Synthesis of Homo- and Heteromultivalent Fucosylated and Sialylated Oligosaccharide Conjugates via Preactivated N-Methyloxyamine Precision Macromolecules and Their Binding to Polyomavirus Capsid Proteins.

Glycoconjugates are a versatile class of bioactive molecules that have found application as vaccines and antivirals and in cancer therapy. Their synthesis typically involves elaborate functionalization and use of protecting groups on the carbohydrate component in order to ensure efficient and selective conjugation. Alternatively, non-functionalized, non-protected carbohydrates isolated from biological sources or derived through biotechnological methods can be directly conjugated via N-methyloxyamine groups. In this study, we introduce such N-methyloxyamine groups into a variety of multivalent scaffolds─from small to oligomeric to polymeric scaffolds─making use of solid-phase polymer synthesis to assemble monodisperse sequence-defined macromolecules. These scaffolds are then successfully functionalized with different types of human milk oligosaccharides deriving a library of homo- and heteromultivalent glycoconjugates. Glycomacromolecules presenting oligosaccharide side chains with either α2,3- or α2,6-linked terminal sialic acid are used in a binding study with two types of polyomavirus capsid proteins showing that the multivalent presentation through the N-methyloxyamine-derived scaffolds increases the number of contacts with the protein. Overall, a straightforward route to derive glycoconjugates from complex oligosaccharides with high variability yet control in the multivalent scaffold is presented, and applicability of the derived structures is demonstrated.

Application of Deep Learning Methods for Binarization of the Choroid in Optical Coherence Tomography Images.

PURPOSE: The purpose of this study was to develop a deep learning model for automatic binarization of the choroidal tissue, separating choroidal blood vessels from nonvascular stromal tissue, in optical coherence tomography (OCT) images from healthy young subjects. METHODS: OCT images from an observational longitudinal study of 100 children were used for training, validation, and testing of 5 fully semantic networks, which provided a binarized output of the choroid. These outputs were compared with ground truth images, generated from a local binarization technique after manually optimizing the analysis window size for each individual image. The performance was evaluated using accuracy and repeatability metrics. The methods were also compared with a fixed window size local binarization technique, which has been commonly used previously. RESULTS: The tested deep learning methods provided a good performance in terms of accuracy and repeatability. With the U-Net and SegNet networks showing >96% accuracy. All methods displayed a high level of repeatability relative to the ground truth. For analysis of the choroidal vascularity index (a commonly used metric derived from the binarized image), SegNet showed the closest agreement with the ground truth and high repeatability. The fixed window size showed a reduced accuracy compared to other methods. CONCLUSIONS: Fully semantic networks such as U-Net and SegNet displayed excellent performance for the binarization task. These methods provide a useful approach for clinical and research applications of deep learning tools for the binarization of the choroid in OCT images. TRANSLATIONAL RELEVANCE: Deep learning models provide a novel, robust solution to automatically binarize the choroidal tissue in OCT images.

Parent-reported prevalence and persistence of 19 common child health conditions.

OBJECTIVE: To estimate prevalence and persistence of 19 common paediatric conditions from infancy to 14-15 years. DESIGN: Population-based prospective cohort study. SETTING: Australia. PARTICIPANTS: Parallel cohorts assessed biennially from 2004 to 2014 from ages 0-1 and 4-5 years to 10-11 and 14-15 years, respectively, in the Longitudinal Study of Australian Children. MAIN OUTCOME MEASURES: 19 health conditions: 17 parent-reported, 2 (overweight/obesity, obesity) directly assessed. Two general measures: health status, special health care needs. ANALYSIS: (1) prevalence estimated in 2-year age-bands and (2) persistence rates calculated at each subsequent time point for each condition among affected children. RESULTS: 10 090 children participated in Wave 1 and 6717 in all waves. From age 2, more than 60% of children were experiencing at least one health condition at any age. Distinct prevalence patterns by age-bands comprised eight conditions that steadily rose (overweight/obesity, obesity, injury, anxiety/depression, frequent headaches, abdominal pain, autism spectrum disorder, attention-deficit hyperactivity disorder). Six conditions fell with age (eczema, sleep problems, day-wetting, soiling, constipation, recurrent tonsillitis), three remained stable (asthma, diabetes, epilepsy) and two peaked in mid-childhood (dental decay, recurrent ear infections). Conditions were more likely to persist if present for 2 years; persistence was especially high for obesity beyond 6-7 (91.3%-95.1% persisting at 14-15). CONCLUSIONS: Beyond infancy, most Australian children are experiencing at least one ongoing health condition at any given time. This study's age-specific estimates of prevalence and persistence should assist families and clinicians to plan care. Conditions showing little resolution (obesity, asthma, attention-deficit hyperactivity disorder) require long-term planning and management.

Early-life exposures predicting onset and resolution of childhood overweight or obesity.

