Large-scale uterine myoma MRI dataset covering all FIGO types with pixel-level annotations.

Uterine myomas are the most common pelvic tumors in women, which can lead to abnormal uterine bleeding, abdominal pain, pelvic compression symptoms, infertility, or adverse pregnancy. In this article, we provide a dataset named uterine myoma MRI dataset (UMD), which can be used for clinical research on uterine myoma imaging. The UMD is the largest publicly available uterine MRI dataset to date including 300 cases of uterine myoma T2-weighted imaging (T2WI) sagittal patient images and their corresponding annotation files. The UMD covers 9 types of uterine myomas classified by the International Federation of Obstetrics and Gynecology (FIGO), which were annotated and reviewed by 11 experienced doctors to ensure the authority of the annotated data. The UMD is helpful for uterine myomas classification and uterine 3D reconstruction tasks, which has important implications for clinical research on uterine myomas.

Secondary cytoreductive surgery followed by olaparib tablets as maintenance therapy in patients with BRCA mutated recurrent ovarian cancer: A multi-center retrospective study.

OBJECTIVE: To evaluate the effect of secondary cytoreductive surgery (SeCRS) followed by platinum-based chemotherapy (PBC) and olaparib tablets as maintenance therapy in patients with BRCA mutated recurrent epithelial ovarian cancer. METHODS: This was a retrospective study of a prospective database. We collected information on 623 patients diagnosed with BRCA mutated recurrent epithelial ovarian cancer, all of whom underwent SeCRS followed by PBC in combination with or without olaparib. Overall survival and progression-free survival were measured to evaluate treatment effectiveness. RESULTS: Of the 623 patients recruited, 240 underwent SeCRS plus hyperthermic intraperitoneal chemotherapy followed by PBC and olaparib maintenance therapy (Group A), 248 underwent SeCRS followed by PBC and olaparib maintenance therapy (Group B), and 135 underwent SeCRS followed by PBC only upon recurrence (Group C). The median progression-free survival for Group A was significantly longer than that for Group B (32.5 vs. 24.2 months, P<0.001), and Group B was significantly longer than Group C (24.2 vs. 15.1 months, P<0.001). The median overall survival for Groups A was significantly longer than that for Group B (71.4 vs. 63.5 months, P<0.001), and Group B was significantly longer than Group C (63.5 vs. 47.5 months, P<0.001). CONCLUSIONS: This study suggested that SeCRS followed by PBC and olaparib maintenance therapy resulted in longer overall survival and progression-free survival than SeCRS followed by PBC only in patients with BRCA mutated recurrent ovarian cancer, especially in patients treated with SeCRS plus hyperthermic intraperitoneal chemotherapy.

Interactive segmentation of medical images using deep learning.

Medical image segmentation algorithms based on deep learning have achieved good segmentation results in recent years, but they require a large amount of labeled data. When performing pixel-level labeling on medical images, labeling a target requires marking ten or even hundreds of points along its edge, which requires a lot of time and labor costs. To reduce the labeling cost, we utilize a click-based interactive segmentation method to generate high-quality segmentation labels. However, in current interactive segmentation algorithms, only the interaction information clicked by the user and the image features are fused as the input of the backbone network (so-called early fusion). The early fusion method has the problem that the interactive information is much sparse at this time. Furthermore, the interactive segmentation algorithms do not take into account the boundary problem, resulting in poor model performance. So we propose early fusion and late fusion strategy to prevent the interaction information from being diluted prematurely and make better use of the interaction information. At the same time, we propose a decoupled head structure, by extracting the image boundary information, and combining the boundary loss function to establish the boundary constraint term, so that the network can pay more attention to the boundary information and further improve the performance of the network. Finally, we conduct experiments on three medical datasets (Chaos, VerSe and Uterine Myoma MRI) to verify the effectiveness of our network. The experimental results show that our network greatly improved compared with the baseline, and NoC@80(the number of interactive clicks over 80% of the IoU threshold) improved by 0.1, 0.1, and 0.2. In particular, we have achieved a NoC@80 score of 1.69 on Chaos. According to statistics, manual annotation takes 25 min to label a case(Uterine Myoma MRI). Annotating a medical image with our method can be done in only 2 or 3 clicks, which can save more than 50% of the cost.

