News in brief: Donor lungs converted to universal blood type; Male doctors choose beards over N95 mask fit; Voice recordings can predict lung function and abnormalities


Donor lungs converted to universal blood type could improve transplant access

Universal donor lung access could improve some day, thanks to a pre-transplant process converting blood type A organs to type O, Canadian researchers suggest.

A study assessed the ability of  two enzymes to safely and effectively strip A antigens from human tissue via ex vivo lung perfusion (EVLP). It showed that treatment with FpGa1NAc deacetylase and FpGalactosaminidase’s removed 97% of endothelial A-Ag from eight ABO-A1 human lungs within four hours — without inducing treatment-related acute lung toxicity.

Three donor lungs were then exposed to ABO-O plasma in a simulated ex vivo model of antibody-mediated rejection, where the treatment appeared to minimise antibody binding, complement deposition and antibody-mediated injury, compared with control lungs, the authors from the Toronto General Hospital Research Institute wrote in Science Translational Medicine.

An earlier phase of the study found the treatment removed 99% and 90% of A-Ag in five human ABO-A1 red blood cell samples and three aorte, respectively, at concentrations “as low as 1 µg/ml”, the paper read.

“These results show that depletion of donor lung A-Ag can be achieved with EVLP treatment.”

“This strategy has the potential to expand ABO-incompatible lung transplantation and lead to improvements in fairness of organ allocation,” the team concluded.

Male doctors choose beards over N95 mask fit

Almost half the male hospital staff required to wear a P2/N95 respirator during the COVID-19 pandemic have beards or facial hair that would potentially render them ineffective, an audit carried out in a Victorian tertiary centre has found.

Despite successfully completing fit testing with a clean shaven face in the preceding year, 45% of the 110 male staff working in three critical areas of a hospital (ICU, ED and operating theatres) were observed to have beards or to not be clean shaven in the face seal zone of their respirators.

Writing in the journal Infection, Disease and Health, the study authors said it was imperative that clinical staff were clean shaven in the face seal zone of their respirators so they are fit-for-purpose and ready to respond during periods of significant community transmission of COVID-19.

“Health care organisations allowing employees to have facial hair where N95 protection is required may be breaching their duty of care under OHS laws,” they wrote.

Male staff “experiencing a loss of identity or significant skin irritation with daily shaving should be supported so that their respiratory protection is optimised, not only to protect themselves from harm, but also to safeguard those they work with and care for,” they added.

Voice recordings can predict lung function and abnormalities

A group of international researchers say they have developed a machine learning approach to predicting pulmonary function and abnormality from voice recordings, and that it works better than published approaches.

The methodology, they claim, could form the basis of future telehealth solutions, such as smartphone-based applications, which have the potential to assist decision making and self-monitoring in patients with asthma.

Dr Zahangir Alam, a researcher at Southampton University, led a team that developed a mechanism designed to separate speech and breathing from 323 recordings, from which certain features related to breathing and speech were extracted and combined with biological factors, such as sex, height and weight, to predict lung function. The researchers then defined three predictive models implementing machine learning algorithms to predict FEV1% and severity of lung function abnormality.

The performance of each model was evaluated on test samples and the results were promising, for example, with one model lung function impairment was predicted with an accuracy of 73%, and another predicted abnormal lung function with accuracy of 85%.

“The predictive models developed in this study can be implemented in smartphone applications offering a convenient and straightforward way to predict lung function,” the authors concluded in the paper, published in frontiers in Digital Health. “The demonstration that it is possible to use machine learning as a surrogate measure for underlying lung function has the potential to lead to the development of telemedicine solutions to improve early diagnosis, reduce unplanned hospital admissions and mortality for respiratory disease through supporting clinical decision making and patient self-monitoring.”

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