Excessive Exogenous Gonadotropins and Genetic and Pregnancy Outcomes After Euploidy Embryo Transfer: A Secondary Analysis of a Randomized Clinical Trial.

Importance: The safety of exogenous gonadotropin treatment, based on its effect on embryos and pregnancy outcomes, remains inconclusive. Objective: To evaluate the associations of different doses and durations of gonadotropins with embryonic genetic status and pregnancy outcomes after euploid embryo transfer in couples with infertility. Design, Setting, and Participants: This study was a post hoc analysis of a multicenter randomized clinical trial (RCT) conducted at 14 reproductive centers throughout China from July 2017 to June 2018 that evaluated the cumulative live birth rate with or without preimplantation genetic testing for aneuploidy (PGT-A) among couples with infertility and good prognosis. The PGT-A group from the original RCT was selected for secondary analysis. Patients were divided into 4 groups according to the total dosage of exogenous gonadotropins and treatment duration: group 1 (≤1500 IU and <10 days), group 2 (≤1500 IU and ≥10 days), group 3 (>1500 IU and <10 days), and group 4 (>1 500 IU and ≥10 days). Group 1 served as the control group. Data were analyzed from June through August 2023. Interventions: Blastocyst biopsy and PGT-A. Main outcomes and measures: The primary outcomes were embryonic aneuploidy, embryonic mosaicism, and cumulative live birth rates after euploid embryo transfer. Results: A total of 603 couples (mean [SD] age of prospective mothers, 29.13 [3.61] years) who underwent PGT-A were included, and 1809 embryos were screened using next-generation sequencing. The embryo mosaicism rate was significantly higher in groups 2 (44 of 339 embryos [13.0%]; adjusted odds ratio [aOR], 1.69 [95% CI, 1.09-2.64]), 3 (27 of 186 embryos [14.5%]; aOR, 1.98 [95% CI, 1.15-3.40]), and 4 (82 of 651 embryos [12.6%]; aOR, 1.60 [95% CI, 1.07-2.38]) than in group 1 (56 of 633 embryos [8.8%]). There were no associations between gonadotropin dosage or duration and the embryo aneuploidy rate. The cumulative live birth rate was significantly lower in groups 2 (83 of 113 couples [73.5%]; aOR, 0.49 [95% CI, 0.27-0.88]), 3 (42 of 62 couples [67.7%]; aOR, 0.41 [95% CI, 0.21-0.82]), and 4 (161 of 217 couples [74.2%]; aOR, 0.53 [95% CI, 0.31-0.89]) than in group 1 (180 of 211 couples [85.3%]). Conclusions and relevance: In this study, excessive exogenous gonadotropin administration was associated with increased embryonic mosaicism and decreased cumulative live birth rate after euploid embryo transfer in couples with a good prognosis. These findings suggest that consideration should be given to minimizing exogenous gonadotropin dosage and limiting treatment duration to improve embryo outcomes and increase the live birth rate. Trial Registration: ClinicalTrials.gov Identifier: NCT03118141.

Enhanced SSD framework for detecting defects in cigarette appearance using variational Bayesian inference under limited sample conditions.

In high-speed cigarette manufacturing industries, occasional minor cosmetic cigarette defects and a scarcity of samples significantly hinder the rapid and accurate detection of defects. To tackle this challenge, we propose an enhanced single-shot multibox detector (SSD) model that uses variational Bayesian inference for improved detection of tiny defects given sporadic occurrences and limited samples. The enhanced SSD model incorporates a bounded intersection over union (BIoU) loss function to reduce sensitivity to minor deviations and uses exponential linear unit (ELU) and leaky rectified linear unit (ReLU) activation functions to mitigate vanishing gradients and neuron death in deep neural networks. Empirical results show that the enhanced SSD300 and SSD512 models increase the model's detection accuracy mean average precision (mAP) by up to 1.2% for small defects. Ablation studies further reveal that the model's mAP increases by 1.5%, which reduces the computational requirements by 5.92 GFLOPs. The model also shows improved inference in scenarios with limited samples, thus highlighting its effectiveness and applicability in high-speed, precision-oriented cigarette manufacturing industries.

Molecular cluster mining of high-grade serous ovarian cancer via multi-omics data analysis aids precise medicine.

