Jutzeler, C. | Search bioRxiv

bioRxiv2026

Voluntary engagement predicts recovery across rehabilitation time points after experimental spinal cord injury

Rehabilitation is the cornerstone of clinical care following spinal cord injury (SCI) and is frequently incorporated into preclinical studies to evaluate emerging therapies. However, the variables that influence the effectiveness of rehabilitation interventions in rodent models remain poorly characterized, limiting the translational value of preclinical research. Here, we performed a meta-analysis of 150 preclinical studies comprising more than 3000 rodents and 292 effect sizes to identify such factors associated with successful rehabilitation after SCI. Rehabilitation improved functional outcomes across multiple behavioral domains, with larger effects observed in thoracic than cervical injury models. Meta-regression identified environmental enrichment as the most effective strategy, particularly when combined with task-specific training. Longer rehabilitation duration was associated with improved recovery, whereas greater daily exposure to forced rehabilitation was associated with smaller gains. In contrast, prolonged access to voluntary rehabilitation did not show this relationship. Rehabilitation improved outcomes when initiated at all time points after injury, with greater benefits when initiated during the late subacute and intermediate phases. Rehabilitation approaches associated with higher stress burden produced reduced benefits, suggesting that stress may limit rehabilitation-induced recovery. Collectively, these findings identify rehabilitation type, duration, and delivery context as important determinants of recovery after SCI and support a broad therapeutic window for intervention. Our results favor low-stress, voluntary rehabilitation strategies that promote sustained engagement and provide principles for designing effective and biologically appropriate rehabilitation strategies, potentially improving translational success for emerging SCI therapies. One Sentence SummarySustained voluntary rehabilitation promotes functional recovery across stages of experimental spinal cord injury, whereas prolonged forced training may reduce its benefits.

neuroscience↗

bioRxiv2026

Antimicrobial peptide databases and prediction tools: Toward a standard evaluation framework

Antimicrobial resistance (AMR) has a profound impact on animal and human health and is associated with substantial morbidity, mortality and public health costs. There is a clear need to develop novel, effective antibiotic agents, which can overcome the current AMR crisis. Antimicrobial peptides (AMPs) may offer such a solution and have attracted growing attention for their potential to combat AMR. In parallel, the growing availability of peptide sequences in public databases has stimulated the development of numerous machine learning and deep learning tools to predict antimicrobial activity computationally. However, it remains unclear how reliably these tools can be compared, as existing studies often rely on heterogeneous datasets and inconsistent evaluation protocols that may lead to data leakage and inflated performance estimates. This raises a central question: what evaluation criteria and benchmark resources are needed to enable fair, reproducible, and biologically meaningful assessment of AMP prediction tools? We address this question by focusing specifically on antibacterial peptides (ABPs). We first provide an overview of AMP databases relevant to antibacterial activity and compare their content, redundancy, and experimental metadata. We then critically assess existing computational tools for ABP prediction, highlighting key limitations related to dataset construction, affinity to certain sequences, data leakage, and inconsistent performance reporting. Based on these limitations, we propose a reference evaluation framework designed to improve comparability, reproducibility, and practical utility in ABP prediction. Finally, we provide targeted recommendations for AMP databases and future tool development to support more robust progress in the computational discovery of ABPs.

bioinformatics↗