N-acetylcysteine and Hydroxychloroquine Ameliorate ADMA-Induced Fetal Growth Restriction in Mice via Regulating Oxidative Stress and Autophagy.

Fetal growth restriction (FGR) seriously threatens perinatal health. The main cause of FGR is placental malperfusion, but the specific mechanism is still unclear, and there is no effective treatment for FGR. We constructed a FGR mouse model by adding exogenous asymmetric dimethylarginine (ADMA) through in vivo experiments and found that ADMA could cause placental dysplasia and induce the occurrence of FGR. Compared with the control group, reactive oxygen species (ROS) production in the placenta was increased in mice with FGR, and the expression of autophagy-related proteins p-AKT/AKT, p-mTOR/mTOR, and P62 was significantly decreased, while the expression of Beclin-1 and LC3-II was significantly increased in the FGR group. Furthermore, ADMA had a favorable effect in promoting the formation of autophagosomes. Hydroxychloroquine (HCQ) and N-acetylcysteine (NAC) improved ADMA-induced disorders of placental development and alleviated ADMA-induced FGR. This study found that ADMA could cause excessive autophagy of trophoblasts by increasing the level of oxidative stress, ultimately leading to the occurrence of FGR, and HCQ and NAC had therapeutic effects on ADMA-induced FGR.

Revolutionizing hysteroscopy outcomes: AI-powered uterine myoma diagnosis algorithm shortens operation time and reduces blood loss.

Background: The application of artificial intelligence (AI) powered algorithm in clinical decision-making is globally popular among clinicians and medical scientists. In this research endeavor, we harnessed the capabilities of AI to enhance the precision of hysteroscopic myomectomy procedures. Methods: Our multidisciplinary team developed a comprehensive suite of algorithms, rooted in deep learning technology, addressing myomas segmentation tasks. We assembled a cohort comprising 56 patients diagnosed with submucosal myomas, each of whom underwent magnetic resonance imaging (MRI) examinations. Subsequently, half of the participants were randomly designated to undergo AI-augmented procedures. Our AI system exhibited remarkable proficiency in elucidating the precise spatial localization of submucosal myomas. Results: The results of our study showcased a statistically significant reduction in both operative duration (41.32 ± 17.83 minutes vs. 32.11 ± 11.86 minutes, p=0.03) and intraoperative blood loss (10.00 (6.25-15.00) ml vs. 10.00 (5.00-15.00) ml, p=0.04) in procedures assisted by AI. Conclusion: This work stands as a pioneering achievement, marking the inaugural deployment of an AI-powered diagnostic model in the domain of hysteroscopic surgery. Consequently, our findings substantiate the potential of AI-driven interventions within the field of gynecological surgery.

Associations between HT, BMI, and allergic rhinitis in perimenopausal women.

BACKGROUND: Increasing evidence suggests that hormone therapy (HT) and obesity exert an influence on allergic rhinitis (AR). It is important to investigate the association and interactions between HT, BMI, and AR in perimenopausal women. METHODS: From May 2020 to March 2021, a cross-sectional survey was completed by patients who visited the Allergy Department and Gynecology Department of Shijitan Hospital. The patients completed a questionnaire and stratified analyses by BMI in tertiles were performed. Logistic analyses were performed to evaluate the relationships between HT, BMI, and AR. RESULTS: A total of 950 patients completed the study, among which, 393 patients were receiving HT. HT was found to be associated with increased risks for AR (OR = 1.51 [95% CI: 1.151-1.985]), asthma (OR = 3.61 [95% CI: 2.21-5.89]), and their accompanying symptoms (OR = 3.54 [95% CI: 2.146-5.831]). In lean women, the use of HT was significantly associated with a higher risks for AR (OR = 2.26 [95% CI: 1.31-3.91]), the time course of AR (OR = 2.54 [95% CI: 1.37-4.74]), hay fever (OR = 2.54 [95% CI: 1.37-4.74]), and accompanying symptoms (including canker sores, diarrhea, and stomachache) (OR = 2.26 [95% CI: 1.309-3.907]) when compared to normal or heavier weight women (course of AR: pinteraction = 0.032; hay fever; pinteraction = 0.006; accompanying symptoms: pinteraction = 0.009). CONCLUSIONS: HT can reduce the risk for AR in perimenopausal women. Lean women who used HT were at a higher risk for AR when compared to overweight women who used AR. There exists an interaction between HT and BMI that influences AR. Furthermore, HT and obesity increase the risk for AR by some common pathways, more follow-up work is needed to explore common pathways.

