Collagen fibers mediate MRI-detected water diffusion and anisotropy in breast cancers

AbstractCollagen 1 (Col1) fibers play an important role in tumor interstitial macromolecular transport and cancer cell dissemination. Our goal was to understand the influence of Col1 fibers on water diffusion, and to examine the potential of using noninvasive diffusion tensor imaging (DTI) to indirectly detect Col1 fibers in breast lesions. We previously observed, in human MDA-MB-231 breast cancer xenografts engineered to fluoresce under hypoxia, relatively low amounts of Col1 fibers in fluorescent hypoxic regions. These xenograft tumors together with human breast cancer samples were used here to investigate the relationship between Col1 fibers, water diffusion and anisotropy, and hypoxia. Hypoxic low Col1 fiber containing regions showed decreased apparent diffusion coefficient (ADC) and fractional anisotropy (FA) compared to normoxic high Col1 fiber containing regions. Necrotic high Col1 fiber containing regions showed increased ADC with decreased FA values compared to normoxic viable high Col1 fiber regions that had increased ADC with increased FA values. A good agreement of ADC and FA patterns was observed between in vivo and ex vivo images. In human breast cancer specimens, ADC and FA decreased in low Col1 containing regions. Our data suggest that a decrease in ADC and FA values observed within a lesion could predict hypoxia, and a pattern of high ADC with low FA values could predict necrosis. Collectively the data identify the role of Col1 fibers in directed water movement and support expanding the evaluation of DTI parameters as surrogates for Col1 fiber patterns associated with specific tumor microenvironments as companion diagnostics and for staging

A Stable Biologically Motivated Learning Mechanism for Visual Feature Extraction to Handle Facial Categorization

The brain mechanism of extracting visual features for recognizing various objects has consistently been a controversial issue in computational models of object recognition. To extract visual features, we introduce a new, biologically motivated model for facial categorization, which is an extension of the Hubel and Wiesel simple-to-complex cell hierarchy. To address the synaptic stability versus plasticity dilemma, we apply the Adaptive Resonance Theory (ART) for extracting informative intermediate level visual features during the learning process, which also makes this model stable against the destruction of previously learned information while learning new information. Such a mechanism has been suggested to be embedded within known laminar microcircuits of the cerebral cortex. To reveal the strength of the proposed visual feature learning mechanism, we show that when we use this mechanism in the training process of a well-known biologically motivated object recognition model (the HMAX model), it performs better than the HMAX model in face/non-face classification tasks. Furthermore, we demonstrate that our proposed mechanism is capable of following similar trends in performance as humans in a psychophysical experiment using a face versus non-face rapid categorization task

Biosensor vital sign detects multiple sclerosis progression.

ObjectiveTo determine whether a small, wearable multisensor device can discriminate between progressive versus relapsing multiple sclerosis (MS) and capture limb progression over a short interval, using finger and foot tap data.MethodsPatients with MS were followed prospectively during routine clinic visits approximately every 6 months. At each visit, participants performed finger and foot taps wearing the MYO-band, which includes accelerometer, gyroscope, and surface electromyogram sensors. Metrics of within-patient limb progression were created by combining the change in signal waveform features over time. The resulting upper (UE) and lower (LE) extremity metrics' discrimination of progressive versus relapsing MS were evaluated with calculation of AUROC. Comparisons with Expanded Disability Status Scale (EDSS) scores were made with Pearson correlation.ResultsParticipants included 53 relapsing and 15 progressive MS (72% female, baseline mean age 48 years, median disease duration 11 years, median EDSS 2.5, median 10 months follow-up). The final summary metrics differentiated relapsing from secondary progressive MS with AUROC UE 0.93 and LE 0.96. The metrics were associated with baseline EDSS (UE P = 0.0003, LE P = 0.0007). While most had no change in EDSS during the short follow-up, several had evidence of progression by the multisensor metrics.InterpretationWithin a short follow-up interval, this novel multisensor algorithm distinguished progressive from relapsing MS and captured changes in limb function. Inexpensive, noninvasive and easy to use, this novel outcome is readily adaptable to clinical practice and trials as a MS vital sign. This approach also holds promise to monitor limb dysfunction in other neurological diseases

Spiritual wellbeing of Iranian patients with acute coronary syndromes: a cross-sectional descriptive study

Author's accepted version (post-print)

Hypoxic Tumor Environments Exhibit Disrupted Collagen I Fibers and Low Macromolecular Transport

<div><p>Hypoxic tumor microenvironments result in an aggressive phenotype and resistance to therapy that lead to tumor progression, recurrence, and metastasis. While poor vascularization and the resultant inadequate drug delivery are known to contribute to drug resistance, the effect of hypoxia on molecular transport through the interstitium, and the role of the extracellular matrix (ECM) in mediating this transport are unexplored. The dense mesh of fibers present in the ECM can especially influence the movement of macromolecules. Collagen 1 (Col1) fibers form a key component of the ECM in breast cancers. Here we characterized the influence of hypoxia on macromolecular transport in tumors, and the role of Col1 fibers in mediating this transport using an MDA-MB-231 breast cancer xenograft model engineered to express red fluorescent protein under hypoxia. Magnetic resonance imaging of macromolecular transport was combined with second harmonic generation microscopy of Col1 fibers. Hypoxic tumor regions displayed significantly decreased Col1 fiber density and volume, as well as significantly lower macromolecular draining and pooling rates, than normoxic regions. Regions adjacent to severely hypoxic areas revealed higher deposition of Col1 fibers and increased macromolecular transport. These data suggest that Col1 fibers may facilitate macromolecular transport in tumors, and their reduction in hypoxic regions may reduce this transport. Decreased macromolecular transport in hypoxic regions may also contribute to poor drug delivery and tumor recurrence in hypoxic regions. High Col1 fiber density observed around hypoxic regions may facilitate the escape of aggressive cancer cells from hypoxic regions.</p></div

Quantification of vascular parameters.

<p>Quantitative comparison of (A) VV and (B) PS for hypoxic and normoxic breast tumor regions. Columns: Mean±standard deviation (SD). We observed significantly lower VV (*<i>p-value = </i>0.000374, N = 10) and PS (**<i>p-value = </i>0.000215, N = 10) in hypoxic regions compared to normoxic regions. Columns: Mean±SD.</p

Co-registration results.

<p>(A) MR anatomical M₀ images of the central four slices of the tumor as reference images (FOV = 16×16×4 mm³). (B) Display of the optical bright field image overlaid with the fluorescence field image. Features extracted from (C) MR images and (D) optical images, respectively. (E) Display of the extracted features from the MR images overlaid with the optical images after co-registration. (F) Registered optical bright field images overlaid with the fluorescence field images. (G) The co-registration error represented by the Dice similarity index for all tumors (N = 10) in this study.</p

Optical imaging and quantification of fiber volume and fiber distribution.

<p>(A) <i>Ex vivo</i> 1× bright field and fluorescence image overlaid showing the locations of hypoxic ROIs on the tumor section. 3D visualization of (B) hypoxic and (C) normoxic FOVs; hypoxic regions are displayed in red and Col1 fibers in green. The FOV image size was 334.91×334.91×60 µm³ with a voxel size of 0.66×0.66×1 µm³. (D) Hypoxic FOVs showed a significantly lower fiber volume as compared to normoxic FOVs (**<i>p-value</i> = 0.000106, N = 10). (E) Hypoxic FOVs have a significantly larger inter-fiber distance as compared to normoxic FOVs (*<i>p-value</i> = 0.000121, N = 10).</p