Littérature scientifique sur le sujet « Lai gang yi yuan »

Abstract Many studies have shown the associations between colorectal cancer (CRC) and gut microbiome. The deep learning models have the potential to detect CRC earlier than the conventional stool screening test. However, the results are very inconsistent, which impedes the application of prediction using gut microbiomes as biomarkers. Therefore, this study aims to construct a novel deep learning (DL) pipeline to better classify CRC, colorectal adenoma, and healthy groups using microbiome data. Tissue or stool samples with sequence data and/or taxonomy profiles from 16 studies were collected from the NCBI SRA database or supplementary data provided in studies. Tumor-adjacent samples were excluded because of highly related to tumor samples. Sequence data were quality controlled and taxonomy assigned by EasyMAP based on the QIIME2 pipeline. All taxonomy profiles were merged and normalized to relative abundance. In total, 136 CRC-associated genera were collected from 38 different region studies. Among the CRC-associated genera, 98 ones with high prevalence were selected as CRC biomarkers. Taxonomy profiles were feature-selected by the CRC biomarkers. A phylogenetic tree of 98 CRC biomarkers was constructed by ete3 package and NCBI taxonomy database. Features in taxonomy profiles were sorted and converted into a 10x10 array by phylogenetic-based order. Samples labeled as healthy, adenoma, or CRC were randomly split into train, validation, and test datasets by 8:1:1. The 2D convolutional neural network DL model was trained with a feature array of the samples in the training dataset. Model performance was evaluated by the test dataset. Moreover, Saliency map was calculated to identify highly contributed features. Our results showed that, by combining the tissue and stool data, the DL model illustrated excellent performance with 81% AUC and 60% accuracy for the test dataset in classifying health, adenoma, and CRC groups. Two-class DL model performed even higher with 85% AUC and 78% accuracy in classifying health and CRC groups. For a model with only stool samples, the model in classifying health and CRC groups achieved comparable results with 84% AUC and 76% accuracy. Finally, CRC biomarkers that highly contributed to the DL model were identified by saliency map. In summary, we developed a new pipeline for CRC classification using 16s rRNA gut microbiome data and identified CRC-specific gut microbiome genera. The pipeline and biomarkers could be used as a non-invasive tool for the early detection of CRC. Citation Format: Nai-Shun Liao, Yuan-Mao Hung, Yi-Jian Tsai, Nam Nhut Phan, Pei-Chun Chen, Liang-Chuan Lai, Mong-Hsun Tsai, Tzu-Pin Lu, Eric Y. Chuang. A novel deep learning pipeline for early detection of colorectal cancer and colorectal adenoma using gut microbiome data [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 3032.

Background:Anti-TNF-α agents have been proven to be effective for patients with immune-medicated inflammatory diseases (IMIDs) including rheumatoid arthritis (RA), ankylosing spondylitis (AS), psoriasis (PsO), psoriatic arthritis (PsA), Crohn’s disease (CD) and ulcerative colitis (UC). Prior studies have shown an increased risk of infection in IMID patients treated with anti-TNF-α but limited studies investigated factors associated with the development of sepsis in patients with IMIDs.Objectives:To investigate factors associated with the development of sepsis in patients with IMIDs using the Taiwanese National Health Insurance Research Database (NHIRD).Methods:We identified all biologic-naïve patients with RA, AS, PsO, PsA, or CD/UC from the claim data via the