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Chronic peritonitis contributes to technique failure in peritoneal dialysis (PD), an effective replacement therapy for chronic kidney failure. Staphylococcus aureus infection is one of the most common causes of peritonitis in PD. Interestingly, mice deficient in the cell surface heparan sulfate proteoglycan, syndecan-1, were reported to clear S. aureus corneal infection more effectively than wild-type mice. The objectives of this study were to examine the protein expression and role of syndecan-1 in leukocyte recruitment, chemokine presentation and S. aureus infection in the microcirculation underlying the parietal peritoneum in wild-type and syndecan-1-/- mice. Immunofluorescence intravital microscopy (IVM) of the parietal peritoneum microcirculation revealed that syndecan-1 was localized to the subendothelial region of venules and the mesothelial layer but does not regulate leukocyte recruitment and is not necessary for presentation of the chemokine MIP-2 in peritoneal venules. IVM was also used to study the effects of a conventional PD solution injected through a peritoneal catheter in a mouse PD model. After 6 weeks of dialysis, the peritoneal catheter implant increased leukocyte rolling and extravasation, fibrosis and angiogenesis in the parietal peritoneum independently from the dialysis solution treatment. Furthermore, the role of syndecan-1 was examined using a 4 week PD model. Four hours after infection with S. aureus through the dialysis catheter or intraperitoneal injection, the dialyzed syndecan-1-/- mice were more susceptible to S. aureus infection than undialyzed syndecan-1-/- controls and wild-type animals. IVM showed that in S. aureus infection, syndecan-1 was removed from the subendothelial surface of peritoneal venules but syndecan-1 deficiency did not affect leukocyte recruitment during S. aureus infection. This study indicates that syndecan-1 in the peritoneum and microcirculation is not a regulator of inflammatory responses but is crucial for providing a barrier to S. aureus infection, which may have important implications for susceptibility to S. aureus infections in PD. / Dissertation / Doctor of Philosophy (Medical Science)

In end-stage kidney disease, kidneys no longer sufficiently perform their intended functions, for example, filtering blood of excess fluid and waste products. Without transplantation or chronic dialysis, this condition results in mortality. Dialysis is the process of artificially replacing some of the kidney’s functionality by passing blood from a patient through an external semi-permeable membrane to remove toxins and excess fluid. The rate of ultrafiltration – the rate of fluid removal from blood – is controlled by the hemodialysis machine per prescription by a nephrologist. While essential for survival, hemodialysis is fraught with clinical challenges. Too high a fluid removal rate could result in hypotensive events where the patient blood pressure drops significantly which is associated with adverse symptoms such as exhaustion, fainting, nausea, and cramps, leading to decreased patient quality of life. Too low a fluid removal rate, in contrast, could leave the patient fluid overloaded often leading to hypertension, which is associated with adverse clinical outcomes. Previous work in our lab demonstrated via simulations that it is possible to design an individualized, model-based ultrafiltration profile with the aim of minimizing hypotensive events during dialysis. The underlying model using in the design of the individualized ultrafiltration profile is a simplified, linearized, continuous-time model derived from a nonlinear model of the patient’s fluid dynamics system. The parameters of the linearized model are estimated from actual patient’s temporal hematocrit response to ultrafiltration. However, the parameter identification approach used in the above work was validated using limited clinical data and often failed to achieve accurate estimation. Against this backdrop, this thesis had three goals: (1) obtain a new, larger set of clinical data, (2) improve the linearized model to account for missing physiological aspects of fluid dynamics, and (3) develop and validate a new approach for identification of model parameters for use in the design of individualized ultrafiltration profiles. The first goal was accomplished by retrofitting an entire in-center, hemodialysis clinic in Holyoke, MA, with online hematocrit sensors (CliC devices), Wi-Fi boards, and a laptop with a radio receiver. Treatment data was wirelessly uploaded to a laptop and redacted files and manual treatment charts were made available for our research per approved study IRB. The second goal was accomplished by examining the nonlinear system of equations governing the relevant dynamics and simplifying the model to an identifiable case. Considerations of refill not accounted for fully in previous works were integrated into the Cardimino 7 linearized model, adding terms but making it generally more accurate to the underlying dynamics. The third goal was accomplished by developing an algorithm to identify major system parameters, using steady-state behavior to effectively reduce the number of parameters to identify. The system was subsequently simulated over an established range for all remaining parameters, compared to collected data, with the lowest RMS error case being taken as the set of identified parameters. While intra-patient identified individual model parameters were associated with a high degree of variability, the system’s steady-state gain and time constants exhibited more consistent estimations, though the time constants still had high variability overall. Parameter sensitivity analysis shows high sensitivity to small changes in individual model parameters. The addition of refill dynamics in the model constituted a significant improvement in the identifiability of the measured dynamics, with up to 70% of data sets resulting in successful estimates. Unmodelled dynamics, resulting from unmeasured input variables, resulted in about 30% of measured data sets unidentifiable. The updated model and associated parameter identification developed in this thesis can be readily integrated with the model-based design of individualized UFR profile.

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京都大学 / 新制・課程博士 / 博士(社会健康医学) / 甲第23820号 / 社医博第120号 / 新制||社医||12(附属図書館) / 京都大学大学院医学研究科社会健康医学系専攻 / (主査)教授 中山 健夫, 教授 小杉 眞司, 教授 柳田 素子 / 学位規則第4条第1項該当 / Doctor of Public Health / Kyoto University / DFAM