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Sumner, M.

Publications and source records attributed to Sumner, M..

2 recordsLinked to original sources

Learning induces unique transcriptional landscapes in the auditory cortex

Learning can induce neurophysiological plasticity in the auditory cortex at multiple timescales. Lasting changes to auditory cortical function that persist over days, weeks, or even a lifetime, require learning to induce de novo gene expression. Indeed, transcription is the molecular determinant for long-term memories to form with a lasting impact on sound-related behavior. However, auditory cortical genes that support auditory learning, memory, and acquired sound-specific behavior are largely unknown. This report is the first to identify in young adult male rats (Sprague-Dawley) genome-wide changes in learning-induced gene expression within the auditory cortex that may underlie the formation of long-lasting discriminative memory for acoustic frequency cues. Auditory cortical samples were collected from animals in the initial learning phase of a two-tone discrimination sound-reward task known to induce sound-specific neurophysiological and behavioral effects (e.g., Shang et al., 2019). Bioinformatic analyses on gene enrichment profiles from bulk RNA sequencing identified cholinergic synapse (KEGG 04725), extra-cellular matrix receptor interaction (KEGG 04512), and neuroactive ligand-receptor interaction (KEGG 04080) as top biological pathways for auditory discrimination learning. The findings characterize key candidate effectors underlying changes in cortical function that support the initial formation of long-term discriminative auditory memory in the adult brain. The molecules and mechanisms identified are potential therapeutic targets to facilitate lasting changes to sound-specific auditory function in adulthood and prime for future gene-targeted investigations.

neuroscience↗

Are Machines-learning Methods More Efficient than Humans in Triaging Literature for Systematic Reviews?

Systematic literature reviews provide rigorous assessments of clinical, cost-effectiveness, and humanistic data. Accordingly, there is a growing trend worldwide among healthcare agencies and decision-makers to require them in order to make informed decisions. Because these reviews are labor-intensive and time consuming, we applied advanced analytic methods (AAM) to determine if machine learning methods could classify abstracts as well as humans. Literature searches were run for metastatic non-small cell lung cancer treatments (mNSCLC) and metastatic castration-resistant prostate cancer (mCRPC). Records were reviewed by humans and two AAMs. AAM-1 involved a pre-trained data-mining model specialized in biomedical literature, and AAM-2 was based on support vector machine algorithms. The AAMs assigned an accept/reject status, with reasons for exclusion. Automatic results were compared to those of humans. For mNSCLC, 5820 records were processed by humans and 440 (8%) records were accepted and the remaining items rejected. AAM-1 correctly accepted 6% of records and correctly excluded 79%. AAM-2 correctly accepted 6% of records and correctly excluded 82%. The review was completed by AAM-1 or AAM-2 in 52 hours, compared to 196 hours for humans. Work saved was estimated to be 76% and 79% by AAM-1 and AAM-2, respectively. For mCRPC, 2434 records were processed by humans and 26% of these were accepted and 74% rejected. AAM-1 correctly accepted 23% of records and rejected 62%. AAM-2 correctly accepted 20% of records and rejected 66%. The review was completed by AAM-1, AAM-2, and humans in 25, 25 and 85 hours, respectively. Work saved was estimated to be 61% and 68% by AAM-1 and AAM-2, respectively. AAMs can markedly reduce the time required for searching and triaging records during a systematic review. Methods similar to AAMs should be assessed in future research for how consistent their performances are in SLRs of economic, epidemiological and humanistic evidence.

bioinformatics↗