OBJECTIVES: To determine which of multiple early-life exposures predict onset or resolution of overweight/obesity during a 9-year period. METHODS: Design: longitudinal cohort from three harmonised community-based cohorts enriched for overweight and obesity. Early-life exposures: child-gestational age; delivery; birth weight; breast feeding; solids introduction; baseline body mass index (BMI); waist circumference; diet; activity; global, physical and psychosocial health. Mother-baseline BMI; education; age; neighbourhood disadvantage; concern for child's weight. Outcome: change in BMI category. Analyses: adjusted logistic regression. RESULTS: On average, the 363 children (57% retention) were 6 and 15 years old at baseline and follow-up. Children were classified as 'never' overweight/obese (38%), 'resolving' overweight/obese (15%), 'becoming' overweight/obese (8%) or 'always' overweight/obese (39%). Compared with 'never overweight/obese' children, odds of 'becoming overweight/obese' were greater with higher child (OR 2.33, 95% CI 1.02 to 5.29) and maternal BMI (OR 1.18, CI 1.07 to 1.31), and lower with higher maternal education (OR 0.09, CI 0.02 to 0.34). Compared with 'always overweight/obese' children, odds of 'resolving overweight/obese' were lower with higher maternal BMI (OR 0.87, CI 0.78 to 0.97), and higher with better child physical health (OR 1.06, CI 1.02 to 1.10) and higher maternal age (OR 1.11, CI 1.01 to 1.22) and education (OR 4.07, CI 1.02 to 16.19). CONCLUSIONS: Readily available baseline information (child/maternal BMI, maternal age, education and child health) were the strongest predictors of both onset and resolution of overweight/obesity between the primary school and adolescent years. Perinatal, breastfeeding and lifestyle exposures were not strongly predictive. Results could stimulate development of algorithms identifying children most in need of targeted prevention or treatment.

Prediction of dissolution time and coating thickness of sustained release formulations using Raman spectroscopy and terahertz pulsed imaging.

Raman spectroscopy was implemented successfully as a non-invasive and rapid process analytical technology (PAT) tool for in-line quantitative monitoring of functional coating. Coating experiments were performed at which diprophylline tablets were coated with a sustained release formulation based on Kollicoat SR 30 D. Using PLS a multivariate model was constructed by correlating Raman spectral data with the mean dissolution time as determined by dissolution testing and the coating thickness as measured by terahertz pulsed imaging. By performing in-line measurements it was possible to monitor the progress of the coating process and to detect the end point of the process, where the acquired coating amount was achieved for the desired MDT or coating thickness.

From Mini to Micro Scale-Feasibility of Raman Spectroscopy as a Process Analytical Tool (PAT).

BACKGROUND: Active coating is an important unit operation in the pharmaceutical industry. The quality, stability, safety and performance of the final product largely depend on the amount and uniformity of coating applied. Active coating is challenging regarding the total amount of coating and its uniformity. Consequently, there is a strong demand for tools, which are able to monitor and determine the endpoint of a coating operation. In previous work, it was shown that Raman spectroscopy is an appropriate process analytical tool (PAT) to monitor an active spray coating process in a pan coater [1]. Using a multivariate model (Partial Least Squares-PLS) the Raman spectral data could be correlated with the coated amount of the API diprophylline. While the multivariate model was shown to be valid for the process in a mini scale pan coater (batch size: 3.5 kg cores), the aim of the present work was to prove the robustness of the model by transferring the results to tablets coated in a micro scale pan coater (0.5 kg). METHOD: Coating experiments were performed in both, a mini scale and a micro scale pan coater. The model drug diprophylline was coated on placebo tablets. The multivariate model, established for the process in the mini scale pan coater, was applied to the Raman measurements of tablets coated in the micro scale coater for six different coating levels. Then, the amount of coating, which was predicted by the model, was compared with reference measurements using UV spectroscopy. RESULTS: For all six coating levels the predicted coating amount was equal to the amounts obtained by UV spectroscopy within the statistical error. Thus, it was possible to predict the total coating amount with an error smaller than 3.6%. The root mean squares of errors for calibration and prediction (root mean square of errors for calibration and prediction-RMSEC and RMSEP) were 0.335 mg and 0.392 mg, respectively, which means that the predictive power of the model is not dependent on the scale or the equipment. CONCLUSION: The scale-down experiment showed that it was possible to transfer the PLS model developed on a mini scale coater to a micro scale coater.

Validation of Raman spectroscopic procedures in agreement with ICH guideline Q2 with considering the transfer to real time monitoring of an active coating process.

A multivariate model was constructed by correlating Raman spectral data with coated amount of the API diprophylline using Partial Least Squares. In agreement with ICH guideline Q2 the method was validated in order to achieve the requirement of demonstrating that Raman spectroscopy is suitable as rapid PAT tool for inline quantitative monitoring of active coating. The present work presents an appropriate approach to transfer the requirements of the guidelines to the Raman method used for inline measurements and demonstrates that the requirements of the validation characteristics were achieved.

Feasibility of Raman spectroscopy as PAT tool in active coating.

BACKGROUND: Active coating is a specific application of film coating where the active ingredient is comprised in the coating layer. This implementation is a challenging operation regarding the achievement of desired amount of coating and coating uniformity. To guarantee the quality of such dosage forms it is desirable to develop a tool that is able to monitor the coating operation and detect the end of the process. METHOD: Coating experiments were performed at which the model drug diprophylline is coated in a pan coater on placebo tablets and tablets containing the active ingredient itself. During the active coating Raman spectra were recorded in-line. The spectral measurements were correlated with the average weight gain and the amount of coated active ingredient at each time point. The developed chemometric model was tested by monitoring further coated batches. Furthermore, the effects of pan rotation speed and working distance on the acquired Raman signal and, hence, resulting effect of the chemometric model were examined. RESULTS: Besides coating on placebo cores it was possible to determine the amount of active ingredient in the film when coated onto cores containing the same active ingredient. In addition, the method is even applicable when varying the process parameters and measurement conditions within a restricted range. CONCLUSION: Raman spectroscopy is an appropriate process analytical technology too.