20 research outputs found
A knowledge-driven GIS modeling technique for groundwater potential mapping at the Upper Langat Basin, Malaysia.
The aim of this paper is to use a knowledge-driven expert-based geographical information system (GIS) model coupling with remote-sensing-derived parameters for groundwater potential mapping in an area of the Upper Langat Basin, Malaysia. In this study, nine groundwater storage controlling parameters that affect groundwater occurrences are derived from remotely sensed imagery, available maps, and associated databases. Those parameters are: lithology, slope, lineament, land use, soil, rainfall, drainage density, elevation, and geomorphology. Then the parameter layers were integrated and modeled using a knowledge-driven GIS of weighted linear combination. The weightage and score for each parameter and their classes are based on the Malaysian groundwater expert opinion survey. The predicted groundwater potential map was classified into four distinct zones based on the classification scheme designed by Department of Minerals and Geoscience Malaysia (JMG). The results showed that about 17% of the study area falls under low-potential zone, with 66% on moderate-potential zone, 15% with high-potential zone, and only 0.45% falls under very-high-potential zone. The results obtained in this study were validated with the groundwater borehole wells data compiled by the JMG and showed 76% of prediction accuracy. In addition statistical analysis indicated that hard rock dominant of the study area is controlled by secondary porosity such as distance from lineament and density of lineament. There are high correlations between area percentage of predicted groundwater potential zones and groundwater well yield. Results obtained from this study can be useful for future planning of groundwater exploration, planning and development by related agencies in Malaysia which provide a rapid method and reduce cost as well as less time consuming. The results may be also transferable to other areas of similar hydrological characteristics
Combinations of QT-prolonging drugs: towards disentangling pharmacokinetic and pharmaco-dynamic effects in their potentially additive nature.
Background: Whether arrhythmia risks will increase if drugs with electrocardiographic (ECG)
QT-prolonging properties are combined is generally supposed but not well studied. Based on
available evidence, the Arizona Center for Education and Research on Therapeutics (AZCERT)
classification defines the risk of QT prolongation for exposure to single drugs. We aimed to
investigate how combining AZCERT drug categories impacts QT duration and how relative drug
exposure affects the extent of pharmacodynamic drug–drug interactions.
Methods: In a cohort of 2558 psychiatric inpatients and outpatients, we modeled whether
AZCERT class and number of coprescribed QT-prolonging drugs correlates with observed
rate-corrected QT duration (QTc) while also considering age, sex, inpatient status, and other
QTc-prolonging risk factors. We concurrently considered administered drug doses and
pharmacokinetic interactions modulating drug clearance to calculate individual weights of
relative exposure with AZCERT drugs. Because QTc duration is concentration-dependent, we
estimated individual drug exposure with these drugs and included this information as weights
in weighted regression analyses.
Results: Drugs attributing a ‘known’ risk for clinical consequences were associated with the
largest QTc prolongations. However, the presence of at least two versus one QTc-prolonging
drug yielded nonsignificant prolongations [exposure-weighted parameter estimates with
95% confidence intervals for ‘known’ risk drugs + 0.93 ms (–8.88;10.75)]. Estimates for
the ‘conditional’ risk class increased upon refinement with relative drug exposure and coadministration of a ‘known’ risk drug as a further risk factor.
Conclusions: These observations indicate that indiscriminate combinations of QTc-prolonging
drugs do not necessarily result in additive QTc prolongation and suggest that QT prolongation
caused by drug combinations strongly depends on the nature of the combination partners and
individual drug exposure. Concurrently, it stresses the value of the AZCERT classification also
for the risk prediction of combination therapies with QT-prolonging drugs