Sleep in children with type 1 diabetes and their parents in the T1D Exchange

Objectives Sleep has physiological and behavioral impacts on diabetes outcomes, yet little is known about the impact of sleep disturbances in children with type 1 diabetes. The current study sought to characterize sleep in children with type 1 diabetes and in their parents and to examine the associations between child sleep, glycemic control and adherence, parent sleep and well-being, parental fear of hypoglycemia, and nocturnal caregiving behavior. Methods Surveys were emailed to parents of 2- to 12-year-old participants in the Type 1 Diabetes (T1D) Exchange clinic registry. Clinical data were obtained from the registry for the 515 respondents. Results In our sample, 67% of children met criteria for poor sleep quality. Child sleep quality was related to glycemic control (HbA1c of 7.9% [63 mmol/mol] in children with poor sleep quality vs 7.6% [60 mmol/mol] in children with non-poor sleep quality; P < 0.001) but not mean frequency of blood glucose monitoring (BGM) (7.6 times/day vs 7.4 in poor/non-poor quality; P = 0.56). Associations were similar for sleep duration. Children with poor sleep quality were more likely to experience severe hypoglycemia (4% in children with poor sleep quality vs 1% in children with non-poor sleep quality; P = 0.05) and more likely to experience DKA (7% vs 4%, respectively; P < 0.001). Poorer child sleep quality was associated with poorer parental sleep quality, parental well-being, and fear of hypoglycemia (P < 0.001 for all). Child sleep was not related to the use of diabetes-related technology (CGM, insulin pump). Conclusions Sleep may be a modifiable factor to improve glycemic control and reduce parental distress

Good practices for 68Ga radiopharmaceutical production

Background: The radiometal gallium-68 (Ga-68) is increasingly used in diagnostic positron emission tomography (PET), with Ga-68-labeled radiopharmaceuticals developed as potential higher-resolution imaging alternatives to traditional Tc-99m agents. In precision medicine, PET applications of Ga-68 are widespread, with Ga-68 radiolabeled to a variety of radiotracers that evaluate perfusion and organ function, and target specific biomarkers found on tumor lesions such as prostate-specific membrane antigen, somatostatin, fibroblast activation protein, bombesin, and melanocortin. Main body: These Ga-68 radiopharmaceuticals include agents such as [Ga-68]Ga-macroaggregated albumin for myocardial perfusion evaluation, [Ga-68]Ga-PLED for assessing renal function, [Ga-68]Ga-t-butyl-HBED for assessing liver function, and [Ga-68]Ga-PSMA for tumor imaging. The short half-life, favourable nuclear decay properties, ease of radiolabeling, and convenient availability through germanium-68 (Ge-68) generators and cyclotron production routes strongly positions Ga-68 for continued growth in clinical deployment. This progress motivates the development of a set of common guidelines and standards for the Ga-68 radiopharmaceutical community, and recommendations for centers interested in establishing Ga-68 radiopharmaceutical production. Conclusion: This review outlines important aspects of Ga-68 radiopharmacy, including Ga-68 production routes using a Ge-68/Ga-68 generator or medical cyclotron, standardized Ga-68 radiolabeling methods, quality control procedures for clinical Ga-68 radiopharmaceuticals, and suggested best practices for centers with established or upcoming Ga-68 radiopharmaceutical production. Finally, an outlook on Ga-68 radiopharmaceuticals is presented to highlight potential challenges and opportunities facing the community

Targeted hepatitis C antibody testing interventions: a systematic review and meta-analysis

Testing for hepatitis C virus (HCV) infection may reduce the risk of liver-related morbidity, by facilitating earlier access to treatment and care. This review investigated the effectiveness of targeted testing interventions on HCV case detection, treatment uptake, and prevention of liver-related morbidity. A literature search identified studies published up to 2013 that compared a targeted HCV testing intervention (targeting individuals or groups at increased risk of HCV) with no targeted intervention, and results were synthesised using meta-analysis. Exposure to a targeted testing intervention, compared to no targeted intervention, was associated with increased cases detected [number of studies (n) = 14; pooled relative risk (RR) 1.7, 95 % CI 1.3, 2.2] and patients commencing therapy (n = 4; RR 3.3, 95 % CI 1.1, 10.0). Practitioner-based interventions increased test uptake and cases detected (n = 12; RR 3.5, 95 % CI 2.5, 4.8; and n = 10; RR 2.2, 95 % CI 1.4, 3.5, respectively), whereas media/information-based interventions were less effective (n = 4; RR 1.5, 95 % CI 0.7, 3.0; and n = 4; RR 1.3, 95 % CI 1.0, 1.6, respectively). This meta-analysis provides for the first time a quantitative assessment of targeted HCV testing interventions, demonstrating that these strategies were effective in diagnosing cases and increasing treatment uptake. Strategies involving practitioner-based interventions yielded the most favourable outcomes. It is recommended that testing should be targeted at and offered to individuals who are part of a population with high HCV prevalence, or who have a history of HCV risk behaviour

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Source Characteristics Influence AI-Enabled Orthopaedic Text Simplification: Recommendations for the Future.

BACKGROUND: This study assesses the effectiveness of large language models (LLMs) in simplifying complex language within orthopaedic patient education materials (PEMs) and identifies predictive factors for successful text transformation. METHODS: We transformed 48 orthopaedic PEMs using GPT-4, GPT-3.5, Claude 2, and Llama 2. The readability, quantified by the Flesch-Kincaid Reading Ease (FKRE) and Flesch-Kincaid Grade Level (FKGL) scores, was measured before and after transformation. Analysis included text characteristics such as syllable count, word length, and sentence length. Statistical and machine learning methods evaluated the correlations and predictive capacity of these features for transformation success. RESULTS: All LLMs improved FKRE and FKGL scores (p &lt; 0.01). GPT-4 showed superior performance, transforming PEMs to a seventh-grade reading level (mean FKGL, 6.72 ± 0.99), with higher FKRE and lower FKGL than other models. GPT-3.5, Claude 2, and Llama 2 significantly shortened sentences and overall text length (p &lt; 0.01). Importantly, correlation analysis revealed that transformation success varied substantially with the model used, depending on original text factors such as word length and sentence complexity. CONCLUSIONS: LLMs successfully simplify orthopaedic PEMs, with GPT-4 leading in readability improvement. This study highlights the importance of initial text characteristics in determining the effectiveness of LLM transformations, offering insights for optimizing orthopaedic health literacy initiatives using artificial intelligence (AI). CLINICAL RELEVANCE: This study provides critical insights into the ability of LLMs to simplify complex orthopaedic PEMs, enhancing their readability without compromising informational integrity. By identifying predictive factors for successful text transformation, this research supports the application of AI in improving health literacy, potentially leading to better patient comprehension and outcomes in orthopaedic care