Predictive Solution for Radiation Toxicity Based on Big Data

Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment planning for each radiotherapy machine. At this time, the dose, volume, device setting information, complication, tumor control probability, etc. are considered as a single-patient treatment for each fraction during radiotherapy process. Thus, these filed-up big data for a long time and numerous patients’ cases are inevitably suitable to produce optimal treatment and minimize the radiation toxicity and complication. Thus, we are going to handle up prostate, lung, head, and neck cancer cases using machine learning algorithm in radiation oncology. And, the promising algorithms as the support vector machine, decision tree, and neural network, etc. will be introduced in machine learning. In conclusion, we explain a predictive solution of radiation toxicity based on the big data as treatment planning decision support system

Improvements of motion vector in variational echo tracking technique by correction of initial guess

Póster presentado en: 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019

Prediction of Cancer Patient Outcomes Based on Artificial Intelligence

Knowledge-based outcome predictions are common before radiotherapy. Because there are various treatment techniques, numerous factors must be considered in predicting cancer patient outcomes. As expectations surrounding personalized radiotherapy using complex data have increased, studies on outcome predictions using artificial intelligence have also increased. Representative artificial intelligence techniques used to predict the outcomes of cancer patients in the field of radiation oncology include collecting and processing big data, text mining of clinical literature, and machine learning for implementing prediction models. Here, methods of data preparation and model construction to predict rates of survival and toxicity using artificial intelligence are described

Hexane Fractions of Bupleurum falcatum L.

Bupleurum falcatum L. has been used traditionally as a medicinal herb in Korean medicine. The hexane fraction of BF (HFBF), which was profiled with Direct Analysis in Real Time-Mass Spectrometry (DART-MS), activates the secretion of glucagon-like peptide-1 (GLP-1) in NCI-H716 cells significantly. We performed a microarray analysis and GLP-1 ELISA assay, as well as calcium imaging experiments with inhibitors, to investigate the mechanism of action of the HFBF. Through the microarray analysis, it was found that the ITPR2 gene that encodes the inositol 1,4,5-trisphosphate (IP3) receptor is up-regulated and the HFBF induces cell depolarization by inhibiting the voltage-gated channel expression in NCI-H716 cells. In addition, we found that the intracellular calcium in NCI-H716 cells, with Gallein, U73122, and 2APB as inhibitors, was decreased. These results suggest that the HFBF activates the GLP-1 secretion through the Gβγ pathways in the enteroendocrine L cells after treatment with the HFBF

Effects of warming and eutrophication on coastal phytoplankton production

Phytoplankton production in coastal waters influences seafood production and human health and can lead to harmful algal blooms. Water temperature and eutrophication are critical factors affecting phytoplankton production, although the combined effects of warming and nutrient changes on phytoplankton production in coastal waters are not well understood. To address this, phytoplankton production changes in natural waters were investigated using samples collected over eight months, and under 64 different initial conditions, established by combining four different water temperatures (i.e., ambient T, + 2, + 4, and + 6 degrees C), and two different nutrient conditions (i.e., non-enriched and enriched). Under the non-enriched conditions, the effect of warming on phytoplankton production was significantly positive in some months, significantly negative in others, or had no effect. However, under enriched conditions, warming affected phytoplankton production positively in all months except one, when the salinity was as low as 6.5. These results suggest that nutrient conditions can alter the effects of warming on phytoplankton production. Of several parameters, the ratio of initial nitrate concentration to chlorophyll a concentration [NCCA, mu M (pg L-1)(-1))] was one of the most critical factors determining the directionality of the warming effects. In laboratory experiments, when NCCA in the ambient or nutrient-enriched waters was >= 1.2, warming increased or did not change phytoplankton production with one exception; however, when NCCA was < 1.2, warming did not change or decreased production. In the time series data obtained from the coastal waters of four target countries, when NCCA was 1.5 or more, warming increased phytoplankton production, whereas when NCCA was lower than 1.5, warming lowered phytoplankton production, Thus, it is suggested that NCCA could be used as an index for predicting future phytoplankton production changes in coastal waters.11Ysciescopu

