In recent years, digitalization and computer-based technologies have greatly revolutionized the field of bioinformatics. Advance research and development of computer-based programs have enhanced various DNA sequencing technologies. This advancement has significantly broadened our understanding of genomic evolution and has widely contributed to the application of clinical genomics. Cancer has been one of the major causes of death across the world. Cancer is mainly caused due to the damage or changes in DNA that affect the function of genes which contain a set of instructions that control various functions of cells. This damage in genes that maintain DNA repair mechanism may lead towards genome instability allowing rapid growth of cancer.   Microsatellite instability (MSI) is one such condition characterized due to genomic alteration leading towards the failure of DNA repair mechanism in cancerous cells. MSI is found in various types of cancer but is most often found in colorectal cancer, gastric cancer, and endometrial cancer. Hence, detection of this MSI can greatly contribute towards cancer therapies and enable to plan for the best treatment. This study mainly focuses on evaluating the performance of MSI calling algorithms using targeted sequencing methods.   The literature provides a detailed outline of various topics related to MSI detection. Moreover, different computational methods like MSIsensor, MSIsensor-ct, MSIsensor-pro, MSings, MiMSI, and MSIsensor2 were used in this study for the detection of MSI in selected samples are thoroughly discussed in the methodology section. Finally, the findings of this study conclude that the MSI calling algorithms mentioned above provide accurate detection of MSI in the chosen samples. Also, these algorithms enable us to determine the MSI status of the chosen samples more precisely

Driver sleepiness is a cause for crashes and it is estimated that 3.9 to 33 % of all crashes might be related to sleepiness at the wheel. It is desirable to get an objective measurement of driver sleepiness for reduced sensitivity to subjective variations. Using deep learning for classification of driver sleepiness could be a step toward this objective. In this master thesis, deep learning was used for investigating classification of electrophysiological data, electroencephalogram (EEG) and electrooculogram (EOG), from drivers into levels of sleepiness. The EOG reflects eye position and EEG reflects brain activity. Initially, the intention was to include electrocardiogram (ECG), which reflects heart activity, in the research but this data were later excluded. Both raw time series data and data transformed into time-frequency domain representations were fed into the developed neural networks and for comparison manually extracted features were used in a shallow neural network architecture. Investigation of using EOG and EEG data separately as input was performed as well as a combination as input. The data were labeled using the Karolinska Sleepiness Scale, and the scale was divided into two labels "fatigue" and "alert" for binary classification or in five labels for comparison of classification and regression. The effect of example length was investigated using 150 seconds, 60 seconds and 30 seconds data. Different variations of the main network architecture were used depending on the data representation and the best result was given when using a combination of a convolutional neural network (CNN) and a long short-term memory (LSTM) network with time distributed 150 seconds EOG data as input. The accuracy was in this case 80.4 % and the majority of both alert and fatigue epochs were classified correctly with 85.7 % and 66.7 % respectively. Using the optimal threshold from the created receiver operating characteristics (ROC) curve resulted in a more balanced classifier with 76.3 % correctly classified alert examples and 79.2 % correctly classified fatigue examples. The results from the EEG data, both in terms of accuracy and distribution of correctly classified examples, were shown to be less promising compared to EOG data. Combining EOG and EEG signals was shown to slightly increase the proportion of correctly classified fatigue examples. However, more promising results were obtained when balancing the classifier for solely EOG signals. The overall result from this project shows that there are patterns in the data connected to sleepiness that the neural network can find which makes further work on applying deep learning to the area of driver sleepiness interesting.

