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Abstract Purpose The study aimed to investigate the potential influence of COVID-19 infection on embryo implantation and early development in women undergoing frozen embryo transfer (FET), with a specific focus on infections occurring at different periods around FET. Methods A retrospective analysis was performed on women who had undergone FET during a period marked by a significant surge in COVID-19 infection in Shanghai. All enrolled women experienced their first documented COVID-19 infection around the time of FET, ensuring that infections did not occur prior to oocyte retrieval. Participants were categorized into six groups based on the timing of infection: uninfected, ≥ 60 days,

BackgroundHereditary leiomyomatosis and renal cell cancer (HLRCC) is a rare autosomal dominant inheritable disease caused by Fumarate hydratase (FH) gene germline mutation. It is speculated that for HRLCC infertility women with multiple uterine leiomyomas, preimplantation genetic testing may help block transmission of mutated FH gene during pregnancy.Case presentationWe present the case of a 26-year-old nulligravida with a history of early-onset uterine leiomyomatosis had a heterozygous nonsense mutation [NM_000143.4 (FH): c.1027C > T(p.Arg343Ter)] in the HRLLC gene. After ovulation induction and in vitro fertilization, preimplantation genetic testing for monogenic disorders (PGT-M) on embryos revealed the absence of the pathogenic allele in two blastomeres. Uterine fibroids were identified before embryo transfer, leading to a submucosal myomectomy and long period of pituitary suppression by Gonadotropin-releasing hormone analog (GnRHa). The patient achieved a healthy live birth after the second cycle of frozen–thawed embryo transfer.ConclusionThis case details the successful treatment of an infertile patient with an HRLLC family history, resulting in a healthy birth through myomectomy and PGT-M selected embryo transplantation. Our literature search indicates the first reported live birth after HRLLC-PGT-M.

Abstract Background Significant lower genital tract (LGT) dysbiosis and an associated lower rate of clinical pregnancy after in vitro fertilization–frozen embryo transfer (IVF-FET) among polycystic ovary syndrome (PCOS) patients have been previously reported by our group. We aimed to assess whether transvaginal Lactobacillus supplementation can reverse LGT dysbiosis and further improve perinatal outcomes in PCOS patients after IVF-FET. Methods/design This is a protocol for a multicenter, open-label, randomized controlled trial in China. Women diagnosed with PCOS who are undergoing IVF-FET treatment will be recruited. Allocation to the intervention/control arms at a ratio of 1:1 will be executed by an electronic randomization system. Participants in the intervention arm will receive the live Lactobacillus capsule vaginally for 10 consecutive days before embryo transfer, while those in the control arm will receive standard individualized care. The primary outcomes will be the clinical pregnancy rate, implantation rate, and live birth rate. 16S rRNA sequencing and liquid chromatography–mass spectrometry will be conducted to evaluate the LGT microbiome and systemic metabonomics before and after the intervention. A sample of 260 participants will provide 95% power to detect a 20% increase in the rate of clinical pregnancy (α = 0.025, one-tailed test, 15% dropout rate). A total of 300 participants will be recruited. Discussion This is the first large and multicenter randomized controlled trial aimed at assessing the efficacy of transvaginal Lactobacillus supplementation on restoring the LGT microbiome and improving perinatal outcomes in PCOS patients after IVF-FET. This pragmatic trial is promising for increasing the rates of clinical pregnancy and live birth in PCOS patients after IVF-FET. Ethics and dissemination Ethical review approval was obtained from the Medical Research Ethics Committees of the International Peace Maternity and Child Health Hospital of Shanghai Jiao Tong University (15 October 2020, GKLW 2020-29). To maximize dissemination, these findings will be reported in open access publications in journals with high impact, and oral and poster conference presentations will be performed. Trial registration ChiCTR ChiCTR2000036460. Registered on 13 September 2020, https://www.chictr.org.cn/showproj.html?proj=59549 .