Use of the Clock Drawing Test and the Rey–Osterrieth Complex Figure Test-copy with convolutional neural networks to predict cognitive impairment

Background The Clock Drawing Test (CDT) and Rey–Osterrieth Complex Figure Test (RCFT) are widely used as a part of neuropsychological test batteries to assess cognitive function. Our objective was to confirm the prediction accuracies of the RCFT-copy and CDT for cognitive impairment (CI) using convolutional neural network algorithms as a screening tool. Methods The CDT and RCFT-copy data were obtained from patients aged 60–80 years who had more than 6 years of education. In total, 747 CDT and 980 RCFT-copy figures were utilized. Convolutional neural network algorithms using TensorFlow (ver. 2.3.0) on the Colab cloud platform ( www.colab.research.google.com ) were used for preprocessing and modeling. We measured the prediction accuracy of each drawing test 10 times using this dataset with the following classes: normal cognition (NC) vs. mildly impaired cognition (MI), NC vs. severely impaired cognition (SI), and NC vs. CI (MI + SI). Results The accuracy of the CDT was better for differentiating MI (CDT, 78.04 ± 2.75; RCFT-copy, not being trained) and SI from NC (CDT, 91.45 ± 0.83; RCFT-copy, 90.27 ± 1.52); however, the RCFT-copy was better at predicting CI (CDT, 77.37 ± 1.77; RCFT, 83.52 ± 1.41). The accuracy for a 3-way classification (NC vs. MI vs. SI) was approximately 71% for both tests; no significant difference was found between them. Conclusions The two drawing tests showed good performance for predicting severe impairment of cognition; however, a drawing test alone is not enough to predict overall CI. There are some limitations to our study: the sample size was small, all the participants did not perform both the CDT and RCFT-copy, and only the copy condition of the RCFT was used. Algorithms involving memory performance and longitudinal changes are worth future exploration. These results may contribute to improved home-based healthcare delivery.The costs for manuscript publication, design of the study, data management, and writing of the manuscript were supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2017S1A6A3A01078538)

Clinical and pathological significance of ROS1 expression in intrahepatic cholangiocarcinoma

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.Background: More knowledge about genetic and molecular features of cholangiocarcinoma is needed to develop effective therapeutic strategies. We investigated the clinical and pathological significance of ROS1 expression in intrahepatic cholangiocarcinoma. Methods: One hundred ninety-four patients with curatively resected intrahepatic cholangiocarcinoma were included in this study. Tumor tissue specimens were collected and analyzed for ROS1 gene rearrangement using fluorescence in situ hybridization (FISH) and ROS1 protein expression using immunohistochemistry (IHC). Results: ROS1 immunohistochemistry was positive (moderate or strong staining) in 72 tumors (37.1 %). ROS1 protein expression was significantly correlated with well differentiated tumors, papillary or mucinous histology, oncocytic/hepatoid or intestinal type tumors, and periductal infiltrating or intraductal growing tumors (vs. mass-forming cholangiocarcinoma). ROS-expressing tumors were associated with better disease-free survival (30.1 months for ROS1 expression (+) tumors vs. 9.0 months for ROS1 (-) tumors, p = 0.006). Moreover, ROS1 expression was an independent predictor of better disease-free survival in a multivariate analysis (HR 0.607, 95 % CI 0.377-0.976; p = 0.039). Although break-apart FISH was successfully performed in 102 samples, a split pattern indicative of ROS1 gene rearrangement was not found in the examined samples. Conclusion: ROS1 protein expression was associated with well-differentiated histology and better survival in our patients with resected intrahepatic cholangiocarcinoma. ROS1 gene rearrangement by break-apart FISH was not found in the examined samples