Dual-angle interferometric scattering microscopy for optical multiparametric particle characterization

Traditional single-nanoparticle sizing using optical microscopy techniques assesses size via the diffusion constant, which requires suspended particles in a medium of known viscosity. However, these assumptions are typically not fulfilled in complex natural sample environments. Here, we introduce dual-angle interferometric scattering microscopy (DAISY), enabling optical quantification of both size and polarizability of individual nanoparticles without requiring a priori information regarding the surrounding media or super-resolution imaging. DAISY achieves this by combining the information contained in concurrently measured forward and backward scattering images through twilight off-axis holography and interferometric scattering (iSCAT). Going beyond particle size and polarizability, single-particle morphology can be deduced from the fact that hydrodynamic radius relates to the outer particle radius while the scattering-based size estimate depends on the internal mass distribution of the particles. We demonstrate this by optically differentiating biomolecular fractal aggregates from spherical particles in fetal bovine serum at the single particle level

Convolutional neural networks for semantic segmentation of FIB-SEM volumetric image data

Focused ion beam scanning electron microscopy (FIB-SEM) is a well-established microscopy technique for 3D imaging of porous materials. We investigate three porous samples of ethyl cellulose microporous films made from ethyl cellulose and hydroxypropyl cellulose (EC/HPC) polymer blends. These types of polymer blends are used as coating materials on various pharmaceutical tablets or pellets and form a continuous network of pores in the film. Understanding the microstructures of these porous networks allow for controlling drug release. We perform semantic segmentation of the image data, separating the solid parts of the material from the pores to accurately quantify the microstructures in terms of porosity. Segmentation of FIB-SEM data is complicated because in each 2D slice there is 2.5D information, due to parts of deeper underlying cross-sections shining through in porous areas. The supposed shine-through effect greatly complicates the segmentation in regards to two factors; uncertainty in the positioning of the microstructural features and overlapping grayscale intensities between pore and solid regions. In this work, we explore different convolutional neural networks (CNNs) for pixelwise classification of FIB-SEM data, where the class of each pixel is predicted using a three-dimensional neighborhood of size (nx; ny; nz). In total, we investigate six types of CNN architectures with different hyperparameters, dimensionalities, and inputs. For assessing the classification performance we consider the mean intersection over union (mIoU), also called Jaccard index. All the investigated CNNs are well suited to the problem and perform good segmentations of the FIB-SEM data. The so-called standard 2DCNN performs the best overall followed by different varieties of 2D and 3D CNN architectures. The best performing models utilize larger neighborhoods, and there is a clear trend that larger neighborhoods boost performance. Our proposed method improves results on all metrics by 1.35 - 3.14 % compared to a previously developed method for the same data using Gaussian scale-space features and a random forest classifier. The porosities for the three HPC samples are estimated to 20.34, 33.51, and 45.75 %, which is in close agreement with the expected porosities of 22, 30, and 45 %. Interesting future work would be to let multiple experts segment the same image to obtain more accurate ground truths, to investigate loss functions that better correlate with the porosity, and to consider other neighborhood sizes. Ensemble learning methods could potentially boost results even further, by utilizing multiple CNNs and/or other machine learning models together

Does vitamin D supplementation have a favorable effect on totalcholesterol, HDL cholesterol or LDL cholesterol in adults with type 2 diabetes?

