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Human mesenchymal stem cells (hMSCs) have multiple potential roles in regenerative medicine. These roles revolve around the exploitation of their multipotent differentiation potential, their immunogenic privilege, or the broad range of molecules they secrete. The functions of these molecules include stimulation of cell migration during wound repair, activation of endogenous tissue-specific stem cell pools, and suppression of T-cell-based inflammatory responses. hMSCs have been exploited clinically in the treatment of graft versus host disease following bone marrow or organ transplant via systemic administration and in other indications including ischemic heart disease, Crohn’s disease, and diabetes mellitus. However, the mechanisms underlying the regenerative potential of hMSCs are still uncertain. Moreover, the effectiveness of hMSCs-based cell therapy has been doubted because the therapeutic actions have often been noticed in the absence of hMSCs homing to the target tissues. Thus, indicating that the level of improvement of diseased tissues is not reciprocal to the observed target tissue homing/differentiation, suggesting that it may play an indirect role in tissue regeneration or it may have a dualistic mode of action. Irrespective MSCs provide a great promise in the treatment of degenerative disorders and inflammatory ailments. However, there are still many challenges to overcome prior to their widespread clinical application. For instance, their ambiguous paracrine mechanism remains a matter for controversy and exploration; moreover, current in vitro cultures have limited success in reflecting many aspects of the in vivo niche – including oxygen level. This study focused on the paracrine properties of hMSCs with a particular focus on their role in immunosuppression and anti-inflammation. Coupled to this we explored oxygen-dependent regulation of the paracrine biology of MSCs focussing on a number of important paracrine factors with roles in immunomodulation. Serum free-conditioned media (SFCM) were used as a model to characterize the immune sentinel function of MSCs. Proteomic analysis indicated that SFCM of hMSCs contained various cytokines which may play an important role in the suppression of inflammation and that the composition and concentration of SFCM was oxygen dependent. Intermittent hypoxia (IH) initiated a stark upregulation of paracrine factor secretion. The therapeutic effectiveness of SFCM was reflected via its efficacy across two in vitro cell line models; SFCM suppressed T cell model activation (Jurkat) in an oxygen independent manner while the modulation of macrophage (THP-1) terminal differentiation was oxygen sensitive. Collectively, IH SFCM suppressed the immune response at T cell and antigen presentation levels while air oxygen (21% O2) cultured hMSC SFCM supressed the immune response at T cell level and maintain the antigen presentation which might elicits the immune response. These optimized in vitro-culture findings support clinical application of hMSCs and/or their secretory factors as a pharmacoregenerative modality for the treatment of non-curative diseases, such as, rheumatoid arthritis, Crohn’s disease, myocardial infarction, and advanced critical limb diseases. Moreover, this property may be harnessed to produce an optimized acellular biological product with immunomodulatory actions similar to hMSCs, leading to production of an “off-the-shelf” biological product. The production of such biological products will have important economic considerations in cell-based therapy.

The Domain Name System (DNS) has a direct and strong impact on the performance of nearly all aspects of the Internet. DNS relies on a delegation-based architecture, where resolution of names to their IP addresses require resolving the names of the servers responsible for those names. The recursive graphs of the inter-dependencies that exist between servers associated with each zone are called Dependency Graphs. We constructed a DNS Dependency Model as a unified representation of these Dependency Graphs. We utilized a set of Structural Metrics defined over this model as indicators of external quality attributes of the DNS. We applied machine learning in order to construct Prediction Models of the perceived quality attributes of the DNS out of the structural metrics of the model and evaluate the accuracy of these models. Operational Bad Smells are configuration and deployment decisions, made by zone administrators, that are not totally errant or technically incorrect and do not currently prevent the system from doing its designated functionality. Instead, they indicate weaknesses that may impose additional overhead on DNS queries, or increase system vulnerability to threats, or increase the risk of failures in the future. We proposed the ISDR (Identification, Specification, Detection and Refactoring) Method that enables DNS administrators to identify bad smells on a high-level abstraction using a consistent taxonomy and reusable vocabulary. We developed techniques for systematic detection and recommendations of reaction mechanisms in the form of graph-based refactoring rules. The ISDR Method along with the DNS Quality Prediction Models are used to build the DNS Quality Assurance Framework and the DNS Advisor Tool. Assessing the perceived quality attributes of the DNS at an early stage enables us to avoid the implications of defective and low-quality designs. We identify configuration changes that improve the availability, security, stability and resiliency postures of the DNS.