Therapeutic drug monitoring of amlodipine and the Z-FHL/HHL ratio: Adherence tools in patients referred for apparent treatment-resistant hypertension

Background. Non-adherence to antihypertensives is a cause of ‘pseudo-treatment-resistant’ hypertension. Objective. To determine whether monitoring plasma amlodipine concentrations and inhibition of angiotensin-converting enzyme (ACE) can be adjunct adherence tools. Methods. Patients with hypertension who were prescribed enalapril and amlodipine were enrolled. Blood pressures (BPs) were monitored and an adherence questionnaire was completed. Steady-state amlodipine was assayed using liquid chromatography-mass spectrometry and degree of ACE inhibition using the Z-FHL/HHL (z-phenylalanine-histidine-leucine/hippuryl-histidine-leucine) ratio. Results. One hundred patients (mean (standard deviation) age 50.5 (12) years, 46% male) were enrolled. Based on plasma assays, 26/97 patients (26.8%) were unsuppressed by enalapril and 20/100 (20%) were sub-therapeutic for amlodipine. There were significant BP differences based on plasma levels of the medication: 21/20 mmHg lower in the group with suppressed ACE and 26/20 mmHg in the group with steady-state amlodipine concentrations. Conclusions. Monitoring antihypertensive adherence by assaying plasma medication concentrations is a feasible option for evaluating true v. pseudo-resistant hypertension.S Afr Med J 2017;107(10):887-89

Fluctuations in measured radioactive decay rates inside a modified Faraday cage: Correlations with space weather

