Mitochondrial DNA (mtDNA) is believed to be particularly susceptible to oxidative damage during aging, resulting in mtDNA point mutations, duplications, and deletions. Although mtDNA deletions have been reported in various human tissues, e.g., the brain, heart, and skeletal muscle, little is known about the occurrence in hair. Therefore, we screened for the presence of mtDNA 13162 bp, 10422 bp, 7663 bp, 7436 bp, 4989 bp, and 4977 bp deletions in 90 hair samples from subjects aged 5 days to 91 years by using polymerase chain reaction (PCR) and investigated the deletion load by TaqMan probe-based real-time PCR. We detected the mtDNA 4977 bp deletion in hair samples, but none of the other deletions that were screened for. The proportion of mtDNA 4977 deletion carriers was 98.3% (89/90) and the deletion loads increased from 0 to 1.436 ± 0.2086% of the total mtDNA with an exponential increase with age (r = 0.677, p < 0.05). These results suggest that mtDNA 4977 bp deletion is a common phenomenon in hair and increases with age. These findings expand our understanding of the tissue-specific distribution of mtDNA deletions.
Drug repositioning, a method that relies on the information from the original drug-disease association matrix, aims to identify new indications for existing drugs and will greatly reduce the cost and...
Purpose
Suicidal ideation (SI) is often overlooked as a risk factor for people with cancer. Because it is often a precursor for suicidal behavior, it is critical to identify and address SI in a timely manner. This study investigated SI incidence and risk factors in a cohort of Chinese patients with mixed cancer types.
Methods
Data from this cross-sectional study were collected from 588 patients receiving medical therapy for tumors at Nanfang Hospital and the Integrated Hospital of Traditional Chinese Medicine at Southern Medical University. SI was measured using the Self-rating Idea of Suicide Scale (SIOSS). Anxiety and depression were assessed using the Hospital Anxiety and Depression Scale (HADS). The Chinese version of the Demoralization Scale II (DS-II-C) was used to assess demoralization. Univariate and correlation analyses were used to identify correlative factors of SI and multiple stepwise linear regression analysis was used to characterize potential risk factors.
Results
SI was reported in 24.7% of participants and the SIOSS score was 14.00 (13.00, 15.00) in the SI group. Multiple linear regression results showed that demoralization, medical financial burden, cancer type, living condition, caretaker, working state, residence, gender, and marital status explained 32.1% of the SI in this cohort (F = 28.705, P < 0.001).
Conclusion
Approximately one-quarter of cancer patients in this study reported SI influenced by both external and internal factors. Characterizing these factors can be informative for prevention and treatment efforts.
Accumulating evidence has demonstrated various associations of long non-coding RNAs (lncRNAs) with human diseases, such as abnormal expression due to microbial influences that cause disease. Gaining a deeper understanding of lncRNA–disease associations is essential for disease diagnosis, treatment, and prevention. In recent years, many matrix decomposition methods have also been used to predict potential lncRNA-disease associations. However, these methods do not consider the use of microbe-disease association information to enrich disease similarity, and also do not make more use of similarity information in the decomposition process. To address these issues, we here propose a correction-based similarity-constrained probability matrix decomposition method (SCCPMD) to predict lncRNA–disease associations. The microbe-disease associations are first used to enrich the disease semantic similarity matrix, and then the logistic function is used to correct the lncRNA and disease similarity matrix, and then these two corrected similarity matrices are added to the probability matrix decomposition as constraints to finally predict the potential lncRNA–disease associations. The experimental results show that SCCPMD outperforms the five advanced comparison algorithms. In addition, SCCPMD demonstrated excellent prediction performance in a case study for breast cancer, lung cancer, and renal cell carcinoma, with prediction accuracy reaching 80, 100, and 100%, respectively. Therefore, SCCPMD shows excellent predictive performance in identifying unknown lncRNA–disease associations.
In recent years, emerging evidence has shown that long noncoding RNAs (lncRNAs) is essential to biological processes, diagnosis, and treatments associated with complex diseases. However, infering the associations between diseases...
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