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Purpose: Pregnant women with a diagnosis of differentiated thyroid cancer (DTC) were potentially high-risk but largely ignored study population. We aimed to explore whether gestational thyrotropin levels were associated with progression of DTC., Methods: We conducted a retrospective cohort study at Peking University Third Hospital in Beijing, China from January 2012 to December 2022. We included pregnant women with a pre-pregnancy DTC managed by active surveillance (under-surveillance DTC) or surgical treatment (after-surgery DTC). Dynamic changes of gestational thyrotropin levels across multiple time points were characterized by both statistical (average level, change instability, longitudinal trajectory) and clinical (thyroid dysfunction, thyrotropin suppression, and achievement of thyrotropin suppression target) indicators. Outcomes were clinician-validated progression of DTC, measured separately for patients under surveillance (tumor enlargement or lymph node metastasis) and those after surgery (≥ 3 mm growth in the size of existing metastatic foci, development of new lymph node metastases, ≥ 2 mm growth in the size of existing cancer foci in the contralateral thyroid, or biochemical progression)., Results: Among 43 and 118 patients with under-surveillance and after-surgery DTC, we observed no evidence of associations between any of the quantitative or clinical indicators of gestational thyrotropin levels and progression-free survival, after a median of 2.63 (IQR: 0.90-4.73) and 4.22 (2.53-6.02) year follow-up, respectively (all P values > 0.05)., Conclusions: Gestational thyrotropin levels appeared to play a minor role in the progression of under-surveillance or after-surgery DTC. Clinicians might focus on the risk of adverse pregnancy outcomes when optimizing thyrotropin levels for pregnant women with a diagnosis of DTC., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Li, Fu, Xiao, Mei, Zhang, Zhang, Chen, Shan, Sun, Song, Yuan and Liu.)

Background: Differentiated thyroid cancer (DTC) has been increasingly common in women of reproductive age. However, the evidence remains mixed regarding the association of DTC with adverse pregnancy outcomes in pregnant women previously diagnosed with DTC., Methods: We conducted a retrospective cohort study in the Peking University Third Hospital in Beijing, China between January 2012 and December 2022. We included singleton-pregnancy women with a pre-pregnancy DTC managed by surgical treatment (after-surgery DTC) or active surveillance (under-surveillance DTC). To reduce the confounding effects, we adopted a propensity score to match the after-surgery and under-surveillance DTC groups with the non-DTC group, respectively, on age, parity, gravidity, pre-pregnancy weight, height, and Hashimoto's thyroiditis. We used conditional logistics regressions, separately for the after-surgery and under-surveillance DTC groups, to estimate the adjusted associations of DTC with both the composite of adverse pregnancy outcomes and the specific mother-, neonate-, and placenta-related pregnancy outcomes., Results: After the propensity-score matching, the DTC and non-DTC groups were comparable in the measured confounders. In the after-surgery DTC group ( n  = 204), the risk of the composite or specific adverse pregnancy outcomes was not significantly different from that of the matched, non-DTC groups ( n  = 816; P  > 0.05), and the results showed no evidence of difference across different maternal thyroid dysfunctions, gestational thyrotropin levels, and other pre-specified subgroup variables. We observed broadly similar results in the under-surveillance DTC group ( n  = 37), except that the risk of preterm birth, preeclampsia, and delivering the low-birth-weight births was higher than that of the matched, non-DTC group [ n  = 148; OR (95% CI): 4.79 (1.31, 17.59); 4.00 (1.16, 13.82); 6.67 (1.59, 27.90)]., Conclusions: DTC was not associated with adverse pregnancy outcomes in pregnant women previously treated for DTC. However, more evidence is urgently needed for pregnant women with under-surveillance DTC, which finding will be clinically significant in individualizing prenatal care., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Li, Mei, Xiao, Zhang, Zhang, Fu, Chen, Shan, Sun, Song, Yuan and Liu.)

Physiological signal monitoring and driver behavior analysis have gained increasing attention in both fundamental research and applied research. This study involved the analysis of driving behavior using multimodal physiological data collected from 35 participants. The data included 59-channel EEG, single-channel ECG, 4-channel EMG, single-channel GSR, and eye movement data obtained via a six-degree-of-freedom driving simulator. We categorized driving behavior into five groups: smooth driving, acceleration, deceleration, lane changing, and turning. Through extensive experiments, we confirmed that both physiological and vehicle data met the requirements. Subsequently, we developed classification models, including linear discriminant analysis (LDA), MMPNet, and EEGNet, to demonstrate the correlation between physiological data and driving behaviors. Notably, we propose a multimodal physiological dataset for analyzing driving behavior(MPDB). The MPDB dataset's scale, accuracy, and multimodality provide unprecedented opportunities for researchers in the autonomous driving field and beyond. With this dataset, we will contribute to the field of traffic psychology and behavior., (© 2024. The Author(s).)

Biphasic face photo-sketch synthesis has significant practical value in wide-ranging fields such as digital entertainment and law enforcement. Previous approaches directly generate the photo-sketch in a global view, they always suffer from the low quality of sketches and complex photograph variations, leading to unnatural and low-fidelity results. In this article, we propose a novel semantic-driven generative adversarial network to address the above issues, cooperating with graph representation learning. Considering that human faces have distinct spatial structures, we first inject class-wise semantic layouts into the generator to provide style-based spatial information for synthesized face photographs and sketches. In addition, to enhance the authenticity of details in generated faces, we construct two types of representational graphs via semantic parsing maps upon input faces, dubbed the intraclass semantic graph (IASG) and the interclass structure graph (IRSG). Specifically, the IASG effectively models the intraclass semantic correlations of each facial semantic component, thus producing realistic facial details. To preserve the generated faces being more structure-coordinated, the IRSG models interclass structural relations among every facial component by graph representation learning. To further enhance the perceptual quality of synthesized images, we present a biphasic interactive cycle training strategy by fully taking advantage of the multilevel feature consistency between the photograph and sketch. Extensive experiments demonstrate that our method outperforms the state-of-the-art competitors on the CUHK Face Sketch (CUFS) and CUHK Face Sketch FERET (CUFSF) datasets.