Introduction intussusception in South African (SA) children is often severe. A proportion of cases require management at quaternary hospitals which are a scare resource in SA. A geospatial investigation of severe paediatric intussusception (SPI) in the KwaZulu-Natal (KZN) province of SA would assist with identifying regions which should be targeted for preventative interventions. This could reduce resource utilisation for this condition at quaternary hospitals. The objective of this study was to determine the geospatial distribution of SPI in KZN. Methods this was a retrospective analysis of data for patients with SPI who were admitted to a quaternary hospital in KZN over an 11-year period. Data related to patient demographics, duration of hospitalization, surgical intervention, inpatient mortality and residential postal code were extracted from the electronic hospital admissions system. Each residential postal code was linked to a corresponding KZN district municipality. Descriptive statistical methods were used to determine the distribution of various characteristics in the study sample. Semi-quantitative geospatial analysis was used to determine the distribution of patients with SPI in each KZN district municipality. Results the study sample consisted of 182 patients with SPI. Most patients were <1 year old (83.5%), male (51.1%) and black African (87.9%). All patients underwent surgical intervention. Inpatient mortality was 2.7%. The majority of patients in the study sample resided in the eThekwini and King Cetshwayo district municipalities (51.1% and 14.8%, respectively). Conclusion preventative interventions for SPI should be considered for rollout in the eThekwini and King Cetshwayo district municipalities of KZN, SA.
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Introduction and Aim: The currently available antifungal therapy for the treatment of cryptococcus infections frequently fails due to antifungal drug resistance and it is well known-fact that among P450, CYP51 is reported to be responsible for drug resistance. However, to date, P450 profiling of Cryptococcus species has not been reported. Therefore, the aim of the study was to perform genome data mining, and annotation of P450s in Cryptococcus species, C. neoformans var. neoformans, C. neoformans var. gattii and C. neoformans var. grubii. Methods: Six Cryptococcus species were used; C. neoformans serotypes and C. Vishniachi, a non-human pathogen as an outgroup. The protein sequences were downloaded and grouped into different protein families using the NCBI Conserved Domain Database: NCBI Batch Web CD-search tool. Proteins belonging under the cytochrome P450 monooxygenases superfamily were selected for further study. The selected P450s were subjected to BLAST analysis at the Cytochrome P450 home page Blast server sequences against all named fungal proteins. Cryptococcus P450s were assigned to families and subfamilies based on amino acid percentage identity, i.e., family members share greater than 40% amino acid identity and members of subfamilies share over 55% amino acid identity, The P450 with less than 40% amino acid identity were assigned to a new family. Results: Genome data-mining and annotation of P450s in six Cryptococcus species revealed the presence of 41 P450s. The P450 count in cryptococcus genomes ranged from 5-16. Among the used Cryptococcus species, C. vishniachi v1.0 showed the highest number of P450s ( 16), while other five species, C. gattii WM276, C. neoformans JEC21, C. neoformans B-3501A, C. gattii R265 and C. grubii H99 showed the lowest number (5). Cyp51 was present in all cryptococcus species. Conclusion: Study revealed P450 profiling in Cryptococcus species.
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