RESUMEN

A high number of cancer patients around the world rely on gemcitabine (GEM) for chemotherapy. During local metastasis of cancers, surgery is beneficial for therapy, but dissemination in distant organs leads to using chemotherapy alone or in combination with surgery to prevent cancer recurrence. Therapy failure can be observed as a result of GEM resistance, threatening life of pancreatic cancer (PC) patients. The mortality and morbidity of PC in contrast to other tumors are increasing. GEM chemotherapy is widely utilized for PC suppression, but resistance has encountered its therapeutic impacts. The purpose of current review is to bring a broad concept about role of biological mechanisms and pathways in the development of GEM resistance in PC and then, therapeutic strategies based on using drugs or nanostructures for overcoming chemoresistance. Dysregulation of the epigenetic factors especially non-coding RNA transcripts can cause development of GEM resistance in PC and miRNA transfection or using genetic tools such as siRNA for modulating expression level of these factors for changing GEM resistance are suggested. The overexpression of anti-apoptotic proteins and survival genes can contribute to GEM resistance in PC. Moreover, supportive autophagy inhibits apoptosis and stimulates GEM resistance in PC cells. Increase in metabolism, glycolysis induction and epithelial-mesenchymal transition (EMT) stimulation are considered as other factors participating in GEM resistance in PC. Drugs can suppress tumorigenesis in PC and inhibit survival factors and pathways in increasing GEM sensitivity in PC. More importantly, nanoparticles can increase pharmacokinetic profile of GEM and promote its blood circulation and accumulation in cancer site. Nanoparticles mediate delivery of GEM with genes and drugs to suppress tumorigenesis in PC and increase drug sensitivity. The basic research displays significant connection among dysregulated pathways and GEM resistance, but the lack of clinical application is a drawback that can be responded in future.

Asunto(s)

Gemcitabina , Neoplasias Pancreáticas , Humanos , Preparaciones Farmacéuticas , Recurrencia Local de Neoplasia , Neoplasias Pancreáticas/tratamiento farmacológico , Neoplasias Pancreáticas/genética , Carcinogénesis , Transformación Celular Neoplásica , Neoplasias Pancreáticas

RESUMEN

The prognosis of laryngeal squamous cell carcinoma (LSCC) patients remains poor, and early diagnosis can distinctly improve the long-term survival of LSCC patients. MicroRNAs (miRs) are a group of endogenous, noncoding, 18-24 nucleotide length single-strand RNAs and have been demonstrated to regulate the expression of many genes, thus modulating various cellular biological processes. In this study, we aimed to identify critical diagnostic miRNAs based on two machine learning algorithms. The GSE133632 dataset was acquired from the Gene Expression Omnibus (GEO) datasets, comprising LSCC tissular samples (57 specimens) and matched neighboring healthy mucosa tissular samples (57 specimens). Differentially expressed miRNAs (DEMs) were screened between 57 LSCC specimens and 57 normal specimens. The LASSO regression model and SVM-RFE analysis were carried out for the identification of critical miRNAs. ROC assays were applied to evaluate discriminatory ability. We identified 32 DEMs between LSCC specimens and normal specimens. Two machine learning algorithms confirmed that hsa-miR-615-3p, hsa-miR-4652-5p, hsa-miR-450a-5p, hsa-miR-196a-5p, hsa-miR-21-3p, hsa-miR-139-5p, and hsa-miR-424-5p were critical diagnostic factors. According to the ROC assays, seven miRNAs had an AUC value of >0.85 for LSCC. Taken together, our findings identified seven critical miRNAs in LSCC patients which can be used to diagnose LSCC patients with high sensitivity and specificity. These results must be verified by large-scale prospective studies.

Asunto(s)

Neoplasias de Cabeza y Cuello , Neoplasias Laríngeas , MicroARNs , Perfilación de la Expresión Génica , Humanos , Neoplasias Laríngeas/diagnóstico , Neoplasias Laríngeas/genética , Neoplasias Laríngeas/patología , MicroARNs/genética , MicroARNs/metabolismo , Estudios Prospectivos , Carcinoma de Células Escamosas de Cabeza y Cuello/diagnóstico , Carcinoma de Células Escamosas de Cabeza y Cuello/genética

RESUMEN

We propose the asynchronous control of anisotropic diffusion (AD) algorithm, and such asynchronous anisotropic diffusion (AAD) algorithm is demonstrated experimentally to reduce noise from the sensing signals obtained from Brillouin distributed optical fiber sensors. The performance of the proposed AAD algorithm is analyzed in detail for different experimental conditions and compared with that of block-matching and 3D filtering, two-dimensional wavelet denoising, AD, and non-local means algorithms. Some key factors of the proposed algorithm, such as the impact of convolution kernel size on the performance of AD algorithms, the influence of low sampling point number (SPN) on the quality of Brillouin frequency shift and the selection of diffusion thresholds are analyzed and discussed with experimental results. The experimental results validate that the AAD algorithm can provide better root-mean-square error (RMSE) and spatial resolution (SR) than the other four algorithms, especially for higher signal-to-noise ratio (SNR) improvement and higher SPNs. For lower SPNs, the performance of AAD is also not inferior to the RMSE performance of NLM and AD. The runtime of the AAD algorithm is also quite low. Moreover, the proposed algorithm offers the best SR performance as compared to other noise reduction algorithms investigated in this study. Thus, the proposed AAD algorithm can be an effective candidate to improve the measurement accuracy of Brillouin distributed optical fiber sensors.

RESUMEN

The objective of present study was to find out an accurate, rapid and nondestructive method to detect total acid content (TA) of pitaya with visible/near-infrared spectrometry, wavelet transform (WT) and successive projections algorithm (SPA), which will provide scientific basis for non- destructive measurement of pitaya. Maya2000 fiber-optic spectrumeter was used to collect spectral data of pitaya on the wavelength in the range of 380~1 099 nm; and then with the methods of WT denosing pretreatment, SPA and partial least squares regression (PLSR) quantitative forecasting model of TA of pitaya was established. The result showed that the precision of WT-SPA-PLSR model, which combine the WT with SPA, was better than that of PLSR model based on the whole wave variables. The relation coefficient of the PLSR model (Rp) that predicted TA based on the original spectrum of all samples as the input variables was 0.851 394 and RMSEP was 0.086 848. The original spectrum variable of the all samples were processed by using wavelet function dbN(N=2, 3, …, 10) for wavelet decomposition and de-noising. The optimal results of noise reduction were decomposed in level 2 using wavelet function db4 (db4-2). The Rp of WT-PLSR model was 0.915 635 and RMSEP was 0.066 752. The prediction of model using wavelet transform de-noising was improved significantly. After the original spectrum processed by db10-3 and SPA, 12 preferred variables were selected from 570 spectrum variables, such as 530, 545, 604, 626, 648, 676, 685, 695, 730, 897, 972, 1 016 nm spectrum variables. The WT-SPA-PLSR model based on these 12 variables as input variables was established. Rp of the WT-SPA-PLSR prediction model was 0.882 83 and RMSEP was 0.077 39. SPA algorithm was suitable for the selection of spectrum variables which could effectively obtain the spectrum variables which were strong correlation with TA and increase the accuracy and stability of the prediction model. The results indicated that the nondestructive detection for TA of pitaya based on the diffuse reflectance visible/near-infrared spectrometry, WT and SPA was feasible.