[EN] For several years, reports have been published about fluctuations in measured radioactive decay time-series and in some instances linked to astrophysical as well as classical environmental influences. Anomalous behaviors of radioactive decay measurement and measurement of capacitance inside and outside a modified Faraday cage were documented by our group in previous work. In the present report, we present an in-depth analysis of our measurement with regard to possible correlations with space weather, i.e. the geomagnetic activity (GMA) and cosmic-ray activity (CRA). Our analysis revealed that the decay and capacitance time-series are statistically significantly correlated with GMA and CRA when specific conditions are met. The conditions are explained in detail and an outlook is given on how to further investigate this important finding. Our discovery is relevant for all researchers investigating radioactive decay measurements since they point out that the space weather condition during the measurement is relevant for partially explaining the observed variability.This work has been partially financed by: grant no. 20170764 (Equipos de deteccion, regulacion e informacion en el sector de los sistemas inteligentes de transporte (ITS). Nuevos modelos y ensayos de compatibilidad y verificacion de funcionamiento) (Spain), by grant no. RTI2018-102256-B-I00 (Spain), by the Generalitat Valenciana (Spain) under project Bioingenieria de las Radiaciones Ionizantes. Biorad (PROMETEO/2018/035) and the project MEMO RADION (IDIFEDER/2018/038) co-financed by the Programa Operativo del Fondo Social Europeo 2014-2020", and by grant No.075-00845-20-01 (Russia).Milián-Sánchez, V.; Scholkmann, F.; Fernández De Córdoba, P.; Mocholí Salcedo, A.; Mocholí-Belenguer, F.; Iglesias-Martínez, ME.; Castro-Palacio, JC.... (2020). Fluctuations in measured radioactive decay rates inside a modified Faraday cage: Correlations with space weather. Scientific Reports. 10(1):1-12. https://doi.org/10.1038/s41598-020-64497-0S112101Milián-Sánchez, V., Mocholí-Salcedo, A., Milián, C., Kolombet, V. A. & Verdú, G. Anomalous effects on radiation detectors and capacitance measurements inside a modified Faraday cage. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 828, 210–228 (2016).G. F. Knoll Radiation Detection and Measurement, 4th Edition. (Wiley, 2010).Jenkins, J. H., Mundy, D. W. & Fischbach, E. Analysis of environmental influences in nuclear half-life measurements exhibiting time-dependent decay rates. Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 620, 332–342 (2010).Jenkins, J. H. et al. Additional experimental evidence for a solar influence on nuclear decay rates. Astroparticle Physics 37, 81–88 (2012).Falkenberg, E. D. Radioactive Decay Caused by Neutrinos? Apeiron 8, 32–45 (2001).A. G. Parkhomov Influence of Relic Neutrinos on Beta Radioactivity. arXiv:1010.1591v1 [physics.gen-ph], (2010).P. A. Sturrock, E. Fischbach, A. G. Parkhamov, J. D. Scargle, G. Steinitz, Concerning the variability of beta-decay measurements. arXiv:1510.05996 [nucl-ex], (2015).Baurov, Y. A. et al. Experimental Investigations of Changes in β-Decay if 60Co and 137Cs. Modern Physics Letters A 16, 2089–2101 (2001).Baurov, Y. A. Research of Global Anisotropy of Physical Space on Investigation Base of Changes in β and α-decay Rate of Radioactive Elements. Motion of Pulsars and Anisotropy of Cosmic Rays. American Journal of Modern Physics 2, 177–184 (2013).Baurov, Y. A., Sobolev, Y. G., Ryabov, Y. V. & Kushniruk, V. F. Experimental investigations of changes in the rate of beta decay of radioactive elements. Physics of Atomic Nuclei 70, 1825–1835 (2009).Baurov, Y. A. The anisotropic phenomenon in the β decay of radioactive elements and in other processes in nature. Bulletin of the Russian Academy of Sciences: Physics 76, 1076–1080 (2012).Baurov, Y. A., Sobolev, Y. G. & Ryabov, Y. V. New force, global anisotropy and the changes in β-decay rate of radioactive elements. American Journal of Astronomy and Astrophysics 2, 8–19 (2014).Pons, D. J., Pons, A. D. & Pons, A. J. Asymmetrical neutrino induced decay of nucleons. Applied Physics Research 7, 1–13 (2015).Pons, D. J., Pons, A. D. & Pons, A. J. Hidden Variable Theory Supports Variability in Decay Rates of Nuclides. Applied Physics Research 7, 18–29 (2015).Kossert, K. & Nähle, O. J. Long-term measurements of 36Cl to investigate potential solar influence on the decay rate. Astroparticle Physics 55, 33–36 (2014).Schrader, H. Seasonal variations of decay rate measurement data and their interpretation. Applied Radiation and Isotopes 114, 202–213 (2016).Pommé, S. et al. Evidence against solar influence on nuclear decay constants. Physics Letters B 761, 281–286 (2016).Bergeson, S. D., Peatross, J. & Ware, M. J. Precision long-term measurements of beta-decay-rate ratios in a controlled environment. Physics Letters B 767, 171–176 (2017).McKnight, Q., Bergeson, S. D., Peatross, J. & Ware, M. J. 2.7 years of beta-decay-rate ratio measurements in a controlled environment. Applied Radiation and Isotopes 142, 113–119 (2018).Pommé, S. et al. On decay constants and orbital distance to the Sun—part I: alpha decay. Metrologia 54, 1–18 (2017).Pommé, S. et al. On decay constants and orbital distance to the Sun—part III: beta plus and electron capture decay. Metrologia 54, 36–50 (2017).Pommé, S., Lutter, G., Marouli, M., Kossert, K. & Nähle, O. On the claim of modulations in radon decay and their association with solar rotation. Astroparticle Physics 97, 38–45 (2018).S. Pommé, K. Kossert, O. Nähle On the Claim of Modulations in 36Cl Beta Decay and Their Association with Solar Rotation. Solar Physics 292 (2017).Pommé, S. et al. Is decay constant? Applied Radiation and Isotopes 134, 6–12 (2018).Bellotti, E., Broggini, C., Di