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INTRODUCTION: Primary small bowel melanoma (PSBM) is a rare form of melanoma that originates from the intestinal mucosa. It is typically asymptomatic; however, it can present with non-specific symptoms, which pose challenges in accurately diagnosing the condition. In rare cases, it may manifest as small bowel obstruction, further adding challenges with diagnosis and management. CASE PRESENTATION: A 57-year-old male presented to the hospital with complaints of chronic constipation, abdominal pain, and abdominal enlargement. Computed tomography (CT) scan revealed thickening of the jejunum wall, while endoscopy and biopsy revealed nothing. During surgery, surgeons identified and excised a jejunal mass. Subsequent pathological analysis confirmed the diagnosis of melanoma, and post-surgical examination failed to identify primary cutaneous melanoma. DISCUSSION: PSBM is a rare and aggressive tumor often misdiagnosed due to non-specific symptoms and challenging imaging interpretations. Obstruction and intussusception are uncommon presentations. Surgical resection offers symptom control and improved prognosis, but achieving negative margins can be challenging. Early recognition and diagnosis are crucial for optimal management. CONCLUSION: The lack of data in the literature presents challenges in identifying and selecting the optimal approach for managing PSBM. Physicians should increase their awareness of this specific type of tumor to facilitate early-stage diagnosis and provide appropriate care for patients.
RESUMO
Although alcohol consumption may produce effects that can be beneficial or harmful, alcohol consumption prevails among communities around the globe. Additionally, alcohol consumption patterns may be associated with several factors among communities and individuals. Numerous technologies and methods are implemented to enhance the detection and tracking of alcohol consumption, such as vehicle-integrated and wearable devices. In this paper, we present a cellular-based Internet of Things (IoT) implementation in a breath analyzer to enable data collection from multiple users via a single device. Cellular technology using hypertext transfer protocol (HTTP) was implemented as an IoT gateway. IoT integration enabled the direct retrieval of information from a database relative to the device and direct upload of data from the device onto the database. A manually developed threshold algorithm was implemented to quantify alcohol concentrations within a range from 0 to 200 mcg/100 mL breath alcohol content using electrochemical reactions in a fuel-cell sensor. Two data collections were performed: one was used for the development of the model and was split into two sets for model development and on-machine validation, and another was used as an experimental verification test. An overall accuracy of 98.16% was achieved, and relative standard deviations within the range from 1.41% to 2.69% were achieved, indicating the reliable repeatability of the results. The implication of this paper is that the developed device (an IoT-integrated breath analyzer) may provide practical assistance for healthcare representatives and researchers when conducting studies involving the detection and data collection of alcohol consumption patterns.