No description available.

No description available.

The parasite Plasmodium falciparum is the causative agent of the most severe form of human malaria. During its intraerythocytic life cycle, P. falciparum transports red blood cell contents to its acidic organelle, known as the food vacuole, where a series of proteases degrade a majority of the host hemoglobin. Two metalloaminopeptidases, PfAPP and PfA-M1, have been previously localized to the food vacuole (in addition to distinct secondary locations for each), implicating them in the final stages of hemoglobin catabolism. Prior genetic work has determined these enzymes are necessary for efficient parasite proliferation, highlighting them as potential anti-malarial drug targets. This study presents the biochemical basis for the catalytic roles of these two enzymes in the hemoglobin degradation pathway. PfAPP, an aminopeptidase P homolog, is specific for hydrolyzing the N-termini of peptides containing penultimate prolines. PfA-M1 is a member of the expansive M1 family of proteases and exhibits a broad specificity towards substrates. The two enzymes are ubiquitous, found in organisms across all kingdoms of life. Their presence in an acidic environment is unique for aminopeptidase P proteins and rare for M1 homologs. Our immunolocalization results have confirmed the dual distribution of these two enzymes in the parasite. Vacuolar targeting was found to be associated with the Plasmodium specific N-terminal extension found in the PfA-M1 sequence by yellow fluorescent protein fusion studies. Kinetic analysis of recombinant forms of PfAPP and PfA-M1 revealed both enzymes are stable and catalytically efficient in the substrate rich, acidic environment of the parasite food vacuole. In addition, mutagenic exploration of the PfA-M1 active site has determined a residue important in dictating substrate specificity among homologs of the same family. These results provide insight into the parasite's functional recruitment of these enzymes to deal with the final stages of hemoglobin catabolism and necessary considerations for inhibitor design. / Ph. D.

M.S.

The object of this study is to determine the effect, if any, of the copper and iron intake upon the hemoglobin levels of student nurses on self-chosen diets.

No description available.

Background SARS-CoV-2 infection causes acute respiratory distress, which may progress to multiorgan failure and death. Severe COVID-19 disease is accompanied by reduced erythrocyte turnover, low hemoglobin levels along with increased total bilirubin and ferritin serum concentrations. Moreover, expansion of erythroid progenitors in peripheral blood together with hypoxia, anemia, and coagulopathies highly correlates with severity and mortality. We demonstrate that SARS-CoV-2 directly infects erythroid precursor cells, impairs hemoglobin homeostasis and aggravates COVID-19 disease. Methods Erythroid precursor cells derived from peripheral CD34+ blood stem cells of healthy donors were infected in vitro with SARS-CoV-2 alpha variant and differentiated into red blood cells (RBCs). Hemoglobin and iron metabolism in hospitalized COVID-19 patients and controls were analyzed in plasma-depleted whole blood samples. Raman trapping spectroscopy rapidly identified diseased cells. Results RBC precursors express ACE2 receptor and CD147 at day 5 of differentiation, which makes them susceptible to SARS-CoV-2 infection. qPCR analysis of differentiated RBCs revealed increased HAMP mRNA expression levels, encoding for hepcidin, which inhibits iron uptake. COVID-19 patients showed impaired hemoglobin biosynthesis, enhanced formation of zinc-protoporphyrine IX, heme-CO2, and CO-hemoglobin as well as degradation of Fe-heme. Moreover, significant iron dysmetablolism with high serum ferritin and low serum iron and transferrin levels occurred, explaining disturbances of oxygen-binding capacity in severely ill COVID-19 patients. Conclusions Our data identify RBC precursors as a direct target of SARS-CoV-2 and suggest that SARS-CoV-2 induced dysregulation in hemoglobin- and iron-metabolism contributes to the severe systemic course of COVID-19. This opens the door for new diagnostic and therapeutic strategies.

<p dir="ltr">Blood hemoglobin (Hgb) testing is a widely used clinical laboratory test for a variety of patient care needs. However, conventional blood Hgb measurements involve invasive blood sampling, exposing patients to potential risks and complications from needle pricks and iatrogenic blood loss. Although noninvasive blood Hgb quantification methods are under development, they still pose challenges in achieving performance comparable to clinical laboratory blood Hgb test results (i.e., gold standard). In particular, optical spectroscopy can provide reliable blood Hgb tests, but its practical utilizations in diagnostics are limited by bulky optical components, high costs, and extended data acquisition time. Mobile health (mHealth) or diagnostic colorimetric applications have a potential for point-of-care blood Hgb testing. However, achieving color accuracy for diagnostic applications is a complex matter, affected by device models, light conditions, and image file formats.</p><p dir="ltr">To address these limitations, we propose biophysics-based machine learning algorithms that combine hyperspectral learning and spectroscopic gamut-informed learning for accurate and precise mHealth blood Hgb assessments in a noninvasive manner. This method utilizes single-shot photographs of peripheral tissue acquired by onboard smartphone cameras. The palpebral conjunctiva (i.e., inner eyelid) serves as an ideal peripheral tissue site, owing to its easy accessibility, relatively uniform microvasculature, and absence of skin pigmentation (i.e., melanocytes). First, hyperspectral learning enables a mapping from red-green-blue (RGB) values of a digital camera into detailed hyperspectral information: an inverse mapping from a sparse space (tristimulus color values) to a dense space (multiple wavelengths). Hyperspectral learning employs a statistical learning framework to reconstruct a high-resolution spectrum from a digital photo of the palpebral conjunctiva, eliminating the need for complex and costly optical instrumentation. Second, comprehensive spectroscopic analyses of peripheral tissue are used to establish a unique blood Hgb gamut and design a diagnostic color reference chart highly sensitive to blood Hgb and peripheral perfusion. Informed by the domain knowledge of tissue optics and machine vision, the Hgb gamut-based learning algorithm offers device/light/format-agnostic color recovery of the palpebral conjunctiva, outperforming the existing color correction methods.</p><p dir="ltr">This mHealth blood Hgb prediction method exhibits comparable accuracy and precision to capillary blood sampling tests (e.g., finger prick) over a wide range of blood Hgb values, ensuring its reliability, consistency, and reproducibility. Importantly, by employing only a digital photograph with the Hgb gamut-learned color recovery, hyperspectral learning-based blood Hgb assessments allow noninvasive, continuous, and real-time reading of blood Hgb levels in resource-limited and at-home settings. Furthermore, our biophysics-based machine learning approaches for digital health applications can lay the foundation for the future of personalized medicine and facilitate the tempo of clinical translation, empowering individuals and frontline healthcare workers.</p>

