1,721,092 research outputs found
An Adapted Convolutional Neural Network for Automatic Measurement of Pancreatic Fat and Pancreatic Volume in Clinical Multi-Protocol Magnetic Resonance Images: A Retrospective Study With Multi-Ethnic External Validation of Various Fat Deposition
No full textAnthropometric indices, such as body mass index (BMI), waist circumference (WC), and waist to height ratio (WHtR) have limitations in accurately predict the pathophysiology of diabetes mellitus, cardiovascular diseases, and metabolic syndrome due to ethnic differences in fat distribution. Recent studies showed that the visceral adipose tissue (VAT) deposition and fat content of internal organs, most notably intra-hepatic and intra-pancreatic fat, has emerged as a more important parameter. Measurement of fat fraction is now regarded as a challenge in clinical settings. Magnetic Resonance Imaging (MRI) based quantification of fat fraction requires highly accurate data reconstruction for the assessment of hepatic and pancreatic fat accumulation in medical diagnostics and biomedical research. So, automated pancreas segmentation and accurate fat content determination from medical images are important for clinical and research applications, including type 2 diabetes risk prediction.
In this study, we tried to assess the coordination between the traditional anthropometric indices and the various fat depositions within different ethnicities in New Zealand. We further established the signal model of oil and water emulsion used for phantom study with a field strength of 3.0 T. Finally, we modified a novel convolutional neural network (CNN) for pancreatic volume and fat fraction segmentation in magnetic resonance imaging (MRI) scans.
We used 104 previous participants with different ethnic backgrounds, including New Zealand European, Māori (the indigenous people of New Zealand), Pacific Islanders (PI), and Asian. Their weight, height, and waist circumference (WC) were measured, and subcutaneous, visceral, intra-hepatic, and intra-pancreatic fat depositions were obtained using magnetic resonance imaging (MRI). The results showed VAT depositions, but not subcutaneous adipose tissue (SAT) depositions, varied significantly at all levels among the three groups. BMI was best associated with L23SAT in NZ Europeans (30%) and L45VAT in Māori/PI (24.3%). Overall, WC and WHtR were correlated well with L45SAT (18.8% and 12.2% respectively). Intra-pancreatic fat deposition had positive Pearson relation with NZ European’s BMI and Māori/PI’s WC, but no regression correlation with anthropometric indices. Conventional anthropometric indices do not correspond to the same fat depositions across different ethnic groups. To accomplish the phantom study for machine learning, we used fat fraction quantification from phantom as a standard gradient to compare hepatic fat fraction and intrapancreatic fat fraction quantification with both algorithm of magnetic resonance spectroscopy (MRS) and Iterative Decomposition with Echo Asymmetry and Least-squares estimation (IDEAL) in MRI. We also compared MRS and IDEAL pancreatic fat fraction quantification with expert manual pancreatic fat measurement. We noticed a strong correlation between true fat volume fraction and the fat fractions from both IDEAL (R2=0.99) and MRS (R2=0.99). Linear correlation and Pearson’s correlation were applied to both the phantom and in vivo measurements. The results of in vivo measurement demonstrated a good correlation between MRI measurements of hepatic fat fractions, but varied for the pancreatic fat fraction. We also observed that the manual operation performed better than IDEAL and MRS pancreatic fat fractions reading, which helped with the further establishment of auto pancreatic fat measurement by machine learning. In this retrospective, prognostic study, we conducted pancreatic boundary identification and fat fraction segmentation. Images were modified to allow CNN with improved super pixel pre-processing. The formal training and testing of the artificial intelligence was established on more than 3,000 abdominal MR images. Validation was then conducted on 200 images from 10 additional patients who were each scanned twice. Our algorithm achieved a dice similarity coefficient (DSC) of 91.2%. This is the first algorithm for automated pancreas volume and intra-pancreatic fat determination with > 90% DSC, which has the potential to be widely used for rapid and accurate pancreatic fat quantification in research and clinical settings when using abdominal MRI
Probiotics and Intermittent Fasting to Improve Prediabetes in Obese Participants
