on the potential patterns of viral diversity and ecological characteristics. In addition, the dataset also provides well-annotated sequence sets of receptor-binding domains (RBDs) and receptor-binding motifs (RBMs) for the scientific community to highlight the potential determinants of successful cross-species transmission that could be aid in risk estimation and strategic design for future emerging infectious disease control and prevention.
| S-EPMC10560225 | biostudies-literature
Project description:Compared to most mammals, human pregnancy is unusual in that it involves chromosomally diverse embryos, cyclical breakdown and regeneration of the uterine mucosa, and intimate integration of fetal and maternal cells at the uteroplacental interface. Not surprisingly, pregnancy often falters in early gestation. Whether these losses result in clinical miscarriages depends on the origins and impacts of chromosomal errors on fetal development and the ability of the decidualizing endometrium to engage in embryo biosensing and selection. Aneuploidy originating in oocytes during meiosis drives the age-related risk of miscarriage. By contrast, the frequency of endometrial cycles with an impaired decidual response may account for the stepwise increase in miscarriage rates with each pregnancy loss independently of maternal age. Additional physiological mechanisms operate in early gestation to ensure that most failing pregnancies are lost before vascular maternal-fetal connections are established by the end of the first trimester. Here, we summarise how investigations into the mechanisms that cause miscarriage led to new insights into the processes that govern maternal selection of human embryos in early gestation.
| S-EPMC9325922 | biostudies-literature
Project description:In the dataset presented in this article, samples belonging to one of the following crops, apple, broccoli, leek, and mushroom, were measured by hyperspectral cameras in the visible/near-infrared spectral domain (430-900 nm). The dataset was compiled by putting together measurements from different calibrated hyperspectral imaging cameras and crops to facilitate the training of artificial intelligence models, helping to overcome the generalization problem of hyperspectral models. In particular, this dataset focuses on estimating dry matter content across various crops by a single model in a non-destructive way using hyperspectral measurements. This dataset contains extracted mean reflectance spectra for each sample (n=1028) and their respective dry matter content (%).
| S-EPMC10823050 | biostudies-literature
Project description:BackgroundIt is challenging to predict the outcome of the pregnancy when fetal heart activity is detected in early pregnancy. However, an accurate prediction is of importance for obstetricians as it helps to provide appropriate consultancy and determine the frequency of ultrasound examinations. The purpose of this study was to investigate the role of the convolutional neural network (CNN) in the prediction of spontaneous miscarriage risk through the analysis of early ultrasound gestational sac images.MethodsA total of 2196 ultrasound images from 1098 women with early singleton pregnancies of gestational age between 6 and 8 weeks were used for training a CNN for the prediction of the miscarriage in the retrospective study. The patients who had positive fetal cardiac activity on their first ultrasound but then experienced a miscarriage were enrolled. The control group was randomly selected in the same database from the fetuses confirmed to be normal during follow-up. Diagnostic performance of the algorithm was validated and tested in two separate test sets of 136 patients with 272 images, respectively. Performance in prediction of the miscarriage was compared between the CNN and the manual measurement of ultrasound characteristics in the prospective study.ResultsThe accuracy of the predictive model was 80.32% and 78.1% in the retrospective and prospective study, respectively. The area under the receiver operating characteristic curve (AUC) for classification was 0.857 (95% confidence interval [CI], 0.793-0.922) in the retrospective study and 0.885 (95%CI, 0.846-0.925) in the prospective study, respectively. Correspondingly, the predictive power of the CNN was higher compared with manual ultrasound characteristics, for which the AUCs of the crown-rump length combined with fetal heart rate was 0.687 (95%CI, 0.587-0.775).ConclusionsThe CNN model showed high accuracy for predicting miscarriage through the analysis of early pregnancy ultrasound images and achieved better performance than that of manual measurement.
| S-EPMC9354356 | biostudies-literature