Emergence of vancomycin resistant Staphylococcus aureus (VRSA) from a tertiary care hospital from northern part of India

BACKGROUND: Glycopeptides such as vancomycin are frequently the antibiotics of choice for the treatment of infections caused by methicillin resistant Staphylococcus aureus (MRSA). For the last 7 years incidence of vancomycin intermediate S. aureus and vancomycin resistant S. aureus (VISA and VRSA respectively) has been increasing in various parts of the world. The present study was carried out to find out the presence of VISA and VRSA in the northern part of India. METHODS: A total 1681 staphylococcal isolates consisting of 783 S. aureus and 898 coagulase negative staphylococci (CoNS) were isolated from different clinical specimens from various outpatient departments and wards. All S. aureus and 93 CoNS were subjected to MIC testing (against vancomycin, teicolplanin and oxacillin); Brain Heart Infusion (BHI) vancomycin screen agar test; disc diffusion testing, and PCR for mecA, vanA and vanB genes detection. RESULTS: Out of 783 S. aureus two S. aureus strains were found to be vancomycin and teicoplanin resistant (one strain with MIC 32 μg/ml and the other strain with MIC 64 μg/ml); six strains of S. aureus have shown to be vancomycin intermediate (two strains with MIC 16 μg/ml and four strains with MIC 8 μg/ml); and two strains with teicoplanin intermediate (MIC 16 μg/ml). One CoNS strain was resistant to vancomycin and teicoplanin (MIC 32 μg/ml), and two CoNS strains were intermediate to vancomycin and teicoplanin (MIC 16 μg/ml). All VRSA, VISA and vancomycin resistant CoNS had shown growth on BHI vancomycin screen agar (vancomycin 6 μg/ml) and were mecA PCR positive. None of these isolates have demonstrated vanA/vanB gene by PCR. CONCLUSION: The present study reveals for the first time emergence of VISA/VRSA from this part of world and indicates the magnitude of antibiotic resistance in and around the study area. The major cause of this may be unawareness and indiscriminate use of broad-spectrum antibiotics

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

Original Article Methicillin resistant Staphylococcus aureus: prevalence and antibiogram in a tertiary care hospital in western Nepal

Background: Methicillin resistant Staphylococcus aureus (MRSA) is a major cause of nosocomial and community infections. Its prevalence varies with country and with hospitals within a country. The current study estimates the prevalence of MRSA strains and investigates their antibiogram in western Nepal. Methodology: A total of 162 S. aureus strains were isolated from various clinical specimens, and antibiotic susceptibility tests were performed using disc diffusion, growth on oxacillin screen agar, and oxacillin minimum inhibitory concentration (MIC). Results: One hundred and twelve (69.1%) strains were found to be MRSA, of which 37 (33.1%) were community acquired and 75 (66.9%) were hospital acquired. Of 112 MRSA strains, 45 (40.1%) were multi-drug resistant. All MRSA strains were found resistant to penicillin, and 91.9%, 87.4%, 77%, and 55.5 % were resistant to amoxicillin, ampicillin, trimethoprim/sulfamethoxazole, and cephalexin, respectively. However, low resistance was observed with amikacin (19%), ciprofloxacin (26.5%), and norfloxacin (30.6%). All strains were sensitive to vancomycin. Conclusion: The reported rate of MRSA prevalence is alarming. Given the ability of MRSA to spread from person to person, it is necessary to adhere to rational use of antibiotics and to raise awareness among the concerned communities and tourists who visit this area

Distribution of Antibiotic-Resistant Enterobacteriaceae Pathogens in Potable Spring Water of Eastern Indian Himalayas: Emphasis on Virulence Gene and Antibiotic Resistance Genes in Escherichia coli

