Estrogen Activation of G-Protein–Coupled Estrogen Receptor 1 Regulates Phosphoinositide 3-Kinase and mTOR Signaling to Promote Liver Growth in Zebrafish and Proliferation of Human Hepatocytes

1. Download high-res image (212KB)
2. Download full-size image

     

Effects of hydroxypyridinone iron chelators in combination with antimalarial drugs on the in vitro growth of Plasmodium falciparum

Using standard in vitro drug susceptibility methods, we assessed the antimalarial activity of 3 orally administered iron chelators (hydroxypyridinones) alone and in combination with conventional antimalarials drugs (quinine, mefloquine, artesunate, tetracycline, atovaquone) against a chloroquine-resistant Plasmodium falciparum isolate. When tested alone, all iron chelators and antimalarial compounds inhibited the growth of the parasites. IC50 values for iron chelators were 60-70 microM, whereas the IC50 values for antimalarial drugs were in nM ranges, with artesunate being the most potent. The derived isobolograms for the interaction of hydroxypyridinones and antimalarial drugs showed addition or mild antagonism, similar to desferroxamine (Sum of Fractional Inhibitory Concentration, sigma FIC < 0.5 or > 4.0). Despite the absence of synergy with conventional drugs, intrinsic antimalarial activity of hydroxypyridinones supports the continued assessment of these iron chelators as treatment adjuncts.     

From the Cover: Satellites can reveal global extent of forced labor in the world’s fishing fleet

While forced labor in the world’s fishing fleet has been widely documented, its extent remains unknown. No methods previously existed for remotely identifying individual fishing vessels potentially engaged in these abuses on a global scale. By combining expertise from human rights practitioners and satellite vessel monitoring data, we show that vessels reported to use forced labor behave in systematically different ways from other vessels. We exploit this insight by using machine learning to identify high-risk vessels from among 16,000 industrial longliner, squid jigger, and trawler fishing vessels. Our model reveals that between 14% and 26% of vessels were high-risk, and also reveals patterns of where these vessels fished and which ports they visited. Between 57,000 and 100,000 individuals worked on these vessels, many of whom may have been forced labor victims. This information provides unprecedented opportunities for novel interventions to combat this humanitarian tragedy. More broadly, this research demonstrates a proof of concept for using remote sensing to detect forced labor abuses.

