Studies with cyanidium caldarium,an anomalously pigmented chlorophyte

Summary Cyanidium caldarium, an alga found in acid hot springs troughout the world, has a morphology and developmental history resembling those of Chlorella, but contains C-phycocyanin and no chlorophyll other than chlorophyll a. The reasons for considering it to be a member of the Chlorophyta are reviewed. Cyanidium is also remarkable for its thermal and acid tolerance. It grows readily in the dark on sugar media. However, light is required for the formation of chlorophyll and phycocyanin except in occasional variant cells which can form limited amounts of these pigments in the dark. Light-grown Cyanidium carries out normal green plant photosynthesis but resembles the red and some of the blue-green algae in that chlorophyll-absorbed light is used with lower efficiency than that absorbed by phycocyanin.The possible significance of the unusual pigmentation of Cyanidium is discussed.Contribution no.23 from the Laboratory of Comparative Physiology and Morphology of The Kaiser Foundation.     

In vivo progesterone regulation of protein phosphatase activity in Xenopus oocytes

Exogenous beta casein, previously phosphorylated in vitro by protein kinase A and casein kinase II, was microinjected into Xenopus oocytes to monitor in vivo protein phosphatase activities. Phosphatase activities were 1.6 and 3.4 fmol/min/oocyte, respectively, for beta casein phosphorylated by casein kinase II and beta casein phosphorylated by protein kinase A. Progesterone induced an early decrease (35% after 10 min) in phosphatase activity restricted to the protein kinase A sites of beta casein.     

Impact of bone marrow-derived mesenchymal stromal cells on experimental xenogeneic graft-versus-host disease

Background aimsGraft-versus-host disease (GVHD) is a life-threatening complication of allogeneic hematopoietic cell transplantation caused by donor T cells reacting against host tissues. Previous studies have suggested that mesenchymal stromal cells (MSCs) could exert potent immunosuppressive effects.MethodsThe ability of human bone marrow derived MSCs to prevent xenogeneic GVHD in non-obese diabetic/severe combined immunodeficient (NOD/SCID) mice and in NOD/SCID/interleukin-2Rγ(null) (NSG) mice transplanted with human peripheral blood mononuclear cells (PBMCs) was assessed.ResultsInjection of 200 × 10⁶ human PBMCs intraperitoneally (IP) into sub-lethally (3.0 Gy) irradiated NOD/SCID mice also given anti-asialo GM₁ antibodies IP 1 day prior and 8 days after transplantation induced lethal xenogeneic GVHD in all tested mice. Co-injection of 2 × 10⁶ MSCs IP on day 0 did not prevent lethal xenogeneic GVHD induced by injection of human PBMCs. Similarly, injection of 30 × 10⁶ human PBMCs IP into sub-lethally (2.5 Gy) irradiated NSG mice induced a lethal xenogeneic GVHD in all tested mice. Injection of 3 × 10⁶ MSCs IP on days 0, 7, 14 and 21 did not prevent lethal xenogeneic GVHD induced by injection of human PBMCs.ConclusionsInjection of MSCs did not prevent xenogeneic GVHD in these two humanized mice models.     

Towards Developing an Initial Programme Theory: Programme Designers and Managers Assumptions on the Antiretroviral Treatment Adherence Club Programme in Primary Health Care Facilities in the Metropolitan Area of Western Cape Province,South Africa

BackgroundThe antiretroviral adherence club intervention was rolled out in primary health care facilities in the Western Cape province of South Africa to relieve clinic congestion, and improve retention in care, and treatment adherence in the face of growing patient loads. We adopted the realist evaluation approach to evaluate what aspects of antiretroviral club intervention works, for what sections of the patient population, and under which community and health systems contexts, to inform guidelines for scaling up of the intervention. In this article, we report on a step towards the development of a programme theory—the assumptions of programme designers and health service managers with regard to how and why the adherence club intervention is expected to achieve its goals and perceptions on how it has done so (or not).MethodsWe adopted an exploratory qualitative research design. We conducted a document review of 12 documents on the design and implementation of the adherence club intervention, and key informant interviews with 12 purposively selected programme designers and managers. Thematic content analysis was used to identify themes attributed to the programme actors, context, mechanisms, and outcomes. Using the context-mechanism-outcome configurational tool, we provided an explanatory focus of how the adherence club intervention is roll-out and works guided by the realist perspective.ResultsWe classified the assumptions of the adherence club designers and managers into the rollout, implementation, and utilisation of the adherence club programme, constructed around the providers, management/operational staff, and patients, respectively. Two rival theories were identified at the patient-perspective level. We used these perspectives to develop an initial programme theory of the adherence club intervention, which will be tested in a later phase.ConclusionThe perspectives of the programme designers and managers provided an important step towards developing an initial programme theory, which will guide our realist evaluation of the adherence club programme in South Africa.     

A Signal Processing Approach for Detection of Hemodynamic Instability before Decompensation

Advanced hemodynamic monitoring is a critical component of treatment in clinical situations where aggressive yet guided hemodynamic interventions are required in order to stabilize the patient and optimize outcomes. While there are many tools at a physician’s disposal to monitor patients in a hospital setting, the reality is that none of these tools allow hi-fidelity assessment or continuous monitoring towards early detection of hemodynamic instability. We present an advanced automated analytical system which would act as a continuous monitoring and early warning mechanism that can indicate pending decompensation before traditional metrics can identify any clinical abnormality. This system computes novel features or bio-markers from both heart rate variability (HRV) as well as the morphology of the electrocardiogram (ECG). To compare their effectiveness, these features are compared with the standard HRV based bio-markers which are commonly used for hemodynamic assessment. This study utilized a unique database containing ECG waveforms from healthy volunteer subjects who underwent simulated hypovolemia under controlled experimental settings. A support vector machine was utilized to develop a model which predicts the stability or instability of the subjects. Results showed that the proposed novel set of features outperforms the traditional HRV features in predicting hemodynamic instability.     

Experiment-specific estimation of peptide identification probabilities using a randomized database

Determining the error rate for peptide and protein identification accurately and reliably is necessary to enable evaluation and crosscomparisons of high throughput proteomics experiments. Currently, peptide identification is based either on preset scoring thresholds or on probabilistic models trained on datasets that are often dissimilar to experimental results. The false discovery rates (FDR) and peptide identification probabilities for these preset thresholds or models often vary greatly across different experimental treatments, organisms, or instruments used in specific experiments. To overcome these difficulties, randomized databases have been used to estimate the FDR. However, the cumulative FDR may include low probability identifications when there are a large number of peptide identifications and exclude high probability identifications when there are few. To overcome this logical inconsistency, this study expands the use of randomized databases to generate experiment-specific estimates of peptide identification probabilities. These experiment-specific probabilities are generated by logistic and Loess regression models of the peptide scores obtained from original and reshuffled database matches. These experiment-specific probabilities are shown to very well approximate "true" probabilities based on known standard protein mixtures across different experiments. Probabilities generated by the earlier Peptide_Prophet and more recent LIPS models are shown to differ significantly from this study's experiment-specific probabilities, especially for unknown samples. The experiment-specific probabilities reliably estimate the accuracy of peptide identifications and overcome potential logical inconsistencies of the cumulative FDR. This estimation method is demonstrated using a Sequest database search, LIPS model, and a reshuffled database. However, this approach is generally applicable to any search algorithm, peptide scoring, and statistical model when using a randomized database.