Acute corneal graft rejection after anti-severe acute respiratory syndrome-coronavirus-2 vaccination: A report of four cases.

PURPOSE: This study intends to add to previous reports on acute corneal graft rejection following anti-severe acute respiratory syndrome-coronavirus-2 vaccination, providing data to corroborate a possible causative relationship between anti-COVID-19 immunization and corneal graft rejection, regardless of vaccine or graft type. METHODS AND RESULTS: This report describes 4 cases of acute-onset rejection as early as 5 days following the first dose of anti-severe acute respiratory syndrome-coronavirus-2 vaccine types not yet referred for corneal allograft. Patients were individually given the Moderna messenger RNA-1273 COVID-19 vaccine (2 patients) and the AstraZeneca COVID-19 vaccine, Vaxzevria, AZD1222 (2 patients). CONCLUSIONS: Even though a direct causative effect is hard to prove, temporal proximity between anti-severe acute respiratory syndrome-coronavirus-2 vaccines of different types and consecutive reports of corneal graft rejection indicates the need for further investigation. Consistent advice must be given to corneal transplant patients regarding such risk.

Facile Synthesis of Gold Nanoparticles by Amino Acid Asparagine: Selective Sensing of Arsenic.

The amino acid asparagine (ASP) was used as a benign reducing and stabilizing agent for the production of monodisperse gold nanoparticles (AuNPs) using green chemistry principles. With an increasing concentration of ASP (0.5 to 10 mM), the absorbance intensity at 525 nm increased; however, no effects on the color, size, or shape of the AuNPs were observed. Transmission electron microscope (TEM) images showed that the AuNPs were either hexagonal or spherical in shape and had an average size of approximately 10 ± 5 nm. Facile colorimetric assays of the AuNPs were applied to detect a variety of heavy metal ion species in water. In this study, the selective detection of arsenic ions (As (III) ions) by quenching, aggregation, and/or red-shifting of the surface plasmon resonance (SPR) was successfully achieved. The AuNPs sensor was sustainable as a visual colorimetric detection system and spectral assay of hazardous As (III) ions in the reaction medium; thus, it will be useful for aqueous assessment without using any sophisticated or expensive instruments.

Dynamic modelling of high biomass density cultivation and biohydrogen production in different scales of flat plate photobioreactors.

This paper investigates the scaling-up of cyanobacterial biomass cultivation and biohydrogen production from laboratory to industrial scale. Two main aspects are investigated and presented, which to the best of our knowledge have never been addressed, namely the construction of an accurate dynamic model to simulate cyanobacterial photo-heterotrophic growth and biohydrogen production and the prediction of the maximum biomass and hydrogen production in different scales of photobioreactors. To achieve the current goals, experimental data obtained from a laboratory experimental setup are fitted by a dynamic model. Based on the current model, two key original findings are made in this work. First, it is found that selecting low-chlorophyll mutants is an efficient way to increase both biomass concentration and hydrogen production particularly in a large scale photobioreactor. Second, the current work proposes that the width of industrial scale photobioreactors should not exceed 0.20 m for biomass cultivation and 0.05 m for biohydrogen production, as severe light attenuation can be induced in the reactor beyond this threshold.

Analysis of green algal growth via dynamic model simulation and process optimization.

Chlamydomonas reinhardtii is a green microalga with the potential to generate sustainable biofuels for the future. Process simulation models are required to predict the impact of laboratory-scale growth experiments on future scaled-up system operation. Two dynamic models were constructed to simulate C. reinhardtii photo-autotrophic and photo-mixotrophic growth. A novel parameter estimation methodology was applied to determine the values of key parameters in both models, which were then verified using experimental results. The photo-mixotrophic model was used to accurately predict C. reinhardtii growth under different light intensities and in different photobioreactor configurations. The optimal dissolved CO2 concentration for C. reinhardtii photo-autotrophic growth was determined to be 0.0643 g·L(-1) , and the optimal light intensity for algal growth was 47 W·m(-2) . Sensitivity analysis revealed that the primary factor limiting C. reinhardtii growth was its intrinsic cell decay rate rather than light attenuation, regardless of the growth mode. The photo-mixotrophic growth model was also applied to predict the maximum biomass concentration at different flat-plate photobioreactors scales. A double-exposure-surface photobioreactor with a lower light intensity (less than 50 W·m(-2) ) was the best configuration for scaled-up C. reinhardtii cultivation. Three different short-term (30-day) C. reinhardtii photo-mixotrophic cultivation processes were simulated and optimised. The maximum biomass productivity was 0.053 g·L(-1) ·hr(-1) , achieved under continuous photobioreactor operation. The continuous stirred-tank reactor was the best operating mode, as it provides both the highest biomass productivity and lowest electricity cost of pump operation.

Discovery of metabolite biomarkers: flux analysis and reaction-reaction network approach.

BACKGROUND: Metabolism is a vital cellular process, and its malfunction can be a major contributor to many human diseases. Metabolites can serve as a metabolic disease biomarker. An detection of such biomarkers plays a significant role in the study of biochemical reaction and signaling networks. Early research mainly focused on the analysis of the metabolic networks. The issue of integrating metabolite networks with other available biological data to reveal the mechanics of disease-metabolite associations is an important and interesting challenge. RESULTS: In this article, we propose two new approaches for the identification of metabolic biomarkers with the incorporation of disease specific gene expression data and the genome-scale human metabolic network. The first approach is to compare the flux interval between the normal and disease sample so as to identify reaction biomarkers. The second one is based on the Reaction-Reaction Network (RRN) to reveal the significant reactions. These two approaches utilize reaction flux obtained by a Linear Programming (LP) based method that can contribute to the discovery of potential novel biomarkers. CONCLUSIONS: Biomarker identification is an important issue in studying biochemical reactions and signaling networks. Two efficient and effective computational methods are proposed for the identification of biomarkers in this article. Furthermore, the biomarkers found by our proposed methods are shown to be significant determinants for diabetes.

Individual-based and stochastic modeling of cell population dynamics considering substrate dependency.

In this article, we propose an individual-based and stochastic modeling approach that is capable of describing the bacterial cell population dynamics during a batch culture. All stochastic nature inherent in intracellular molecular level reactions and cell division processes were considered in a single model framework by embedding a sub-model describing individual cell's growth kinetics in a discrete event simulation algorithm. The resultant unique feature of the model is that the effects of the stochasticities on the cell population dynamics can be investigated for different substrate-dependent cell growth kinetics. When Monod kinetics was used as the sub-model, the stochasticities only slightly affected the cell mass increase and substrate consumption profiles during the batch culture although they were still important in describing the changes of cell population distributions. When Andrews substrate inhibition kinetics was used, however, it was revealed that the overall cell population dynamics could be seriously influenced by the stochasticities. Under a critical initial substrate level, the cell population could proliferate against the substrate inhibition only when the stochasticities were considered.