Forced labor in fisheries, a type of modern slavery, is increasingly recognized as a human rights crisis. The International Labor Organization (ILO) defines forced labor as “all work or service which is exacted from any person under the menace of any penalty and for which the said person has not offered himself voluntarily” (1). The ILO provides a framework of 11 forced labor risk indicators (2) that have all been documented within the fisheries sector, including indicators representative of debt-bonded labor, as well as indicators representative of servitude or slave labor such as abusive working and living conditions. In 2015, reports emerged on forced labor in Thai fisheries (3) and the role of forced labor in producing seafood imported to the United States (4). More recent reports have described the global nature of the problem (5), and there has been a call to integrate social responsibility into ocean science (6). Despite widespread condemnation and ambitious commitments, forced labor remains poorly understood in the fisheries sector. Here we show that recently available high-frequency vessel monitoring of the global industrial fishing fleet can shed new light on forced labor at a much finer resolution. We combine expertise from on-the-ground human rights practitioners and satellite vessel monitoring data for over 16,000 industrial fishing vessels to estimate 1) the number of high-risk vessels and the number of crew who may be victims working on those vessels, 2) where these vessels fish, and 3) what ports these vessels visit. This information can inform new market, policy, and enforcement interventions to combat forced labor in global fisheries. This research more generally demonstrates how remote sensing can detect forced labor abuses by observing dynamic behavior.Current estimates of forced labor in fisheries are coarse and are based on country-level statistics. Using country-level household surveys, the ILO estimated that 16 million people were victims of forced labor in 2016, with 11% of these in agriculture, forestry, or fisheries (7). The Global Slavery Index reports that the seven countries with highest slavery risk in 2018 generated 39% of global fisheries catch (3, 8), and Tickler et al. found that the United States has slavery risks of 0.2 kg per metric ton for domestic seafood and 3.1 kg per metric ton for imported seafood (9). While these studies are important for broadly understanding which countries have risk, current methods are unable to detect this problem at the level of individual fishing vessels, which will be essential for targeted interventions.We empirically examine whether vessels reported to exhibit any of the ILO indicators of forced labor behave in ways that are systematically different from other vessels, and then exploit this information using machine learning to discriminate between vessels that use forced labor from those that do not. We do so by measuring a suite of features that can be observed using satellite Automatic Identification System (AIS) vessel monitoring data made available by Global Fishing Watch (GFW) (10). There may be many behavioral correlates with forced labor that could help to differentiate between high-risk and low-risk vessels. To determine which model features to include, we first conducted a literature review of investigative journalism reports and looked for instances of forced labor case accounts that detailed specific behaviors that could be observed using vessel monitoring data. We next conducted informal phone interviews with experts from several nongovernmental organizations (NGOs) working in this field, during which we asked interviewees what observable vessel behaviors they would look for if they wanted to identify suspicious activity. The machine-learning approach we use does not assume that vessels behave in any particular way; rather, it merely uses the features identified by literature review and expert insight to exploit any observed empirical differences between vessels that use forced labor and other vessels. NGO experts and investigative journalism suggest that gaps in AIS transmission, port avoidance, transshipment, and extended time at sea may indicate the presence of forced labor (11). Certain features, like information on catch and the species being targeted, could also be helpful in discriminating between high- and low-risk behavior by providing more context on the fishing taking place. However, these data are not currently available at the vessel level on a global scale. Data on recruitment practices and vessel ownership and information on from where the crew originates could also be helpful, but, again, these data are not widely available. We arrived at a list of 27 vessel behavior and characteristic features for which we have globally available data at the vessel level (SI Appendix, Table S1, and SI Appendix).To build a predictive model for identifying high-risk vessels, we developed a training dataset that includes the behavior and characteristics of known forced-labor vessels, as well as the behavior and characteristics of other vessels. We compiled a comprehensive database of vessels that were reported to display one or more of the ILO forced labor indicators (2); these vessels are labeled as “positives.” We do not, however, know which vessels do not use forced labor (“negatives”). Rather, any vessel that we do not label as positive is “unlabeled,” and may in fact be a positive vessel that has not yet been identified or may truly be a negative vessel. This is an example of “positive-unlabeled (PU)” learning, a less straightforward problem than traditional supervised machine learning (12). We use PU learning to predict whether or not 16,261 longliner, trawler, and squid jigger fishing vessels were high-risk during each year they operated between 2012 and 2018 (“vessel-years”). We focus on this subset of vessels because they broadcasted sufficient and reliable AIS positions and because these are the only fishing gear types with documented cases of forced labor aboard vessels that broadcasted sufficient AIS data. These vessels represent 33% of the total time at sea spent by all fishing vessels operating in this time period tracked by GFW. Our PU approach leverages information from all positively labeled vessels (n = 22 unique vessels across 22 vessel-years using our baseline model assumption), but places less emphasis on unlabeled vessels given their uncertain nature (n = 16,257 unique vessels across 66,314 vessel-years using our baseline model assumption).     

Increased Antifungal Drug Resistance in Clinical Isolates of Cryptococcus neoformans in Uganda

Cryptococcal antigen screening is recommended among people living with AIDS when entering HIV care with a CD4 count of <100 cells/μl, and preemptive fluconazole monotherapy treatment is recommended for those with subclinical cryptococcal antigenemia. Yet, knowledge is limited of current antimicrobial resistance in Africa. We examined antifungal drug susceptibility in 198 clinical isolates collected from Kampala, Uganda, between 2010 and 2014 using the CLSI broth microdilution assay. In comparison with two previous studies from 1998 to 1999 that reported an MIC₅₀ of 4 μg/ml and an MIC₉₀ of 8 μg/ml prior to widespread human fluconazole and agricultural azole fungicide usage, we report an upward shift in the fluconazole MIC₅₀ to 8 μg/ml and an MIC₉₀ value of 32 μg/ml, with 31% of isolates with a fluconazole MIC of ≥16 μg/ml. We observed an amphotericin B MIC₅₀ of 0.5 μg/ml and an MIC₉₀ of 1 μg/ml, of which 99.5% of isolates (197 of 198 isolates) were still susceptible. No correlation between MIC and clinical outcome was observed in the context of amphotericin B and fluconazole combination induction therapy. We also analyzed Cryptococcus susceptibility to sertraline, with an MIC₅₀ of 4 μg/ml, suggesting that sertraline is a promising oral, low-cost, available, novel medication and a possible alternative to fluconazole. Although the CLSI broth microdilution assay is ideal to standardize results, limit human bias, and increase assay capacity, such assays are often inaccessible in low-income countries. Thus, we also developed and validated an assay that could easily be implemented in a resource-limited setting, with similar susceptibility results (P = 0.52).     

