Clinical Application of Double Ovulation Stimulation in Patients with Diminished Ovarian Reserve and Asynchronous Follicular Development Undergoing Assisted Reproduction Technology.

OBJECTIVE: This study aimed to compare the clinical effects of double ovulation stimulation (DouStim) applied during the follicular and luteal phases with the antagonist protocol in patients with diminished ovarian reserve (DOR) and asynchronous follicular development undergoing assisted reproductive technology (ART). METHODS: The clinical data of patients with DOR and asynchronous follicular development receiving ART from January 2020 to December 2021 were retrospectively analyzed. The patients were divided into two groups according to their ovulation stimulation protocol: DouStim group (n=30) and antagonist group (n=62). Assisted reproduction and clinical pregnancy outcomes were compared between the two groups. RESULTS: In the DouStim group, the number of oocytes retrieved, metaphase II (MII) oocytes, two-pronuclei (2PN), day 3 (D3) embryos, D3 high-quality embryos as well as blastocyst formation, implantation, and human chorionic gonadotropin-positive rates were significantly greater than those in the antagonist group (all P<0.05). No significant differences were found in MII, fertilization, or continued pregnancy rates at the first frozen embryo transfer (FET), in-vitro fertilization (IVF) cancellation, or early medical abortion rates between the groups (all P>0.05). Except for the early medical abortion rate, the DouStim group generally had favorable outcomes. In the DouStim group, the dosage and duration of gonadotropin and the fertilization rate were significantly greater in the first ovulation stimulation induction than in the second ovulation stimulation induction (P<0.05). CONCLUSION: The DouStim protocol efficiently and economically obtained more mature oocytes and high-quality embryos for patients with DOR and asynchronous follicular development.

Corrigendum: Identification of key endometrial microRNAs and their target genes associated with pathogenesis of recurrent implantation failure by integrated bioinformatics analysis.

[This corrects the article DOI: 10.3389/fgene.2022.919301.].

Identification of Key Endometrial MicroRNAs and Their Target Genes Associated With Pathogenesis of Recurrent Implantation Failure by Integrated Bioinformatics Analysis.

Purpose: Recurrent implantation failure (RIF) is an enormous challenge for in vitro fertilization (IVF) clinicians. An understanding of the molecular mechanisms of RIF helps to predict prognosis and develop new therapeutic strategies. The study is designed to identify diagnostic biomarkers for RIF as well as the potential mechanisms underlying RIF by utilizing public databases together with experimental validation. Methods: Two microarray datasets of RIF patients and the healthy control endometrium were downloaded from the Gene Expression Omnibus (GEO) database. First, differentially expressed microRNAs (miRNAs) (DEMs) were identified and their target genes were predicted. Then, we identified differentially expressed genes (DEGs) and selected hub genes through protein-protein interaction (PPI) analyses. Functional enrichment analyses of DEGs and DEMs were conducted. Furthermore, the key DEMs which targeted these hub genes were selected to obtain the key miRNA-target gene network. The key genes in the miRNA-target gene network were validated by a single-cell RNA-sequencing dataset of endometrium from GEO. Finally, we selected two miRNA-target gene pairs for further experimental validation using dual-luciferase assay and quantitative polymerase chain reaction (qPCR). Results: We identified 49 DEMs between RIF patients and the fertile group and found 136,678 target genes. Then, 325 DEGs were totally used to construct the PPI network, and 33 hub genes were selected. Also, 25 DEMs targeted 16 key DEGs were obtained to establish a key miRNA-target gene network, and 16 key DEGs were validated by a single-cell RNA-sequencing dataset. Finally, the target relationship of hsa-miR-199a-5p-PDPN and hsa-miR-4306-PAX2 was verified by dual-luciferase assay, and there were significant differences in the expression of those genes between the RIF and fertile group by PCR (p < 0.05). Conclusion: We constructed miRNA-target gene regulatory networks associated with RIF which provide new insights regarding the underlying pathogenesis of RIF; hsa-miR-199a-5p-PDPN and hsa-miR-4306-PAX2 could be further explored as potential biomarkers for RIF, and their detection in the endometrium could be applied in clinics to estimate the probability of successful embryo transfer.

[Application and Progress of Convolutional Neural Network-based Pathological Diagnosis of Gastric Cancer].

The incidence of gastric cancer is the highest among all kinds of malignant tumors in China. Because gastric cancer is very hard to identify in its early stage, the early diagnosis rate of gastric cancer in China is relatively low. At present, the pathological diagnosis of gastric cancer mainly depends on the diagnosis of pathologists. However, the gradual improvement of people's living standards and the growing demand for medical and health care have exacerbated the shortage of medical resources, which has become a even more serious problem. Therefore, there is an urgent need for new technologies to help deal with this challenge. In recent years, with the rapid development of artificial intelligence (AI) and digital pathology, AI-aided pathological diagnosis based on convolutional neural network (CNN) as the core technology is showing promises for improving the diagnostic efficiency of gastric cancer. It is also of great significance for the early diagnosis and treatment of the disease and the reduction of its high incidence and mortality. We herein summarize the application and progress of deep-learning CNN in pathological diagnosis of gastric cancer, as well as the existing problems and prospects of future development.

Applying Statistical and Complex Network Methods to Explore the Key Signaling Molecules of Acupuncture Regulating Neuroendocrine-Immune Network.

The mechanisms of acupuncture are still unclear. In order to reveal the regulatory effect of manual acupuncture (MA) on the neuroendocrine-immune (NEI) network and identify the key signaling molecules during MA modulating NEI network, we used a rat complete Freund's adjuvant (CFA) model to observe the analgesic and anti-inflammatory effect of MA, and, what is more, we used statistical and complex network methods to analyze the data about the expression of 55 common signaling molecules of NEI network in ST36 (Zusanli) acupoint, and serum and hind foot pad tissue. The results indicate that MA had significant analgesic, anti-inflammatory effects on CFA rats; the key signaling molecules may play a key role during MA regulating NEI network, but further research is needed.