Background Previously, we reported a model for assessing ovarian reserves using 4 predictors: anti-Müllerian hormone (AMH) level, antral follicle count (AFC), follicle-stimulating hormone (FSH) level, and female age. This model is referred as the AAFA (anti-Müllerian hormone level–antral follicle count–follicle-stimulating hormone level–age) model. Objective This study aims to explore the possibility of establishing a model for predicting ovarian reserves using only 3 factors: AMH level, FSH level, and age. The proposed model is referred to as the AFA (anti-Müllerian hormone level–follicle-stimulating hormone level–age) model. Methods Oocytes from ovarian cycles stimulated by gonadotropin-releasing hormone antagonist were collected retrospectively at our reproductive center. Poor ovarian response (<5 oocytes retrieved) was defined as an outcome variable. The AFA model was built using a multivariable logistic regression analysis on data from 2017; data from 2018 were used to validate the performance of AFA model. Measurements of the area under the curve (AUC), sensitivity, specificity, positive predictive value, and negative predicative value were used to evaluate the performance of the model. To rank the ovarian reserves of the whole population, we ranked the subgroups according to the predicted probability of poor ovarian response and further divided the 60 subgroups into 4 clusters, A-D, according to cut-off values consistent with the AAFA model. Results The AUCs of the AFA and AAFA models were similar for the same validation set, with values of 0.853 (95% CI 0.841-0.865) and 0.850 (95% CI 0.838-0.862), respectively. We further ranked the ovarian reserves according to their predicted probability of poor ovarian response, which was calculated using our AFA model. The actual incidences of poor ovarian response in groups from A-D in the AFA model were 0.037 (95% CI 0.029-0.046), 0.128 (95% CI 0.099-0.165), 0.294 (95% CI 0.250-0.341), and 0.624 (95% CI 0.577-0.669), respectively. The order of ovarian reserve from adequate to poor followed the order from A to D. The clinical pregnancy rate, live-birth rate, and specific differences in groups A-D were similar when predicted using the AFA and AAFA models. Conclusions This AFA model for assessing the true ovarian reserve was more convenient, cost-effective, and objective than our original AAFA model.

Anti-Müllerian hormone (AMH) is a functional marker of fetal Sertoli cells. The germ cell number in adults depends on the number of Sertoli cells produced during perinatal development. Recently, AMH has received increasing attention in research of disorders related to male fertility. This paper reviews and summarizes the articles on the regulation of AMH in males and the serum levels of AMH in male fertility-related disorders. We have determined that follicle-stimulating hormone (FSH) promotes AMH transcription in the absence of androgen signaling. Testosterone inhibits the transcriptional activation of AMH. The undetectable levels of serum AMH and testosterone levels indicate a lack of functional testicular tissue, for example, that in patients with anorchia or severe Klinefelter syndrome suffering from impaired spermatogenesis. The normal serum testosterone level and undetectable AMH are highly suggestive of persistent Müllerian duct syndrome (PMDS), combined with clinical manifestations. The levels of both AMH and testosterone are always subnormal in patients with mixed disorders of sex development (DSD). Mixed DSD is an early-onset complete type of disorder with fetal hypogonadism resulting from the dysfunction of both Leydig and Sertoli cells. Serum AMH levels are varying in patients with male fertility-related disorders, including pubertal delay, severe congenital hypogonadotropic hypogonadism, nonobstructive azoospermia, Klinefelter syndrome, varicocele, McCune-Albright syndrome, and male senescence.

In conclusion, maintenance rituximab after RCHOP improves progression-free survival. In addition, overall survival is improved for patients with an IPI ≥3 risk profile receiving MR.

Objective. Pregnancy loss has negative impacts on both the physical and the mental health of expectant mothers, which calls for an in-depth investigation. In this study, we examined the effects of case management on patients with pregnancy loss after in vitro fertilization and embryo transfer (IVF-ET). Methods. 100 participants that had suffered pregnancy loss after IVF-ET-assisted pregnancy from January 2019 to March 2020 were divided into routine care and case management groups, each with 50 cases. For the routine care group, a doctor led the diagnostic and treatment processes and a nurse assisted with the treatment. For the case management group, a nurse led the patient diagnostic and treatment processes and a doctor controlled the diagnosis and treatment plan formulation. Case management models were established according to the comprehensive peripregnancy loss care of patients with pregnancy loss after IVF-ET-assisted pregnancy. The participants’ outcomes (satisfaction, anxiety, and depression) were assessed at the time of pregnancy loss and 1 and 3 months after pregnancy loss during follow-up of the routine care and case management groups. Results. There was no statistical difference between the patients in the two groups with regard to their general information statistics ( P > 0.05 ) or their satisfaction, anxiety, and depression at the time of pregnancy loss ( P > 0.05 ). One month after pregnancy loss, there was no statistical difference in anxiety between the two groups ( P > 0.05 ), but satisfaction was greater and depression was significantly reduced in the case management group compared with the routine care group ( P < 0.05 ). Conclusion. Case management care can have a positive effect on improving the satisfaction, anxiety, and depression of patients that have had pregnancy loss after IVF-ET.