Articular cartilage has depth-varying composition and structure that are manifest as depth-varying biomechanical properties. In addition, the subchondral bone region has a hydraulic permeability which is markedly increased in osteoarthritis. Thus, the effects of the depth-varying properties of articular cartilage and of the permeability of the deep boundary on the biomechanical behavior of cartilage were determined for oscillatory confined compression both experimentally and using a poroelastic multi-layer model. Homogeneous (a) and inhomogeneous (b) sample structures were both experimentally tested as well as simulated in two confined compression testing setups, one with an impermeable base (I) and a freely-permeable base (II) at 15% offset. The model and experimental results demonstrate that the high modulus and low permeability of the deep layer of cartilage normally provide a sealing effect, such that fluid pressurization and dynamic stiffness are maintained much more similarly for conditions Ib and IIb than for conditions Ia and IIa, with these differences between I and II diminishing at lower frequencies for both sample structures. In the simulation, strain and solid stress were more distributed throughout the sample depth and propagated deeper into the tissue from permeable boundary conditions in Ia and IIa than in Ib and IIb. In summary, the depth varying properties of the inhomogeneous sample and an impermeable deep boundary provide increased fluid pressurization capability that when combined have a cumulative effect, effectively shielding the tissue from large strain and stress levels

Background

Use of functional MRI (fMRI) in pre-surgical planning is a non-invasive method for pre-operative functional mapping for patients with brain tumors, especially tumors located near eloquent cortex. Currently, this practice predominantly involves task-based fMRI (T-fMRI). Resting state fMRI (RS-fMRI) offers an alternative with several methodological advantages. Here, we compare group-level analyses of RS-fMRI vs. T-fMRI as methods for language localization.

Purpose

To contrast RS-fMRI vs. T-fMRI as techniques for localization of language function.

Methods

We analyzed data obtained in 35 patients who had both T-fMRI and RS-fMRI scans during the course of pre-surgical evaluation. The RS-fMRI data were analyzed using a previously trained resting-state network classifier. The T-fMRI data were analyzed using conventional techniques. Group-level results obtained by both methods were evaluated in terms of two outcome measures: (1) inter-subject variability of response magnitude and (2) sensitivity/specificity analysis of response topography, taking as ground truth previously reported maps of the language system based on intraoperative cortical mapping as well as meta-analytic maps of language task fMRI responses.

Results

Both fMRI methods localized major components of the language system (areas of Broca and Wernicke) although not with equal inter-subject consistency. Word-stem completion T-fMRI strongly activated Broca's area but also several task-general areas not specific to language. RS-fMRI provided a more specific representation of the language system.

Conclusion

We demonstrate several advantages of classifier-based mapping of language representation in the brain. Language T-fMRI activated task-general (i.e., not language-specific) functional systems in addition to areas of Broca and Wernicke. In contrast, classifier-based analysis of RS-fMRI data generated maps confined to language-specific regions of the brain.

Pancreatic ductal adenocarcinoma (PDA) is the most lethal of common human malignancies, with no truly effective therapies for advanced disease. Preclinical studies have suggested a therapeutic benefit of targeting the Hedgehog (Hh) signaling pathway, which is activated throughout the course of PDA progression by expression of Hh ligands in the neoplastic epithelium and paracrine response in the stromal fibroblasts. Clinical trials to test this possibility, however, have yielded disappointing results. To further investigate the role of Hh signaling in the formation of PDA and its precursor lesion, pancreatic intraepithelial neoplasia (PanIN), we examined the effects of genetic or pharmacologic inhibition of Hh pathway activity in three distinct genetically engineered mouse models and found that Hh pathway inhibition accelerates rather than delays progression of oncogenic Kras-driven disease. Notably, pharmacologic inhibition of Hh pathway activity affected the balance between epithelial and stromal elements, suppressing stromal desmoplasia but also causing accelerated growth of the PanIN epithelium. In striking contrast, pathway activation using a small molecule agonist caused stromal hyperplasia and reduced epithelial proliferation. These results indicate that stromal response to Hh signaling is protective against PDA and that pharmacologic activation of pathway response can slow tumorigenesis. Our results provide evidence for a restraining role of stroma in PDA progression, suggesting an explanation for the failure of Hh inhibitors in clinical trials and pointing to the possibility of a novel type of therapeutic intervention.

