Perfusion by Arterial Spin Labelling following Single Dose Tadalafil in Small Vessel Disease (PASTIS): study protocol for a randomized controlled trial

Background Cerebral small vessel disease is a common cause of vascular cognitive impairment in older people, with no licensed treatment. Cerebral blood flow is reduced in small vessel disease. Tadalafil is a widely prescribed phosphodiesterase-5 inhibitor that increases blood flow in other vascular territories. The aim of this trial is to test the hypothesis that tadalafil increases cerebral blood flow in older people with small vessel disease. Methods/design Perfusion by Arterial Spin labelling following Single dose Tadalafil In Small vessel disease (PASTIS) is a phase II randomised double-blind crossover trial. In two visits, 7-30 days apart, participants undergo arterial spin labelling to measure cerebral blood flow and a battery of cognitive tests, pre- and post-dosing with oral tadalafil (20 mg) or placebo. Sample size: 54 participants are required to detect a 15% increase in cerebral blood flow in subcortical white matter (p < 0.05, 90% power). Primary outcomes are cerebral blood flow in subcortical white matter and deep grey nuclei. Secondary outcomes are cortical grey matter cerebral blood flow and performance on cognitive tests (reaction time, information processing speed, digit span forwards and backwards, semantic fluency). Discussion Recruitment started on 4th September 2015 and 36 participants have completed to date (19th April 2017). No serious adverse events have occurred. All participants have been recruited from one centre, St George’s University Hospitals NHS Foundation Trust. Trial registration European Union Clinical Trials Register: EudraCT number 2015-001235-20. Registered on 13 May 2015

Prophylactic ciprofloxacin treatment prevented high mortality, and modified systemic and intestinal immune function in tumour-bearing rats receiving dose-intensive CPT-11 chemotherapy

Infectious complications are a major cause of morbidity and mortality from dose-intensive cancer chemotherapy. In spite of the importance of intestinal bacteria translocation in these infections, information about the effect of high-dose chemotherapy on gut mucosal immunity is minimal. We studied prophylactic ciprofloxacin (Cipro) treatment on irinotecan (CPT-11) toxicity and host immunity in rats bearing Ward colon tumour. Cipro abolished chemotherapy-related mortality, which was 45% in animals that were not treated with Cipro. Although Cipro reduced body weight loss and muscle wasting, it was unable to prevent severe late-onset diarrhoea. Seven days after CPT-11, splenocytes were unable to proliferate (stimulation index=0.10±0.02) and produce proliferative and inflammatory cytokines (i.e., Interleukin (IL)-2, interferon-γ (IFN-γ), tumour necrosis factor-α (TNF-α) IL-1β, IL-6) on mitogen stimulation in vitro (P<0.05 vs controls), whereas mesenteric lymph node (MLN) cells showed a hyper-proliferative response and a hyper-production of pro-inflammatory cytokines on mitogen stimulation. This suggests compartmentalised effects by CPT-11 chemotherapy on systemic and intestinal immunity. Cipro normalised the hyper-responsiveness of MLN cells, and in the spleen, it partially restored the proliferative response and normalised depressed production of IL-1β and IL-6. Taken together, Cipro prevented infectious challenges associated with immune hypo-responsiveness in systemic immune compartments, and it may also alleviate excessive pro-inflammatory responses mediating local gut injury

First Results from the LUX Dark Matter Experiment at the Sanford Underground Research Facility

The Large Underground Xenon (LUX) experiment is a dual-phase xenon time-projection chamber operating at the Sanford Underground Research Facility (Lead, South Dakota). The LUX cryostat was filled for the first time in the underground laboratory in February 2013. We report results of the first WIMP search data set, taken during the period from April to August 2013, presenting the analysis of 85.3 live days of data with a fiducial volume of 118 kg. A profile-likelihood analysis technique shows our data to be consistent with the background-only hypothesis, allowing 90% confidence limits to be set on spin-independent WIMP-nucleon elastic scattering with a minimum upper limit on the cross section of 7.6 × 10−46 cm2 at a WIMP mass of 33 GeV=c2. We find that the LUX data are in disagreement with lowmass WIMP signal interpretations of the results from several recent direct detection experiments

Aptamers for pharmaceuticals and their application in environmental analytics

Aptamers are single-stranded DNA or RNA oligonucleotides, which are able to bind with high affinity and specificity to their target. This property is used for a multitude of applications, for instance as molecular recognition elements in biosensors and other assays. Biosensor application of aptamers offers the possibility for fast and easy detection of environmental relevant substances. Pharmaceutical residues, deriving from human or animal medical treatment, are found in surface, ground, and drinking water. At least the whole range of frequently administered drugs can be detected in noticeable concentrations. Biosensors and assays based on aptamers as specific recognition elements are very convenient for this application because aptamer development is possible for toxic targets. Commonly used biological receptors for biosensors like enzymes or antibodies are mostly unavailable for the detection of pharmaceuticals. This review describes the research activities of aptamer and sensor developments for pharmaceutical detection, with focus on environmental applications

AI is a viable alternative to high throughput screening: a 318-target study

: High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery