model

A Novel Approach to Adolescents With Type 1 Diabetes: The Team Clinic Model

Jennifer K. Raymond
Feb 1, 2015; 28:68-71
Care Innovations




model

The Breakthrough Series: IHI's Collaborative Model for Achieving Breakthrough Improvement


Apr 1, 2004; 17:97-101
Articles




model

Overview of Peer Support Models to Improve Diabetes Self-Management and Clinical Outcomes

Michele Heisler
Oct 1, 2007; 20:214-221
Articles




model

Determination of globotriaosylceramide analogs in the organs of a mouse model of Fabry disease [Lipids]

Fabry disease is a heritable lipid disorder caused by the low activity of α-galactosidase A and characterized by the systemic accumulation of globotriaosylceramide (Gb3). Recent studies have reported a structural heterogeneity of Gb3 in Fabry disease, including Gb3 isoforms with different fatty acids and Gb3 analogs with modifications on the sphingosine moiety. However, Gb3 assays are often performed only on the selected Gb3 isoforms. To precisely determine the total Gb3 concentration, here we established two methods for determining both Gb3 isoforms and analogs. One was the deacylation method, involving Gb3 treatment with sphingolipid ceramide N-deacylase, followed by an assay of the deacylated products, globotriaosylsphingosine (lyso-Gb3) and its analogs, by ultra-performance LC coupled to tandem MS (UPLC-MS/MS). The other method was a direct assay established in the present study for 37 Gb3 isoforms and analogs/isoforms by UPLC-MS/MS. Gb3s from the organs of symptomatic animals of a Fabry disease mouse model were mainly Gb3 isoforms and two Gb3 analogs, such as Gb3(+18) containing the lyso-Gb3(+18) moiety and Gb3(−2) containing the lyso-Gb3(−2) moiety. The total concentrations and Gb3 analog distributions determined by the two methods were comparable. Gb3(+18) levels were high in the kidneys (24% of total Gb3) and the liver (13%), and we observed Gb3(−2) in the heart (10%) and the kidneys (5%). These results indicate organ-specific expression of Gb3 analogs, insights that may lead to a deeper understanding of the pathophysiology of Fabry disease.




model

Quantitative proteomics of human heart samples collected in vivo reveal the remodeled protein landscape of dilated left atrium without atrial fibrillation

Nora Linscheid
Apr 14, 2020; 0:RA119.001878v1-mcp.RA119.001878
Research




model

Predictions and Policymaking: Complex Modelling Beyond COVID-19

1 April 2020

Yasmin Afina

Research Assistant, International Security Programme

Calum Inverarity

Research Analyst and Coordinator, International Security Programme
The COVID-19 pandemic has highlighted the potential of complex systems modelling for policymaking but it is crucial to also understand its limitations.

GettyImages-1208425931.jpg

A member of the media wearing a protective face mask works in Downing Street where Britain's Prime Minister Boris Johnson is self-isolating in central London, 27 March 2020. Photo by TOLGA AKMEN/AFP via Getty Images.

Complex systems models have played a significant role in informing and shaping the public health measures adopted by governments in the context of the COVID-19 pandemic. For instance, modelling carried out by a team at Imperial College London is widely reported to have driven the approach in the UK from a strategy of mitigation to one of suppression.

Complex systems modelling will increasingly feed into policymaking by predicting a range of potential correlations, results and outcomes based on a set of parameters, assumptions, data and pre-defined interactions. It is already instrumental in developing risk mitigation and resilience measures to address and prepare for existential crises such as pandemics, prospects of a nuclear war, as well as climate change.

The human factor

In the end, model-driven approaches must stand up to the test of real-life data. Modelling for policymaking must take into account a number of caveats and limitations. Models are developed to help answer specific questions, and their predictions will depend on the hypotheses and definitions set by the modellers, which are subject to their individual and collective biases and assumptions. For instance, the models developed by Imperial College came with the caveated assumption that a policy of social distancing for people over 70 will have a 75 per cent compliance rate. This assumption is based on the modellers’ own perceptions of demographics and society, and may not reflect all societal factors that could impact this compliance rate in real life, such as gender, age, ethnicity, genetic diversity, economic stability, as well as access to food, supplies and healthcare. This is why modelling benefits from a cognitively diverse team who bring a wide range of knowledge and understanding to the early creation of a model.

The potential of artificial intelligence

Machine learning, or artificial intelligence (AI), has the potential to advance the capacity and accuracy of modelling techniques by identifying new patterns and interactions, and overcoming some of the limitations resulting from human assumptions and bias. Yet, increasing reliance on these techniques raises the issue of explainability. Policymakers need to be fully aware and understand the model, assumptions and input data behind any predictions and must be able to communicate this aspect of modelling in order to uphold democratic accountability and transparency in public decision-making.

In addition, models using machine learning techniques require extensive amounts of data, which must also be of high quality and as free from bias as possible to ensure accuracy and address the issues at stake. Although technology may be used in the process (i.e. automated extraction and processing of information with big data), data is ultimately created, collected, aggregated and analysed by and for human users. Datasets will reflect the individual and collective biases and assumptions of those creating, collecting, processing and analysing this data. Algorithmic bias is inevitable, and it is essential that policy- and decision-makers are fully aware of how reliable the systems are, as well as their potential social implications.

The age of distrust

Increasing use of emerging technologies for data- and evidence-based policymaking is taking place, paradoxically, in an era of growing mistrust towards expertise and experts, as infamously surmised by Michael Gove. Policymakers and subject-matter experts have faced increased public scrutiny of their findings and the resultant policies that they have been used to justify.

This distrust and scepticism within public discourse has only been fuelled by an ever-increasing availability of diffuse sources of information, not all of which are verifiable and robust. This has caused tension between experts, policymakers and public, which has led to conflicts and uncertainty over what data and predictions can be trusted, and to what degree. This dynamic is exacerbated when considering that certain individuals may purposefully misappropriate, or simply misinterpret, data to support their argument or policies. Politicians are presently considered the least trusted professionals by the UK public, highlighting the importance of better and more effective communication between the scientific community, policymakers and the populations affected by policy decisions.

Acknowledging limitations

While measures can and should be built in to improve the transparency and robustness of scientific models in order to counteract these common criticisms, it is important to acknowledge that there are limitations to the steps that can be taken. This is particularly the case when dealing with predictions of future events, which inherently involve degrees of uncertainty that cannot be fully accounted for by human or machine. As a result, if not carefully considered and communicated, the increased use of complex modelling in policymaking holds the potential to undermine and obfuscate the policymaking process, which may contribute towards significant mistakes being made, increased uncertainty, lack of trust in the models and in the political process and further disaffection of citizens.