PURPOSE: HGSOC is a kind of gynecological cancer with high mortality and strong heterogeneity. The study used multi-omics and multiple algorithms to identify novel molecular subtypes, which can help patients obtain more personalized treatments. METHODS: Firstly, the consensus clustering result was obtained using a consensus ensemble of ten classical clustering algorithms, based on mRNA, lncRNA, DNA methylation, and mutation data. The difference in signaling pathways was evaluated using the single-sample gene set enrichment analysis (ssGSEA). Meanwhile, the relationship between genetic alteration, response to immunotherapy, drug sensitivity, prognosis, and subtypes was further analyzed. Finally, the reliability of the new subtype was verified in three external datasets. RESULTS: Three molecular subtypes were identified. Immune desert subtype (CS1) had little enrichment in the immune microenvironment and metabolic pathways. Immune/non-stromal subtype (CS2) was enriched in the immune microenvironment and metabolism of polyamines. Immune/stromal subtype (CS3) not only enriched anti-tumor immune microenvironment characteristics but also enriched pro-tumor stroma characteristics, glycosaminoglycan metabolism, and sphingolipid metabolism. The CS2 had the best overall survival and the highest response rate to immunotherapy. The CS3 had the worst prognosis and the lowest response rate to immunotherapy but was more sensitive to PARP and VEGFR molecular-targeted therapy. The similar differences among three subtypes were successfully validated in three external cohorts. CONCLUSION: We used ten clustering algorithms to comprehensively analyze four types of omics data, identified three biologically significant subtypes of HGSOC patients, and provided personalized treatment recommendations for each subtype. Our findings provided novel views into the HGSOC subtypes and could provide potential clinical treatment strategies.

An Interpretable Multitask Framework BiLAT Enables Accurate Prediction of Cyclin-Dependent Protein Kinase Inhibitors.

The cyclin-dependent protein kinases (CDKs) are protein-serine/threonine kinases with crucial effects on the regulation of cell cycle and transcription. CDKs can be a hallmark of cancer since their excessive expression could lead to impaired cell proliferation. However, the selectivity profile of most developed CDK inhibitors is not enough, which have hindered the therapeutic use of CDK inhibitors. In this study, we propose a multitask deep learning framework called BiLAT based on SMILES representation for the prediction of the inhibitory activity of molecules on eight CDK subtypes (CDK1, 2, 4-9). The framework is mainly composed of an improved bidirectional long short-term memory module BiLSTM and the encode layer of the Transformer framework. Additionally, the data enhancement method of SMILES enumeration is applied to improve the performance of the model. Compared with baseline predictive models based on three conventional machine learning methods and two multitask deep learning algorithms, BiLAT achieves the best performance with the highest average AUC, ACC, F1-score, and MCC values of 0.938, 0.894, 0.911, and 0.715 for the test set. Moreover, we constructed a targeted external data set CDK-Dec for the CDK family, which mainly contains bait values screened by 3D similarity with active compounds. This dataset was utilized in the subsequent evaluation of our model. It is worth mentioning that the BiLAT model is interpretable and can be used by chemists to design and synthesize compounds with improved activity. To further verify the generalization ability of the multitask BiLAT model, we also conducted another evaluation on three public datasets (Tox21, ClinTox, and SIDER). Compared with several currently popular models, BiLAT shows the best performance on two datasets. These results indicate that BiLAT is an effective tool for accelerating drug discovery.

Gold core-satellite nanostructure linked by oligonucleotides for detection of glutathione with LSPR scattering spectrum.

We demonstrated a sensitive method for detection of glutathione (GSH) based on LSPR scattering spectrum using gold core-satellite nanostructure linked by T-Hg2+-T base pair. The core-satellite assembly caused coupling between plasmonic nanoparticles, which inducing distinct change of LSPR peak wavelength. As the interaction between Hg2+ and GSH, the core-satellite nanostructure would be disassembled, which accompanied with spectral blue-shift of the scattering spectrum. By using this method, GSH could be quantitatively detected, and the detection limits can reach to 0.1 µM.

[Study on preparation of intravenous submicron emulsions of Oleum Cinnamomi oil of Miao nationality herbal].

OBJECTIVE: To study the prescription and preparation of intravenous submicron emulsion of Oleum Cinnamomi oil of Miao nationality herbal. METHOD: Using the high speed blender mixed round the Oleum Cinnamomi oil with the soybean phospholipids and Pluronic F68 as emulsifier, then using the high pressure homogenizer made the intravenous submicron emulsion of the Oleum Cinnamomi oil and investigate its grain path and distributing. RESULT: Having been done by using hydroextractor 4,500 r min(-1) 15 minutes the submicron emulsion grain path has well proportioned distribution. CONCLUSION: The preparation technology is simple and has good stability, so it can be used as a method to make the intravenous submicron emulsion of the Oleum Cinnamomi oil of Miao nationality herbal.