Establishment and Validation of a Machine Learning-Based Prediction Model for Termination of Pregnancy via Cesarean Section.

Objective: This study aimed to investigate the risk factors of cesarean section and establish a prediction model for cesarean section based on the characteristics of pregnant women. Methods: The clinical characteristics of 2552 singleton pregnant women who delivered a live baby between January 2020 and December 2021 were retrospectively reviewed. They were divided into vaginal delivery group (n = 1850) and cesarean section group (n = 702). These subjects were divided into training set (2020.1-2021.6) and validation set (2021.7-2021.12). In the training set, univariate analysis, Lasso regression, and Boruta were used to screen independent risk factors for cesarean section. Four models, including Logistic Regression (LR), K-Nearest Neighbor (KNN), Classification and Regression Tree (CART), and Random forest (RF), were established in the training set using K-fold cross validation, hyperparameter optimization, and random oversampling techniques. The best model was screened, and Sort graph of feature variables, univariate partial dependency profile, and Break Down profile were delineated. In the validation set, the confusion matrix parameters were calculated, and receiver operating characteristic curve (ROC), precision recall curve (PRC), calibration curve, and clinical decision curve analysis (DCA) were delineated. Results: The risk factors of cesarean section included age and height of women, weight at delivery, weight gain, para, assisted reproduction, abnormal blood glucose during pregnancy, pregnancy hypertension, scarred uterus, premature rupture of membrane (PROM), placenta previa, fetal malposition, thrombocytopenia, floating fetal head, and labor analgesia. RF had the best performance among the four models, and the accuracy of confusion matrix parameters was 0.8956357. The Matthews correlation coefficient (MCC) was 0.753012. The area under ROC (AUC-ROC) was 0.9790787, and the area under PRC (AUC-PRC) was 0.957888. Conclusion: RF prediction model for caesarean section has high discrimination performance, accuracy and consistency, and outstanding generalization ability.

Altered expression of the L-arginine/nitric oxide pathway in ovarian cancer: metabolic biomarkers and biological implications.

