Global ETD Search

1	KARBAMAZEPIN-INDUCERAD LEVERTOXICITET - ETT LITTERATURARBETE Yilmaz, Ezgi January 2017 (has links) Introduktion: Läkemedelsbiverkningar delas in i två grupper; typ A och typ B. Typ A-biverkningar är dosberoende medan typ B-biverkningar är idiosynkrasiska och beroende av immunsystemet. Levern är kroppens huvudsakliga metabola organ, och drabbas ofta av läkemedelinducerad toxicitet. Ibland inducerar läkemedelsmetaboliter levertoxicitet, vilket kan medieras av immunsystemet. Karbamazepin är ett antiepileptikum och orsakar levertoxicitet, men den exakta mekanismen är inte klarlagd. Syfte: Syftet med detta arbete är att undersöka om karbamazepins levertoxicitet är beroende av metabolismen av karbamazepin och/eller immunsystemet. Material och metoder: En strukturerad litteraturundersökning utfördes med hjälp av databasen PubMed. 7 artiklar inkluderades i sammanställningen. Resultat: Resultat från in vivo-studier identifierade metaboliter producerade av cytokrom P450-monooxygenaser (CYP450) hos de individer som utvecklade levertoxicitet inducerad av karbamazepin. Samtidigt noterades en ökad nivå CYP3A. Expressionen av en rad immunsystemsmarkörer ökade också vid karbamazepin-inducerad levertoxicitet, exempelvis TNF-α, som leder till apoptos. Slutsats: Utifrån inkluderade studier kan slutsatsen dras att karbamazepins levertoxicitet induceras av dess metaboliter via immunsystemet. Undersökningarna var huvudsakligen associationsstudier, vilket försvårar slutsatser kring kausalitet. Därför behöver ytterligare studier göras så att mekanismen helt kan klargöras. karbamazepin levertoxicitet toxiska metaboliter immunsystem Pharmacology and Toxicology Farmakologi och toxikologi
2	Characterization of the equine metabolites of LGD-4033 in urine using UHPLC-MS(/MS) for doping control purposes Liora, Jackson January 2017 (has links) The aim of this project was to study and characterize the metabolitesof LGD-4033 in equine urine, with the final aim of using the results indoping controls in equestrian sports. Urine samples had been taken atdifferent points in time from three horses that had received thesubstance intravenously. The samples were both directly analysed usinga so called dilute-and-shoot approach and were also prepared with amixed mode anion exchange solid phase extraction. All analysis weredone with UHPLC-ESI-MS(/MS) in negative mode. A total of eightmetabolites were found, which were all combinations of phase Ihydroxylation and/or phase II glucuronidation. Of these a total of four(one that is both monohydroxylated and glucuronidated, one that isdihydroxylated, as well as two glucuronidated metabolites) would besuitable for doping control. equine doping metabolites characterization kemiteknik läkemedel doping hplc ms metaboliter Other Chemical Engineering Annan kemiteknik
3	Exercise and the microbiome : Health effects of exercise on gut microbiome modulation in healthy, prediabetic, and diabetic cohorts / Träning och mikrobiomet : Träningens förändring av tarmens mikrobiom med hälsoeffekter hos friska människor, prediabetiker och diabetiker Brengesjö, Linnea January 2021 (has links) Diabetes has caused many deaths worldwide but can be combated at least partially by diet and physical activity. The gut microbiome shows correlation with both type 1 and type 2 diabetes, has modulatory effects on the immune system and implicates brain functions through the gut-brain axis, in part by microbial metabolites. Diet has long been known to impact the microbiome but exercise has gained interest within the last decade, with studies mostly done on rodents and athletes with somewhat positive results on its modulation of the microbiome. This literature study aims to evaluate whether exercise can influence the microbiome for healthy, prediabetic, and diabetic cohorts and what this might mean for host health. The database PubMed was searched for articles in January 2021 and inclusion criteria yielded 7 articles for review. These differed in methods, cohorts, and exercise interventions, and therefore cannot grant any strong evidence but indicate along with previous research that exercise affects the microbiome, with slight differences in responses depending on the individual’s current state and exercising methods. / Diabetes har orsakat många dödsfall världen över men kan bekämpas åtminstone delvis med hjälp av diet och fysisk aktivitet. Tarmens mikrobiom har visats korrelera med både typ 1 och typ 2 diabetes, har reglerande effekter på immunsystemet och inverkar på hjärnfunktioner genom tarm-hjärna-axeln, delvis via metaboliter från mikroberna. Det har länge varit känt att mat kan påverka mikrobiomet, men träning har också väckt intresse över det senaste årtiondet med studier som fokuserat mest på möss och atleter med någorlunda positiva resultat för dess förändring