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Biology subjects

Shani, S.

Publications and source records attributed to Shani, S..

2 recordsLinked to original sources

Bedside Assessment of Visual Tracking in Traumatic Brain Injury: Comparing Simple and Predictive Paradigms Using Multiple Oculomotor Markers

Oculomotor function is a sensitive marker of neurological impairment with smooth pursuit deficiencies investigated in various disorders. However, the oculomotor deficits following traumatic brain injury (TBI) have not been fully characterized. In this study, we employed a novel bedside eye-tracking paradigm to assess oculomotor dysfunction in 30 TBI patients and 30 age-matched controls. Our paradigm utilized short, repeated linear tracking segments with head-free recording, enabling the extraction of multiple oculomotor indices, including saccadic pursuit, tracking deviation under occlusion, initial tracking speed, initial saccade latency, pupil response, and vergence instability. TBI patients exhibited widespread deficits across these indices (AUC = 0.71-0.84), which correlated significantly with functional recovery, as measured by the Functional Independence Measure (R = 0.41-0.78, p < 0.001) but not with the initial Glasgow Coma Scale scores. These findings suggest that TBI disrupts multiple components of the oculomotor system, extending to predictive tracking, pupil-linked arousal, and binocular coordination. Additionally, preliminary testing in disorders of consciousness (DOC) patients revealed fragmented tracking, suggesting a potential application for assessing perceptual awareness. Our findings support the use of eye tracking as a promising tool for quantifying brain function in TBI, with potential applications in prognosis, rehabilitation monitoring, and broader neurological assessment.

neuroscience↗

RNAlysis: analyze your RNA sequencing data without writing a single line of code

BackgroundAmongst the major challenges in next-generation sequencing experiments are exploratory data analysis, interpreting trends, identifying potential targets/candidates, and visualizing the results clearly and intuitively. These hurdles are further heightened for researchers who are not experienced in writing computer code, since the majority of available analysis tools require programming skills. Even for proficient computational biologists, an efficient and replicable system is warranted to generate standardized results. ResultsWe have developed RNAlysis, a modular Python-based analysis software for RNA sequencing data. RNAlysis allows users to build customized analysis pipelines suiting their specific research questions, going all the way from raw FASTQ files, through exploratory data analysis and data visualization, clustering analysis, and gene-set enrichment analysis. RNAlysis provides a friendly graphical user interface, allowing researchers to analyze data without writing code. We demonstrate the use of RNAlysis by analyzing RNA data from different studies using C. elegans nematodes. We note that the software is equally applicable to data obtained from any organism. ConclusionsRNAlysis is suitable for investigating a variety of biological questions, and allows researchers to more accurately and reproducibly run comprehensive bioinformatic analyses. It functions as a gateway into RNA sequencing analysis for less computer-savvy researchers, but can also help experienced bioinformaticians make their analyses more robust and efficient, as it offers diverse tools, scalability, automation, and standardization between analyses.

bioinformatics↗