Convolutional neural network can recognize drug resistance of single cancer cells

It is known that single or isolated tumor cells enter cancer patients' circulatory systems. These circulating tumor cells (CTCs) are thought to be an effective tool for diagnosing cancer malignancy. However, handling CTC samples and evaluating CTC sequence analysis results are challenging. Recently, the convolutional neural network (CNN) model, a type of deep learning model, has been increasingly adopted for medical image analyses. However, it is controversial whether cell characteristics can be identified at the single-cell level by using machine learning methods. This study intends to verify whether an AI system could classify the sensitivity of anticancer drugs, based on cell morphology during culture. We constructed a CNN based on the VGG16 model that could predict the efficiency of antitumor drugs at the single-cell level. The machine learning revealed that our model could identify the effects of antitumor drugs with ~0.80 accuracies. Our results show that, in the future, realizing precision medicine to identify effective antitumor drugs for individual patients may be possible by extracting CTCs from blood and performing classification by using an AI system

Initial Evaluation of [18F]FAPI-74 PET for Various Histopathologically Confirmed Cancers and Benign Lesions

The 18F-labeled fibroblast activation protein inhibitor (FAPI) [18F]FAPI- 74 has the benefit of a higher synthetic yield and better image resolution than 68Ga-labeled FAPI. We preliminarily evaluated the diagnostic performance of [18F]FAPI-74 PET in patients with various histopathologically confirmed cancers or suspected malignancies. Methods: We enrolled 31 patients (17 men and 14 women) with lung cancer (n = 7), breast cancer (n = 5), gastric cancer (n = 5), pancreatic cancer (n = 3), other cancers (n = 5), and benign tumors (n = 6). Twenty-seven of the 31 patients were treatment-naïve or preoperative, whereas recurrence was suspected in the remaining 4 patients. Histopathologic confirmation was obtained for the primary lesions of 29 of the 31 patients. In the remaining 2 patients, the final diagnosis was based on the clinical course. [18F]FAPI-74 PET scanning was performed 60min after the intravenous injection of [18F]FAPI-74 (240631 MBq). The [18F]FAPI-74 PET images were compared between the primary or local recurrent lesions of malignant tumors (n = 21) and nonmalignant lesions (n 5 8: type-B1 thymomas, granuloma, solitary fibrous tumor, and postoperative or posttherapeutic changes). The uptake and number of detected lesions on [18F]FAPI-74 PET were also compared with those on [18F]FDG PET for available patients (n = 19). Results: [18F]FAPI-74 PET showed higher uptake in primary lesions of various cancers than in nonmalignant lesions (median SUVmax, 9.39 [range, 1.83-25.28] vs. 3.49 [range, 2.21-15.58]; P = 0.053), but some of the nonmalignant lesions showed high uptake. [18F]FAPI-74 PET also showed significantly higher uptake than [18F]FDG PET (median SUVmax, 9.44 [range, 2.50-25.28] vs. 5.45 [range, 1.22-15.06] in primary lesions [P 5 0.010], 8.86 [range, 3.51-23.33] vs. 3.84 [range, 1.01-9.75] in lymph node metastases [P 5 0.002], and 6.39 [range, 0.55-12.78] vs. 1.88 [range, 0.73-8.35] in other metastases [P 5 0.046], respectively). In 6 patients, [18F]FAPI-74 PET detected more metastatic lesions than [18F]FDG PET. Conclusion: [18F]FAPI-74 PET showed higher uptake and detection rates in primary and metastatic lesions than did [18F]FDG PET. [18F]FAPI-74 PET is a promising novel diagnostic modality for various tumors, especially for precise staging before treatment, including characterization of tumor lesions before surgery. Moreover, 18F-labeled FAPI ligand might serve a higher demand in clinical care in the future.This research was originally published in JNM. Tadashi Watabe, Sadahiro Naka, Mitsuaki Tatsumi et.al. Initial Evaluation of [18F]FAPI-74 PET for Various Histopathologically Confirmed Cancers and Benign Lesions. J Nucl Med. 2023, 64(8), 1225-1231. © SNMMI

高知県室戸市で確認されたカミガモソウGratiola fluviatilis（ゴマノハグサ科）の新産地とその生育状況

A new locality of Gratiola fluviatilis Koidz.（Scrophulariaceae）, a threatened species endemic to Japan, was discov-ered in a mountain bog in Muroto City, Kochi Prefecture, southwestern Japan, in 2004. Sixty-three patches of G. flu-viatilis were recorded at the site, with most patches being distributed under the canopy of trees growing around the bog. Extrapolating from the area of the patches and the density of individual plants, the total number of individuals was estimated at ca. 47,000. In conclusion, the study site represents the largest population of G. fluviatilis in Japan. To maintain habitat conditions suitable for the growth and reproduction of this threatened species, a comprehensive investigation of plant life history strategy, environmental requirements and habitat disturbance regime is urgently re-quired

Collagen adhesion gene is associated with blood stream infections caused by methicillin-resistant Staphylococcus aureus

Objectives: Methicillin-resistant Staphylococcus aureus (MRSA) causes hospital- and community-acquired infections. It is not clear whether genetic characteristics of the bacteria contribute to disease pathogenesis in MRSA infection. We hypothesized that whole genome analysis of MRSA strains could reveal the key gene loci and/or the gene mutations that affect clinical manifestations of MRSA infection. Methods: Whole genome sequences (WGS) of MRSA of 154 strains were analyzed with respect to clinical manifestations and data. Further, we evaluated the association between clinical manifestations in MRSA infection and genomic information. Results: WGS revealed gene mutations that correlated with clinical manifestations of MRSA infection. Moreover, 12 mutations were selected as important mutations by Random Forest analysis. Cluster analysis revealed strains associated with a high frequency of bloodstream infection (BSI). Twenty seven out of 34 strains in this cluster caused BSI. These strains were all positive for collagen adhesion gene (cna) and have mutations in the locus, those were selected by Random Forest analysis. Univariate and multivariate analysis revealed that these gene mutations were the predictor for the incidence of BSI. Interestingly, mutant CNA protein showed lower attachment ability to collagen, suggesting that the mutant protein might contribute to the dissemination of bacteria. Conclusions: These findings suggest that the bacterial genotype affects the clinical characteristics of MRSA infection. (c) 2019 The Author(s). Published by Elsevier Ltd on behalf of International Society for Infectious Diseases