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40 ± 0.11 dB/0.849 ± 0.003), deep image prior (DIP) (24.22 ± 0.43 dB/0.737 ± 0.017), and MR-DIP (27.65 ± 0.42 dB/0.879 ± 0.007). Furthermore, we experimentally visualized the behavior of the optimization process, which is often unknown in unsupervised CNN-based restoration problems. For preclinical (using [18F]FDG and [11C]raclopride) and clinical (using [18F]florbetapir) studies, the proposed method demonstrates state-of-the-art denoising performance while retaining spatial resolution and quantitative accuracy, despite using a common network architecture for various noisy PET images with 1/10th of the full counts. These results suggest that the proposed MR-GDD can reduce PET scan times and PET tracer doses considerably without impacting patients.Shape reconstruction from sparse point clouds/images is a challenging and relevant task required for a variety of applications in computer vision and medical image analysis (e.g. Erastin order surgical navigation, cardiac motion analysis, augmented/virtual reality systems). A subset of such methods, viz. 3D shape reconstruction from 2D contours, is especially relevant for computer-aided diagnosis and intervention applications involving meshes derived from multiple 2D image slices, views or projections. We propose a deep learning architecture, coined Mesh Reconstruction Network (MR-Net), which tackles this problem. MR-Net enables accurate 3D mesh reconstruction in real-time despite missing data and with sparse annotations. Using 3D cardiac shape reconstruction from 2D contours defined on short-axis cardiac magnetic resonance image slices as an exemplar, we demonstrate that our approach consistently outperforms state-of-the-art techniques for shape reconstruction from unstructured point clouds. Our approach can reconstruct 3D cardiac meshes to within 2.5-mm point-to-point error, concerning the ground-truth data (the original image spatial resolution is ∼1.8×1.8×10mm3). We further evaluate the robustness of the proposed approach to incomplete data, and contours estimated using an automatic segmentation algorithm. MR-Net is generic and could reconstruct shapes of other organs, making it compelling as a tool for various applications in medical image analysis.In the time of transition to parenthood, many physical, psychological and social changes may affect the multidimensional theme of sexuality. This area plays a significant role in the overall well-being of the individual, the couple and the family. The aim of this systematic review is to consider current and emerging trends in the study of sexual function during pregnancy and after childbirth, evaluating the available evidence in the literature reported in specific reviews, and pulling together the suggestions that various authors have brought forward as being useful for daily clinical practice. A total of 4 databases were searched on EBSCOhost MEDLINE, Cochrane reviews, CINAHAL, and PsychInfo. A systematic search strategy was formulated using the key terms Sexuality, Sexual, Pregnancy, Postpartum, Puerperium, Perinatal, and Review. Eleven articles were included. The results revealed a gradual decline in the frequency of sexual behaviour throughout pregnancy, sharper in the third trimester. Sexual activity started to be resumed around 6-8 weeks after childbirth, to fully recover only after 6 months. A simultaneous change in sexual function was also found, such as less orgasm, sexual desire and satisfaction, more dyspareunia. Many aspects are related to these changes physical, psychological and social factors, fears about negative consequences of sexual intercourse, inadequate or absent professional counselling about sexuality, method of delivery and breastfeeding. Healthcare professionals need to adequately inform couples about the common fluctuations in sexual activity, interest, desire, and responsiveness over the course of the pregnancy and following childbirth. Joint counselling, if possible, is preferred.

To (1) describe the prevalence of key reproductive health outcomes (e.g., pregnancy, unintended pregnancy; abortion); and (2) examine social-structural correlates, including HIV stigma, of having key sexual and reproductive health (SRH) priorities met by participants' primary HIV provider, among women living with HIV.

Data were drawn from a longitudinal community-based open cohort (SHAWNA) of women living with HIV. The associations between social-structural factors and two outcomes representing having SRH priorities met by HIV providers ('being comfortable discussing sexual health [SH] and/or getting a Papanicolaou test' and 'being comfortable discussing reproductive health [RH] and/or pregnancy needs') were analyzed using bivariate and multivariable logistic regression models with generalized estimating equations for repeated measures over time. Adjusted odds ratios (AOR) and 95% confidence intervals [95% CIs] are reported.

Of 314 participants, 77.1% reported having SH priorities met while 64.7% report and/or gender minority identity and those who experience enacted HIV stigma. HIV providers should create safe, non-judgmental environments to facilitate discussions on SRH. These environments should be affirming of all sexual orientations and gender identities, culturally safe, culturally humble and use trauma-informed approaches.

Our findings suggest that there remain unmet priorities for safe SRH care and practice among women living with HIV, and in particular, for women living with HIV with sexual and/or gender minority identity and those who experience enacted HIV stigma. HIV providers should create safe, non-judgmental environments to facilitate discussions on SRH. These environments should be affirming of all sexual orientations and gender identities, culturally safe, culturally humble and use trauma-informed approaches.

