Self-esteem, depression, anxiety and sexual function in Mayer-Rokitansky-Küster-Hauser syndrome with neovagina: A case series.

BACKGROUND: The Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome is a rare condition with significant psychological implications. However, our understanding of its impact on postoperative sexual function and mental health is still limited. AIM: Evaluate the mental health status and sexual functioning of women with MRKH syndrome after vaginoplasty surgery. METHODS: We enrolled 53 cases with MRKH syndrome who underwent artificial vaginoplasty. The participants were asked to participate in a two-round survey conducted between February 2021 during the covid-19 period and March 2023. The survey included questionnaires to measure depression, anxiety, self-esteem, and sexual functioning. Differences between scores over time were analysed using a paired sample t-test, and we assessed the correlation between mental health and sexual functioning. RESULTS: In the first round, patients' mean ± SD age at surgery was 23.6 ± 4.5 years old, and the mean ± SD time that had elapsed since surgery at the time of the survey was 34.2 ± 20.8 months. None of the patients reported low self-esteem, 45.3 % reported mild-to-moderate depression, and 34.0 % reported mild anxiety. Thirty patients have had vaginal intercourse during the last six months. The mean ± SD Female Sexual Functioning Index score was 24.6 ± 4.4, and 60.0 % had a score of 23.5 or higher, indicating high sexual functioning. The sexual functioning scores were positively correlated with self-esteem scores and negatively correlated with depression or anxiety scores (p < 0.05). There was no significant improvement in patient's mental health status and sexual function between the second round survey (71.3 ± 17.8 months after surgery) and the first round survey (p > 0.05). In contrast, the sexual arousal of FSFI were significantly higher in the second survey round (p < 0.05). CONCLUSION: Most patients undergoing vaginoplasty reported persisting mental health challenges. However, the majority reported good sexual functioning.

Association of sleep traits with risk of hypertensive disorders of pregnancy: a mendelian randomization study.

BACKGROUND: Unhealthy sleep patterns are common during pregnancy and have been associated with an increased risk of developing hypertensive disorders of pregnancy (HDPs) in observational studies. However, the causality underlying these associations remains uncertain. This study aimed to evaluate the potential causal association between seven sleep traits and the risk of HDPs using a two-sample Mendelian randomization study. METHODS: Genome-wide association study (GWAS) summary statistics were obtained from the FinnGen consortium, UK Biobank, and other prominent consortia, with a focus on individuals of European ancestry. The primary analysis utilized an inverse-variance-weighted MR approach supplemented by sensitivity analyses to mitigate potential biases introduced by pleiotropy. Furthermore, a two-step MR framework was employed for mediation analyses. RESULTS: The data analyzed included 200 000-500 000 individuals for each sleep trait, along with approximately 15 000 cases of HDPs. Genetically predicted excessive daytime sleepiness (EDS) exhibited a significant association with an increased risk of HDPs [odds ratio (OR) 2.96, 95% confidence interval (95% CI) 1.40-6.26], and the specific subtype of preeclampsia/eclampsia (OR 2.97, 95% CI 1.06-8.3). Similarly, genetically predicted obstructive sleep apnea (OSA) was associated with a higher risk of HDPs (OR 1.27, 95% CI 1.09-1.47). Sensitivity analysis validated the robustness of these associations. Mediation analysis showed that BMI mediated approximately 25% of the association between EDS and HDPs, while mediating up to approximately 60% of the association between OSA and the outcomes. No statistically significant associations were observed between other genetically predicted sleep traits, such as chronotype, daytime napping, sleep duration, insomnia, snoring, and the risk of HDPs. CONCLUSION: Our findings suggest a causal association between two sleep disorders, EDS and OSA, and the risk of HDPs, with BMI acting as a crucial mediator. EDS and OSA demonstrate promise as potentially preventable risk factors for HDPs, and targeting BMI may represent an alternative treatment strategy to mitigate the adverse impact of sleep disorders.

SDO-Based Command Filtered Adaptive Neural Tracking Control for MIMO Nonlinear Systems With Time-Varying Constraints.

In this article, an adaptive neural tracking control based on saturation disturbance observer (SDO) and command filter is studied for multiple-input-multiple-output nonlinear systems with time-varying constraints and system uncertainties. By employing neural networks (NNs), the system uncertainties are approximated. The SDO is proposed to estimate the composited disturbances which consist of NN approximation errors and the external bounded disturbances. Compared with the traditional disturbance observer, the SDO can reduce the estimation error to some extent. The control requirements are achieved based on the multiconstraints which contain three layers: 1) prescribed performance functions (PPFs); 2) actual constraints; and 3) virtual constraints. The errors remain within the prescribed small neighborhood of zero by using the PPFs, the error constraints ensure that the time-varying constraints are never violated even if the PPFs are not available, and the virtual constraints are applied in a new time-varying barrier Lyapunov function (TVBLF) to design virtual controllers and controller to solve the singularity problem of the traditional TVBLF. In addition, the command filter is introduced to solve the problem of "explosion of complexity." Finally, a numerical simulation verifies the effectiveness of the proposed scheme for a flight control of unmanned aerial vehicle.

Adaptive NN Tracking Control for Uncertain MIMO Nonlinear System With Time-Varying State Constraints and Disturbances.