Titel: Har vitamin D-supplementering gynnsam effekt på total-, HDLeller LDL-kolesterol hos vuxna med typ 2 diabetes? Författare: Sofie Bolwede och Fredrik Skärberg Handledare: Frode Slinde Examinator: Anna Winkvist Linje: Dietistprogrammet, 180/240 hp Typ av arbete: Självständigt arbete i klinisk nutrition, 15 hp Datum: 2016-05-26 Bakgrund: Diabetes mellitus är en av de största folksjukdomarna och är starkt associerat med ökad risk för hjärt-kärlsjukdomar. Ett flertal observationsstudier, prospektiva meta-analyser och interventionsstudier har granskat länken mellan vitamin D-brist och risken till att utveckla hjärt-kärlsjukdomar och dess riskmarkörer. I dagens läge finns det teorier om hur vitamin D skulle kunna påverka serumlipider, både direkt och indirekt genom serum-PTH och/eller kalciumbalansen. Syfte: Syftet med denna systematiska översiktsartikel var att sammanställa evidensen om vitamin D-supplementerings effekt på utvalda serumlipider hos vuxna med typ 2 diabetes. Sökväg: Litteratursökningen gjordes i PubMed och Scopus med synonymer och MeSHtermer för sökorden kolesterol, vitamin D-supplementering och diabetes där artiklarna först bedömdes på titelnivå och därefter på abstractnivå. Efter litteratursökningen gjordes även så kallad snowballing där två till artiklar inkluderades. Urvalskriterier: Inklusionskriterierna på studiedesignen var RCT-studier gjorda på människor skrivna på engelska och svenska. Studiepopulationer som inkluderades var vuxna kvinnor och män ≥ 18 år diagnostiserade med typ 2 diabetes. Interventionen fick endast vara vitamin Dsupplementering via oral tablett. Studier som inkluderade viktnedgång exkluderades. Datainsamling och analys: För kvalitetsgranskningen användes “SBUs kvalitetsgranskningsmall för randomiserade studier”. Evidensgraderingen gjordes med hjälp av Göteborgs universitets “Underlag för sammanvägd bedömning enligt GRADE”. Resultat: Endast en av sex artiklar visade en signifikant minskning av totalkolesterol med vitamin D-supplementering. De resterande fem studierna visade ingen signifikant effekt på totalkolesterol, HDL eller LDL. Slutsats: Denna systematiska översiktsartikel visar måttlig evidens(+++) för att det inte finns någon gynnsam effekt av vitamin D-supplementering på total-, HDL- eller LDL-kolesterol hos vuxna med typ 2 diabetes. Nyckelord: kolesterol, HDL, LDL, blodlipider, serumlipider, vitamin D, calcitriol, cholecalciferol, vitamin D3, kostsupplement, diabetesTitle: Does vitamin D supplementation have a favorable effect on totalcholesterol, HDL cholesterol or LDL cholesterol in adults with type 2 diabetes? Author: Sofie Bolwede and Fredrik Skärberg Supervisor: Frode Slinde Examiner: Anna Winkvist Programme: Programme in dietetics, 180/240 ECTS Type of paper: Bachelor’s thesis in clinical nutrition, 15 hp Date: May 26, 2016 Background: Diabetes mellitus is one of the greatest public health diseases and is strongly associated with increased risk of cardiovascular diseases. A number of observational studies, prospective meta-analysis and interventional studies have examined the link between vitamin D deficiency and the risk of developing cardiovascular diseases and its risk markers. Nowadays there are theories of how vitamin D could affect serum lipid levels, both directly and indirectly through its effect on serum PTH and/or the calcium balance. Objective: The aim of this systematic review was to compile the evidence of the effect of vitamin D supplementations on serum lipids in adults with type 2 diabetes. Search strategy: The literature search was done in PubMed and Scopus with synonyms and MeSH-terms for search words for cholesterol, vitamin D supplementation and diabetes where the articles were assessed by their title and their abstract. All the references of the included articles were checked, using so called snowballing, and two more articles were included. Selection criteria: The inclusion criterion on study design was RCT made on humans written in English or Swedish. Study populations which were included were those that included males or females ≥ 18 years diagnosed with type 2 diabetes. The intervention had to be vitamin D supplementation via oral tablet. Studies that included weight loss were excluded. Data collection and analysis: “SBUs kvalitetsgranskning för randomiserade studier” was used for the quality examination. “Underlag för sammanvägd bedömning enligt GRADE” by the University of Gothenburg was used for the evidence examination. Main results: Only one of the six articles showed a significant reduction in total cholesterol with vitamin D supplementation. The remaining five articles did not show any significant effect on total cholesterol, HDL or LDL. Conclusions: This systematic review shows moderate evidence(+++) for that there is no favorable effects of vitamin D supplementation on total cholesterol, HDL or LDL in adults with type 2 diabetes. Keywords: cholesterol, HDL, LDL, blood lipids, serum lipids, vitamin D, calcitriol, cholecalciferol, vitamin D3, dietary supplement, diabete

Maskininlärning för diagnosticering av perifer neuropati

This report investigates the possibility of diagnosing peripheral neuropathy with the help of non-parametic classification methods. Peripheral neuropathy is a disease state characterized by damage on the nerves furthest out in the nervous system, with symptoms first occuring in the feet. The data used in this project comes from Dr. William Kennedys research group at University of Minnesota. The data contains 401 observations of 120 healthy controls and 65 individuals with presumed peripheral neuropathy due to chemotherapy, (where 18 individuals have been confirmed having peripheral neuropathy through other examination procedures). The data is collected with a dynamic sweat test, a new diagnostic method to discover unusual sweating patterns and therefore also peripheral neuropathy. In this project we compare three different machine learning methods to classify subjects as sick (peripheral neuropathy) and healthy (no peripheral neuropathy): k-NN, random forest and neural networks. These methods differ in their complexity, all with their disadvantages and advantages. To evauluate which classification method that works the best a cross-validation was performed, with a modified version of Cohen’s kappa. How good these classification methods perform depends on which measuring area the data comes from, either foot, calf or foot and calf combined. The best classification method was shown to be random forest, this for the calf-measurements where the covarariates are chosen by backward stepwise selection. This method correctly classifies 67% of the sick individuals and 96% of the healthy controls. With the best model trained on foot-measurements most undetermined sick individuals are being classified as sick, while for the best model trained on calf-measurement most of the undetermined sick individuals are classified as healthy. This could hint towards that the symptoms of peripheral neuropathy first appears in the feet, something that is in line with the clinical reality