A major goal of cancer research is to understand how mutations distributed across diverse genes affect common cellular systems, including multiprotein complexes and assemblies. Two challenges—how to comprehensively map such systems and how to identify which are under mutational selection—have hindered this understanding. Accordingly, we created a comprehensive map of cancer protein systems integrating both new and published multi-omic interaction data at multiple scales of analysis. We then developed a unified statistical model that pinpoints 395 specific systems under mutational selection across 13 cancer types. This map, called NeST (Nested Systems in Tumors), incorporates canonical processes and notable discoveries, including a PIK3CA-actomyosin complex that inhibits phosphatidylinositol 3-kinase signaling and recurrent mutations in collagen complexes that promote tumor proliferation. These systems can be used as clinical biomarkers and implicate a total of 548 genes in cancer evolution and progression. This work shows how disparate tumor mutations converge on protein assemblies at different scales.

Background Clinicians vary markedly in their ability to detect murmurs during cardiac auscultation and identify the underlying pathological features. Deep learning approaches have shown promise in medicine by transforming collected data into clinically significant information. The objective of this research is to assess the performance of a deep learning algorithm to detect murmurs and clinically significant valvular heart disease using recordings from a commercial digital stethoscope platform. Methods and Results Using >34 hours of previously acquired and annotated heart sound recordings, we trained a deep neural network to detect murmurs. To test the algorithm, we enrolled 962 patients in a clinical study and collected recordings at the 4 primary auscultation locations. Ground truth was established using patient echocardiograms and annotations by 3 expert cardiologists. Algorithm performance for detecting murmurs has sensitivity and specificity of 76.3% and 91.4%, respectively. By omitting softer murmurs, those with grade 1 intensity, sensitivity increased to 90.0%. Application of the algorithm at the appropriate anatomic auscultation location detected moderate-to-severe or greater aortic stenosis, with sensitivity of 93.2% and specificity of 86.0%, and moderate-to-severe or greater mitral regurgitation, with sensitivity of 66.2% and specificity of 94.6%. Conclusions The deep learning algorithm's ability to detect murmurs and clinically significant aortic stenosis and mitral regurgitation is comparable to expert cardiologists based on the annotated subset of our database. The findings suggest that such algorithms would have utility as front-line clinical support tools to aid clinicians in screening for cardiac murmurs caused by valvular heart disease. Registration URL: https://clinicaltrials.gov; Unique Identifier: NCT03458806.

The cell is a multi-scale structure with modular organization across at least four orders of magnitude¹. Two central approaches for mapping this structure-protein fluorescent imaging and protein biophysical association-each generate extensive datasets, but of distinct qualities and resolutions that are typically treated separately^2,3. Here we integrate immunofluorescence images in the Human Protein Atlas⁴ with affinity purifications in BioPlex⁵ to create a unified hierarchical map of human cell architecture. Integration is achieved by configuring each approach as a general measure of protein distance, then calibrating the two measures using machine learning. The map, known as the multi-scale integrated cell (MuSIC 1.0), resolves 69 subcellular systems, of which approximately half are to our knowledge undocumented. Accordingly, we perform 134 additional affinity purifications and validate subunit associations for the majority of systems. The map reveals a pre-ribosomal RNA processing assembly and accessory factors, which we show govern rRNA maturation, and functional roles for SRRM1 and FAM120C in chromatin and RPS3A in splicing. By integration across scales, MuSIC increases the resolution of imaging while giving protein interactions a spatial dimension, paving the way to incorporate diverse types of data in proteome-wide cell maps.