Carlo, G., Laubenstein, M. & Menegazzo, R. Search for time modulations in the decay constant of 40 K and 226 Ra at the underground Gran Sasso Laboratory. Physics Letters B 780, 61–65 (2018).Borrello, J. A., Wuosmaa, A. & Watts, M. Non-dependence of nuclear decay rates of 123 I and 99m Tc on Earth-Sun distance. Applied Radiation and Isotopes 132, 189–194 (2018).Sturrock, P. A., Steinitz, G., Fischbach, E., Parkhomov, A. & Scargle, J. D. Analysis of beta-decay data acquired at the Physikalisch-Technische Bundesanstalt: Evidence of a solar influence. Astroparticle Physics 84, 8–14 (2016).Stancil, D. D., Balci Yegen, S., Dickey, D. A. & Gould, C. R. Search for possible solar influences in Ra-226 decays. Results in Physics 7, 385–406 (2017).P. A. Sturrock, G. Steinitz & E. Fischbach Analysis of Ten Years of Radon-Chain Decay Measurements: Evidence of Solar Influences and Inferences Concerning Solar Internal Structure and the Role of Neutrinos. arXiv:1705.03010 [astro-ph.SR], (2017).Sturrock, P. A., Steinitz, G. & Fischbach, E. Concerning the variability of nuclear decay rates: Rebuttal of an article by Pomme et al. [1]. Astroparticle Physics 98, 9–12 (2018).Pommé, S., Lutter, G., Marouli, M., Kossert, K. & Nähle, O. A reply to the rebuttal by Sturrock et al. Astroparticle Physics 107, 22–25 (2019).S. Pommé, Solar influence on radon decay rates: irradiance or neutrinos? The European Physical Journal C. 79 (2019).Barnes, V. E. et al. Upper limits on perturbations of nuclear decay rates induced by reactor electron antineutrinos. Applied Radiation and Isotopes 149, 182–199 (2019).Pommé, S., Stroh, H. & Van Ammel, R. The 55Fe half-life measured with a pressurised proportional counter. Applied Radiation and Isotopes 148, 27–34 (2019).Elmaghraby, E. E. Configuration Mixing in Particle Decay and Reaction. Progress in Physics 13, 150–155 (2017).Shnoll, S. E. et al. Realization of discrete states during fluctuations in macroscopic processes. Physics-Uspekhi 41, 1025–1035 (1998).Namiot, V. A. & Shnoll, S. E. On the possible mechanism of periodicity in fine structure of histograms during nuclear decay processes. Physics Letters A 359, 249–251 (2006).Panchelyuga, V. A. & Panchelyuga, M. S. Fractal dimension and histogram method: Algorithm and some preliminary results of noise-like time series analysis. Biophysics 58, 283–289 (2013).Panchelyuga, V. A. & Panchelyuga, M. S. Local fractal analysis of noise-like time series by the all-permutations method for 1–115 min periods. Complex Systems Biophysics 60, 317–330 (2015).T. A. Zenchenko, A. A. Konradov, K. I. Zenchenko In Biophotonics and Coherent Systems in Biology. chap. Chapter 18, pp. 225–233 (2005).Jenkins, J. H. & Fischbach, E. Perturbation of nuclear decay rates during the solar flare of 2006 December 13. Astroparticle Physics 31, 407–411 (2009).F. Scholkmann et al., Anomalous effects of radioactive decay rates and capacitance values measured inside a modified Faraday cage: Correlations with space weather. EPL (Europhysics Letters) 117 (2017).M. E. Iglesias-Martínez et al. Correlations between Background Radiation Inside a Multilayer Interleaving Structure, Geomagnetic Activity, and Cosmic Radiation: A Fourth-Order Cumulant-Based Correlation Analysis. Mathematics 8 (2020).Karinen, A. & Mursula, K. A new reconstruction of the Dst index for 1932-2002. Annales Geophysicae 23, 475–485 (2005).A. Karinen, K. Mursula Correcting the Dst index: Consequences for absolute level and correlations. Journal of Geophysical Research 111 (2006).Nakamura, T., Uwamino, Y., Ohkubo, T. & Hara, A. Altitude Variation of Cosmic-ray Neutrons. Health Physics 53, 509–517 (1987).Hendrick, L. D. & Edge, R. D. Cosmic-Ray Neutrons near the Earth. Physical Review 145, 1023–1025 (1966).Yamashita, M., Stephens, L. D. & Patterson, H. W. Cosmic-ray-produced neutrons at ground level: Neutron production rate and flux distribution. Journal of Geophysical Research 71, 3817–3834 (1966).Mohsinally, T. et al. Evidence for correlations between fluctuations in 54Mn decay rates and solar storms. Astroparticle Physics 75, 29–37 (2016).Snyder, C. W., Neugebauer, M. & Rao, U. R. The solar wind velocity and its correlation with cosmic-ray variations and with solar and geomagnetic activity. Journal of Geophysical Research 68, 6361–6370 (1963).Kharayat, H., Prasad, L., Mathpal, R., Garia, S. & Bhatt, B. Study of Cosmic Ray Intensity in Relation to the Interplanetary Magnetic Field and Geomagnetic Storms for Solar Cycle 23. Solar Physics 291, 603–611 (2016).M. Tsichla, M. Gerontidou, H. Mavromichalaki, Spectral Analysis of Solar and Geomagnetic Parameters in Relation to Cosmic-ray Intensity for the Time Period 1965 – 2018. Solar Physics 294 (2019).Singh, Y. P. Badruddin, Short- and mid-term oscillations of solar, geomagnetic activity and cosmic-ray intensity during the last two solar magnetic cycles. Planetary and Space Science 138, 1–6 (2017).B. Adhikari, N. Sapkota, P. Baruwal, N. P. Chapagain & C. R. Braga Impacts on Cosmic-Ray Intensity Observed During Geomagnetic Disturbances. Solar Physics 292 (2017).Grigoryev, V. G., Starodubtsev, S. A. & Gololobov, P. Y. Monitoring geomagnetic disturbance predictors using data of ground measurements of cosmic rays. Bulletin of the Russian Academy of Sciences: Physics 81, 200–202 (2017).W. Reich Selected Writings: An Introduction to Orgonomy. (Farrar, Straus and Cudahy, 1960).Fischbach, E. et al. Time-Dependent Nuclear Decay Parameters: New Evidence for New Forces? Space Science Reviews 145, 285–335 (2009).Javorsek, D. et al. Power spectrum analyses of nuclear decay rates. Astroparticle Physics 34, 173–178 (2010).Bellotti, E., Broggini, C., Di Carlo, G., Laubenstein, M. & Menegazzo, R. Search for time dependence of the 137Cs decay constant. Physics Letters B 710, 114–117 (2012)

