Deep learning-driven imaging of cell division and cell growth across an entire eukaryotic life cycle.

The life cycle of biomedical and agriculturally relevant eukaryotic microorganisms involves complex transitions between proliferative and non-proliferative states such as dormancy, mating, meiosis, and cell division. New drugs, pesticides, and vaccines can be created by targeting specific life cycle stages of parasites and pathogens. However, defining the structure of a microbial life cycle often relies on partial observations that are theoretically assembled in an ideal life cycle path. To create a more quantitative approach to studying complete eukaryotic life cycles, we generated a deep learning-driven imaging framework to track microorganisms across sexually reproducing generations. Our approach combines microfluidic culturing, life cycle stage-specific segmentation of microscopy images using convolutional neural networks, and a novel cell tracking algorithm, FIEST, based on enhancing the overlap of single cell masks in consecutive images through deep learning video frame interpolation. As proof of principle, we used this approach to quantitatively image and compare cell growth and cell cycle regulation across the sexual life cycle of Saccharomyces cerevisiae. We developed a fluorescent reporter system based on a fluorescently labeled Whi5 protein, the yeast analog of mammalian Rb, and a new High-Cdk1 activity sensor, LiCHI, designed to report during DNA replication, mitosis, meiotic homologous recombination, meiosis I, and meiosis II. We found that cell growth preceded the exit from non-proliferative states such as mitotic G1, pre-meiotic G1, and the G0 spore state during germination. A decrease in the total cell concentration of Whi5 characterized the exit from non-proliferative states, which is consistent with a Whi5 dilution model. The nuclear accumulation of Whi5 was developmentally regulated, being at its highest during meiotic exit and spore formation. The temporal coordination of cell division and growth was not significantly different across three sexually reproducing generations. Our framework could be used to quantitatively characterize other single-cell eukaryotic life cycles that remain incompletely described. An off-the-shelf user interface Yeastvision provides free access to our image processing and single-cell tracking algorithms.

Biofilms: Formation, drug resistance and alternatives to conventional approaches.

Biofilms are aggregates of bacteria, in most cases, which are resistant usually to broad-spectrum antibiotics in their typical concentrations or even in higher doses. A trend of increasing multi-drug resistance in biofilms, which are responsible for emerging life-threatening nosocomial infections, is becoming a serious problem. Biofilms, however, are at various sensitivity levels to environmental factors and are versatile in infectivity depending on virulence factors. This review presents the fundamental information about biofilms: formation, antibiotic resistance, impacts on public health and alternatives to conventional approaches. Novel developments in micro-biosystems that help reveal the new treatment tools by sensing and characterization of biofilms will also be discussed. Understanding the formation, structure, physiology and properties of biofilms better helps eliminate them by the usage of appropriate antibiotics or their control by novel therapy approaches, such as anti-biofilm molecules, effective gene editing, drug-delivery systems and probiotics.

An Unanticipated Worsening of Glycemic Control Following a Mild COVID-19 Infection.

We describe a case of acute-onset worsening of a patient's previously well-controlled type 2 diabetes mellitus (T2DM) following his recovery from a novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. A 78-year-old male with a three-year medical history of well-controlled T2DM (controlled by diet and metformin) presented to the outpatient clinic to discuss his regularly scheduled six-month lab work. He mentioned having a mild coronavirus disease 2019 (COVID-19) infection lasting one week which required no medical treatment approximately two months before his current visit. His labs, taken one week prior to his current visit, were notable for fasting hyperglycemia, 301mg/dL, and an elevated hemoglobin A1C (HbA1C), 11%. A fasting blood glucose level was recorded at his current in-office visit and was found to be 403mg/dL. These findings were not anticipated - our patient reported no change in his meals, medications, or exercise routines. The only notable change he reported between visits was his COVID-19 infection. This case report explores the link between this virus and our patient's exacerbation of his previously well-controlled T2DM. Whether it be through insulin resistance or deficiency (or another unknown mechanism), our patient's prior novel COVID-19 infection could potentially be associated with his unprecedented altered glucose metabolism.

Cell cycle-independent integration of stress signals by Xbp1 promotes Non-G1/G0 quiescence entry.

Cellular quiescence is a nonproliferative state required for cell survival under stress and during development. In most quiescent cells, proliferation is stopped in a reversible state of low Cdk1 kinase activity; in many organisms, however, quiescent states with high-Cdk1 activity can also be established through still uncharacterized stress or developmental mechanisms. Here, we used a microfluidics approach coupled to phenotypic classification by machine learning to identify stress pathways associated with starvation-triggered high-Cdk1 quiescent states in Saccharomyces cerevisiae. We found that low- and high-Cdk1 quiescent states shared a core of stress-associated processes, such as autophagy, protein aggregation, and mitochondrial up-regulation, but differed in the nuclear accumulation of the stress transcription factors Xbp1, Gln3, and Sfp1. The decision between low- or high-Cdk1 quiescence was controlled by cell cycle-independent accumulation of Xbp1, which acted as a time-delayed integrator of the duration of stress stimuli. Our results show how cell cycle-independent stress-activated factors promote cellular quiescence outside G1/G0.

A Focus Group Evaluation of Breathe Easier: A Mindfulness-Based mHealth App for Survivors of Lung Cancer and Their Family Members.

PURPOSE: To explore attitudes and acceptance (i.e., intent for future adoption) of survivors of lung cancer and their family members toward a dyad-focused mHealth mindfulness-based intervention (MBI). APPROACH: Focus groups. SETTING: Community hospital setting in South Carolina. PARTICIPANTS: Survivors n = 11 (M = 64.6 years; 73% female; 64% African American) of non-small cell lung cancer (stage I-IIIa) and their family members, n = 8 (M = 58.6 years; 38% female; 75% African American). INTERVENTION: A fully functional prototype mHealth app to deliver a tailored MBI for survivors of lung cancer and their family members. METHOD: Semi-structured focus groups were conducted and assessed using thematic data analysis to identify the benefits, concerns, needs, and expectations of the app. RESULTS: Convenience and health were the top benefits of using the app, while cost and difficulty of use were the top concerns. Survivors mentioned benefits more than their family members did. Participants felt positively about adding a community network to the app. Finally, participants expected to hear about Breathe Easier from their care provider. CONCLUSION: Participants perceived a benefit to having credible health information delivered through an mHealth app. Guidance and credible health information regarding lung cancer survivorship should be accessible and convenient for everyone impacted by the disease. Thus, future research should explore platforms for a virtual support system and understanding dissemination of mHealth apps through health care providers.