Bioinformatics Research

KEY FINDING: Identified 14 significant loci associated with OPLL, including 8 previously unreported loci.

KEY FINDING: Identified 693 and 885 DEGs in old and young mice, respectively, after peripheral nerve injury, with shared DEGs involved in inflammatory and immune responses.

KEY FINDING: Principal component analysis (PCA) identified six factor components (FC) explaining 77% and 82% of the total variance in the SCI and non-SCI cohorts, respectively.

KEY FINDING: Whole genome sequencing identified 260 genes with increased single-nucleotide variations in exonic regions among individuals with high intramuscular adipose tissue levels and recurrent PIs.

KEY FINDING: AI algorithms, including random forests, deep neural networks, and convolutional neural networks, have been used to predict motor outcomes after stroke with AUCs ranging from 0.7 to 0.9.

KEY FINDING: The study identified five distinct subgroups of tSCI patients based on baseline variables such as age, BMI, injury severity (AIS grade), primary location of injury (PLI), and baseline FIM motor score.

KEY FINDING: Patients in the LLRR group (Group B) showed significantly higher FAC (functional ambulation category) scores after 10 weeks of training compared to the conventional RT group (Group A).

KEY FINDING: The classification accuracy of the proposed method has reached 74.61 and 81.19% on datasets 2a and 2b, respectively.

KEY FINDING: The experimental group (using the AI system) showed increased strength in all measured variables, whereas the control group showed constant or reduced results.

KEY FINDING: ANNs predicted the prognosis of patients with cervical SCI more accurately than MLR analysis (75.0% vs 31.3%).