A smart tablet-phone-based high-performance pancreatic cancer cell biosensing system for drug screening.

Exploring more efficient pancreatic cancer drug screening platforms is of significant importance for accelerating the drug development process. In this study, we developed a high-sensitivity bioluminescence system based on smartphones and smart tablets, and constructed a pancreatic cancer drug screening platform (PCDSP) by combining the pancreatic cancer cell sensing model (PCCSM) on the multiwell plates (MTP). A smart tablet was used as the light source and a smartphone as the colorimetric sensing device. The smartphone dynamically controls the color and brightness displayed on the smart tablet to achieve lower LOD and wider detection ranges. We constructed PCCSM for 24 h, 48 h, and 72 h , and performed colorimetric experiments using both PCDSP and a commercial plate reader (CPR). The results showed that the PCDSP had a lower LOD than that of CPR. Moreover, PCDSP even exhibited a lower LOD for 24 h PCCSM testing compared to CPR for 48 h PCCSM testing, effectively shortening the drug evaluation process. Additionally, the PCDSP offers higher portability and efficiency compared with CPR, making it a promising platform for efficient pancreatic cancer drug screening.

The prognostic value of multiparametric cardiac magnetic resonance in patients with systemic light chain amyloidosis.

Background: Late gadolinium enhancement (LGE) is a classic imaging modality derived from cardiac magnetic resonance (CMR), which is commonly used to describe cardiac tissue characterization. T1 mapping with extracellular volume (ECV) and native T1 are novel quantitative parameters. The prognostic value of multiparametric CMR in patients with light chain (AL) amyloidosis remains to be thoroughly investigated. Methods: A total of 89 subjects with AL amyloidosis were enrolled from April 2016 to January 2021, and all of them underwent CMR on a 3.0 T scanner. The clinical outcome and therapeutic effect were observed. Cox regression was used to investigate the effect of multiple CMR parameters on outcomes in this population. Results: LGE extent, native T1 and ECV correlated well with cardiac biomarkers. During a median follow-up of 40 months, 21 patients died. ECV (hazard ratio [HR]: 2.087 for per 10% increase, 95% confidence interval [CI]: 1.379-3.157, P < 0.001) and native T1 (HR: 2.443 for per 100 ms increase, 95% CI: 1.381-4.321, P=0.002) were independently predictive of mortality. A novel prognostic staging system based on median native T1 (1344 ms) and ECV (40%) was similar to Mayo 2004 Stage, and the 5-year estimated overall survival rates in Stage I, II, and III were 95%, 80%, and 53%, respectively. In patients with ECV > 40%, receiving autologous stem cell transplantation had higher cardiac and renal response rates than conventional chemotherapy. Conclusion: Both native T1 and ECV independently predict mortality in patients with AL amyloidosis. Receiving autologous stem cell transplantation is effective and significantly improves the clinical outcomes in patients with ECV > 40%.

Micheliolide inhibits gastric cancer growth in vitro and in vivo via blockade of the IL-6/STAT3 pathway.

Upregulation of pro-inflammatory cytokine interleukin (IL)-6 is observed in gastric cancer tissue, and high IL-6 serum levels predict a poor prognosis of gastric cancer patients. The IL-6/STAT3 pathway has been confirmed to play essential roles in the process of carcinogenesis, including gastric cancer. Thus, blockade of the IL-6/STAT3 pathway may be a potentially effective therapeutic option for gastric cancer. Micheliolide (MCL), a guaianolide sesquiterpene lactone, possesses anti-inflamma tory properties and can attenuate the IL-6 level. In addition, MCL has been widely reported to possess anti-tumor activity. But the anti-cancer effect of MCL on gastric cancer is unclear. In this study, we detected the effects of MCL on gastric cancer cell proliferation and apoptosis by performing MTT, colony formation, TUNEL and western blot assays, and found that MCL inhibited gastric cancer cell proliferation and promoted apoptosis in vitro. We further investigated the molecular mechanism by which MCL played an efficient role against gastric cancer, and found that the IL-6/STAT3 pathway is involved in the anti-cancer effect of MCL on gastric cancer. In vivo experiments further confirmed this conclusion. Taken together, MCL inhibits gastric cancer growth in vitro and in vivo via blockade of IL-6/STAT3 pathway.

A Hybrid Short-Term Traffic Flow Prediction Model Based on Singular Spectrum Analysis and Kernel Extreme Learning Machine.

Short-term traffic flow prediction is one of the most important issues in the field of intelligent transport system (ITS). Because of the uncertainty and nonlinearity, short-term traffic flow prediction is a challenging task. In order to improve the accuracy of short-time traffic flow prediction, a hybrid model (SSA-KELM) is proposed based on singular spectrum analysis (SSA) and kernel extreme learning machine (KELM). SSA is used to filter out the noise of traffic flow time series. Then, the filtered traffic flow data is used to train KELM model, the optimal input form of the proposed model is determined by phase space reconstruction, and parameters of the model are optimized by gravitational search algorithm (GSA). Finally, case validation is carried out using the measured data of an expressway in Xiamen, China. And the SSA-KELM model is compared with several well-known prediction models, including support vector machine, extreme learning machine, and single KLEM model. The experimental results demonstrate that performance of the proposed model is superior to that of the comparison models. Apart from accuracy improvement, the proposed model is more robust.