The potential contribution of complexity modelling to the work of policymakers is undeniable. However, it is imperative to appreciate the inner workings and limitations of these models, such as the biases that underpin their functioning and the uncertainties that they will not be fully capable of accounting for, in spite of their immense power. They must be tested against the data, again and again, as new information becomes available or there is a risk of scientific models becoming embroiled in partisan politicization and potentially weaponized for political purposes. It is therefore important not to consider these models as oracles, but instead as one of many contributions to the process of policymaking.




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PARP-1-targeted Auger emitters display high-LET cytotoxic properties in vitro but show limited therapeutic utility in solid tumor models of human neuroblastoma

The currently available therapeutic radiopharmaceutical for high-risk neuroblastoma, 131I-MIBG, is ineffective at targeting micrometastases due to the low linear energy transfer (LET) properties of high-energy beta particles. In contrast, Auger radiation has high-LET properties with nanometer ranges in tissue, efficiently causing DNA damage when emitted in close proximity to DNA. The aim of this study was to evaluate the cytotoxicity of targeted Auger therapy in pre-clinical models of high-risk neuroblastoma. Methods: Using a radiolabeled poly(ADP-ribose) polymerase (PARP) inhibitor, 125I-KX1, we delivered an Auger emitter iodine-125 to PARP-1: a chromatin-binding enzyme overexpressed in neuroblastoma. In vitro cytotoxicity of 125I-KX1 was assessed in nineteen neuroblastoma cell lines, followed by in-depth pharmacological analysis in a sensitive and resistant pair of cell lines. Immunofluorescence microscopy was used to characterize 125I-KX1-induced DNA damage. Finally, in vitro/in vivo microdosimetry was modeled from experimentally derived pharmacological variables. Results: 125I-KX1 was highly cytotoxic in vitro across a panel of neuroblastoma cell lines, directly causing double strand DNA breaks. Based on subcellular dosimetry, 125I-KX1 was approximately twice as effective compared to 131I-KX1, whereas cytoplasmic 125I-MIBG demonstrated low biological effectiveness. Despite the ability to deliver focused radiation dose to the cell nuclei, 125I-KX1 remained less effective than its alpha-emitting analog 211At-MM4, and required significantly higher activity for equivalent in vivo efficacy based on tumor microdosimetry. Conclusion: Chromatin-targeted Auger therapy is lethal to high-risk neuroblastoma cells with potential use in micrometastatic disease. This study provides the first evidence for cellular lethality from a PARP-1 targeted Auger emitter, calling for further investigation into targeted Auger therapy.




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Imaging P-glycoprotein Induction at the Blood-Brain Barrier of a Beta-Amyloidosis Mouse Model with 11C-Metoclopramide PET

P-glycoprotein (ABCB1) plays an important role at the blood-brain barrier (BBB) in promoting the clearance of neurotoxic beta-amyloid (Aß) peptides from the brain into the blood. ABCB1 expression and activity were found to be decreased in the brains of Alzheimer disease (AD) patients. Treatment with drugs which induce cerebral ABCB1 activity may be a promising approach to delay the build-up of Aß deposits in the brain by enhancing the clearance of Aß peptides from the brain. The aim of this study was to investigate whether PET with the weak ABCB1 substrate radiotracer 11C-metoclopramide can measure ABCB1 induction at the BBB in a beta-amyloidosis mouse model (APP/PS1-21 mice) and in wild-type mice. Methods: Groups of wild-type and APP/PS1-21 mice aged 50 or 170 days underwent 11C-metoclopramide baseline PET scans or scans after intraperitoneal treatment with the rodent pregnane X receptor (PXR) activator 5-pregnen-3β-ol-20-one-16α-carbonitrile (PCN, 25 mg/kg) or its vehicle over 7 days. At the end of the PET scans, brains were harvested for immunohistochemical analysis of ABCB1 and Aß levels. In separate groups of mice, radiolabeled metabolites of 11C-metoclopramide were determined in plasma and brain at 15 min after radiotracer injection. As an outcome parameter of cerebral ABCB1 activity, the elimination slope of radioactivity washout from the brain (kE,brain) was calculated. Results: PCN treatment resulted in an increased clearance of radioactivity from the brain as reflected by significant increases in kE,brain (from +26% to +54% relative to baseline). Immunohistochemical analysis confirmed ABCB1 induction in the brains of PCN-treated APP/PS1-21 mice with a concomitant decrease in Aß levels. There was a significant positive correlation between kE,brain values and ABCB1 levels in the brain. In wild-type mice, a significant age-related decrease in kE,brain values was found. Metabolite analysis showed that the majority of radioactivity in the brain was composed of unmetabolized 11C-metoclopramide in all animal groups. Conclusion: 11C-metoclopramide can measure ABCB1 induction in the mouse brain without the need to consider an arterial input function and may find potential application in AD patients to non-invasively evaluate strategies to enhance the clearance properties of the BBB.




model

Early Detection in a Mouse Model of Pancreatic Cancer by Imaging DNA Damage Response Signalling

Rationale: Despite its widespread use in oncology, the PET radiotracer 18F-FDG is ineffective for improving early detection of pancreatic ductal adenocarcinoma (PDAC). An alternative strategy for early detection of pancreatic cancer involves visualisation of high-grade pancreatic intraepithelial neoplasias (PanIN-3), generally regarded as the non-invasive precursors of PDAC. The DNA damage response is known to be hyper-activated in late-stage PanINs. Therefore, we investigated whether the SPECT imaging agent, 111In-anti-H2AX-TAT, allows visualisation of the DNA damage repair marker H2AX in PanIN-3s in an engineered mouse model of PDAC, to facilitate early detection of PDAC. Methods: Genetically engineered KPC mice (KRasLSL.G12D/+; p53LSL.R172H/+; PdxCre) were imaged with 18F-FDG and 111In-anti-H2AX-TAT. PanIN/PDAC presence visualised by histology was compared with autoradiography and immunofluorescence. Separately, the survival of KPC mice imaged with 111In-anti-H2AX-TAT was evaluated. Results: In KPC mouse pancreata, H2AX expression was increased in high-grade PanINs, but not in PDAC, corroborating earlier results obtained from human pancreas sections. Uptake of 111In-anti-H2AX-TAT, but not 111In-IgG-TAT or 18F-FDG, within the pancreas was positively correlated with the age of KPC mice, which was correlated with the number of high-grade PanINs. 111In-anti-H2AX-TAT localises preferentially in high-grade PanIN lesions, but not in established PDAC. Younger, non-tumour-bearing KPC mice that show uptake of 111In-anti-H2AX-TAT in the pancreas survive significantly shorter than mice with physiological 111In-anti-H2AX-TAT uptake. Conclusion: 111In-anti-H2AX-TAT imaging allows non-invasive detection of DNA damage repair signalling upregulation in pre-invasive PanIN lesions and is a promising new tool to aid in the early detection and staging of pancreatic cancer.




