Management and surgical treatment of parathyroid carcinoma: a 6-year experience of a single centre of endocrine surgery unit.

Background: Parathyroid carcinoma (PC) affects 0.1-0.3% of the general population and represents the rarest malignant neoplasms among endocrinological diseases, comprising less than 1%. The best therapeutic treatment and management methods are still debated in the literature. The aim of this study is to evaluate the management and surgical treatment of parathyroid carcinoma after 6 years of enrolment with the Endocrine Surgery Unit of the University Hospital of Bari. Materials and methods: A retrospective observational study was carried out using a prospectively maintained database of patients affected by primary hyperparathyroidism between January 2017 and September 2022. Consecutive patients over 18 years old with a final histopathological finding of PC were included in the study. Patients with secondary or tertiary hyperparathyroidism, parathyroid hyperplasia, and parathyroid adenoma were excluded. All patients underwent follow-up every 6 months for the first 2 years, and annually thereafter. Results: In this study, 9 out of 40 patients affected by hyperparathyroidism were included; 6 (66.6%) were female and 3 (33.3%) were male patients, with a median age of 59 years (IQR 46-62). None had a family history of PC. No mortality was recorded while the incidence of recurrence was 22.2%, with a disease-free survival of 8 and 10 months. Parathyroidectomy was performed in five patients, while four patients underwent parathyroidectomy with concurrent thyroidectomy for thyroid goitre. No intraoperative complications were recorded. Open parathyroidectomy was performed with a mini-cervicotomy in seven patients, while two patients underwent robotic surgery. All patients were discharged on the second postoperative day. Conclusion: PC represents a great challenge in terms of preoperative diagnosis, management and treatment. A surgical approach represents the first best option for PC in referral endocrine surgery units. The early identification of risky patients should be the dominant goal to plan an appropriate therapy and to perform adequate en bloc surgery.

Cytoplasmic movements of the early human embryo: imaging and artificial intelligence to predict blastocyst development.

RESEARCH QUESTION: Can artificial intelligence and advanced image analysis extract and harness novel information derived from cytoplasmic movements of the early human embryo to predict development to blastocyst? DESIGN: In a proof-of-principle study, 230 human preimplantation embryos were retrospectively assessed using an artificial neural network. After intracytoplasmic sperm injection, embryos underwent time-lapse monitoring for 44 h. For comparison, standard embryo assessment of each embryo by a single embryologist was carried out to predict development to blastocyst stage based on a single picture frame taken at 42 h of development. In the experimental approach, in embryos that developed to blastocyst or destined to arrest, cytoplasm movement velocity was recorded by time-lapse monitoring during the first 44 h of culture and analysed with a Particle Image Velocimetry algorithm to extract quantitative information. Three main artificial intelligence approaches, the k-Nearest Neighbour, the Long-Short Term Memory Neural Network and the hybrid ensemble classifier were used to classify the embryos. RESULTS: Blind operator assessment classified each embryo in terms of ability to develop to blastocyst, with 75.4% accuracy, 76.5% sensitivity, 74.3% specificity, 74.3% precision and 75.4% F1 score. Integration of results from artificial intelligence models with the blind operator classification, resulted in 82.6% accuracy, 79.4% sensitivity, 85.7% specificity, 84.4% precision and 81.8% F1 score. CONCLUSIONS: The present study suggests the possibility of predicting human blastocyst development at early cleavage stages by detection of cytoplasm movement velocity and artificial intelligence analysis. This indicates the importance of the dynamics of the cytoplasm as a novel and valuable source of data to assess embryo viability.

Chromatin organization and timing of polar body I extrusion identify developmentally competent mouse oocytes.

In the mouse, the use of the DNA-binding fluorochrome Hoechst 33342 allows the classification of fully-grown antral oocytes into two categories distinguished by their chromatin conformation: surrounding nucleolus (SN) and not-surrounding nucleolus (NSN) oocytes, the former capable of completing development, the latter unable to proceed beyond the 2-cell stage. In the present study, time-lapse observation of SN and NSN oocyte GV-to-MII transition highlighted differences in the timing of germinal vesicle breakdown (GVBD) and polar body I (PB-I) extrusion. PB-I extrusion, but not GVBD, revealed the presence of three main groups of significantly different oocytes: Group A (456-576 min) comprising mainly SN oocytes (91.4%), group B (584-728 min) entailing an almost equivalent percentage of SN (52.7%) and NSN (47.3%) oocytes, whereas group C (736-896 min) consisting of almost all NSN (94.4%) oocytes. In a further set of time-lapse experiments, GV oocytes were in vitro matured without Hoechst staining and, depending on the timing of PB-I extrusion, sorted into group A, B or C, inseminated with sperm and observed throughout preimplantation. The results show that 26.2 ± 12.3% of group A, 2.4 ± 5.0% of group B and none of group C MII oocytes developed to blastocyst. Overall, this study shows that SN oocytes that complete MI earlier are those with a better developmental competence. The possibility to avoid the use of the invasive DNA-binding fluorochrome Hoechst is relevant for future applications in human and domestic animal reproductive technologies.

IVM of mouse fully grown germinal vesicle oocytes upon a feeder layer of selected cumulus cells enhances their developmental competence.

In the ovary, acquisition of oocyte developmental competence depends on a bidirectional exchange between the gamete and its companion cumulus cells (CCs). In this study we investigated the contribution of CCs surrounding oocytes of known developmental competence or incompetence to the acquisition of oocyte developmental competence. To this end, feeder layers of CCs (FL-CCs) were prepared using CCs isolated either from: (1) developmentally competent mouse oocytes whose nucleolus was surrounded by a chromatin ring (FL-SN-CCs); or (2) developmentally incompetent mouse oocytes whose nucleolus was not surrounded by a chromatin ring (FL-NSN-CCs). Denuded, fully grown oocytes (DOs) were matured to the MII stage on either FL-SN-CCs or FL-NSN-CCs, inseminated with spermatozoa and cultured throughout preimplantation development. FL-SN-CCs significantly improved the acquisition of oocyte developmental competence, with a blastocyst development rate equal to that for maturation of intact cumulus-oocyte-complexes. In contrast, DOs matured on FL-NSN-CCs or in the absence of CCs exhibited developmental failure, with embryos arresting at either the 4-cell or morula stage. These results set a culture platform to further improve the protocols for the maturation of DOs and to unravel the molecules involved in the cross-talk between the gamete and its companion CCs during the germinal vesicle to MII transition.

A Neural Network-Based Identification of Developmentally Competent or Incompetent Mouse Fully-Grown Oocytes.

Infertility clinics would benefit from the ability to select developmentally competent vs. incompetent oocytes using non-invasive procedures, thus improving the overall pregnancy outcome. We recently developed a classification method based on microscopic live observations of mouse oocytes during their in vitro maturation from the germinal vesicle (GV) to the metaphase II stage, followed by the analysis of the cytoplasmic movements occurring during this time-lapse period. Here, we present detailed protocols of this procedure. Oocytes are isolated from fully-grown antral follicles and cultured for 15 h inside a microscope equipped for time-lapse analysis at 37 °C and 5% CO2. Pictures are taken at 8 min intervals. The images are analyzed using the Particle Image Velocimetry (PIV) method that calculates, for each oocyte, the profile of Cytoplasmic Movement Velocities (CMVs) occurring throughout the culture period. Finally, the CMVs of each single oocyte are fed through a mathematical classification tool (Feed-forward Artificial Neural Network, FANN), which predicts the probability of a gamete to be developmentally competent or incompetent with an accuracy of 91.03%. This protocol, set up for the mouse, could now be tested on oocytes of other species, including humans.