Proteoglycan 4: A dynamic regulator of skeletogenesis and parathyroid hormone skeletal anabolism

Proteoglycan 4 ( Prg4 ), known for its lubricating and protective actions in joints, is a strong candidate regulator of skeletal homeostasis and parathyroid hormone (PTH) anabolism. Prg4 is a PTH‐responsive gene in bone and liver. Prg4 null mutant mice were used to investigate the impact of proteoglycan 4 on skeletal development, remodeling, and PTH anabolic actions. Young Prg4 mutant and wild‐type mice were administered intermittent PTH(1–34) or vehicle daily from 4 to 21 days. Young Prg4 mutant mice had decreased growth plate hypertrophic zones, trabecular bone, and serum bone formation markers versus wild‐type mice, but responded with a similar anabolic response to PTH. Adult Prg4 mutant and wild‐type mice were administered intermittent PTH(1–34) or vehicle daily from 16 to 22 weeks. Adult Prg4 mutant mice had decreased trabecular and cortical bone, and blunted PTH‐mediated increases in bone mass. Joint range of motion and animal mobility were lower in adult Prg4 mutant versus wild‐type mice. Adult Prg4 mutant mice had decreased marrow and liver fibroblast growth factor 2 (FGF‐2) mRNA and reduced serum FGF‐2, which were normalized by PTH. A single dose of PTH decreased the PTH/PTHrP receptor (PPR), and increased Prg4 and FGF‐2 to a similar extent in liver and bone. Proteoglycan 4 supports endochondral bone formation and the attainment of peak trabecular bone mass, and appears to support skeletal homeostasis indirectly by protecting joint function. Bone‐ and liver‐derived FGF‐2 likely regulate proteoglycan 4 actions supporting trabeculae formation. Blunted PTH anabolic responses in adult Prg4 mutant mice are associated with altered biomechanical impact secondary to joint failure. © 2012 American Society for Bone and Mineral ResearchPeer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/89450/1/508_ftp.pd

Systematic Identification of Combinatorial Drivers and Targets in Cancer Cell Lines

There is an urgent need to elicit and validate highly efficacious targets for combinatorial intervention from large scale ongoing molecular characterization efforts of tumors. We established an in silico bioinformatic platform in concert with a high throughput screening platform evaluating 37 novel targeted agents in 669 extensively characterized cancer cell lines reflecting the genomic and tissue-type diversity of human cancers, to systematically identify combinatorial biomarkers of response and co-actionable targets in cancer. Genomic biomarkers discovered in a 141 cell line training set were validated in an independent 359 cell line test set. We identified co-occurring and mutually exclusive genomic events that represent potential drivers and combinatorial targets in cancer. We demonstrate multiple cooperating genomic events that predict sensitivity to drug intervention independent of tumor lineage. The coupling of scalable in silico and biologic high throughput cancer cell line platforms for the identification of co-events in cancer delivers rational combinatorial targets for synthetic lethal approaches with a high potential to pre-empt the emergence of resistance

The consumer scam: an agency-theoretic approach

Despite the extensive body of literature that aims to explain the phenomenon of consumer scams, the structure of information in scam relationships remains relatively understudied. The purpose of this article is to develop an agency-theoretical approach to the study of information in perpetrator-victim interactions. Drawing a distinction between failures of observation and failures of judgement in the pre-contract phase, we introduce a typology and a set of propositions that explain the severity of adverse selection problems in three classes of scam relationships. Our analysis provides a novel, systematic explanation of the structure of information that facilitates scam victimisation, while also enabling critical scrutiny of a core assumption in agency theory regarding contract design. We highlight the role of scam perpetrators as agents who have access to private information and exercise considerable control over the terms and design of scam relationships. Focusing on the consumer scam context, we question a theoretical assumption, largely taken for granted in the agency literature, that contact design is necessarily in the purview of the uninformed principal

Transmission of MDR and XDR Tuberculosis in Shanghai, China

Background: Multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB) are global health problems. We sought to determine the characteristics, prevalence, and relative frequency of transmission of MDR and XDR TB in Shanghai, one of the largest cities in Asia. Methods: TB is diagnosed in district TB hospitals in Shanghai, China. Drug susceptibility testing for first-line drugs was performed for all culture positive TB cases, and tests for second-line drugs were performed for MDR cases. VNTR-7 and VNTR-16 were used to genotype the strains, and prior treatment history and treatment outcomes were determined for each patient. Results: There were 4,379 culture positive TB cases diagnosed with drug susceptibility test results available during March 2004 through November 2007. 247 (5.6%) were infected with a MDR strain of M. tuberculosis and 11 (6.3%) of the 175 MDR patients whose isolate was tested for susceptibility to second-line drugs, were XDR. More than half of the patients with MDR and XDR were newly diagnosed and had no prior history of TB treatment. Nearly 57 % of the patients with MDR were successfully treated. Discussion: Transmission of MDR and XDR strains is a serious problem in Shanghai. While a history of prior anti-TB treatment indicates which individuals may have acquired MDR or XDR TB, it does not accurately predict which TB patients have disease caused by transmission of MDR and XDR strains. Therefore, universal drug susceptibility testing i

