Most of the tested polyphenols significantly influenced the outcome of translesion synthesis, either through an error-free (apigenin, baicalein, sakuranetin, and myricetin) or a mutagenic pathway (epicatechin, chalcone, genistein, magnolol, and honokiol).Despite improvements in assisted reproduction techniques (ARTs), live birth rates remain suboptimal, particularly in women with advanced maternal age (AMA). The leading cause of poor reproductive outcomes demonstrated in women with AMA, as well as women with recurrent miscarriage and repetitive implantation failure, is thought to be due to high rates of embryonic aneuploidy. Preimplantation genetic testing for aneuploidies (PGT-A) aims to select an euploid embryo for transfer and therefore improve ART outcomes. Early PGT-A studies using fluorescent in situ hybridization on mainly cleavage-stage biopsies failed to show improved delivery rates and, in certain cases, were even found to be harmful. However, the development of comprehensive chromosome screening, as well as improvements in culture media and vitrification techniques, has resulted in an emerging body of evidence in favor of PGT-A, demonstrating higher implantation, pregnancy, and live birth rates. While there are concerns regarding the potential harm of invasive biopsy and the cost implications of PGT-A, the introduction of noninvasive techniques and the development of new high-throughput methods which lower costs are tackling these issues. This review aims to assess the evidence for PGT-A, address possible concerns regarding PGT-A, and also explore the future direction of this technology.

 We aimed to develop a survey instrument to assess the maturity level of consumer health informatics (ConsHI) in low-middle income countries (LMIC).

 We deduced items from unified theory of acceptance and use of technology (UTAUT), UTAUT2, patient activation measure (PAM), and ConsHI levels to constitute a pilot instrument. We proposed a total of 78 questions consisting of 14 demographic and 64 related maturity variables using an iterative process. We used a multistage convenient sampling approach to select 351 respondents from all three countries.

 Our results supported the earlier assertion that mobile devices and technology are standard today than ever, thus confirming that mobile devices have become an essential part of human activities. We used the Wilcoxon Signed-Rank Test (WSRT) and item response theory (IRT) to reduce the ConsHI-related items from 64 to 43. The questionnaire consisted of 10 demographic questions and 43 ConsHI relevant questions on the maturity of citizens for ConsHI in LMIC. Also, the results supported some moderators such as age and gender. Vorinostat chemical structure Additionally, more demographic items such as marital status, educational level, and location of respondents were validated using IRT and WSRT.

 We contend that this is the first composite instrument for assessing the maturity of citizens for ConsHI in LMIC. Specifically, it aggregates multiple theoretical models from information systems (UTAUT and UTAUT2) and health (PAM) and the ConsHI level.

 We contend that this is the first composite instrument for assessing the maturity of citizens for ConsHI in LMIC. Specifically, it aggregates multiple theoretical models from information systems (UTAUT and UTAUT2) and health (PAM) and the ConsHI level.

 This study aimed to develop a semi-automated process to convert legacy data into clinical data interchange standards consortium (CDISC) study data tabulation model (SDTM) format by combining human verification and three methods data normalization; feature extraction by distributed representation of dataset names, variable names, and variable labels; and supervised machine learning.

 Variable labels, dataset names, variable names, and values of legacy data were used as machine learning features. Because most of these data are string data, they had been converted to a distributed representation to make them usable as machine learning features. For this purpose, we utilized the following methods for distributed representation Gestalt pattern matching, cosine similarity after vectorization by Doc2vec, and vectorization by Doc2vec. In this study, we examined five algorithms-namely decision tree, random forest, gradient boosting, neural network, and an ensemble that combines the four algorithms-to identify the one that could generate the best prediction model.

 The accuracy rate was highest for the neural network, and the distribution of prediction probabilities also showed a split between the correct and incorrect distributions. By combining human verification and the three methods, we were able to semi-automatically convert legacy data into the CDISC SDTM format.

 By combining human verification and the three methods, we have successfully developed a semi-automated process to convert legacy data into the CDISC SDTM format; this process is more efficient than the conventional fully manual process.

 By combining human verification and the three methods, we have successfully developed a semi-automated process to convert legacy data into the CDISC SDTM format; this process is more efficient than the conventional fully manual process.

Semantic textual similarity (STS) captures the degree of semantic similarity between texts. It plays an important role in many natural language processing applications such as text summarization, question answering, machine translation, information retrieval, dialog systems, plagiarism detection, and query ranking. STS has been widely studied in the general English domain. However, there exists few resources for STS tasks in the clinical domain and in languages other than English, such as Japanese.

The objective of this study is to capture semantic similarity between Japanese clinical texts (Japanese clinical STS) by creating a Japanese dataset that is publicly available.

We created two datasets for Japanese clinical STS (1) Japanese case reports (CR dataset) and (2) Japanese electronic medical records (EMR dataset). The CR dataset was created from publicly available case reports extracted from the CiNii database. The EMR dataset was created from Japanese electronic medical records.

We used an approach based on bidirectional encoder representations from transformers (BERT) to capture the semantic similarity between the clinical domain texts.