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Background: Enhancing self-management in health care through digital tools is a promising strategy to empower patients with type 2 diabetes (T2D) to improve self-care.

Objective: This study evaluates whether the Greenhabit (mobile health [mHealth]) behavioral treatment enhances T2D outcomes compared with standard care.

Methods: A 12-week, parallel, single-blind randomized controlled trial was conducted with 123 participants (62/123, 50%, female; mean age 58.

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Although iron deficiency anemia is common, interpreting iron laboratory test results can be challenging in patients with comorbidities. We aimed to study the accuracy of common iron biomarkers compared with bone marrow iron staining in a large retrospective dataset of hematological patients. We collected from 6610 patients (median age 66 years) results of iron staining, with their concurrent ferritin, transferrin saturation, soluble transferrin receptor, transferrin, hemoglobin, and mean red blood cell volume results from Helsinki University Hospital electronic health records.

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Aim: To evaluate the clinical effectiveness of ozonated sunflower oil (Oz) as an adjunctive of non-surgical periodontal therapy in patients with type 2 diabetes mellitus (DM2), on fibroblast cell viability and migration and the effectiveness of Oz on a Candida albicans (C. albicans) culture.

Methodology: In total, 32 sites in 16 DM2 with moderate to advanced periodontal disease with periodontal pocket depths ≥5mm were selected.

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Objective: Brucellosis is a multisystem infectious disease and may cause an increase in acute phase reactants. This study aimed to examine the platelet mass index (PMI), the neutrophil-lymphocyte ratio (NLR), and systemic immune-inflammation index (SII) in children with brucellosis and to determine their roles in focal involvement.

Methods: This retrospective observational study included 69 patients with brucellosis and a control group of 69 healthy children.

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Background: Recent research has revealed the potential value of machine learning (ML) models in improving prognostic prediction for patients with trauma. ML can enhance predictions and identify which factors contribute the most to posttraumatic mortality. However, no studies have explored the risk factors, complications, and risk prediction of preoperative and postoperative traumatic coagulopathy (PPTIC) in patients with trauma.

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