Search

Introduction Type 2 diabetes mellitus (T2DM) is a powerful risk factor for cardiovascular disease (CVD), conferring a greater relative risk in women than men. We sought to examine sex differences in cardiometabolic risk factors and management in the contemporary cohort represented by the Glycemia Reduction Approaches in Diabetes: A Comparative Effectiveness Study (GRADE).Research design and methods GRADE enrolled 5047 participants (1837 women, 3210 men) with T2DM on metformin monotherapy at baseline. The current report is a cross-sectional analysis of baseline data collected July 2013 to August 2017.Results Compared with men, women had a higher mean body mass index (BMI), greater prevalence of severe obesity (BMI≥40 kg/m2), higher mean LDL cholesterol, greater prevalence of low HDL cholesterol, and were less likely to receive statin treatment and achieve target LDL, with a generally greater prevalence of these risk factors in younger women. Women with hypertension were equally likely to achieve blood pressure targets as men; however, women were less likely to receive ACE inhibitors or angiotensin receptor blockers. Women were more likely to be divorced, separated or widowed, and had fewer years of education and lower incomes.Conclusions This contemporary cohort demonstrates that women with T2DM continue to have a greater burden of cardiometabolic and socioeconomic risk factors than men, particularly younger women. Attention to these persisting disparities is needed to reduce the burden of CVD in women.Trial registration number ClinicalTrials.gov (NCT01794143)

Large Language Model (LLM) artificial intelligence tools present a unique challenge for educators who teach programming languages. While LLMs like ChatGPT have been well documented for their ability to complete exams and create prose, there is a noticeable lack of research into their ability to solve problems using high-level programming languages. Like many other university educators, those teaching programming courses would like to detect if students submit assignments generated by an LLM. To investigate grade performance and the likelihood of instructors identifying code generated by artificial intelligence (AI) tools, we compare code generated by students and ChatGPT for introductory Python homework assignments. Our research reveals mixed results on both counts, with ChatGPT performing like a mid-range student on assignments and seasoned instructors struggling to detect AI-generated code. This indicates that although AI-generated results may not always be identifiable, they do not currently yield results approaching those of diligent students. We describe our methodology for selecting and evaluating the code examples, the results of our comparison, and the implications for future classes. We conclude with recommendations for how instructors of programming courses can mitigate student use of LLM tools as well as articulate the inherent value of preserving students' individual creativity in producing programming languages.

Background: Our goal is to review current literature regarding the role of multi-parametric magnetic resonance imaging (mpMRI) in the active surveillance (AS) of prostate cancer (PCa) and identify trends in rate of reclassification of risk category, performance of fusion biopsy (FB) versus systematic biopsy (SB), and progression-free survival. Methods: We performed a comprehensive literature search in PubMed and identified 121 articles. A narrative summary was performed. Results: Thirty-two articles were chosen to be featured in this review. SB and FB are complementary in detecting higher-grade disease in follow-up. While FB was more likely than SB to detect clinically significant disease, FB missed 6.4–11% of clinically significant disease. Imaging factors that predicted upgrading include number of lesions on magnetic resonance imaging (MRI), lesion density, and MRI suspicion level. Conclusion: Incorporating mpMRI FB in conjunction with SB should be part of contemporary AS protocols. mpMRI should additionally be used routinely for follow-up; however, mpMRI is not currently sensitive enough in detecting disease progression to replace biopsy in the surveillance protocol.

Islets of Langerhans are multicellular microorgans located in the pancreas that play a central role in whole-body energy homeostasis. Through secretion of insulin and other hormones they regulate postprandial storage and interprandial usage of energy-rich nutrients. In these clusters of hormone-secreting endocrine cells, intricate cell-cell communication is essential for proper function. Electrical coupling between the insulin-secreting beta cells through gap junctions composed of connexin36 is particularly important, as it provides the required, most important, basis for coordinated responses of the beta cell population. The increasing evidence that gap-junctional communication and its modulation are vital to well-regulated secretion of insulin has stimulated immense interest in how subpopulations of heterogeneous beta cells are functionally arranged throughout the islets and how they mediate intercellular signals. In the last decade, several novel techniques have been proposed to assess cooperation between cells in islets, including the prosperous combination of multicellular imaging and network science. In the present contribution, we review recent advances related to the application of complex network approaches to uncover the functional connectivity patterns among cells within the islets. We first provide an accessible introduction to the basic principles of network theory, enumerating the measures characterizing the intercellular interactions and quantifying the functional integration and segregation of a multicellular system. Then we describe methodological approaches to construct functional beta cell networks, point out possible pitfalls, and specify the functional implications of beta cell network examinations. We continue by highlighting the recent findings obtained through advanced multicellular imaging techniques supported by network-based analyses, giving special emphasis to the current developments in both mouse and human islets, as well as outlining challenges offered by the multilayer network formalism in exploring the collective activity of islet cell populations. Finally, we emphasize that the combination of these imaging techniques and network-based analyses does not only represent an innovative concept that can be used to describe and interpret the physiology of islets, but also provides fertile ground for delineating normal from pathological function and for quantifying the changes in islet communication networks associated with the development of diabetes mellitus.

GM1 gangliosidosis (GM1) is a fatal neurodegenerative lysosomal storage disease that occurs most commonly in young children, with no effective treatment available. Long-term follow-up of GM1 cats treated by bilateral thalamic and deep cerebellar nuclei (DCN) injection of adeno-associated virus (AAV)-mediated gene therapy has increased lifespan to 8 years of age, compared with an untreated lifespan of ~8 months. Due to risks associated with cerebellar injection in humans, the lateral ventricle was tested as a replacement route to deliver an AAVrh8 vector expressing feline β-galactosidase (β-gal), the defective enzyme in GM1. Treatment via the thalamus and lateral ventricle corrected storage, myelination, astrogliosis, and neuronal morphology in areas where β-gal was effectively delivered. Oligodendrocyte number increased, but only in areas where myelination was corrected. Reduced AAV and β-gal distribution were noted in the cerebellum with subsequent increases in storage, demyelination, astrogliosis, and neuronal degeneration. These postmortem findings were correlated with endpoint MRI and magnetic resonance spectroscopy (MRS). Compared with the moderate dose with which most cats were treated, a higher AAV dose produced superior survival, currently 6.5 years. Thus, MRI and MRS can predict therapeutic efficacy of AAV gene therapy and non-invasively monitor cellular events within the GM1 brain.

Abstract Background Blood–brain barrier dysfunction is associated with many late-stage neurodegenerative diseases. An emerging question is whether the mutations associated with neurodegenerative diseases can independently lead to blood–brain barrier (BBB) dysfunction. Studies from patient-derived induced pluripotent stem cells suggest that mutations associated with neurodegenerative disease are non-cell autonomous, resulting in gain of toxic function in derived neurons and astrocytes. Here we assess whether selected mutations associated with neurodegenerative diseases can contribute to impairment of the blood–brain barrier. Methods We assessed barrier function of confluent monolayers of human brain microvascular endothelial cells (hBMECs) derived from induced pluripotent stem cells (iPSC) from three healthy individuals and eight individuals with neurodegenerative disease. We systematically assessed protein and gene expression of BBB biomarkers, transendothelial resistance (TEER), permeability of Lucifer yellow, permeability of d-glucose, permeability of rhodamine 123, the efflux ratio of rhodamine 123, and P-gp inhibition using Tariquidar for confluent monolayers of human brain microvascular endothelial cell (hBMECs). Results We provide evidence supporting the hypothesis that mutations associated with neurodegenerative disease can independently cause BBB dysfunction. These functional changes are not catastrophic since barrier breakdown would result in BBB impairment during development. Synergistic interactions between non-cell autonomous cerebrovascular dysfunction and the effects of gain-of-toxic function in neurons (e.g. toxic oligomers) are likely to increase disease burden through a positive feedback mechanism. Conclusions These results suggest that the accumulation of defects in brain microvascular endothelial cells may ultimately lead to impairment of the BBB. Small changes in barrier function over time could lead to accumulated defects that result in positive feedback to unrelated central nervous system diseases.

Abstract Background Idiopathic Pulmonary Fibrosis (IPF) is a fatal lung disease of unknown etiology with only two federally approved drug options. Given the complex molecular pathogenesis of IPF involving multiple cell types and multiple pathways, we explore the effects of a potential antifibrotic and antioxidant drug combination. Curcumin is a polyphenolic compound derived from turmeric with significant biological activity including a potential antifibrotic capacity. N-acetylcysteine (NAC) is a precursor to the antioxidant glutathione. To advance our understanding of these molecules, and to identify a clinical application, we present a small number of focused experiments that interrogates the effect of curcumin and NAC on pathways relevant to IPF in both fibroblasts and epithelial cells. Methods Primary epithelial cell and fibroblasts isolated from patients with IPF were challenged with a combination treatment of NAC and curcumin. Evaluation of the antifibrotic potential and effect on oxidative stress was performed through QPCR gene expression analysis and functional assays including scratch tests, viability assays, and measurement of induced reactive oxygen species. Results We demonstrate that curcumin alone does have antifibrotic potential, but that effect is accompanied by proapoptotic increases in oxidative stress. Coupled with this, we find that NAC alone can reduce oxidative stress, but that epithelial cell viability is decreased through this treatment. However, co-administration of these two molecules decreases oxidative stress and maintains high cell viability in both cell types. In addition, this co-treatment maintains an antifibrotic potential. Conclusions These findings suggest a novel application for these molecules in IPF and encourage further exploration of this potential therapeutic approach.

Abstract Background The mitogen-activated protein kinase (MAPK) family is involved in signal transduction networks that underpin many different biological processes in plants, ranging from development to biotic and abiotic stress responses. To date this class of enzymes has received little attention in Triticeae species, which include important cereal crops (wheat, barley, rye and triticale) that represent over 20% of the total protein food-source worldwide. Results The work presented here focuses on two subfamilies of Triticeae MAPKs, the MAP kinases (MPKs), and the MAPK kinases (MKKs) whose members phosphorylate the MPKs. In silico analysis of multiple Triticeae sequence databases led to the identification of 152 MAPKs belonging to these two sub-families. Some previously identified MAPKs were renamed to reflect the literature consensus on MAPK nomenclature. Two novel MPKs, MPK24 and MPK25, have been identified, including the first example of a plant MPK carrying the TGY activation loop sequence common to mammalian p38 MPKs. An EF-hand calcium-binding domain was found in members of the Triticeae MPK17 clade, a feature that appears to be specific to Triticeae species. New insights into the novel MEY activation loop identified in MPK11s are offered. When the exon-intron patterns for some MPKs and MKKs of wheat, barley and ancestors of wheat were assembled based on transcript data in GenBank, they showed deviations from the same sequence predicted in Ensembl. The functional relevance of MAPKs as derived from patterns of gene expression, MPK activation and MKK-MPK interaction is discussed. Conclusions A comprehensive resource of accurately annotated and curated Triticeae MPK and MKK sequences has been created for wheat, barley, rye, triticale, and two ancestral wheat species, goat grass and red wild einkorn. The work we present here offers a central information resource that will resolve existing confusion in the literature and sustain expansion of MAPK research in the crucial Triticeae grains.