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Background: We aimed to determine the best-performing machine learning (ML)-based algorithm for predicting gestational diabetes mellitus (GDM) with sociodemographic and obstetrics features in the pre-conceptional period., Methods: We collected the data of pregnant women who were admitted to the obstetric clinic in the first trimester. The maternal age, body mass index, gravida, parity, previous birth weight, smoking status, the first-visit venous plasma glucose level, the family history of diabetes mellitus, and the results of an oral glucose tolerance test of the patients were evaluated. The women were categorized into groups based on having and not having a GDM diagnosis and also as being nulliparous or primiparous. 7 common ML algorithms were employed to construct the predictive model., Results: 97 mothers were included in the study. 19 and 26 nulliparous were with and without GDM, respectively. 29 and 23 primiparous were with and without GDM, respectively. It was found that the greatest feature importance variables were the venous plasma glucose level, maternal BMI, and the family history of diabetes mellitus. The eXtreme Gradient Boosting (XGB) Classifier had the best predictive value for the two models with the accuracy of 66.7% and 72.7%, respectively., Discussion: The XGB classifier model constructed with maternal sociodemographic findings and the obstetric history could be used as an early prediction model for GDM especially in low-income countries., (© 2024. The Author(s).)

This research paper investigates the integration of blockchain technology to enhance the security of Android mobile app data storage. Blockchain holds the potential to significantly improve data security and reliability, yet faces notable challenges such as scalability, performance, cost, and complexity. In this study, we begin by providing a thorough review of prior research and identifying critical research gaps in the field. Android's dominant position in the mobile market justifies our focus on this platform. Additionally, we delve into the historical evolution of blockchain and its relevance to modern mobile app security in a dedicated section. Our examination of encryption techniques and the effectiveness of blockchain in securing mobile app data storage yields important insights. We discuss the advantages of blockchain over traditional encryption methods and their practical implications. The central contribution of this paper is the Blockchain-based Secure Android Data Storage (BSADS) framework, now consisting of six comprehensive layers. We address challenges related to data storage costs, scalability, performance, and mobile-specific constraints, proposing technical optimization strategies to overcome these obstacles effectively. To maintain transparency and provide a holistic perspective, we acknowledge the limitations of our study. Furthermore, we outline future directions, stressing the importance of leveraging lightweight nodes, tackling scalability issues, integrating emerging technologies, and enhancing user experiences while adhering to regulatory requirements.

A bis(acyl)phosphane oxide (BAPO) photoinitiator was conveniently synthesized in an efficient one-pot process. It shows excellent dispersibility in water, good storage stability, and high photo-reactivity in 3D printing of hydrogels under visible-light irradiation (460 nm).

Objectives: This study aims to investigate the relationship of serum insulin-like growth factor 1 (IGF-1) level with obstructive sleep apnea, the Pittsburgh sleep quality index (PSQI), age, body mass index, and fibromyalgia impact questionnaire (FIQ) in fibromyalgia syndrome (FMS) patients., Patients and Methods: A total of 105 female patients (mean age 41.8±9.0 years; range 26 to 55 years) with fibromyalgia (FMS group) who were diagnosed according to 2010 American College of Rheumatology criteria, and 51 female patients (mean age 39.9±10.8 years; range 24 to 54 years) with mechanical low back pain (MLBP) (control group) were included in the study. Age, smoking, educational status, tender point number, body mass index, duration of disease, FIQ, PSQI, and polysomnographic assessment of both FMS and control groups were recorded., Results: Sleep disorder was detected in 88 patients in FMS group and 15 patients in control group (p<0.05). The PSQI score was higher in the FMS group compared to the control group (9.9±4.6 vs. 5.7±3.5). The FIQ score was higher in the FMS group compared to the control group (53.4±17.4 vs. 26.4±13.9; p<0.05). The serum IGF-1 level of FMS group was significantly lower than that of the control group (140.6±49.5 ng/mL vs. 177.2±58.5 ng/mL; p<0.05). In the FMS group, an examination of the correlation between serum IGF-1 level with the age, body mass index, obstructive sleep apnea syndrome, FIQ, and PSQI revealed a negative correlation between serum IGF-1 with the age and PSQI. The obstructive sleep apnea syndrome ratios of study groups were comparable with regard to the frequencies of mild, moderate, and severe obstructive sleep apnea syndrome., Conclusion: In FMS patients, serum IGF-1 levels may decrease due to age and PSQI; however, this may not be related to the severity of obstructive sleep apnea., Competing Interests: Conflict of Interest: The authors declared no conflicts of interest with respect to the authorship and/or publication of this article.