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Cytotoxic chemotherapy remains the mainstay of treatment for advanced pancreatic adenocarcinoma (PDAC). Emerging studies support metronomic chemotherapy (MCT) as effective, challenging established paradigms of dosing and schedules. The blood-based ChemoSensitivity Assay has been shown to predict response and survival in advanced PDAC patients treated with standard chemotherapy. The current study combines these concepts for a highly personalized treatment approach. This was a retrospective analysis; a pilot (n = 50) and validation cohort (n = 45) were studied. The ChemoSensitivity Assay was performed at baseline and during therapy; results were correlated to drugs administered and patient outcomes. MCT was administered based on the assay results at the treating physician′s discretion. Patients in the pilot cohort experienced favorable survival compared with historical controls (median overall survival (mOS) 16.8 mo). Patients whose treatment closely matched the ChemoSensitivity Assay predictions experienced longer median time on lines of therapy (5.3 vs. 3.3 mo, p = 0.02) and showed a trend for longer mOS (20.9 vs. 12.5 mo, p = 0.055) compared with those not closely matched. These findings were confirmed in the validation cohort. Overall, patients treated with MCT closely matching Assay results experienced a remarkable mOS of 27.7 mo. ChemoSensitivity profiling-guided MCT is a promising approach for personalized therapy in advanced PDAC.

Pancreatic adenocarcinoma (PDAC) remains a refractory disease; however, modern cytotoxic chemotherapeutics can induce tumor regression and extend life. A blood-based, pharmacogenomic, chemosensitivity assay using gene expression profiling of circulating tumor and invasive cells (CTICs) to predict treatment response was previously developed. The combination regimen of 5-fluorouracil, leucovorin, irinotecan, and oxaliplatin (FOLFIRINOX) and gemcitabine/nab-paclitaxel (G/nab-P) are established frontline approaches for treating advanced PDAC; however, there are no validated biomarkers for treatment selection. A similar unmet need exists for choosing second-line therapy.The chemosensitivity assay was evaluated in metastatic PDAC patients presenting for frontline treatment. A prospective study enrolled patients (n = 70) before receiving either FOLFIRINOX or G/nab-P at a 1:1 ratio. Six milliliters of peripheral blood was collected at baseline and at time of disease progression. CTICs were isolated, gene-expression profiling was performed, and the assay was used to predict effective and ineffective chemotherapeutic agents. Treating physicians were blinded to the assay prediction results.Patients receiving an effective regimen as predicted by the chemosensitivity assay experienced significantly longer median progression-free survival (mPFS; 7.8 months vs. 4.2 months; hazard ratio [HR], 0.35; p = .0002) and median overall survival (mOS; 21.0 months vs. 9.7 months; HR, 0.40; p = .005), compared with an ineffective regimen. Assay prediction for effective second-line therapy was explored. The entire study cohort experienced favorable outcomes compared with historical controls, 7.1-month mPFS and 12.3-month mOS.Chemosensitivity assay profiling is a promising tool for guiding therapy in advanced PDAC. Further prospective validation is under way (clinicaltrials.gov NCT03033927).

Recently, the fungal sphingolipid glucosylceramide (GlcCer) synthesis has emerged as a highly promising new target for drug discovery of next-generation antifungal agents, and we found two aromatic acylhydrazones as effective inhibitors of GlcCer synthesis based on HTP screening. In the present work, we have designed libraries of new aromatic acylhydrazones, evaluated their antifungal activities (MIC(80) and time-kill profile) against C. neoformans and performed an extensive SAR study, which led to the identification of five promising lead compounds, exhibiting excellent fungicidal activities with very large selectivity index. Moreover, two compounds demonstrated broad spectrum antifungal activity against six other clinically relevant fungal strains. These five lead compounds were examined for their synergism/cooperativity with five clinical drugs against seven fungal strains and very encouraging results were obtained, e.g., the combination of all five lead compounds with voriconazole exhibited either synergistic or additive effect to all seven fungal strains.

Fungal infections are a universal problem and are routinely associated with high morbidity and mortality rates in immunocompromised patients. Existing therapies comprise five different classes of antifungal agents, four of which target the synthesis of ergosterol and cell wall glucans. However, the currently available antifungals have many limitations, including poor oral bioavailability, narrow therapeutic indices, and emerging drug resistance resulting from their use, thus making it essential to investigate the development of novel drugs which can overcome these limitations and add to the antifungal armamentarium. Advances have been made in antifungal drug discovery research and development over the past few years as evidenced by the presence of several new compounds currently in various stages of development. In the following minireview, we provide a comprehensive summary of compounds aimed at one or more novel molecular targets. We also briefly describe potential pathways relevant for fungal pathogenesis that can be considered for drug development in the near future.