model

177Lu-NM600 targeted radionuclide therapy extends survival in syngeneic murine models of triple-negative breast cancer

Triple negative breast cancer (TNBC) remains the most aggressive subtype of breast cancer leading to the worst prognosis. Because current therapeutic approaches lack efficacy, there is a clinically unmet need for effective treatment alternatives. Herein, we demonstrate a promising strategy utilizing a tumor-targeting alkylphosphocholine (NM600) radiolabeled with 177Lu for targeted radionuclide therapy (TRT) of TNBC. In two murine syngeneic models of TNBC, we confirmed excellent tumor targeting and rapid normal tissue clearance of the PET imaging analog 86Y-NM600. Based on longitudinal PET/CT data acquired with 86Y-NM600, we estimated the dosimetry of therapeutic 177Lu-NM600, which showed larger absorbed doses in the tumor compared to normal tissues. Administration of 177Lu-NM600 resulted in significant tumor growth inhibition and prolonged overall survival in mice bearing syngeneic 4T07 and 4T1 tumors. Complete response was attained in 60% of 4T07 bearing mice, but animals carrying aggressive 4T1 tumor grafts succumbed to metastatic progression. The injected activities used for treatment (9.25 and 18.5 MBq) were well tolerated, and only mild transient cytopenia was noted. Overall, our results suggest that 177Lu-NM600 TRT has potential for treatment of TNBC and merits further exploration in a clinical setting.




model

PET/CT imaging with a 18F-labeled galactodendritic unit in a galectin-1 overexpressing orthotopic bladder cancer model

Galectins are carbohydrate-binding proteins overexpressed in bladder cancer (BCa) cells. Dendritic galactose moieties have a high affinity for galectin-expressing tumor cells. We radiolabeled a dendritic galactose carbohydrate with fluorine-18 – 18F-labeled galactodendritic unit 4 – and examined its potential in imaging urothelial malignancies. Methods: The 18F-labeled 1st generation galactodendritic unit 4 was obtained from its tosylate precursor. We conducted in vivo studies in galectin-expressing UMUC3 orthotopic BCa model to determine the ability of 18F-labeled galactodendritic unit 4 to image BCa. Results: Intravesical administration of 18F-labeled galactodendritic unit 4 allowed specific accumulation of the carbohydrate radiotracer in galectin-1 overexpressing UMUC3 orthotopic tumors when imaged with PET. The 18F-labeled galactodendritic unit 4 was not found to accumulate in non-tumor murine bladders. Conclusion: The 18F-labeled galactodendritic unit 4 and similar analogs may be clinically relevant and exploitable for PET imaging of galectin-1 overexpressing bladder tumors.




model

Kinetic modeling and test-retest reproducibility of 11C-EKAP and 11C-FEKAP, novel agonist radiotracers for PET imaging of the kappa opioid receptor in humans

The kappa opioid receptor (KOR) is implicated in various neuropsychiatric disorders. We previously evaluated an agonist tracer, 11C-GR103545, for PET imaging of KOR in humans. Although 11C-GR103545 showed high brain uptake, good binding specificity, and selectivity to KOR, it displayed slow kinetics and relatively large test-retest variability (TRV) of distribution volume (VT) estimates (15%). Therefore we set out to develop two novel KOR agonist radiotracers, 11C-EKAP and 11C-FEKAP, and in nonhuman primates, both tracers exhibited faster kinetics and comparable binding parameters to 11C-GR103545. The aim of this study was to assess their kinetic and binding properties in humans. Methods: Six healthy subjects underwent 120-min test-retest PET scans with both 11C-EKAP and 11C-FEKAP. Metabolite-corrected arterial input functions were measured. Regional time-activity curves (TACs) were generated for 14 regions of interest. One- and two-tissue compartment models (1TC, 2TC) and the multilinear analysis-1 (MA1) method were applied to the regional TACs to calculate VT. Time-stability of VT values and test-retest reproducibility were evaluated. Levels of specific binding, as measured by the non-displaceable binding potential (BPND) for the three tracers (11C-EKAP, 11C-FEKAP and 11C-GR103545), were compared using a graphical method. Results: For both tracers, regional TACs were fitted well with the 2TC model and MA1 method (t*=20min), but not with the 1TC model. Given unreliably estimated parameters in several fits with the 2TC model and a good match between VT values from MA1 and 2TC, MA1 was chosen as the appropriate model for both tracers. Mean MA1 VT values were highest for 11C-GR103545, followed by 11C-EKAP, then 11C-FEKAP. Minimum scan time for stable VT measurement was 90 and 110min for 11C-EKAP and 11C-FEKAP, respectively, compared with 140min for 11C-GR103545. The mean absolute TRV in MA1 VT estimates was 7% and 18% for 11C-EKAP and 11C-FEKAP, respectively. BPND levels were similar for 11C-FEKAP and 11C-GR103545, but ~25% lower for 11C-EKAP. Conclusion: The two novel KOR agonist tracers showed faster tissue kinetics than 11C-GR103545. Even with slightly lower BPND, 11C-EKAP is judged to be a better tracer for imaging and quantification of KOR in humans, based on the shorter minimum scan time and excellent test-retest.




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Dietary plant stanol ester supplementation reduces peripheral symptoms in a mouse model of Niemann-Pick type C1 disease.