MOTIVATION: Ovarian cancer (OC) is a highly lethal gynecological malignancy. Extensive research has shown that OC cells undergo significant metabolic alterations during tumorigenesis. In this study, we aim to leverage these metabolic changes as potential biomarkers for assessing ovarian cancer. METHODS: A functional module-based approach was utilized to identify key gene expression pathways that distinguish different stages of ovarian cancer (OC) within a tissue biopsy cohort. This cohort consisted of control samples (n = 79), stage I/II samples (n = 280), and stage III/IV samples (n = 1016). To further explore these altered molecular pathways, minimal spanning tree (MST) analysis was applied, leading to the formulation of metabolic biomarker hypotheses for OC liquid biopsy. To validate, a multiple reaction monitoring (MRM) based quantitative LCMS/MS method was developed. This method allowed for the precise quantification of targeted metabolite biomarkers using an OC blood cohort comprising control samples (n = 464), benign samples (n = 3), and OC samples (n = 13). RESULTS: Eleven functional modules were identified as significant differentiators (false discovery rate, FDR < 0.05) between normal and early-stage, or early-stage and late-stage ovarian cancer (OC) tumor tissues. MST analysis revealed that the metabolic L-arginine/nitric oxide (L-ARG/NO) pathway was reprogrammed, and the modules related to "DNA replication" and "DNA repair and recombination" served as anchor modules connecting the other nine modules. Based on this analysis, symmetric dimethylarginine (SDMA) and arginine were proposed as potential liquid biopsy biomarkers for OC assessment. Our quantitative LCMS/MS analysis on our OC blood cohort provided direct evidence supporting the use of the SDMA-to-arginine ratio as a liquid biopsy panel to distinguish between normal and OC samples, with an area under the ROC curve (AUC) of 98.3%. CONCLUSION: Our comprehensive analysis of tissue genomics and blood quantitative LC/MSMS metabolic data shed light on the metabolic reprogramming underlying OC pathophysiology. These findings offer new insights into the potential diagnostic utility of the SDMA-to-arginine ratio for OC assessment. Further validation studies using adequately powered OC cohorts are warranted to fully establish the clinical effectiveness of this diagnostic test.

WA-ResUNet: A Focused Tail Class MRI Medical Image Segmentation Algorithm.

Medical image segmentation can effectively identify lesions in medicine, but some small and rare lesions cannot be well identified. Existing studies do not take into account the uncertainty of the occurrence of diseased tissue, and the problem of long-tailed distribution of medical data. Meanwhile, the grayscale image obtained from Magnetic Resonance Imaging (MRI) detection has problems, such as the features being difficult to extract and invalid features being difficult to distinguish. In order to solve these problems, we propose a new weighted attention ResUNet (WA-ResUNet) and a class weight formula based on the number of images contained in the class, which improves the performance of the model in the low-frequency class and the overall effect of the model by improving the degree of attention paid to the valid features and invalid ones and rebalancing the learning efficiency among the classes. We evaluated our method on an uterine MRI dataset and compared it with the ResUNet. WA-ResUNet increased Intersection over Union (IoU) in the low-frequency class (Nabothian cysts) by 21.87%, and the overall mIoU increased by more than 6.5%.

Platelet-rich fibrin improves repair and regeneration of damaged endometrium in rats.

Purpose: Intrauterine adhesion (IUA) is the most common cause of uterine infertility. This study aims to evaluate whether platelet-rich fibrin (PRF) treatment can stimulate damaged endometrium regeneration in rats. Methods: First, hematoxylin and eosin (HE) staining, scanning and transmission electron microscopy, and ELISAs were used to evaluate the microstructure of PRF. Then, mechanical damage was used to establish an IUA rat model. A total of 40 SD female rats were randomized to three groups: PRF transplantation group, IUA group, and sham group. Rats were sacrificed at 3, 7, and 14 days and uteruses were obtained for further analysis. Finally, functional and histological recovery of the damaged endometrium was analyzed by pregnancy test, HE staining, Masson's staining, and immunohistochemistry. Results: PRF has two distinct zones, platelets and fibrin zone. Long and narrow fibrin fibers interconnected with each other and formed a three-dimensional, flexible, and elastic structure; platelet aggregates were trapped in fibrin fibers, and each platelet is associated with several fibrin fibers. PRF exudates promoted endometrial stromal cell proliferation and migration in vitro. PRF transplantation was beneficial for maintaining uterine structure, promoting endometrial luminal epithelium and endometrial gland regeneration, and decreasing fibrotic areas in vivo. Conclusion: Intrauterine administration of PRF was demonstrated to be effective in preventing IUA and stimulating damaged endometrium regeneration in rats. This study not only provided a promising method for its potential in endometrial regeneration in women who suffer from uterine infertility but also may prevent IUA after intrauterine surgery in clinical cases.

Deep convolutional neural networks for multiple histologic types of ovarian tumors classification in ultrasound images.

