BACKGROUND
Estrogens regulate disparate female physiological processes, thus ensuring reproduction. Altered estrogen levels and signaling have been associated with increased risks of pregnancy failure and complications, including hypertensive disorders and low birthweight babies. However, the role of estrogens in the periconceptional period and early pregnancy is still understudied.


OBJECTIVE AND RATIONALE
This review aims to summarize the current evidence on the role of maternal estrogens during the periconceptional period and the first trimester of pregnancies conceived naturally and following ART. Detailed molecular mechanisms and related clinical impacts are extensively described.


SEARCH METHODS
Data for this narrative review were independently identified by seven researchers on Pubmed and Embase databases. The following keywords were selected: 'estrogens' OR 'estrogen level(s)' OR 'serum estradiol' OR 'estradiol/estrogen concentration', AND 'early pregnancy' OR 'first trimester of pregnancy' OR 'preconceptional period' OR 'ART' OR 'In Vitro Fertilization (IVF)' OR 'Embryo Transfer' OR 'Frozen Embryo Transfer' OR 'oocyte donation' OR 'egg donation' OR 'miscarriage' OR 'pregnancy outcome' OR 'endometrium'.


OUTCOMES
During the periconceptional period (defined here as the critical time window starting 1 month before conception), estrogens play a crucial role in endometrial receptivity, through the activation of paracrine/autocrine signaling. A derailed estrogenic milieu within this period seems to be detrimental both in natural and ART-conceived pregnancies. Low estrogen levels are associated with non-conception cycles in natural pregnancies. On the other hand, excessive supraphysiologic estrogen concentrations at time of the LH peak correlate with lower live birth rates and higher risks of pregnancy complications. In early pregnancy, estrogen plays a massive role in placentation mainly by modulating angiogenic factor expression-and in the development of an immune-tolerant uterine micro-environment by remodeling the function of uterine natural killer and T-helper cells. Lower estrogen levels are thought to trigger abnormal placentation in naturally conceived pregnancies, whereas an estrogen excess seems to worsen pregnancy development and outcomes.


WIDER IMPLICATIONS
Most current evidence available endorses a relation between periconceptional and first trimester estrogen levels and pregnancy outcomes, further depicting an optimal concentration range to optimize pregnancy success. However, how estrogens co-operate with other factors in order to maintain a fine balance between local tolerance towards the developing fetus and immune responses to pathogens remains elusive. Further studies are highly warranted, also aiming to identify the determinants of estrogen response and biomarkers for personalized estrogen administration regimens in ART.

https://academic.oup.com/humupd/advance-article-pdf/doi/10.1093/humupd/dmad016/50693700/dmad016.pdf

The pathophysiological role of estrogens in the initial stages of pregnancy: molecular mechanisms and clinical implications for pregnancy outcome from the periconceptional period to end of the first trimester.

Early diagnosis of cancer is very important as it significantly increases the chances of appropriate treatment and survival. To this end, Deep Learning models are increasingly used in the classification and segmentation of histopathological images, as they obtain high accuracy index and can help specialists. In most cases, images need to be preprocessed for these models to work correctly. In this paper, a comparative study of different preprocessing methods and deep learning models for a set of breast cancer images is presented. For this purpose, the statistical test ANOVA with data obtained from the performance of five different deep learning models is analyzed. An important conclusion from this test can be obtained; from the point of view of the accuracy of the system, the main repercussion is the deep learning models used, however, the filter used for the preprocessing of the image, has no statistical significance for the behavior of the system.

