The carbonization of aromatic molecules with three-dimensional structures affords carbon materials with controlled pore sizes at the Ångstrom-level

分子構造により細孔径を制御したカーボンを開発. 京都大学プレスリリース. 2021-05-24.Carbon materials with controlled pore sizes at the nanometer level have been obtained by template methods, chemical vapor desorption, and extraction of metals from carbides. However, to produce porous carbons with controlled pore sizes at the Ångstrom-level, syntheses that are simple, versatile, and reproducible are desired. Here, we report a synthetic method to prepare porous carbon materials with pore sizes that can be precisely controlled at the Ångstrom-level. Heating first induces thermal polymerization of selected three-dimensional aromatic molecules as the carbon sources, further heating results in extremely high carbonization yields (>86%). The porous carbon obtained from a tetrabiphenylmethane structure has a larger pore size (4.40 Å) than those from a spirobifluorene (4.07 Å) or a tetraphenylmethane precursor (4.05 Å). The porous carbon obtained from tetraphenylmethane is applied as an anode material for sodium-ion battery

Risk factors for CAR-T cell manufacturing failure among DLBCL patients: A nationwide survey in Japan

CAR-T細胞製造を成功させるためのレシピ --アフェレーシス前の下ごしらえでの工夫--. 京都大学プレスリリース. 2023-04-27.For successful chimeric antigen receptor T (CAR-T) cell therapy, CAR-T cells must be manufactured without failure caused by suboptimal expansion. In order to determine risk factors for CAR-T cell manufacturing failure, we performed a nationwide cohort study in Japan and analysed patients with diffuse large B-cell lymphoma (DLBCL) who underwent tisagenlecleucel production. We compared clinical factors between 30 cases that failed (7.4%) with those that succeeded (n = 378). Among the failures, the proportion of patients previously treated with bendamustine (43.3% vs. 14.8%; p < 0.001) was significantly higher, and their platelet counts (12.0 vs. 17.0 × 10⁴/μL; p = 0.01) and CD4/CD8 T-cell ratio (0.30 vs. 0.56; p < 0.01) in peripheral blood at apheresis were significantly lower than in the successful group. Multivariate analysis revealed that repeated bendamustine use with short washout periods prior to apheresis (odds ratio [OR], 5.52; p = 0.013 for ≥6 cycles with washout period of 3–24 months; OR, 57.09; p = 0.005 for ≥3 cycles with washout period of <3 months), low platelet counts (OR, 0.495 per 105/μL; p = 0.022) or low CD4/CD8 ratios (<one third) (OR, 3.249; p = 0.011) in peripheral blood at apheresis increased the risk of manufacturing failure. Manufacturing failure remains an obstacle to CAR-T cell therapy for DLBCL patients. Avoiding risk factors, such as repeated bendamustine administration without sufficient washout, and risk-adapted strategies may help to optimize CAR-T cell therapy for DLBCL patients

The whole blood transcriptional regulation landscape in 465 COVID-19 infected samples from Japan COVID-19 Task Force

「コロナ制圧タスクフォース」COVID-19患者由来の血液細胞における遺伝子発現の網羅的解析 --重症度に応じた遺伝子発現の変化には、ヒトゲノム配列の個人差が影響する--. 京都大学プレスリリース. 2022-08-23.Coronavirus disease 2019 (COVID-19) is a recently-emerged infectious disease that has caused millions of deaths, where comprehensive understanding of disease mechanisms is still unestablished. In particular, studies of gene expression dynamics and regulation landscape in COVID-19 infected individuals are limited. Here, we report on a thorough analysis of whole blood RNA-seq data from 465 genotyped samples from the Japan COVID-19 Task Force, including 359 severe and 106 non-severe COVID-19 cases. We discover 1169 putative causal expression quantitative trait loci (eQTLs) including 34 possible colocalizations with biobank fine-mapping results of hematopoietic traits in a Japanese population, 1549 putative causal splice QTLs (sQTLs; e.g. two independent sQTLs at TOR1AIP1), as well as biologically interpretable trans-eQTL examples (e.g., REST and STING1), all fine-mapped at single variant resolution. We perform differential gene expression analysis to elucidate 198 genes with increased expression in severe COVID-19 cases and enriched for innate immune-related functions. Finally, we evaluate the limited but non-zero effect of COVID-19 phenotype on eQTL discovery, and highlight the presence of COVID-19 severity-interaction eQTLs (ieQTLs; e.g., CLEC4C and MYBL2). Our study provides a comprehensive catalog of whole blood regulatory variants in Japanese, as well as a reference for transcriptional landscapes in response to COVID-19 infection

