Cardiovascular dysfunction is a major complication associated with sepsis induced mortality. Cardiac fibrosis plays a critical role in sepsis induced cardiac dysfunction. The mechanisms of the activation of cardiac fibrosis is unclarified. In this study, we found that microRNA-23b (miR-23b) was up-regulated in heart tissue during cecal ligation and puncture (CLP)-induced sepsis and transfection of miR-23b inhibitor improved survival in late sepsis. Inhibition of miR-23b in the myocardium protected against cardiac output and enhanced left ventricular systolic function. miR-23b inhibitor also alleviated cardiac fibrosis in late sepsis. MiR-23b mediates the activation of TGF-β1/Smad2/3 signaling to promote the differentiation of cardiac fibroblasts through suppression of 5′TG3′-interacting factor 1 (TGIF1). MiR-23b also induces AKT/N-Cadherin signaling to contribute to the deposition of extracellular matrix by inhibiting phosphatase and tensin homologue (PTEN). TGIF1 and PTEN were confirmed as the targets of miR-23b in vitro by Dual-Glo Luciferase assay. miR-23b inhibitor blocked the activation of adhesive molecules and restored the imbalance of pro-fibrotic and anti-fibrotic factors. These data provide direct evidence that miR-23b is a critical contributor to the activation of cardiac fibrosis to mediate the development of myocardial dysfunction in late sepsis. Blockade of miR-23b expression may be an effective approach for prevention sepsis-induced cardiac dysfunction.

Background microRNA-23b (miR-23b) is a multiple functional miRNA. We hypothesize that miR-23b plays a role in the pathogenesis of sepsis. Our study investigated the effect of miR-23b on sepsis-induced immunosuppression. Methods Mice were treated with miR-23b inhibitors by tail vein injection 2 days after cecal ligation puncture (CLP)-induced sepsis. Apoptosis in spleens and apoptotic signals were investigated, and survival was monitored. T-cell immunoreactivities were examined during late sepsis. Nuclear factor B (NF-B)-inducing kinase (NIK), tumor necrosis factor (TNF)-receptor associated factor 1 (TRAF1), and X-linked inhibitor of apoptosis protein (XIAP), the putative targets of miR-23b, were identified by a dual-luciferase reporter assay. Results miR-23b expression is upregulated and sustained during sepsis. The activation of the TLR4/TLR9/p38 MAPK/STAT3 signal pathway contributes to the production of miR-23b in CLP-induced sepsis. miR-23b inhibitor decreased the number of spleen cells positive by terminal deoxynucleotidyl transferase dUTP nick-end labeling and improved survival. miR-23b inhibitor restored the immunoreactivity by alleviating the development of T-cell exhaustion and producing smaller amounts of immunosuppressive interleukin 10 and interleukin 4 during late sepsis. We demonstrated that miR-23b mediated immunosuppression during late sepsis by inhibiting the noncanonical NF-B signal and promoting the proapoptotic signal pathway by targeting NIK, TRAF1, and XIAP. Conclusions Inhibition of miR-23b reduces late-sepsis-induced immunosuppression and improves survival. miR-23b might be a target for immunosuppression.

Sepsis-induced myeloid-derived suppressor cells (MDSCs) increase mortality risk. We previously identified that long non-coding RNA Hotairm1 supports myeloid precursor shifts to Gr1+CD11b+ MDSCs during mouse sepsis. A major unanswered question is what molecular processes control Hotairm1 expression. In this study, we found by a genetic deletion that a specific PU.1-binding site is indispensable in controlling Hotairm1 transcription. We then identified H3K4me3 and H3K27me3 at the PU.1 site on the Hotairm1 promoter. Controlling an epigenetic switch of Hotairm1 transcription by PU.1 was histone KDM6A demethylase for H3K27me3 that derepressed its transcription with possible contributions from Ezh2 methyltransferase for H3K27me3. KDM6A knockdown in MDSCs increased H3K27me3, decreased H3K4me3, and inhibited Hotairm1 transcription activation by PU.1. These results enlighten clinical translation research of PU.1 epigenetic regulation as a potential sepsis immune-checkpoint treatment site.

Sepsis is an organ dysfunction life-threatening disease that is caused by a dysregulated body response to infection. Sepsis is difficult to detect at an early stage, and when not detected early, is difficult to treat and results in high mortality rates. Developing improved methods for identifying patients in high risk of suffering septic shock has been the focus of much research in recent years. Building on this body of literature, this dissertation develops an improved method for septic shock prediction. Using the data from the MMIC-III database, an ensemble classifier is trained to identify high-risk patients. A robust prediction model is built by obtaining a risk score from fitting the Cox Hazard model on multiple input features. The score is added to the list of features and the Random Forest ensemble classifier is trained to produce the model. The Cox Enhanced Random Forest (CERF) proposed method is evaluated by comparing its predictive accuracy to those of extant methods.

