The impact of heterotopic ovarian tissue transplantation with varied volumes on reproductive endocrine function in oophorectomized rats.

BACKGROUND: This study aimed to investigate the effects of different volumes of ovarian tissue transplantation on the reproductive endocrine function of rats after oophorectomy. METHODS: Female rats were selected to establish a castration model and then underwent different volumes of ovarian tissue transplantation. Group I served as the sham operation group. The transplantation group was divided into five subgroups based on the calculated ratio of ovarian weight to body weight in normal female rats, δ = (2.52 ± 0.17) ×10-4: Group II: transplanted ovarian volume was δ; Group III: 0.75δ; Group IV: 0.5δ; Group V: 0.25δ; Group VI: without ovarian transplantation. The post-transplant oestrous cycle recovery was observed, and blood samples were collected every 2 weeks to measure serum hormone levels. Histological evaluation was performed at the end of the observation period. RESULTS: Rats in Group V exhibited disrupted oestrous cycles after transplantation, which were significantly longer than those in Group I. Rats in Groups II, III, and IV showed no cyclic changes. At 6 weeks post-transplantation, rats in Group V had lower E2 and AMH levels and higher FSH levels compared to Group I. The uterine wet weight and the number of normal follicles in Group V were significantly lower than those in Group I, but the number of atretic follicles was higher than in Group I. CONCLUSION: The larger ovarian tissue transplantation resulted in a faster recovery with a higher survival rate of the uterus and normal follicles, compared to smaller ovarian tissue transplantation.

With advancements in science and technology, ovarian transplantation techniques have become increasingly mature. However, there are still many questions that need to be addressed. For instance, the large size of the transplanted ovarian tissues may cause over-recruitment of the primordial follicles. When the transplanted ovarian tissue is too small, it can only exert limited functionality and may not meet the patient's needs. This study aimed to investigate the effects of different volumes of ovarian tissue transplantation on the reproductive endocrine function in rats after oophorectomy, and to provide a theoretical basis for determining the minimum effective volume of heterotopic ovarian tissue transplantation.

Integrating predictive coding and a user-centric interface for enhanced auditing and quality in cancer registry data.

Data curation for a hospital-based cancer registry heavily relies on the labor-intensive manual abstraction process by cancer registrars to identify cancer-related information from free-text electronic health records. To streamline this process, a natural language processing system incorporating a hybrid of deep learning-based and rule-based approaches for identifying lung cancer registry-related concepts, along with a symbolic expert system that generates registry coding based on weighted rules, was developed. The system is integrated with the hospital information system at a medical center to provide cancer registrars with a patient journey visualization platform. The embedded system offers a comprehensive view of patient reports annotated with significant registry concepts to facilitate the manual coding process and elevate overall quality. Extensive evaluations, including comparisons with state-of-the-art methods, were conducted using a lung cancer dataset comprising 1428 patients from the medical center. The experimental results illustrate the effectiveness of the developed system, consistently achieving F1-scores of 0.85 and 1.00 across 30 coding items. Registrar feedback highlights the system's reliability as a tool for assisting and auditing the abstraction. By presenting key registry items along the timeline of a patient's reports with accurate code predictions, the system improves the quality of registrar outcomes and reduces the labor resources and time required for data abstraction. Our study highlights advancements in cancer registry coding practices, demonstrating that the proposed hybrid weighted neural-symbolic cancer registry system is reliable and efficient for assisting cancer registrars in the coding workflow and contributing to clinical outcomes.