Digital marketing is constantly adapting and evolving in line with technological advances. One of these advances is the digitalization, which has given rise to Programmatic Advertising (PA). In order to practice PA, the companies need to collect data about consumers' preferences and personal interests. On the other hand, consumers have a need to protect their privacy. The needs of these two parties cross each other which creates the tension, called the Personalization-privacy paradox. In this study, we intend to investigate consumers' attitudes towards PA and whether personalization gives rise to improved browsing experiences or evenviolates their privacy. A quantitative study was conducted, where the independent variable was called Personalization and the dependent variables - Attractiveness, Annoyance, Invasiveness and Trade-off. The results of bivariate regression analysis showed that all of the dependent variables of the study were statistically significant. The results also illustrated that the majority of the respondents experienced PA ads as beneficial, but also invasive. These results are in line with the Utility maximization theory, as PA ads were considered both beneficial and risky. Therefore, consumers would have incentives to disclose their personal information as long as the percieved benefits would outweigh perceived risks, generated by PA. We concluded that there are no clear, predetermined answers to what attitudes consumers have towards PA, but this can vary from case to case, which is in line with both the Privacy calculus theory and the Utility maximization theory. This involves a risk-benefit analysis, performed by consumers, where perceived benefits exceeding perceived risks would generate positive attitudes and vice versa

Digital tools are rapidly evolving (Quach et al. 2022), and this is a contributing factor to the increase in breaches of consumer privacy. This creates a need to understand how consumers relate to marketing using Artificial Intelligence (AI) and personal information to enable personalization (Kumar et al. 2019). AI can generate benefits both for marketers and consumers. It is also used in personal marketing by collecting and managing data in order to make decisions (Huang & Rust 2021a). In contrast, the privacy risks associated with AI are when data is collected without consent and AI systems draw conclusions from individual customer data or interaction data with other consumers (Hermann 2022). There is a need to understand how consumers relate to personal information being used in marketing. For this reason, a qualitative study is being conducted on consumer privacy in relation to the use of AI in digital marketing and how the approach differs between different generations. The study is based on theories to analyze the data collection from interviews to be able to answer the research question. The theories are used to form key concepts that are the basis for a thematic analysis.  The conclusions show that AI can contribute to influence consumer behavior and that there are differences as well as similarities in the generations' approach to digital marketing and AI. Participants in the study have a negative perspective on the subject, while at the same time, some respondents state that personal marketing is preferred. In addition to this, it appears in the study that all participants have insufficient knowledge of how to assess risks when providing personal information, and that they share a concern of AI managing their personal information. The reasons differ depending on the generation of the participants. / Digitala verktyg genomgår, och har under en tid genomgått en snabb utveckling (Quach et al. 2022), och det är en bidragande faktor till att överträdelser på konsumenters integritet ökar. Detta ger ett växande behov av att förstå hur konsumenter förhåller sig till att marknadsföring använder Artificiell Intelligens (AI) och personlig information för att möjliggöra personalisering (Kumar et al. 2019). AI kan generera fördelar för både marknadsförare och kunder. Det används även inom personlig marknadsföring genom att samla in och hantera data för att kunna ta beslut (Huang & Rust 2021a). De integritetsrisker som finns kopplade till AI är däremot när data samlas in utan samtycke och AI-system drar slutsatser från individuella kunddata eller interaktionsdata med andra kunder (Hermann 2022). Här finns ett ökat behov av att förstå hur konsumenter förhåller sig till att personlig information används i marknadsföring. Av denna anledning genomförs en kvalitativ studie om konsumenters integritet i samband med AI i digital marknadsföring, samt hur förhållningssättet skiljer mellan olika generationer.   Studien utgår från teorier för att analysera datainsamlingen med hjälp av semistrukturerade intervjuer för att därefter kunna besvara forskningsfrågan. Teorierna används för att ta fram värdebegrepp som ligger till grund för en tematisk analys.   Slutsatserna visar på att AI kan bidra med påverkan på konsumentbeteenden och att det finns skillnader samt likheter i generationernas förhållningssätt till digital marknadsföring och AI. Deltagare i studien förhåller sig negativt till ämnet, samtidigt som vissa respondenter framför att personlig marknadsföring föredras. Utöver detta framkommer det i studien att samtliga deltagare har otillräckliga kunskaper för hur de ska bedöma risker när de ska tillhandahålla personlig information, samt att de delar en oro av att AI hanterar deras information, däremot skiljer sig orsakerna beroende på deltagarnas ålder och generation.