Objective: This study aimed to evaluate and validate the performance of deep convolutional neural networks when discriminating different histologic types of ovarian tumor in ultrasound (US) images. Material and methods: Our retrospective study took 1142 US images from 328 patients from January 2019 to June 2021. Two tasks were proposed based on US images. Task 1 was to classify benign and high-grade serous carcinoma in original ovarian tumor US images, in which benign ovarian tumor was divided into six classes: mature cystic teratoma, endometriotic cyst, serous cystadenoma, granulosa-theca cell tumor, mucinous cystadenoma and simple cyst. The US images in task 2 were segmented. Deep convolutional neural networks (DCNN) were applied to classify different types of ovarian tumors in detail. We used transfer learning on six pre-trained DCNNs: VGG16, GoogleNet, ResNet34, ResNext50, DensNet121 and DensNet201. Several metrics were adopted to assess the model performance: accuracy, sensitivity, specificity, FI-score and the area under the receiver operating characteristic curve (AUC). Results: The DCNN performed better in labeled US images than in original US images. The best predictive performance came from the ResNext50 model. The model had an overall accuracy of 0.952 for in directly classifying the seven histologic types of ovarian tumors. It achieved a sensitivity of 90% and a specificity of 99.2% for high-grade serous carcinoma, and a sensitivity of over 90% and a specificity of over 95% in most benign pathological categories. Conclusion: DCNN is a promising technique for classifying different histologic types of ovarian tumors in US images, and provide valuable computer-aided information.

A retrospective analysis of secondary cytoreductive surgery plus hyperthermic intraperitoneal chemotherapy in patients with recurrent ovarian cancer.

AIM: To evaluate the effect of secondary cytoreductive surgery (SeCRS) plus hyperthermic intraperitoneal chemotherapy (HIPEC) in recurrent epithelial ovarian cancer patients. METHODS: This retrospective study analyzed a prospective database. We collected information of 389 patients who were diagnosed with recurrent epithelial ovarian cancer. All patients underwent SeCRS with or without HIPEC. Overall survival and progression-free survival (PFS) were used to evaluate the treatment effectiveness. RESULTS: Of the 389 patients collected, 123 underwent primary or interval cytoreductive surgery at initial treatment and SeCRS at recurrence (Group A), 130 underwent primary or interval cytoreductive surgery at initial and SeCRS plus HIPEC at recurrence (Group B), and 136 underwent primary or interval cytoreductive surgery plus HIPEC at initial and SeCRS plus HIPEC at recurrence (Group C). The median overall survival for Groups A, B, and C were 49.1 months (95% confidence interval [CI]: 47.6-50.5), 56.0 months (95% CI: 54.2-57.7), and 64.4 months (95% CI: 63.1-65.6), respectively. The median PFS for Groups A, B, and C were 13.1 months (95% CI: 12.6-13.5), 15.0 months (95% CI: 14.2-15.7), and 16.8 months (95% CI: 16.1-17.4), respectively. There were no significant difference in incidence and grade of adverse events among groups. CONCLUSIONS: This study suggested that SeCRS plus HIPEC followed by chemotherapy resulted in longer overall survival and PFS than only SeCRS followed by chemotherapy in patients with recurrent ovarian cancer, especially in patients who were treated with repeat HIPEC.

An Instance Segmentation Model Based on Deep Learning for Intelligent Diagnosis of Uterine Myomas in MRI.

