311 research outputs found
In Vitro Chemosensitivity Using the Histoculture Drug Response Assay in Human Epithelial Ovarian Cancer
The choice of chemotherapeutic drugs to treat patients with epithelial ovarian cancer has not depended on individual patient characteristics. We have investigated the correlation between in vitro chemosensitivity, as determined by the histoculture drug response assay (HDRA), and clinical responses in epithelial ovarian cancer. Fresh tissue samples were obtained from 79 patients with epithelial
ovarian cancer. The sensitivity of these samples to 11 chemotherapeutic agents was tested using the HDRA method according to established methods, and we analyzed the results retrospectively. HDRA showed that they were more chemosensitive to carboplatin, topotecan and belotecan, with inhibition rates of 49.2%, 44.7%, and 39.7%, respectively, than to cisplatin, the traditional drug of choice in epithelial ovarian cancer. Among the 37 patients with FIGO stage Ⅲ/Ⅳ serous adenocarcinoma
who were receiving carboplatin combined with paclitaxel, those with carboplatin-sensitive samples on HDRA had a significantly longer median disease-free interval than patients with carboplatin-
resistant samples (23.2 vs. 13.8 months, p<0.05), but median overall survival did not differ significantly
(60.4 vs. 37.3 months, p=0.621). In conclusion, this study indicates that HDRA could provide useful information for designing individual treatment strategies in patients with epithelial ovarian cancer
CropCat: Data Augmentation for Smoothing the Feature Distribution of EEG Signals
Brain-computer interface (BCI) is a communication system between humans and
computers reflecting human intention without using a physical control device.
Since deep learning is robust in extracting features from data, research on
decoding electroencephalograms by applying deep learning has progressed in the
BCI domain. However, the application of deep learning in the BCI domain has
issues with a lack of data and overconfidence. To solve these issues, we
proposed a novel data augmentation method, CropCat. CropCat consists of two
versions, CropCat-spatial and CropCat-temporal. We designed our method by
concatenating the cropped data after cropping the data, which have different
labels in spatial and temporal axes. In addition, we adjusted the label based
on the ratio of cropped length. As a result, the generated data from our
proposed method assisted in revising the ambiguous decision boundary into
apparent caused by a lack of data. Due to the effectiveness of the proposed
method, the performance of the four EEG signal decoding models is improved in
two motor imagery public datasets compared to when the proposed method is not
applied. Hence, we demonstrate that generated data by CropCat smooths the
feature distribution of EEG signals when training the model.Comment: 4 pages, 1 tabl
Artificial intelligence-estimated biological heart age using a 12-lead electrocardiogram predicts mortality and cardiovascular outcomes
A Case of Lambert-Eaton Myasthenic Syndrome Associated with Atypical Bronchopulmonary Carcinoid Tumor
The Lambert-Eaton myasthenic syndrome (LEMS) is typically recognized as a paraneoplastic syndrome associated with a small cell lung carcinoma (SCLC), whereas LEMS with other neuroendocrine lung tumors, including carcinoids or large cell lung carcinoma, are highly unusual. Here, we report a rare case of LEMS with atypical bronchopulmonary carcinoid tumor: A 65-yr-old man presented with progressive leg weakness and a diagnosis of LEMS was made by serial repetitive nerve stimulation test. Chest CT revealed a lung nodule with enlargement of paratracheal lymph nodes, and surgically resected lesion showed pathological features of atypical carcinoid tumor. We concluded that LEMS could be associated with rare pulmonary neuroendocrine tumor other than SCLC, which necessitates pathologic confirmation followed by aggressive treatment for optimal management in these rare cases
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