Management of self-inflicted orchiectomy in psychiatric patient. Case report and non-systematic review of the literature.

INTRODUCTION: Self-inflicted orchidectomy and auto-castration, also known as "Eshmun complex" is a rare phenomenon. The aim of our study it to present the management of a patient who performed a self orchiectomy and propose a non-systematic review of literature about self-orchiectomy. MATERIAL AND METHOD: A 27-years old male Patient with psychiatric disorder was admitted to our ward to have been cutted his scrotum with scissors and cut away his left testicle causing active bleeding from the left spermatic artery. The patient underwent emergency surgery with clamping of the spermatic cord and hemostasis of the wound. RESULTS: After surgery the clinical condition of the patient remained good during whole hospitalization. Urgent psychiatric evaluation was performed in order to administer proper therapy for acute management. To best of our knowledge, only 11 cases of self-orchidectomy are reported in literature and all of them except 1 case, underwent surgical exploration. CONCLUSIONS: Self-orchidectomy is an extremely rare phenomenon, often associated with psychiatric disorders, compounded by the use of drugs. In our opinion, emergency surgery should be the first choice of treatment, offering diagnostic and hemostatic purpose in a single act, aimed to prevent acute and postacute complications.

Blue-Light Induced Iron-Catalyzed Synthesis of γ,δ-Unsaturated Ketones.

A visible-light-induced iron-catalyzed α-alkylation of ketones with allylic and propargylic alcohols as pro-electrophiles is reported. The diaminocyclopentadienone iron tricarbonyl complex plays a dual role by harvesting light and facilitating dehydrogenation and reduction steps without the help of any exogenous photosensitizer. γ,δ-Unsaturated ketones can now be accessed through this borrowing hydrogen methodology at room temperature. Mechanistic investigations revealed that the steric hindrance on the δ-position of either the dienone or ene-ynone intermediate is the key feature to prevent or decrease the competitive 1,6-reduction (and consequently the formation of the saturated ketone) and to favor the synthesis of a set of non-conjugated enones and ynones.

68Ga-PSMA-11 PET/CT in Bilateral Clear Cell Renal Cell Carcinoma: an Intra-patient Comparison Between High and Low Grade Tumors.

Recent studies have outlined the emerging role of 68Ga-PSMA-11 PET/CT in the diagnostic algorithm of clear cell renal cell carcinoma (ccRCC). We report a unique intra-patient comparison of bilateral primary ccRCC imaged with 68Ga-PSMA-11 PET/CT. Although both tumors resulted 68Ga-PSMA-11 avid, we found a remarkable discrepancy in uptake intensity between the high grade and the low grade ccRCC. This case confirms previous evidence reporting that SUVmax on 68Ga-PSMA-11 PET/CT could be used to discriminate aggressive high grade from more indolent low grade ccRCC, due to their different endothelial expression of PSMA.

[18F]FDG PET-TC radiomics and machine learning in the evaluation of prostate incidental uptake.

AIM: To evaluate the relevance of incidental prostate [18F]FDG uptake (IPU) and to explore the potential of radiomics and machine learning (ML) to predict prostate cancer (PCa). METHODS: We retrieved [18F]FDG PET/CT scans with evidence of IPU performed in two institutions between 2015 and 2021. Patients were divided into PCa and non-PCa, according to the biopsy. Clinical and PET/CT-derived information (comprehensive of radiomic analysis) were acquired. Five ML models were developed and their performance in discriminating PCa vs non-PCa IPU was evaluated. Radiomic analysis was investigated to predict ISUP Grade. RESULTS: Overall, 56 IPU were identified and 31 patients performed prostate biopsy. Eighteen of those were diagnosed as PCa. Only PSA and radiomic features (eight from CT and nine from PET images, respectively) showed statistically significant difference between PCa and non-PCa patients. Eight features were found to be robust between the two institutions. CT-based ML models showed good performance, especially in terms of negative predictive value (NPV 0.733-0.867). PET-derived ML models results were less accurate except the Random Forest model (NPV = 0.933). Radiomics could not accurately predict ISUP grade. CONCLUSIONS: Paired with PSA, radiomic analysis seems to be promising to discriminate PCa/non-PCa IPU. ML could be a useful tool to identify non-PCa IPU, avoiding further investigations.