METHODS: Genome of C. freundii B9-C2 was sequenced on an Illumina MiSeq platform. The assembled genome was annotated and deposited into GenBank under the accession number CP027849.
RESULTS: Multiple antimicrobial resistance genes including blaCMY-66 were identified. Further, the presence of 15 antibiotic efflux pump-encoding resistance genes, including crp, baeR, hns, patA, emrB, msbA, acrA, acrB, emrR, mdtC, mdtB, mdtG, kdpE, mdfA and msrB, were detected and likely to account for the observed cephalosporins, carbapenems, aminoglycosides and monobactams resistance in C. freundii B9-C2. The isolate also presented unique virulence genes related to biofilm formation, motility and iron uptake. The genome was compared to publicly available genomes and it was closely related to strains with environmental origins.
CONCLUSION: To the best of our knowledge, this is the first report of intestinal carriage of colistin-resistant C. freundii from the stool of a neonate in Malaysia. Using genomic analysis, we have contributed to the understanding of the potential mechanism of resistance and the phylogenetic relationship of the isolates with draft genomes available in the public domain.
METHODS: Sixteen computed tomography scan of SC patients (8 months-6 years old) were imported to Materialise Interactive Medical Image Control System (MIMICS) and Materialise 3-matics software. Three-dimensional (3D) OC models were fabricated, and linear measurements were obtained. Mathematical formulas were used for calculation of OC volume and surface area from the 3D model. The same measurements were obtained from the software and used as ground truth. Data normality was investigated before statistical analyses were performed. Wilcoxon test was used to validate differences of OC volume and surface area between 3D model and software.
RESULTS: The mean values for OC surface area for 3D model and MIMICS software were 103.19 mm2 and 31.27 mm2, respectively, whereas the mean for OC volume for 3D model and MIMICS software were 184.37 mm2 and 147.07 mm2, respectively. Significant difference was found between OC volume (P = 0.0681) and surface area (P = 0.0002) between 3D model and software.
CONCLUSION: Optic canal in SC is not a perfect conical frustum thus making 3D model measurement and mathematical formula for surface area and volume estimation not ideal. Computer software remains the best modality to gauge dimensional parameter and is useful to elucidates the relationship of OC and eye function as well as aiding intervention in SC patients.
METHODS: Scopus, PubMed, and Wiley Online Libraries were searched up to the date November 24, 2019. Two reviewers were requested to independently extract study characteristics and to assess the bias and applicability risks with reference to the study inclusion criteria. Meta-analyses were performed to specify the relationship between dietary intake and the risk of ovarian cancer identifying 97 cohort studies.
RESULTS: No significant association was found between dietary intake and risk of ovarian cancer. The results of subgroup analyses indicated that green leafy vegetables (RR = 0.91, 95%, 0.85-0.98), allium vegetables (RR = 0.79, 95% CI 0.64-0.96), fiber (RR = 0.89, 95% CI 0.81-0.98), flavonoids (RR = 0.83, 95% CI 0.78-0.89) and green tea (RR = 0.61, 95% CI 0.49-0.76) intake could significantly reduce ovarian cancer risk. Total fat (RR = 1.10, 95% CI 1.02-1.18), saturated fat (RR = 1.11, 95% CI 1.01-1.22), saturated fatty acid (RR = 1.19, 95% CI 1.04-1.36), cholesterol (RR = 1.13, 95% CI 1.04-1.22) and retinol (RR = 1.14, 95% CI 1.00-1.30) intake could significantly increase ovarian cancer risk. In addition, acrylamide, nitrate, water disinfectants and polychlorinated biphenyls were significantly associated with an increased risk of ovarian cancer.
CONCLUSION: These results could support recommendations to green leafy vegetables, allium vegetables, fiber, flavonoids and green tea intake for ovarian cancer prevention.
Methods: We searched PubMed, EMBASE, Cochrane library, and Econlit for articles published from inception to 31 July 2019. Original articles reporting costs or full economic evaluation related with snakebites were included. The methods and reporting quality were assessed. Costs were presented in US dollars (US$) in 2018.
Results: Twenty-three cost of illness studies and three economic evaluation studies related to snakebites were included. Majority of studies (18/23, 78.26%) were conducted in Low- and Middle-income countries. Most cost of illness studies (82.61%) were done using hospital-based data of snakebite patients. While, four studies (17.39%) estimated costs of snakebites in communities. Five studies (21.74%) used societal perspective estimating both direct and indirect costs. Only one study (4.35%) undertook incidence-based approach to estimate lifetime costs. Only three studies (13.04%) estimated annual national economic burdens of snakebite which varied drastically from US$126 319 in Burkina Faso to US$13 802 550 in Sri Lanka. Quality of the cost of illness studies were varied and substantially under-reported. All three economic evaluation studies were cost-effectiveness analysis using decision tree model. Two of them assessed cost-effectiveness of having full access to antivenom and reported cost-effective findings.
Conclusions: Economic burdens of snakebite were underestimated and not extensively studied. To accurately capture the economic burdens of snakebites at both the global and local level, hospital data should be collected along with community survey and economic burdens of snakebites should be estimated both in short-term and long-term period to incorporate the lifetime costs and productivity loss due to premature death, disability, and consequences of snakebites.