Construction of ceRNA network and key gene screening in cervical squamous intraepithelial lesions.

BACKGROUND: This study aimed to construct an endogenous competition network for cervical squamous intraepithelial lesions using differential gene screening. METHODS: GSE149763 was used to screen differentially expressed long non-coding RNAs (lncRNAs) and mRNAs to predict correlated microRNAs (miRNAs). The correlated miRNAs and GSE105409 were used to screen differentially expressed miRNAs for differential co-expression analysis, and the co-expressed differentially expressed miRNAs were used to predict correlated mRNAs. Differentially expressed mRNAs, miRNAs, and lncRNAs were visualized, and differential gene screening, enrichment, and pathway analysis were performed. RESULTS: The ceRNA network of cervical squamous intraepithelial was successfully established and a potential differentially expressed network was identified. The key genes were VEGFA and FOS, and the key pathway was the MAPK signaling pathway. CONCLUSIONS: The differential expression and potential effects of the lncRNA BACH1-IT1/miR-140-5p/VEGFA axis, key genes, VEGFA and FOS, and MAPK signaling in CIN were clarified, and the occurrence and potential effects of CIN were further clarified. The underlying molecular mechanism provides a certain degree of reference for subsequent treatments and experimental research.

A hierarchical system of covalent and dual non-covalent crosslinks promotes the toughness and self-healing properties of polymer hydrogels.

While it is challenging to simultaneously achieve both high mechanical performance and self-healing ability within one polymer hydrogel network, we, herein, synthesized a novel class of hydrogels based on a combination of chemical and dual non-covalent crosslinks via micellar polymerization of 3-isocyanatomethyl-3,5,5-trimethylcyclohexyl isocyanate, end-capped by 2-hydroxyethyl methacrylate (IPDI-HEMA), with acrylamide (AM). The prepared hydrogels were demonstrated to possess a tensile elongation at a break of at least 1900%, a fracture energy of 138.4 kJ m-3, and remarkable self-healing behaviors (e.g., a strong self-healing ability achieved at ambient temperature without the need for any stimulus or healing agent). The multiple crosslinks developed in this study for one polymer hydrogel network are significant steps to construct the desired functional hydrogels with excellent self-healing and mechanical properties.

Highly tough and rapid self-healing dual-physical crosslinking poly(DMAA-co-AM) hydrogel.

Introducing double physical crosslinking reagents (i.e., a hydrophobic monomer micelle and the LAPONITE® XLG nano-clay) into the copolymerization reaction of hydrophilic monomers of N,N-dimethylacrylamide (DMAA) and acrylamide (AM) is reported here by a thermally induced free-radical polymerization method, resulting in a highly tough and rapid self-healing dual-physical crosslinking poly(DMAA-co-AM) hydrogel. The mechanical and self-healing properties can be finely tuned by varying the weight ratio of nanoclay to DMAA. The tensile strength and elongation at break of the resulting nanocomposite hydrogel can be modulated in the range of 7.5-60 kPa and 1630-3000%, respectively. Notably, such a tough hydrogel also exhibits fast self-healing properties, e.g., its self-healing rate reaches 48% and 80% within 2 and 24 h, respectively.

Predicting PM2.5 in Well-Mixed Indoor Air for a Large Office Building Using Regression and Artificial Neural Network Models.

Although the exposure to PM2.5 has serious health implications, indoor PM2.5 monitoring is not a widely applied practice. Regulations on the indoor PM2.5 level and measurement schemes are not well established. Compared to other indoor settings, PM2.5 prediction models for large office buildings are particularly lacking. In response to these challenges, statistical models were developed in this paper to predict the PM2.5 concentration in well-mixed indoor air in a commercial office building. The performances of different modeling methods, including multiple linear regression (MLR), partial least squares regression (PLS), distributed lag model (DLM), least absolute shrinkage selector operator (LASSO), simple artificial neural networks (ANN), and long-short term memory (LSTM), were compared. Various combinations of environmental and meteorological parameters were used as predictors. The root-mean-square error (RMSE) of the predicted hourly PM2.5 was 1.73 µg/m3 for the LSTM model and in the range of 2.20-4.71 µg/m3 for the other models when regulatory ambient PM2.5 data were used as predictors. The LSTM models outperformed other modeling approaches across the performance metrics used by learning the predictors' temporal patterns. Even without any ambient PM2.5 information, the developed models still demonstrated relatively high skill in predicting the PM2.5 levels in well-mixed indoor air.