We present the case of a 46-year-old male with atypically large left elbow pilomatrix carcinoma present for 10 years with emergent excision after developing life-threatening hemorrhage. Pilomatrix carcinoma is a dermal-based malignant tumor typically of the head and neck region. Histopathology shows islands of basaloid cells, shadow cells, and fibromyxoid fibroma. Reoccurrence is seen in 23% of cases on an average of six months after primary excision. The current standard of care is wide excision with close follow-up. Copyright © 2020, Eckhoff et al.Cardiac myxoma is an uncommon diagnosis but presents with common manifestations. There is a wide range of symptomatology from non-specific fever and weight loss to stroke-like symptoms. It is also one of the rare causes of cardiac syncope and thromboembolic events. We present a case of a 67-year-old female who presented with seemingly benign vertigo symptoms which she ignored for years before seeking medical attention. An echocardiogram revealed a 3.5 x 3.0 x 1.0 cm atrial myxoma which was surgically excised. Her symptoms have resolved entirely with no recurrence since surgery. Copyright © 2019, Jupalli et al.Transcatheter aortic valve replacement (TAVR) is now the preferred choice of treatment for severe symptomatic aortic stenosis (AS) patients who are at intermediate to high risk for surgery. Rare complications like valve embolization have been described and we report a case with unique cause for such complication. A 79-year-old female presented with new onset dyspnea on exertion for evaluation and work up to the outside hospital and was found to have severe AS and referred to us for TAVR evaluation. She had a history of coronary artery bypass grafts surgery and bioprosthetic mitral valve replacement (MVR) 10 years ago. Preoperative transesophageal echocardiogram (TEE) revealed normally functioning bioprosthetic mitral valve and severe AS with peak/mean gradients of 67/44 mm Hg. She underwent transfemoral TAVR using a 26-mm Edwards Sapien S3 TAVR valve. During the slow deployment of the TAVR valve while rapid pacing, the valve appeared to move a little. Shortly after the removal of the delivery system out of the valve, the TAVR valve embolized to ascending aorta. It was carefully withdrawn into the aortic arch past the great vessels with an inflated balloon aortic valvuloplasty (BAV) catheter. https://www.selleckchem.com/products/imlunestrant.html Then, BAV was performed x 2 to plan for TAVR with a second valve, but the BAV balloon water-melon seeded repeatedly. We concluded that in this case, the rigid struts of bioprosthetic mitral valve encroaching on LVOT resulted in TAVR valve embolization and a decision was made to abort further attempts at TAVR valve implantation. This patient later under surgical aortic valve replacement (SAVR) and is clinically doing well at six months of clinical follow-up. Copyright © 2019, Nagabandi et al.Cervical cancer disproportionally affects women in low- and middle-income countries, in part due to the difficulty of implementing existing cervical cancer screening and diagnostic technologies in low-resource settings. Single-board computers offer a low-cost alternative to provide computational support for automated point-of-care technologies. Here we demonstrate two new devices for cervical cancer prevention that use a single-board computer 1) a low-cost imaging system for real-time detection of cervical precancer and 2) a low-cost reader for real-time interpretation of lateral flow-based molecular tests to detect cervical cancer biomarkers. Using a Raspberry Pi computer to provide real-time image collection and processing, we developed 1) a low-cost, portable high-resolution microendoscope system (PiHRME); and 2) a low-cost automatic lateral flow test reader (PiReader). The PiHRME acquired high-resolution ([Formula see text]) images of the cervix at half the cost of existing high-resolution microendoscope systems; image analysis algorithms based on convolutional neural networks were implemented to provide real-time image interpretation. The PiReader acquired and analyzed images of a point-of-care human papillomavirus (HPV) serology test with the same contrast and accuracy as a standard flatbed high-resolution scanner coupled to a laptop computer, for less than one-fifth of the cost. Raspberry Pi single-board computers provide a low-cost means to implement point-of-care tools with automatic image analysis. This work demonstrates the promise of single-board computers to develop and translate low-cost, point-of-care technologies for use in low-resource settings.BACKGROUND Computer-aided disease detection schemes from wireless capsule endoscopy (WCE) videos have received great attention by the researchers for reducing physicians' burden due to the time-consuming and risky manual review process. While single disease classification schemes are greatly dealt by the researchers in the past, developing a unified scheme which is capable of detecting multiple gastrointestinal (GI) diseases is very challenging due to the highly irregular behavior of diseased images in terms of color patterns. METHOD In this paper, a computer-aided method is developed to detect multiple GI diseases from WCE videos utilizing linear discriminant analysis (LDA) based region of interest (ROI) separation scheme followed by a probabilistic model fitting approach. Commonly in training phase, as pixel-labeled images are available in small number, only the image-level annotations are used for detecting diseases in WCE images, whereas pixel-level knowledge, although a major source for learning the disease characteristics, is left unused. In view of learning the characteristic disease patterns from pixel-labeled images, a set of LDA models are trained which are later used to extract the salient ROI from WCE images both in training and testing stages. The intensity patterns of ROI are then modeled by a suitable probability distribution and the fitted parameters of the distribution are utilized as features in a supervised cascaded classification scheme. RESULTS For the purpose of validation of the proposed multi-disease detection scheme, a set of pixel-labeled images of bleeding, ulcer and tumor are used to extract the LDA models and then, a large WCE dataset is used for training and testing. A high level of accuracy is achieved even with a small number of pixel-labeled images. CONCLUSION Therefore, the proposed scheme is expected to help physicians in reviewing a large number of WCE images to diagnose different GI diseases.