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Coming dissertations at Uppsala university

  • Immunometabolic patterns in psychiatric disease Author: Mikaela Syk Link: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-459466 Publication date: 2021-12-21 13:13

    Many forms of immune system dysregulation are linked to psychiatric disorders. This thesis examines specific types of immune dysregulation in broad cohorts with psychiatric disease. The first section focuses on adipokines and other immunometabolic biomarkers and their interaction with state vs. trait symptoms. Direct-acting autoantibodies are an increasingly recognized mechanism for causing psychosis and obsessive-compulsive disorder, but it is unclear how prevalent this patient group is. To identify which patients to investigate more extensively, superior methods are needed. Therefore, the second section addresses the value of clinical red flags in predicting elevated central nervous system (CNS) damage biomarkers and other CNS pathology.

    In paper I-III, a psychiatric cohort of young adults was examined for plasma immunometabolic biomarkers, depressive symptom severity, bulimia nervosa and neurotic traits. Psychiatric diagnoses were based on diagnostic interviews while depressive symptom severity was assessed with the self-rating version of the Montgomery-Åsberg Depression Rating Scale. Personality traits were evaluated using the Swedish universities Scales of Personality. Young adults with higher leptin levels self-reported more severe depressive symptoms (paper I) and leptin levels were independently linked to neuroticism (paper III). Neuroticism was also linked to other immunometabolic alterations. Women with bulimia nervosa had elevated plasma adiponectin levels that remained stable over time (paper II), suggesting long-term metabolic changes.

    In paper IV, a psychiatric patient cohort enriched for clinical signs of suspected autoimmune psychiatric disease was investigated for psychiatric symptoms, neurological findings and signs of CNS pathology in radiological, neurophysiological, blood and CSF analyses. In this cohort, 27% had CSF signs of CNS tissue damage and 21% had CSF signs of neuroinflammation or blood-brain barrier dysfunction. Six percent had known anti-neuronal autoantibodies in serum and 2% in CSF. CNS damage biomarkers in CSF were also linked to red flags and specific psychiatric features.

    In summary, the thesis confirms different patterns of immunometabolic biomarkers and associations with trait and state symptoms in a psychiatric patient cohort that may have important implications for the future health of young adults with psychiatric morbidity. The final study supports clinical red flags in previous guidelines, indicating that a more comprehensive inclusion of patients with diverse psychiatric symptoms (not restricted to purely psychosis) is necessary to find all psychiatric patients requiring further investigation for immune system involvement.

  • The extent of gynaecological cancer : Evaluation, outcome and quality of life Author: Björg Jónsdottir Link: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-459491 Publication date: 2021-12-21 12:11

    The overall aim of this thesis was to enhance treatment planning for gynaecological cancer patients and identify women that are more likely to have impaired quality of life (QoL) after treatment. 

    In a retrospective cohort study on ovarian cancer, the peritoneal cancer index (PCI) was examined in relation to incomplete cytoreductive surgeries (CRS) and surgical complications (n=167). The PCI was found to be an excellent predictor of incomplete CRS (AUC 0.94). Complete CRS was obtained for only 67.2% of the patients with a PCI score higher than 24, who also experienced an increased rate of complications (p = 0.008). In a prospective study, radiologic PCI assessed with integrated PET/MRI and DW-MRI was compared with the surgical PCI as the gold standard (n=34). The median total PCI for PET/MRI (21.5) was closer to the surgical PCI (24.5) (p = 0.6) than to DW-MRI (20.0, p = 0.007). PET/MRI was more accurate (p = 0.3) for evaluating patients at primary diagnosis and for evaluating high tumour burden in inoperable patients.

    In a nationwide study, endometrial cancer patients included in the Swedish Quality Registry for Gynaecologic Cancer 2017-2019 (n=1401) were analysed with the aim of describing methods of evaluating myometrial invasion (MI). The main methods for the MI assessment were transvaginal sonography (59%) and MRI (28%). The sensitivity of transvaginal sonography (65.6%) was lower than for the other methods.

    In a longitudinal questionnaire-study, QoL in women with advanced gynaecological cancer was compared to women with local disease (n=372). No difference in QoL was found at the one-year follow-up. With multiple regression analyses, previous psychiatric illness, high BMI and comorbidities were identified as risk factors for impaired QoL.

    In conclusion, the PCI is an excellent predictor of incomplete CRS, and PCI ≥24 is a possible cut-off. PET/MRI is superior to DW-MRI for estimating total PCI. The assessment of MI in endometrial cancer in Sweden is usually performed with transvaginal sonography, but the sensitivity is lower than for other methods. Women with advanced gynaecological cancer have equally good QoL one year after diagnosis as women with limited disease, and psychiatric illness, high BMI, and comorbidities are risk factors for impaired mental health.

  • Scalable Data Management for Internet of Things Author: Khalid Mahmood Link: http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-458420 Publication date: 2021-12-21 10:14

    Internet of Things (IoT) often involve considerable numbers of sensors that produce large volumes of data. In this context, efficient management of data could potentially enable automatic decision making based on analytics of sensors on equipment. However, these sensors are often geographically distributed and generate diverse formats of data in form of sensor streams at a high rate. The combination of these properties of IoT pose significant challenges for the existing database management systems (DBMSs) to provide scalable data storage and analytics.

    The problem of providing efficient data management of distributed IoT applications using DBMS technologies is addressed in this thesis. Initially, we developed a prototype system, Fused LOg database Query Processor (FLOQ), which enables general query processingover collections of relational databases that are deployed locally on distributed sites to store sensor measurement logs. Although FLOQ provides efficient query execution when scaling the number of distributed databases, it exhibits complexity and scalability issues for large IoT applications having heterogeneous data. The limitations of FLOQ are primarily inherent to its use of relational database backends for storage of sensor logs.

    When a relational database is used to store large-scale IoT data, it exhibits several challenges. The loading of massive logs produced at high rates is not fast enough due to its strong consistency mechanisms. Furthermore, it could demonstrate a single point of failure that limits the availability, and the inflexible schemas make it difficult to manage heterogeneity. In contrast to relational databases, distributed NoSQL data stores could provide scalable storage of heterogeneous data through data partitioning, replication, and high availability by sacrificing strong consistency. To understand the suitability of NoSQL databases, this thesis also investigates to what degree NoSQL DBMSs provide scalable storage and analytics of IoT applications by comparing a variety of state-of-the-art relational and NoSQL databases for real-world industrial IoT data. 

    The experimental evaluations reveal that the scalability can be provided by the distributed NoSQL data stores; however, the support of advanced data analytics is difficult due to their limited query processing capabilities. Furthermore, data management of distributed IoT applications often requires seamless integration between a real-time edge analytics platform, a distributed storage manager, effective data integration, and query processing techniques for handling heterogeneity. Therefore, in order to provide a holistic data management solution, this thesis developed the Extended Query Processing (EQP) system, which enables advanced analytics for supporting both edge and offline analytics for large-scale IoT applications.

    These contributions enable efficient data management of large-scale heterogeneous IoT applications and supports advanced analytics.

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