NHIRD who started their first anti-TNF-α agent (etanercept (ETN), adalimumab (ADA) or golimumab (GOL)) between 2003 and 2017 as study subjects. The index date was the first date of anti-TNF-α prescription. Sepsis was defined based on the sepsis-3 definition. We identified sepsis patients using a validated ICD-9-CM coding system proposed by Angus et al, in which a diagnosis of bacterial/fungal infection with one or more acute organ dysfunction is required to define an episode of sepsis. All study subjects were followed up till the date of first hospitalization due to sepsis, 90 days after the last date of anti-TNF-α prescription, withdrawal from NHIRD or death, whichever came first. We used a Cox regression analysis to assess the associations of covariates with the risk of sepsis shown as hazard ratios (HRs) with 95% confidence interval (CIs). Covariates included anti-TNF-α agent, IMID, age, sex, insured amount, level of urbanization, disease duration, Charlson comorbidity index (CCI), a history of prior hospitalization due to sepsis within 3 months before the index date and medication use within 12 months before the index date and during the follow-up period.Results:We identified 18105 biologic-naïve patients with IMIDs, including 8123 ETN users, 7623 ADA users and 2359 GOL users. The incidence rates (IRs) of sepsis in patients treated with ETN, ADA and GOL were 1080, 1181, and 617 per 105years respectively. Multivariable regression analyses showed that factors associated with an increased risk of sepsis were use of ADA (ETN as reference: HR, 1.21; 95% CI, 1.02–1.42), male (HR, 1.24; 95% CI, 1.04–1.48), age (HR, 1.06; 95% CI, 1.05–1.07), CD/UC (HR, 2.35; 95% CI, 1.57–3.53), CCI (HR, 1.30; 95% CI, 1.23–1.38), prior sepsis (HR, 2.42; 95% CI, 1.78–3.29), prior use of sulfasalazine (HR, 1.25, 95% CI, 1.00-1.55), lower levels of urbanization (level III: HR, 1.37; 95% CI, 1.06–1.77; level IV: HR, 1.68, 95% CI, 1.35–2.10). Factors associated with a decreased risk of sepsis were use of GOL (ETN as reference: HR, 0.59; 95% CI, 0.39–0.84), use of methotrexate (HR, 0.78; 95% CI, 0.65–1.00), and higher insured amount (reference: ≤ 15480 NTD; 15480–28800 NTD: HR, 0.83; 95% CI, 0.68–0.99; 28800–45800 NTD: HR, 0.58; 95% CI, 0.45–0.74; >45800 NTD: HR, 0.33; 95% CI, 0.21–0.54).Conclusion:Our study revealed that among biologic-naïve IMID patients initiating anti-TNF-α treatment, use of ADA, age, sex, CD/UC, CCI, prior sepsis, prior use of sulfasalazine and lower levels of urbanization were associated with an increased risk of sepsis, while use of GOL, use of methotrexate, and higher insured amount were associated with a decreased risk of sepsis.Disclosure of Interests:BO-CHUEN HSU: None declared, Hsin-Hua Chen: None declared, Ching-Heng Lin: None declared, Yi-Ming Chen: None declared, Kuo-Lung Lai: None declared, Der-Yuan Chen: None declared, Wen-Nan Huang: None declared, Yi-Hsing Chen Grant/research support from: Taiwan Ministry of Science and Technology, Taiwan Department of Health, Taichung Veterans General Hospital, National Yang-Ming University, GSK, Pfizer, BMS., Consultant of: Pfizer, Novartis, Abbvie, Johnson & Johnson, BMS, Roche, Lilly, GSK, Astra& Zeneca, Sanofi, MSD, Guigai, Astellas, Inova Diagnostics, UCB, Agnitio Science Technology, United Biopharma, Thermo Fisher, Gilead., Paid instructor for: Pfizer, Novartis, Johnson & Johnson, Roche, Lilly, Astra& Zeneca, Sanofi, Astellas, Agnitio Science Technology, United Biopharma., Speakers bureau: Pfizer, Novartis, Abbvie, Johnson & Johnson, BMS, Roche, Lilly, GSK, Astra& Zeneca, Sanofi, MSD, Guigai, Astellas, Inova Diagnostics, UCB, Agnitio Science Technology, United Biopharma, Thermo Fisher, Gilead.

