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Esophageal motility disorders have a severe impact on patients’ quality of life. While high-resolution manometry (HRM) is the gold standard in the diagnosis of esophageal motility disorders, intermittently occurring muscular deficiencies often remain undiscovered if they do not lead to an intense level of discomfort or cause suffering in patients. Ambulatory long-term HRM allows us to study the circadian (dys)function of the esophagus in a unique way. With the prolonged examination period of 24 h, however, there is an immense increase in data which requires personnel and time for evaluation not available in clinical routine. Artificial intelligence (AI) might contribute here by performing an autonomous analysis.

On the basis of 40 previously performed and manually tagged long-term HRM in patients with suspected temporary esophageal motility disorders, we implemented a supervised machine learning algorithm for automated swallow detection and classification.

For a set of 24 h of long-term HRM by means of this algorithm, the evaluation time could be reduced from 3 days to a core evaluation time of 11 min for automated swallow detection and clustering plus an additional 10-20 min of evaluation time, depending on the complexity and diversity of motility disorders in the examined patient. In 12.5% of patients with suggested esophageal motility disorders, AI-enabled long-term HRM was able to reveal new and relevant findings for subsequent therapy.

This new approach paves the way to the clinical use of long-term HRM in patients with temporary esophageal motility disorders and might serve as an ideal and clinically relevant application of AI.

This new approach paves the way to the clinical use of long-term HRM in patients with temporary esophageal motility disorders and might serve as an ideal and clinically relevant application of AI.

In the past, image-based computer-assisted diagnosis and detection systems have been driven mainly from the field of radiology, and more specifically mammography. Nevertheless, with the availability of large image data collections (known as the “Big Data” phenomenon) in correlation with developments from the domain of artificial intelligence (AI) and particularly so-called deep convolutional neural networks, computer-assisted detection of adenomas and polyps in real-time during screening colonoscopy has become feasible.

With respect to these developments, the scope of this contribution is to provide a brief overview about the evolution of AI-based detection of adenomas and polyps during colonoscopy of the past 35 years, starting with the age of “handcrafted geometrical features” together with simple classification schemes, over the development and use of “texture-based features” and machine learning approaches, and ending with current developments in the field of deep learning using convolutional neural networks. In parallel, the need and necessity of large-scale clinical data will be discussed in order to develop such methods, up to commercially available AI products for automated detection of polyps (adenoma and benign neoplastic lesions). Finally, a short view into the future is made regarding further possibilities of AI methods within colonoscopy.

Research of image-based lesion detection in colonoscopy data has a 35-year-old history. Milestones such as the Paris nomenclature, texture features, big data, and deep learning were essential for the development and availability of commercial AI-based systems for polyp detection.

Research of image-based lesion detection in colonoscopy data has a 35-year-old history. Milestones such as the Paris nomenclature, texture features, big data, and deep learning were essential for the development and availability of commercial AI-based systems for polyp detection.

Acetylsalicylic acid (ASA) has been investigated for a potential anticancer role in several cancers, such as colorectal, ovarian, and endometrial cancer. Moreover, ASA has been shown to abrogate various processes that contribute to tumor growth and progression.

The aim of this study was to evaluate the effects of ASA on cutaneous melanoma (CM) and uveal melanoma (UM).

Human CM and UM cells were treated with 5 mM ASA and assessed for changes in cellular functions. Antiangiogenic effects of ASA were determined using an ELISA-based assay for 10 proangiogenic cytokines, and then validated by Western blot. Finally, proteomic analysis of ASA-treated cells was performed to elucidate the changes that may be responsible for ASA-mediated effects in melanoma cells.

Treatment with ASA significantly inhibited the proliferation, invasion, and migration capabilities, and caused a significant decrease in angiogenin and PIGF secretion in both CM and UM. Mass spectrometry revealed 179 protein changes associated with ASA in the CM and UM cell lines.

These results suggest that ASA may be effective as an adjuvant therapy in metastatic CM and UM. Future studies are needed to determine the regulating targets that are responsible for the antitumor effects of ASA.

These results suggest that ASA may be effective as an adjuvant therapy in metastatic CM and UM. this website Future studies are needed to determine the regulating targets that are responsible for the antitumor effects of ASA.Primary rhabdoid tumors are highly malignant, rare tumors occurring in the renal, extrarenal soft tissue or central nervous system. They have non-specific radiological features and present with several histological components that create a problem in differential diagnosis with other embryonal tumors. We report a rare case of malignant rhabdoid tumor of the retina that presented with clinical features like those of retinoblastoma.

A masquerade syndrome is an atypical presentation of a neoplastic process that mimics an inflammatory condition. In this paper, we focus on orbital pseudocellulitis.

Our case series includes 5 retinoblastoma patients with orbital pseudocellulitis at presentation. In 3 patients the disease was bilateral, in 1 trilateral, and in 1 unilateral. The eyes with pseudocellulitis were enucleated, while the fellow eyes were treated conservatively, when affected. Four patients responded well to the therapy and showed remission of the tumor. The patient with trilateral retinoblastoma did not respond to therapy and died of disease.

Differential diagnosis with infectious orbital cellulitis is extremely important. Patients with orbital cellulitis present with fever, sinusitis, leukocytosis, and raised inflammatory markers, while ophthalmoscopic examination is negative and imaging studies show sinus involvement. On the contrary, patients with retinoblastoma do not show systemic inflammation, while ophthalmoscopic examination reveals leukocoria, buphthalmos, and an intraocular tumor mass associated with retinal detachment.