Changing Mortality and Patterns of Death Causes in HIV Infected Patients—China,2013-2022

What is already known about this topic?The advent of antiretroviral therapy(ART)has markedly decreased mortality rates among patients infected with human immunodeficiency virus(HIV).Globally,there has been a 43%reduction in acquired immunodeficiency syndrome(AIDS)-related deaths from 2010 to 2022.Additionally,prior research indicates that the initiation of ART at an early stage within China has substantially lowered mortality rates.What is added by this report?Over the previous decade,following the implementation of China’s universal ART access strategy,the patterns of mortality causes among HIVinfected individuals across the country have undergone significant alterations.In 2022,the all-cause mortality rate among this population was reported at 2.7%,with the non-AIDS-related mortality rate at 1.8%.However,it is important to consider that the accuracy of death reporting could contribute to potential misclassification of the underlying causes of death.What are the implications for public health practice?Efforts to enhance health outcomes should persist in emphasizing the advancement of ART strategies,with a particular focus on mitigating non-AIDS-related mortality in the future.

Active learning for the power factor prediction in diamond-like thermoelectric materials

The Materials Genome Initiative requires the crossing of material calculations,machine learning,and experiments to accelerate the material development process.In recent years,data-based methods have been applied to the thermoelectric field,mostly on the transport properties.In this work,we combined data-driven machine learning and first-principles automated calculations into an active learning loop,in order to predict the p-type power factors(PFs)of diamond-like pnictides and chalcogenides.Our active learning loop contains two procedures(1)based on a high-throughput theoretical database,machine learning methods are employed to select potential candidates and(2)computational verification is applied to these candidates about their transport properties.The verification data will be added into the database to improve the extrapolation abilities of the machine learning models.Different strategies of selecting candidates have been tested,finally the Gradient Boosting Regression model of Query by Committee strategy has the highest extrapolation accuracy(the Pearson R=0.95 on untrained systems).Based on the prediction from the machine learning models,binary pnictides,vacancy,and small atom-containing chalcogenides are predicted to have large PFs.The bonding analysis reveals that the alterations of anionic bonding networks due to small atoms are beneficial to the PFs in these compounds.