No full textNew Zealand has the third highest rate of obesity within OECD countries with 8% of the adult population presenting type 2 diabetes and 25% living with pre-diabetes. Obesity and type 2 diabetes significantly increase the risk of cardiovascular disease and cancer and are critical modifiable risk factors associated with these conditions. In this study we determined whether relative loss of fat from visceral (compared to subcutaneous fat) depots can be manipulated by probiotics among participants with obesity and pre-diabetes who restricted caloric intake through intermittent fasting (IF). A randomized double-blind, two parallel arm study was performed in Auckland, New Zealand. Of the total 33 participants who were randomized, 26 completed the study, of whom 22 had magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) data at baseline and 12 weeks after starting treatment. All participants commenced 12-week of intermittent fasting (IF) as 2 days per week of caloric restriction to 600-650 kcal/day (IF5:2) and were randomized 1:1 to either daily probiotic (Lacticaseibacillus rhamnosus HN001) or matching placebo during this period. subcutaneous adipose tissue (SCAT), visceral adipose tissue (VAT), liver fat (LF), and pancreatic fat (PF), as quantified through MRI/MRS, were compared in probiotic versus placebo groups. Changes in fat depots were also compared with changes in haemoglobin A1c (HbA1c) and body mass index (BMI.) Overall, the average weight loss ranged from 94.2 to 89.5 kg and was mild-positively correlated with percentage of VAT to SAT ratio (%V:S) (r = 0.215, (p = 0.392). There was a correlation with %V:S and improvements in HbA1c (r = -0.007, p = 0.973). There was no difference in %V:S between probiotic versus placebo groups at 12 week. Intermittent fasting resulted in a modest weight loss of 5% which, when combined with probiotics, is not able to better target visceral fat loss compared to placebo. There was a correlation between %V:S fat reduction and improvement in HbA1c in overweight people with pre-diabetes. Therefore, intermittent fasting is beneficial in reducing weight and improve biomarkers in obese pre-diabetic participants, however, the beneficial effects of probiotics need to be further investigated for proper conclusion
Bariatric Surgery and Type 2 Diabetes - Analysis of existing and development of new prediction models for post-surgery remission of type 2 diabetes
No full textFull Text is available to authenticated members of The University of Auckland only.Background: Type 2 diabetes (T2D) is a current global health issue that needs to be addressed as it causes premature morbidity and mortality. Importantly T2D is an expensive disease to medically manage which places a financial burden on the New Zealand healthcare system. Obesity is a key contributor to the increasing prevalence of T2D worldwide. Conversely, weight loss is linearly associated with the likelihood of T2D remission. Bariatric surgery provides effective and sustainable long term weight loss and diabetes remission. However, due to surgical expenses there is limited capacity to provide bariatric surgery in the public health system. Those with the greatest likelihood of remission of T2D are prioritised for cost-effectiveness. Several T2D remission prediction tools have been developed but these tools have yet to be tested in the New Zealand population. Improved prediction models for the NZ population are yet to be developed for short term (1-year) or long term (5-year) T2D remission post bariatric surgery. Aims: (1) To test the performance of existing T2D remission prediction tools in our New Zealand cohort and identify the most effective existing prediction tool. (2) To develop new prediction models to predict short term (1-year) or long term (5-year) T2D remission following Silastic Ring Laparoscopic Roux-en-Y Gastric Bypass (SR-LRYGB) or Laparoscopic Sleeve Gastrectomy (LSG). Methods: 114 participants aged 20-55-years with obesity (BMI 35-65kg/m2) and T2D who had been randomised to SR-LRYGB or LSG were followed up 1-year (100%) and 5-years post bariatric surgery (95%). T2D remission was defined as an HbA1c of <6% (42mmol/mol) without the need of any glucose-lowering medication, assessed at the 1-year and 5-year post-operative visits. A prediction score was manually calculated for each patient based on the scoring criteria of each existing prediction tool. Six prediction tools were tested: ABCD tool for RYGB (ABCDGBP), ABCD for SG (ABCDSG), DiaRem and Ad-DiaRem for RYGB and Metabolic Surgery Diabetes Remission (MDR) and DiaBetter tools for both RYGB and SG. Discrimination and calibration performance of each existing prediction tool was tested in our cohort using ROC curves to identify the area under the receiver operating characteristic curve (AUC) and calibration plots. The performance of each existing prediction tool at the literature identified cut-off score indicative of T2D remission was tested in our cohort by calculating sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and predictive accuracy. New cut off scores indicative of T2D remission in our cohort were identified from the ROC curves by maximising the Youden index. A new prediction model was developed for 1 and 5 year T2D remission by identifying the statistically significant predictors using univariate analysis followed by multiple logistic regression analysis. These were internally validated using bootstrap resampling methodology based on the AUC as the criterion for predictive accuracy. Calibration performance of the new prediction models were assessed through visual interpretation of the calibration plots.
