No abstract available.
Liver* ; Ultrasonography*

Paraffinoma is a destructive complication of paraffin oil injection, usually associated with massive tissue destruction, thus requiring radical surgery and subsequent complex reconstruction. Although breast and penile paraffinomas have been widely described and their management is quite standardized, paraffinomas of the knee are still rare and only few case reports or small case series are available in the current literature. We describe the case of a 77-year-old man with a large paraffinoma of the right knee that occurred after self-injection of paraffin oil, 58 years before. He underwent wide surgical resection of the soft tissues overlying the knee and subsequent two-stage reconstruction by using acellular dermal matrix and, after 20 days, split-thickness skin grafts. Follow-up after 16 months showed no signs of skin ulcerations or inflammation, with an overall improvement in function. Even though conventional flap reconstructions may be still useful, the authors believe that acellular dermal matrices represent a safe, reliable, and less invasive alternative for challenging soft tissue reconstructions even in elderly patients with multiple medical problems.
Acellular Dermis* ; Aged ; Breast ; Follow-Up Studies ; Humans ; Inflammation ; Knee ; Paraffin ; Skin ; Skin Ulcer ; Transplants

Abnormal levels of amino acids have been reported in patients with schizophrenia and have also been investigated as a biomarker to monitor antipsychotic treatment, however results have been inconsistent. The purpose of the present review is to summarize the evidence in the literature of whether amino acid levels can be a biomarker and predict the treatment outcome in schizophrenia. The current review does not support amino acid concentration as a useful biomarker for monitoring antipsychotic response in patients with schizophrenia, although there is evidence that high levels of serum homocysteine and glutamate might be considered as a trait marker for schizophrenia. This review has also highlighted a considerable dearth of studies, specifically of studies evaluating antipsychotic side-effects.
Amino Acids ; Antipsychotic Agents ; Glutamic Acid ; Homocysteine ; Humans ; Schizophrenia* ; Treatment Outcome

No abstract available.
Animals ; Dogs ; Ear

No abstract available.
Fasciitis, Necrotizing

No abstract available.
Knee*

OBJECTIVE: Hyperthermic intraperitoneal chemotherapy (HIPEC) has been proposed as a treatment in ovarian cancer. A recently published RCT demonstrated that HIPEC prolongs disease-free survival (DFS) and overall survival (OS) in ovarian cancer. The aim of the study was to investigate oncologic results of cytoreductive surgery+HIPEC compared with cytoreductive surgery alone in advanced primary ovarian cancer with a particular attention to the pattern of recurrence. METHODS: This is a retrospective case control study with a propensity score (PS) matching of the patients. All the patients treated for primary advanced ovarian cancer who underwent interval surgery with or without HIPEC were collected; a PS was calculated in order to match cases to controls. RESULTS: Among 77 eligible patients 56 patients were included in the study. Preoperative patients' characteristics were homogeneous. No difference in morbidity and mortality after surgery were recorded. DFS was not different among the 2 groups (13.2 vs. 13.9 months, p=0.454) but OS was better in patients treated with HIPEC with no median reached vs. 35.5 months (p=0.048). Patients treated with cytoreductive surgery alone were more likely to have a peritoneal recurrence (43% vs. 14%). CONCLUSION: HIPEC seems to affect the relapse pattern with lesser peritoneal recurrence. This difference in relapse pattern seems to affect the OS with better results in patients treated with HIPEC. Further studies are needed to confirm these findings.
Case-Control Studies* ; Disease-Free Survival ; Drug Therapy* ; Humans ; Hyperthermia, Induced ; Mortality ; Ovarian Neoplasms* ; Propensity Score* ; Recurrence* ; Retrospective Studies

Purpose: To explore the role of artificial intelligence and machine learning (ML) techniques in oncological urology. In recent years, our group investigated the prostate cancer gene 3 (PCA3) score, prostate-specific antigen (PSA), and free-PSA predictive role for prostate cancer (PCa), using the classical binary logistic regression (LR) modeling. In this research, we approached the same clinical problem by several different ML algorithms, to evaluate their performances and feasibility in a real-world evidence PCa detection trial. Materials and Methods: The occurrence of a positive biopsy has been studied in a large cohort of 1,246 Italian men undergoing first or repeat biopsy. Seven supervised ML algorithms were selected to build biomarkers-based predictive models: generalized linear model, gradient boosting machine, eXtreme gradient boosting machine (XGBoost), distributed random forest/ extremely randomized forest, multilayer artificial Deep Neural Network, naïve Bayes classifier, and an automatic ML ensemble function. Results: All the ML models showed better performances in terms of area under curve (AUC) and accuracy, when compared to LR model. Among them, an XGBoost model tuned by the autoML function reached the best metrics (AUC, 0.830), well overtaking LR results (AUC, 0.738). In the variable importance ranking coming from this XGBoost model (accuracy, 0.824), the PCA3 score importance was 3-fold and 4-fold larger, when compared to that of free-PSA and PSA, respectively. Conclusions The ML approach proved to be feasible and able to achieve good predictive performances with reproducible results: it may thus be recommended, when applied to PCa prediction based on biomarkers fluctuations.

Purpose: To explore the role of artificial intelligence and machine learning (ML) techniques in oncological urology. In recent years, our group investigated the prostate cancer gene 3 (PCA3) score, prostate-specific antigen (PSA), and free-PSA predictive role for prostate cancer (PCa), using the classical binary logistic regression (LR) modeling. In this research, we approached the same clinical problem by several different ML algorithms, to evaluate their performances and feasibility in a real-world evidence PCa detection trial. Materials and Methods: The occurrence of a positive biopsy has been studied in a large cohort of 1,246 Italian men undergoing first or repeat biopsy. Seven supervised ML algorithms were selected to build biomarkers-based predictive models: generalized linear model, gradient boosting machine, eXtreme gradient boosting machine (XGBoost), distributed random forest/ extremely randomized forest, multilayer artificial Deep Neural Network, naïve Bayes classifier, and an automatic ML ensemble function. Results: All the ML models showed better performances in terms of area under curve (AUC) and accuracy, when compared to LR model. Among them, an XGBoost model tuned by the autoML function reached the best metrics (AUC, 0.830), well overtaking LR results (AUC, 0.738). In the variable importance ranking coming from this XGBoost model (accuracy, 0.824), the PCA3 score importance was 3-fold and 4-fold larger, when compared to that of free-PSA and PSA, respectively. Conclusions The ML approach proved to be feasible and able to achieve good predictive performances with reproducible results: it may thus be recommended, when applied to PCa prediction based on biomarkers fluctuations.

No abstract available.
Carcinoma, Merkel Cell* ; Eyelids*