Pregnant women in China are among those most affected by COVID-19. This article assesses Chinese pregnant women's COVID-19 and pregnancy knowledge levels, including the modality through which such knowledge was acquired, the degree of difficulty in acquiring the knowledge, the means of confirming the accuracy of the knowledge, and difficulties in seeking help from people who possess relevant medical knowledge.

The Mantel-Haenszel chi-square test was used to assess trends in binomial proportions. Multivariable binary logistic regression was performed to identify the association between knowledge acquisition and anxiety among pregnant women.

Low scores on knowledge about pregnancy, acquiring COVID-19 and pregnancy information through communication with others, verifying COVID-19 and pregnancy information either independently or via friends, and experiencing difficulties in seeking professional help regarding COVID-19 and pregnancy significantly increased anxiety among pregnant women.

Pregnant women's anxiety can be effectively reduced through developing and disseminating targeted information, including how to cope in an emergency (such as a major disease outbreak), through popular and social media, along with the provision of convenient consultation services.

Pregnant women's anxiety can be effectively reduced through developing and disseminating targeted information, including how to cope in an emergency (such as a major disease outbreak), through popular and social media, along with the provision of convenient consultation services.Prolactin (PRL) is produced by the pituitary gland and plays a vital role in the production of milk after a baby is born. PRL levels are normally elevated in pregnant and nursing women, and high levels of PRL in the human body cause hyperprolactinemia, infertility, galactorrhea, infrequent or irregular periods, amenorrhea, breast pain, and loss of libido. Accordingly, herein, a novel label-free immunosensor using a bismuth sulfide/polypyrrole (Bi2S3/PPy)-modified screen-printed electrode (SPE) for the fast and facile detection of the peptide hormone PRL. Bi2S3 nanorods were synthesized via a facile hydrothermal technique, and PPy was prepared by chemical polymerization method. Subsequently, the Bi2S3/PPy/ SPE was modified with 3-mercaptopropionic acid (MPA) and EDC/NHS. Owing to the cross-linking effect of EDC/NHS, antibody-PRL (anti-PRL) was firmly stabilized on the modified SPE surface. These layer-by-layer modifications enhanced the conducting properties, anti-PRL loading capacity, and sensitivity of the developed immunosensor. Under optimized conditions, the PRL immunosensor demonstrated a broad linear range of approximately 1-250 ng/mL, a low detection limit of approximately 0.130 ng/mL (3 × SD/b), good specificity, reproducibility, and stability. PRL was successfully evaluated in human and mouse serum samples, and the corresponding outcomes were compared with those of the electrochemical and ELISA methods.COVID-19 is one of the biggest challenges that human beings have faced recently. Many researchers have proposed different prediction methods for establishing a virus transmission model and predicting the trend of COVID-19. Among them, the methods based on artificial intelligence are currently the most interesting and widely used. However, only using artificial intelligence methods for prediction cannot capture the time change pattern of the transmission of infectious diseases. To solve this problem, this paper proposes a COVID-19 prediction model based on time-dependent SIRVD by using deep learning. This model combines deep learning technology with the mathematical model of infectious diseases, and forecasts the parameters in the mathematical model of infectious diseases by fusing deep learning models such as LSTM and other time prediction methods. In the current situation of mass vaccination, we analyzed COVID-19 data from January 15, 2021, to May 27, 2021 in seven countries - India, Argentina, Brazil, South Korea, Russia, the United Kingdom, France, Germany, and Italy. The experimental results show that the prediction model not only has a 50% improvement in single-day predictions compared to pure deep learning methods, but also can be adapted to short- and medium-term predictions, which makes the overall prediction more interpretable and robust.Channel attention, a channel-wise method often used in computer vision tasks, including liver tumor segmentation tasks, is able to model the channel relationship to augment the representation ability of feature maps. link2 Channel attention could adaptively generate channel-wise responses using global pooling, which aggregates spatial information roughly. link3 Actually, global pooling may introduce the loss of fine information, which is vital for segmentation tasks. Hence, we rethink the problem and propose the channel attention with adaptive global pooling(short for CAAGP), which preserves spatial and fine-grained information for liver tumor segmentation tasks when channel attention is generated. The model consists of three main parts, including improved self-attention, adaptive global pooling and responses generation modules. Self-attention achieves excellent performance in the computing of the spatial attention, while introducing serious calculation and memory burdens. In order to remedy these burdens, we improve self-attention and consider aggregating spatial information from x and y directions respectively. Extensive experiments have been conducted to verify the effectiveness of our proposed method. Our CAAGP outperforms other attention mechanisms significantly in liver tumor segmentation, especially for tumors with small size.