The purpose of this study was to determine whether adults with type 2 diabetes participating in American Diabetes Association (ADA) diabetes self-management education (DSME) randomly assigned to an intensive follow-up group (IFG), utilizing continuous glucose monitoring system (CGMS), or a standard follow-up group (SFG) have any significant differences in mean HgbA1c values and health belief scores over time. Baseline HgbA1c values and health beliefs were measured using the revised Expanded Health Belief Model (HBM) questionnaire. The questionnaire measured the 8 HBM domains: perceived susceptibility; severity; treatment benefit; cues to action; motivation; barriers; self-efficacy and structural elements. Twelve weeks after DSME, patients returned for follow-up based on random assignment. The SFG received routine follow-up care: HgbA1c measurements; behavioral goals and education assessments. The IFG received routine follow-up and CGMS. Patients wore the CSMS for 72 hours and recorded their daily food, blood glucose values, medications and physical activities. Results were analyzed and reviewed with patients. Both groups returned in 24 weeks for HgbA1c measurements and to complete the HBM questionnaire. A repeated measure ANOVA analysis showed a statistically significant reduction in mean HgbA1c at each time period (F=86.75. p>.0001 ) from week 1 to week 12 (SFG 8.6-7.1; IFG 8.5 --7.1,) and from week 12 to week 24 ( SFG 7.1 to 6.9; IFG 7.1 -- 7.0). There were no significant differences found between the groups. (F = 0.17 p > 0.87). Following DMSE and follow-up intervention some health belief scores improved but no significant differences were found between groups except for severity scores. (SFG 27.05, IFG 25.00, p=0.03). The power of the study to detect small differences between the groups was affected by the higher than anticipated attrition and the significant lowering of HgbA1c in the education arm of the study. Both groups achieved a high success rate (58% IFG; 55% SFG) to lower the HgbA1c to the ADA goal of less than 7. DSME and follow-up care (both standard follow-up and more intensive follow-up) achieved a significant lowering of HgbA1c (1.6%), which has been shown to reduce diabetes related morbidity and health costs.

Humans are exposed to electrophilically reactive compounds, both formed endogenously and from exogenous exposure. Such compounds could react and form stable reaction products, adducts, at nucleophilic sites in proteins and DNA. The formation of adducts constitutes a risk for effects, such as cancer and contact allergy, and plays a role in ageing processes. Adducts to proteins offer a possibility to measure electrophilic compounds in vivo. Adductomic approaches aim to study the totality of adducts, to specific biomolecules, by mass spectrometric screening. This thesis describes the development and application of an adductomic approach for the screening of unknown adducts to N-terminal valine (Val) in hemoglobin (Hb) by liquid chromatography tandem mass spectrometry (LC/MS/MS). The adductomic approach is based on the FIRE procedure, a modified Edman procedure for the analysis of adducts to N-terminal Val in Hb by LC/MS/MS. The adduct screening was performed by stepwise scanning of precursor ions in small mass increments and monitoring four fragments common for derivatives of detached Val adducts, in the multiple reaction monitoring mode. Samples from 12 smokers/nonsmokers were screened with the adductomic approach, and seven previously identified adducts and 19 unknown adducts were detected. A semiquantitative approach was applied for approximate quantification of adduct levels. A strategy for identifying unknown Hb adducts using adductome LC/MS/MS data was formulated and applied for the identification of unknown adducts. Identifications were based on the observed m/z of precursor ions and retention times combined with databases and Log P calculations. Hypothesized adducts were generated in vitro for comparison and matching with the corresponding unknown adducts. Five identified adducts correspond to the precursor electrophiles ethyl vinyl ketone (EVK), glyoxal, methylglyoxal, acrylic acid, and 1-octen-3-one. These adducts, except the adducts corresponding to glyoxal and methylglyoxal, have not been observed as protein adducts before.  Probable exposure sources to these electrophiles are diet and/or endogenous formation. The observation of these adducts motivate further studies to evaluate possible contributions to health risks, as well as their potential as biomarkers of exposure. The adduct from EVK was quantitatively assessed through different experiments to estimate the daily internal dose (area under the concentration-time-curve, AUC). EVK is about 2 × 103 more reactive than the reference compound acrylamide. The EVK adduct was shown to be unstable, with a relatively short half-life. The daily AUC in humans of EVK was estimated to be about 20 times lower than the corresponding AUC of acrylamide from intake via food. To confirm the observation of the detected unknown adducts and obtain a statistical foundation, analysis of unknown adducts were performed in large sets of blood samples (n = 50–120) from human cohorts. The majority of the previously detected unknown adducts were found in all analyzed samples, and the levels of many adducts showed large variations between individuals. The cause and significance of these observed variations are not yet clarified, but are of importance for the directions of future studies. In conclusion, a new approach for identification of unknown human exposure to electrophiles was developed and successfully applied. / <p>At the time of the doctoral defense, the following paper was unpublished and had a status as follows: Paper 4: Submitted.</p>