Designing of fragment based inhibitors with improved activity against E. coli AmpC ß-lactamase compared to the conventional antibiotics.

One of the most common primary resistance mechanism of multi-drug resistant (MDR) Gram negative pathogenic bacteria to combat ß-lactam antibiotics, such as penicillins, cephalosporins and carbapenems is the generation of ß- lactamases. The uropathogenic E. coli is mostly getting multi-drug resistance due to the synthesis of AmpC ß-lactamases and therefore new antibiotics and inhibitors are needed to treat the evolving infections. The current study was designed for targetting AmpC ß-lactamase of E. coli using molecular docking based virtual screening, linking fragments for designing novel compounds and binding mode analysis using molecular dynamic simulation with target protein. The FCH group all-purpose fragment library consisting of 9388 fragments has been screened against AmpC ß-lactamase protein of E. coli and the antibiotics and anti-infectives used in treatment of Urinary tract Infections (UTIs) were also screened with AmpC ß-lactamase protein. Among the 9388 fragments, 339 fragment candidates were selected and linked with cefepime antibiotic having maximum binding affinity for AmpC target protein. Computational analysis of interactions as well as molecular dynamics (MD) simulations were also conducted for identifying the most promising ligand-pocket complexes from docking investigations to comprehend their thermodynamic properties and verify the docking outcomes as well. Overall, the linked complexes (LCs) showed good binding interactions with AmpC ß-lactamase. Interestingly, our fragment-based LCs remained relatively stable in comparison with cefepime antibiotic. Moreover, S12 fragment linked complex remained the most stable during 50 ns with remarkable number of interactions indicating it as promising candidate in novel lead discovery against MDR E. coli infections.

Marine plastic pollution detection and identification by using remote sensing-meta analysis.

The persistent plastic litter, originating from different sources and transported from rivers to oceans, has posed serious biological, ecological, and chemical effects on the marine ecosystem, and is considered a global issue. In the past decade, many studies have identified, monitored, and tracked marine plastic debris in coastal and open ocean areas using remote sensing technologies. Compared to traditional surveying methods, high-resolution (spatial and temporal) multispectral or hyperspectral remote sensing data have been substantially used to monitor floating marine macro litter (FMML). In this systematic review, we present an overview of remote sensing data and techniques for detecting FMML, as well as their challenges and opportunities. We reviewed the studies based on different sensors and platforms, spatial and spectral resolution, ground sampling data, plastic detection methods, and accuracy obtained in detecting marine litter. In addition, this study elaborates the usefulness of high-resolution remote sensing data in Visible (VIS), Near-infrared (NIR), and Short-Wave InfraRed (SWIR) range, along with spectral signatures of plastic, in-situ samples, and spectral indices for automatic detection of FMML. Moreover, the Thermal Infrared (TIR), Synthetic aperture radar (SAR), and Light Detection and Ranging (LiDAR) data were introduced and these were demonstrated that could be used as a supplement dataset for the identification and quantification of FMML.

Spatial mapping of temporal risk to improve prevention measures: A case study of dengue epidemic in Lahore.

BACKGROUND: Dengue is identified as serious vector born infectious disease by WHO, threating around 2.5 billion people around the globe. Pakistan is facing dengue epidemic since 1994 but 2010 and 2011 dengue outbreaks were worst. During 2011 dengue outbreak 22,562 cases were reported and 363 died due to this fatal infection in Pakistan. In this study, Lahore District was chosen as it was severely affected in 2011 dengue outbreak with 14,000 reported cases and 300 deaths. There is no vaccine developed yet for the disease control, so only effective early warning, prevention and control measures can reduce the potential disease risk. METHODS: This study proposes a method for detecting spatial autocorrelation of temporal dynamics of disease using Local Index of Spatial Autocorrelation (LISA) using three temporal indices: (a) how often the dengue cases occur, frequency index; (b) how long the epidemic wave prevails, duration index; (c) how significant dengue cases occur in successive periods, severity index. Overlay analysis of LISA value for each temporal index resulted in eight risk types. RESULTS: The mapping of spatio-temporal risk indices and their overlay analysis identified that 10.6% area of Lahore (184.3km2 and population density 119,110persons/km2) had high values for frequency, duration, and severity index (p<0.05) and 16% area (having 25% population) is at potential risk of dengue. CONCLUSION: Spatial risk identification by using local spatial-autocorrelation helps in identifying other possible causes of disease risk and further strategic planning for prevention and control measures.