Surgical intervention combined with weight-bearing walking training promotes recovery in patients with chronic spinal cord injury:a randomized controlled study

For patients with chronic spinal cord injury,the co nventional treatment is rehabilitation and treatment of spinal cord injury complications such as urinary tract infection,pressure sores,osteoporosis,and deep vein thrombosis.Surgery is rarely perfo rmed on spinal co rd injury in the chronic phase,and few treatments have been proven effective in chronic spinal cord injury patients.Development of effective therapies fo r chronic spinal co rd injury patients is needed.We conducted a randomized controlled clinical trial in patients with chronic complete thoracic spinal co rd injury to compare intensive rehabilitation(weight-bearing walking training)alone with surgical intervention plus intensive rehabilitation.This clinical trial was registered at ClinicalTrials.gov(NCT02663310).The goal of surgical intervention was spinal cord detethering,restoration of cerebrospinal fluid flow,and elimination of residual spinal cord compression.We found that surgical intervention plus weight-bearing walking training was associated with a higher incidence of American Spinal Injury Association Impairment Scale improvement,reduced spasticity,and more rapid bowel and bladder functional recovery than weight-bearing walking training alone.Overall,the surgical procedures and intensive rehabilitation were safe.American Spinal Injury Association Impairment Scale improvement was more common in T7-T11 injuries than in T2-T6 injuries.Surgery combined with rehabilitation appears to have a role in treatment of chronic spinal cord injury patients.

BrcaSeg:A Deep Learning Approach for Tissue Quantification and Genomic Correlations of Histopathological Images

Epithelial and stromal tissues are components of the tumor microenvironment and play a major role in tumor initiation and progression.Distinguishing stroma from epithelial tissues is critically important for spatial characterization of the tumor microenvironment.Here,we propose BrcaSeg,an image analysis pipeline based on a convolutional neural network(CNN)model to classify epithelial and stromal regions in whole-slide hematoxylin and eosin(H&E)stained histopathological images.The CNN model is trained using well-annotated breast cancer tissue microarrays and validated with images from The Cancer Genome Atlas(TCGA)Program.BrcaSeg achieves a classification accuracy of 91.02%,which outperforms other state-of-the-art methods.Using this model,we generate pixel-level epithelial/stromal tissue maps for 1000 TCGA breast cancer slide images that are paired with gene expression data.We subsequently estimate the epithelial and stromal ratios and perform correlation analysis to model the relationship between gene expression and tissue ratios.Gene Ontology(GO)enrichment analyses of genes that are highly correlated with tissue ratios suggest that the same tissue is associated with similar biological processes in different breast cancer subtypes,whereas each subtype also has its own idiosyncratic biological processes governing the development of these tissues.Taken all together,our approach can lead to new insights in exploring relationships between image-based phenotypes and their underlying genomic events and biological processes for all types of solid tumors.BrcaSeg can be accessed at https://github.com/Serian1992/ImgBio.

Characterization of intratumor microbiome in cancer immunotherapy

Dear Editor,As an illuminating cancer hallmark,polymorphic microbiomes profoundly impact cancer phenotypes by promoting or repressing cancer initiation and progression.1 Diversity and composition in the gut microbiome are significantly associated with the response rate of anti-PD1 immunotherapy in melanoma.2 In addition to the gut microbiome,a large number of microbiomes colonizing in human tumors have been shown to play significant roles in cancer development.3 However,a comprehensive understanding of intratumor microbiomes in cancer immunotherapy is lacking,largely due to the challenge of investigating intratumor microbiomes in anti-cancer immunotherapy.

Artificial Intelligence in Omics

Artificial intelligence(AI)is a powerful approach for solving complex problems in the processing,analysis,and interpretation of omics data,as well as the integration of multi-omics and clinical data.In recent years,AI has enabled remarkable breakthroughs across diverse biomedical fields,such as genomic variant interpretation,protein structure prediction,disease diagnosis,and drug discovery.Aiming to provide a forum for advances in the development and application of AI-based tools in omics,we have organized a special issue“Artificial Intelligence in Omics”for the journal Genomics,Proteomics&Bioinformatics(GPB).