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)

Introduction The shape of the glucose curve during an oral glucose tolerance test (OGTT) reflects β-cell function in populations without diabetes but has not been as well studied in those with diabetes. A monophasic shape has been associated with higher risk of diabetes, while a biphasic pattern has been associated with lower risk. We sought to determine if phenotypic or metabolic characteristics were associated with glucose response curve shape in adults with type 2 diabetes treated with metformin alone.Research design and methods This is a cross-sectional analysis of 3108 metformin-treated adults with type 2 diabetes diagnosed

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.

Abstract Metabolite extraction is the critical first-step in metabolomics experiments, where it is generally regarded to inactivate and remove proteins. Here, arising from efforts to improve extraction conditions for polar metabolomics, we discover a proteomic landscape of over 1000 proteins within metabolite extracts. This is a ubiquitous feature across several common extraction and sample types. By combining post-resuspension stable isotope addition and enzyme inhibitors, we demonstrate in-extract metabolite interconversions due to residual transaminase activity. We extend these findings with untargeted metabolomics where we observe extensive protein-mediated metabolite changes, including in-extract formation of glutamate dipeptide and depletion of total glutathione. Finally, we present a simple extraction workflow that integrates 3 kDa filtration for protein removal as a superior method for polar metabolomics. In this work, we uncover a previously unrecognized, protein-mediated source of observer effects in metabolomics experiments with broad-reaching implications across all research fields using metabolomics and molecular metabolism.

Here, we provide an in-depth analysis of the usefulness of single-sample metabolite/RNA extraction for multi-‘omics readout. Using pulverized frozen livers of mice injected with lymphocytic choriomeningitis virus (LCMV) or vehicle (Veh), we isolated RNA prior (RNA) or following metabolite extraction (MetRNA). RNA sequencing (RNAseq) data were evaluated for differential expression analysis and dispersion, and differential metabolite abundance was determined. Both RNA and MetRNA clustered together by principal component analysis, indicating that inter-individual differences were the largest source of variance. Over 85% of LCMV versus Veh differentially expressed genes were shared between extraction methods, with the remaining 15% evenly and randomly divided between groups. Differentially expressed genes unique to the extraction method were attributed to randomness around the 0.05 FDR cut-off and stochastic changes in variance and mean expression. In addition, analysis using the mean absolute difference showed no difference in the dispersion of transcripts between extraction methods. Altogether, our data show that prior metabolite extraction preserves RNAseq data quality, which enables us to confidently perform integrated pathway enrichment analysis on metabolomics and RNAseq data from a single sample. This analysis revealed pyrimidine metabolism as the most LCMV-impacted pathway. Combined analysis of genes and metabolites in the pathway exposed a pattern in the degradation of pyrimidine nucleotides leading to uracil generation. In support of this, uracil was among the most differentially abundant metabolites in serum upon LCMV infection. Our data suggest that hepatic uracil export is a novel phenotypic feature of acute infection and highlight the usefulness of our integrated single-sample multi-‘omics approach.

Summary: Targeting programmed cell death protein 1 (PD-1) is an important component of many immune checkpoint blockade (ICB) therapeutic approaches. However, ICB is not an efficacious strategy in a variety of cancer types, in part due to immunosuppressive metabolites in the tumor microenvironment. Here, we find that αPD-1-resistant cancer cells produce abundant itaconate (ITA) due to enhanced levels of aconitate decarboxylase (Acod1). Acod1 has an important role in the resistance to αPD-1, as decreasing Acod1 levels in αPD-1-resistant cancer cells can sensitize tumors to αPD-1 therapy. Mechanistically, cancer cells with high Acod1 inhibit the proliferation of naive CD8+ T cells through the secretion of inhibitory factors. Surprisingly, inhibition of CD8+ T cell proliferation is not dependent on the secretion of ITA but is instead a consequence of the release of small inhibitory peptides. Our study suggests that strategies to counter the activity of Acod1 in cancer cells may sensitize tumors to ICB therapy.