Gastric cancer is a leading cause of cancer death worldwide. Systemic treatment comprising chemotherapy and targeted therapy is the standard of care in advanced/metastatic gastric cancer. Comprehensive molecular characterization of gastric adenocarcinomas by the TCGA Consortium and ACRG has resulted in the definition of distinct molecular subtypes. These efforts have in parallel built a basis for the development of novel molecularly stratified treatment approaches. Based on this molecular characterization, an increasing number of specific genomic alterations can potentially serve as treatment targets. Consequently, the development of promising compounds is ongoing. In this review, key molecular alterations in gastric and gastroesophageal junction cancers will be addressed. Finally, the current status of the translation of targeted therapy towards clinical applications will be reviewed.

The aim of this study was to compare effects of an individualized with a standardized risk assessment for adverse drug reactions to improve drug treatment with antithrombotic drugs in older adults. A randomized controlled trial was conducted in general practitioner (GP) offices. Patients aged 60 years and older, multi-morbid, taking antithrombotic drugs and at least one additional drug continuously were randomized to individualized and standardized risk assessment groups. Patients were followed up for nine months. A composite endpoint defined as at least one bleeding, thromboembolic event or death reported via a trigger list was used. Odds ratios (OR) and 95% confidence intervals (CI) were calculated. In total, N = 340 patients were enrolled from 43 GP offices. Patients in the individualized risk assessment group met the composite endpoint more often than in the standardized group (OR 1.63 [95%CI 1.02–2.63]) with multiple adjustments. The OR was higher in patients on phenprocoumon treatment (OR 1.99 [95%CI 1.05–3.76]), and not significant on DOAC treatment (OR 1.52 [95%CI 0.63–3.69]). Pharmacogenenetic variants of CYP2C9, 2C19 and VKORC1 were not observed to be associated with the composite endpoint. The results of this study may indicate that the time point for implementing individualized risk assessments is of importance.

Longitudinal personalized electronic health record (LPHR) provides a holistic view of health records for individuals and offers a consistent patient-controlled information system for managing the health care of patients. Except for the patients in Veterans Affairs health care service, however, no LPHR is available for the general population in the U.S. that can integrate the existing patients' electronic health records throughout life of care. Such a gap may be contributed mainly by the fact that existing patients' electronic health records are scattered across multiple health care facilities and often not shared due to privacy and security concerns from both patients and health care organizations. The main objective of this dissertation is to address these roadblocks by designing a scalable and interoperable LPHR with patient-controlled and mutually-trusted security and privacy. Privacy and security are complex problems. Specifically, without a set of access control policies, encryption alone cannot secure patient data due to insider threat. Moreover, in a distributed system like LPHR, so-called race condition occurs when access control policies are centralized while decisions making processes are localized. We propose a formal definition of secure LPHR and develop a blockchain-enabled next generation access control (BeNGAC) model. The BeNGAC solution focuses on patient-managed secure authorization for access, and NGAC operates in open access surroundings where users can be centrally known or unknown. We also propose permissioned blockchain technology - Hyperledger Fabric (HF) - to ease the shortcoming of race condition in NGAC that in return enhances the weak confidentiality protection in HF. Built upon BeNGAC, we further design a blockchain-enabled secure and trusted (BEST) LPHR prototype in which data are stored in a distributed yet decentralized database. The unique feature of the proposed BEST-LPHR is the use of blockchain smart contracts allowing BeNGAC policies to govern the security, privacy, confidentiality, data integrity, scalability, sharing, and auditability. The interoperability is achieved by using a health care data exchange standard called Fast Health Care Interoperability Resources. We demonstrated the feasibility of the BEST-LPHR design by the use case studies. Specifically, a small-scale BEST-LPHR is built for sharing platform among a patient and health care organizations. In the study setting, patients have been raising additional ethical concerns related to consent and granular control of LPHR. We engineered a Web-delivered BEST-LPHR sharing platform with patient-controlled consent granularity, security, and privacy realized by BeNGAC. Health organizations that holding the patient's electronic health record (EHR) can join the platform with trust based on the validation from the patient. The mutual trust is established through a rigorous validation process by both the patient and built-in HF consensus mechanism. We measured system scalability and showed millisecond-range performance of LPHR permission changes. In this dissertation, we report the BEST-LPHR solution to electronically sharing and managing patients' electronic health records from multiple organizations, focusing on privacy and security concerns. While the proposed BEST-LPHR solution cannot, expectedly, address all problems in LPHR, this prototype aims to increase EHR adoption rate and reduce LPHR implementation roadblocks. In a long run, the BEST-LPHR will contribute to improving health care efficiency and the quality of life for many patients. / Doctor of Philosophy / Longitudinal personalized electronic health record (LPHR) provides a holistic view of health records for individuals and offers a consistent patient-controlled information system for managing the health care of patients. Except for the patients in Veterans Affairs health care service, however, no LPHR is available for the general population in the U.S. that can integrate the existing patients' electronic health records throughout life of care. Such a gap may be contributed mainly by the fact that existing patients' electronic health records are scattered across multiple health care facilities and often not shared due to privacy and security concerns from both patients and health care organizations. The main objective of this dissertation is to address these roadblocks by designing a scalable and interoperable LPHR with patient-controlled and mutually-trusted security and privacy. We propose a formal definition of secure LPHR and develop a novel blockchain-enabled next generation access control (BeNGAC) model, that can protect security and privacy of LPHR. Built upon BeNGAC, we further design a blockchain-enabled secure and trusted (BEST) LPHR prototype in which data are stored in a distributed yet decentralized database. The health records on BEST-LPHR are personalized to the patients with patient-controlled security, privacy, and granular consent. The unique feature of the proposed BEST-LPHR is the use of blockchain technology allowing BeNGAC policies to govern the security, privacy, confidentiality, data integrity, scalability, sharing, and auditability. The interoperability is achieved by using a health care data exchange standard. We demonstrated the feasibility of the BEST-LPHR design by the use case studies. Specifically, a small-scale BEST-LPHR is built for sharing platform among a patient and health care organizations. We engineered a Web-delivered BEST-LPHR sharing platform with patient-controlled consent granularity, security, and privacy realized by BeNGAC. Health organizations that holding the patient's electronic health record (EHR) can join the platform with trust based on the validation from the patient. The mutual trust is established through a rigorous validation process by both the patient and built-in blockchain consensus mechanism. We measured system scalability and showed millisecond-range performance of LPHR permission changes. In this dissertation, we report the BEST-LPHR solution to electronically sharing and managing patients' electronic health records from multiple organizations, focusing on privacy and security concerns. While the proposed BEST-LPHR solution cannot, expectedly, address all problems in LPHR, this prototype aims to increase EHR adoption rate and reduce LPHR implementation roadblocks. In a long run, the BEST-LPHR will contribute to improving health care efficiency and the quality of life for many patients.