The incidence of invasive fungal infections is rising due to the increase in susceptible populations. Current clinically available drugs have therapeutic limitations due to toxicity, a narrow spectrum of activity, and, more importantly, the consistent rise of fungal species that are intrinsically resistant or that develop resistance due to prolonged therapy. Thus, there is an urgent need for new broad-spectrum antifungal agents with low toxicity and a novel mechanism of action. We previously reported a new class of potent antifungal compounds, acylhydrazones, that target the fungal sphingolipid pathway. Based upon our initial lead molecules, (E)-N′-(5-bromo-2-hydroxybenzylidene)-2-methylbenzohydrazide and D13, we performed a structure-activity relationship study, synthesizing ca. 300 new compounds. Of these, 5 compounds were identified to be the most promising for further studies, based on their broad-spectrum activity and low toxicity in mammalian cells lines. Among these top 5 lead compounds, we report here the impressive in vivo activity of 2,4-dibromo-N′-(5-bromo-2-hydroxybenzylidene)benzohydrazide (SB-AF-1002) in several models of systemic fungal infection. Our data show that SB-AF-1002 is efficacious and outperforms current standard-of-care drugs in models of invasive fungal infections, such as cryptococcosis, candidiasis, and aspergillosis. Specifically, animals treated with SB-AF-1002 not only survived the infection but also showed a clearing of fungal cells from key organs. Moreover, SB-AF-1002 was very effective in an aspergillosis model as a prophylactic therapy. SB-AF-1002 also displayed acceptable pharmacokinetic properties in mice, similar to those of the parent compound, D13. These results clearly indicate that our novel acylhydrazones constitute a new class of highly potent and efficacious antifungal agents which warrant further development for the treatment of invasive fungal infections.

Metal-organic frameworks (MOFs) interfaced with visible-light-absorbing semiconductors offer a novel approach to improve photoelectrochemical performances. When tested under 1-sun illumination, a naphthalene diimide (NDI)-based monolayer immobilized at p-type Si(111) undergoes two sequential one-electron reductions close to their thermodynamic potentials. No photovoltage is observed until the NDI monolayer is expanded in three dimensions in a PIZOF-type Zr(NDI) MOF (PIZOF = porous interpenetrated zirconium organic framework). The surface-grown MOF thin film promotes photo-induced charge separation and electron transfer across the interface and through the film, resulting in reduction of the molecular linkers at formal potentials >300 mV positive of their thermodynamic potentials. The apparent diffusion coefficient is similar to that measured at a conductive electrode (10-10 cm2 s-1), indicating that the observed photocurrent is governed by charge diffusion through the Zr(NDI) MOF film. The charges accumulated in the NDI-based MOF can be extracted by an external electron acceptor, demonstrating sufficient conductivity throughout the MOF film to power reductive transformations. When grown on GaP(100), the potentials of the NDI reductions in the MOF film are shifted anodically by >700 mV compared to those of the same MOF on conductive substrates. This photovoltage, among the highest reported for GaP in photoelectrochemical applications, illustrates the power of MOF thin films to improve photocathodic performance.

In this work the authors present the first publicly available, open-source simulation model of the Third Generation Partnership Project (3GPP) Release 14 Cellular Vehicle to Everything (C-V2X) standard. The model specifically addresses Mode 4, in which vehicles communicate directly in peer to peer mode using a distributed resource allocation algorithm. Importantly, the model is fully compliant with existing service and application layers already defined by the automotive and standards communities, providing a fully standardised cross-layer communication model spanning application layer based on the ITS-G5 standard to physical layer from the new C-V2X Mode 4 standard. This enables enhancements of the lower layers to support future vehicular use cases. The simulation model has been implemented by extending SimuLTE, an OMNeT++ cellular communication stack that enables simulation of LTE networks as well as extending and integrating Artery, an ETSI compliant ITS-G5 implementation. The developed model is validated using published 3GPP simulation evaluation scenarios and is available at www.cs.ucc.ie/cv2x.