A New Method to Reconstruct Recombination Events at a Genomic Scale

Recombination is one of the main forces shaping genome diversity, but the information it generates is often overlooked. A recombination event creates a junction between two parental sequences that may be transmitted to the subsequent generations. Just like mutations, these junctions carry evidence of the shared past of the sequences. We present the IRiS algorithm, which detects past recombination events from extant sequences and specifies the place of each recombination and which are the recombinants sequences. We have validated and calibrated IRiS for the human genome using coalescent simulations replicating standard human demographic history and a variable recombination rate model, and we have fine-tuned IRiS parameters to simultaneously optimize for false discovery rate, sensitivity, and accuracy in placing the recombination events in the sequence. Newer recombinations overwrite traces of past ones and our results indicate more recent recombinations are detected by IRiS with greater sensitivity. IRiS analysis of the MS32 region, previously studied using sperm typing, showed good concordance with estimated recombination rates. We also applied IRiS to haplotypes for 18 X-chromosome regions in HapMap Phase 3 populations. Recombination events detected for each individual were recoded as binary allelic states and combined into recotypes. Principal component analysis and multidimensional scaling based on recotypes reproduced the relationships between the eleven HapMap Phase III populations that can be expected from known human population history, thus further validating IRiS. We believe that our new method will contribute to the study of the distribution of recombination events across the genomes and, for the first time, it will allow the use of recombination as genetic marker to study human genetic variation

