Potential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning: A prospective cohort study in breast cancer patients.

An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin (H&E) histology, such as frozen section, are time-, resource-, and labor-intensive, and involve specimen-consuming concerns. Here, we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography (D-FFOCT), a label-free optical imaging method, and deep learning for bedside tumor diagnosis during surgery. To classify the benign and malignant breast tissues, we conducted a prospective cohort trial. In the modeling group (n = 182), D-FFOCT images were captured from April 26 to June 20, 2018, encompassing 48 benign lesions, 114 invasive ductal carcinoma (IDC), 10 invasive lobular carcinoma, 4 ductal carcinoma in situ (DCIS), and 6 rare tumors. Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches. Subsequently, from June 22 to August 17, 2018, independent tests (n = 42) were conducted on 10 benign lesions, 29 IDC, 1 DCIS, and 2 rare tumors. The model yielded excellent performance, with an accuracy of 97.62%, sensitivity of 96.88% and specificity of 100%; only one IDC was misclassified. Meanwhile, the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures. In the simulated intraoperative margin evaluation procedure, the time required for our novel workflow (approximately 3 min) was significantly shorter than that required for traditional procedures (approximately 30 min). These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraoperative cancer diagnosis independently of traditional pathology laboratory procedures.

Bioengineered human tissue regeneration and repair using endogenous stem cells.

We describe a general approach to produce bone and cartilaginous structures utilizing the self-regenerative capacity of the intercostal rib space to treat a deformed metacarpophalangeal joint and microtia. Anatomically precise 3D molds were positioned on the perichondro-periosteal or perichondral flap of the intercostal rib without any other exogenous elements. We find anatomically precise metacarpal head and auricle constructs within the implanted molds after 6 months. The regenerated metacarpal head was used successfully to surgically repair the deformed metacarpophalangeal joint. Auricle reconstructive surgery in five unilateral microtia patients yielded good aesthetic and functional results. Long-term follow-up revealed the auricle constructs were safe and stable. Single-cell RNA sequencing analysis reveal early infiltration of a cell population consistent with mesenchymal stem cells, followed by IL-8-stimulated differentiation into chondrocytes. Our results demonstrate the repair and regeneration of tissues using only endogenous factors and a viable treatment strategy for bone and tissue structural defects.

An analysis of aging-related genes derived from the Genotype-Tissue Expression project (GTEx).

Aging is a complex biological process that is far from being completely understood. Analyzing transcriptional differences across age might help uncover genetic bases of aging. In this study, 1573 differentially expressed genes, related to chronological age, from the Genotype-Tissue Expression (GTEx) project, were categorized as upregulated age-associated genes (UAGs) and downregulated age-associated genes (DAGs). Characteristics in evolution, expression, function and molecular networks were comprehensively described and compared for UAGs, DAGs and other genes. Analyses revealed that UAGs are more clustered, more quickly evolving, more tissue specific and have accumulated more single-nucleotide polymorphisms (SNPs) and disease genes than DAGs. DAGs were found with a lower evolutionary rate, higher expression level, greater homologous gene number, smaller phyletic age and earlier expression in body development. UAGs are more likely to be located in the extracellular region and to occur in both immune-relevant processes and cancer-related pathways. By contrast, DAGs are more likely to be located intracellularly and to be enriched in catabolic and metabolic processes. Moreover, DAGs are also critical in a protein-protein interaction (PPI) network, whereas UAGs have more influence on a signaling network. This study highlights characteristics of the aging transcriptional landscape in a healthy population, which may benefit future studies on the aging process and provide a broader horizon for age-dependent precision medicine.

EVA1A/TMEM166 Regulates Embryonic Neurogenesis by Autophagy.

Self-renewal and differentiation of neural stem cells is essential for embryonic neurogenesis, which is associated with cell autophagy. However, the mechanism by which autophagy regulates neurogenesis remains undefined. Here, we show that Eva1a/Tmem166, an autophagy-related gene, regulates neural stem cell self-renewal and differentiation. Eva1a depletion impaired the generation of newborn neurons, both in vivo and in vitro. Conversely, overexpression of EVA1A enhanced newborn neuron generation and maturation. Moreover, Eva1a depletion activated the PIK3CA-AKT axis, leading to the activation of the mammalian target of rapamycin and the subsequent inhibition of autophagy. Furthermore, addition of methylpyruvate to the culture during neural stem cell differentiation rescued the defective embryonic neurogenesis induced by Eva1a depletion, suggesting that energy availability is a significant factor in embryonic neurogenesis. Collectively, these data demonstrated that EVA1A regulates embryonic neurogenesis by modulating autophagy. Our results have potential implications for understanding the pathogenesis of neurodevelopmental disorders caused by autophagy dysregulation.