In this article, an adaptive neural network (NN) tracking control scheme is proposed for uncertain multi-input-multi-output (MIMO) nonlinear system in strict-feedback form subject to system uncertainties, time-varying state constraints, and bounded disturbances. The radial basis function NNs (RBFNNs) are adopted to approximate the system uncertainties. By constructing the intermediate variables, the external disturbances that cannot be directly measured are approximated by the disturbance observers. The time-varying barrier Lyapunov function (TVBLF) is constructed to guarantee the boundedness of the errors lie in the sets. To overcome the potential singularity problem that the denominator of the barrier function term approaches zero in controller design, the adaptive NN tracking control scheme with time-varying state constraints is proposed. Based on the TVBLF, the controller will be designed to guarantee tracking performance without violating the appropriate error constraints. The analysis of TVBLF shows that all closed-loop signals remain semiglobally uniformly ultimately bounded (SGUUB). The simulation results are performed to validate the validity of the proposed scheme.

Event-Triggered Fractional-Order Tracking Control for an Uncertain Nonlinear System With Output Saturation and Disturbances.

In this article, an event-triggered (ET) fractional-order adaptive tracking control scheme (ATCS) is studied for the uncertain nonlinear system with the output saturation and the external disturbances by using the nonlinear disturbance observer (NDO) and the neural networks (NNs). Based on NNs, the system uncertainties are approximated. An NN-based NDO is designed to estimate the bounded disturbances. Combining the NNs, the output of the designed NDO, the fractional-order theory, and the ET mechanism, an ATCS is proposed under the output saturation. According to the stability analysis, all the closed-loop signals are semiglobally uniformly ultimately bounded based on the investigative ATCS. The simulation results and the comparative experiment verifications are shown to indicate the viability of the developed control scheme.

Psychological intervention in women with Mayer-Rokitansky-Küster-Hauser syndrome after artificial vaginoplasty: a prospective study.

INTRODUCTION AND HYPOTHESIS: The negative psychological impact on women with Mayer-Rokitansky-Küster-Hauser (MRKH) syndrome is long-lasting, resulting from not only the disease itself, but also the cumbersome and painful treatment process. However, little is known about the postoperative psychological status of these patients and related interventions to improve mental health. Here, in our study, we postulated that mental disorders exist in MRKH patients with a surgical neovagina and that psychological intervention will be helpful. METHODS: Thirty MRKH women who had undergone vaginoplasty were enrolled. All patients had received psychological interventions since February 2020. Depression and anxiety questionnaires prior to and 2 weeks after the final intervention were recorded. RESULTS: Before intervention, among 30 MRKH patients after artificial vaginoplasty, the median depression score was 6.00 (25th/75th percentile, 0.00/7.00), and the median anxiety score was 4.00 (25th/75th percentile, 1.00/7.00). After intervention, women's depression (p < 0.001) and anxiety (p < 0.001) scores significantly decreased. The median depression score was 0.00 (25th/75th percentile, 0.00/3.00), and the median anxiety score was 1.00 (25th/75th percentile, 0.00/3.25). Furthermore, stratified analysis found that the depression (p = 0.029) and anxiety (p = 0.019) scores both improved when intervention was performed within 12 months postoperatively. CONCLUSIONS: MRKH patients are at a great risk of depression and anxiety problems after artificial vaginoplasty. Early psychological intervention can alleviate these symptoms. Ongoing psychological support was needed to eliminate emotional burden during MRKH treatment, and further study is sorely needed to identify its appropriate timing and method.

Circadian Rhythms Within the Female HPG Axis: From Physiology to Etiology.

Declining female fertility has become a global health concern. It results partially from an abnormal circadian clock caused by unhealthy diet and sleep habits in modern life. The circadian clock system is a hierarchical network consisting of central and peripheral clocks. It not only controls the sleep-wake and feeding-fasting cycles but also coordinates and maintains the required reproductive activities in the body. Physiologically, the reproductive processes are governed by the hypothalamic-pituitary-gonadal (HPG) axis in a time-dependent manner. The HPG axis releases hormones, generates female characteristics, and achieves fertility. Conversely, an abnormal daily rhythm caused by aberrant clock genes or abnormal environmental stimuli contributes to disorders of the female reproductive system, such as polycystic ovarian syndrome and premature ovarian insufficiency. Therefore, breaking the "time code" of the female reproductive system is crucial. In this paper, we review the interplay between circadian clocks and the female reproductive system and present its regulatory principles, moving from normal physiology regulation to disease etiology.

Adaptive Discrete-Time Flight Control Using Disturbance Observer and Neural Networks.

This paper studies the adaptive neural control (ANC)-based tracking problem for discrete-time nonlinear dynamics of an unmanned aerial vehicle subject to system uncertainties, bounded time-varying disturbances, and input saturation by using a discrete-time disturbance observer (DTDO). Based on the approximation approach of neural network, system uncertainties are tackled approximately. To restrain the negative effects of bounded disturbances, a nonlinear DTDO is designed. Then, a backstepping technique-based ANC strategy is proposed by utilizing a constructed auxiliary system and a discrete-time tracking differentiator. The boundness of all signals is proven in the closed-loop system under the discrete-time Lyapunov analysis. Finally, the feasibility of the proposed ANC technique is further specified based on numerical simulation results.

Adaptive Neural Control of Uncertain Nonlinear Systems Using Disturbance Observer.

This paper studies the problem of prescribed performance adaptive neural control for a class of uncertain multi-input and multi-output (MIMO) nonlinear systems in the presence of external disturbances and input saturation based on a disturbance observer. The system uncertainties are tackled by neural network (NN) approximation. To handle unknown disturbances, a Nussbaum disturbance observer is presented. By incorporating the disturbance observer and NNs, an adaptive prescribed performance neural control scheme is further developed. Then, the expected asymptotically convergent tracking errors between system output signals and desired signals are achieved. Numerical simulation results demonstrate the effectiveness of the proposed control scheme.