The quadrivalent HPV vaccine targeted at types 6, 11, 16, and 18 was safe and immunogenic in HIV-infected women aged 13-45 years. Women with HIV RNA load >10 000 copies/mL and/or CD4 count <200 cells/µL had lower rates of seroconversion rates. Clinical Trials Registration. NCT00604175.

Solid tumors are frequently associated with resistance to chemotherapy because the fraction of hypoxic tumor cells is substantial. To understand the underlying mechanism of hypoxia on silver nanoparticle (AgNPs)-induced apoptosis, the expression of hypoxia-inducible factor (HIF)-1α, a hallmark of hypoxia, was measured in the presence and absence of AgNPs. The results showed that HIF-1α expression was upregulated after AgNPs treatment under both hypoxic and normoxic conditions. Cell viability assays showed that AgNPs promoted cell death in cancer cells but not in non-cancer cells, as cancer cells are slightly more acidic than normal cells. However, reactive oxygen species generation induced by AgNPs in lung cancer cells caused high susceptibility to oxidative stress, whereas pre-exposure to hypoxia blocked AgNPs-induced oxidative stress. Notably, HIF-1α inhibited AgNPs-induced mitochondria-mediated apoptosis by regulating autophagic flux through the regulation of ATG5, LC3-II, and p62. Further, cell viability after treatment of cancer cells with AgNPs under hypoxic conditions was lower in HIF-1α siRNA-transfected cells than in control siRNA-transfected cells, indicating that HIF-1α knockdown enhances hypoxia induced decrease in cell viability. Our results suggest that hypoxia-mediated autophagy may be a mechanism for the resistance of AgNPs-induced apoptosis and that strategies targeting HIF-1α may be used for cancer therapy.

Continuous-time, multistate processes can be used to represent a variety of biological processes in the public health sciences; yet the analysis of such processes is complex when they are observed only at a limited number of time points. Inference methods for such panel data have been developed for time homogeneous Markov models, but there has been little research done for other classes of processes. We develop likelihood-based methods for panel data from a semi-Markov process, where transition intensities depend on the duration of time in the current state. The proposed methods account for possible misclassification of states. To illustrate the methods, we investigate a three- and a four-state models in detail and apply the results to model the natural history of oncogenic genital human papillomavirus infections in women.

Part of the Geriatrics Commons, and the Occupational Therapy Commons This Article is brought to you for free and open access by the Jefferson Digital Commons. The Jefferson Digital Commons is a service of Thomas Jefferson University's Center for Teaching and Learning (CTL). The Commons is a showcase for Jefferson books and journals, peer-reviewed scholarly publications, unique historical collections from the University archives, and teaching tools. The Jefferson Digital Commons allows researchers and interested readers anywhere in the world to learn about and keep up to date with Jefferson scholarship. This article has been accepted for inclusion in Department of Occupational Therapy Faculty Papers by an authorized administrator of the Jefferson Digital Commons.

BackgroundPredicting prognosis in patients from large-scale genomic data is a fundamentally challenging problem in genomic medicine. However, the prognosis still remains poor in many diseases. The poor prognosis may be caused by high complexity of biological systems, where multiple biological components and their hierarchical relationships are involved. Moreover, it is challenging to develop robust computational solutions with high-dimension, low-sample size data.ResultsIn this study, we propose a Pathway-Associated Sparse Deep Neural Network (PASNet) that not only predicts patients’ prognoses but also describes complex biological processes regarding biological pathways for prognosis. PASNet models a multilayered, hierarchical biological system of genes and pathways to predict clinical outcomes by leveraging deep learning. The sparse solution of PASNet provides the capability of model interpretability that most conventional fully-connected neural networks lack. We applied PASNet for long-term survival prediction in Glioblastoma multiforme (GBM), which is a primary brain cancer that shows poor prognostic performance. The predictive performance of PASNet was evaluated with multiple cross-validation experiments. PASNet showed a higher Area Under the Curve (AUC) and F1-score than previous long-term survival prediction classifiers, and the significance of PASNet’s performance was assessed by Wilcoxon signed-rank test. Furthermore, the biological pathways, found in PASNet, were referred to as significant pathways in GBM in previous biology and medicine research.ConclusionsPASNet can describe the different biological systems of clinical outcomes for prognostic prediction as well as predicting prognosis more accurately than the current state-of-the-art methods. PASNet is the first pathway-based deep neural network that represents hierarchical representations of genes and pathways and their nonlinear effects, to the best of our knowledge. Additionally, PASNet would be promising due to its flexible model representation and interpretability, embodying the strengths of deep learning. The open-source code of PASNet is available at https://github.com/DataX-JieHao/PASNet.

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Efforts to increase HIV case identification through routine, voluntary HIV testing are hindered by high refusal rates. Our objective was to identify patients most likely to refuse routine HIV testing. We developed a new HIV testing program at four Massachusetts urgent care centers. Patients were asked if they were interested in routine HIV testing. We performed analyses to assess differences in characteristics between those who refused testing and those who accepted it. Data were available for 9129/10,354 (88%) patients offered routine HIV testing from January to December 2002. Of these 9129 patients, 67% refused testing. In the crude analysis, HIV test refusal was associated with female gender, white race, older age, and higher educational level. In multivariate analysis, non-English-speaking patients who were Hispanic, Haitian, and other race were more likely to refuse testing than their English-speaking counterparts. Among all patients, "not at risk" and "already tested" were the most common reasons for test refusal. Two thirds of patients refused routine HIV testing when it was offered in a statewide urgent care-based program. If routine HIV testing programs are to be successful, strategies must be developed to increase HIV test acceptance among patients most likely to refrain from testing.

Little is known about the production of fluorescent dissolved organic matter (FDOM) in the anoxic oceanic sediments. In this study, sediment pore waters were sampled from four different sites in the Chukchi-East Siberian Seas area to examine the bulk dissolved organic carbon (DOC) and their optical properties. The production of FDOM, coupled with the increase of nutrients, was observed above the sulfate-methane-transition-zone (SMTZ). The presence of FDOM was concurrent with sulfate reduction and increased alkalinity (R2 > 0.96, p < 0.0001), suggesting a link to organic matter degradation. This inference was supported by the positive correlation (R2 > 0.95, p < 0.0001) between the net production of FDOM and the modeled degradation rates of particulate organic carbon sulfate reduction. The production of FDOM was more pronounced in a shallow shelf site S1 with a total net production ranging from 17.9 to 62.3 RU for different FDOM components above the SMTZ depth of ca. 4.1 mbsf, which presumably underwent more accumulation of particulate organic matter than the other three deeper sites. The sediments were generally found to be the sources of CDOM and FDOM to the overlying water column, unearthing a channel of generally bio-refractory and pre-aged DOM to the oceans.