Effects of omega-3 supplementation on interleukin and neurotrophin levels in an animal model of schizophrenia

New studies suggest that polyunsaturated fatty acids, such as omega-3, may reduce the symptoms of schizophrenia. The present study evaluated the preventive effect of omega-3 on interleukines (IL) and neurotrophin brain-derived neurotrophic factor (BDNF) levels in the brains of young rats subjected to a model of schizophrenia. Treatment was performed over 21 days, starting on the 30th day of rat’s life. After 14 days of treatment with omega-3 or vehicle, a concomitant treatment with saline or ketamine (25 mg/kg) was started and maintained until the last day of the experiment. BDNF levels in the rat’s prefrontal cortex were decreased at 1 h and 24 h after the last administration of ketamine, whereas the group administered with ketamine and omega-3 showed a decrease in BDNF levels only after 24 h. In contrast, both interventions induced similar responses in levels of IL-1β and IL6. These findings suggest that the similarity of IL-1β and IL6 levels in our experimental groups is due to the mechanism of action of ketamine on the immune system. More studies have to be carried out to explain this pathology. In conclusion, according to previous studies and considering the current study, we could suggest a prophylactic role of omega-3 against the outcome of symptoms associated with schizophrenia

How reliable is assessment of true vocal cord-arytenoid unit mobility in patients affected by laryngeal cancer? a multi-institutional study on 366 patients from the ARYFIX collaborative group

Purpose: In clinical practice the assessment of the "vocal cord-arytenoid unit" (VCAU) mobility is crucial in the staging, prognosis, and choice of treatment of laryngeal squamous cell carcinoma (LSCC). The aim of the present study was to measure repeatability and reliability of clinical assessment of VCAU mobility and radiologic analysis of posterior laryngeal extension. Methods: In this multi-institutional retrospective study, patients with LSCC-induced impairment of VCAU mobility who received curative treatment were included; pre-treatment endoscopy and contrast-enhanced imaging were collected and evaluated by raters. According to their evaluations, concordance, number of assigned categories, and inter- and intra-rater agreement were calculated. Results: Twenty-two otorhinolaryngologists evaluated 366 videolaryngoscopies (total evaluations: 2170) and 6 radiologists evaluated 237 imaging studies (total evaluations: 477). The concordance of clinical rating was excellent in only 22.7% of cases. Overall, inter- and intra-rater agreement was weak. Supraglottic cancers and transoral endoscopy were associated with the lowest inter-observer reliability values. Radiologic inter-rater agreement was low and did not vary with imaging technique. Intra-rater reliability of radiologic evaluation was optimal. Conclusions: The current methods to assess VCAU mobility and posterior extension of LSCC are flawed by weak inter-observer agreement and reliability. Radiologic evaluation was characterized by very high intra-rater agreement, but weak inter-observer reliability. The relevance of VCAU mobility assessment in laryngeal oncology should be re-weighted. Patients affected by LSCC requiring imaging should be referred to dedicated radiologists with experience in head and neck oncology

Malignant tumors of the maxillary sinus: Prognostic impact of neurovascular invasion in a series of 138 patients

Background: Maxillary sinus cancer is a rare disease with heterogeneous biologic behavior. The pattern of neurovascular invasion is known to be an important prognosticator in head and neck cancers, but has not been studied in maxillary malignancies. Materials and methods: Patients undergoing surgery-based treatment with curative intent for a malignancy of the maxillary sinus at the Unit of Otorhinolaryngology \u2013 Head and Neck Surgery of the University of Brescia between November 2000 and October 2018 were included. A description of the characteristics of the patients, tumors, and treatments has been performed along with uni- and multi-variate analysis of prognostic factors. Tumors were classified based on the presence of perineural (P0/P1) and lymphovascular invasion (V0/V1) in 4 categories: P0V0, P1V0, P0V1, and P1V1. Results: One hundred-thirty-eight patients were included. Mean age at surgery was 61.0 years. Most patients (60.1%) were affected by non-salivary carcinomas, and most tumors (73.9%) were high-grade cancers. One hundred-seven (77.5%) tumors were classified as pT4. The large majority of patients received bi- or tri-modality treatment. Sixty-three (45.7%) cases were classified as P0V0, 32 (23.2%) as P1V0, 7 (5.1%) as P0V1, and 36 (26.1%) as P1V1. T category, nodal status, and neurovascular invasion were significantly associated with prognosis. Perineural and lymphovascular invasion were associated with the topographical growth of the tumor. Conclusions: Maxillary cancer is often diagnosed at an advanced stage and in most cases requires a multimodal approach. Perineural and lymphovascular invasion are frequent and have a different impact on prognosis and topographical extension of the tumor

Application of bioendoscopy filters in endoscopic assessment of sinonasal Schneiderian papillomas

none10noneCarobbio A.L.C, Vallin A, Ioppi, A, Missale F, Ascoli A, Mocellin D, Bagnasco D, Mora, R, Peretti G, Canevari F.R.M.Carobbio A. L., C; Vallin, A; Ioppi, A; Missale, F; Ascoli, A; Mocellin, D; Bagnasco, D; Mora, R; Peretti, G; Canevari, F. R. M

Deep Learning Applied to White Light and Narrow Band Imaging Videolaryngoscopy: Toward Real-Time Laryngeal Cancer Detection

Objectives: To assess a new application of artificial intelligence for real-time detection of laryngeal squamous cell carcinoma (LSCC) in both white light (WL) and narrow-band imaging (NBI) videolaryngoscopies based on the You-Only-Look-Once (YOLO) deep learning convolutional neural network (CNN). Study Design: Experimental study with retrospective data. Methods: Recorded videos of LSCC were retrospectively collected from in-office transnasal videoendoscopies and intraoperative rigid endoscopies. LSCC videoframes were extracted for training, validation, and testing of various YOLO models. Different techniques were used to enhance the image analysis: contrast limited adaptive histogram equalization, data augmentation techniques, and test time augmentation (TTA). The best-performing model was used to assess the automatic detection of LSCC in six videolaryngoscopies. Results: Two hundred and nineteen patients were retrospectively enrolled. A total of 624 LSCC videoframes were extracted. The YOLO models were trained after random distribution of images into a training set (82.6%), validation set (8.2%), and testing set (9.2%). Among the various models, the ensemble algorithm (YOLOv5s with YOLOv5m\u2014TTA) achieved the best LSCC detection results, with performance metrics in par with the results reported by other state-of-the-art detection models: 0.66 Precision (positive predicted value), 0.62 Recall (sensitivity), and 0.63 mean Average Precision at 0.5 intersection over union. Tests on the six videolaryngoscopies demonstrated an average computation time per videoframe of 0.026 seconds. Three demonstration videos are provided. Conclusion: This study identified a suitable CNN model for LSCC detection in WL and NBI videolaryngoscopies. Detection performances are highly promising. The limited complexity and quick computational times for LSCC detection make this model ideal for real-time processing. Level of Evidence: 3 Laryngoscope, 2021