Inês Magro dos Reis
Apr 14, 2020; 0:jlr.RA120000632v1-jlr.RA120000632
Research Articles




model

Dietary plant stanol ester supplementation reduces peripheral symptoms in a mouse model of Niemann-Pick type C1 disease. [Research Articles]

Niemann–Pick type C1 (NPC1) disease is a rare genetic condition in which the function of the lysosomal cholesterol transporter NPC1 protein is impaired. Consequently, sphingolipids and cholesterol accumulate in lysosomes of all tissues, triggering a cascade of pathological events that culminate in severe systemic and neurological symptoms. Lysosomal cholesterol accumulation is also a key-factor in the development of atherosclerosis and non-alcoholic steatohepatitis (NASH). In these two metabolic diseases, the administration of plant stanol esters has been shown to ameliorate cellular cholesterol accumulation and inflammation. Given the overlap of pathological mechanisms among atherosclerosis, NASH and NPC1 disease, we sought to investigate whether dietary supplementation with plant stanol esters improves the peripheral features of NPC1 disease. To this end, we used an NPC1 murine model featuring an Npc1 null allele (Npc1nih), creating a dysfunctional NPC1 protein. Npc1nih mice were fed a two or six percent plant stanol esters–enriched diet over the course of 5 weeks. During this period, hepatic and blood lipid and inflammatory profiles were assessed. Npc1nih mice fed the plant stanol–enriched diet exhibited lower hepatic cholesterol accumulation, damage and inflammation than regular chow–fed Npc1nih mice. Moreover, plant stanol consumption shifted circulating T-cells and monocytes in particular towards an anti-inflammatory profile. Overall, these effects were stronger following dietary supplementation with 6% stanols, suggesting a dose-dependent effect. The findings of our study highlight the potential use of plant stanols as an affordable complementary means to ameliorate disorders in hepatic and blood lipid metabolism and reduce inflammation in NPC1 disease.




model

Predictions and Policymaking: Complex Modelling Beyond COVID-19

1 April 2020

Yasmin Afina

Research Assistant, International Security Programme

Calum Inverarity

Research Analyst and Coordinator, International Security Programme
The COVID-19 pandemic has highlighted the potential of complex systems modelling for policymaking but it is crucial to also understand its limitations.

GettyImages-1208425931.jpg

A member of the media wearing a protective face mask works in Downing Street where Britain's Prime Minister Boris Johnson is self-isolating in central London, 27 March 2020. Photo by TOLGA AKMEN/AFP via Getty Images.

Complex systems models have played a significant role in informing and shaping the public health measures adopted by governments in the context of the COVID-19 pandemic. For instance, modelling carried out by a team at Imperial College London is widely reported to have driven the approach in the UK from a strategy of mitigation to one of suppression.

Complex systems modelling will increasingly feed into policymaking by predicting a range of potential correlations, results and outcomes based on a set of parameters, assumptions, data and pre-defined interactions. It is already instrumental in developing risk mitigation and resilience measures to address and prepare for existential crises such as pandemics, prospects of a nuclear war, as well as climate change.

The human factor

In the end, model-driven approaches must stand up to the test of real-life data. Modelling for policymaking must take into account a number of caveats and limitations. Models are developed to help answer specific questions, and their predictions will depend on the hypotheses and definitions set by the modellers, which are subject to their individual and collective biases and assumptions. For instance, the models developed by Imperial College came with the caveated assumption that a policy of social distancing for people over 70 will have a 75 per cent compliance rate. This assumption is based on the modellers’ own perceptions of demographics and society, and may not reflect all societal factors that could impact this compliance rate in real life, such as gender, age, ethnicity, genetic diversity, economic stability, as well as access to food, supplies and healthcare. This is why modelling benefits from a cognitively diverse team who bring a wide range of knowledge and understanding to the early creation of a model.

The potential of artificial intelligence

Machine learning, or artificial intelligence (AI), has the potential to advance the capacity and accuracy of modelling techniques by identifying new patterns and interactions, and overcoming some of the limitations resulting from human assumptions and bias. Yet, increasing reliance on these techniques raises the issue of explainability. Policymakers need to be fully aware and understand the model, assumptions and input data behind any predictions and must be able to communicate this aspect of modelling in order to uphold democratic accountability and transparency in public decision-making.

In addition, models using machine learning techniques require extensive amounts of data, which must also be of high quality and as free from bias as possible to ensure accuracy and address the issues at stake. Although technology may be used in the process (i.e. automated extraction and processing of information with big data), data is ultimately created, collected, aggregated and analysed by and for human users. Datasets will reflect the individual and collective biases and assumptions of those creating, collecting, processing and analysing this data. Algorithmic bias is inevitable, and it is essential that policy- and decision-makers are fully aware of how reliable the systems are, as well as their potential social implications.

The age of distrust

Increasing use of emerging technologies for data- and evidence-based policymaking is taking place, paradoxically, in an era of growing mistrust towards expertise and experts, as infamously surmised by Michael Gove. Policymakers and subject-matter experts have faced increased public scrutiny of their findings and the resultant policies that they have been used to justify.

This distrust and scepticism within public discourse has only been fuelled by an ever-increasing availability of diffuse sources of information, not all of which are verifiable and robust. This has caused tension between experts, policymakers and public, which has led to conflicts and uncertainty over what data and predictions can be trusted, and to what degree. This dynamic is exacerbated when considering that certain individuals may purposefully misappropriate, or simply misinterpret, data to support their argument or policies. Politicians are presently considered the least trusted professionals by the UK public, highlighting the importance of better and more effective communication between the scientific community, policymakers and the populations affected by policy decisions.

Acknowledging limitations

While measures can and should be built in to improve the transparency and robustness of scientific models in order to counteract these common criticisms, it is important to acknowledge that there are limitations to the steps that can be taken. This is particularly the case when dealing with predictions of future events, which inherently involve degrees of uncertainty that cannot be fully accounted for by human or machine. As a result, if not carefully considered and communicated, the increased use of complex modelling in policymaking holds the potential to undermine and obfuscate the policymaking process, which may contribute towards significant mistakes being made, increased uncertainty, lack of trust in the models and in the political process and further disaffection of citizens.

The potential contribution of complexity modelling to the work of policymakers is undeniable. However, it is imperative to appreciate the inner workings and limitations of these models, such as the biases that underpin their functioning and the uncertainties that they will not be fully capable of accounting for, in spite of their immense power. They must be tested against the data, again and again, as new information becomes available or there is a risk of scientific models becoming embroiled in partisan politicization and potentially weaponized for political purposes. It is therefore important not to consider these models as oracles, but instead as one of many contributions to the process of policymaking.




model

Bayesian Proteoform Modeling Improves Protein Quantification of Global Proteomic Measurements [Technology]

As the capability of mass spectrometry-based proteomics has matured, tens of thousands of peptides can be measured simultaneously, which has the benefit of offering a systems view of protein expression. However, a major challenge is that with an increase in throughput, protein quantification estimation from the native measured peptides has become a computational task. A limitation to existing computationally-driven protein quantification methods is that most ignore protein variation, such as alternate splicing of the RNA transcript and post-translational modifications or other possible proteoforms, which will affect a significant fraction of the proteome. The consequence of this assumption is that statistical inference at the protein level, and consequently downstream analyses, such as network and pathway modeling, have only limited power for biomarker discovery. Here, we describe a Bayesian model (BP-Quant) that uses statistically derived peptides signatures to identify peptides that are outside the dominant pattern, or the existence of multiple over-expressed patterns to improve relative protein abundance estimates. It is a research-driven approach that utilizes the objectives of the experiment, defined in the context of a standard statistical hypothesis, to identify a set of peptides exhibiting similar statistical behavior relating to a protein. This approach infers that changes in relative protein abundance can be used as a surrogate for changes in function, without necessarily taking into account the effect of differential post-translational modifications, processing, or splicing in altering protein function. We verify the approach using a dilution study from mouse plasma samples and demonstrate that BP-Quant achieves similar accuracy as the current state-of-the-art methods at proteoform identification with significantly better specificity. BP-Quant is available as a MatLab ® and R packages at https://github.com/PNNL-Comp-Mass-Spec/BP-Quant.




