Biology subjects

Georgiou, A.

Publications and source records attributed to Georgiou, A..

6 recordsLinked to original sources

bioRxiv2025

Suspended Tissue Engineering with Assemblable Microfluidics (STEAM)

Suspended tissue culture systems enable cellular responses to mechanical forces critical for tissue development and function. Tissues develop in complex environments containing both mechanical and chemical cues that vary spatiotemporally; thus modeling both of these physiochemistries in vitro through integration of spatial patterning with mechanical manipulation simultaneously is an important aspect in microphysiological tissue modeling which has yet to be achieved. Here, we introduce Suspended Tissue Engineering with Assemblable Microfluidics (STEAM), a modular tissue fabrication platform that allows for spatially heterogeneous suspended tissue architectures. With STEAM, we achieve tissue constructs with multiple regions through the addition of capillary pinning features to control hydrogel precursor flow. STEAM tissues can easily be moved from patterning setup to well-plate to microscope slide, which also enables stacking of separately generated layers. Mechanical manipulation post-fabrication is also possible via static stretching, where cell-embedded 3D tissues can be stretched farther apart to induce strain along an axis. To demonstrate the utility of post fabrication strain ability, we showed that myotube alignment increases when strain is applied to STEAM generated engineered muscle tissue containing mouse myoblasts. Finally, by modifying the channel geometry of the fluidic-based patterning rails, we generate complex nonplanar suspended tissues. STEAM leverages microfluidic principles to generate suspended tissues that integrate patterning precision, mechanical functionality, and experimental versatility, providing a suite of construct combinations for modeling tissue behaviors from the interplay of spatial organization and mechanical forces.

bioengineering↗

bioRxiv2025

Metformin Shows Greater Potential Than Semaglutide in Reducing Alzheimer's Risk in Diabetes Type II via Dual Actions: Tackling Disease Pathways and Environmental Herpesvirus Triggers

Alzheimers disease (AD) and Diabetes Mellitus Type II (DM2) share overlapping pathological mechanisms, with DM2 increasing AD risk. Disease-modifying therapies (DMTs) for DM2, including Metformin and Semaglutide, have been explored for neuroprotection, yet their efficacy in AD remains unclear. Here, we introduce a novel integrative framework combining comparative network pharmacology, Mendelian Randomization (MR), and transcriptomic validation to systematically assess the therapeutic impact of 39 DM2 DMTs in preventing AD in DM2. Metformin emerged as the top-ranked therapy, whereas Semaglutide ranked among the least effective based on comparative analysis within the DM2-AD pathway-pathway comorbidity network. Notably, a two-sample MR analysis finds no evidence supporting a genetic liability to either drug being associated with AD risk, suggesting that their benefits may arise through non-direct mechanisms or that their relationship could be confounded by third factors. Metformins neuroprotective impact is mediated through AMPK, insulin, and adipocytokine signaling, which regulate key AD-related processes. Additionally, Metformin may indirectly affect herpesviruses, emerging environmental contributors to AD, potentially enhancing its neuroprotective effects. In contrast, Semaglutide, despite its growing clinical prominence as a weight loss therapy, exhibits minimal engagement with core neurodegenerative pathways within the DM2-AD comorbidity network, highlighting variability in neuroprotective potential across DM2 DMTs. Furthermore, specific dual-action therapies (e.g., Insulin Glargine and Lixisenatide, Insulin Degludec and Liraglutide) exhibit efficacy comparable to Metformin, reinforcing the need for a precision medicine approach. These findings challenge the assumption that all DM2 DMTs confer equal neuroprotection, revealing significant differences in their impact on AD-related pathways. While some show strong potential for AD prevention, others appear far less effective. Metformins effects depend on genotype, disease state, and environmental factors, underscoring the need to re-evaluate DM2 DMTs for AD prevention in population-specific clinical trials. Metformin stands out as a strong candidate for targeted investigations in DM2 patients at high risk of AD.

bioinformatics↗

bioRxiv2024

Random Tree Model of Meaningful Memory

Traditional studies of memory for meaningful narratives focus on specific stories and their semantic structures but do not address common quantitative features of recall across different narratives. We introduce a statistical ensemble of random trees to represent narratives as hierarchies of key points, where each node is a compressed representation of its descendant leaves, which are the original narrative segments. Recall is modeled as constrained by working memory capacity from this hierarchical structure. Our analytical solution aligns with observations from large-scale narrative recall experiments. Specifically, our model explains that (1) average recall length increases sublinearly with narrative length, and (2) individuals summarize increasingly longer narrative segments in each recall sentence. Additionally, the theory predicts that for sufficiently long narratives, a universal, scale-invariant limit emerges, where the fraction of a narrative summarized by a single recall sentence follows a distribution independent of narrative length.

neuroscience↗

bioRxiv2023

Using large language models to study human memory for meaningful narratives

One of the most impressive achievements of the AI revolution is the development of large language models that can generate meaningful text and respond to instructions in plain English with no additional training necessary. Here we show that language models can be used as a scientific instrument for studying human memory for meaningful material. We developed a pipeline for designing large scale memory experiments and analyzing the obtained results. We performed online memory experiments with a large number of participants and collected recognition and recall data for narratives of different lengths. We found that both recall and recognition performance scale linearly with narrative length. Furthermore, in order to investigate the role of narrative comprehension in memory, we repeated these experiments using scrambled versions of the presented stories. We found that even though recall performance declined significantly, recognition remained largely unaffected. Interestingly, recalls in this condition seem to follow the original narrative order rather than the scrambled presentation, pointing to a contextual reconstruction of the story in memory.

neuroscience↗

bioRxiv2022

Forgetting Dynamics For Items of Different Categories

AO_SCPLOWBSTRACTC_SCPLOWHow the dynamic evolution of forgetting changes for different materials is unexplored. By using a common experimental paradigm with stimuli of different types, we were able to directly cross-examine the emerging dynamics and we found that even though the presentation sets differ minimally by design, the obtained curves appear to fall on a discrete spectrum. Furthermore, we have previously proposed a model of forgetting based on the notion of retrograde interference with a single integer parameter. All measured curves were compatible with the model with different values of the parameter, hinting to a potential common underlying mechanism of forgetting.

neuroscience↗

bioRxiv2019

Retroactive Interference Model of Power-Law Forgetting

Memory and forgetting constitute two sides of the same coin, and although the first has been rigorously investigated, the latter is often overlooked. A number of experiments under the realm of psychology and experimental neuroscience have described the properties of forgetting in humans and animals, showing that forgetting exhibits a power-law relationship with time. These results indicate a counter-intuitive property of forgetting, namely that old memories are more stable than younger ones. We have devised a phenomenological model that is based on the principle of retroactive interference, such that stronger memories acquired after weaker ones erase the latter. The model has only one free integer parameter and can be solved analytically. Recognition experiments with long streams of words were performed, resulting in a good match to a five-dimensional version of the model.

neuroscience↗