Publications by authors named "G I Jenkins"

Introduction: Increasing emphasis has been placed on measurement of quality of life (QOL) as a central criterion for assessment of success of any medical treatment. The aim of our study was to assess the nutritional and quality of life of patient-reported outcomes among patients who have undergone laser resection of tongue cancer.

Materials And Methods: A cross-sectional study was undertaken of patients treated with KTP laser resection of T1/T2 tongue squamous cell carcinoma (SCC) between 2011-2019.

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Background: The 5-year overall survival (OS) rates of T-cell lymphocytic leukemia (T-ALL) are better for children (>90%) compared to adults (~57%). The early T-cell precursor (ETP) T-ALL subtype is prognostically unfavorable in adults, but less significant in pediatric T-ALL, and the diagnosis and prognosis of "near"-ETP is controversial. We compared protein and RNA expression patterns in pediatric and adult T-ALL to identify prognostic subgroups, and to further characterize ETP and near-ETP T-ALL in both age groups.

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Objective: Human papillomavirus (HPV) vaccine uptake remains suboptimal. Our stepped-wedge cluster randomized trial found that reminder-recall letters sent to parents of age-eligible children significantly increased vaccine uptake compared to usual care. Subsequently, we conducted a process evaluation to assess the mechanisms of the letter's effectiveness.

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Background Esophageal cancer is a prevalent and highly lethal malignancy worldwide, comprising two main subtypes: esophageal squamous cell carcinoma (ESCC) and esophageal adenocarcinoma (EAC). While both subtypes are frequently encountered, ESCC has historically been more common globally. However, in recent decades, EAC has emerged as the predominant type in industrialized nations, often developing from Barrett's esophagus, a condition driven by chronic gastroesophageal reflux disease (GERD).

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Biopharmaceutical companies generate a wealth of data, ranging from in silico physicochemical properties and machine learning models to both low and high-throughput in vitro assays and in vivo studies. To effectively harnesses this extensive data, we introduce a statistical methodology facilitated by Accuracy, Utility, and Rank Order Assessment (AURA), which combines basic statistical analyses with dynamic data visualizations to evaluate endpoint effectiveness in predicting intestinal absorption. We demonstrated that various physicochemical properties uniquely influence intestinal absorption on a project-specific basis, considering factors like intestinal efflux, passive permeability, and clearance.

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