Predictors of Multidrug- and Extensively Drug-Resistant Tuberculosis in a High HIV Prevalence Community

BACKGROUND: Multidrug-resistant (MDR) and extensively drug-resistant (XDR) tuberculosis (TB) have emerged in high-HIV-prevalence settings, which generally lack laboratory infrastructure for diagnosing TB drug resistance. Even where available, inherent delays with current drug-susceptibility testing (DST) methods result in clinical deterioration and ongoing transmission of MDR and XDR-TB. Identifying clinical predictors of drug resistance may aid in risk stratification for earlier treatment and infection control. METHODS: We performed a retrospective case-control study of patients with MDR (cases), XDR (cases) and drug-susceptible (controls) TB in a high-HIV-prevalence setting in South Africa to identify clinical and demographic risk factors for drug-resistant TB. Controls were selected in a 1:1:1 ratio and were not matched. We calculated odds ratios (OR) and performed multivariate logistic regression to identify independent predictors. RESULTS: We enrolled 116, 123 and 139 patients with drug-susceptible, MDR, and XDR-TB. More than 85% in all three patient groups were HIV-infected. In multivariate analysis, MDR and XDR-TB were each strongly associated with history of TB treatment failure (adjusted OR 51.7 [CI 6.6-403.7] and 51.5 [CI 6.4-414.0], respectively) and hospitalization more than 14 days (aOR 3.8 [CI 1.1-13.3] and 6.1 [CI 1.8-21.0], respectively). Prior default from TB treatment was not a risk factor for MDR or XDR-TB. HIV was a risk factor for XDR (aOR 8.2, CI 1.3-52.6), but not MDR-TB. Comparing XDR with MDR-TB patients, the only significant risk factor for XDR-TB was HIV infection (aOR 5.3, CI 1.0-27.6). DISCUSSION: In this high-HIV-prevalence and drug-resistant TB setting, a history of prolonged hospitalization and previous TB treatment failure were strong risk factors for both MDR and XDR-TB. Given high mortality observed among patients with HIV and drug-resistant TB co-infection, previously treated and hospitalized patients should be considered for empiric second-line TB therapy while awaiting confirmatory DST results in settings with a high-burden of MDR/XDR-TB

Machine learning-based prediction of breast cancer growth rate in-vivo

BackgroundDetermining the rate of breast cancer (BC) growth in vivo, which can predict prognosis, has remained elusive despite its relevance for treatment, screening recommendations and medicolegal practice. We developed a model that predicts the rate of in vivo tumour growth using a unique study cohort of BC patients who had two serial mammograms wherein the tumour, visible in the diagnostic mammogram, was missed in the first screen.MethodsA serial mammography-derived in vivo growth rate (SM-INVIGOR) index was developed using tumour volumes from two serial mammograms and time interval between measurements. We then developed a machine learning-based surrogate model called Surr-INVIGOR using routinely assessed biomarkers to predict in vivo rate of tumour growth and extend the utility of this approach to a larger patient population. Surr-INVIGOR was validated using an independent cohort.ResultsSM-INVIGOR stratified discovery cohort patients into fast-growing versus slow-growing tumour subgroups, wherein patients with fast-growing tumours experienced poorer BC-specific survival. Our clinically relevant Surr-INVIGOR stratified tumours in the discovery cohort and was concordant with SM-INVIGOR. In the validation cohort, Surr-INVIGOR uncovered significant survival differences between patients with fast-growing and slow-growing tumours.ConclusionOur Surr-INVIGOR model predicts in vivo BC growth rate during the pre-diagnostic stage and offers several useful applications

Standardized Treatment of Active Tuberculosis in Patients with Previous Treatment and/or with Mono-resistance to Isoniazid: A Systematic Review and Meta-analysis

Performing a systematic review of studies evaluating retreatment of tuberculosis or treatment of isoniazid mono-resistant infection, Dick Menzies and colleagues find a paucity of evidence to support the WHO-recommended regimen

Impact of 3D cell culture on bone regeneration potential of mesenchymal stromal cells

As populations age across the world, osteoporosis and osteoporosis-related fractures are becoming the most prevalent degenerative bone diseases. More than 75 million patients suffer from osteoporosis in the US, the EU and Japan. Furthermore, it is anticipated that the number of patients affected by osteoporosis will increase by a third by 2050. Although conventional therapies including bisphosphonates, calcitonin and oestrogen-like drugs can be used to treat degenerative diseases, they are often associated with serious side effects including the development of oesophageal cancer, ocular inflammation, severe musculoskeletal pain, and osteonecrosis of the jaw. The use of autologous mesenchymal stromal cells/mesenchymal stem cells (MSCs) is a possible alternative therapeutic approach to tackle osteoporosis while overcoming the limitations of traditional treatment options. However, osteoporosis can cause a decrease in the numbers of MSCs, induce their senescence, and lower their osteogenic differentiation potential. Three-dimensional (3D) cell culture is an emerging technology that allows a more physiological expansion and differentiation of stem cells compared to cultivation on conventional flat systems. This review will discuss current understanding of the effects of different 3D cell culture systems on proliferation, viability, osteogenic differentiation, as well as on the immunomodulatory and anti-inflammatory potential of MSCs