Every year millions of people die due to fatal waterborne diseases around the world especially in developing countries like India. Sikkim, a northeastern state of India, greatly depends on natural water sources. About 80% of the population of Sikkim depends on natural spring water for domestic as well as agricultural use. Recent waterborne disease outbreaks in the state raises a concerning question on water quality. In this study, we analyzed water quality especially for the detection of Enterobacteriaceae members from four districts of the state. Isolation with selective culture media techniques and taxonomic characterization of Enterobacteriaceae bacteria with 16S rRNA gene showed the prevalence of Escherichia coli (37.50%), Escherichia fergusonii (29.41%), Klebsiella oxytoca (36.93%), Citrobacter freundii (37.92%), Citrobacter amalonaticus (43.82%), Enterobacter sp. (43.82%), Morganella morganii (43.82%), Hafnia alvei (32.42%), Hafnia paralvei (38.74%), and Shigella flexneri (30.47%) in the spring water of Sikkim. Antibiotic susceptibility test (AST) showed resistance of the isolates to common antibiotics like ampicillin, amoxicillin as well as to third generation antibiotics like ceftazidime and carbapenem. None of the isolates showed resistance to chloramphenicol. E. coli isolated from spring water of Sikkim showed presence of different virulence genes such as stx1 (81.81%), elt (86.66%), and eae (66.66%) along with resistance gene for ampicillin (CITM) (80%), quinolones (qnrB) (44.44%), tetracycline (tetO) (66.66%), and streptomycin (aadA1) (66.66%). The data indicates a high incidence rate of multiple antibiotic resistant enteric bacteria in the spring water of Sikkim. Additionally, the presence of enteric bacteria in the water samples indicates widespread fecal contamination of the spring water

Prevalence of antibiotic resistance in commensal <i>Escherichia coli</i> among the children in rural hill communities of Northeast India

<div><p>Commensal bacteria are the representative of the reservoir of antibiotic resistance genes present in a community. The usage of antibiotics along with the demographic factors is generally associated with an increase in antibiotics resistance in pathogens. Northeast (NE) India is untapped with regard to antibiotic resistance prevalence and spread. In the current study, the prevalence of antibiotic-resistant commensal <i>Escherichia coli</i> in pre-school and school-going children (n = 550, 1–14 years old) from the rural areas of the state of Sikkim—an NE Indian state, with respect to associated demographic factors was investigated. A total of 550 fecal <i>E</i>. <i>coli</i> isolates were collected during July 2015 to June 2017. A structured questionnaire was used to collect data to ascertain the potential factors associated with the carriage of antibiotic resistance <i>E</i>. <i>coli</i> among the children. Statistical analysis along with a logistic regression identified potential external factors affecting the observed antibiotic resistance pattern. The data indicated a high prevalence of resistance to common antibiotics like ampicillin (92%), ceftazidime (90%), cefoxitin (88%), streptomycin (40%) and tetracycline (36%), but no resistance to chloramphenicol. The resistance to the combination of penicillin and quinolone group of antibiotics was observed in fifty-two percent of the isolates. A positive correlation between the harboring of antibiotics resistant <i>E</i>. <i>coli</i> with different demographic factors was observed such as, with children living in nuclear family (<i>vs</i> joint family 63.15%, OR 0.18, 95% CI:0.11–0.28, p < 0.01), below higher secondary maternal education (<i>vs</i> college graduates 59.27% OR 0.75, 95% CI:0.55–1.02, p < 0.02). A close association between different demographic factors and the high prevalence of antibiotic-resistant commensal <i>E</i>. <i>coli</i> in the current study suggests a concern over rising misuse of antibiotics that warrants a future threat of emergence of multidrug-resistant pathogen isolates.</p></div

Demographic details of community participants.

<p>Demographic details of community participants and their families are represented as a percentage of the population against the total population (N = 550). a: ‘Nuclear family’ referred to those families having only parents and children living in one household premises and ‘Joint family’ referred to the families having parents, children and their relatives living in one household premises; b = Family below poverty line refers to those families having possession of “Below Poverty Line (BPL)’ card issued by Government of India (GOI); c = Scheduled castes, backward castes and scheduled tribes are special status groups of socially and economically deprived classes of people as defined by the GOI; I: Gender of Children; II: Family type; III: Economic status; IV: Caste; V: Number of family members; VI: Paternal education; VII: Maternal education; VIII: Maternal occupation; IX: Antibiotics used last month.</p

Cluster analysis representing the relationship between demographic factors and pattern of antibiotic resistance.

<p>It formed three distinct groups/clusters based on the antibiotic resistance pattern. Group 1 represents the <i>E</i>. <i>coli</i> isolates from male and female which showed similar resistance pattern. Group 2 represents the demographic factors which make children more prone to carry antibiotic-resistant commensal <i>E</i>. <i>coli</i>. Group 3 represents the demographic factor associated with least antibiotic resistance.</p

Resistance pattern of <i>E</i>. <i>coli</i> isolates against individual antibiotics.

<p>Resistance pattern of <i>E</i>. <i>coli</i> isolates against individual antibiotics.</p

Resistance pattern of <i>E</i>. <i>coli</i> isolates against a combination of antibiotics.

<p>(Group A = Penicillin, B = Quinolones/Fluoroquinolones, C = Cephalosporin, D = Carbapenem, E = Aminoglycosides, F = Tetracycline, G = Polypeptide).</p