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Translating Divergent Environmental Stresses into a Common Proteome Response through Hik33 in a Model Cyanobacterium [Research]

The histidine kinase Hik33 plays important roles in mediating cyanobacterial response to divergent types of abiotic stresses including cold, salt, high light (HL), and osmotic stresses. However, how these functions are regulated by Hik33 remains to be addressed. Using a hik33-deficient strain (hik33) of Synechocystis sp. PCC 6803 (Synechocystis) and quantitative proteomics, we found that Hik33 depletion induces differential protein expression highly similar to that induced by divergent types of stresses. This typically includes downregulation of proteins in photosynthesis and carbon assimilation that are necessary for cell propagation, and upregulation of heat shock proteins, chaperons, and proteases that are important for cell survival. This observation indicates that depletion of Hik33 alone mimics divergent types of abiotic stresses, and that Hik33 could be important for preventing abnormal stress response in the normal condition. Moreover, we found the majority of proteins of plasmid origin were significantly upregulated in hik33, though their biological significance remains to be addressed. Together, the systematically characterized Hik33-regulated cyanobacterial proteome, which is largely involved in stress responses, builds the molecular basis for Hik33 as a general regulator of stress responses.




model

Quantitative proteomics of human heart samples collected in vivo reveal the remodeled protein landscape of dilated left atrium without atrial fibrillation [Research]

Genetic and genomic research has greatly advanced our understanding of heart disease. Yet, comprehensive, in-depth, quantitative maps of protein expression in hearts of living humans are still lacking. Using samples obtained during valve replacement surgery in patients with mitral valve prolapse (MVP), we set out to define inter-chamber differences, the intersect of proteomic data with genetic or genomic datasets, and the impact of left atrial dilation on the proteome of patients with no history of atrial fibrillation (AF).  We collected biopsies from right atria (RA), left atria (LA) and left ventricle (LV) of seven male patients with mitral valve regurgitation with dilated LA but no history of AF. Biopsy samples were analyzed by high-resolution mass spectrometry (MS), where peptides were pre-fractionated by reverse phase high-pressure liquid chromatography prior to MS measurement on a Q-Exactive-HF Orbitrap instrument. We identified 7,314 proteins based on 130,728 peptides. Results were confirmed in an independent set of biopsies collected from three additional individuals. Comparative analysis against data from post-mortem samples showed enhanced quantitative power and confidence level in samples collected from living hearts. Our analysis, combined with data from genome wide association studies suggested candidate gene associations to MVP, identified higher abundance in ventricle for proteins associated with cardiomyopathies and revealed the dilated LA proteome, demonstrating differential representation of molecules previously associated with AF, in non-AF hearts. This is the largest dataset of cardiac protein expression from human samples collected in vivo. It provides a comprehensive resource that allows insight into molecular fingerprints of MVP and facilitates novel inferences between genomic data and disease mechanisms. We propose that over-representation of proteins in ventricle is consequent not to redundancy but to functional need, and conclude that changes in abundance of proteins known to associate with AF are not sufficient for arrhythmogenesis.




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Effects of omega-O-acylceramide structures and concentrations in healthy and diseased skin barrier lipid membrane models [Research Articles]

Ceramides (Cers) with ultralong (~32-carbon) chains and -esterified linoleic acid, composing a subclass called omega-O-acylceramides (acylCers), are indispensable components of the skin barrier. Normal barriers typically contain acylCer concentrations of ~10 mol%; diminished concentrations, along with altered or missing long periodicity lamellar phase (LPP), and increased permeability accompany an array of skin disorders, including atopic dermatitis, psoriasis, and ichthyoses. We developed model membranes to investigate the effects of the acylCer structure and concentration on skin lipid organization and permeability. The model membrane systems contained six to nine Cer subclasses as well as fatty acids, cholesterol, and cholesterol sulfate; acylCer content—namely, acylCers containing sphingosine (Cer EOS), dihydrosphingosine (Cer EOdS), and phytosphingosine (Cer EOP) ranged from zero to 30 mol%. Systems with normal physiologic concentrations of acylCer mixture mimicked the permeability and nanostructure of human skin lipids (with regard to LPP, chain order, and lateral packing). The models also showed that the sphingoid base in acylCer significantly affects the membrane architecture and permeability and that Cer EOP, notably, is a weaker barrier component than Cer EOS and Cer EOdS. Membranes with diminished or missing acylCers displayed some of the hallmarks of diseased skin lipid barriers (i.e., lack of LPP, less ordered lipids, less orthorhombic chain packing, and increased permeability). These results could inform the rational design of new and improved strategies for the barrier-targeted treatment of skin diseases.




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Model systems for studying the assembly, trafficking, and secretion of apoB lipoproteins using fluorescent fusion proteins [Research Articles]

apoB exists as apoB100 and apoB48, which are mainly found in hepatic VLDLs and intestinal chylomicrons, respectively. Elevated plasma levels of apoB-containing lipoproteins (Blps) contribute to coronary artery disease, diabetes, and other cardiometabolic conditions. Studying the mechanisms that drive the assembly, intracellular trafficking, secretion, and function of Blps remains challenging. Our understanding of the intracellular and intraorganism trafficking of Blps can be greatly enhanced, however, with the availability of fusion proteins that can help visualize Blp transport within cells and between tissues. We designed three plasmids expressing human apoB fluorescent fusion proteins: apoB48-GFP, apoB100-GFP, and apoB48-mCherry. In Cos-7 cells, transiently expressed fluorescent apoB proteins colocalized with calnexin and were only secreted if cells were cotransfected with microsomal triglyceride transfer protein. The secreted apoB-fusion proteins retained the fluorescent protein and were secreted as lipoproteins with flotation densities similar to plasma HDL and LDL. In a rat hepatoma McA-RH7777 cell line, the human apoB100 fusion protein was secreted as VLDL- and LDL-sized particles, and the apoB48 fusion proteins were secreted as LDL- and HDL-sized particles. To monitor lipoprotein trafficking in vivo, the apoB48-mCherry construct was transiently expressed in zebrafish larvae and was detected throughout the liver. These experiments show that the addition of fluorescent proteins to the C terminus of apoB does not disrupt their assembly, localization, secretion, or endocytosis. The availability of fluorescently labeled apoB proteins will facilitate the exploration of the assembly, degradation, and transport of Blps and help to identify novel compounds that interfere with these processes via high-throughput screening.