<p>In recent years, the pharmaceutical industry has embarked on an extensive program to modernize its manufacturing resources. Recognizing the limitations of traditional mass manufacturing, the industry is now focused on developing new production systems that can deliver high-quality medicines with enhanced efficiency, flexibility, agility, and reliability. To realize this vision, innovations in three key areas are being pursued: continuous processing, personalized medicine, and Industry 4.0.</p> <p>This thesis contributes towards the industry’s goal by focusing on the development of an advanced system to manufacture solid oral drug products. This system is centered around a pharmaceutical additive manufacturing technology called drop on demand (DoD) printing. This technology is highly effective in making personalized drug products, which allow for customizing dose attributes such as drug loading, release behavior, formulation type, and dosage form, based on patient requirements. To develop the DoD printer into an advanced production system, technologies such as end-to-end continuous processing, real time quality assurance, and automated operation need to be incorporated into it. </p> <p>The studies presented in this thesis implement different aspects of these technologies in the printer. To enable end to end operation, a novel solvent switch process called three phase settling is developed to integrate the DoD system with upstream steps for synthesizing the active ingredient. To facilitate automated processing of formulations with different active ingredients, excipients, and particle concentrations, a model framework is developed to recommend operating conditions for the DoD platform that can deliver on-spec printer operation. To expand the range of personalized dosage forms offered by the system, the manufacturing of a new category of drug products, called mini-tablets, is demonstrated. To provide reconfigurability and quality assurance capabilities in the platform, modular design and process monitoring tools are implemented. To aid optimization and control of drug production processes, a digital twin is developed by combining the models developed for DoD, solvent switch, synthesis, and crystallization operations. </p> <p>The research presented in this thesis lays the foundation for developing the next generation of manufacturing systems for drug products. Incorporation of scalability, autonomous operation, and real time release prediction are critical steps in facilitating the next phase of its development – deployment in real-world manufacturing scenarios.  </p>

Gardening has been proven to have multiple health benefits, both mentally and physically. It is a common activity and it’s estimated that almost 78% of home-owners in the USA do it on a regular basis. Cities are growing and have created the challenge to make room for new green spaces to preserve the current biodiversity. This makes preserving or creating garden diversity more important than ever. This study investigates if a user interface could potentially impact and create incentive in gardening for users, with the help of personalized garden information. A prototype was developed and tested in collaboration with Husqvarna Group. The prototype was tested on 5 participants over a 10 day period combined with a qualitative interview to find out if it generated incentive for gardening. The study was done as a conceptual proof of concept and the results were evaluated using the Technology Acceptance Model (TAM). The result implied that the conceptual solution would be perceived useful and accepted on the condition that it provides a personalized user experience. Future research is suggested to look into how to achieve a personalized user experience for user interfaces for gardening. / Trädgårdsarbete har visat sig ha flera hälsofördelar, både mentalt och fysiskt. Det är en vanligt förekommande aktivitet och det uppskattas att nästan 78% av att alla husägare i USA gör det regelbundet. Moderna städers tillväxt har resulterat i en utmaning att skapa nya grönområde för att bevara den nuvarande biologiska mångfalden. Den tillväxten gör det viktigare än någonsin att bevara eller skapa ny biologisk mångfald i trädgårdarna. Denna studie undersöker om mobila gränssnittet potentiellt kan påverka och skapa incitament i trädgårdsarbete för användare med hjälp av personlig information om trädgården. En prototyp utvecklades och testades i samarbete med Husqvarna Group. Prototypen testades på 5 deltagare under en 10-dagarsperiod i kombination med en kvalitativ intervju. Detta för att ta reda på om det genererade incitament för trädgårdsarbete. Studien gjordes som ett konceptet-test och resultaten utvärderades med efter: Technology Acceptance Model (TAM). Resultatet visade att den konceptuella lösningen skulle upplevas användbar och accepterad, under förutsättning att den ger en personlig användarupplevelse. Framtida forskning föreslås att undersöka hur man kan uppnå en personlig användarupplevelse med gränssnittet för trädgårdsarbete.

