We investigated whether permeability transition-mediated release of mitochondrial cytochrome c is a potential therapeutic target for treating acute spinal cord injury (SCI). Based on previous reports, minocycline, a second-generation tetracycline, exerts neuroprotection partially by inhibiting mitochondrial cytochrome c release and reactive microgliosis. We first evaluated cytochrome c release at the injury epicenter after a T10 contusive SCI in rats. Cytochrome c release peaked at Ϸ4 -8 h postinjury. A dose-response study generated a safe pharmacological regimen that enabled i.p. minocycline to significantly lower cytosolic cytochrome c at the epicenter 4 h after SCI. In the long-term study, i.p. minocycline (90 mg͞kg administered 1 h after SCI followed by 45 mg͞kg administered every 12 h for 5 days) markedly enhanced long-term hind limb locomotion relative to that of controls. Coordinated motor function and hind limb reflex recoveries also were improved significantly. Histopathology suggested that minocycline treatment alleviated later-phase tissue loss, with significant sparing of white matter and ventral horn motoneurons at levels adjacent to the epicenter. Furthermore, glial fibrillary acidic protein and 2 ,3 cyclic nucleotide 3 phosphodiesterase immunocytochemistry showed an evident reduction in astrogliosis and enhanced survival of oligodendrocytes. Therefore, release of mitochondrial cytochrome c is an important secondary injury mechanism in SCI. Drugs with multifaceted effects in antagonizing this process and microgliosis may protect a proportion of spinal cord tissue that is clinically significant for functional recovery. Minocycline, with its proven clinical safety, capability to cross the blood-brain barrier, and demonstrated efficacy during a clinically relevant therapeutic window, may become an effective therapy for acute SCI.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues.