Detection of human brain cancers using genomic and immune cell characterization of cerebrospinal fluid through CSF-BAM.

Unlabelled: Patients who have radiographically detectable lesions in their brain or other symptoms compatible with brain tumors pose challenges for diagnosis. The only definitive way to diagnose such patients is through brain biopsy, an obviously invasive and dangerous procedure. Here we present a new workflow termed "CSF-BAM" that simultaneously identifies cell or T cell receptor rearrangements, neuploidy, and using PCR-mediated amplification of both strands of the DNA from CSF samples.

Reality and imagination intertwined: A sensorimotor paradox interpretation.

As a hypothesis on the origins of mind and language, the evolutionary theory of the sensorimotor paradox suggests that capacities for imagination, self-representation and abstraction would operate from a dissociation in what is known as the forward model. In some studies, sensory perception is understood as a system of prediction and confirmation (feedforward and feedback processes) that would share common yet distinct and overlapping neural networks with mental imagery. The latter would then mostly operate through internal feedback processes.

Morphologic and Molecular Heterogeneity of High-grade Serous Carcinoma Precursor Lesions.

Serous tubal intraepithelial carcinoma (STIC) is the fallopian tube precursor lesion for most cases of pelvic high-grade serous carcinoma (HGSC). To date, the morphologic, molecular, and clinical heterogeneity of STIC and a less atypical putative precursor lesion, termed serous tubal intraepithelial lesion, has not been well characterized. Better understanding of precursor heterogeneity could impact the clinical management of women with incidental STICs (without concurrent carcinoma) identified in cases of prophylactic or opportunistic salpingectomy.

Machine learning to detect the SINEs of cancer.

We previously described an approach called RealSeqS to evaluate aneuploidy in plasma cell-free DNA through the amplification of ~350,000 repeated elements with a single primer. We hypothesized that an unbiased evaluation of the large amount of sequencing data obtained with RealSeqS might reveal other differences between plasma samples from patients with and without cancer. This hypothesis was tested through the development of a machine learning approach called Alu Profile Learning Using Sequencing (A-PLUS) and its application to 7615 samples from 5178 individuals, 2073 with solid cancer and the remainder without cancer.