/pdf/impact-of-image-preprocessing-methods-and-deep-learning-1b1an629.pdf

Impact of Image Preprocessing Methods and Deep Learning Models for Classifying Histopathological Breast Cancer Images

The aging population and the prevalence of infectious diseases have impacted the traditional medical order, significantly increasing the burden on healthcare and adversely affecting the socio-economic system. Medical sensors based on artificial intelligence (AI) provide new ideas for modern medical data collection to monitor the health status of individuals and environmental changes. Meanwhile, with the aid of AI algorithms, the big data processing capabilities of sensor systems have been greatly improved, further realizing early predictions and timely diagnoses. In this paper, a brief overview is offered on the development status of AI-enabled medical sensors for off-body detection, near-body monitoring, disease prediction, and clinical decision support system, and the ongoing challenges and future prospects to move from concept to implementation are discussed. In the foreseeable future, breakthroughs in the combination of medical sensors and AI algorithms are expected to pave the way for early detection and clinical decision support and improve the accuracy and efficiency for disease diagnosis. 

Artificial Intelligence‐Based Medical Sensors for Healthcare System

Breast cancer (BC) is closely linked with the maximum mortality rate for cancer detection across the globe and has become a predominant public health issue. Earlier detection might increase the possibility of survival and successful treatment. However, it is a time-consuming and very challenging task that depends on the diagnostician’s experience. For patients and their prognosis, it is essential that BC cancer can be automatically detected by the analysis of histopathological images. Conventional feature extraction method extracts some lower-level features of images, and preceding knowledge is essential for selecting suitable features that could be heavily impacted by human beings. The deep learning (DL) technique extracts higher-level abstract features from an image automatically. Therefore, this study develops a new Ensemble Learning with Symbiotic Organism Search Optimization Algorithm for Breast Cancer Classification (ELSOSA-BCC) technique on Histopathological Images. In the ELSOSA-BCC technique, the noise is removed using Gabor filtering (GF). In addition, the ELSOSA-BCC technique employs the EfficientNet-B0 model for feature extraction and optimal hyperparameter tuning using the SOS algorithm. Finally, the ensemble learning-based classification process is performed by three classifiers namely deep stacked autoencoder (DSAE), kernel extreme learning machine (KELM), and bidirectional long short-term memory (BiLSTM). In this study, ELSOSA-BCC simulation values are tested on a medical dataset. ELSOSA-BCC has been shown to perform better than other models in the experimental results.

Ensemble Learning With Symbiotic Organism Search Optimization Algorithm for Breast Cancer Classification and Risk Identification of Other Organs on Histopathological Images

The adoption of “artificial intelligence (AI) in drug discovery”, where AI is used in the process of pharmaceutical research and development, is progressing. By using the ability to process large amounts of data, which is a characteristic of AI, and achieving advanced data analysis and inference, there are benefits such as shortening development time, reducing costs, and reducing the workload of researchers. There are various problems in drug development, but the following two issues are particularly problematic: (1) the yearly increases in development time and cost of drugs and (2) the difficulty in finding highly accurate target genes. Therefore, screening and simulation using AI are expected. Researchers have high demands for data collection and the utilization of infrastructure for AI analysis. In the field of drug discovery, for example, interest in data use increases with the amount of chemical or biological data available. The application of AI in drug discovery is becoming more active due to improvement in computer processing power and the development and spread of machine-learning frameworks, including deep learning. To evaluate performance, various statistical indices have been introduced. However, the factors affected in performance have not been revealed completely. In this study, we summarized and reviewed the applications of deep learning for drug discovery with BigData.

/pdf/applications-of-deep-learning-for-drug-discovery-systems-30et8wab.pdf

Applications of Deep Learning for Drug Discovery Systems with BigData

Abstract The frozen section (FS) diagnoses of pathology experts are used in China to determine whether sentinel lymph nodes of breast cancer have metastasis during operation. Direct implementation of a deep neural network (DNN) in clinical practice may be hindered by misdiagnosis of the algorithm, which affects a patient's treatment decision. In this study, we first obtained the prediction result of the commonly used patch-DNN, then we present a relative risk classification and regression tree (RRCART) to identify the misdiagnosed whole-slide images (WSIs) and recommend them to be reviewed by pathologists. Applying this framework to 2362 WSIs of breast cancer lymph node metastasis, test on frozen section results in the mean area under the curve (AUC) reached 0.9851. However, the mean misdiagnosis rate (0.0248), was significantly higher than the pathologists’ misdiagnosis rate ( p &lt; 0.01). The RRCART distinguished more than 80% of the WSIs as a high-accuracy group with an average accuracy reached to 0.995, but the difference with the pathologists’ performance was not significant ( p &gt; 0.01). However, the other low-accuracy group included most of the misdiagnoses of DNN models. Our research shows that the misdiagnosis from deep learning model can be further enriched by our method, and that the low-accuracy WSIs must be selected for pathologists to review and the high-accuracy ones may be ready for pathologists to give diagnostic reports. 

