Characterizing the pH-dependent release kinetics of food-grade spray drying encapsulated iron microcapsules

“This work was carried out with the aid of a grant from Canada’s International Development Research Centre (IDRC), and with financial support from the Government of Canada, provided through Global Affairs Canada (GAC).”This detailed study models the release behavior of reverse enteric spray-dried microcapsules under different pH conditions, to better understand the release of iron fortificants. Spray drying is one of the mechanisms used for microencapsulating water-soluble iron salts with desired coating materials. Food fortification is an inexpensive and effective method to increase the intake of iron without compromising dietary customs. Of three models, the Eudragit coating, due to higher solid content, can handle a higher amount of iron payload. The new methodology developed for studying iron release of microencapsulated microparticles could be used in future applications in food fortification research

Labelling imaging datasets on the basis of neuroradiology reports: a validation study

Natural language processing (NLP) shows promise as a means to automate the labelling of hospital-scale neuroradiology magnetic resonance imaging (MRI) datasets for computer vision applications. To date, however, there has been no thorough investigation into the validity of this approach, including determining the accuracy of report labels compared to image labels as well as examining the performance of non-specialist labellers. In this work, we draw on the experience of a team of neuroradiologists who labelled over 5000 MRI neuroradiology reports as part of a project to build a dedicated deep learning-based neuroradiology report classifier. We show that, in our experience, assigning binary labels (i.e. normal vs abnormal) to images from reports alone is highly accurate. In contrast to the binary labels, however, the accuracy of more granular labelling is dependent on the category, and we highlight reasons for this discrepancy. We also show that downstream model performance is reduced when labelling of training reports is performed by a non-specialist. To allow other researchers to accelerate their research, we make our refined abnormality definitions and labelling rules available, as well as our easy-to-use radiology report labelling app which helps streamline this process

Spray Encapsulation of Iron in Chitosan Biopolymer for Tea Fortification

Aim. Tea was studied as a carrier for iron in a fortification strategy to reduce iron deficiency. Iron forms insoluble coloured complexes with tea polyphenols which lower consumer acceptability. Complexation of iron by polyphenols and quinones derived from tea inhibits iron absorption in the first segment of the small intestine. Spray-dried chitosan-iron microcapsules were prepared to prevent iron-polyphenol interaction before the beverage is consumed. A competing chelating agent (EDTA) or antioxidant (sodium ascorbate) was added to prevent interactions and help improve iron bioavailability. Methods. The effect of concentration of chitosan (0.2–1.5%w/w), iron loading (10–60% w/w FeSO4), addition of secondary coatings on particle morphology, surface iron exposure and release, and bioaccessibility were evaluated. Tea-containing chitosan microcapsules and chelating agents to enable iron absorption were evaluated for sensory acceptability. Results. The iron release profile at pH 1 and pH 7 exhibited reverse enteric behaviour of non-cross-linked chitosan microcapsules. Increasing the iron content leads to more iron exposure on the surface due to a high core to coat ratio. Cross-linked chitosan effectively encapsulated iron, and its release in tea was inhibited, as indicated by lower delta E values in comparison with untreated tea and positive sensory testing scores. The use of maltodextrin as secondary coating slightly improved the spray process and produced larger particles, with less exposed iron on the surface. However, it did not improve the colour performance in milk tea. Conclusions. Tea fortified with encapsulated iron and a chelating agent providing 40% of the daily iron requirement of an adult, prepared in a traditional South Asian manner, i.e., with milk and water, resulted in tea with acceptable colour and taste. However, further research is required to develop an encapsulation formulation for stable iron encapsulation in hot tea and exploration of equivalent plant-based chitosan sources to address concerns of consumers with dietary restrictions

Activating glucokinase (GCK) mutations as a cause of medically responsive congenital hyperinsulinism: prevalence in children and characterisation of a novel GCK mutation

OBJECTIVE: Activating glucokinase (GCK) mutations are a rarely reported cause of congenital hyperinsulinism (CHI), but the prevalence of GCK mutations is not known. METHODS: From a pooled cohort of 201 non-syndromic children with CHI from three European referral centres (Denmark, n=141; Norway, n=26; UK, n=34), 108 children had no K(ATP)-channel (ABCC8/KCNJ11) gene abnormalities and were screened for GCK mutations. Novel GCK mutations were kinetically characterised. RESULTS: In five patients, four heterozygous GCK mutations (S64Y, T65I, W99R and A456V) were identified, out of which S64Y was novel. Two of the mutations arose de novo, three were dominantly inherited. All the five patients were medically responsive. In the combined Danish and Norwegian cohort, the prevalence of GCK-CHI was estimated to be 1.2% (2/167, 95% confidence interval (CI) 0-2.8%) of all the CHI patients. In the three centre combined cohort of 72 medically responsive children without K(ATP)-channel mutations, the prevalence estimate was 6.9% (5/72, 95% CI 1.1-12.8%). All activating GCK mutations mapped to the allosteric activator site. The novel S64Y mutation resulted in an increased affinity for the substrate glucose (S(0.5) 1.49+/-0.08 and 7.39+/-0.05 mmol/l in mutant and wild-type proteins respectively), extrapolating to a relative activity index of approximately 22 compared with the wild type. CONCLUSION: In the largest study performed to date on GCK in children with CHI, GCK mutations were found only in medically responsive children who were negative for ABCC8 and KCNJ11 mutations. The estimated prevalence (approximately 7%) suggests that screening for activating GCK mutations is warranted in those patients

