PurposeCisplatin plus gemcitabine (GEM) is a standard regimen for the first-line treatment of advanced non-small cell lung cancer. The aim of this study was to prepare biocompatible and biodegradable polymeric prodrugs and construct nanoparticles (NPs) with layer-by-layer (LbL) technique.MethodsPlatinum (Pt) (IV) complex with a carboxyl group was conjugated to the amino group of chitosan (CH), resulting in a CH-Pt conjugation with positive charge. GEM with amino group was conjugated to the carboxyl group of hyaluronic acid (HA), resulting in a HA-GEM conjugation with negative charge. Novel LbL NPs consisting of the CH-Pt core and the HA-GEM layer, named as HA-GEM/CH-Pt NPs, were constructed. The physicochemical properties of the HA-GEM/CH-Pt NPs were investigated. In vitro cytotoxicity against human non-small lung cancer cells (NCl-H460 cells) was investigated, and in vivo antitumor efficiency was evaluated on mice bearing NCl-H460 cells xenografts.ResultsHA-GEM/CH-Pt NPs have a size of about 187 nm, a zeta potential value of −21 mV and high drug encapsulation efficiency of 90%. The drug release of HA-GEM/CH-Pt NPs exhibited a sustained behavior. HA-GEM/CH-Pt NPs could significantly enhance in vitro cytotoxicity and in vivo antitumor effect against lung cancer animal model compared to the single-drug-loaded NPs and free drug solutions.ConclusionThe results demonstrated that the HA-GEM/CH-Pt NPs might be a promising system for the synergetic treatment of lung carcinoma.

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Background: For subjects with out-of-hospital cardiac arrest (OHCA), bag-valve mask (BVM), endotracheal intubation (ETI), and laryngeal mask airway (LMA) are the most common methods of ventilatory support; however, the best choice remains controversial. Methods: A comprehensive search of online databases was performed. A traditional meta-analysis was performed to determine the risk ratio of BVM vs. LMA and ETI vs. LMA. Indirect treatment comparisons (ITCs) were conducted to compare BVM and ETI. Results: A total of 13 full-text articles reporting the efficacy of BVM, ETI, and LMA were considered in this analysis. BVM and LMA had the same effect regarding return of spontaneous circulation (ROSC) (23% vs. 24%; RR =0.84), survival rate at admission (19% vs. 21%; RR =0.82) or discharge (6% vs. 4%; RR =0.61). ETI was superior to LMA in terms of ROSC (48% vs. 23%; RR =0.72) and survival rate at both admission (27% vs. 19%; RR =0.85) and discharge (12% vs. 4%; RR =0.90). BVM was inferior to ETI in terms of ROSC (24% vs. 48%; RR =0.86), survival to admission rate (21% vs. 27%; RR =1.037), and survival to discharge rate (6% vs. 12%; RR =1.476). Conclusions: ETI should be considered for airway management as early as possible, which can improve the subject's success rate of recovery and survival to admission rate. In future, large-scale, multi-center, randomized controlled studies should be conducted to evaluate the exact efficacy of BVM, ETI, and LMA for the first aid of subjects with OHCA.

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive tumors with an extremely low 5-year survival rate. Accumulating evidence has unveiled that inflammatory response promotes tumor progression, enhances angiogenesis, and causes local immunosuppression. Herein, we aim to develop an inflammatory related prognostic signature, and found it could be used to predict gemcitabine response in PDAC.Methods: PDAC cohorts with mRNA expression profiles and clinical information were systematically collected from the four public databases. An inflammatory response related genes (IRRGs) prognostic signature was constructed by LASSO regression analysis. Kaplan–Meier survival analysis, receiver operating characteristic analysis, principal component analysis, and univariate and multivariate Cox analyses were carried out to evaluate effectiveness, and reliability of the signature. The correlation between gemcitabine response and risk score was evaluated in the TCGA-PAAD cohort. The GDSC database, pRRophetic algorithm, and connectivity map analysis were used to predict gemcitabine sensitivity and identify potential drugs for the treatment of PDAC. Finally, we analyzed differences in frequencies of gene mutations, infiltration of immune cells, as well as biological functions between different subgroups divided by the prognostic signature.Results: We established a seven IRRGs (ADM, DCBLD2, EREG, ITGA5, MIF, TREM1, and BTG2) signature which divided the PDAC patients into low- and high-risk groups. Prognostic value of the signature was validated in 11 PDAC cohorts consisting of 1337 PDAC patients from 6 countries. A nomogram that integrated the IRRGs signature and clinicopathologic factors of PDAC patients was constructed. The risk score showed positive correlation with gemcitabine resistance. Two drugs (BMS-536924 and dasatinib) might have potential therapeutic implications in high-risk PDAC patients. We found that the high-risk group had higher frequencies of KRAS, TP53, and CDKN2A mutations, increased infiltration of macrophages M0, neutrophils, and macrophages M2 cells, as well as upregulated hypoxia and glycolysis pathways, while the low-risk group had increased infiltration of CD8+ T, naïve B, and plasma and macrophages M1 cells.Conclusion: We constructed and validated an IRRGs signature that could be used to predict the prognosis and gemcitabine response of patients with PDAC, as well as two drugs (BMS-536924 and dasatinib) may contribute to PDAC treatment.

