An initial analysis today from Poland's National Veterinary Institute of nine viruses from cats infected with H5N1 avian influenza suggests they are related to viruses found in the country's poultry and wild birds, according to a statement translated and posted by Avian Flu Diary, an infectious disease news blog. The sicknesses and deaths in cats have been reported from a wide geographic area, with some noted in indoor cats, making the source of the virus unclear.

The report said the viruses belong to the same genotype and are most closely related to a sample collected in June from a white stork. The genotype was seen during the peak of the 2022-23 season and was detected mainly in poultry in Wielkopolska province and in wild birds in multiple parts of Poland.

The report didn't note any mutations that may make the virus more adapted to mammals. Scientists noted that the feline H5N1 viruses they have examined so far came from a single unidentified source and that more detailed genetic analysis is under way to better gauge the zoonotic potential.

CDC updates H5N1 technical report

In other avian flu developments, the US Centers for Disease Control and Prevention (CDC) recently updated its technical information on H5N1, which covers the latest sporadic infections in humans and outbreaks in wild birds, poultry, and other animals. (The document, dated June 30, doesn't include information on the H5N1 detections in Polish cats.)

The CDC said the H5N1 viruses currently circulating in wild birds and poultry don't have the ability to easily bind to human upper-airway tract receptors. Though outbreaks in US poultry flocks have declined substantially over spring and summer, the H5N1 strain continues to circulate in wild birds, posing an ongoing transmission threat that requires close monitoring.

Regarding H5N1 in mammals, the CDC said data suggest the virus may evolve to replicate more efficiently in the respiratory tract, but so far the changes haven't been linked to increased transmissibility in humans.

An analysis of fecal metagenomes and household survey data suggests that access to clean water and sanitation could be a strategy to curb antimicrobial resistance (AMR) in low- and middle-income countries (LMICs), an international team of researchers reported late last week in The Lancet Microbe.

Using 1,589 publicly available human fecal metagenomes from 26 countries, a team led by researchers from the University of California, Berkeley, identified antibiotic resistance genes (ARGs) and calculated their relative abundance by region. They then analyzed data from Demographic and Health Surveys—which include questions about access to water, sanitation and hygiene (WASH)—from geospatially tagged and nationally representative datasets to determine associations between ARG abundance and access to improved WASH.

The mean abundance of ARGs was highest in Africa compared with Europe (P = 0.014), North America (P = 0.0032), and the Western Pacific (P = 0.011), and second highest in Southeast Asia compared with Europe (P = 0.047) and North America (P = 0.014). Abundance trends varied by drug class, with beta-lactamase gene abundance highest in Africa and Southeast Asia and tetracycline resistance ubiquitous throughout all regions.

Going from 0% to 100% access to improved WASH was associated with an estimated 0.22 (95% confidence interval [CI], –0.39 to –0.05) reduction in AGR abundance. The magnitude of the association was higher for improved sanitation access alone (–0.13; 95% CI, ­–0.31 to –0.05) compared with improved drinking water access alone (–0.08; 95% CI, –0.28 to 0.11), and in urban (–0.32; 95% CI, –0.63 to 0.00) compared with rural (–0.16; 95% CI, –0.38 to 0.07) areas.

The most statistically significant reduction in ARGs was associated with access to combined improvements in water and sanitation.

The study authors note that while the findings are limited by the observational, ecological nature of the study, and additional research is needed to determine if there is a causal relationship between improved WASH and the burden of AMR, the findings nonetheless suggest that efforts to improve WASH access should be part of national AMR action plans.

"We found the most statistically significant reduction in ARGs was associated with access to combined improvements in water and sanitation, suggesting that comprehensive WASH access might be more effective than single interventions," they wrote.

A deep-learning (DL) model that analyzes the initial chest x-rays of patients who have community-acquired pneumonia (CAP) may predict the risk of death by 30 days more accurately than an established risk-prediction tool, finds a new study published in the American Journal of Roentgenology.

For the study, a team led by Seoul National University Hospital researchers evaluated the ability of a DL model developed using 7,105 CAP patients at a single center from March 2013 to December 2019 to predict risk of all-cause death by 30 days.

The team tested the model using the initial x-rays of emergency department (ED) patients at the same center as the development cohort from January to December 2020 (temporal test cohort, 947 patients), a second center (external test cohort A, 467) over the same period, and a third center (external test cohort B, 381) from March 2019 to October 2021.

The results of the DL model and the established CURB-65 risk-prediction tool were compared, and the combination of the two tests was assessed using a logistic regression model.

Higher specificity at same sensitivity

The area under the curve (AUC, a measure of diagnostic accuracy) for risk of death by 30 days was higher for the DL model than for CURB-65 in the temporal test cohort (0.77 vs 0.67), but the result wasn't statistically significant in external test cohort A (0.80 vs 0.73) or B (0.80 vs 0.72).

The DL model showed higher specificity (range, 61% to 69% vs 44% to 58%) at the same sensitivity as the CURB-65 score in all three groups. Combined, the DL model and CURB-65 scores increased the AUC in the temporal cohort (0.77) and external cohort B (0.80) but the increase was not significant in cohort A (0.80).

The deep learning (DL) model may guide clinical decision-making.

"The deep learning (DL) model may guide clinical decision-making in the management of patients with CAP by identifying high-risk patients who warrant hospitalization and intensive treatment," coauthor Eui Jin Hwang, MD, PhD, of Seoul National University, said in an American Roentgen Ray Society news release.