Medicina
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Background and Objectives: Type two diabetes mellitus (T2DM) is a chronic disease with debilitating complications and high mortality. Evidence indicates that good glycemic control delays disease progression and is hence a target of disease management protocols. Nonetheless, some patients cannot maintain glycemic control. ⋯ In multivariate analysis, serum leptin levels significantly lowered the risk of having poor glycemic control (OR = 0.985; CI: 0.976-0.994; p = 0.002); moreover, the GA genotype of rs2167270 was protective against poor glycemic control compared to the GG genotype (OR = 0.417; CI: 0.245-0.712; p = 0.001). Conclusions: Higher serum leptin and the GA genotype of the rs2167270 SNP of the LEP gene were associated with good glycemic control in T2DM patients on metformin therapy. Further studies with a larger sample size from multiple institutions are required to validate the findings.
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Background and Objectives: Antiresorptive drugs are widely used in osteology and oncology. An important adverse effect of these drugs is medication-induced osteonecrosis of the jaw (MRONJ). There is scientific uncertainty about the underlying pathomechanism of MRONJ. ⋯ The presence of MRONJ and periodontitis was proven clinically, radiologically and histologically. Conclusions: The results of this study provide further evidence that the infectious processes without prior dentoalveolar surgical interventions can trigger MRONJ. Therefore, iatrogenic disruption of the oral mucosa cannot be the decisive step in the pathogenesis of MRONJ.
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Background and Objectives: Studies on rotator cuff tears (RCT) in patients younger than 50 years have focused on the post-operative outcomes. Little is known about cuff tear etiopathogenesis, although it is a common belief that most tears are due to trauma. We have retrospectively verified the prevalence of medical conditions, whose role in tendon degeneration development have been widely demonstrated, in a group of patients younger than 50 years with postero-superior RCT. ⋯ Conclusions: In our series, three quarters of patients with RCT had a smoking habit or medical conditions predisposing them to a tendon tear; therefore, the role of trauma in RCT onset in patients younger than 50 years is markedly resized. It is plausible that in the remaining 25%, RCT may be due to trauma or to genetic or acquired degeneration. Level of Evidence: IV.
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Case Reports
Drug-Induced Thrombocytopenia Due to Nintedanib during Treatment of Idiopathic Pulmonary Fibrosis.
Nintedanib is a tyrosine kinase inhibitor that was approved for the treatment of patients with idiopathic pulmonary fibrosis in 2014. The most common side effect of Nintedanib is diarrhea, and thrombocytopenia is a rare side effect of Nintedanib. The exact mechanism is unknown, and the literature lacks case reports of this phenomenon. ⋯ Additionally, the onset of thrombocytopenia was delayed, 3 months after the initiation of Nintedanib. We also highlight the various literature regarding drug-induced thrombocytopenia and explore the necessary work-up needed to exclude other potential diagnoses. We hope to advocate for multidisciplinary teams to be aware of patients with pulmonary fibrosis on Nintedanib so that this adverse effect can be recognized promptly.
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Background and Objectives: The development of liver fibrosis as a consequence of continuous inflammation represents a turning point in the evolution of chronic liver diseases. The recent developments of artificial intelligence (AI) applications show a high potential for improving the accuracy of diagnosis, involving large sets of clinical data. For this reason, the aim of this systematic review is to provide a comprehensive overview of current AI applications and analyze the accuracy of these systems to perform an automated diagnosis of liver fibrosis. ⋯ Nevertheless, the findings of these studies need to be confirmed through clinical trials to be implemented into clinical practice. Conclusions: The current systematic review provides a comprehensive analysis of the performance of AI systems in diagnosing liver fibrosis. Automatic diagnosis, staging, and risk stratification for liver fibrosis is currently possible considering the accuracy of the AI systems, which can overcome the limitations of non-invasive diagnosis methods.