Results: At 1-year post bariatric surgery, 71.9% of our cohort had T2D remission which decreased to 39.8% 5-years post bariatric surgery. AUC ranged from 0.69-0.88 and 0.76-0.82 for the six existing prediction tools applied to our cohort 1 and 5-years post bariatric surgery, respectively. The MDR tool (utilising pre-operative age, HOMA-B, diabetes duration, HbA1c) performed best at predicting T2D remission 1-year post bariatric surgery (AUC 0.88) compared to all other tools. In our cohort the MDR cut-off score of 5.4 had the highest Youden index making it the score with highest sensitivity and specificity for predicting T2D remission at both 1 and 5 years post bariatric surgery. This compares to the literature identified cut-off score of ≥4 for the MDR tool which had the highest predictive accuracy of 81.58% compared to the other tools for the prediction of 1-year T2D remission in our cohort. The DiaRem tool had the highest predictive accuracy of 75.47% at the literature identified cut-off score of <7 for predicting 5-year T2D remission compared to the other tools. The DiaBetter tool (utilising pre-operative HbA1c, diabetes duration and anti-diabetic medication) performed best at predicting T2D remission 5-years post bariatric surgery (AUC 0.82) compared to all other tools. In our cohort the DiaBetter cut off score of 5.5 had the highest Youden index making it the score with highest sensitivity plus specificity for predicting T2D remission at both 1 and 5 years post bariatric surgery. No existing cut-off score had been identified in the literature for the DiaBetter tool. Multiple logistic regression analysis demonstrated weight loss to be a statistically significant independent post-operative predictor of T2D remission 5-years post RYGB and SG in our cohort (p= 0.0006). Diabetes duration (p= 0.0024), insulin use (p= 0.0030) and HbA1c (p <0.0001) were statistically significant independent preoperative predictors of T2D remission 1-year post RYGB or SG. Only diabetes duration (p <0.0001) and HbA1c (p= 0.0315) were independent preoperative predictors of T2D remission 5-years post RYGB or SG. These independent preoperative predictors were used to build the New Zealand Diabetes Remission Model (NZ-DRM). The 1-year and 5 year NZ-DRM AUC was 0.90 and 0.81 respectively with an average optimism from internal validation of 0.032 and 0.035 (bootstrap-corrected predictive accuracy 0.86 and 0.78) respectively.
