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This research has suggested a link between major depressive disorder (MDD) and infertility, with interleukin‐18 (IL‐18) being proposed as a potential mediator due to its connections to both conditions. A Mendelian randomization (MR) approach was utilized in this study, which drew on genetic data from 500,199 European participants studied for MDD, along with additional IL‐18 and reproductive health data from the FinnGen consortium and GWAS datasets. Single nucleotide polymorphisms were employed as instrumental variables to examine the causal relationships between MDD, genetically predicted IL‐18 levels, and infertility. In our study, bidirectional MR analysis revealed a significant inverse causal relationship between MDD and genetically predicted IL‐18 levels, with a higher genetic predisposition to MDD, correlating with reduced IL‐18 levels (β: −0.40; 95% confidence interval (CI): −0.69 to −0.11; P = 7.09 × 10−3). Additionally, MDD is found to significantly increase the risk of female infertility. Notably, genetically predicted IL‐18 levels demonstrated a protective effect against female infertility (odds ratio (OR): 0.92; 95% CI: 0.86–0.98; P = 1.17 × 10−2). Mediation analysis indicated that genetically predicted IL‐18 levels partially mediated the impact of MDD on female infertility associated with cervical, vaginal, other or unspecified origin, accounting for up to 14.61% of this effect. No evidence of pleiotropy or heterogeneity was detected. The role of genetic predispositions to MDD in influencing genetically predicted IL‐18 levels, and subsequently, female infertility, was highlighted by our study, offering insights into the complex interplay between mental health and reproductive biology. These findings contribute to a deeper understanding of the genetic and molecular pathways influencing these conditions, suggesting new directions for research and potential therapeutic interventions. [ABSTRACT FROM AUTHOR]

Facile and efficient photocatalysts using sunlight, as well as fast and sensitive surface-enhanced Raman spectroscopy (SERS) substrates, are urgently needed for practical degradation of tetracycline (TC). To meet these requirements, a new paradigm for PI/TiO2/Ag organic‒inorganic ternary flexible microfibers based on semiconducting titanium dioxide (TiO2), the noble metal silver (Ag) and the conjugated polymer polyimide (PI) was developed by engineering a simple method. Under sunlight, the photocatalytic characteristics of the PI/TiO2/Ag flexible microfibers containing varying amounts of Ag quantum dots (QDs) were evaluated with photocatalytic degradation of TC in aqueous solution. The results demonstrated that the amount of Ag affected the photocatalytic activity. Among the tested samples, PI/TiO2/Ag-0.07 (93.1%) exhibited a higher photocatalytic degradation rate than PI/TiO2 (25.7%), PI/TiO2/Ag-0.05 (77.7%), and PI/TiO2/Ag-0.09 (63.3%). This observation and evaluation conducted in the present work strongly indicated a charge transfer mechanism. Moreover, the PI/TiO2/Ag-0.07 flexible microfibers exhibited highly sensitive SERS detection, as demonstrated by the observation of the Raman peaks for TC even at an extremely low concentration of 10–10 moles per liter. The excellent photocatalytic performance and SERS detection capability of the PI/TiO2/Ag flexible microfibers arose from the Schottky barrier formed between Ag and TiO2 and also from the outstanding plasmonic resonance and visible light absorptivity of Ag, along with immobilization by the PI. The successful synthesis of PI/TiO2/Ag flexible microfibers holds significant promise for sensitive detection and efficient photocatalytic degradation of antibiotics. [ABSTRACT FROM AUTHOR]

Autonomous railway inspection with unmanned aerial vehicles (UAVs) has huge advantages over traditional inspection methods. As a prerequisite for UAV‐based autonomous following of railway lines, it is quite essential to develop intelligent railway track detection algorithms. However, there are no existing algorithms currently that can efficiently adapt to the demand for the various forms and changing inclination angles of railway tracks in the UAV aerial images. To address the challenge, this paper proposes a novel anchor‐adaptive railway track detection network (ARTNet), which constructs a dual‐branch architecture based on projection length discrimination to realize full‐angle railway track detection for the UAV aerial images taken from arbitrary viewing angles. Considering the potential capacity imbalance of the two branches that can be caused by the uneven distribution of railway tracks in the dataset, a balanced transpose co‐training strategy is proposed to train the two branches coordinately. Moreover, an extra customized transposed consistency loss is designed to guide the training of the network without increasing any computational complexity. A set of experiments have been conducted to verify the feasibility and superiority of the ARTNet. It is demonstrated that our approach can effectively realize full‐angle railway track detection and outperform other popular algorithms greatly in terms of both detection accuracy and reasoning efficiency. ARTNet can achieve a mean F1 of 76.12 and run at a speed of 50 more frames per second. [ABSTRACT FROM AUTHOR]