Search Results (6)

Osteoblastic bone metastases are commonly detected in patients with advanced prostate cancer (PCa) and are associated with an increased mortality rate. Dickkopf-1 (DKK-1) antagonizes canonical WNT/β-catenin signaling and plays a complex role in bone metastases. We explored the function of cancer cell-specific DKK-1 in PCa growth, metastasis, and cancer–bone interactions using the osteoblastic canine PCa cell line, Probasco. Probasco or Probasco + DKK-1 (cells transduced with human DKK-1) were injected into the tibia or left cardiac ventricle of athymic nude mice. Bone metastases were detected by bioluminescent imaging in vivo and evaluated by micro-computed tomography and histopathology. Cancer cell proliferation, migration, gene/protein expression, and their impact on primary murine osteoblasts and osteoclasts, were evaluated in vitro. DKK-1 increased cancer growth and stimulated cell migration independent of canonical WNT signaling. Enhanced cancer progression by DKK-1 was associated with increased cell proliferation, up-regulation of NF-kB/p65 signaling, inhibition of caspase-dependent apoptosis by down-regulation of non-canonical WNT/JNK signaling, and increased expression of epithelial-to-mesenchymal transition genes. In addition, DKK-1 attenuated the osteoblastic activity of Probasco cells, and bone metastases had decreased cancer-induced intramedullary woven bone formation. Decreased bone formation might be due to the inhibition of osteoblast differentiation and stimulation of osteoclast activity through a decrease in the OPG/RANKL ratio in the bone microenvironment. The present study indicated that the cancer-promoting role of DKK-1 in PCa bone metastases was associated with increased growth of bone metastases, reduced bone induction, and altered signaling through the canonical WNT-independent pathway. DKK-1 could be a promising therapeutic target for PCa. Full article

This article centers on the nonprofit landscape in Vancouver, Canada, a city that occupies the territories of the xʷməθkʷəy̓əm (Musqueam), sḵwx̱wú7mesh (Squamish), and səlilwətaɬ (Tsleil-Waututh) nations, which have never been ceded to the colonial occupation of Canada. Vancouver has a competitive nonprofit field, with an estimated 1600+ nonprofits operating within city limits. This descriptive review starts by defining what a nonprofit industrial complex (NPIC) is, then outlines an abbreviated history of the nonprofit sector on the aforementioned lands. The article then explores issues related to colonialism, anti-poor legislation, neoliberal governance, the fusing of the public and private sectors, and the bureaucratization of social movements and care work as mechanisms to uphold the status quo social order and organization of power. Focusing on under-examined issues related to the business imperatives of nonprofit organizations in the sectors of housing, health and social services, community policing, and research, this work challenges the positive default framing of nonprofits and charities. Instead, we contend that Vancouver’s NPIC allows the government and the wealthy to shirk responsibility for deepening health and social inequities, while shaping nonprofits’ revenue-generating objectives and weakening their accountability to the community. Full article

Defective DNA mismatch repair is one pathogenic pathway to colorectal cancer. It is characterised by microsatellite instability which provides a molecular biomarker for its detection. Clinical guidelines for universal testing of this biomarker are not met due to resource limitations; thus, there is interest in developing novel methods for its detection. Raman spectroscopy (RS) is an analytical tool able to interrogate the molecular vibrations of a sample to provide a unique biochemical fingerprint. The resulting datasets are complex and high-dimensional, making them an ideal candidate for deep learning, though this may be limited by small sample sizes. This study investigates the potential of using RS to distinguish between normal, microsatellite stable (MSS) and microsatellite unstable (MSI-H) adenocarcinoma in human colorectal samples and whether deep learning provides any benefit to this end over traditional machine learning models. A 1D convolutional neural network (CNN) was developed to discriminate between healthy, MSI-H and MSS in human tissue and compared to a principal component analysis–linear discriminant analysis (PCA–LDA) and a support vector machine (SVM) model. A nested cross-validation strategy was used to train 30 samples, 10 from each group, with a total of 1490 Raman spectra. The CNN achieved a sensitivity and specificity of 83% and 45% compared to PCA–LDA, which achieved a sensitivity and specificity of 82% and 51%, respectively. These are competitive with existing guidelines, despite the low sample size, speaking to the molecular discriminative power of RS combined with deep learning. A number of biochemical antecedents responsible for this discrimination are also explored, with Raman peaks associated with nucleic acids and collagen being implicated. Full article

Estimates of global population growth are often cited as a significant challenge for global food production. It is estimated that by 2050 there will be approximately two- billion additional people on earth, with the greatest proportion of that growth occurring in central Africa. To meet recommended future protein needs (60 g/d), approximately 120 million kg of protein must be produced daily. The production of ruminant meat (particularly beef cattle) offers the potential to aid in reaching increased global protein needs. However, advancements in beef cattle production are necessary to secure the industry’s future sustainability. This article draws attention to a subset of sustainable beef cattle production challenges, including the role of ruminant livestock in meeting global human protein needs, the environmental relationships of advanced beef cattle production, and big data and machine learning in beef cattle production. Considering the significant quantities of resources necessary to produce this form of protein, such advancements are not just a moral imperative but critical to developing advanced beef cattle production practices and predictive models that will reduce costs and liabilities and advance industry sustainability. Full article

Raman Spectroscopy has long been anticipated to augment clinical decision making, such as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far inhibited their routine use in clinical settings. Traditional machine learning models have been used to help exploit this information, but recent advances in deep learning have the potential to improve the field. However, there are a number of potential pitfalls with both traditional and deep learning models. We conduct a literature review to ascertain the recent machine learning methods used to classify cancers using Raman spectral data. We find that while deep learning models are popular, and ostensibly outperform traditional learning models, there are many methodological considerations which may be leading to an over-estimation of performance; primarily, small sample sizes which compound sub-optimal choices regarding sampling and validation strategies. Amongst several recommendations is a call to collate large benchmark Raman datasets, similar to those that have helped transform digital pathology, which researchers can use to develop and refine deep learning models. Full article

The marine environment is a complex system, and with growing human demand, the sustainable use of multiple marine resources is continually challenged. The increasing complexity of overlapping marine activities causes pressures on the environment. Here, we review the fundamental aspects for effective marine management, particularly the role of science and scientific evidence to inform marine policy and decision making. The outcomes of internal expert workshops were used to analyse currently applied marine management practices in the UK using four marine sectors in English waters based on the expertise: environmental impact assessments; dredge and disposal operations; marine protected areas; and offshore renewable energy. Strengths, weaknesses, and commonalities between these sectors were assessed in terms of their effectiveness for marine management. Finally, we make recommendations based on the outputs to better inform effective yet sustainable marine management. The importance of increasing accessibility to data, hypothesis-driven environmental monitoring, streamlining funding opportunities and ensuring effective dissemination of data to ensure scientific outcomes and achieve increased robustness of assessments is emphasised. We also recommend that assessment drivers align with the outputs and approaches should be holistic and engage with the public to ensure a shared understanding and vision. Full article