Uterine myomas affect 70% of women of reproductive age, potentially impacting their fertility and health. Manual film reading is commonly used to identify uterine myomas, but it is time-consuming, laborious, and subjective. Clinical treatment requires the consideration of the positional relationship among the uterine wall, uterine cavity, and uterine myomas. However, due to their complex and variable shapes, the low contrast of adjacent tissues or organs, and indistinguishable edges, accurately identifying them in MRI is difficult. Our work addresses these challenges by proposing an instance segmentation network capable of automatically outputting the location, category, and masks of each organ and lesion. Specifically, we designed a new backbone that facilitates learning the shape features of object diversity, and filters out background noise interference. We optimized the anchor box generation strategy to provide better priors in order to enhance the process of bounding box prediction and regression. An adaptive iterative subdivision strategy ensures that the mask boundary details of objects are more realistic and accurate. We conducted extensive experiments to validate our network, which achieved better average precision (AP) results than those of state-of-the-art instance segmentation models. Compared to the baseline network, our model improved AP on the uterine wall, uterine cavity, and myomas by 8.8%, 8.4%, and 3.2%, respectively. Our work is the first to realize multiclass instance segmentation in uterine MRI, providing a convenient and objective reference for the clinical development of appropriate surgical plans, and has significant value in improving diagnostic efficiency and realizing the automatic auxiliary diagnosis of uterine myomas.

Improving the Segmentation Accuracy of Ovarian-Tumor Ultrasound Images Using Image Inpainting.

Diagnostic results can be radically influenced by the quality of 2D ovarian-tumor ultrasound images. However, clinically processed 2D ovarian-tumor ultrasound images contain many artificially recognized symbols, such as fingers, crosses, dashed lines, and letters which assist artificial intelligence (AI) in image recognition. These symbols are widely distributed within the lesion's boundary, which can also affect the useful feature-extraction-utilizing networks and thus decrease the accuracy of lesion classification and segmentation. Image inpainting techniques are used for noise and object elimination from images. To solve this problem, we observed the MMOTU dataset and built a 2D ovarian-tumor ultrasound image inpainting dataset by finely annotating the various symbols in the images. A novel framework called mask-guided generative adversarial network (MGGAN) is presented in this paper for 2D ovarian-tumor ultrasound images to remove various symbols from the images. The MGGAN performs to a high standard in corrupted regions by using an attention mechanism in the generator to pay more attention to valid information and ignore symbol information, making lesion boundaries more realistic. Moreover, fast Fourier convolutions (FFCs) and residual networks are used to increase the global field of perception; thus, our model can be applied to high-resolution ultrasound images. The greatest benefit of this algorithm is that it achieves pixel-level inpainting of distorted regions without clean images. Compared with other models, our model achieveed better results with only one stage in terms of objective and subjective evaluations. Our model obtained the best results for 256 × 256 and 512 × 512 resolutions. At a resolution of 256 × 256, our model achieved 0.9246 for SSIM, 22.66 for FID, and 0.07806 for LPIPS. At a resolution of 512 × 512, our model achieved 0.9208 for SSIM, 25.52 for FID, and 0.08300 for LPIPS. Our method can considerably improve the accuracy of computerized ovarian tumor diagnosis. The segmentation accuracy was improved from 71.51% to 76.06% for the Unet model and from 61.13% to 66.65% for the PSPnet model in clean images.

Pre-pregnancy body mass index has greater influence on newborn weight and perinatal outcome than weight control during pregnancy in obese women.

BACKGROUND: The pre-pregnancy weight and gestational weight gain (GWG) are closely related to perinatal maternal and infant complications, but the relationship between pre-pregnancy weight and GWG and the pattern of interaction have not been reported. This study investigated the influence of weight control during pregnancy on the perinatal maternal and infant outcomes. METHODS: A total of 835 singleton pregnant women who were hospitalized between January 2018 and December 2018 were retrospectively included in this study and divided into two groups: the diet guidance (DG) group (n = 167) and the control group (n = 668). The pre-pregnancy body mass index (BMI), GWG, and perinatal outcomes of the women and infants were determined in two groups. RESULTS: The dietary modification and reasonable exercise during pregnancy effectively reduced the GWG, and even some women with pre-pregnancy obesity achieved weight loss during pregnancy. The GWG in the DG group was significantly lower than in the control group, especially in the second and third trimesters. GWG was positively related to birth weight. The birth weight in the DG group was significantly lower than in the control group when their mothers had similar GWG. In women with pre-pregnancy obesity, GWG seemed to be negatively related to birth weight. However, after adjusting the mean BMI, the pre-pregnancy BMI and GWG were positively related to the birth weight. Compared with the control group, the incidences of dystocia, postpartum hemorrhage, macrosomia, small for gestational age infants and neonatal complications significantly reduced in the DG group, and the preterm birth rate was comparable between two groups. Some women with pre-pregnancy obesity lose weight during pregnancy, but there was no premature birth or small for gestational age infant. The incidences of macrosomia, postpartum hemorrhage, dystocia, cesarean section and gestational diabetes increased significantly with the increase of pre-pregnancy BMI. CONCLUSION: For women with increased pre-pregnancy BMI, strict weight control is required to reduce obesity-related complications of the mother and infant. The weight control in the second and third trimesters is especially important and most likely to prevent GWG. Compared with GWG, pre-pregnancy BMI has greater influence on the birth weight and maternal and infant complications, and may even compromise the benefits of weight control during pregnancy. Thus, weight control is recommended before pregnancy.