DOCK2 is involved in the host genetics and biology of severe COVID-19

「コロナ制圧タスクフォース」COVID-19疾患感受性遺伝子DOCK2の重症化機序を解明 --アジア最大のバイオレポジトリーでCOVID-19の治療標的を発見--. 京都大学プレスリリース. 2022-08-10.Identifying the host genetic factors underlying severe COVID-19 is an emerging challenge. Here we conducted a genome-wide association study (GWAS) involving 2, 393 cases of COVID-19 in a cohort of Japanese individuals collected during the initial waves of the pandemic, with 3, 289 unaffected controls. We identified a variant on chromosome 5 at 5q35 (rs60200309-A), close to the dedicator of cytokinesis 2 gene (DOCK2), which was associated with severe COVID-19 in patients less than 65 years of age. This risk allele was prevalent in East Asian individuals but rare in Europeans, highlighting the value of genome-wide association studies in non-European populations. RNA-sequencing analysis of 473 bulk peripheral blood samples identified decreased expression of DOCK2 associated with the risk allele in these younger patients. DOCK2 expression was suppressed in patients with severe cases of COVID-19. Single-cell RNA-sequencing analysis (n = 61 individuals) identified cell-type-specific downregulation of DOCK2 and a COVID-19-specific decreasing effect of the risk allele on DOCK2 expression in non-classical monocytes. Immunohistochemistry of lung specimens from patients with severe COVID-19 pneumonia showed suppressed DOCK2 expression. Moreover, inhibition of DOCK2 function with CPYPP increased the severity of pneumonia in a Syrian hamster model of SARS-CoV-2 infection, characterized by weight loss, lung oedema, enhanced viral loads, impaired macrophage recruitment and dysregulated type I interferon responses. We conclude that DOCK2 has an important role in the host immune response to SARS-CoV-2 infection and the development of severe COVID-19, and could be further explored as a potential biomarker and/or therapeutic target

Application of convolutional neural networks for evaluating Helicobacter pylori infection status on the basis of endoscopic images

Background and aim: We recently reported the role of artificial intelligence in the diagnosis of Helicobacter pylori (H. pylori) gastritis on the basis of endoscopic images. However, that study included only H. pylori-positive and -negative patients, excluding patients after H. pylori-eradication. In this study, we constructed a convolutional neural network (CNN) and evaluated its ability to ascertain all H. pylori infection statuses.Methods: A deep CNN was pre-trained and fine-tuned on a dataset of 98,564 endoscopic images from 5236 patients (742 H. pylori-positive, 3649 -negative, and 845 -eradicated). A separate test data set (23,699 images from 847 patients; 70 positive, 493 negative, and 284 eradicated) was evaluated by the CNN.Results: The trained CNN outputs a continuous number between 0 and 1 as the probability index for H. pylori infection status per image (Pp, H. pylori-positive; Pn, negative; Pe, eradicated). The most probable (largest number) of the three infectious statuses was selected as the ‘CNN diagnosis’. Among 23,699 images, the CNN diagnosed 418 images as positive, 23,034 as negative, and 247 as eradicated.Because of the large number of H. pylori negative findings, the probability of H. pylori-negative was artificially re-defined as Pn 0.9, after which 80% (465/582) of negative diagnoses were accurate, 84% (147/174) eradicated, and 48% (44/91) positive. The time needed to diagnose 23,699 images was 261 seconds.Conclusion: We used a novel algorithm to construct a CNN for diagnosing H. pylori infection status on the basis of endoscopic images very quickly.Abbreviations: H. pylori: Helicobacter pylori; CNN: convolutional neural network; AI: artificial intelligence;EGD: esophagogastroduodenoscopies