Circulating Tumor DNA as a Sensitive Marker in Patients Undergoing Irreversible Electroporation for Pancreatic Cancer

Background/Aims: Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at an advanced stage, resulting in extremely poor 5-year survival. Late diagnosis of PDAC is mainly due to lack of a reliable method of early detection. Carbohydrate antigen (CA) 19-9 is often used as a tumor biomarker in PDAC; however, the test lacks sensitivity and specificity. Therefore, new sensitive and minimally invasive diagnostic tools are required to detect pancreatic cancer. Methods: Here, we investigated circulating tumor DNA (ctDNA) which contained KRAS-mutated as a potential diagnostic tool for PDAC patients who underwent irreversible electroporation (IRE). We used droplet digital polymerase chain reaction (ddPCR) to detect the expression of KRAS-mutated genes in plasma samples of 65 PDAC patients who underwent IRE. Results: In these 65 cases, ctDNA was detected in 20 (29.2%) samples. The median overall survival (OS) was 11.4 months with ctDNA+ patients and 14.3 months for ctDNA- patients. ctDNA+ patients had a obviously poorer prognosis associated to overall survival (P < 0.001). Conclusion: Our results suggested that the existence of ctDNA was a predictor of survival for PDAC patients. Therefore, ctDNA may be a new sensitive biomarker for monitoring treatment outcome in PDAC

Allogenic Natural Killer Cell Immunotherapy Combined with Irreversible Electroporation for Stage IV Hepatocellular Carcinoma: Survival Outcome

Background/Aims: We evaluated the clinical effectiveness of irreversible electroporation (IRE) in combination with immunotherapy using allogenic natural killer cells (NK) for stage IV hepatocellular carcinoma (HCC). Methods: The study involved 40 patients with stage IV HCC who were divided equally into two groups: 1) simple IRE; and 2) IRE plus allogenic NK cells (IRE-NK); we mainly assessed the overall survival (OS). Results: The effect of the IRE-NK treatment was synergistic, i.e., not only did it enhance immune function, it also decreased alpha-fetoprotein expression and showed significantly good clinical effectiveness. At the median 7.6-month follow-up (range, 3.8–12.1 months), median OS was higher in the IRE-NK group (10.1 months) than in the IRE group (8.9 months, P = 0.0078). Conclusion: IRE combined with allogeneic NK cell immunotherapy significantly increases the median OS of patients with stage IV HCC

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Sea-Land Clutter Classification Based on Graph Spectrum Features

In this paper, an approach for radar clutter, especially sea and land clutter classification, is considered under the following conditions: the average amplitude levels of the clutter are close to each other, and the distributions of the clutter are unknown. The proposed approach divides the dataset into two parts. The first data sequence from sea and land is used to train the model to compute the parameters of the classifier, and the second data sequence from sea and land under the same conditions is used to test the performance of the algorithm. In order to find the essential structure of the data, a new data representation method based on the graph spectrum is utilized. The method reveals the nondominant correlation implied in the data, and it is quite different from the traditional method. Furthermore, this representation is combined with the support vector machine (SVM) artificial intelligence algorithm to solve the problem of sea and land clutter classification. We compare the proposed graph feature set with nine exciting valid features that have been used to classify sea clutter from the radar in other works, especially when the average amplitude levels of the two types of clutter are very close. The experimental results prove that the proposed extraction can represent the characteristics of the raw data efficiently in this application