Effects of a ketogenic diet on reproductive and metabolic phenotypes in mice with polycystic ovary syndrome†.

Polycystic ovary syndrome (PCOS) is one of the most common female reproductive and metabolic disorders. The ketogenic diet (KD) is a diet high in fat and low in carbohydrate. The beneficial effects of KD intervention have been demonstrated in obese women with PCOS. The underlying mechanisms, however, remain unknown. The aim of the present study was to investigate the effects of a KD on both reproductive and metabolic phenotypes of dehydroepiandrosterone (DHEA)-induced PCOS mice. Female C57BL/6 mice were divided into three groups, designated Control, DHEA, and DHEA+KD groups. Mice of both Control and DHEA groups were fed the control diet, whereas DHEA+KD mice were fed a KD with 89%(kcal) fat for 1 or 3 weeks after PCOS mouse model was completed. At the end of the experiment, both reproductive and metabolic characteristics were assessed. Our data show that KD treatment significantly increased blood ketone levels, reduced body weight and random and fasting blood glucose levels in DHEA+KD mice compared with DHEA mice. Glucose tolerance, however, was impaired in DHEA+KD mice. Ovarian functions were improved in some DHEAmice after KD feeding, especially in mice treated with KD for 3 weeks. In addition, inflammation and cell apoptosis were inhibited in the ovaries of DHEA+KD mice. Results from in vitro experiments showed that the main ketone body ß-hydroxybutyrate reduced inflammation and cell apoptosis in DHEA-treated KGN cells. These findings support the therapeutic effects of KD and reveal a possible mechanism by which KD improves ovarian functions in PCOS mice.

Changes in Maternal Serum Levels of C3 and C4 Complement Components in Different Delivery Methods and Postpartum Hemorrhage.

BACKGROUND: Activation of the complement system may play a role in the pathophysiology of human labor. Yet no unanimous conclusion has been drawn. OBJECTIVE: To compare the differences in maternal complement components C3 and C4 serum levels in cesarean section and the vaginal delivery at term and in the postpartum hemorrhage. METHODS: One hundred and sixty six women delivered at term were enrolled in this study. Maternal blood samples were obtained from 47 cases of elective cesarean section and 119 cases of the vaginal delivery. Serum complement levels were measured subsequently by immuno-scatter turbidimetry. RESULTS: The maternal complement levels declined significantly during delivery by both the cesarean section and the vaginal delivery (p＜0.01) in comparison with the baseline. A much larger drop of C3 serum level was found in the postpartum hemorrhage and in the vaginal delivery, and the incidence of the postpartum hemorrhage has a positive correlation with the complement decline rate. CONCLUSION: The complement system may be involved in the delivery process and represents a predictive value in postpartum hemorrhage.

Ketogenic diet may be a new approach to treatment stress urinary incontinence in obese elderly women: report of five cases.

