Sex and aging influence the human immune system, resulting in disparate responses to infection, autoimmunity, and cancer. However, the impact of sex and aging on the immune system is not yet fully elucidated. Using small conditional RNA sequencing, we found that females had a lower percentage of natural killer (NK) cells and a higher percentage of plasma cells in peripheral blood compared with males. Bioinformatics revealed that young females exhibited an overrepresentation of pathways that relate to T and B cell activation. Moreover, cell–cell communication analysis revealed evidence of increased activity of the BAFF/APRIL systems in females. Notably, aging increased the percentage of monocytes and reduced the percentage of naïve T cells in the blood and the number of differentially expressed genes between the sexes. Aged males expressed higher levels of inflammatory genes. Collectively, the results suggest that females have more plasma cells in the circulation and a stronger BAFF/APRIL system, which is consistent with a stronger adaptive immune response. In contrast, males have a higher percentage of NK cells in blood and a higher expression of certain proinflammatory genes. Overall, this work expands our knowledge of sex differences in the immune system in humans.

Retinal fundus diseases can lead to irreversible visual impairment without timely diagnoses and appropriate treatments. Single disease-based deep learning algorithms had been developed for the detection of diabetic retinopathy, age-related macular degeneration, and glaucoma. Here, we developed a deep learning platform (DLP) capable of detecting multiple common referable fundus diseases and conditions (39 classes) by using 249,620 fundus images marked with 275,543 labels from heterogenous sources. Our DLP achieved a frequency-weighted average F1 score of 0.923, sensitivity of 0.978, specificity of 0.996 and area under the receiver operating characteristic curve (AUC) of 0.9984 for multi-label classification in the primary test dataset and reached the average level of retina specialists. External multihospital test, public data test and tele-reading application also showed high efficiency for multiple retinal diseases and conditions detection. These results indicate that our DLP can be applied for retinal fundus disease triage, especially in remote areas around the world.

PurposeTo assess the correlation and agreement between the Topcon built-in algorithm and our graph-based algorithm in measuring the total and regional macular thickness for normal and glaucoma subjects.MethodsA total of 228 normal eyes and 93 glaucomatous eyes were enrolled in our study. All patients underwent comprehensive ophthalmic examination and Topcon 3D-OCT 2000 scan. One eye was randomly selected for each subject. The thickness of each layer and the total and regional macular thickness on an Early Treatment of Diabetic Retinopathy Study (ETDRS) chart were measured using the Topcon algorithm and our three-dimensional graph-based algorithm. Correlation and agreement analyses between these two algorithms were performed.ResultsOur graph search algorithm exhibited a strong correlation with Topcon algorithm. The macular GCC thickness values for normal and glaucoma subjects ranged from 0.86 to 0.91 and from 0.78 to 0.90, and the regional macular thickness values ranged from 0.79 to 0.96 and 0.70 to 0.95, respectively. Small differences were observed between the Topcon algorithm and our graph-based algorithm. The span of 95% limits of agreement of macular GCC thickness was less than 28 μm in both normal and glaucoma subjects, respectively. These limits of total and regional macular thickness were 15.5 μm and 23.1 μm for normal subjects and 29.1 μm and 46.4 μm for glaucoma subjects, respectively.ConclusionOur graph-based algorithm exhibited a high degree of agreement with the Topcon algorithm with respect to thickness measurements in normal and glaucoma subjects. Moreover, our graph-based algorithm can segment the retina into more layers than the Topcon algorithm does.