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As a result of their cell structures, elastomeric foams exhibit high compressibility and are frequently used as buffer cushions in energy absorption. Foam pads between two surfaces typically withstand uniaxial loads. In this paper, we considered the effects of porosity and cell size on the mechanical behavior of random elastomeric foams, and proposed a constitutive model based on an artificial neural network (ANN). Uniform cell size distribution was used to represent monodisperse foam. The constitutive relationship between Cauchy stress and the four input variables of axial stretch λ U , lateral stretch λ L , porosity φ, and cell size θ was given by con-ANN. The mechanical responses of 500 different foam structures (20% < φ < 60%, 0.1 mm < θ < 0.5 mm) under compression and tension loads (0.4 < λ U < 3) were simulated, and a dataset containing 100,000 samples was constructed. We also introduced a pre-ANN to predict lateral stretch to address the issue of missing lateral strain data in practical applications. By combining physical experience, we chose appropriate input forms and activation functions to improve ANN's extrapolation capability. The results showed that pre-ANN and con-ANN could provide reasonable predictions for λ U outside the dataset. We can obtain accurate lateral stretch and axial stress predictions from two ANNs. The porosity affects the stress and λ L , while the cell size only affects the stress during foam compression.

Background: Malignant pleural effusion is one of the common clinical manifestations of patients with lung adenocarcinoma. Patients with pleural effusion at the initial diagnosis of lung adenocarcinoma usually indicate poor prognosis. Epidermal growth factor receptor (EGFR) mutations mainly occur in patients with lung adenocarcinoma. Patients with different mutant subtypes have different prognosis. The clinical characteristics and prognostic factors of patients with EGFR mutated lung adenocarcinoma of different molecular subtypes combined with pleural effusion at initial diagnosis are still unclear. This study was designed to explore the clinical characteristics and prognostic factors of these patients in order to provide management recommendations for them., Methods: A retrospective analysis of the clinical characteristics, treatment, outcomes and progression-free survival (PFS) of first-line treatment in patients with EGFR mutated lung adenocarcinoma combined with pleural effusion at initial diagnosis admitted to Department of Medical Oncology and Radiation Sickness, Peking University Third Hospital from January 2012 to June 2021 was performed. Pearson's chi-square test or Fisher's exact test were performed for comparison between groups. Kaplan-Meier method was performed for survival analysis and Cox proportional risk regression model was performed for multivariate analysis., Results: 76 patients met the inclusion criteria in this study. The incidences of EGFR classical mutations 19del, 21L858R and non-classical mutations were 46.0%, 38.2% and 15.8%, respectively among these patients. There was no significant difference between the three mutations in terms of gender, age, presence of dyspnea at presentation, whether other distant metastases were combined, site of pleural effusion, volume of pleural effusion, presence of other combined effusions, tumor-node-metastasis (TNM) stage, presence of other gene mutations, and treatment of pleural effusion (P>0.05). In patients with EGFR classical mutations 19del or 21L858R or non-classical mutations subtype, the proportion of chemotherapy in first-line regimens were 17.1%, 20.7% and 58.3%, respectively (P=0.001); and first-line disease control rates were 94.3%, 75.9% and 50%, respectively (P=0.003); pleural effusion control rates were 94.3%, 79.3% and 66.7%, respectively (P=0.04); PFS were 287 d, 327 d and 55 d, respectively (P=0.001). Univariate analysis showed that EGFR mutation subtype, control of pleural effusion, first-line treatment agents, and first-line treatment efficacy were significantly associated with PFS (P<0.05). Cox multifactorial analysis showed that only EGFR mutation subtype and first-line treatment efficacy were independent prognostic factors for PFS (P<0.05)., Conclusions: PFS was significantly better for classical mutations than for non-classical mutations in patients with EGFR mutated lung adenocarcinoma combined with pleural effusion at initial diagnosis. Improving the efficacy of first-line therapy is the key to improve the prognosis of these patients.

This study was designed to investigate the significance of the effect of miR-34a on 5-fluorouracil (5-FU) sensitivity in vitro and in vivo . miR-34a expression in tumor tissues or serum was determined by quantitative polymerase chain reaction. CRC cell lines HCT116 and SW480 were used to evaluate cell viability, cell apoptosis, and the cell cycle using a cell proliferation assay, flow cytometry, and Western blotting, respectively. For the in vivo studies, xenografts derived from SW480 cells were established to assess the antitumor activity between miR-34a and 5-FU. Patients with high levels of miR-34a expression were found to benefit more from 5-FU-based chemotherapy than patients with low levels of miR-34a expression, regardless of disease stage. Ectopic expression of miR-34a alone or 5-FU alone was found to inhibit CRC cell growth in vitro and in vivo . Moreover, cell growth in vitro and in vivo was further inhibited when miR-34a combined with 5-FU through increasing the rate of cell apoptosis. The potential targets of miR-34a, including CREB1, Bcl-2, Notch 1, Sirt1, and E2F3, were predicted and preliminarily validated and merit further study., Conclusion: miR-34a might function as a predictor of fluorouracil chemosensitivity in CRC, and a combination strategy of miR-34a with fluorouracil was expected to be more beneficial for CRC patients., Competing Interests: None.

Gastric cancer (GC) remains the second tumor caused death threat worldwide, and personalized medicine for GC is far from expectation. Finding novel, recurrently mutated genes through next-generation sequencing (NGS) is a powerful and productive approach. However, previous genomic data for GC are based on surgical resected samples while a large proportion of advanced gastric cancer (AGC) patients have already missed the chance for operation. The aim of this study is to assess frequent genomic alteration in AGC via biopsy samples. Here we performed targeted genomic sequencing of 78 AGC patients' tumor biopsies along with matched lymphocyte samples based on a 118 cancer related gene panel. In total, we observed 301 somatic nonsynonymous genomic alterations in 92 different genes, as well as 37 copy number gain events among 15 different genes (fold change 2-12), and validated the fold changes of ERBB2 copy number gains with IHC and FISH test showed an accuracy of 81.8%. Previously reported driver genes for gastric cancer ( TP53, KMT2D, KMT2B, EGFR, PIK3CA, GNAQ , and ARID1A ), and several unreported mutations ( TGFBR2, RNF213, NF1, NSD1 , and LRP2 ) showed high non-silent mutation prevalence (7.7%-34.6%). When comparing intestinal-type gastric cancer (IGC) with diffuse-type gastric cancer (DGC), TP53 and GNAQ appear to be more frequently mutated in IGC ( P =0.028 and P =0.023, respectively), whereas LRP2, BRCA2 and FGFR3 mutations are not observed in IGC, but have 12.8%, 7.7% and 7.7% mutation rates, respectively, in DGC patients. Patients with one or more mutations in adherens junction pathway ( CREBBP, EP300, CDH1, CTNNB1, EGFR, MET, TGFBR2 and ERBB2 ) or TGF-β signaling pathway ( CREBBP, EP300, MYST4, KRAS and TGFBR2 ) showed significantly better overall survival ( P =0.007 and P =0.014, respectively), consistent with The Cancer Genome Atlas (TCGA) cohort data. Importantly, 57 (73.1%) patients harbored at least one genomic alteration with potential treatments, making NGS-based drug target screening a viable option for AGC patients. Our study established a comprehensive genomic portrait of AGC, and identified several mutation signatures highly associated with clinical features, survival outcomes, which may be used to design future personalized treatments., Competing Interests: None.