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Advances in two-dimensional semiconducting thin films enable the realization of wearable electronic devices in the form factor of flexible substrate/thin films that can be seamlessly adapted in our daily lives. For wearable gas sensing, two-dimensional materials, such as SnSe, are particularly favorable because of their high surface-to-volume ratio and strong adsorption of gas molecules. Chemical vapor deposition and liquid/mechanical exfoliation are the widely applied techniques to obtain SnSe thin films. However, these methods normally result in non-uniform and isolated flakes which cannot apply to the practical industrial-scale wearable electronic devices. Here, we demonstrate large-scale (10 cm × 10 cm), uniform, and self-standing SnSe nanoplate arrays by co-evaporation process on flexible polyimide substrates. Both structural and morphological properties of the resulting SnSe nanoplates are systematically investigated. Particularly, the single-crystalline SnSe nanoplates are achieved. Furthermore, we explore the application of the polyimide/SnSe nanoplate arrays as wearable gas sensors for detecting methane. The wearable gas sensors show high sensitivity, fast response and recovery, and good uniformity. Our approach not only provides an efficient technique to obtain large-area, uniform and high-quality single-crystalline SnSe nanoplates, but also impacts on the future developments of layered metal dichalcogenides-based wearable devices.

We aimed to investigate the influence of long non-coding RNA (lncRNA) PTEN pseudogene-1 (PTENP1) on the proliferation, migration and cycle of breast cancer cells and its mechanism. Lentiviral vectors expressing PTENP1 were synthesized and breast cancer cells MCF7 were transfected with LV003-GFP-PTENP1 and LV003-GFP, respectively. The proliferation capacities of breast cancer cells were detected using CCK-8 assay, and the migration capacities of breast cancer cells were detected using scratch assay; flow cytometry was used to detect the cell cycles and Western blot was used to detect the expression levels of cyclin A2, CDK2, p-p44/42 MAPK, t-p44/42 MAPK, p-p38 MAPK, t-p38 MAPK, p-AKT, t-AKT in AKT and MAPK pathways. The absorbance values (A450) of cells in experimental group at 48 and 72 h were 1.4±0.3 and 2.3±0.47, respectively, which were significantly lower than those in control group (3.2±0.39, 3.4±0.58) (P<0.05). The number of cell colonies in experimental group was (48±13), which was significantly lower than that in control group (159±16) (P<0.01). The cell migration rate in experimental group was 22.8±3.3%, which was significantly lower than that in control group 61.8±5.2% (P<0.01). Western blot detection showed that the expression levels of cyclin A2, CDK2, p-AKT, p-p44/42 MAPK and p-p38 MAPK in experimental group were significantly decreased compared with those in control group. LncRNA PTENP1 can inhibit the proliferation and migration of breast cancer cells via the AKT and MAPK signaling pathways.

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Purpose This study aimed to develop deep learning (DL) models based on multicentre biparametric magnetic resonance imaging (bpMRI) for the diagnosis of clinically significant prostate cancer (csPCa) and compare the performance of these models with that of the Prostate Imaging and Reporting and Data System (PI-RADS) assessment by expert radiologists based on multiparametric MRI (mpMRI). Methods We included 1861 consecutive male patients who underwent radical prostatectomy or biopsy at seven hospitals with mpMRI. These patients were divided into the training (1216 patients in three hospitals) and external validation cohorts (645 patients in four hospitals). PI-RADS assessment was performed by expert radiologists. We developed DL models for the classification between benign and malignant lesions (DL-BM) and that between csPCa and non-csPCa (DL-CS). An integrated model combining PI-RADS and the DL-CS model, abbreviated as PIDL-CS, was developed. The performances of the DL models and PIDL-CS were compared with that of PI-RADS. Results In each external validation cohort, the area under the receiver operating characteristic curve (AUC) values of the DL-BM and DL-CS models were not significantly different from that of PI-RADS (P > 0.05), whereas the AUC of PIDL-CS was superior to that of PI-RADS (P < 0.05), except for one external validation cohort (P > 0.05). The specificity of PIDL-CS for the detection of csPCa was much higher than that of PI-RADS (P < 0.05). Conclusion Our proposed DL models can be a potential non-invasive auxiliary tool for predicting csPCa. Furthermore, PIDL-CS greatly increased the specificity of csPCa detection compared with PI-RADS assessment by expert radiologists, greatly reducing unnecessary biopsies and helping radiologists achieve a precise diagnosis of csPCa.