Inducible clindamycin resistance and nasal carriage rates of Staphylococcus aureus among healthcare workers and community members.

Background: Nasal carriage of Staphylococcus aureus is becoming an increasing problem among healthcare workers and community individuals Objectives: To determine the prevalence of methicillin-resistant S. aureus (MRSA) nasal colonization and inducible clindamycin resistance (ICR) of S. aureus among healthcare workers at Soba University Hospital and community members in Khartoum State, Sudan. Methods: Five hundred nasal swabs samples were collected during March 2009 to April 2010. Isolates were identified using conventional laboratory assays and MRSA determined by the disk diffusion method. The D-test was performed for detection of ICR isolates with Clinical Laboratory Standard Institute guidelines. Results: Of the 114 S. aureus isolated, 20.2% represented MRSA. The occurrence of MRSA was significantly higher among healthcare worker than community individuals [32.7% (18/55) vs. 6.9% (5/59)] (p=0.001). Overall the 114 S. aureus isolates tested for ICR by D-test, 29 (25.4%) yielded inducible resistance. Significantly higher (p=0.026) ICR was detected among MRSA (43.5%) than methicillin-susceptible S. aureus (MSSA) (20.9%). Conclusion: MRSA nasal carriage among healthcare workers needs infection control practice in hospitals to prevent transmission of MRSA. The occurrence of ICR in S. aureus is of a great concern, D- test should be carried out routinely in our hospitals to avoid therapeutic failure

Incorporating radiomics into clinical trials: expert consensus on considerations for data-driven compared to biologically-driven quantitative biomarkers

Existing Quantitative Imaging Biomarkers (QIBs) are associated with known biological tissue characteristics and follow a well-understood path of technical, biological and clinical validation before incorporation into clinical trials. In radiomics, novel data-driven processes extract numerous visually imperceptible statistical features from the imaging data with no a priori assumptions on their correlation with biological processes. The selection of relevant features (radiomic signature) and incorporation into clinical trials therefore requires additional considerations to ensure meaningful imaging endpoints. Also, the number of radiomic features tested means that power calculations would result in sample sizes impossible to achieve within clinical trials. This article examines how the process of standardising and validating data-driven imaging biomarkers differs from those based on biological associations. Radiomic signatures are best developed initially on datasets that represent diversity of acquisition protocols as well as diversity of disease and of normal findings, rather than within clinical trials with standardised and optimised protocols as this would risk the selection of radiomic features being linked to the imaging process rather than the pathology. Normalisation through discretisation and feature harmonisation are essential pre-processing steps. Biological correlation may be performed after the technical and clinical validity of a radiomic signature is established, but is not mandatory. Feature selection may be part of discovery within a radiomics-specific trial or represent exploratory endpoints within an established trial; a previously validated radiomic signature may even be used as a primary/secondary endpoint, particularly if associations are demonstrated with specific biological processes and pathways being targeted within clinical trials

A global research priority agenda to advance public health responses to fatty liver disease

Background & aims An estimated 38% of adults worldwide have non-alcoholic fatty liver disease (NAFLD). From individual impacts to widespread public health and economic consequences, the implications of this disease are profound. This study aimed to develop an aligned, prioritised fatty liver disease research agenda for the global health community. Methods Nine co-chairs drafted initial research priorities, subsequently reviewed by 40 core authors and debated during a three-day in-person meeting. Following a Delphi methodology, over two rounds, a large panel (R1 n = 344, R2 n = 288) reviewed the priorities, via Qualtrics XM, indicating agreement using a four-point Likert-scale and providing written feedback. The core group revised the draft priorities between rounds. In R2, panellists also ranked the priorities within six domains: epidemiology, models of care, treatment and care, education and awareness, patient and community perspectives, and leadership and public health policy. Results The consensus-built fatty liver disease research agenda encompasses 28 priorities. The mean percentage of ‘agree’ responses increased from 78.3 in R1 to 81.1 in R2. Five priorities received unanimous combined agreement (‘agree’ + ‘somewhat agree’); the remaining 23 priorities had >90% combined agreement. While all but one of the priorities exhibited at least a super-majority of agreement (>66.7% ‘agree’), 13 priorities had 90% combined agreement. Conclusions Adopting this multidisciplinary consensus-built research priorities agenda can deliver a step-change in addressing fatty liver disease, mitigating against its individual and societal harms and proactively altering its natural history through prevention, identification, treatment, and care. This agenda should catalyse the global health community’s efforts to advance and accelerate responses to this widespread and fast-growing public health threat. Impact and implications An estimated 38% of adults and 13% of children and adolescents worldwide have fatty liver disease, making it the most prevalent liver disease in history. Despite substantial scientific progress in the past three decades, the burden continues to grow, with an urgent need to advance understanding of how to prevent, manage, and treat the disease. Through a global consensus process, a multidisciplinary group agreed on 28 research priorities covering a broad range of themes, from disease burden, treatment, and health system responses to awareness and policy. The findings have relevance for clinical and non-clinical researchers as well as funders working on fatty liver disease and non-communicable diseases more broadly, setting out a prioritised, ranked research agenda for turning the tide on this fast-growing public health threat