av mikrobiomet. Denna litteraturstudie syftar till att undersöka om träning kan ha effekt på mikrobiomet hos såväl friska människor som prediabetiker och diabetiker, och vad detta kan betyda för hälsan. En litteratursökning gjordes i databasen PubMed i januari 2021 som efter sortering enligt inkluderande kriterier gav 7 artiklar för granskning. Dessa använde olika metoder, undersökta grupper och träningsupplägg, vilket försvårar jämförelser men indikerar i linje med tidigare forskning att träning påverkar mikrobiomet, med en del skillnader i resultat beroende på individens status och träningsupplägg. Microbiome microbiota gastrointestinal exercise diabetes prediabetes metabolites SCFA inflammation Mikrobiom mikrobiota tarm träning diabetes prediabetes metaboliter kortkedjiga fettsyror inflammation Medical and Health Sciences Medicin och hälsovetenskap Microbiology Mikrobiologi
4	Applied Machine Learning Predicts the Postmortem Interval from the Metabolomic Fingerprint Arpe, Jenny January 2024 (has links) In forensic autopsies, accurately estimating the postmortem interval (PMI) is crucial. Traditional methods, relying on physical parameters and police data, often lack precision, particularly after approximately two days have passed since the person's death. New methods are increasingly focusing on analyzing postmortem metabolomics in biological systems, acting as a 'fingerprint' of ongoing processes influenced by internal and external molecules. By carefully analyzing these metabolomic profiles, which span a diverse range of information from events preceding death to postmortem changes, there is potential to provide more accurate estimates of the PMI. The limitation of available real human data has hindered comprehensive investigation until recently. Large-scale metabolomic data collected by the National Board of Forensic Medicine (RMV, Rättsmedicinalverket) presents a unique opportunity for predictive analysis in forensic science, enabling innovative approaches for improving PMI estimation. However, the metabolomic data appears to be large, complex, and potentially nonlinear, making it difficult to interpret. This underscores the importance of effectively employing machine learning algorithms to manage metabolomic data for the purpose of PMI predictions, the primary focus of this project. In this study, a dataset consisting of 4,866 human samples and 2,304 metabolites from the RMV was utilized to train a model capable of predicting the PMI. Random Forest (RF) and Artificial Neural Network (ANN) models were then employed for PMI prediction. Furthermore, feature selection and incorporating sex and age into the model were explored to improve the neural network's performance. This master's thesis shows that ANN consistently outperforms RF in PMI estimation, achieving an R2 of 0.68 and an MAE of 1.51 days compared to RF's R2 of 0.43 and MAE of 2.0 days across the entire PMI-interval. Additionally, feature selection indicates that only 35% of total metabolites are necessary for comparable results with maintained predictive accuracy. Furthermore, Principal Component Analysis (PCA) reveals that these informative metabolites are primarily located within a specific cluster on the first and second principal components (PC), suggesting a need for further research into the biological context of these metabolites. In conclusion, the dataset has proven valuable for predicting PMI. This indicates significant potential for employing machine learning models in PMI estimation, thereby assisting forensic pathologists in determining the time of death. Notably, the model shows promise in surpassing current methods and filling crucial gaps in the field, representing an important step towards achieving accurate PMI estimations in forensic practice. This project suggests that machine learning will play a central role in assisting with determining time since death in the future. Forensic autopsies postmortem interval (PMI) postmortem metabolomics biological systems metabolomic fingerprints postmortem changes National Board of Forensic Medicine RMV Rättsmedicinalverket forensic science PMI estimation machine learning algorithms metabolomic data metabolites Random Forest (RF) Artificial Neural Network (ANN) feature selection Principal Component Analysis (PCA) forensic pathologists forensic practice. Rättsmedicinska obduktioner postmortalt intervall (PMI) postmortal metabolomik biologiska system metabolomiska fingeravtryck postmortala förändringar Rättsmedicinalverket RMV PMI-estimering maskininlärningsalgoritmer metabolomiska data metaboliter Random Forest (RF) Artificiella neurala nätverk (ANN) feature selection Principal Component Analysis (PCA) rättsläkare rättsmedicinsk praxis. Other Medical Engineering Annan medicinteknik

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