Practice patterns and 90-day treatment-related morbidity in early-stage cervical cancer

To evaluate the impact of the Laparoscopic Approach to Cervical Cancer (LACC) Trial on patterns of care and surgery-related morbidity in early-stage cervical cancer

Intelligenza artificiale e sicurezza: opportunità, rischi e raccomandazioni

L'IA (o intelligenza artificiale) è una disciplina in forte espansione negli ultimi anni e lo sarà sempre più nel prossimo futuro: tuttavia è dal 1956 che l’IA studia l’emulazione dell’intelligenza da parte delle macchine, intese come software e in certi casi hardware. L’IA è nata dall’idea di costruire macchine che - ispirandosi ai processi legati all’intelligenza umana - siano in grado di risolvere problemi complessi, per i quali solitamente si ritiene che sia necessario un qualche tipo di ragionamento intelligente. La principale area di ricerca e applicazione attuale dell’IA è il machine learning (algoritmi che imparano e si adattano in base ai dati che ricevono), che negli ultimi anni ha trovato ampie applicazioni grazie alle reti neurali (modelli matematici composti da neuroni artificiali) che a loro volta hanno consentito la nascita del deep learning (reti neurali di maggiore complessità). Appartengono al mondo dell’IA anche i sistemi esperti, la visione artificiale, il riconoscimento vocale, l’elaborazione del linguaggio naturale, la robotica avanzata e alcune soluzioni di cybersecurity. Quando si parla di IA c'è chi ne è entusiasta pensando alle opportunità, altri sono preoccupati poiché temono tecnologie futuristiche di un mondo in cui i robot sostituiranno l'uomo, gli toglieranno il lavoro e decideranno al suo posto. In realtà l'IA è ampiamente utilizzata già oggi in molti campi, ad esempio nei cellulari, negli oggetti smart (IoT), nelle industry 4.0, per le smart city, nei sistemi di sicurezza informatica, nei sistemi di guida autonoma (drive o parking assistant), nei chat bot di vari siti web; questi sono solo alcuni esempi basati tutti su algoritmi tipici dell’intelligenza artificiale. Grazie all'IA le aziende possono avere svariati vantaggi nel fornire servizi avanzati, personalizzati, prevedere trend, anticipare le scelte degli utenti, ecc. Ma non è tutto oro quel che luccica: ci sono talvolta problemi tecnici, interrogativi etici, rischi di sicurezza, norme e legislazioni non del tutto chiare. Le organizzazioni che già adottano soluzioni basate sull’IA, o quelle che intendono farlo, potrebbero beneficiare di questa pubblicazione per approfondirne le opportunità, i rischi e le relative contromisure. La Community for Security del Clusit si augura che questa pubblicazione possa fornire ai lettori un utile quadro d’insieme di una realtà, come l’intelligenza artificiale, che ci accompagnerà sempre più nella vita personale, sociale e lavorativa.AI (or artificial intelligence) is a booming discipline in recent years and will be increasingly so in the near future.However, it is since 1956 that AI has been studying the emulation of intelligence by machines, understood as software and in some cases hardware. AI arose from the idea of building machines that-inspired by processes related to human intelligence-are able to solve complex problems, for which it is usually believed that some kind of intelligent reasoning is required. The main current area of AI research and application is machine learning (algorithms that learn and adapt based on the data they receive), which has found wide applications in recent years thanks to neural networks (mathematical models composed of artificial neurons), which in turn have enabled the emergence of deep learning (neural networks of greater complexity). Also belonging to the AI world are expert systems, computer vision, speech recognition, natural language processing, advanced robotics and some cybersecurity solutions. When it comes to AI there are those who are enthusiastic about it thinking of the opportunities, others are concerned as they fear futuristic technologies of a world where robots will replace humans, take away their jobs and make decisions for them. In reality, AI is already widely used in many fields, for example, in cell phones, smart objects (IoT), industries 4.0, for smart cities, cybersecurity systems, autonomous driving systems (drive or parking assistant), chat bots on various websites; these are just a few examples all based on typical artificial intelligence algorithms. Thanks to AI, companies can have a variety of advantages in providing advanced, personalized services, predicting trends, anticipating user choices, etc. But not all that glitters is gold: there are sometimes technical problems, ethical questions, security risks, and standards and legislation that are not entirely clear. Organizations already adopting AI-based solutions, or those planning to do so, could benefit from this publication to learn more about the opportunities, risks, and related countermeasures. Clusit's Community for Security hopes that this publication will provide readers with a useful overview of a reality, such as artificial intelligence, that will increasingly accompany us in our personal, social and working lives

Benign glomus tumor of the urinary bladder

Glomus tumors are rare, mesenchymal neoplasms of adulthood, which occur in both the sexes with equal frequency. Most of these tumors are benign, but some cases with atypical/malignant behavior have been reported. They most often occur in the extremities, typically in the subungual region of the fingers, and rarely involve the internal organs. We report the case of a 63-year-old man who presented with hematuria. The cystoscopy showed a polypoid lesion of the anterior wall of the bladder, which was diagnosed on biopsy as a benign glomus tumor. To the best of our knowledge, this is the first case of benign glomus tumor of the bladder described in the literature. This report widens the spectrum of the differential diagnoses of bladder neoplasms