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ANGPTL3, PCSK9, and statin therapy drive remarkable reductions in hyperlipidemia and atherosclerosis in a mouse model [Commentary]




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Is CYP2C70 the key to new mouse models to understand bile acids in humans? [Commentary]




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Problem Notes for SAS®9 - 65852: The PANEL procedure produces incorrect results for certain models when the NOINT and RANONE options are specified

The estimation results might be incorrect in PROC PANEL when the RANONE and NOINT options are specified in the MODEL statement.




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Problem Notes for SAS®9 - 34294: A missing discrete dependent variable in the selection model together with a OUTPUT statement might cause an Access Violation error

If the following conditions are met in PROC QLIM: the SELECT option and DISCRETE option are specified in the same MODEL statement or ENDOGENOUS statement the same dependent variable with S




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Connecting Rodent and Human Pharmacokinetic Models for the Design and Translation of Glucose-Responsive Insulin

Despite considerable progress, development of glucose-responsive insulins (GRI) still largely depends on empirical knowledge and tedious experimentation – especially on rodents. To assist the rational design and clinical translation of the therapeutic, we present a Pharmacokinetic Algorithm Mapping GRI Efficacies in Rodents and Humans (PAMERAH), built upon our previous human model. PAMERAH constitutes a framework for predicting the therapeutic efficacy of a GRI candidate from its user-specified mechanism of action, kinetics, and dosage, which we show is accurate when checked against data from experiments and literature. Results from simulated glucose clamps also agree quantitatively with recent GRI publications. We demonstrate that the model can be used to explore the vast number of permutations constituting the GRI parameter space, and thereby identify the optimal design ranges that yield desired performance. A design guide aside, PAMERAH more importantly can facilitate GRI’s clinical translation by connecting each candidate’s efficacies in rats, mice, and humans. The resultant mapping helps find GRIs which appear promising in rodents but underperform in humans (i.e. false-positives). Conversely, it also allows for the discovery of optimal human GRI dynamics not captured by experiments on a rodent population (false-negatives). We condense such information onto a translatability grid as a straightforward, visual guide for GRI development.




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Erratum. Multiethnic Genome-Wide Association Study of Diabetic Retinopathy Using Liability Threshold Modeling of Duration of Diabetes and Glycemic Control. Diabetes 2019;68:441--456




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The Histone Methyltransferase MLL1 Directs Macrophage-Mediated Inflammation in Wound Healing and Is Altered in a Murine Model of Obesity and Type 2 Diabetes

Andrew S. Kimball
Sep 1, 2017; 66:2459-2471
Immunology and Transplantation




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The High-Fat Diet-Fed Mouse: A Model for Studying Mechanisms and Treatment of Impaired Glucose Tolerance and Type 2 Diabetes

Maria Sörhede Winzell
Dec 1, 2004; 53:S215-S219
Section V: The Incretin Pathway




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Pancreas Pathology of Latent Autoimmune Diabetes in Adults (LADA) in Patients and in a LADA Rat Model Compared With Type 1 Diabetes

Anne Jörns
Apr 1, 2020; 69:624-633
Islet Studies




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Subscription models and small venues the future

AS ARTISTES and musicians try to manage the situation brought on by COVID-19’s stranglehold on the world and its economy, in the interim, many have resorted to hosting live-stream events. But that only succeeds to a point. Performers retain their...




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Pancreas Pathology of Latent Autoimmune Diabetes in Adults (LADA) in Patients and in a LADA Rat Model Compared With Type 1 Diabetes

Approximately 10% of patients with type 2 diabetes suffer from latent autoimmune diabetes in adults (LADA). This study provides a systematic assessment of the pathology of the endocrine pancreas of patients with LADA and for comparison in a first rat model mimicking the characteristics of patients with LADA. Islets in human and rat pancreases were analyzed by immunohistochemistry for immune cell infiltrate composition, by in situ RT-PCR and quantitative real-time PCR of laser microdissected islets for gene expression of proinflammatory cytokines, the proliferation marker proliferating cell nuclear antigen (PCNA), the anti-inflammatory cytokine interleukin (IL) 10, and the apoptosis markers caspase 3 and TUNEL as well as insulin. Human and rat LADA pancreases showed differences in areas of the pancreas with respect to immune cell infiltration and a changed ratio between the number of macrophages and CD8 T cells toward macrophages in the islet infiltrate. Gene expression analyses revealed a changed ratio due to an increase of IL-1β and a decrease of tumor necrosis factor-α. IL-10, PCNA, and insulin expression were increased in the LADA situation, whereas caspase 3 gene expression was reduced. The analyses into the underlying pathology in human as well as rat LADA pancreases provided identical results, allowing the conclusion that LADA is a milder form of autoimmune diabetes in patients of an advanced age.




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Talk Evidence covid-19 update - hydroxy/chloroquinine, prognostic models and facemaskss

For the next few months Talk Evidence is going to focus on the new corona virus pandemic. There is an enormous amount of uncertainty about the disease, what the symptoms are, fatality rate, treatment options, things we shouldn't be doing. We're going to try to get away from the headlines and talk about what we need to know - to hopefully give...




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Predictive Modeling of Type 1 Diabetes Stages Using Disparate Data Sources

This study aims to model genetic, immunologic, metabolomics, and proteomic biomarkers for development of islet autoimmunity (IA) and progression to type 1 diabetes in a prospective high-risk cohort. We studied 67 children: 42 who developed IA (20 of 42 progressed to diabetes) and 25 control subjects matched for sex and age. Biomarkers were assessed at four time points: earliest available sample, just prior to IA, just after IA, and just prior to diabetes onset. Predictors of IA and progression to diabetes were identified across disparate sources using an integrative machine learning algorithm and optimization-based feature selection. Our integrative approach was predictive of IA (area under the receiver operating characteristic curve [AUC] 0.91) and progression to diabetes (AUC 0.92) based on standard cross-validation (CV). Among the strongest predictors of IA were change in serum ascorbate, 3-methyl-oxobutyrate, and the PTPN22 (rs2476601) polymorphism. Serum glucose, ADP fibrinogen, and mannose were among the strongest predictors of progression to diabetes. This proof-of-principle analysis is the first study to integrate large, diverse biomarker data sets into a limited number of features, highlighting differences in pathways leading to IA from those predicting progression to diabetes. Integrated models, if validated in independent populations, could provide novel clues concerning the pathways leading to IA and type 1 diabetes.




