Microplate-Based Cell Viability Assay as a Cost-Effective Alternative to Flow Cytometry for Microalgae Analysis.

Cell viability is a critical indicator for assessing culture quality in microalgae cultivation for biorefinery and bioremediation. Fluorescent dyes that distinguish viable from nonviable cells can enable viability quantification based on the percentage of live cells. However, fluorescence analysis using the typical flow cytometry method is costly and impractical for industrial applications. To address this, we developed new microplate assays utilizing fluorescein diacetate as a live cell stain and erythrosine B as a dead cell stain. These assays provide a low-cost, simple, and reliable method of assessing cell viability. The proposed microplate assays were successfully applied to monitor the viability of the microalgae Dunaliella viridis under carbon and nitrogen limitation stresses and demonstrated good agreement with flow cytometry measurements. We conducted a systematic investigation of the effects of dye concentration, incubation time, and background fluorescence on the microplate assays' performance. Further, we provide a comprehensive review of commonly used fluorescent dyes for microalgae staining, discuss strategies to enhance assay performance, and offer recommendations for dye selection and protocol development. This study presents a comprehensive new method for microplate-based viability analysis, providing valuable insights for future microalgae viability assessments and applications.

Automated Video Analysis of Audio-Visual Approaches to Predict and Detect Mild Cognitive Impairment and Dementia in Older Adults.

BACKGROUND: Early identification of different stages of cognitive impairment is important to provide available intervention and timely care for the elderly. OBJECTIVE: This study aimed to examine the ability of the artificial intelligence (AI) technology to distinguish participants with mild cognitive impairment (MCI) from those with mild to moderate dementia based on automated video analysis. METHODS: A total of 95 participants were recruited (MCI, 41; mild to moderate dementia, 54). The videos were captured during the Short Portable Mental Status Questionnaire process; the visual and aural features were extracted using these videos. Deep learning models were subsequently constructed for the binary differentiation of MCI and mild to moderate dementia. Correlation analysis of the predicted Mini-Mental State Examination, Cognitive Abilities Screening Instrument scores, and ground truth was also performed. RESULTS: Deep learning models combining both the visual and aural features discriminated MCI from mild to moderate dementia with an area under the curve (AUC) of 77.0% and accuracy of 76.0%. The AUC and accuracy increased to 93.0% and 88.0%, respectively, when depression and anxiety were excluded. Significant moderate correlations were observed between the predicted cognitive function and ground truth, and the correlation was strong excluding depression and anxiety. Interestingly, female, but not male, exhibited a correlation. CONCLUSION: The study showed that video-based deep learning models can differentiate participants with MCI from those with mild to moderate dementia and can predict cognitive function. This approach may offer a cost-effective and easily applicable method for early detection of cognitive impairment.

Effects of UV-C Disinfection on N95 and KN95 Filtering Facepiece Respirator Reuse.

The objective of this study was to evaluate the effectiveness of UV technology for virus disinfection to allow FFR reuse. UV is a proven decontamination tool for microbial pathogens, including the SARS-CoV-2 virus. Research findings suggest that the impacts of UV-C treatment on FFR material degradation should be confirmed using microbial surrogates in addition to the commonly performed abiotic particle testing. This study used the surrogates, E. coli and MS-2 bacteriophage, as they bracket the UV response of SARS-CoV-2. Lower log inactivation was observed on FFRs than predicted by aqueous-based UV dose-response data for MS-2 bacteriophage and E. coli. In addition, the dose-response curves did not follow the trends commonly observed with aqueous data for E. coli and MS-2. The dose-response curves for the respirators in this study had a semicircle shape, where the inactivation reached a peak and then decreased. This decrease in UV inactivation is thought to be due to the degradation of the fibers of the FFR and allows for more viral and bacterial cells to wash through the layers of the respirator. This degradation phenomenon was observed at UV doses at and above 2,000 mJ/cm2. Results have demonstrated that FFR materials yield various results in terms of effective disinfection in experiments conducted on KN95 and N95 face respirators. The highest inactivation for both surrogates was observed with the KN95 respirator made by Purism, yielding 3 and 2.75 log inactivation for E. coli and MS-2 at UV doses of 1,500 mJ/cm2. The KN95 made by Anboruo yielded the lowest inactivation for MS-2 at 0.75 log when exposed to 1,000 mJ/cm2. To further test the degradation theory, experiments used a collimated beam device to test the hypothesis further that degradation is occurring at and above UV doses of 1,500 mJ/cm2. The experiment aimed to determine the effect of "predosing" a respirator with UV before inoculating the respirator with MS-2. In this test, quantification of the penetrated irradiance value and the ability of each layer to retain MS-2 were quantified. The results of the experiments varied from the intact FFR degradation experiments but displayed some data to support the degradation theory. IMPORTANCE Research suggests degradation of FFR materials at high UV doses is important. There appears to be a peak inactivation dose at approximately 1,500 mJ/cm2. The subsequent dose increases appear to have the reverse effect on inactivation values; these trends have shown true with both the N95 and KN95-Purism respirators.

Dynamic Modeling of Microalgae Growth and Lipid Production under Transient Light and Nitrogen Conditions.

We developed a new dynamic model to characterize how light and nitrogen regulate the cellular processes of photosynthetic microalgae leading to transient changes in the production of neutral lipids, carbohydrates, and biomass. Our model recapitulated the versatile neutral lipid synthesis pathways via (i) carbon reuse from carbohydrate metabolism under nitrogen sufficiency and (ii) fixed carbon redirection under nitrogen depletion. We also characterized the effects of light adaptation, light inhibition hysteresis, and nitrogen limitation on photosynthetic carbon fixation. The formulated model was calibrated and validated with experimental data of Dunaliella viridis cultivated in a lab-scale photobioreactor (PBR) under various light (low/moderate/high) and nitrogen (sufficient/limited) conditions. We conducted the identifiability, uncertainty, and sensitivity analyses to verify the model reliability using the profile likelihood method, the Markov chain Monte Carlo (MCMC) technique, and the extended Fourier Amplitude Sensitivity Test (eFAST). Our model predictions agreed well with experimental observations and suggested potential model improvement by incorporating a lipid degradation mechanism. The insights from our model-driven analysis helped improve the mechanistic understanding of transient algae growth and bioproducts formation under environmental variations and could be applied to optimize biofuel and biomass production.