Nephrotoxicity Comparison of Two Commercially Available Generic Vancomycin Products

To date, no comparative clinical studies have investigated the effects of different vancomycin products on nephrotoxicity. The objective of this single-center, retrospective, matched-cohort study was to investigate the impact of two different vancomycin products on the development of nephrotoxicity. The study population included adults receiving a single vancomycin product, from either Pfizer or Hospira, for their entire course of therapy. Patients were matched based on underlying nephrotoxicity risk factors. Secondary outcomes included the need for renal replacement therapy, length of hospital stay, and in-hospital mortality. One-hundred forty-six matched pairs (n = 292) were included, and they had no significant differences in demographics, comorbid conditions, severity of illness, or vancomycin-associated nephrotoxicity risk factors. The frequency of nephrotoxicity was 8.9% in the Pfizer group and 11.0% in the Hospira group as defined by the 2009 consensus vancomycin guidelines (P = 0.56), 17.1% in the Pfizer group and 13.0% in the Hospira group as defined by the Acute Kidney Injury Network (AKIN) (P = 0.33), and 10.3% in the Pfizer group and 11.6% in the Hospira group as defined by RIFLE (risk, injury, failure, loss, and end-stage renal disease) criteria (P = 0.71). There were no differences between groups in regard to nephrotoxicity by any definition or in secondary outcomes. In multivariate analysis of overall nephrotoxicity risk factors, the type of vancomycin product was not independently associated with increased odds of developing nephrotoxicity according to the RIFLE criteria. Based on our results, there are no discernible differences between Pfizer and Hospira vancomycin products in the frequency of nephrotoxicity. Confirmation of these results with other types of vancomycin and different patient populations is warranted.     

Antenatal sonographic diagnosis of choledochal cyst: Case report and imaging review

In this report, we present the antenatal two‐ and three‐dimensional sonographic findings from a fetus with choledochal cyst as well as confirmatory postnatal MRI. A delayed diagnosis of choledochal cyst is common, leading to significant morbidity and mortality. Visualizing bile ducts entering a right upper quadrant cyst is pathognomonic, and early diagnosis can facilitate definitive treatment with Roux‐en‐Y hepaticojejunostomy. © 2014 Wiley Periodicals, Inc. J Clin Ultrasound 43 :581–583, 2015     

Novel antibodies directed against the human erythropoietin receptor: creating a basis for clinical implementation

Recombinant human erythropoietin (rHuEPO) is an effective treatment for anaemia but concerns that it causes disease progression in cancer patients by activation of EPO receptors (EPOR) in tumour tissue have been controversial and have restricted its clinical use. Initial clinical studies were flawed because they used polyclonal antibodies, later shown to lack specificity for EPOR. Moreover, multiple isoforms of EPOR caused by differential splicing have been reported in cancer cell lines at the mRNA level but investigations of these variants and their potential impact on tumour progression, have been hampered by lack of suitable antibodies. The EpoCan consortium seeks to promote improved pathological testing of EPOR, leading to safer clinical use of rHuEPO, by producing well characterized EPOR antibodies. Using novel genetic and traditional peptide immunization protocols, we have produced mouse and rat monoclonal antibodies, and show that several of these specifically recognize EPOR by Western blot, immunoprecipitation, immunofluorescence, flow cytometry and immunohistochemistry in cell lines and clinical material. Widespread availability of these antibodies should enable the research community to gain a better understanding of the role of EPOR in cancer, and eventually to distinguish patients who can be treated safely by rHuEPO from those at increased risk from treatment.