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A Novel Model of Diabetic Complications: Adipocyte Mitochondrial Dysfunction Triggers Massive {beta}-Cell Hyperplasia

Obesity-associated type 2 diabetes mellitus (T2DM) entails insulin resistance and loss of β-cell mass. Adipose tissue mitochondrial dysfunction is emerging as a key component in the etiology of T2DM. Identifying approaches to preserve mitochondrial function, adipose tissue integrity, and β-cell mass during obesity is a major challenge. Mitochondrial ferritin (FtMT) is a mitochondrial matrix protein that chelates iron. We sought to determine whether perturbation of adipocyte mitochondria influences energy metabolism during obesity. We used an adipocyte-specific doxycycline-inducible mouse model of FtMT overexpression (FtMT-Adip mice). During a dietary challenge, FtMT-Adip mice are leaner but exhibit glucose intolerance, low adiponectin levels, increased reactive oxygen species damage, and elevated GDF15 and FGF21 levels, indicating metabolically dysfunctional fat. Paradoxically, despite harboring highly dysfunctional fat, transgenic mice display massive β-cell hyperplasia, reflecting a beneficial mitochondria-induced fat-to-pancreas interorgan signaling axis. This identifies the unique and critical impact that adipocyte mitochondrial dysfunction has on increasing β-cell mass during obesity-related insulin resistance.




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Retinopathy in a Diet-Induced Type 2 Diabetic Rat Model and Role of Epigenetic Modifications

Type 2 diabetes accounts for 90% of the population with diabetes, and these patients are generally obese and hyperlipidemic. In addition to hyperglycemia, hyperlipidemia is also closely related with diabetic retinopathy. The aim was to investigate retinopathy in a model closely mimicking the normal progression and metabolic features of the population with type 2 diabetes and elucidate the molecular mechanism. Retinopathy was evaluated in rats fed a 45% kcal as fat diet for 8 weeks before administering streptozotocin, 30 mg/kg body weight (T2D), and compared with age- and duration-matched type 1 diabetic rats (T1D) (60 mg/kg streptozotocin). The role of epigenetic modifications in mitochondrial damage was evaluated in retinal microvasculature. T2D rats were obese and severely hyperlipidemic, with impaired glucose and insulin tolerance compared with age-matched T1D rats. While at 4 months of diabetes, T1D rats had no detectable retinopathy, T2D rats had significant retinopathy, their mitochondrial copy numbers were lower, and mtDNA and Rac1 promoter DNA methylation was exacerbated. At 6 months, retinopathy was comparable in T2D and T1D rats, suggesting that obesity exaggerates hyperglycemia-induced epigenetic modifications, accelerating mitochondrial damage and diabetic retinopathy. Thus, maintenance of good lifestyle and BMI could be beneficial in regulating epigenetic modifications and preventing/retarding retinopathy in patients with diabetes.




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Multimodality Imaging of Inflammation and Ventricular Remodeling in Pressure-Overload Heart Failure

Inflammation contributes to ventricular remodeling after myocardial ischemia, but its role in nonischemic heart failure is poorly understood. Local tissue inflammation is difficult to assess serially during pathogenesis. Although 18F-FDG accumulates in inflammatory leukocytes and thus may identify inflammation in the myocardial microenvironment, it remains unclear whether this imaging technique can isolate diffuse leukocytes in pressure-overload heart failure. We aimed to evaluate whether inflammation with 18F-FDG can be serially imaged in the early stages of pressure-overload–induced heart failure and to compare the time course with functional impairment assessed by cardiac MRI. Methods: C57Bl6/N mice underwent transverse aortic constriction (TAC) (n = 22), sham surgery (n = 12), or coronary ligation as an inflammation-positive control (n = 5). MRI assessed ventricular geometry and contractile function at 2 and 8 d after TAC. Immunostaining identified the extent of inflammatory leukocyte infiltration early in pressure overload. 18F-FDG PET scans were acquired at 3 and 7 d after TAC, under ketamine-xylazine anesthesia to suppress cardiomyocyte glucose uptake. Results: Pressure overload evoked rapid left ventricular dilation compared with sham (end-systolic volume, day 2: 40.6 ± 10.2 μL vs. 23.8 ± 1.7 μL, P < 0.001). Contractile function was similarly impaired (ejection fraction, day 2: 40.9% ± 9.7% vs. 59.2% ± 4.4%, P < 0.001). The severity of contractile impairment was proportional to histology-defined myocardial macrophage density on day 8 (r = –0.669, P = 0.010). PET imaging identified significantly higher left ventricular 18F-FDG accumulation in TAC mice than in sham mice on day 3 (10.5 ± 4.1 percentage injected dose [%ID]/g vs. 3.8 ± 0.9 %ID/g, P < 0.001) and on day 7 (7.8 ± 3.7 %ID/g vs. 3.0 ± 0.8 %ID/g, P = 0.006), though the efficiency of cardiomyocyte suppression was variable among TAC mice. The 18F-FDG signal correlated with ejection fraction (r = –0.75, P = 0.01) and ventricular volume (r = 0.75, P < 0.01). Western immunoblotting demonstrated a 60% elevation of myocardial glucose transporter 4 expression in the left ventricle at 8 d after TAC, indicating altered glucose metabolism. Conclusion: TAC induces rapid changes in left ventricular geometry and contractile function, with a parallel modest infiltration of inflammatory macrophages. Metabolic remodeling overshadows inflammatory leukocyte signal using 18F-FDG PET imaging. More selective inflammatory tracers are requisite to identify the diffuse local inflammation in pressure overload.