/pdf/identification-of-misdiagnosis-by-deep-neural-networks-on-a-2og317bz.pdf

Identification of misdiagnosis by deep neural networks on a histopathologic review of breast cancer lymph node metastases

Diffusion-weighted, intravoxel incoherent motion, and diffusion kurtosis tensor MR imaging in chronic kidney diseases: Correlations with histology.

To predict miscarriage outcome within 12 weeks of gestational age by evaluating values of serum estradiol, progesterone and β-human chorionic gonadotropin (β-HCG) within 9 weeks of gestation.One hundred sixty-five women with singleton pregnancies were retrospectively studied. Estradiol, progesterone and β-HCG levels were measured at 5-6 weeks of gestation and the measurements were repeated at 7-9 weeks. According to pregnancy outcome at 12 weeks of gestation, 71 cases were categorized into miscarriage group, and 94 cases into group of normal pregnancy. Each group was further divided into 5-6 and 7-9 weeks of gestation sub-group. Predictive values of estradiol, progesterone and β- HCG levels at 5-6 weeks and 7-9 weeks of gestation were analyzed with receiver operating characteristic (ROC) curves and logistic regression.Serum levels of estradiol at 7-9 weeks identified miscarriage with an area under the ROC curve (AUC) of 0.866 (95% CI 0. 793 ~ 0.938, P = 0.000), diagnostic cutoff value of 576 pg/ml, sensitivity of 0.804, and specificity of 0.829 respectively at the optimal threshold, according to Youden index. Progesterone levels at 7-9 weeks were with AUC of 0.766 (95% CI 0. 672 ~ 0.861, P = 0.000), cutoff value of 15.27 ng/ml, sensitivity of 0.921, and specificity of 0.558, respectively; Estradiol at 5-6 weeks were with AUC of 0.709 (95% CI 0. 616 ~ 0.801, P < 0.001), the diagnostic cutoff value of 320 pg/ml, sensitivity of 0.800, and specificity of 0.574, respectively. The performance of the dual markers of estradiol and progesterone analysis (AUC 0.871, CI 0.793-0.950), three-markers analysis (AUC 0.869, CI 0.759-0.980)were slightly better than the single marker at 7-9 weeks. β-HCG or progesterone provide additional utility of estradiol prediction at 5-6 weeks with AUC 0.770 (0.672-0.869) for β-HCG and estradiol, AUC0.768(CI 0.670-0.866) for β-HCG, estradiol and progesterone and AUC 0.739 (CI 0.651-0.827) for progesterone and estradiol.Low serum levels such as dual of estradiol and progesterone or estradiol alone at 7-9 weeks, β-HCG or progesterone combing estradiol at 5-6 weeks of gestation can be used better to predict miscarriage in first trimester. 

https://bmcpregnancychildbirth.biomedcentral.com/track/pdf/10.1186/s12884-021-04158-w

Prediction of miscarriage in first trimester by serum estradiol, progesterone and β-human chorionic gonadotropin within 9 weeks of gestation

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

J. Du

Author Tools

Chat about Author

Papers

Identification of misdiagnosis by deep neural networks on a histopathologic review of breast cancer lymph node metastases

Diffusion-weighted, intravoxel incoherent motion, and diffusion kurtosis tensor MR imaging in chronic kidney diseases: Correlations with histology.

Prediction of miscarriage in first trimester by serum estradiol, progesterone and β-human chorionic gonadotropin within 9 weeks of gestation