MATERIAL AND METHODS: This cross-sectional multi-center study was performed between August 1, 2021, and August 30, 2022, across 11 countries. A total of 2411 responses meeting the inclusion criteria (being a family member or caregiver involved in patient care) were collected. The sleep quality was assessed using the self-reported Pittsburgh Sleep Quality Index (PSQI) 12. Total scores ranged from 0 to 21. A ≥5 indicated poor sleep quality with 89.6% sensitivity and 86.5% specificity.
RESULTS: A total of 2411 responses meeting the inclusion criteria showed that mean PSQI scores (P = 0.3604) were higher in caregivers of hospitalized patients than in patients isolated at home. Approximately 62.4% of caregivers reported sleep quality problems while caring for their patients.
CONCLUSION: The results showed that the majority of caregivers of patients with COVID-19 reported disturbances in sleep quality and impaired sleep was more common among caregivers of hospitalized patients, perhaps because hospitalization is associated with a more severe course of the disease. There is a pressing need to take measures to improve the mental health of these caregivers. There should be treatment programs set up to reverse sleep disturbances in this population sufficiently.
METHODS: MiRNA profiling was conducted on plasma samples from 18 patients with primary aldosteronism taken during adrenal venous sampling on an Illumina MiSeq platform. Bioinformatics and machine learning identified 9 miRNAs for validation by reverse transcription real-time quantitative polymerase chain reaction. Validation was performed on a cohort consisting of 108 patients with known subdifferentiation. A 30-patient subset of the validation cohort involved both adrenal venous sampling and peripheral, the rest only peripheral samples. A neural network model was used for feature selection and comparison between adrenal venous sampling and peripheral samples, while a deep-learning model was used for classification.
RESULTS: Our model identified 10 miRNA combinations achieving >85% accuracy in distinguishing unilateral primary aldosteronism and bilateral adrenal hyperplasia on a 30-sample subset, while also confirming the suitability of peripheral samples for analysis. The best model, involving 6 miRNAs, achieved an area under curve of 87.1%. Deep learning resulted in 100% accuracy on the subset and 90.9% sensitivity and 81.8% specificity on all 108 samples, with an area under curve of 86.7%.
CONCLUSIONS: Machine learning analysis of circulating miRNAs offers a minimally invasive alternative for primary aldosteronism lateralization. Early identification of bilateral adrenal hyperplasia could expedite treatment initiation without the need for further localization, benefiting both patients and health care providers.