Objective: NF1 is a tumor suppressor gene and its protein product, neurofibromin, is a negative regulator of the RAS pathway. NF1 is one of the top driver mutations in sporadic breast cancer such that 27 % of breast cancers exhibit damaging NF1 alterations. NF1 loss-of-function is a frequent event in the genomic evolution of estrogen receptor (ER)+ breast cancer metastasis and endocrine resistance. Individuals with Neurofibromatosis type 1 (NF) – a disorder caused by germline NF1 mutations – have an increased risk of dying from breast cancer [1–4]. NF-related breast cancers are associated with decreased overall survival compared to sporadic breast cancer. Despite numerous studies interrogating the role of RAS mutations in tumor metabolism, no study has comprehensively profiled the NF1-deficient breast cancer metabolome to define patterns of energetic and metabolic reprogramming. The goals of this investigation were (1) to define the role of NF1 deficiency in estrogen receptor-positive (ER+) breast cancer metabolic reprogramming and (2) to identify potential targeted pathway and metabolic inhibitor combination therapies for NF1-deficient ER + breast cancer. Methods: We employed two ER+ NF1-deficient breast cancer models: (1) an NF1-deficient MCF7 breast cancer cell line to model sporadic breast cancer, and (2) three distinct, Nf1-deficient rat models to model NF-related breast cancer [1]. IncuCyte proliferation analysis was used to measure the effect of NF1 deficiency on cell proliferation and drug response. Protein quantity was assessed by Western Blot analysis. We then used RNAseq to investigate the transcriptional effect of NF1 deficiency on global and metabolism-related transcription. We measured cellular energetics using Agilent Seahorse XF-96 Glyco Stress Test and Mito Stress Test assays. We performed stable isotope labeling and measured [U–13C]-glucose and [U–13C]-glutamine metabolite incorporation and measured total metabolite pools using mass spectrometry. Lastly, we used a Bliss synergy model to investigate NF1-driven changes in targeted and metabolic inhibitor synergy. Results: Our results revealed that NF1 deficiency enhanced cell proliferation, altered neurofibromin expression, and increased RAS and PI3K/AKT pathway signaling while constraining oxidative ATP production and restricting energetic flexibility. Neurofibromin deficiency also increased glutamine influx into TCA intermediates and dramatically increased lipid pools, especially triglycerides (TG). Lastly, NF1 deficiency alters the synergy between metabolic inhibitors and traditional targeted inhibitors. This includes increased synergy with inhibitors targeting glycolysis, glutamine metabolism, mitochondrial fatty acid transport, and TG synthesis. Conclusions: NF1 deficiency drives metabolic reprogramming in ER+ breast cancer. This reprogramming is characterized by oxidative ATP constraints, glutamine TCA influx, and lipid pool expansion, and these metabolic changes introduce novel metabolic-to-targeted inhibitor synergies.

During the main COVID-19 global pandemic lockdown period of 2020 an impromptu set of pollination ecologists came together via social media and personal contacts to carry out standardised surveys of the flower visits and plants in gardens. The surveys involved 67 rural, suburban and urban gardens, of various sizes, ranging from 61.18° North in Norway to 37.96° South in Australia, resulting in a data set of 25,174 rows, with each row being a unique interaction record for that date/site/plant species, and comprising almost 47,000 visits to flowers, as well as records of flowers that were not visited by pollinators, for over 1,000 species and varieties belonging to more than 460 genera and 96 plant families. The more than 650 species of flower visitors belong to 12 orders of invertebrates and four of vertebrates. In this first publication from the project, we present a brief description of the data and make it freely available for any researchers to use in the future, the only restriction being that they cite this paper in the first instance. The data generated from these global surveys will provide scientific evidence to help us understand the role that private gardens (in urban, rural and suburban areas) can play in conserving insect pollinators and identify management actions to enhance their potential.

Objective. To evaluate maternal and fetal outcomes in pregnant patients with fibrodysplasia ossificans progressiva (FOP; OMIM#135100), an ultrarare genetic disorder characterized by progressive heterotopic ossification of soft tissues and cumulative disability. Methods. This is a retrospective case series of three patients with FOP who were admitted to Grady Memorial Hospital in Atlanta, Georgia, from to February 2011 to July 2021. Results. Three women delivered preterm infants at our institution. These cases posed unique anesthetic and obstetric technical challenges, particularly when securing the airway and performing cesarean delivery. Importantly, each patient received perioperative glucocorticoids for prevention of further heterotopic ossification. Conclusion. FOP is a unique clinical diagnosis encountered by obstetricians and requires multidisciplinary management for optimal outcomes.

Chronic inflammation has been closely linked to the development and progression of various cancers. The epithelial–mesenchymal transition (EMT) is a process involving the acquisition of mesenchymal features by carcinoma cells and is an important link between inflammation and cancer development. Inflammatory mediators in the tumour micro-environment, such as cytokines and chemokines, can promote EMT changes in cancer cells. The aim of this systematic review is to analyse the effect of cytokines on EMT in gynaecological cancers and discuss their possible therapeutic implications. A search of the databases CINAHL, Cochrane, Embase, Medline, PubMed, TRIP, and Web of Science was performed using the keywords: “cytokines” AND “epithelial mesenchymal transition OR transformation” AND “gynaecological cancer”. Seventy-one articles reported that various cytokines, such as TGF-β, TNF-α, IL-6, etc., promoted EMT changes in ovarian, cervical, and endometrial cancers. The EMT changes included from epithelial to mesenchymal morphological change, downregulation of the epithelial markers E-cadherin/β-catenin, upregulation of the mesenchymal markers N-cadherin/vimentin/fibronectin, and upregulation of the EMT-transformation factors (EMT-TF) SNAI1/SNAI2/TWIST/ZEB. Cytokine-induced EMT can lead to gynaecological cancer development and metastasis and hence novel therapies targeting the cytokines or their EMT signalling pathways could possibly prevent cancer progression, reduce cancer recurrence, and prevent drug-resistance.