CCNB1 may as a biomarker for the adipogenic differentiation of adipose-derived stem cells in the postoperative fat transplantation of breast cancer.

Background: Adipose-derived stem cells (ADSCs) are closely associated with the survival rate of transplanted fat in breast reconstruction after breast cancer surgery. Nevertheless, the intrinsic mechanisms regulating ADSCs adipogenic differentiation remain ambiguous. The aim of our study was to explore the relevant genes and pathways to elucidate the potential mechanisms of adipogenic differentiation in ADSCs. Methods: The Gene Expression Omnibus (GEO) dataset GSE61302 was downloaded and analyzed to identify differentially expressed genes (DEGs). Key genes and signaling pathways were obtained through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional and enrichment analysis. Protein-protein interaction (PPI) network and hub gene analyses were performed with the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database and Cytoscape software. Finally, the transcription levels of hub genes in the adipogenic differentiated group and undifferentiated group of ADSCs were compared via real-time quantitative polymerase chain reaction (RT-qPCR). Results: In total, 1,091 DEGs were identified through bioinformatics analysis of the adipogenic differentiated group and undifferentiated group. If was then found that the 10 downregulated key genes, CCNB1, NUSAP1, DLGAP5, TTK, CCNB2, KIF23, BUB1B, CDC20, CDCA8, and KIF11 may play important roles in the adipogenic differentiation of ADSCs. Subsequent in vitro experimental verification also revealed that the messenger RNA (mRNA) expression levels of cyclin B1 in adipogenic differentiated cells and undifferentiated cells were significantly different at the early stage (P<0.05), but there was no significant difference at the late stage (P>0.05). Conclusions: As a key gene, CCNB1 might be a potential biomarker in the adipogenic differentiation of ADSCs at the early stage.

Stability of Mixed Lead Halide Perovskite Films Encapsulated in Cyclic Olefin Copolymer at Room and Cryogenic Temperatures.

Lead Mixed Halide Perovskites (LMHPs), CsPbBrI2, have attracted significant interest as promising candidates for wide bandgap absorber layers in tandem solar cells due to their relative stability and red-light emission with a bandgap ∼1.7 eV. However, these materials segregate into Br-rich and I-rich domains upon continuous illumination, affecting their optical properties and compromising the operational stability of devices. Herein, we track the microscopic processes occurring during halide segregation by using combined spectroscopic measurements at room and cryogenic temperatures. We also evaluate a passivation strategy to mitigate the halide migration of Br/I ions in the films by overcoating with cyclic olefin copolymer (COC). Our results explain the correlation between grain size, intensity dependencies, phase segregation, activation energy barrier, and their influence on photoinduced carrier lifetimes. Importantly, COC treatment increases the lifetime charge carriers in mixed halide thin films, improving efficient charge transport in perovskite solar cell applications.

High rate of epidermal growth factor receptor-mutated primary lung cancer in patients with primary breast cancer.

Background: With increased survival in breast cancer, resulting from advances in treatment, patients incur the possibility of subsequent primary malignancies, especially lung cancer. The aim of this study was to assess the frequency of CT-detected pulmonary ground-glass nodules and lung cancer following breast cancer diagnosis, the associations between breast cancer and lung cancer, the pathological features of double primary cancer, and the status of epidermal growth factor receptor (EGFR) mutations in second primary lung cancer. Methods: Clinical data from more than 9000 individuals who were diagnosed with primary breast cancer at Jiangsu Province Hospital (Jiangsu, China) between January 2008 and December 2021 were retrospectively analyzed. Results: Of the 9179 patients, 6512 underwent diagnostic CT, 55 (0.8%) were diagnosed with a second primary lung cancer, which accounted for approximately 18.4% of the pulmonary ground-glass nodules (GGNs) detected. The incidence was higher than in the general female population (standardized incidence ratio 1.4 [95% confidence interval (CI): 1.25-1.55]). Patients who experienced a second primary lung cancer exhibited a significantly higher rate of EGFR mutation (78.5%) than those with lung adenocarcinoma alone, with most exhibiting low-grade malignancy, older age, estrogen receptor negativity, low Ki67, and no lymph node metastasis. Conclusions: Breast cancer patients, especially those with low-grade malignancy, were at high risk for developing primary lung cancer. For isolated GGN in patients with high-risk factors, clinicians should insist on close follow-up. Furthermore, EGFR may play an important role in primary lung adenocarcinomas and breast cancer.

Deep learning-enabled prediction of 2D material breakdown.

Characterizing electrical breakdown limits of materials is a crucial step in device development. However, methods for repeatable measurements are scarce in two-dimensional materials, where breakdown studies have been limited to destructive methods. This restricts our ability to fully account for variability in local electronic properties induced by surface contaminants and the fabrication process. To tackle this, we implement a two-step deep-learning model to predict the breakdown mechanism and breakdown voltage of monolayer MoS2devices with varying channel lengths and resistances using current measured in the low-voltage regime as inputs. A deep neural network (DNN) first classifies between Joule and avalanche breakdown mechanisms using partial current traces from 0 to 20 V. Following this, a convolutional long short-term memory network (CLSTM) predicts breakdown voltages of these classified devices based on partial current traces. We test our model with electrical measurements collected using feedback-control of the applied voltage to prevent device destruction, and show that the DNN classifier achieves an accuracy of 79% while the CLSTM model has a 12% error when requiring only 80% of the current trace as inputs. Our results indicate that information encoded in the current behavior far from the breakdown point can be used for breakdown predictions, which will enable non-destructive and rapid material characterization for 2D material device development.

Prognostic evaluation of microRNA-210 in various carcinomas: Evidence from 19 studies.

BACKGROUND: We performed this meta-analysis to provide a comprehensive evaluation of the role of MicroRNA-210 (miR-210) expression on the overall survival (OS) rate of cancers. METHODS: We searched for relevant available literatures on miR-210 and cancer until November 1st, 2016 on the databases PubMed, EMBASE, Cochrane Library, and Science Direct database. We calculated the pooled hazard ratio (HR) with 95% confidence intervals (CIs) for OS, which compared the high and low expression levels of miR-210 in patients of the available studies. Subgroup analysis was performed to evaluate the specific role of miR-210 in ethnicity and the type of cancers. Publication bias was evaluated using Begg funnel plots and Egger regression test. RESULTS: Overall, 19 studies were involved in this meta-analysis. The result indicated that upregulated miR-210 might be associated with poor OS outcome in various carcinomas, with the pooled HR of 1.80 (95% CI: 1.29-2.51). When stratified by disease, significant results were detected in breast cancer (HR = 2.67, 95% CI: 1.24-5.76) and glioma (HR = 2.42, 95% CI: 1.32-4.43). Besides, in the subgroup analysis by ethnicity, significant results were detected only in Asian populations (HR = 2.14, 95% CI: 1.37-3.34). CONCLUSION: The present meta-analysis suggests that high expressed miR-210 is significantly associated with OS in cancer patients, which has the potential to be a prognostic marker in cancers.