TNF autovaccination induces self anti-TNF antibodies and inhibits metastasis in a murine melanoma model

TNF is a proinflammatory cytokine involved in the pathogenesis of chronic inflammatory diseases, but also in metastasis in certain types of cancer. In terms of therapy, TNF is targeted by anti-TNF neutralising monoclonal antibodies or soluble TNF receptors. Recently, a novel strategy based on the generation of self anti-TNF antibodies (TNF autovaccination) has been developed. We have previously shown that TNF autovaccination successfully generates high anti-TNF antibody titres, blocks TNF and ameliorates collagen-induced arthritis in DBA/1 mice. In this study, we examined the ability of TNF autovaccination to generate anti-TNF antibody titres and block metastasis in the murine B16F10 melanoma model. We found that immunisation of C57BL/6 mice with TNF autovaccine produces a 100-fold antibody response to TNF compared to immunisation with phosphate-buffered saline vehicle control and significantly reduces both the number (P<0.01) and size of metastases (P<0.01) of B16F10 melanoma cells. This effect is also observed when an anti-TNF neutralising monoclonal antibody is administered, confirming the essential role TNF plays in metastasis in this model. This study suggests that TNF autovaccination is a cheaper and highly efficient alternative that can block TNF and reduce metastasis in vivo and trials with TNF autovaccination are already underway in patients with metastatic cancer

Acquiring a pet dog significantly reduces stress of primary carers for children with autism spectrum disorder: a prospective case control study

This study describes the impact of pet dogs on stress of primary carers of children with Autism Spectrum Disorder (ASD). Stress levels of 38 primary carers acquiring a dog and 24 controls not acquiring a dog were sampled at: Pre-intervention (17 weeks before acquiring a dog), post-intervention (3–10 weeks after acquisition) and follow-up (25–40 weeks after acquisition), using the Parenting Stress Index. Analysis revealed significant improvements in the intervention compared to the control group for Total Stress, Parental Distress and Difficult Child. A significant number of parents in the intervention group moved from clinically high to normal levels of Parental Distress. The results highlight the potential of pet dogs to reduce stress in primary carers of children with an ASD

Ovarian cysts in women receiving tamoxifen for breast cancer

Tamoxifen is a nonsteroidal anti-oestrogen with gynaecological side-effects. Only recently, ovarian cyst formation during tamoxifen treatment has been reported. The present study aimed to evaluate patient-related parameters that determine ovarian cyst formation in women using tamoxifen for breast cancer. A cross-sectional study was performed in 142 breast cancer patients using tamoxifen. Forty-five patients were also examined prior to tamoxifen treatment. Gynaecological assessment, transvaginal ultrasonography (TVU) and serum oestradiol (E2) and follicle stimulating hormone (FSH) analysis were performed. Follow-up assessments were performed twice a year. Uni- or bilateral ovarian cysts were detected by TVU in 24 tamoxifen-using patients and in one patient before tamoxifen treatment. Multiple regression analysis showed that cyst development is related (multiple R = 0.73) to high E2 (P < 0.001), younger age (P < 0.001) and absence of high-dose chemotherapy (P = 0.007). Patients with ovarian cysts had higher serum E2 levels compared to patients without cysts (1.95 vs 0.05 nmol l−1; P < 0.001). All patients after high-dose chemotherapy or older than 50 years had E2 < 0.10 nmol l−1 and/or amenorrhoea > 1 year and did not develop ovarian cysts. Patients still having a menstrual cycle during tamoxifen had a high chance (81%) of developing ovarian cysts. Breast cancer patients receiving tamoxifen only develop ovarian cysts if their ovaries are able to respond to FSH stimulation as shown by E2 production. © 1999 Cancer Research Campaig

Specialization training in Malawi: A qualitative study on the perspectives of medical students graduating from the University of Malawi College of Medicine