Abstract Background Type 1 gastric neuroendocrine tumors (NETs) are relatively rare to the extent that some physicians have little experience in diagnosing and treating them. The purpose of this study was to increase the understanding of the disease by analyzing and summarizing the management and prognoses of patients with type 1 gastric NETs at our center. Methods The data of 229 patients (59.4% female) with type 1 gastric NETs who were treated at our center during 2011–2022 were retrospectively analyzed. Results The average patient age was 50.5 ± 10.8 years. Multiple tumors affected 72.5% of the patients; 66.4% of the tumors were

ResNet-based correlation models excel in age recognition algorithms, but specific age recognition research is currently limited and often plagued by substantial errors. We introduce an enhanced portrait age recognition algorithm based on ResNet, using CORAL (consistent rank logits) rank consistent ordered regression instead of traditional classification to predict precise ages. We further improve this approach by incorporating DCN (deformable convolution), resulting in the DCN-R model. DCN dynamically adjusts convolution kernels for diverse faces, improving accuracy and robustness. We tested DCN-R34 and DCN-R50 against the SOTA model, achieving better results with the same complexity. This reduces the computational load while maintaining or enhancing performance.

When people tell lies, they often exhibit tension and emotional fluctuations, reflecting a complex psychological state. However, the scarcity of labeled data in datasets and the complexity of deception information pose significant challenges in extracting effective lie features, which severely restrict the accuracy of lie detection systems. To address this, this paper proposes a semi-supervised lie detection algorithm based on integrating multiple speech emotional features. Firstly, Long Short-Term Memory (LSTM) and Auto Encoder (AE) network process log Mel spectrogram features and acoustic statistical features, respectively, to capture the contextual links between similar features. Secondly, the joint attention model is used to learn the complementary relationship among different features to obtain feature representations with richer details. Lastly, the model combines the unsupervised loss Local Maximum Mean Discrepancy (LMMD) and supervised loss Jefferys multi-loss optimization to enhance the classification performance. Experimental results show that the algorithm proposed in this paper achieves better performance.

BackgroundCareful assessment of the reasons for discontinuation of active surveillance (AS) is required for men with prostate cancer (PCa).ObjectiveUsing Movember's Global Action Plan Prostate Cancer Active Surveillance initiative (GAP3) database, we report on reasons for AS discontinuation.Design, setting, and participantsWe compared data from 10296 men on AS from 21 centres across 12 countries.Outcome measurements and statistical analysisCumulative incidence methods were used to estimate the cumulative incidence rates of AS discontinuation.Results and limitationsDuring 5-yr follow-up, 27.5% (95% confidence interval [CI]: 26.4-28.6%) men showed signs of disease progression, 12.8% (95% CI: 12.0-13.6%) converted to active treatment without evidence of progression, 1.7% (95% CI: 1.5-2.0%) continued to watchful waiting, and 1.7% (95% CI: 1.4-2.1%) died from other causes. Of the 7049 men who remained on AS, 2339 had follow-up for >5yr, 4561 had follow-up for

How to effectively extract features with high representation ability has always been a research topic and a challenge for classification tasks. Most of the existing methods mainly solve the problem by using deep convolutional neural networks as feature extractors. Although a series of excellent network structures have been successful in the field of Chinese ink-wash painting classification, but most of them adopted the methods of only simple augmentation of the network structures and direct fusion of different scale features, which limit the network to further extract semantically rich and scale-invariant feature information, thus hindering the improvement of classification performance. In this paper, a novel model based on multi-level attention and multi-scale feature fusion is proposed. The model extracts three types of feature maps from the low-level, middle-level and high-level layers of the pretrained deep neural network firstly. Then, the low-level and middle-level feature maps are processed by the spatial attention module, nevertheless the high-level feature maps are processed by the scale invariance module to increase the scale-invariance properties. Moreover, the conditional random field module is adopted to fuse the optimized three-scale feature maps, and the channel attention module is followed to refine the features. Finally, the multi-level deep supervision strategy is utilized to optimize the model for better performance. To verify the effectiveness of the model, extensive experimental results on the Chinese ink-wash painting dataset created in this work show that the classification performance of the model is better than other mainstream research methods.