Conclusions: The NZ-DRM offers a model with comparable performance to the DiaBetter tool for prediction of long term (5-year) T2D remission in the New Zealand cohort
Bile Acids and FGF19 in the Remission of Type 2 Diabetes After Sleeve Gastrectomy and Gastric Bypass
No full textPrevalence of type 2 diabetes (T2DM) is increasing sharply. There is a direct relationship between obesity and T2DM. Currently, T2DM has no effective cure. However, there are several diabetes management such as pharmacotherapy, diet and bariatric surgery considered as an effective treatment, and bariatric surgery is considered to be the most effective. There are four types of bariatric surgeries including adjustable gastric banding, biliopancreatic diversion with/without duodenal switch, gastric bypass (GB) and sleeve gastrectomy (SG). GB and SG are the most common bariatric surgery procedures for the treatment of T2DM and obesity. To evaluate the effect of an intervention on the treatment of chronic diseases such as obesity and T2DM, clinical follow-up studies are essential. Biomarkers are currently used in basic and clinical research. Their roles as essential endpoints in clinical trials are accepted universally. In this study two candidate biomarkers, fibroblast growth factor19 (FGF19) and bile acids (BAs) were assumed to play contributory roles in the remission of T2DM after bariatric surgery. Therefore, the main aim of this study was to quantify fasting and postprandial BAs and their computed compositions, and fasting and postprandial FGF19, on the remission of T2DM a year after SG and GB. Also, body composition and diabetes indices were measured a year after SG and GB. SG and GB were compared in this study to investigate which one is superior regarding diabetes alleviation. Due to the very low concentration of BAs in human plasma, liquid chromatography tandem-mass spectrometry (LC-MS/MS) has been used as a reliable method to measure BAs. BAs in this study were measured by using LC-MS/MS method, and FGF19 were quantified by using sandwich ELISA assay. It was found that fasting and postprandial BAs and FGF19 significantly increased a year after either SG or GB. However, fasting and postprandial BAs and FGF19 were not significantly differed between SG and GB. According to the definition of diabetes remission, one-year after SG and GB, the remissions of diabetes were scored. In this study, 38% and 40% of patients who underwent SG and GB, respectively, achieved complete remission of T2DM. A year after bariatric surgery, patients were divided into two groups, remitted and non-remitted. Then, a comparison between the actual values and changes in different BA fractions (fasting and AUC0-60min) and FGF19 (fasting and prandial) were performed to see whether they significantly differed between remitted and non-remitted. Despite the increased level of FGF19 and BAs within the remitted group, they were not significantly different from those of the non-remitted group. Therefore, it is concluded that firstly, both SG and GB are equally effective on the remission of T2DM. Secondly, the increased level of BAs and FGF19 a year after SG and GB play a contributory role for the remission of T2DM, but they are not the main reason for diabetes remission after bariatric surgery
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Exploring the Role of the Inflammasome Pathway in Diabetic Retinopathy
No full textBackground: Diabetic retinopathy (DR) is the leading cause of vision loss among working-age adults worldwide, yet current treatments are inadequate to halt its progression. Studies suggest that dysregulated nucleotide-binding oligomerization domain (NOD)-like receptor protein 3 (NLRP3) inflammasome activation, which orchestrates inflammation in the innate immunity, is associated with DR. However, the impact of inflammasome activation on DR progression remains uncertain. Therefore, this thesis seeks to investigate the role of inflammasome activation in DR progression.
Methods: Expressions of inflammasome-associated proteins were characterised in human retina and vitreous of donors without diabetes and donors with diabetes without and with DR. Correlation between markers of activated inflammasome and DR severity as well as the relationship between systemic and ocular inflammasome activation were assessed in a systematic literature review (SLR). To evaluate the use of systemic inflammasome biomarkers in predicting DR progression, plasma inflammasome biomarker levels pre- and post-bariatric surgery were measured in patients who regressed, remained stable, or progressed in DR post-surgery. The role of interleukin (IL)-18, an inflammasome activation biomarker, in DR pathogenesis was further examined using a novel human organotypic retinal culture (HORC) model, where donor retinal explants were cultured in IL-18 without and with high glucose.
Results: The NLRP3 inflammasome is activated at the onset of DR, and a significant increase in vitreous IL-18 levels was found in donors with DR compared to normal controls. The SLR demonstrates a correlation between systemic and vitreous levels of IL-1β and IL-18 and DR progression. Plasma analysis of bariatric surgery patients suggests that the group with DR progression exhibited a greater relative increase in IL-18 and C-reactive protein (CRP) levels post-surgery compared to the regressed and stable groups. Using the HORC model, IL-18 in high glucose was able to prime, but not activate, the inflammasome in donor retinal explants.
Conclusion: NLRP3 inflammasome activation in the human retina occurs at early stages of DR, and that both increased systemic and intraocular inflammasome activation correlate with increased DR severity. Therefore, inhibiting inflammasome activation presents a therapeutic approach to stop DR progression. Furthermore, systemically elevated levels of CRP, a commonly measured systemic inflammation marker, can potentially be used to predict DR progression, suggesting the potential of a blood-based DR progression screening tool
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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