BACKGROUND: Stress urinary incontinence (SUI) as a serious social problem restricted women's daily life and affect their quality of life, especially for obese women. The mechanism of stress urinary incontinence is unclear. Weight loss is the first line of treatment for stress incontinence in obese women. Ketogenic diet is a special diet with high fat, low carbohydrate and moderate protein, which can reduce body mass faster than the traditional diet. There exist no reports on the therapeutic effect of ketogenic diet on SUI in obese women. CASE PRESENTATION: Five postmenopausal obese women are diagnosed as mild to moderate stress urinary incontinence, which affected their quality of life for medical treatment. After 4 weeks ketogenic diet, we found that ketogenic diet can significantly improve urine leakage, reduce body weight, decrease visceral fat area, reduce body fat percentage, and reduce BMI. CONCLUSION: Reports in this case reveal that ketogenic diet may become one of the effective methods for the treatment of stress urinary incontinence in obese women in the future, providing a minimally invasive, highly profitable and highly compliant treatment for stress urinary incontinence in obese women.

A multicenter, randomized, open, controlled trial to evaluate the efficacy of Honglilai Vaginal Cream and Premarin Vaginal Cream for Genitourinary Syndrome of Menopause in different subgroups of Chinese postmenopausal women.

AIM: In a randomized, multicenter, open, controlled trial, we compared the effects of Honglilai Vaginal Cream and Premarin Vaginal Cream in different age subgroups and menopausal year subgroups (trial registration numbers: 02003L00493). METHODS: Postmenopausal women with Genitourinary Syndrome of Menopause (GSM) were divided into Honglilai group (n = 319) and Premarin group (n = 116), while subgroups were divided according to their different characteristics of age and menopausal years. Honglilai Vaginal Cream (0.625 mg/g) or Premarin Vaginal Cream (0.625 mg/g) once daily for 3 weeks. RESULTS: In the subgroup of participates >60 years, there were no significant differences of Vaginal Cell Maturation Index (VMI) between the two groups after treatment (p = .171). In the subgroup of 50-59 years, the VMI of Honglilai group was significantly lower than Premarin group (Honglilai group: 74.37 ± 22.76; Premarin group: 80.06 ± 16.15; p = .02). There were no significant differences of Vaginal symptom scores between Honglilai group and Premarin group in every sub-group (p > .05). CONCLUSIONS: Honglilai Vaginal Cream had comparable efficacy with Premarin Vaginal Cream in Chinese women older than 60 years.

Analysis of Fertility Preservation by Ovarian Tissue Cryopreservation in Pediatric Children in China.

Background: Ovarian tissue cryopreservation (OTC) is the only method of fertility preservation (FP) in prepubertal girls, but the experience remains limited. This study investigates the effectiveness and feasibility of FP of OTC in children facing gonadotoxicity treatment in Chinese first ovarian tissue cryobank. Procedure: OTC and evaluation of 49 children ≤14 years old in the cryobank of Beijing Obstetrics and Gynecology Hospital, Capital Medical University, from July 2017 to May 19, 2022, were analyzed retrospectively. We compared children's general characteristics, follicle numbers, and hormone levels with and without chemotherapy before OTC. Results: The age of 49 children at the time of OTC was 7.55 (1-14) years old. There were 23 cases of hematological non-malignant diseases, eight cases of hematological malignant diseases, four cases of gynecological malignant tumors, one case of neurological malignant tumors, one case of bladder cancer, five cases of sarcoma, three cases of mucopolysaccharidosis, one case of metachromatic leukodystrophy, two cases of dermatomyositis, one case of Turner's syndrome. The median follicular count per 2-mm biopsy was 705. Age and AMH were not correlated (r = 0.084, P = 0.585). Age and follicle count per 2-mm biopsy was not correlated (r = -0.128, P = 0.403). Log10 (follicle count per 2-mm biopsy) and Log10 (AMH) were not correlated (r = -0.118, P = 0.456). Chemotherapy before OTC decreased AMH levels but had no significant effect on the number of follicles per 2-mm biopsy. Conclusions: OTC is the only method to preserve the fertility of prepubertal girls, and it is safe and effective. Chemotherapy before OTC is not a contraindication to OTC.