Anandakrishnan, R. | Search bioRxiv

bioRxiv2024

Effect of quantified cranial osteopathic manipulation on wild type and transgenic rat models of Alzheimer's disease

Alzheimers Disease is a chronic progressive neurodegenerative disorder that impairs the cerebral lymphatic system and compartmental fluid exchange leading to a decline in cognitive function. Due to the lack of disease modifying medications, non-pharmacological Cranial Osteopathic Manipulation (COM) is evolving as a potential minimally invasive treatment choice. In this work, the effect of quantified COM treatment, using a nanosensor glove, on 3-month-old (Yg) and 18-month-old transgenic (Tg) rat model of Alzheimers Disease were studied using the Morris Water Maze (MWM), Western Blots, and Proteomics and Transcriptomics assays. The results revealed that COM had minimal to no significant difference in the behavioral and biochemical parameters in the Yg rats, suggesting COM treatment was harmless. While COM exhibited no significant differences in Tg rat MWM escape latency, navigation to the platform was significantly different on testing days 5 and 6, with p-values of signed initial heading error were 0.014 and 0.034 respectively. This indicates a difference in learning and spatial working memory. A proteomic assay on Tg rat hippocampus identified 51 significantly differentially expressed proteins with 34 associated with neurological disorders, while transcriptome remained indifferent. In this study, for the first time we have established a technique to quantify the force applied during COM treatment on an animal model of AD, offering a more objective approach for evaluating the effect of such treatments. Our results indicate that a quantifiable COM can be applied to rodents and to study the resulting behavioral and biochemical phenotypes.

bioengineering↗

bioRxiv2023

BiGPICC: a graph-based approach to identifying carcinogenic gene combinations from mutation data

Genome data from cancer patients represents relationships between the presence of a gene mutation and cancer occurrence in a patient. Different types of cancer in human are thought to be caused by combinations of two to nine gene mutations. Identifying these combinations through traditional exhaustive search requires the amount of computation that scales exponentially with the combination size and in most cases is intractable even for cutting-edge supercomputers. We propose a parameter-free heuristic approach that leverages the intrinsic topology of gene-patient mutations to identify carcinogenic combinations. The biological relevance of the identified combinations is measured by using them to predict the presence of tumor in previously unseen samples. The resulting classifiers for 16 cancer types perform on par with exhaustive search results, and score the average of 80.1% sensitivity and 91.6% specificity for the best choice of hit range per cancer type. Our approach is able to find higher-hit carcinogenic combinations targeting which would take years of computations using exhaustive search.

bioinformatics↗