UNIVERZITA KARLOVA Lékařská fakulta v Plzni Dizertační práce Endothelial glycocalyx - diagnostic approach and intervention assessment MUDr.Jiří Pouska ABSTRACT Endothelial glycocalyx (EG) is fine structure on the surface of endothelium. After extensive research in past years, revisited Starling principle was finally formulated. It describes fluid physiology in capillaries precisely. EG has pivotal role in keeping endothelium semipermeable and thus avoiding extensive filtration of fluids to interstitium. Assessment of EG is clinically difficult. Many pathological conditions lead to damage of EG (sepsis etc.). Intravenous fluid therapy is mainstay of treatment of such conditions. Our aim was to determine the changes of EG integrity depending on the choice of intravenous fluid and its infusion time in physiological and pathological conditions. Key words: Endothelial glycocalyx, infusion therapy, anaesthesia, sepsis, microcirculation.

Varje år drabbas omkring 48,9 miljoner människor av sepsis runt om i världen och av dessa avlider cirka 11 miljoner. Sepsis är ett allvarligt tillstånd vilket kräver snabb upptäckt och snabbt påbörjad behandling för att minska dödligheten. Denna studies syfte var att beskriva sjuksköterskors upplevelse av att identifiera sepsis hos patienter på sjukhus. Studien har genomförts som en integrerad litteraturstudie där vi genom systematiska sökningar i databaserna PubMed och Cinahl sökt efter relevanta artiklar. Analysen resulterade i fem slutkategorier, “Att sjuksköterskor känner en osäkerhet”, “Att stress orsakar förseningar i vården”, “Att utveckla en klinisk blick”, “Att bli säker i sin roll att identifiera svikt av vitala tecken och symtom” och “Att arbeta i team”.  Denna studie belyser bland annat behov av utbildning för sjuksköterskor i att upptäcka tecken och symptom av sepsis. Vidare forskning om hur sjuksköterskor upplever att stress, arbetsbelastning och teamarbete påverkar deras bedömning av patienters vitala funktioner genom att upptäcka tecken och symptom i tid behövs. I nuläget verkar det vara stort fokus på kvantitativ forskning medan kvalitativa ansatser, där sjuksköterskor själva får berätta hur deras upplevelse är, är mer sparsam.

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Patients admitted to intensive care units (ICUs) often have a higher risk of sepsis due to weakened immune systems. Early sepsis diagnosis is crucial for timely treatment, emphasizing the need to improve the predictive capabilities of sepsis prediction models. Although machine learning models have demonstrated success in predicting sepsis onset, there is limited work done on how model assessment is affected by sequential prediction rather than evaluating on one prediction per patient. This thesis assesses the effectiveness of the evaluation procedures employed by such models and explore different prediction conditions to enhance sepsis prediction. Data was collected from the MIMIC-IV data set,and includes variables commonly used in real ICU settings relevant to sepsis diagnosis. Random onset matching is used to select time points for patients with and without sepsis, with the data analyzed using XGBoost. Evaluation metrics are calculated both once per patient, and is compared to sequential measurements for all patients from 40 hours before sepsis up until sepsis onset. Results shows that a model trained on data close to sepsis onset has strong predictive performance up to 25 hours before sepsis onset. In addition,different restrictive conditions on predictions are considered and evaluated. As the test set is limited it is important that the results are validated further, as it could provide insights regarding interpretation in the practical implementation of similar prediction models for support of healthcare professionals through timely interventions.

Last observation carried forward (LOCF) is a common imputation method, regularly used for clinical data. It is based on the principle that the most recent observation that is known is carried forward to replace missing values. In this thesis, we investigate the effect that variable age has on sepsis prediction when used as a conditional decision variable for imputation. In an iterative experiment,  we combine the LOCF method with a more passive approach of model-inbuilt ways of handling missing data, using tree-based models. A measurement of variable age is created by measuring the distance in time between missing observations and the most recent known value.  Based on this measurement, different cut-off values based on variable-specific percentiles are evaluated during imputation. In the event of missing values, where the last known value is more or equally recent as the decided cutoff, imputation is made through LOCF. The remaining entries are retained as missing and handled by the model during prediction. Results based on out-of-sample prediction performance for increasing variable age percentile cutoffs suggest that too restrictive constraints on the variable age decrease predictive performance for CART and Random forest, whereas no such performance decrease is found for XGBoost. In addition, tendencies of a  slight decrease in performance are seen for higher variable percentiles as compared to the variable age interval that was found optimal in most cases. Finally, SHAP and LIME values show that there is a clear association between the variable age and prediction contributions for some variables. Further research is necessary to confirm and extend the results.