Quality control by tissue microarray in immunohistochemistry

Aims: An external positive control section is included in each immunohistochemical analysis as a well recognised and validated technique for standardising results. The method is time-consuming and expensive. On the contrary, internal controls are warranted and inexpensive, but their use is only feasible in selected diagnoses. The aim of this work is to show how the method of the authors allows improving the interpretation and cuts costs in the immunohistochemical analysis of bone marrow specimens. Methods: A paraffin-embedded tonsil tissue cylinder was sampled from a donor block using an automated sampler and included as an 'internal control' together with a bone marrow biopsy in a recipient block, avoiding the use of external tonsil tissue control. To validate this technique, the authors compared the quality of immunohistochemistry, the workload and costs with routine external control in 50 consecutive bone marrow biopsies. Results: Processing simultaneously the sample and the tissue control in the same block, 60 external positive control tests were spared. Only a few minutes were taken for the preparation of the recipient blocks, and no particular technical skill was required. Considering that the volume of antibodies used for the analysis of each sample was not increased, a considerable amount of the disposable material was saved. The workload of technicians was decreased and some potential technical bias was avoided. The time required for pathologists to interpret the slides was also reduced. Conclusions: In conclusion, this seems to be a feasible, cost-cutting and quality-improving technique, not limited to haematopathology but potentially extensible to other fields of pathology

A case of acute esophageal necrosis (black esophagus) in a young man with Down syndrome

Acute esophageal necrosis, commonly referred to as 'black esophagus', is a rare clinical entity arising from a combination of ischemic insult, corrosive injury and decreased function of mucosal barrier systems and reparative mechanisms present in debilitating diseases. We describe the case of an 18-year-old man affected by Down syndrome, presenting with a streptococcus β-hemolytic group A infection of the upper airways. Although the patient was intensively treated with antibiotics, he developed a streptococcal toxic shock-like syndrome and died 5 days after admission. At autopsy, circumferential black discoloration of the esophageal mucosa that extended along the entire esophagus and ended abruptly at the esophageal-gastric junction was found. Neither ulceration nor esophageal perforation were present. Black esophagus is well known to the gastroenterology community, whereas it has been described only twice in the pathology literature. To the best of our knowledge, this is the first case ever reported complicating a streptococcal infection. © 2013 The Japan Esophageal Society and Springer

Deep Learning Approaches for the Segmentation of Glomeruli in Kidney Histopathological Images

Deep learning is widely applied in bioinformatics and biomedical imaging, due to its ability to perform various clinical tasks automatically and accurately. In particular, the application of deep learning techniques for the automatic identification of glomeruli in histopathological kidney images can play a fundamental role, offering a valid decision support system tool for the automatic evaluation of the Karpinski metric. This will help clinicians in detecting the presence of sclerotic glomeruli in order to decide whether the kidney is transplantable or not. In this work, we implemented a deep learning framework to identify and segment sclerotic and non-sclerotic glomeruli from scanned Whole Slide Images (WSIs) of human kidney biopsies. The experiments were conducted on a new dataset collected by both the Siena and Trieste hospitals. The images were segmented using the DeepLab V2 model, with a pre-trained ResNet101 encoder, applied to 512 × 512 patches extracted from the original WSIs. The results obtained are promising and show a good performance in the segmentation task and a good generalization capacity, despite the different coloring and typology of the histopathological images. Moreover, we present a novel use of the CD10 staining procedure, which gives promising results when applied to the segmentation of sclerotic glomeruli in kidney tissues

Deep Learning Approaches for the Segmentation of Glomeruli in Kidney Histopathological Images

Deep learning is widely applied in bioinformatics and biomedical imaging, due to its ability to perform various clinical tasks automatically and accurately. In particular, the application of deep learning techniques for the automatic identification of glomeruli in histopathological kidney images can play a fundamental role, offering a valid decision support system tool for the automatic evaluation of the Karpinski metric. This will help clinicians in detecting the presence of sclerotic glomeruli in order to decide whether the kidney is transplantable or not. In this work, we implemented a deep learning framework to identify and segment sclerotic and non-sclerotic glomeruli from scanned Whole Slide Images (WSIs) of human kidney biopsies. The experiments were conducted on a new dataset collected by both the Siena and Trieste hospitals. The images were segmented using the DeepLab V2 model, with a pre-trained ResNet101 encoder, applied to 512 × 512 patches extracted from the original WSIs. The results obtained are promising and show a good performance in the segmentation task and a good generalization capacity, despite the different coloring and typology of the histopathological images. Moreover, we present a novel use of the CD10 staining procedure, which gives promising results when applied to the segmentation of sclerotic glomeruli in kidney tissues