In order to analyse body composition of MRI (Magnetic Resonance Imaging) volumes, atlas-based segmentation is often used to retrieve information from specific organs or anatomical regions. The method behind this technique is to use an already segmented image volume, an atlas, to segment a target image volume by registering the volumes to each other. During this registration a deformation field will be calculated, which is applied to a segmented part of the atlas, resulting in the same anatomical segmentation in the target. The drawback with this method is that the quality of the segmentation is highly dependent on the similarity between the target and the atlas, which means that many atlases are needed to obtain good segmentation results in large sets of MRI volumes. One potential solution to overcome this problem is to create the deformation field between a target and an atlas as a sequence of small deformations between more similar bodies.  In this master thesis a new method for atlas-based segmentation has been developed, with the anticipation of obtaining good segmentation results regardless of the level of similarity between the target and the atlas. In order to do so, 4000 MRI volumes were used to create a manifold of human bodies, which represented a large variety of different body types. These MRI volumes were compared to each other and the calculated similarities were saved in matrices called similarity models. Three different similarity measures were used to create the models which resulted in three different versions of the model. In order to test the hypothesis of achieving good segmentation results when the deformation field was constructed as a sequence of small deformations, the similarity models were used to find the shortest path (the path with the least dissimilarity) between a target and an atlas in the manifold.  In order to evaluate the constructed similarity models, three MRI volumes were chosen as atlases and 100 MRI volumes were randomly picked to be used as targets. The shortest paths between these volumes were used to create the deformation fields as a sequence of small deformations. The created fields were then used to segment the anatomical regions ASAT (abdominal subcutaneous adipose tissue), LPT (left posterior thigh) and VAT (visceral adipose tissue). The segmentation performance was measured with Dice Index, where segmentations constructed at AMRA Medical AB were used as ground truth. In order to put the results in relation to another segmentation method, direct deformation fields between the targets and the atlases were also created and the segmentation results were compared to the ground truth with the Dice Index. Two different types of transformation methods, one non-parametric and one affine transformation, were used to create the deformation fields in this master thesis. The evaluation showed that good segmentation results can be achieved for the segmentation of VAT for one of the constructed similarity models. These results were obtained when a non-parametric registration method was used to create the deformation fields. In order to achieve similar results for an affine registration and to improve the segmentation of other anatomical regions, further investigations are needed.

In modern clinical practice, gamma camera is one of the most important imaging modalities for tumour diagnosis. The standard technique uses scintillator-based gamma cameras equipped with parallel-hole collimator to detect the planar position of γ photon interaction (scintillation). However, the positioning is of insufficient resolution and linearity for breast imaging. With the aim to improve spatial resolution and positioning linearity, a new gamma camera configuration was described specifically for breast-imaging. This breast-specific gamma camera was supposed to have the following technical features: variable angle slant-hole collimator; double SiPM arrays readout at the front and back sides of the scintillator; diffusive reflectors at the edges around the scintillator. Because slant-hole collimator was used, a new 3D scintillation positioning method was introduced and tested. The setup of the gamma detector was created in a Monte Carlo simulation toolkit, and a library of a number of light distributions from known positions was acquired through optical simulation. Two library-based positioning algorithms, similarity comparison and maximum likelihood, were developed to estimate the 3D scintillation position by comparing the responses from simulated gamma interactions and the responses from library. Results indicated that the planar spatial resolution and positioning linearity estimated with this gamma detector setup and positioning algorithm was higher than the conventional gamma detectors. The depth-of-interaction estimation was also of high linearity and resolution. With the results presented, the gamma detector setup and positioning method is promising in future breast cancer diagnosis.

I samband med uppbyggnaden av Nya Karolinska Solna designas ett nytt sjukhusövergripande datalager för vitalparametrar, med arbetsnamnet T5, där insamlad data ska följa patienten genom hela sjukhusvistelsen. Inför upphandlingen av systemet behövs en genomgång av vilka standarder, regelverk samt riktlinjer som gäller vid framställning och drift av T5. Genom djupgående litteraturstudier och intervjuer med personer insatta i områden som anses relevanta för projektet, levereras som slutprodukt en rekommendation om hur regelverken och standarderna kan tänkas appliceras på systemet. Projektets resultat visar att om det data som hanteras i T5 är tänkt att användas i medicinskt syfte enligt Lagen om medicintekniska produkter, så är systemet en medicinteknisk produkt. Vidare bör systemet klassificeras som riskklass I, förutsatt att informationen i T5 inte ska användas för patientövervakning i realtid. / As the opening of emergency hospital Nya Karolinska Solna approaches, a data warehouse for vital signs is being designed. The system is referred to as T5 and the intention is to allow collected medical data to follow the patient throughout the entire hospital stay. Before the procurement of the system there is a need for a review of legal frameworks, standards and guidelines applicable to T5. The project was carried out through research of documents and interviews with professionals involved with subjects relevant to the project. As a final product, a recommendation on how the standards and legal frameworks could be applied to the system was compiled. Project results show that if data managed in T5 is aimed to be used in a medical purpose, the system qualifies as a medical device. Furthermore, the system should be classified according to hazard class I, assuming that the information in T5 will not be used for real time monitoring of patient conditions.

