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Pathology services are limited in most areas of sub-Saharan Africa. This study’s aim was to survey anatomic and clinical pathology services and laboratory infrastructure in Mozambique.

A survey was conducted from October-December 2018 across the four central hospitals of Mozambique to determine infrastructure and pathology services available.

Most laboratory/pathology services in Mozambique are limited to the four central hospitals. Only 14 pathologists practice in the country despite a population of 29.5 million for the world’s fifth worst workforce/population ratio. Approximately 35,000 anatomic pathology specimens are evaluated annually. Standard services across chemistry, hematology, microbiology, and blood bank are available at the four central hospitals. Esoteric laboratory testing and immunohistochemistry are generally only available in Maputo.

While most pathology services are available in Mozambique, many are available only at the Maputo laboratory. Expansion of pathology services and infrastructure will improve provision of effective and efficient health care as access to timely and accurate clinical diagnoses increases in Mozambique.

While most pathology services are available in Mozambique, many are available only at the Maputo laboratory. Expansion of pathology services and infrastructure will improve provision of effective and efficient health care as access to timely and accurate clinical diagnoses increases in Mozambique.How to produce expressive molecular representations is a fundamental challenge in artificial intelligence-driven drug discovery. Brequinar Graph neural network (GNN) has emerged as a powerful technique for modeling molecular data. However, previous supervised approaches usually suffer from the scarcity of labeled data and poor generalization capability. Here, we propose a novel molecular pre-training graph-based deep learning framework, named MPG, that learns molecular representations from large-scale unlabeled molecules. In MPG, we proposed a powerful GNN for modelling molecular graph named MolGNet, and designed an effective self-supervised strategy for pre-training the model at both the node and graph-level. After pre-training on 11 million unlabeled molecules, we revealed that MolGNet can capture valuable chemical insights to produce interpretable representation. The pre-trained MolGNet can be fine-tuned with just one additional output layer to create state-of-the-art models for a wide range of drug discovery tasks, including molecular properties prediction, drug-drug interaction and drug-target interaction, on 14 benchmark datasets. The pre-trained MolGNet in MPG has the potential to become an advanced molecular encoder in the drug discovery pipeline.Platelets have been hypothesized to promote certain neoplastic malignancies; however, antiplatelet drugs are still not part of routine pharmacological cancer prevention and treatment protocols. Paracrine interactions between platelets and cancer cells have been implicated in potentiating the dissemination, survival within the circulation, and extravasation of cancer cells at distant sites of metastasis. Signals from platelets have also been suggested to confer epigenetic alterations, including upregulating oncoproteins in circulating tumor cells, and secretion of potent growth factors may play roles in promoting mitogenesis, angiogenesis, and metastatic outgrowth. Thrombocytosis remains a marker of poor prognosis in patients with solid tumors. Experimental data suggest that lowering of platelet count may reduce tumor growth and metastasis. On the basis of the mechanisms by which platelets could contribute to cancer growth and metastasis, it is conceivable that drugs reducing platelet count or platelet activation might attenuate cancer progression and improve outcomes. We will review select pharmacological approaches that inhibit platelets and may affect cancer development and propagation. We begin by presenting an overview of clinical cancer prevention and outcome studies with low-dose aspirin. We then review current nonclinical development of drugs targeted to platelet binding, activation, and count as potential mitigating agents in cancer.The poly (ADP-ribose) polymerase-1 (PARP1) has been regarded as a vital target in recent years and PARP1 inhibitors can be used for ovarian and breast cancer therapies. However, it has been realized that most of PARP1 inhibitors have disadvantages of low solubility and permeability. Therefore, by discovering more molecules with novel frameworks, it would have greater opportunities to apply it into broader clinical fields and have a more profound significance. In the present study, multiple virtual screening (VS) methods had been employed to evaluate the screening efficiency of ligand-based, structure-based and data fusion methods on PARP1 target. The VS methods include 2D similarity screening, structure-activity relationship (SAR) models, docking and complex-based pharmacophore screening. Moreover, the sum rank, sum score and reciprocal rank were also adopted for data fusion methods. The evaluation results show that the similarity searching based on Torsion fingerprint, six SAR models, Glide docking and pharmacophore screening using Phase have excellent screening performance. The best data fusion method is the reciprocal rank, but the sum score also performs well in framework enrichment. In general, the ligand-based VS methods show better performance on PARP1 inhibitor screening. These findings confirmed that adding ligand-based methods to the early screening stage will greatly improve the screening efficiency, and be able to enrich more highly active PARP1 inhibitors with diverse structures.

Under opt-out organ donation policies, individuals are automatically considered to have agreed to donate their organs in the absence of a recorded opt-out decision. Growing evidence suggests that the language used within organ donation campaigns influences donor intentions and decision-making.

As awareness campaigns to promote opt-out consent in the UK are ongoing, the objectives of this study were to investigate the effect of language and message framing used in opt-out organ donation campaigns on donor intentions and psychological reactance.

Individuals from Scotland and England (N = 1,350) completed this online experiment. Participants were randomized to view one of four messages, designed in the format of a newspaper article, which described the upcoming opt-out system. This followed a 2 × 2 design whereby the degree of threatening language (high threat vs. low threat) and message framing (loss vs. gain) of the newspaper article was experimentally manipulated. Measures of intention (pre-exposure and postexposure) and postmessage reactance (threat to freedom and anger and counter-arguing) were obtained.