Throughout most of the developing brain, including the hippocampus, GABAergic synapses are the first to become functional. Several features of GABAergic signaling change across development, suggesting that this signaling in the immature brain may play important roles in the growth of young neurons and the establishment of networks. To determine whether GABA(A) receptor (GABA(A)R)-containing synapses in new neurons born in the adult dentate gyrus have similar immature features, we examined spontaneous and evoked GABA(A)R-mediated synaptic currents in young (POMC-EGFP or doublecortin-immunostained) granule cells in acute slice preparations from adult mice and rats. Spontaneous inhibitory postsynaptic currents (IPSCs) were observed in nearly all immature granule cells, but their frequency was considerably lower and their decay time constant was nearly two times longer than in neighboring mature (doublecortin-non-immunoreactive or EGFP-non-expressing) granule cells within the sub-granular zone. Evoked IPSCs (eIPSCs) in mature granule cells, but not immature granule cells, were sensitive to zolpidem, suggesting a maturational increase in GABA(A)R alpha1-subunit expression. Perforated-patch recording revealed that eIPSCs depolarized young neurons, but hyperpolarized mature neurons. The early establishment of synaptic GABAergic inputs slow IPSC decay time, and depolarizing action of eIPSCs are remarkably similar to features previously seen in neurons during development, suggesting that they are intrinsic features of immature neurons and not functions of the surrounding circuitry. These developmental features in adult-born granule cells could play a role in maturational processes such as developmental cell death. However, treatment of adult mice with GABA(A)R agonists and an inverse agonist did not significantly alter the number of 4- to 14-day-old BrdU-labeled cells.

Girls and women experience numerous types of vaginal bleeding. These include healthy reproductive processes, such as menstruation and bleeding after childbirth, but also bleeding related to health conditions, such as fibroids or cancer. In most societies, the management of menstruation is handled covertly, something girls are often instructed about at menarche. The management of other vaginal bleeding is often similarly discreet, although behaviours are not well documented. In many societies, cultural taboos frequently hinder open discussion around vaginal bleeding, restricting information and early access to healthcare. Additionally, the limited availability of clean, accessible water and sanitation facilities in many low and middle-income countries augments the challenges girls and women face in conducting daily activities while managing vaginal bleeding, including participating in school or work, going to the market or fetching water. This paper aims to highlight the key vaginal bleeding experiences throughout a woman’s life course and the intersection of these bleeding experiences with their access to adequate water and sanitation facilities, information and education sources, and supplies. The aim is to address the silence around girls and women’s vaginal bleeding and their related social, physical and clinical management needs across the life course; and highlight critical gaps that require attention in research, practice and policy around this neglected topic of health and gender equality.

The use of optoacoustic imaging takes advantage of the photoacoustic effect to generate high-contrast, high-resolution medical images at penetration depths of up to 5 cm. Multispectral optoacoustic tomography (MSOT) is a type of optoacoustic imaging system that has seen promising preclinical success with a recent emergence into the clinic. Multiwavelength illumination of tissue allows for the mapping of multiple chromophores, which are generated endogenously or exogenously. However, translation of MSOT to the clinic is still in its preliminary stages. For successful translation, MSOT requires refinement of probes and data-acquisition systems to tailor to the human body, along with more intuitive, real-time visualization settings. The possibilities of optoacoustic imaging, namely MSOT, in the clinic are reviewed here.

