Relationship between scalp histamine levels and dandruff within an Indian population: A confirmation study using LC/MS/MS method.

Dandruff is a common and challenging complaint associated with type of scalp, skin and population. Scalp seborrheic dermatitis (SD) is a more severe manifestation of dandruff associated with very severe itching and inflammation. Histamine is an interesting biomarker released in scalp affected by dandruff and SD even though the mechanism is not well understood yet. A monocentre clinical study was conducted to confirm the relationship between dandruff/SD and scalp histamine level in an Indian population. Highly sensitive liquid chromatography coupled with mass spectrometry was used for histamine quantification in scalp from samples obtained non-invasively. Results showed that scalps with dandruff and mild to moderate SD had higher histamine levels compared with healthy scalps.

COVID-19 impacts of inbound tourism on Australian economy.

The pandemic COVID-19 has severely impacted upon the world economy, devastating the tourism industry globally. This paper estimates the short-run economic impacts of the inbound tourism industry on the Australian economy during the pandemic. The analysis covers effects both at the macroeconomic as well as at the industry and occupation level, from direct contribution (using tourism satellite accounts) to economy-wide effects (using the computable general equilibrium modelling technique). Findings show that the pandemic affects a range of industries and occupations that are beyond the tourism sector. The paper calls for strong support from the government on tourism as the recovery of tourism can deliver spillover benefits for other sectors and across the whole spectrum of occupations in the labour market.

Genomic prediction of tuberculosis drug-resistance: benchmarking existing databases and prediction algorithms.

BACKGROUND: It is possible to predict whether a tuberculosis (TB) patient will fail to respond to specific antibiotics by sequencing the genome of the infecting Mycobacterium tuberculosis (Mtb) and observing whether the pathogen carries specific mutations at drug-resistance sites. This advancement has led to the collation of TB databases such as PATRIC and ReSeqTB that possess both whole genome sequences and drug resistance phenotypes of infecting Mtb isolates. Bioinformatics tools have also been developed to predict drug resistance from whole genome sequencing (WGS) data. Here, we evaluate the performance of four popular tools (TBProfiler, MyKrobe, KvarQ, PhyResSE) with 6746 isolates compiled from publicly available databases, and subsequently identify highly probable phenotyping errors in the databases by genetically predicting the drug phenotypes using all four software. RESULTS: Our results show that these bioinformatics tools generally perform well in predicting the resistance status for two key first-line agents (isoniazid, rifampicin), but the accuracy is lower for second-line injectables and fluoroquinolones. The error rates in the databases are also non-trivial, reaching as high as 31.1% for prothionamide, and that phenotypes from ReSeqTB are more susceptible to errors. CONCLUSIONS: The good performance of the automated software for drug resistance prediction from TB WGS data shown in this study further substantiates the usefulness and promise of utilising genetic data to accurately profile TB drug resistance, thereby reducing misdiagnoses arising from error-prone culture-based drug susceptibility testing.