Abstract Homologous recombination deficiency (HRD) assays determine patient eligibility for treatment with PARP inhibitors and other DNA repair targeting drugs; therefore, understanding variability in how these assays measure and report HRD is critical for patients and providers. HRD assays measure various factors to determine HRD status including causes (i.e., inactivation in HR pathway genes) and consequences (i.e., genomic scarring). Methodological variability across HRD assays has led to a suggestion that the assays may not agree on a per patient basis. An empirical assessment of assay variability may guide our understanding of how to implement “HRD status” as a biomarker. Friends of Cancer Research (Friends) initiated a unique research partnership to assess levels of agreement and variability across HRD assays. We previously presented an analysis of the HRD status of 348 TCGA ovarian cancer samples across 11 assays. Concordance across assays was analyzed by measuring all possible pairings of samples and assays leading to a median (and IQR) positive percent agreement (PPA) of 74% (51-89%) and negative percent agreement (NPA) of 81% (64-92%). The median percent positivity (percent of patients with HRD) was 49% (range 9-67%). However, some groups modified their HRD pipelines to analyze the in-silico data and we were unable to interrogate the influence of patient and sample characteristics on HRD calls. To establish a more comprehensive dataset, we identified 142 archival specimens from the University of Alabama at Birmingham from patients with stage III-IV high grade serous ovarian cancer diagnosed between 2011 and 2022. Full clinical information is available including response to platinum therapy. FFPE tumor from debulking surgery was sectioned for the 99 samples with adequate tissue and underwent DNA and RNA extraction by the Molecular Characterization Laboratory (MoCha) at the NCI Frederick National Laboratory. MoCha shipped identical aliquots of DNA and/or RNA from the 90 samples that passed QC for independent sequencing and HRD measurement by the 13 different assays. Statisticians from the NCI performed statistical analyses to assess concordance across assays. Three of the 13 assays considered mutations in non-BRCA1/2 HR pathway genes and 7 measured gLOH as determinants for HRD status among other factors. HRD calls resulted in a median pairwise PPA of 81% (69-91%) and a median pairwise NPA of 74% (61-89%). The median percent positivity was 53% (range 23-74%). Ongoing analyses will consider how each of the HRD assays predict responsiveness to platinum-based chemotherapy. Additional analyses will consider assay, patient, and sample characteristics that may drive variation in HRD calls. Preliminary findings demonstrate variability in HRD calls across HRD assays, similar to the in-silico analysis. These findings will help characterize the variability of HRD assays and inform best practices for measuring and reporting HRD. Citation Format: Hillary Stires, Lisa M. McShane, Rebecca C. Arend, Alyssa K. Chapman, Li Chen, Tommaso Coletta, Yuan Ding, Mohit Gupta, Nikita Kotlov, Alexander J. Lazar, Ming-Chung Li, Yi-Hsuan Lucy Lai, Wenjie Li, Brittany A. McKelvey, Jerod R. Parsons, Ethan S. Sokol, Elizabeth R. Starks, Mark D. Stewart, Peihua Wang, Zhiwei Zhang, Yingdong Zhao, ShiPing Zou, Jeff Allen. An analysis of 13 independently performed assays to measure homologous recombination deficiency using 90 freshly extracted high grade serous ovarian tumors: findings from the friends of cancer research hrd harmonization project [abstract]. In: Proceedings of the AACR Special Conference on Ovarian Cancer; 2023 Oct 5-7; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(5 Suppl_2):Abstract nr A017.

