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This letter to the editor published in the journal Leukemia & Lymphoma discusses a rare case of a patient with hypereosinophilic syndrome (HES) and leptomeningeal involvement. The letter provides insights into diagnosing and treating hypereosinophilia, highlights unusual presentations of HES in classical Hodgkin Lymphoma (cHL), and presents a case study of a patient with cHL complicated by hypereosinophilia with multi-organ involvement. The patient experienced symptoms such as fatigue, weight loss, and abdominal bloating, and further evaluation revealed significant eosinophilia and lymphadenopathy. The article emphasizes the challenges of managing secondary complications from HES while treating cHL and suggests the need for further research in optimizing treatment for cHL with HES. [Extracted from the article]

The article offers information on the role of perivascular adipose tissue (PVAT) in cardiovascular physiology and diseases highlighting a brief overview on the composition of PVAT, proinflammatory phenotypes, and the benefits of PVAT in the normal functions of the human cardiovascular system.

To train a generic deep learning software (DLS) to classify breast cancer on ultrasound images and to compare its performance to human readers with variable breast imaging experience. In this retrospective study, all breast ultrasound examinations from January 1, 2014 to December 31, 2014 at our institution were reviewed. Patients with post-surgical scars, initially indeterminate, or malignant lesions with histological diagnoses or 2-year follow-up were included. The DLS was trained with 70% of the images, and the remaining 30% were used to validate the performance. Three readers with variable expertise also evaluated the validation set (radiologist, resident, medical student). Diagnostic accuracy was assessed with a receiver operating characteristic analysis. 82 patients with malignant and 550 with benign lesions were included. Time needed for training was 7 min (DLS). Evaluation time for the test data set were 3.7 s (DLS) and 28, 22 and 25 min for human readers (decreasing experience). Receiver operating characteristic analysis revealed non-significant differences (p-values 0.45–0.47) in the area under the curve of 0.84 (DLS), 0.88 (experienced and intermediate readers) and 0.79 (inexperienced reader). DLS may aid diagnosing cancer on breast ultrasound images with an accuracy comparable to radiologists, and learns better and faster than a human reader with no prior experience. Further clinical trials with dedicated algorithms are warranted. DLS can be trained classify cancer on breast ultrasound images high accuracy even with comparably few training cases. The fast evaluation speed makes real-time image analysis feasible. [ABSTRACT FROM AUTHOR]