Diaphragm plication is an effective procedure with lasting results.

Recent advancements in biomedical tissue engineering are gaining wide interest. Implementing biology of living cells and organisms using technological solutions such as incorporating 4D printing and bioprinting for tissue regeneration/tissue repair, organ regeneration, early diagnosis of deadly diseases (particularly cancer, cardiac disorders and tuberculosis) has successfully opened a new generation of biomedical research. The present review primarily addresses the clinical application of 4D printing and bioprinting techniques for applications such as early detection of diseases and drug delivery. Notably, this review continues the discussion from part I regarding published informative data, in vitro and in vivo findings, commercial biosensors for early disease diagnosis, drug delivery and current challenges in 4D printing/bioprinting.

INTRODUCTION Wilkie’s Syndrome, also known as Superior Mesenteric Artery Syndrome (SMAS), is a rare cause of bowel obstruction that can contribute to vague abdominal symptoms on clinical presentation. This syndrome occurs when the aortomesenteric angle decreases, compressing the third portion of the duodenum between the aorta and the superior mesenteric artery. An acute decrease in the mesenteric fat pad cushion between these two blood vessels is the primary etiology, although other causes (e.g., anatomical, postoperative, functional, and pubescent etiologies) have also been described. CASE PRESENTATION In the present cases, 2 females with a common history of recent weight loss presented to our institution with similar symptoms of abdominal pain, nausea and vomiting. Each patient was subsequently diagnosed with SMAS following imaging studies. Both patients experienced successful resolution of symptoms with conservative nutritional management. DISCUSSION Common presenting complaints of SMAS include nausea, vomiting, early satiety and postprandial pain. These symptoms overlap with other gastrointestinal disorders (i.e., mesenteric ischemia, intestinal volvulus, peptic ulcer disease) making diagnosis difficult. SMAS can be identified through imaging modalities including barium studies and computer tomography. First line therapies typically include conservative nutritional support and promotion of weight gain. If conservative therapies fail, various surgical procedures can be pursued. Delayed diagnosis can lead to further pathological sequelae, including duodenal compromise, ischemia and necrosis. As the syndrome progresses, success of conservative nutritional support is less likely, and surgical correction becomes increasingly necessary. CONCLUSION Therefore, a clinical goal for SMAS should include as swift a recognition and diagnosis as possible.

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Background: Early detection of cancer can be done using machine learning approaches with high precision. The brain tumor is a very dangerous disease that may cause the death of cancerous patients. Every year, thousands of people die from that disease all over the world. Proper detection of cancerous cells in the body can save their lives. Methods: To segment the brain tumor region from brain MR images and classify tumorous and normal brain images into different-different classes is very crucial to cure death leading diseases like cancer. There are various techniques or methods for segmenting the tumorous part or area from the medical images. Magnetic resonance imaging is the most important technique to capture the images of the body parts because it has more information than any other imaging method, such as CT scan, etc. K-means clustering is used for segmentation of the tumor region, and SVM classifier is used for classification purposes. Results: The classification was done through the support vector machines in MATLAB 2019a. 350 images were classified with an accuracy of 89.7 %. Conclusion: In this paper, MRI images have been used for tumor detection and classification of those images in different-different classes with the help of MATLAB software. We calculated the accuracy of the classification using machine learning techniques. It is very efficient for early detection of cancer regions to cure the death leading diseases.