The journal is pleased to publish the abstracts of the six finalists of the 2023 Manufacturing and Service Operations Management Society’s student paper competition. The 2023 prize committee was chaired by Ersin Korpeoglu (UCL), Simone Marinesi (Wharton), and Nur Sunar (UNC). The judges were Adam Elmachtoub, Adem Orsdemir, Agni Orfanoudaki, Alper Nakkas, Amrita Kundu, Antoine Desir, Antoine Feylessoufi, Anton Ovchinnikov, Anyan Qi, Arian Aflaki, Arzum Akkas, Ashish Kabra, Auyon Siddiq, Bilal Gokpinar, Bin Hu, Bob Batt, Bora Keskin, Brent Moritz, Can Zhang, Chloe Glaeser, Cuihong Li, Daniel Freund, Daniel Lin, David Drake, Divya Singhvi, Dongyuan Zhan, Ekaterina Astashkina, Elena Belavina, Elodie Adida, Emre Nadar, Enis Kayis, Fabian Sting, Fanyin Zheng, Fei Gao, Florin Ciocan, Francisco Castro, George Chen, Georgina Hall, Gloria Urrea, Gonzalo Romero, Guihua Wang, Guoming Lai, Heikki Peura, Hessam Bavafa, Hummy Song, Huseyin Gurkan, Ioannis Stamatopoulos, Iris Wang, Jiankun Sun, Jiayi Joey Yu, Jing Wu, Joel Wooten, John Silberholz, Jonas Oddur Jonasson, Jonathan Helm, Jose Guajardo, Junyu Cao, Kaitlin Daniels, Karen Zheng, Ken Moon, Kostas Bimpikis, Lennart Baardman, Lesley Meng, Lina Song, Luyi Yang, Mazhar Arikan, Mehmet Ayvaci, Meng Li, Mengzhenyu Zhang, Miao Bai, Michael Freeman, Mika Sumida, Ming Hu, Morvarid Rahmani, Mostafa Rezaei, Mumin Kurtulus, Nan Yang, Nazli Sonmez, Negin Golrezaei, Nektarios Oraiopoulos, Nikhil Garg, Nikos Trichakis, Nil Karacaoglu, Olga Perdikaki, Onesun Steve Yoo, Ovunc Yilmaz, Ozan Candogan, Panos Markou, Pengyi Shi, Philipp Cornelius, Qiuping Yu, Renyu Zhang, Robert Bray, Ruth Beer, Ruxian Wang, Saed Alizamir, Safak Yucel, Sanjith Gopalakrishnan, Santiago Gallino, Sarah Yini Gao, Scott Rodilitz, Sebastien Martin, Seyed Emadi, Sheng Liu, Shouqiang Wang, Siddharth Singh, Sidika Candogan, Sina Khorasani, So Yeon Chun, Somya Singhvi, Soo-Haeng Cho, Sriram Dasu, Stefanus Jasin, Stephen Leider, Suresh Muthulingam, Sytske Wijnsma, Taghi Khaniyev, Tian Chan, Tim Kraft, Tom Tan, Tugce Martagan, Vasiliki Kostamj, Velibor Misic, Vishal Agrawal, Xiaojia Guo, Xiaoshuai Fan, Xiaoyang Long, Yannis Bellos, Yao Cui, Yehua Wei, Yiangos Papanastasiou, Yi-Chun Chen, Yinghao Zhang, Ying-Ju Chen, Yinghao Zhang, Yuan-Mao Kao, Yuexing Li, Zhaohui (Zoey) Jiang, Zhaowei She, and Zumbul Atan.

The geometries, stabilities of VGen0/- (n = 9 - 13) clusters were systematically studied by the density functional theory (DFT) using the BP86 functional and LANL2DZ basis set. Several possible multiplicities of each cluster were tested to determine the most stable structure among the isomers. The average binding energy per atom, fragmentation energy, second order energy difference and HOMO-LUMO gaps were evaluated. The results indicated that the neutral and anionic clusters possess higher stability when n = 10 and 12. The vertical detachment energy (VDE) and adiabatic detachment energy (ADE) were also calculated for anionic cluster to investigate their stabilities. Among neutral clusters, VGe10 had both the highest vertical ionization potential (VIP) and chemical hardness. Keywords BP86/LANL2DZ, binding energy, VGen0/- clusters, structure of clusters References [1] Shunping Shi, Yiliang Liu, Chuanyu Zhang, Banglin Deng, Gang Jiang (2015). 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Graphene-based composites have received a great deal of attention in recent year because the presence of graphene can enhance the conductivity, strength of bulk materials and help create composites with superior qualities. Moreover, the incorporation of metal oxide nanoparticles such as Fe3O4 can improve the catalytic efficiency of composite material. In this work, we have synthesized a composite material with the combination of reduced graphene oxide (rGO), and Fe3O4 modified tissue-paper (mGO-PP) via a simple hydrothermal method, which improved the removal efficiency of the of methylene blue (MB) in water. MB blue is used as the model of contaminant to evaluate the catalytic efficiency of synthesized material by using a Fenton-like reaction. The obtained materials were characterized by SEM, XRD. The removal of materials with methylene blue is investigated by UV-VIS spectroscopy, and the result shows that mGO-PP composite is the potential composite for the color removed which has the removal efficiency reaching 65% in acetate buffer pH = 3 with the optimal time is 7 h. Keywords Graphene-based composite, methylene blue, Fenton-like reaction. References [1] Ma Joshi, Rue Bansal, Reng Purwar, Colour removal from textile effluents, Indian Journal of Fibre & Textile Research, 29 (2004) 239-259 http://nopr.niscair.res.in/handle/123456789/24631.[2] Kannan Nagar, Sundaram Mariappan, Kinetics and mechanism of removal of methylene blue by adsorption on various carbons-a comparative study, Dyes and pigments, 51 (2001) 25-40 https://doi.org/10.1016/S0143-7208(01)00056-0.[3] K Rastogi, J. N Sahu, B. C Meikap, M. 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