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Objective. Handcrafted radiomics features or deep learning model-generated automated features are commonly used to develop computer-aided diagnosis schemes of medical images. The objective of this study is to test the hypothesis that handcrafted and automated features contain complementary classification information and fusion of these two types of features can improve CAD performance. Approach. We retrospectively assembled a dataset involving 1,535 lesions (740 malignant and 795 benign). Regions of interest (ROI) surrounding suspicious lesions are extracted and two types of features are computed from each ROI. The first one includes 40 radiomic features and the second one includes automated features computed from a VGG16 network using a transfer learning method. A single channel ROI image is converted to three channel pseudo-ROI images by stacking the original image, a bilateral filtered image, and a histogram equalized image. Two VGG16 models using pseudo-ROIs and 3 stacked original ROIs without pre-processing are used to extract automated features. Five linear support vector machines (SVM) are built using the optimally selected feature vectors from the handcrafted features, two sets of VGG16 model-generated automated features, and the fusion of handcrafted and each set of automated features, respectively. Main Results. Using a 10-fold cross-validation, the fusion SVM using pseudo-ROIs yields the highest lesion classification performance with area under ROC curve (AUC=0.756±0.042), which is significantly higher than those yielded by other SVMs trained using handcrafted or automated features only (p<0.05). Significance. This study demonstrates that both handcrafted and automated futures contain useful information to classify breast lesions. Fusion of these two types of features can further increase CAD performance.

This paper undertakes an examination of the espoused commitment to CSR principles and its relationship with the labour management systems in two Australian mining companies. The paper explores the extent to which the adoption of a CSR strategy has affected the management of labour in three respects: "collective" relations with unions; "partnership-style" relations with unions and workers; and the adoption of HPWS measures. The paper concludes that while each company has made substantial "in principle" commitments as a result of CSR, there is nothing to suggest that CSR considerations are sufficiently powerful in themselves to bring about systemic change in the management of labour. Copyright (c) 2007 The Authors; Journal compilation (c) 2007 Blackwell Publishing Ltd.

Inflammatory diseases include a wide variety of highly prevalent conditions with high mortality rates in severe cases ranging from cardiovascular disease, to rheumatoid arthritis, to chronic obstructive pulmonary disease, to graft vs. host disease, to a number of gastrointestinal disorders. Many diseases that are not considered inflammatory per se are associated with varying levels of inflammation. Imaging of the immune system and inflammatory response is of interest as it can give insight into disease progression and severity. Clinical imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) are traditionally limited to the visualization of anatomical information; then, the presence or absence of an inflammatory state must be inferred from the structural abnormalities. Improvement in available contrast agents has made it possible to obtain functional information as well as anatomical. In vivo imaging of inflammation ultimately facilitates an improved accuracy of diagnostics and monitoring of patients to allow for better patient care. Highly specific molecular imaging of inflammatory biomarkers allows for earlier diagnosis to prevent irreversible damage. Advancements in imaging instruments, targeted tracers, and contrast agents represent a rapidly growing area of preclinical research with the hopes of quick translation to the clinic.

Objective: Radiomics and deep transfer learning are two popular technologies used to develop computer-aided detection and diagnosis (CAD) schemes of medical images. This study aims to investigate and to compare the advantages and the potential limitations of applying these two technologies in developing CAD schemes. Methods: A relatively large and diverse retrospective dataset including 3000 digital mammograms was assembled in which 1496 images depicted malignant lesions and 1504 images depicted benign lesions. Two CAD schemes were developed to classify breast lesions. The first scheme was developed using four steps namely, applying an adaptive multi-layer topographic region growing algorithm to segment lesions, computing initial radiomics features, applying a principal component algorithm to generate an optimal feature vector, and building a support vector machine classifier. The second CAD scheme was built based on a pre-trained residual net architecture (ResNet50) as a transfer learning model to classify breast lesions. Both CAD schemes were trained and tested using a 10-fold cross-validation method. Several score fusion methods were also investigated to classify breast lesions. CAD performances were evaluated and compared by the areas under the ROC curve (AUC). Results: The ResNet50 model-based CAD scheme yielded AUC = 0.85 ± 0.02, which was significantly higher than the radiomics feature-based CAD scheme with AUC = 0.77 ± 0.02 (p < 0.01). Additionally, the fusion of classification scores generated by the two CAD schemes did not further improve classification performance. Conclusion: This study demonstrates that using deep transfer learning is more efficient to develop CAD schemes and it enables a higher lesion classification performance than CAD schemes developed using radiomics-based technology.