Printing functional, metal parts for mass production using powder bed fusion additive manufacturing requires the ability to deposit more energy at the working plane to increase build rate. Conventional approaches serially add fiber lasers and scanners to create dual and quad laser machines, but scaling this strategy is limited by the size of powder bed while current scan parameters do not utilize the full output potential of available laser sources. Instead, a processing head containing an array of lower power laser sources can melt wider regions of a powder bed simultaneously to increase build rate. A 16 channel processing head comprised of fiber-coupled direct diodes capable of outputting up to 960W of combined power is presented. Fiber arrays arranged to melt widened tracks of CoCr powder demonstrated builds with >99% density at 2x the build rate of conventional, single laser systems. Details of the array layout, optical system and controls are presented along with scan strategies for melt tracks of varying widths. Most importantly, the array configuration can be scaled to multi-kW outputs spread over larger areas without requiring new parameter development as the energy per unit area remains unchanged with channel count.

The aircraft engine industry manufactures many ring-like metal parts of large diameter but small cross-sectional area. Designers of these parts require increasingly complex geometries for improved aerodynamic efficiency and cooling while manufacturers of these parts require larger and faster equipment for high productivity and low cost. The combination of these industrial requirements inspired the development of a new Direct Metal Laser Melting (DMLM) architecture, reported here, which incorporates a rotating powder bed. The system coordinates the rotational motion of the powder bed with an ascending laser scanner and recoater to build parts in a helical fashion. A single-point powder feeder delivers metal powder near the inner radius of an annular build volume, and a recoater spreads the powder to the outer radius in a “snow plow” fashion. Because the recoater and laser scanner are installed at different angular positions, they operate independently and simultaneously. Encoder feedback from both the rotational stage and the galvanometers assures accuracy of the laser scan path. A prototype system was built to demonstrate this new concept for an aircraft engine combustor liner (600-mm dia. x 150-mm ht.) and showed continuous laser utilization exceeding 97%. Build rates were shown to triple conventional DMLM systems while powder requirements were decreased by more than 4x.

Drug-drug interaction related adverse events (DIAE) signals are a major public health issue. Drug safety analysts must sift through thousands of adverse event reports submitted daily to U.S. Food and Drug Administration (FDA) to discover unexpected DIAE signals, which if addressed can lead to life-saving actions. To facilitate the DIAE discovery from these massive data sets, we design several technological innovations that together are integrated into an interactive visual analytics system called DEVES 1. First, our state- of-the-art DIAE mining algorithm efficiently infers potential DIAE signals from these reports, and then ranks them based on their significance score. For interpretability of these inferred DIAE signals, domain knowledge of adverse events and already known drug interactions is extracted from external authoritative data sources and then seamlessly integrated with the inferred signal set. Guided by this augmented signal model, DEVES supports advanced signal analytics - empowering the analyst to interact with linked visualizations offering complementary perspectives into the signal set and its supporting evidence in the form of reports. Our demonstration showcases the technological innovations of DEVES using real-world FDA datasets, demonstrating that DEVES effectively supports the core regulatory tasks from signal screening, signal prioritization to signal validation.

Hundreds of thousands of patients report adverse reactions from using one or more drugs each year. It is impossible to test every drug combination in clinical trials before a drug reaches the market. Thus, the FDA performs post-marketing surveillance to identify drug combinations that produce harmful reactions. To facilitate this surveillance, consumers and health care professionals submit drug safety reports that include the drugs a patient takes and the reactions they experience. In this research, we propose techniques to support high-fidelity rule mining of interesting drug combinations from safety reports by developing drug name matching, reaction name standardization, and known-rule matching strategies. For evaluation, we design a sensibility metric for drug name matching. We demonstrate that our technique achieves a sensibility score of 0.855, corresponding to a 91% accuracy. We compare methods for reaction name standardization and their effects on known-rule matching, identifying 427 known rules from 4652 generated signals when using our techniques as opposed to 61 known rules from 3276 generated signals without the application of our techniques.

The water chemistry and economics of a commercial-scale inland marine integrated multi-trophic aquaculture (IMTA) system was investigated. The system used a combination of a constructed wetland, sand filter followed by a constructed wetland, and geotextile bags to treat solids waste. This study is a demonstration of a zero-discharge system where commercial nursery production of wetland plants was combined with a marine RAS to treat saline solid waste. Total suspended solids (TSS), chemical oxygen demand (COD), total nitrogen (TN), and total phosphorus (TP) concentrations were measured. Plant and soil samples were analyzed for TN and TP. A significant ( p