Machine learning and integrative analysis identify the common pathogenesis of azoospermia complicated with COVID-19.

Background: Although more recent evidence has indicated COVID-19 is prone to azoospermia, the common molecular mechanism of its occurrence remains to be elucidated. The aim of the present study is to further investigate the mechanism of this complication. Methods: To discover the common differentially expressed genes (DEGs) and pathways of azoospermia and COVID-19, integrated weighted co-expression network (WGCNA), multiple machine learning analyses, and single-cell RNA-sequencing (scRNA-seq) were performed. Results: Therefore, we screened two key network modules in the obstructive azoospermia (OA) and non-obstructive azoospermia (NOA) samples. The differentially expressed genes were mainly related to the immune system and infectious virus diseases. We then used multiple machine learning methods to detect biomarkers that differentiated OA from NOA. Enrichment analysis showed that azoospermia patients and COVID-19 patients shared a common IL-17 signaling pathway. In addition, GLO1, GPR135, DYNLL2, and EPB41L3 were identified as significant hub genes in these two diseases. Screening of two different molecular subtypes revealed that azoospermia-related genes were associated with clinicopathological characteristics of age, hospital-free-days, ventilator-free-days, charlson score, and d-dimer of patients with COVID-19 (P < 0.05). Finally, we used the Xsum method to predict potential drugs and single-cell sequencing data to further characterize whether azoospermia-related genes could validate the biological patterns of impaired spermatogenesis in cryptozoospermia patients. Conclusion: Our study performs a comprehensive and integrated bioinformatics analysis of azoospermia and COVID-19. These hub genes and common pathways may provide new insights for further mechanism research.

Follow-up study on COVID-19 survivors one year after discharge from hospital.

OBJECTIVE: To evaluate the long-term consequences of COVID-19 survivors one year after recovery, and to identify the risk factors associated with abnormal patterns in chest imaging manifestations or impaired lung function. METHODS: COVID-19 patients were recruited and prospectively followed up with symptoms, health-related quality of life, psychological questionnaires, 6-minute walking test, chest computed tomography (CT), pulmonary function tests, and blood tests. Multivariable logistic regression models were used to evaluate the association between the clinical characteristics and chest CT abnormalities or pulmonary function. RESULTS: Ninety-four patients with COVID-19 were recruited between January 16 and February 6, 2021. Muscle fatigue and insomnia were the most common symptoms. Chest CT scans were abnormal in 71.28% of participants. The results of multivariable regression showed an increased odds in age. Ten patients had diffusing capacity of the lung for carbon monoxide (DLCO) impairment. Urea nitrogen concentration on admission was significantly associated with impaired DLCO. IgG levels and neutralizing activity were significantly lower compared with those in the early phase. CONCLUSIONS: One year after hospitalization for COVID-19, a cohort of survivors were mainly troubled with muscle fatigue and insomnia. Pulmonary structural abnormalities and pulmonary diffusion capacities were highly prevalent in surviving COVID-19 patients. It is necessary to intervene in the main target population for long-term recovery.