Int J Med Sci
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Objective: To analyze the blood test indicators of patients after infection of COVID-19 in Chongqing and analyze the clinical indicators of 8 patients with diarrhea. Materials and Methods: From January 26, 2019 to February 13, 2020, 70 patients diagnosed with 2019-nCoV according to the World Health Organization interim guidance for NCP and divided into diarrhea and non-diarrhea groups. The laboratory tests liver and kidney function, blood routine, coagulation function, and immune status. ⋯ Among these indicators, only Lymphocyte, CRP, Prealbumin and Cystatin C positive rate is more than 50%. Although there is no statistical difference in GGT, 100% of the 7 patients tested decreased. Conclusion: Our data recommended that the ESR, CRP, PT, IL6, lymphocyte count, GGT, prealbumin and CD4 have important value in the diagnosis of COVID-19, and the decrease of GGT may be an important indicator for judging the intestinal dysfunction of patients.
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Both the herpes zoster virus and suid herpesvirus type 1 (SuHV-1) belong to the Varicellovirus genus of the α-herpesviridae subfamily. They may cause opportunistic infections especially in patients with kidney diseases, varying from latent illness to overt lethality. Under these circumstances, impaired renal function is both the culprit for and victim of the infection. ⋯ Then in a manner similar to the gradient overlay, SuHV-1 encephalitis was discussed focusing on its neurotropic features, specific MRI findings and exclusive test of high throughput sequencing. Our report highlighted novel presentations of the Varicellovirus genus infection by providing a productive multidisciplinary communication with pointed disclosure of the renal involvement. It may therefore be of great medical relevance and educational value for clinicians, especially the unseasoned ones, to foresee and manage similar cases in susceptible patients.
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Background: As 2019 ends coronavirus disease start expanding all over the world. It is highly transmissible disease that can affect respiratory tract and can leads to organ failure. In 2020 it is declared by world health organization as "Public health emergency of international concerns". ⋯ Results: A Deep Neural Network model provides a significant contribution in terms of detecting COVID-19 and provides effective analysis of chest related diseases with respect to age and gender. Our model achieves 89% accuracy in terms of Gan based synthetic data and four different types of deep learning- based models which provided state of the art comparable results. Conclusion: If the gap in identifying of all viral pneumonias is not filled with effective automation of chest disease detection the healthcare industry may have to bear unfavorable circumstances.
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Guanidinoacetic acid (GAA, also known as glycocyamine or betacyamine) is a naturally-occurring derivative of glycine and a direct metabolic precursor of creatine, a key player in high-phosphate cellular bioenergetics. GAA is found in human serum and urine, with circulating GAA likely reflects an equilibrium between its endogenous production and utilization/excretion. GAA deficiency (as indicated by low serum GAA) has been reported in various conditions yet this intriguing clinical entity appears to be poorly characterized as yet, either as a primary deficit or a sequel of secondary disease. This minireview article summarizes the inherited and acquired disorders with apparent GAA deficiency and discusses a possible relevance of GAA shortfall in clinical medicine.
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Artificial intelligence (AI), as an advanced science technology, has been widely used in medical fields to promote medical development, mainly applied to early detections, disease diagnoses, and management. Owing to the huge number of patients, kidney disease remains a global health problem. ⋯ Here, we review the current studies of AI applications in kidney disease in alerting systems, diagnostic assistance, guiding treatment and evaluating prognosis. Although the number of studies related to AI applications in kidney disease is small, the potential of AI in the management of kidney disease is well recognized by clinicians; AI will greatly enhance clinicians' capacity in their clinical practice in the future.