Effects of X-ray dose on rhizosphere studies using X-ray computed tomography

X-ray Computed Tomography (CT) is a non-destructive imaging technique originally designed for diagnostic medicine, which was adopted for rhizosphere and soil science applications in the early 1980s. X-ray CT enables researchers to simultaneously visualise and quantify the heterogeneous soil matrix of mineral grains, organic matter, air-filled pores and water-filled pores. Additionally, X-ray CT allows visualisation of plant roots in situ without the need for traditional invasive methods such as root washing. However, one routinely unreported aspect of X-ray CT is the potential effect of X-ray dose on the soil-borne microorganisms and plants in rhizosphere investigations. Here we aimed to i) highlight the need for more consistent reporting of X-ray CT parameters for dose to sample, ii) to provide an overview of previously reported impacts of X-rays on soil microorganisms and plant roots and iii) present new data investigating the response of plant roots and microbial communities to X-ray exposure. Fewer than 5% of the 126 publications included in the literature review contained sufficient information to calculate dose and only 2.4% of the publications explicitly state an estimate of dose received by each sample. We conducted a study involving rice roots growing in soil, observing no significant difference between the numbers of root tips, root volume and total root length in scanned versus unscanned samples. In parallel, a soil microbe experiment scanning samples over a total of 24 weeks observed no significant difference between the scanned and unscanned microbial biomass values. We conclude from the literature review and our own experiments that X-ray CT does not impact plant growth or soil microbial populations when employing a low level of dose (<30 Gy). However, the call for higher throughput X-ray CT means that doses that biological samples receive are likely to increase and thus should be closely monitored

Intellectual enrichment and genetic modifiers of cognition and brain volume in Huntington's disease

An important step towards the development of treatments for cognitive impairment in ageing and neurodegenerative diseases is to identify genetic and environmental modifiers of cognitive function and understand the mechanism by which they exert an effect. In Huntington’s disease, the most common autosomal dominant dementia, a small number of studies have identified intellectual enrichment, i.e. a cognitively stimulating lifestyle and genetic polymorphisms as potential modifiers of cognitive function. The aim of our study was to further investigate the relationship and interaction between genetic factors and intellectual enrichment on cognitive function and brain atrophy in Huntington’s disease. For this purpose, we analysed data from Track-HD, a multi-centre longitudinal study in Huntington’s disease gene carriers and focused on the role of intellectual enrichment (estimated at baseline) and the genes FAN1, MSH3, BDNF, COMT and MAPT in predicting cognitive decline and brain atrophy. We found that carrying the 3a allele in the MSH3 gene had a positive effect on global cognitive function and brain atrophy in multiple cortical regions, such that 3a allele carriers had a slower rate of cognitive decline and atrophy compared with non-carriers, in agreement with its role in somatic instability. No other genetic predictor had a significant effect on cognitive function and the effect of MSH3 was independent of intellectual enrichment. Intellectual enrichment also had a positive effect on cognitive function; participants with higher intellectual enrichment, i.e. those who were better educated, had higher verbal intelligence and performed an occupation that was intellectually engaging, had better cognitive function overall, in agreement with previous studies in Huntington’s disease and other dementias. We also found that intellectual enrichment interacted with the BDNF gene, such that the positive effect of intellectual enrichment was greater in Met66 allele carriers than non-carriers. A similar relationship was also identified for changes in whole brain and caudate volume; the positive effect of intellectual enrichment was greater for Met66 allele carriers, rather than for non-carriers. In summary, our study provides additional evidence for the beneficial role of intellectual enrichment and carrying the 3a allele in MSH3 in cognitive function in Huntington’s disease and their effect on brain structure

Short-Lived Trace Gases in the Surface Ocean and the Atmosphere

The two-way exchange of trace gases between the ocean and the atmosphere is important for both the chemistry and physics of the atmosphere and the biogeochemistry of the oceans, including the global cycling of elements. Here we review these exchanges and their importance for a range of gases whose lifetimes are generally short compared to the main greenhouse gases and which are, in most cases, more reactive than them. Gases considered include sulphur and related compounds, organohalogens, non-methane hydrocarbons, ozone, ammonia and related compounds, hydrogen and carbon monoxide. Finally, we stress the interactivity of the system, the importance of process understanding for modeling, the need for more extensive field measurements and their better seasonal coverage, the importance of inter-calibration exercises and finally the need to show the importance of air-sea exchanges for global cycling and how the field fits into the broader context of Earth System Science

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability