Publications by authors named "E Bismuth"

The natural history of type 1 diabetes (T1D) evolves from stage 1 (islet autoimmunity with normoglycemia; ICD-10 diagnostic code E10.A1) to stage 2 (autoimmunity with dysglycemia; E10.A2) and subsequent clinical stage 3 (overt hyperglycemia), which is commonly the first time of referral.

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Background: Tympanostomy tube insertion is a standard surgical procedure in children to address middle ear infections and effusion-related hearing and speech development issues. Perioperative treatments like ear drops containing antibiotics, steroids, and tube irrigation with saline aim to prevent complications, yet no universal gold standard treatment exists. Despite guidelines, practice preferences among ENT specialists vary, motivating this study to investigate perioperative management practices in Israel.

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Article Synopsis
  • The study aimed to assess the long-term effects of using a hybrid closed loop (HCL) system on glucose levels and body mass index (BMI) in children with type 1 diabetes during puberty.* -
  • Over 30 months, data showed that improvements in HbA1c levels were maintained and BMI scores remained stable in participants, with no severe hypoglycemic events and only one unrelated ketoacidosis case.* -
  • Findings suggest that prolonged HCL usage can help manage glucose control during puberty without negatively affecting BMI in children with type 1 diabetes.*
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Article Synopsis
  • The study investigates the varying glycaemic profiles in individuals with type 1 diabetes to better understand their complexities.
  • Using a unique methodology and the DDRTree algorithm, researchers identified seven distinct glycaemic phenotypes among 618 participants.
  • The findings suggest that relying solely on traditional metrics may overlook important subtleties of glycaemic management, emphasizing the need for more tailored strategies in treatment.*
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We train prediction and survival models using multi-omics data for disease risk identification and stratification. Existing work on disease prediction focuses on risk analysis using datasets of individual data types (metabolomic, genomics, demographic), while our study creates an integrated model for disease risk assessment. We compare machine learning models such as Lasso Regression, Multi-Layer Perceptron, XG Boost, and ADA Boost to analyze multi-omics data, incorporating ROC-AUC score comparisons for various diseases and feature combinations.

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