Factors affecting the labelling accuracy of brain MRI studies relevant for deep learning abnormality detection

Unlocking the vast potential of deep learning-based computer vision classification systems necessitates large data sets for model training. Natural Language Processing (NLP)—involving automation of dataset labelling—represents a potential avenue to achieve this. However, many aspects of NLP for dataset labelling remain unvalidated. Expert radiologists manually labelled over 5,000 MRI head reports in order to develop a deep learning-based neuroradiology NLP report classifier. Our results demonstrate that binary labels (normal vs. abnormal) showed high rates of accuracy, even when only two MRI sequences (T2-weighted and those based on diffusion weighted imaging) were employed as opposed to all sequences in an examination. Meanwhile, the accuracy of more specific labelling for multiple disease categories was variable and dependent on the category. Finally, resultant model performance was shown to be dependent on the expertise of the original labeller, with worse performance seen with non-expert vs. expert labellers

Automated Labelling using an Attention model for Radiology reports of MRI scans (ALARM)

Labelling large datasets for training high-capacity neural networks is a major obstacle to the development of deep learning-based medical imaging applications. Here we present a transformer-based network for magnetic resonance imaging (MRI) radiology report classification which automates this task by assigning image labels on the basis of free-text expert radiology reports. Our model's performance is comparable to that of an expert radiologist, and better than that of an expert physician, demonstrating the feasibility of this approach. We make code available online for researchers to label their own MRI datasets for medical imaging applications

Deep learning to automate the labelling of head MRI datasets for computer vision applications

OBJECTIVES: The purpose of this study was to build a deep learning model to derive labels from neuroradiology reports and assign these to the corresponding examinations, overcoming a bottleneck to computer vision model development. METHODS: Reference-standard labels were generated by a team of neuroradiologists for model training and evaluation. Three thousand examinations were labelled for the presence or absence of any abnormality by manually scrutinising the corresponding radiology reports (‘reference-standard report labels’); a subset of these examinations (n = 250) were assigned ‘reference-standard image labels’ by interrogating the actual images. Separately, 2000 reports were labelled for the presence or absence of 7 specialised categories of abnormality (acute stroke, mass, atrophy, vascular abnormality, small vessel disease, white matter inflammation, encephalomalacia), with a subset of these examinations (n = 700) also assigned reference-standard image labels. A deep learning model was trained using labelled reports and validated in two ways: comparing predicted labels to (i) reference-standard report labels and (ii) reference-standard image labels. The area under the receiver operating characteristic curve (AUC-ROC) was used to quantify model performance. Accuracy, sensitivity, specificity, and F1 score were also calculated. RESULTS: Accurate classification (AUC-ROC > 0.95) was achieved for all categories when tested against reference-standard report labels. A drop in performance (ΔAUC-ROC > 0.02) was seen for three categories (atrophy, encephalomalacia, vascular) when tested against reference-standard image labels, highlighting discrepancies in the original reports. Once trained, the model assigned labels to 121,556 examinations in under 30 min. CONCLUSIONS: Our model accurately classifies head MRI examinations, enabling automated dataset labelling for downstream computer vision applications. KEY POINTS: • Deep learning is poised to revolutionise image recognition tasks in radiology; however, a barrier to clinical adoption is the difficulty of obtaining large labelled datasets for model training. • We demonstrate a deep learning model which can derive labels from neuroradiology reports and assign these to the corresponding examinations at scale, facilitating the development of downstream computer vision models. • We rigorously tested our model by comparing labels predicted on the basis of neuroradiology reports with two sets of reference-standard labels: (1) labels derived by manually scrutinising each radiology report and (2) labels derived by interrogating the actual images. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s00330-021-08132-0

Outcome study of the Pipeline Vantage Embolization Device (second version) in unruptured (and ruptured) aneurysms (PEDVU(R) study)

Background The Pipeline Vantage Embolization Device (PEDV) is the fourth-generation pipeline flow diverter for intracranial aneurysm treatment. There are no outcome studies for the second PEDV version. We aimed to evaluate safety and efficacy outcomes. Primary and secondary objectives were to determine outcomes for unruptured and ruptured cohorts, respectively.Methods In this multicenter retrospective and prospective study, we analyzed outcome data from eight centers using core laboratory assessments. We determined 30-day and ≥3-month mortality and morbidity rates, and 6- and 18-month radiographic aneurysm occlusion rates for procedures performed during the period July 2021–March 2023.Results We included 121 consecutive patients with 131 aneurysms. The adequate occlusion rate for the unruptured cohort at short-term and medium-term follow up, and also for the ruptured cohort at short-term follow up, was >90%. Two aneurysms (1.5%) underwent retreatment. When mortality attributed to a palliative case in the unruptured cohort, or subarachnoid hemorrhage in the ruptured cohort, was excluded then the overall major adverse event rate in respective cohorts was 7.5% and 23.5%, with 0% mortality rates for each. When all event causes were included on an intention-to-treat basis, the major adverse event rates in respective cohorts were 8.3% and 40.9%, with 0.9% and 22.7% mortality rates.Conclusions For unruptured aneurysm treatment, the second PEDV version appears to have a superior efficacy and similar safety profile to previous-generation PEDs. These are acceptable outcomes in this pragmatic and non-industry-sponsored study. Analysis of ruptured aneurysm outcomes is limited by cohort size. Further prospective studies, particularly for ruptured aneurysms, are needed