Hedgehog Signaling in Tumor Cells Facilitates Osteoblast-Enhanced Osteolytic Metastases

The remodeling process in bone yields numerous cytokines and chemokines that mediate crosstalk between osteoblasts and osteoclasts and also serve to attract and support metastatic tumor cells. The metastatic tumor cells disturb the equilibrium in bone that manifests as skeletal complications. The Hedgehog (Hh) pathway plays an important role in skeletogenesis. We hypothesized that the Hh pathway mediates an interaction between tumor cells and osteoblasts and influences osteoblast differentiation in response to tumor cells. We have determined that breast tumor cells have an activated Hh pathway characterized by upregulation of the ligand, IHH and transcription factor GLI1. Breast cancer cells interact with osteoblasts and cause an enhanced differentiation of pre-osteoblasts to osteoblasts that express increased levels of the osteoclastogenesis factors, RANKL and PTHrP. There is sustained expression of osteoclast-promoting factors, RANKL and PTHrP, even after the osteoblast differentiation ceases and apoptosis sets in. Moreover, tumor cells that are deficient in Hh signaling are compromised in their ability to induce osteoblast differentiation and consequently are inefficient in causing osteolysis. The stimulation of osteoblast differentiation sets the stage for osteoclast differentiation and overall promotes osteolysis. Thus, in the process of developing newer therapeutic strategies against breast cancer metastasis to bone it would worthwhile to keep in mind the role of the Hh pathway in osteoblast differentiation in an otherwise predominant osteolytic phenomenon

Atrichia congenita

Atrichia congenita is a rare genodermatoses is characterized by a mutation of the human hairless (HR) gene on chromosome 8p22. There is loss of scalp hair between one to six months of age, after which no growth occurs. Eyebrow, eyelash, and body hair may also be sparse or absent; patients may have a few pubic and axillary hairs. The condition may present in isolation or along with other defects

ROAD: Robotics-Assisted Onsite Data Collection and Deep Learning Enabled Robotic Vision System for Identification of Cracks on Diverse Surfaces

Crack detection on roads is essential nowadays because it has a significant impact on ensuring the safety and reliability of road infrastructure. Thus, it is necessary to create more effective and precise crack detection techniques. A safer road network and a better driving experience for all road users can result from the implementation of the ROAD (Robotics-Assisted Onsite Data Collecting) system for spotting road cracks using deep learning and robots. The suggested solution makes use of a robot vision system’s capabilities to gather high-quality data about the road and incorporates deep learning methods for automatically identifying cracks. Among the tested algorithms, Xception stands out as the most accurate and predictive model, with an accuracy of over 90% during the validation process and a mean square error of only 0.03. In contrast, other deep neural networks, such as DenseNet201, InceptionResNetV2, MobileNetV2, VGG16, and VGG19, result in inferior accuracy and higher losses. Xception also achieves high accuracy and recall scores, indicating its capability to accurately identify and classify different data points. The high accuracy and superior performance of Xception make it a valuable tool for various machine learning tasks, including image classification and object recognition

Case Report - Juvenile hyaline fibromatosis in siblings

Juvenile hyaline fibromatosis (JHF) is a rare, autosomal recessively inherited disorder. We report two siblings with multiple large tumors on the scalp, translucent papules on the nape of the neck, hypertrophic gingiva, and severe flexural contractures of large joints. The histopathology from the skin lesions showed features characteristic of juvenile hyaline fibromatosis. The cases are being reported on account of the extreme rarity of the condition

Activation of Hh signaling promotes differentiation and mineralization activity of osteoblasts.

<p>MC3T3 E1 Sc-14 cells were grown in differentiation medium (DM) supplemented with either with 100 nM recombinant SHH or IHH or without. Cells grown in normal growth medium were used as control. (<b>A</b>) Differentiation was assessed by alkaline phosphatase (ALP) assay. Under differentiation conditions that included recombinant IHH or SHH, the ALP activity was significantly greater († indicates p<0.0001) relative to DM alone. (<b>B</b>) Relative to control (growth medium), DM induced the formation of mineralized nodules. Relative to DM the media spiked with recombinant IHH and SHH supports the formation of significantly greater number of mineralized nodules († indicates p = 0.012 and 0.002, respectively). The number of nodules formed due to each treatment is represented as a percentage of the total number of cells present in each field of view. Control represents growth medium. The number of mineralized nodules in 12 independent fields per well were scored. (<b>C</b>) The expression of osteoblast differentiation marker genes, BSP († indicates p = 0.0027) and osteocalcin († indicates p = 0.0004) is significantly elevated in presence of SHH at the end of 14 days. Cells were harvested and RNA extracted which was used in real time quantitative RT-PCR assay. The fold change in expression is represented relative to control (growth medium).</p

Active Hh signaling in the tumor cells is vital to their ability to cause osteolysis.

<p>MDA-MB-435 tumor cells were injected into the left cardiac ventricle of athymic mice; mice were euthanized 4–6 weeks later and radiographically imaged and assessed for osteolysis at the tibio-femoral junction. As represented in the Table, cells that were silenced for GLI1 expression showed an attenuated ability of osteolysis. The percent incidence of osteolysis is depicted in the adjacent graph.</p