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Theranostics Targeting Fibroblast Activation Protein in the Tumor Stroma: 64Cu- and 225Ac-Labeled FAPI-04 in Pancreatic Cancer Xenograft Mouse Models

Fibroblast activation protein (FAP), which promotes tumor growth and progression, is overexpressed in cancer-associated fibroblasts of many human epithelial cancers. Because of its low expression in normal organs, FAP is an excellent target for theranostics. In this study, we used radionuclides with relatively long half-lives, 64Cu (half-life, 12.7 h) and 225Ac (half-life, 10 d), to label FAP inhibitors (FAPIs) in mice with human pancreatic cancer xenografts. Methods: Male nude mice (body weight, 22.5 ± 1.2 g) were subcutaneously injected with human pancreatic cancer cells (PANC-1, n = 12; MIA PaCa-2, n = 8). Tumor xenograft mice were investigated after the intravenous injection of 64Cu-FAPI-04 (7.21 ± 0.46 MBq) by dynamic and delayed PET scans (2.5 h after injection). Static scans 1 h after the injection of 68Ga-FAPI-04 (3.6 ± 1.4 MBq) were also acquired for comparisons using the same cohort of mice (n = 8). Immunohistochemical staining was performed to confirm FAP expression in tumor xenografts using an FAP-α-antibody. For radioligand therapy, 225Ac-FAPI-04 (34 kBq) was injected into PANC-1 xenograft mice (n = 6). Tumor size was monitored and compared with that of control mice (n = 6). Results: Dynamic imaging of 64Cu-FAPI-04 showed rapid clearance through the kidneys and slow washout from tumors. Delayed PET imaging of 64Cu-FAPI-04 showed mild uptake in tumors and relatively high uptake in the liver and intestine. Accumulation levels in the tumor or normal organs were significantly higher for 64Cu-FAPI-04 than for 68Ga-FAPI-04, except in the heart, and excretion in the urine was higher for 68Ga-FAPI-04 than for 64Cu-FAPI-04. Immunohistochemical staining revealed abundant FAP expression in the stroma of xenografts. 225Ac-FAPI-04 injection showed significant tumor growth suppression in the PANC-1 xenograft mice, compared with the control mice, without a significant change in body weight. Conclusion: This proof-of-concept study showed that 64Cu-FAPI-04 and 225Ac-FAPI-04 could be used in theranostics for the treatment of FAP-expressing pancreatic cancer. α-therapy targeting FAP in the cancer stroma is effective and will contribute to the development of a new treatment strategy.




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Prediction models for diagnosis and prognosis of covid-19 infection: systematic review and critical appraisal




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Use of electronic medical records in development and validation of risk prediction models of hospital readmission: systematic review




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Estimated population wide benefits and risks in China of lowering sodium through potassium enriched salt substitution: modelling study




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Impact of Treating Oral Disease on Preventing Vascular Diseases: A Model-Based Cost-effectiveness Analysis of Periodontal Treatment Among Patients With Type 2 Diabetes

OBJECTIVE

Previous randomized trials found that treating periodontitis improved glycemic control in patients with type 2 diabetes (T2D), thus lowering the risks of developing T2D-related microvascular diseases and cardiovascular disease (CVD). Some payers in the U.S. have started covering nonsurgical periodontal treatment for those with chronic conditions, such as diabetes. We sought to identify the cost-effectiveness of expanding periodontal treatment coverage among patients with T2D.

RESEARCH DESIGN AND METHODS

A cost-effectiveness analysis was conducted to estimate lifetime costs and health gains using a stochastic microsimulation model of oral health conditions, T2D, T2D-related microvascular diseases, and CVD of the U.S. population. Model parameters were obtained from the nationally representative National Health and Nutrition Examination Survey (NHANES) (2009–2014) and randomized trials of periodontal treatment among patients with T2D.

RESULTS

Expanding periodontal treatment coverage among patients with T2D and periodontitis would be expected to avert tooth loss by 34.1% (95% CI –39.9, –26.5) and microvascular diseases by 20.5% (95% CI –31.2, –9.1), 17.7% (95% CI –32.7, –4.7), and 18.4% (95% CI –34.5, –3.5) for nephropathy, neuropathy, and retinopathy, respectively. Providing periodontal treatment to the target population would be cost saving from a health care perspective at a total net savings of $5,904 (95% CI –6,039, –5,769) with an estimated gain of 0.6 quality-adjusted life years per capita (95% CI 0.5, 0.6).

CONCLUSIONS

Providing nonsurgical periodontal treatment to patients with T2D and periodontitis would be expected to significantly reduce tooth loss and T2D-related microvascular diseases via improved glycemic control. Encouraging patients with T2D and poor oral health conditions to receive periodontal treatment would improve health outcomes and still be cost saving or cost-effective.




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A Mathematical Model for the Determination of Total Area Under Glucose Tolerance and Other Metabolic Curves

Mary M Tai
Feb 1, 1994; 17:152-154
Short Report




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A Prospective Analysis of the HOMA Model: The Mexico City Diabetes Study

Steven M Haffner
Oct 1, 1996; 19:1138-1141
Short Report




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Brain model links herpes virus to development of Alzheimer's disease

Bio-engineered models of the human brain infected with herpes simplex virus-1 develop many of the same characteristics found in Alzheimer's disease, according to a new analysis published by Science Advances.




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Dapagliflozin Versus Placebo on Left Ventricular Remodeling in Patients With Diabetes and Heart Failure: The REFORM Trial

OBJECTIVE

To determine the effects of dapagliflozin in patients with heart failure (HF) and type 2 diabetes mellitus (T2DM) on left ventricular (LV) remodeling using cardiac MRI.

RESEARCH DESIGN AND METHODS

We randomized 56 patients with T2DM and HF with LV systolic dysfunction to dapagliflozin 10 mg daily or placebo for 1 year, on top of usual therapy. The primary end point was difference in LV end-systolic volume (LVESV) using cardiac MRI. Key secondary end points included other measures of LV remodeling and clinical and biochemical parameters.

RESULTS

In our cohort, dapagliflozin had no effect on LVESV or any other parameter of LV remodeling. However, it reduced diastolic blood pressure and loop diuretic requirements while increasing hemoglobin, hematocrit, and ketone bodies. There was a trend toward lower weight.

CONCLUSIONS

We were unable to determine with certainty whether dapagliflozin in patients with T2DM and HF had any effect on LV remodeling. Whether the benefits of dapagliflozin in HF are due to remodeling or other mechanisms remains unknown.




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K-12 Instructional Models for English Learners: What They Are and Why They Matter

Marking the release of an MPI brief, experts on this webinar examine the key features of English Learner (EL) instructional models and discuss state- and district-level approaches to supporting schools in implementing effective EL program models, with a particular focus on what is being done in New York and Madison Wisconsin. 




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Diapression: An Integrated Model for Understanding the Experience of Individuals With Co-Occurring Diabetes and Depression

Paul Ciechanowski
Apr 1, 2011; 29:43-49
Feature Articles




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'Pay for Success' Funding Model Focus of Policy Toolkit

The Urban Institute released a toolkit aimed at policymakers and investors interested in using private dollars to pay for public programs, such as prekindergarten.




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Source Murray Model - method for determining permitted take in the Victorian Murray, Kiewa and Ovens SDL resource units.




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Modelling carp biomass : estimates for the year 2023 / Charles R. Todd, John D. Koehn, Tim R. Brown, Ben Fanson, Shane Brooks and Ivor Stuart.