Background At the global and country levels, several important sanitation improvement initiatives were launched in the last decade. This study aimed to explore the temporal trend of and factors associated with access to residential toilets among the middle-aged and elderly in rural China from 2011 to 2018. Methods This study used the 2011, 2013, 2015, and 2018 data of China Health and Retirement Longitudinal Study (CHARLS). CHARLS was conducted among adults aged ≥ 45 years in 28 provinces of China. We used descriptive statistics and logistic regressions for data analysis. Results We found that residential toilet coverage increased by about 6% among population aged ≥ 45 years in rural China from 2011 to 2018. The coverage of flushable toilets and toilets with seats among this sector of the population increased by more than 10% during this period. We also found that being female, higher levels of education, higher annual per capita household consumption, having running water in the residence, larger household size, and better health status were significantly associated with an increased likelihood of residential toilet ownership among population aged ≥ 45 years in rural China. Conclusions China made progress in sanitation improvement in rural areas from 2011 to 2018. However, considering the current coverage levels of residential toilets and the vulnerable subgroups who are more prone to toilet deprivation in rural areas, we suggest to the government to implement further targeted toilet improvement interventions to ensure universal coverage of sanitation facilities for the whole of the Chinese population.

Identifying effective biomarkers in osteosarcoma (OS) is important for predicting prognosis. We investigated the prognostic value of ferroptosis-related genes (FRGs) in OS. Transcriptome and clinical data were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. FRGs were obtained from the ferroptosis database. Univariate COX regression and LASSO regression screening were performed and an FRG-based prognostic model was constructed, which was validated using the Gene Expression Omnibus cohort. The predictive power of the model was assessed via a subgroup analysis. A nomogram was constructed using clinical markers with independent prognostic significance and risk score results. The CIBERSORT algorithm was used to detect the correlation between prognostic genes and 22 tumor-infiltrating lymphocytes. The expression of prognostic genes in erastin-treated OS cell lines was verified via real-time PCR. Six prognostic FRGs (ACSL5, ATF4, CBS, CDO1, SCD, and SLC3A2) were obtained and used to construct the risk prognosis model. Subjects were divided into high- and low-risk groups. Prognosis was worse in the high-risk group, and the model had satisfactory prediction performance for patients younger than 18 years, males, females, and those with non-metastatic disease. Univariate COX regression analysis showed that metastasis and risk score were independent risk factors for patients with OS. Nomogram was built on independent prognostic factors with superior predictive power and patient benefit. There was a significant correlation between prognostic genes and tumor immunity. Six prognostic genes were differentially expressed in ferroptosis inducer-treated OS cell lines. The identified prognostic genes can regulate tumor growth and progression by affecting the tumor microenvironment.

Although over-expression of hypoxia-inducible factor-2 alpha (HIF-2α) can result in cartilage destruction and osteoarthritis (OA) development, the underlying mechanisms remain poorly understood. Here, we investigated the molecular mechanisms in chondrocytes over-expressing HIF-2α. The GeneCloud of Biotechnology Information platform was used to identify differentially expressed genes (DEGs). Using the GEO GSE104794 dataset of control (empty adenovirus, n = 4) and experimental (recombinant adenovirus expressing HIF-2α, n = 4) groups, we performed DEG, Gene Ontology, pathway, pathway network, and gene signal network analyses. Similarly, DEG analysis was performed for the GEO GSE51588 dataset of control (non-OA, n = 4) and experimental (OA, n = 20) groups. Thereafter, intersection of GSE104794 gene signal network analysis and GSE51588 DEG analysis was performed for the key genes, validated by quantitative reverse transcription-polymerase chain reaction. A total of 542 DEGs were identified, among which, the 10 most significant genes in the gene signal network were Nfkb1, Tlr2, Nt5e, Enpp1, Entpd3, Vegfa, Ptgs2, Socs3, Fos, and Epas1. The key genes in OA were LUM, ENTPD3, SMPD3, FGFR3, GPX3, IRAK3, EREG, HTR2A, TLR2, and CDA. Taken together, we screened key genes that are potentially involved in osteoarthritis, thereby providing a basis for identifying valuable markers for this disease.