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  1. Turabi N, Tomar SP, Anyagwa OE, Durganaudu H, Ishwarya V, Kivan H, et al.
    Indian J Community Med, 2023;48(5):676-683.
    PMID: 37970177 DOI: 10.4103/ijcm.ijcm_690_22
    BACKGROUND: Since the coronavirus (COVID-19) pandemic began, several studies were published on the possible prevention and treatment of the disease caused by severe acute respiratory syndrome coronavirus (SARSCoV-2), and its complications. However, one aspect that was overlooked is the impact on the mental health of the caregivers of COVID-19 patients. The current study endeavors to investigate sleep quality disturbances in the caregivers of COVID-19 patients in different countries.

    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.

  2. Vékony B, Nyirő G, Herold Z, Fekete J, Ceccato F, Gruber S, et al.
    Hypertension, 2024 Dec;81(12):2479-2488.
    PMID: 39417220 DOI: 10.1161/HYPERTENSIONAHA.124.23418
    BACKGROUND: Distinguishing between unilateral and bilateral primary aldosteronism, a major cause of secondary hypertension, is crucial due to different treatment approaches. While adrenal venous sampling is the gold standard, its invasiveness, limited availability, and often difficult interpretation pose challenges. This study explores the utility of circulating microRNAs (miRNAs) and machine learning in distinguishing between unilateral and bilateral forms of primary aldosteronism.

    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.

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