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Abstract. This paper presents an improved modeling method for bridge-type mechanism by taking the input displacement loss into consideration, and establishes an amplification ratio model of bridge-type mechanism according to compliance matrix method and elastic beam theory. Moreover, the amplification ratio of the designed bridge-type nano-positioner is obtained by taking the guiding mechanism as the external load of bridge-type mechanism. Comparing with existing methods, the proposed model is more accurate, which is further verified by finite element analysis(FEA) and experimental test. The consistency of the results obtained from theoretical model, FEA and experimental testing indicates that the proposed model can accurately predict the amplification characteristics of nano-positioners, which helps the analysis and design of bridge-type nano-positioners in practical applications.

Despite the development of technology, the prognosis of patients with lung adenocarcinoma (LUAD) has not improved. Therefore, we sought to investigate the potential clinical utility of a risk prognosis model in prognostic stratification. For this purpose, DNA damage repair gene-related (DDRG-related) long noncoding RNAs (lncRNAs) were screened based on a single-cell RNA transcriptome to construct such a model. A total of 510 LUAD samples were selected from The Cancer Genome Atlas-LUAD (TCGA-LUAD) dataset. Samples were divided into two immune subtypes (S1 and S2) after calculating the stromal score, immune score, tumor purity, and immune infiltration in each TCGA-LUAD cohort based on the Estimation of Stromal and Immune cells in MAlignant Tumour tissues using Expression data (ESTIMATE) and the NbClust package. Subsequently, DDRG-related lncRNAs were selected by single-cell data analysis combined with bulk sequencing. Next, DDRG-related lncRNAs were screened through the least absolute shrinkage and selection operator, as well as univariate and multivariate Cox regression analyses to develop a precise DDRG-related-lncRNA prognosis risk model. The functions of the target genes of these lncRNAs were described by the Gene Ontology (GO) enrichment analysis. The prognostic capability of the model was tested by analyzing the expression data of LUAD samples downloaded from the Gene Expression Omnibus database. The correlations between tumor mutational burden, N6-methyladenosine (m6A) gene expression, risk score, and immune score were analyzed. A seven DDRG-related lncRNAs risk prognosis model was established. Based on the model, the TCGA-LUAD cohorts and testing sets were classified into low- and high-risk groups. The former group had better overall survival versus the latter group. In addition, a nomogram based on the risk score and clinical stage showed good calibration and moderate discriminative ability. Using single-cell transcriptome datasets, we constructed and verified a useful DDRG-related-lncRNA risk prognosis model for prognostic stratification in LUAD.

The ability to achieve accurate and reliable lifetime prognostics is critical to predictive maintenance of nano/micro motion systems. However, techniques to accomplish this task have not been well studied. This paper presents a stiffness-based degradation model for lifetime monitoring of a considered nanopositioning stage. Based on the dynamic model of the stage, an analysis of the relationship between system stiffness, running time and failure lifetime is investigated. A failure lifetime prognostic model for the stage is formulated by means of a dynamic stiffness analysis. This methodology is evaluated with numerical simulation and verified based on our experimental setup. The results reveal that this model can be used to effectively predict the failure lifetime and the remaining life of the investigated stage, which suggests its promising potentials in real applications.