Small strategic perturbations of the molecular structures impart significant variation in the evolution dynamics and properties of supramolecular self-assembled architectures. However, probing the in situ evolution dynamics of molecular assembly remains a challenge. Herein, we unraveled the real-time, early stage environment-sensitive dynamic molecular self-assembly processes and stimuli-induced reversible morphological transformation between supramolecular fibers and spherical nanoaggregates of alkyl chain-substituted phenanthroimidazole-based luminogen (BPIB1) through fluorescence lifetime imaging microscopy (FLIM). The presence of an octyl chain and basic nitrogen centers in BPIB1 led to a distinct self-assembly pattern. The gradual progression in the fluorescence lifetime provides a unique strategy for elucidating the dynamics of the self-assembly process leading to distinct nano/microarchitectures. Among the varied self-assembled structures, the smaller-sized (diameter ∼ 20–30 nm), highly photostable, water-dispersible, and biocompatible fluorescent nanoaggregates of BPIB1 were employed for tracking the dynamics of lipid droplets in live cells and a model organism, C. elegans, using fluorescence correlation spectroscopy and FLIM. Thus, a combined microscopic and spectroscopic approach demonstrated in the present study opens up new avenues to explore the formation pathways of diverse molecular aggregates and their use to decipher complex organelle dynamics.

Biological processes at the cellular level are stochastic in nature, and the immune response system is no different. Therefore, models that attempt to explain this system need to also incorporate noise or fluctuations that can account for the observed variability. In this work, a stochastic model of the immune response system is presented in terms of the dynamics of T cells and virus particles. Making use of the Green’s function and the Wilemski–Fixman approximation, this model is then solved to obtain the analytical expression for the joint probability density function of these variables in the early and late stages of infection. This is then also used to calculate the average level of virus particles in the system. Upon comparing the theoretically predicted average virus levels to those of COVID-19 patients, it is hypothesized that the long-lived dynamics that are characteristics of such viral infections are due to the long range correlations in the temporal fluctuations of the virions. This model, therefore, provides an insight into the effects of noise on viral dynamics.

Microfluidic devices, through their vast applicability as tools for miniaturized experimental setups, have become indispensable for cutting edge research and diagnostics. However, the high operational cost and the requirement of sophisticated equipment and clean room facility for the fabrication of these devices make their use unfeasible for many research laboratories in resource limited settings. Therefore, with the aim of increasing accessibility, in this article, we report a novel, cost-effective micro-fabrication technique for fabricating multi-layer microfluidic devices using only common wet-lab facilities, thereby significantly lowering the cost. Our proposed process-flow-design eliminates the need for a mastermold, does not require any sophisticated lithography tools, and can be executed successfully outside a clean room. In this work, we also optimized the critical steps (such as spin coating and wet etching) of our fabrication process and validated the process flow and the device by trapping and imaging Caenorhabditis elegans. The fabricated devices are effective in conducting lifetime assays and flushing out larvae, which are, in general, manually picked from Petri dishes or separated using sieves. Our technique is not only cost effective but also scalable, as it can be used to fabricate devices with multiple layers of confinements ranging from 0.6 to more than 50 μm, thus enabling the study of unicellular and multicellular organisms. This technique, therefore, has the potential to be adopted widely by many research laboratories for a variety of applications.

The slowly decaying viral dynamics, even after 2–3 weeks from diagnosis, is one of the characteristics of COVID-19 infection that is still unexplored in theoretical and experimental studies. This long-lived characteristic of viral infections in the framework of inherent variations or noise present at the cellular level is often overlooked. Therefore, in this work, we aim to understand the effect of these variations by proposing a stochastic non-Markovian model that not only captures the coupled dynamics between the immune cells and the virus but also enables the study of the effect of fluctuations. Numerical simulations of our model reveal that the long-range temporal correlations in fluctuations dictate the long-lived dynamics of a viral infection and, in turn, also affect the rates of immune response. Furthermore, predictions of our model system are in agreement with the experimental viral load data of COVID-19 patients from various countries.