The purpose of this thesis is to examine familial hypercholesterolemia (FH) and emerging pharmacogenomics therapies that propose to lower serum low density lipid (LDL) levels. The search of various data bases resulted in nine research articles being selected for review. Syntheses of the articles suggest emerging phamacogenomic drug therapy can improve treatment outcomes for individuals with a diagnosis of FH. The Human Genome Project (HGP) has had far reaching applications for genomic technologies and pharmacagenomic interventions, tailored to human conditions associated with select genomic traits. Synthesis of nine research articles demonstrate that little is known on the topic and reveals extensive gaps in the evidence. This thesis concludes with implications for nursing education, practice, policy and research along with limitations are noted.

Pancreatic cancer is the seventh leading cause of cancer related mortalities worldwide and incidences are increasing. The prognosis remains poor as the 5-year survival rate is below 10%. This can be partly explained by the silent progression of disease as most patients present with advanced disease at time of diagnosis. In turn, surgical resection, the only potential curative measure, is not possible in nearly 80% of cases due to the occurrence of distant metastasis and/or infiltration of major vessels in close proximity to the pancreas. In patients with localized but advanced disease, resectability can be achieved in some cases by initiation of a neoCTx. However, as neoCTx is commonly conducted by administering multi-drug treatments, severe side effects occur frequently, which require an adaption of drug doses administered. In this study, we revealed the negative impact of these drug dose changes during neoCTx on the patients´ treatment outcome. R0 resections were significantly less frequently observed, and the N-status significantly impacted by the tumor regression grade, which in turn trended towards minor response in the cohort of patients that did not sustain full dose course prior surgery. In turn, treatment of LA PDAC could be improved by increasing the proportion of patients that undergo neoCTx without any changes of the treatment schedule. Patient-derived PDAC organoid could serve as an avatar of patients´ tumor disease on which optimal treatment protocols could be tested. In this study, a large living PDAC PDO biobank successfully has been established from surgical resection specimens as well as EUS guided FNA samples. Subsequently, a new protocol for molecular subtyping of PDAC on organoids was established by assessing the expression level of KRT81 and CFTR, as a replacement for HNF1a, using IF staining. Strikingly, we observed identical PDAC subtypes in PDOs and their respective tissue of origin in nearly all cases. This observation allowed the assumption that PDOs could indeed be used as patient-individual avatars to identify treatment sensitivities and resistances, as they share fundamental molecular properties with the tissue they have been initiated from. Extensive pharmacotyping was performed for many PDO lines by testing the response behavior to the multi-drug regimens FOLFIRINOX and Gem/Pac, as well as their respective single drug compounds. As a result, we observed diverse response patterns for each PDAC PDO line. A poor response to FOLFIRINOX did not necessarily imply a resistance to Gem/Pac. PDO pharmacotyping could guide treatment decision making in the foreseeable future. Moreover, when the non-efficient drug was removed, no changes of overall efficacy of treatment in PDOs was observed, implying that additional therapy improvements could be possible using this ex vivo model. This observation was true for both commonly used chemotherapy protocols, FOLFIRINOX and Gem/Pac and could result in less drug mediated side effects under (neo)adjuvant CTx without impacting treatment efficacy. Yet, the main goal of this study was to assess if PDAC PDOs can be used to predict the neoCTx outcome of PDAC patients. All methods required to address this issue in a prospective clinical trial have been established as a protocol for PDAC PDOs initiation from minimal starting material has been established and subsequently improved resulting in take rates of up to 80%. To support this study, we successfully secured patient enrollment from a second clinical center, which will increase the number of recruited patients in the future. Unfortunately, at the time of writing this thesis, patient numbers were not sufficient to answer the question of the predictive value of PDAC PDOs in regard to the current standard of care.

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