Technology innovation to reduce health inequality in skin diagnosis and to improve patient outcomes for people of color: a thematic literature review and future research agenda.

The health inequalities experienced by ethnic minorities have been a persistent and global phenomenon. The diagnosis of different types of skin conditions, e.g., melanoma, among people of color is one of such health domains where misdiagnosis can take place, potentially leading to life-threatening consequences. Although Caucasians are more likely to be diagnosed with melanoma, African Americans are four times more likely to present stage IV melanoma due to delayed diagnosis. It is essential to recognize that additional factors such as socioeconomic status and limited access to healthcare services can be contributing factors. African Americans are also 1.5 times more likely to die from melanoma than Caucasians, with 5-year survival rates for African Americans significantly lower than for Caucasians (72.2% vs. 89.6%). This is a complex problem compounded by several factors: ill-prepared medical practitioners, lack of awareness of melanoma and other skin conditions among people of colour, lack of information and medical resources for practitioners' continuous development, under-representation of people of colour in research, POC being a notoriously hard to reach group, and 'whitewashed' medical school curricula. Whilst digital technology can bring new hope for the reduction of health inequality, the deployment of artificial intelligence in healthcare carries risks that may amplify the health disparities experienced by people of color, whilst digital technology may provide a false sense of participation. For instance, Derm Assist, a skin diagnosis phone application which is under development, has already been criticized for relying on data from a limited number of people of color. This paper focuses on understanding the problem of misdiagnosing skin conditions in people of color and exploring the progress and innovations that have been experimented with, to pave the way to the possible application of big data analytics, artificial intelligence, and user-centred technology to reduce health inequalities among people of color.

Purification and characterization of a novel chitinase from Trichosanthes dioica seed with antifungal activity.

Chitinases are a group of enzymes that show differences in their molecular structure, substrate specificity, and catalytic mechanism and widely found in organisms like bacteria, yeasts, fungi, arthropods actinomycetes, plants and humans. A novel chitinase enzyme (designated as TDSC) was purified from Trichosanthes dioica seed with a molecular mass of 39±1 kDa in the presence and absence of ß-mercaptoethanol. The enzyme was a glycoprotein in nature containing 8% neutral sugar. The N-terminal sequence was determined to be EINGGGA which did not match with other proteins. Amino acid analysis performed by LC-MS revealed that the protein was rich in leucine. The enzyme was stable at a wide range of pH (5.0-11.0) and temperature (30-90 °C). Chitinase activity was little bit inhibited in the presence of chelating agent EDTA (ethylenediaminetetraaceticacid), urea and Ca(2+). A strong fluorescence quenching effect was found when dithiothreitol and sodium dodecyl sulfate were added to the enzyme. TDSC showed antifungal activity against Aspergillus niger and Trichoderma sp. as tested by MTT assay and disc diffusion method.