Breast cancer molecular subtype classification using deep features: Preliminary results

Radiogenomics is a field of investigation that attempts to examine the relationship between imaging characteris-tics of cancerous lesions and their genomic composition. This could offer a noninvasive alternative to establishing genomic characteristics of tumors and aid cancer treatment planning. While deep learning has shown its supe-riority in many detection and classification tasks, breast cancer radiogenomic data suffers from a very limited number of training examples, which renders the training of the neural network for this problem directly and with no pretraining a very difficult task. In this study, we investigated an alternative deep learning approach referred to as deep features or off-the-shelf network approach to classify breast cancer molecular subtypes using breast dynamic contrast enhanced MRIs. We used the feature maps of different convolution layers and fully connected layers as features and trained support vector machines using these features for prediction. For the feature maps that have multiple layers, max-pooling was performed along each channel. We focused on distinguishing the Luminal A subtype from other subtypes. To evaluate the models, 10 fold cross-validation was performed and the final AUC was obtained by averaging the performance of all the folds. The highest average AUC obtained was 0.64 (0.95 CI: 0.57-0.71), using the feature maps of the last fully connected layer. This indicates the promise of using this approach to predict the breast cancer molecular subtypes. Since the best performance appears in the last fully connected layer, it also implies that breast cancer molecular subtypes may relate to high level image features

Rosavin Ameliorates Hepatic Inflammation and Fibrosis in the NASH Rat Model via Targeting Hepatic Cell Death

Background: Non-alcoholic fatty liver disease (NAFLD) represents the most common form of chronic liver disease that urgently needs effective therapy. Rosavin, a major constituent of the Rhodiola Rosea plant of the family Crassulaceae, is believed to exhibit multiple pharmacological effects on diverse diseases. However, its effect on non-alcoholic steatohepatitis (NASH), the progressive form of NAFLD, and the underlying mechanisms are not fully illustrated. Aim: Investigate the pharmacological activity and potential mechanism of rosavin treatment on NASH management via targeting hepatic cell death-related (HSPD1/TNF/MMP14/ITGB1) mRNAs and their upstream noncoding RNA regulators (miRNA-6881-5P and lnc-SPARCL1-1:2) in NASH rats. Results: High sucrose high fat (HSHF) diet-induced NASH rats were treated with different concentrations of rosavin (10, 20, and 30 mg/kg/day) for the last four weeks of dietary manipulation. The data revealed that rosavin had the ability to modulate the expression of the hepatic cell death-related RNA panel through the upregulation of both (HSPD1/TNF/MMP14/ITGB1) mRNAs and their epigenetic regulators (miRNA-6881-5P and lnc-SPARCL1-1:2). Moreover, rosavin ameliorated the deterioration in both liver functions and lipid profile, and thereby improved the hepatic inflammation, fibrosis, and apoptosis, as evidenced by the decreased protein levels of IL6, TNF-α, and caspase-3 in liver sections of treated animals compared to the untreated NASH rats. Conclusion: Rosavin has demonstrated a potential ability to attenuate disease progression and inhibit hepatic cell death in the NASH animal model. The produced effect was correlated with upregulation of the hepatic cell death-related (HSPD1, TNF, MMP14, and ITGB1) mRNAs—(miRNA-6881-5P—(lnc-SPARCL1-1:2) RNA panel