Intravenous fluid infusion during surgeries is based on clinical practice guidelines. Many factors impact the fluid distribution in the body, mainly the effect of anesthetic gases and surgical stress. Volume kinetics is a method to simulate the distribution and elimination of infusion fluids by considering the dilution of plasma over time. In this work, two volume kinetic models for fluid therapy are described – the single and two-fluid space model. The goal was to estimate five volume kinetic parameters for implementation in a population kinetic model. The method was based on data from an experiment at the University of Texas Medical Branch where the purpose was to examine the effect of the anesthetic gas isoflurane on fluid distribution after a controlled bleeding. In this project, measured hemoglobin concentrations from the experiment were used to determine the plasma dilution over time. Volume kinetic models were constructed by approximating terms in corresponding differential equations. As opposed to the single-fluid space model, the two-fluid space model gave a closer estimation to the experimental data. The two-fluid space model parameters were considered to be suitable for further population kinetic analysis.

Dörröppningar till en operationssal under en pågående operation ökar infektionsrisken. På Centraloperation, Karolinska Universitetssjukhuset Solna, öppnas dörrarna till operationssalen fler gånger än nödvändigt. Muntlig information till personalen har inte minskat antalet dörröppningar på lång sikt. En trådlös tekniklösning som räknar och visualiserar antalet dörröppningar tros ha en mer långvarig effekt, därför skulle en prototyp tas fram. En litteraturstudie gjordes för att hitta komponenter och för att med dessa skapa prototypen.   Prototypen kan räkna och visualisera antalet dörröppningar från en dörr. Då en operationssal oftast har tre dörrar är visualiseringen förberedd för att kunna visa antalet för dessa. Prototypen behöver vidareutvecklas för att kunna användas på under en operation. / Door openings to an operating theatre, during an ongoing operation, increases the risk of infection. At the Central operation department, Karolinska University Hospital Solna, the doors open more frequently than necessary. Oral information to the staff has not reduced the number of door openings for a longer time. A wireless solution that counts and visualizes is believed to have a more longlasting effect, therefore a prototype would be created. A literature study was performed in order to find components and with those build the prototype. The prototype is able to count and visualize the number of door openings from one door. However, since many operating theatres have three doors, the visualization is prepared for this. To be able to use this system during an operation the prototype needs to be further developed.

Abstract Time-consuming diagnosis of bacterial blood stream infections and inappropriate antibiotic therapy have critical implications for patient outcome – with mortality figures rising for every hour of delayed treatment. The development of diagnostic methods that are capable of selective and rapid bacteria detection, and do not rely on preliminary blood culturing and Gram-staining procedures, is imperative in providing effective therapy and preventing multi-resistance. The aim of this dissertation was to develop a quantum dot based and Gram-specific bacteria labelling protocol. Focused on the detection of Gram-negative species, a two-step conjugation protocol was produced to functionalise quantum dots with anti-lipid A antibodies. Ionic adsorption and EDC chemistry were used to obtain oriented and covalent conjugation of antibodies to the quantum dot surface. In order to reduce non-specific binding of unreacted carboxylic groups on the conjugates to the bacterial membrane, and optimise the accuracy of detection, blocking experiments were conducted with molecules that could provide a neutral surface charge and sterically block open sites. To access lipid A on E. coli cells, three different antigen retrieval methods were tested. As a result, the developed quantum dot-anti lipid A conjugates were able to detect and specifically label Gram-negative E. coli cells after treatment with 0.6mM EDTA or acetic acid pH 3.58 at 42.5°C. 1% BSA reduced non-specific binding to untreated E. coli cells. Furthermore, in comparison to experiments performed with Tris as a blocking agent, the protein reduced non-specific binding to Gram-positive cells. The results obtained in this project are a step further in the development of a new method to rapidly detect bacteria Gram-specifically.

During the last years many mobile health applications have emerged on the market. Most of these collect and compiles physical data that can be followed over time. Now the next generation of health care applications are on their way. With an increasing capacity and high quality sensors, smartphones have the potential to be used as diagnostic tools. Calmark Sweden AB is a company that has developed a smartphone based diagnostic platform for analysis of colorimetric assays integrated on a disposable plastic chip. Their first product, the hilda Neo system is a Point of care test (POCT) for semi quantitative measurement of the biomarker Lac- tate dehydrogenase (LDH). The system consists of a disposable colorimetric LDH test with inte- grated chemical assay, a separate light-box for controlled light conditions and a smartphone appli- cation for image acquisition and test analysis. The purpose of this Master Thesis project was to develop and evaluate a method for smartphone based semi quantitative colorimetric analysis of the hilda Neo LDH test that would work without the light-box in different light settings. The method was to be implementable as an iPhone applica- tion and should be able do correctly determine LDH activity in the four LDH ranges; 0-300, 300- 600, 600-900 and >900 units per litre (U/L). Also, the computed LDH levels among cards run with the same sample were not to have a standard deviation higher than 50 U/L. Two methods based on continuous measurements of the colour stimuli given from the assay site were developed. In both methods, measurements were made by using the iPhone camera for taking an image series following the colour development of the assay over time. The image series was then processed in MATLAB and the LDH level was computed in two different ways. None of the two proposed methods did reach the stated objectives. Neither of the methods gave the correct LDH interval in all evaluation cards and the computed LDH levels had a larger standard deviation then aimed for. However the results indicate that the variation in light settings is not the only factor for the unreached objectives. It is believed that with further studies of the colour proper- ties of the hilda Neo assay and with the continuing development of smartphone technology, it is possible to find a method for smartphone-based colorimetric analysis without having to control the light setting. / Under de senaste åren har många hälso-applikationer introducerats på marknaden. De flesta samlar och sammanställer hälso-data som sedan kan följas över tiden. Nu är en ny generation av hälso- applikationer på ingång. Med ökande kapacitet och högkvalitativa sensorer har våra smartphones potential att användas för medicinsk diagnostik. Calmark Sweden AB är ett företag som har utvecklat en smartphone-baserad diagnostisk plattform för analys av kolorimetriska tester i form av plastkort för engångsbruk. Deras första produkt hilda Neo är ett patientnära test för semikvantitativ mätning av biomarkören Lactatdehydrogenas (LDH). Systemet består av det kolorimetriska engångstestet för mätning av LDH, en separat ljus-box för kontrollerade ljusförhållanden och en smartphone applikation för bildtagning och test analys. Målet med detta masterexamensarbete var att utveckla och utvärdera en metod för smartphone- baserad semikvantitativ kolorimetrisk analys av hilda Neo testet som fungerar utan ljus-box i olika ljussättningar. Metoden skulle vara implementerbar som en iPhone applikation och skulle kunna bestämma LDH aktivitet inom fyra intervall; 0-300, 300-600, 600-900 och >900 enheter per liter (U/L). De beräknade LDH nivåerna för kort körda med samma prov skulle inte heller ha en standardavvikelse över 50 U/L. Två metoder baserade på kontinuerlig mätning av provets färgutveckling togs fram. För båda metoderna användes iPhone kameran för att ta en bildserie som följde testets färgutveckling över tiden. Bildserien behandlades sedan i MATLAB och ett LDH värde beräknades med de två olika metoderna. Ingen av de två föreslagna metoderna uppnådde de uppsatta målen. Ingen av metoderna gav rätt LDH intervall för alla kort som användes för utvärdering och de beräknade LDH nivåerna hade en för hög standard avvikelse. Dock indikerade resultaten på att variationer i ljussättningen inte var den enda faktorn som bidrog till de ouppnådda målen. Författaren tror att med fortsatt studerande av hilda Neo testets färgegenskaper och med den fortlöpande utvecklingen av smartphone tekniken, kommer det att vara möjligt att hitta en metod för smartphone-baserad kolorimetrisk analys utan kontrollerad ljussättning.

Aging in humans is often associated with reduced muscle strength and difficulty in elevating the arm and sustaining it at a certain position. The aim of this master thesis is to propose a number of technical solutions integrated into a complete electronic system which can be used to support the user's muscle capacity and partially resist gravitational load. An electronic system consisting of sensors, a control unit and an actuator has been developed. The system is able to detect the user's motion intention based on an angle detection algorithm and perform kinematic control over the user's arm by adjusting the level of support at different degrees of elevation. A force control algorithm has been developed for controlling the actuating mechanism, providing the user with a natural and intuitive support during arm elevation. The implemented system is a first step towards the development of a medical assistive device for the elderly or patients with reduced muscle strength allowing them to independently perform a number of personal activities of daily life where active participation of the upper limb is required.