Background: There is a critical shortage of healthcare workers in sub-Saharan Africa, and Malawi has one of the lowest physician densities in the region. One of the reasons for this shortage is inadequate retention of medical school graduates, partly due to the desire for specialization training. The University of Malawi College of Medicine has developed specialty training programs, but medical school graduates continue to report a desire to leave the country for specialization training. To understand this desire, we studied medical students' perspectives on specialization training in Malawi. Methods. We conducted semi-structured interviews of medical students in the final year of their degree program. We developed an interview guide through an iterative process, and recorded and transcribed all interviews for analysis. Two independent coders coded the manuscripts and assessed inter-coder reliability, and the authors used an "editing approach" to qualitative analysis to identify and categorize themes relating to the research aim. The University of Pittsburgh Institutional Review Board and the University of Malawi College of Medicine Research and Ethics Committee approved this study and authors obtained written informed consent from all participants. Results: We interviewed 21 medical students. All students reported a desire for specialization training, with 12 (57%) students interested in specialties not currently offered in Malawi. Students discussed reasons for pursuing specialization training, impressions of specialization training in Malawi, reasons for staying or leaving Malawi to pursue specialization training and recommendations to improve training. Conclusions: Graduating medical students in Malawi have mixed views of specialization training in their own country and still desire to leave Malawi to pursue further training. Training institutions in sub-Saharan Africa need to understand the needs of the country's healthcare workforce and the needs of their graduating medical students to be able to match opportunities and retain graduating students. © 2014 Sawatsky et al.; licensee BioMed Central Ltd

Working relationships between obstetric care staff and their managers: a critical incident analysis

Background Malawi continues to experience critical shortages of key health technical cadres that can adequately respond to Malawi’s disease burden. Difficult working conditions contribute to low morale and frustration among health care workers. We aimed to understand how obstetric care staff perceive their working relationships with managers. Methods A qualitative exploratory study was conducted in health facilities in Malawi between October and December 2008. Critical Incident Analysis interviews were done in government district hospitals, faith-based health facilities, and a sample of health centres’ providing emergency obstetric care. A total of 84 service providers were interviewed. Data were analyzed using NVivo 8 software. Results Poor leadership styles affected working relationships between obstetric care staff and their managers. Main concerns were managers’ lack of support for staff welfare and staff performance, lack of mentorship for new staff and junior colleagues, as well as inadequate supportive supervision. All this led to frustrations, diminished motivation, lack of interest in their job and withdrawal from work, including staff seriously considering leaving their post. Conclusions Positive working relationships between obstetric care staff and their managers are essential for promoting staff motivation and positive work performance. However, this study revealed that staff were demotivated and undermined by transactional leadership styles and behavior, evidenced by management by exception and lack of feedback or recognition. A shift to transformational leadership in nurse-manager relationships is essential to establish good working relationships with staff. Improved providers’ job satisfaction and staff retentionare crucial to the provision of high quality care and will also ensure efficiency in health care delivery in Malawi

The clinical and functional significance of c-Met in breast cancer: a review

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.CMH-Y is funded by a Cancer Research UK Clinical Research Fellowship. JLJ is funded by the Breast Cancer Campaign Tissue Bank

Accurate Learning with Few Atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods

Accurate whole-brain segmentation, or brain extraction, of magnetic resonance imaging (MRI) is a critical first step in most neuroimage analysis pipelines. The majority of brain extraction algorithms have been developed and evaluated for adult data and their validity for neonatal brain extraction, which presents age-specific challenges for this task, has not been established. We developed a novel method for brain extraction of multi-modal neonatal brain MR images, named ALFA (Accurate Learning with Few Atlases). The method uses a new sparsity-based atlas selection strategy that requires a very limited number of atlases ‘uniformly’ distributed in the low-dimensional data space, combined with a machine learning based label fusion technique. The performance of the method for brain extraction from multi-modal data of 50 newborns is evaluated and compared with results obtained using eleven publicly available brain extraction methods. ALFA outperformed the eleven compared methods providing robust and accurate brain extraction results across different modalities. As ALFA can learn from partially labelled datasets, it can be used to segment large-scale datasets efficiently. ALFA could also be applied to other imaging modalities and other stages across the life course

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases