Objectives: To survey the difference of frailty prevalence in elderly inpatients amongdifferent wards; to compare the diagnostic performance of five frailty measurements (Clinical Frailty Scale [CFS], FRAIL, Fried, Edmonton, Frailty Index [FI]) in identifying frailty; and to explore the risk factors of frailty in elderly inpatients. Participants and methods: This was a cross-sectional study including 1000 inpatients (mean age 75.2±6.7 years, 51.5% male; 542, 229, and 229 patients from cardiology, nonsurgical, and surgical wards, respectively) in a tertiary hospital from September 2018 to February 2019. We applied the combined index to integrate the five frailty measurements mentioned above as the gold standard of frailty diagnosis. Multivariate logistic regression models were used to determine the independent risk factors of frailty. Results: Frailty prevalence was 32.3% (Fried), 36.2% (CFS), 19.2% (FRAIL), 25.2% (Edmonton), 35.1% (FI) in all patients. The frailty was more common in non-surgical wards, regardless of the frailty assessment tools used (non-surgical wards: 27.5% to 51.5%; cardiology ward: 14.9% to 29.3%; surgical wards: 18.8% to 41.9%). CFS≥5 showed a sensitivity of 94.1% and a specificity of 85.2% for all patients. FI≥0.25 showed a sensitivity of 94.8% and a specificity of 87.0% for all patients. Age [odds ratio (OR) = 1.089, P<0.001], education level (OR = 0.782, P=0.001), heart rate (OR = 1.025, P<0.001), albumin (OR = 0.911, P=0.002), log D-dimer (OR = 2.940, P<0.001), ≥5 comorbidities (OR = 2.164, P=0.002), and ≥5 medications (OR = 2.819, P<0.001) were independently associated with frailty in all participants. Conclusion: Frailty is common among elderly inpatients, especially in non-surgical wards. CFS is a preferred screening tool and FI may be an optimal assessment tool. Old age, low educational level, fast heart rate, low albumin, high D-dimer, ≥5 comorbidities, and polypharmacy are independent risk factors of frailty in elderly hospitalized patients.

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Mapping DNase I hypersensitive sites (DHSs) within nuclear chromatin is a traditional and powerful method of identifying genetic regulatory elements. DHSs have been mapped by capturing the ends of long DNase I-cut fragments (>100,000 bp), or 100–1200 bp DNase I-double cleavage fragments (also called double-hit fragments). But next generation sequencing requires a DNA library containing DNA fragments of 100–500 bp. Therefore, we used short DNA fragments released by DNase I digestion to generate DNA libraries for next generation sequencing. The short segments are 100–300 bp and can be directly cloned and used for high-throughput sequencing. We identified 83,897 DHSs in 2,343,479 tags across the human genome. Our results indicate that the DHSs identified by this DHS assay are consistent with those identified by longer fragments in previous studies. We also found: (1) the distribution of DHSs in promoter and other gene regions of similarly expressed genes differs among different chromosomes; (2) silenced genes had a more open chromatin structure than previously thought; (3) DHSs in 3′untranslated regions (3′UTRs) are negatively correlated with level of gene expression.

No abstract

MicroRNAs (miRNAs) are mediators of the aging process. The purpose of this work was to analyze the miRNA expression profiles of spermatozoa from men of different ages with normal fertility. Twenty-seven donors were divided into three groups by age (Group A, n = 8, age: 20–30 years; Group B, n = 10, age: 31–40 years; and Group C, n = 9, age: 41–55 years) for high-throughput sequencing analysis. Samples from 65 individuals (22, 22, and 21 in Groups A, B, and C, respectively) were used for validation by quantitative real-time polymerase chain reaction (qRT-PCR). A total of 2160 miRNAs were detected: 1223 were known, 937 were newly discovered and unnamed, of which 191 were expressed in all donors. A total of 7, 5, and 17 differentially expressed microRNAs (DEMs) were found in Group A vs B, Group B vs C, and Group A vs C comparisons, respectively. Twenty-two miRNAs were statistically correlated with age. Twelve miRNAs were identified as age-associated miRNAs, including hsa-miR-127-3p, mmu-miR-5100_L+2R-1, efu-miR-9226_L-2_1ss22GA, cgr-miR-1260_L+1, hsa-miR-652-3p_R+1, pal-miR-9993a-3p_L+2R-1, hsa-miR-7977_1ss6AG, hsa-miR-106b-3p_R-1, hsa-miR-186-5p, PC-3p-59611_111, hsa-miR-93-3p_R+1, and aeca-mir-8986a-p5_1ss1GA. There were 9165 target genes of age-associated miRNAs. Gene Ontology (GO) analysis of the target genes identified revealed enrichment of protein binding, membrane, cell cycle, and so on. The Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis of age-related miRNAs for target genes revealed 139 enriched pathways, such as signaling pathways regulating stem cell pluripotency, metabolic pathways, and the Hippo signaling pathway. This suggests that miRNAs play a key role in male fertility changes with increasing age and provides new evidence for the study of the mechanism of age-related male fertility decline.