Influential Usage of Big Data and Artificial Intelligence in Healthcare.

Artificial intelligence (AI) is making computer systems capable of executing human brain tasks in many fields in all aspects of daily life. The enhancement in information and communications technology (ICT) has indisputably improved the quality of people's lives around the globe. Especially, ICT has led to a very needy and tremendous improvement in the health sector which is commonly known as electronic health (eHealth) and medical health (mHealth). Deep machine learning and AI approaches are commonly presented in many applications using big data, which consists of all relevant data about the medical health and diseases which a model can access at the time of execution or diagnosis of diseases. For example, cardiovascular imaging has now accurate imaging combined with big data from the eHealth record and pathology to better characterize the disease and personalized therapy. In clinical work and imaging, cancer care is getting improved by knowing the tumor biology and helping in the implementation of precision medicine. The Markov model is used to extract new approaches for leveraging cancer. In this paper, we have reviewed existing research relevant to eHealth and mHealth where various models are discussed which uses big data for the diagnosis and healthcare system. This paper summarizes the recent promising applications of AI and big data in medical health and electronic health, which have potentially added value to diagnosis and patient care.

Gastric aggressive fibromatosis: report of a case and review of the literature.

OBJECTIVES: To describe a rare case of aggressive fibromatosis of the stomach and discuss the differential diagnoses. METHODS: A 47-year-old man presented with nonspecific abdominal pain. Gastroscopy revealed stomach wall swelling. An antral gastrectomy was performed. Histological examination revealed spindle-shaped cells and morphology typical of aggressive fibromatosis. We performed a literature search to identify conditions with features similar to those of aggressive fibromatosis. RESULTS: Aggressive fibromatosis does not metastasize, but it is locally invasive and has a tendency to relapse; however, our patient has not had recurrence > 1 year after surgery. Aggressive fibromatosis of the stomach may be confused with an inflammatory fibroid polyp, a gastrointestinal stromal tumor, schwannoma, leiomyoma, inflammatory myofibroblastic tumor, scirrhous carcinoma of the stomach, follicular dendritic cell sarcoma, inflammatory malignant fibrous histiocytoma, myofibroma/myofibromatosis, and solitary fibrous tumor of the stomach. CONCLUSIONS: Aggressive fibromatosis of the stomach is a rare spindle cell tumor that must be differentiated from a variety of conditions.

Application of the SCCmec element in the molecular typing of methicillin-resistant Staphylococcus aureus.

Acquisition of the staphylococcal chromosome cassette mec (SCCmec) is one of the key reasons for the resistance of methicillin-resistant Staphylococcus aureus (MRSA). SCCmec is composed of a mec gene complex encoding the PBP2a determinant that is responsible for the ß-lactam resistance of MRSA, and a ccr gene complex encoding recombinases that mediate the integration of SCCmec into and its excision from the recipient chromosome, and so-called three junkyard (J) regions of different sizes. The SCCmec elements carried by MRSA from different geographic locations are diverse, and each type contains characteristic DNA fragments in size. These characteristics of SCCmec element may facilitate the usage of SCCmec in the molecular typing of MRSA strains. In this review, we summarize the structure and function of SCCmec elecments, and discuss the application of SCCmec elements in the molecular typing of MRSA.