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2. A User-Friendly, Web-Based Integrative Tool (ESurv) for Survival Analysis: Development and Validation Study

Author

Pak, Kyoungjune, Oh, Sae-Ock, Goh, Tae Sik, Heo, Hye Jin, Han, Myoung-Eun, Jeong, Dae Cheon, Lee, Chi-Seung, Sun, Hokeun, Kang, Junho, Choi, Suji, Lee, Soohwan, Kwon, Eun Jung, Kang, Ji Wan, and Kim, Yun Hak

Subjects
Computer applications to medicine. Medical informatics, R858-859.7, Public aspects of medicine, RA1-1270
Abstract

BackgroundPrognostic genes or gene signatures have been widely used to predict patient survival and aid in making decisions pertaining to therapeutic actions. Although some web-based survival analysis tools have been developed, they have several limitations. ObjectiveTaking these limitations into account, we developed ESurv (Easy, Effective, and Excellent Survival analysis tool), a web-based tool that can perform advanced survival analyses using user-derived data or data from The Cancer Genome Atlas (TCGA). Users can conduct univariate analyses and grouped variable selections using multiomics data from TCGA. MethodsWe used R to code survival analyses based on multiomics data from TCGA. To perform these analyses, we excluded patients and genes that had insufficient information. Clinical variables were classified as 0 and 1 when there were two categories (for example, chemotherapy: no or yes), and dummy variables were used where features had 3 or more outcomes (for example, with respect to laterality: right, left, or bilateral). ResultsThrough univariate analyses, ESurv can identify the prognostic significance for single genes using the survival curve (median or optimal cutoff), area under the curve (AUC) with C statistics, and receiver operating characteristics (ROC). Users can obtain prognostic variable signatures based on multiomics data from clinical variables or grouped variable selections (lasso, elastic net regularization, and network-regularized high-dimensional Cox-regression) and select the same outputs as above. In addition, users can create custom gene signatures for specific cancers using various genes of interest. One of the most important functions of ESurv is that users can perform all survival analyses using their own data. ConclusionsUsing advanced statistical techniques suitable for high-dimensional data, including genetic data, and integrated survival analysis, ESurv overcomes the limitations of previous web-based tools and will help biomedical researchers easily perform complex survival analyses.

Published
2020
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19. Monocytes release cystatin F dimer to associate with Aβ and aggravate amyloid pathology and cognitive deficits in Alzheimer’s disease

Author

Qiang Li, Bing Li, Li Liu, Kang-Ji Wang, Ming-Yue Liu, Yu Deng, Ze Li, Wei-Dong Zhao, Li-Yong Wu, Yu-Hua Chen, and Ke Zhang

Subjects
Alzheimer’s disease, Amyloid beta, Cystatin F, Monocyte, Peripheral clearance, Neurology. Diseases of the nervous system, RC346-429
Abstract

Abstract Background Understanding the molecular mechanisms of Alzheimer’s disease (AD) has important clinical implications for guiding therapy. Impaired amyloid beta (Aβ) clearance is critical in the pathogenesis of sporadic AD, and blood monocytes play an important role in Aβ clearance in the periphery. However, the mechanism underlying the defective phagocytosis of Aβ by monocytes in AD remains unclear. Methods Initially, we collected whole blood samples from sporadic AD patients and isolated the monocytes for RNA sequencing analysis. By establishing APP/PS1 transgenic model mice with monocyte-specific cystatin F overexpression, we assessed the influence of monocyte-derived cystatin F on AD development. We further used a nondenaturing gel to identify the structure of the secreted cystatin F in plasma. Flow cytometry, enzyme-linked immunosorbent assays and laser scanning confocal microscopy were used to analyse the internalization of Aβ by monocytes. Pull down assays, bimolecular fluorescence complementation assays and total internal reflection fluorescence microscopy were used to determine the interactions and potential interactional amino acids between the cystatin F protein and Aβ. Finally, the cystatin F protein was purified and injected via the tail vein into 5XFAD mice to assess AD pathology. Results Our results demonstrated that the expression of the cystatin F protein was specifically increased in the monocytes of AD patients. Monocyte-derived cystatin F increased Aβ deposition and exacerbated cognitive deficits in APP/PS1 mice. Furthermore, secreted cystatin F in the plasma of AD patients has a dimeric structure that is closely related to clinical signs of AD. Moreover, we noted that the cystatin F dimer blocks the phagocytosis of Aβ by monocytes. Mechanistically, the cystatin F dimer physically interacts with Aβ to inhibit its recognition and internalization by monocytes through certain amino acid interactions between the cystatin F dimer and Aβ. We found that high levels of the cystatin F dimer protein in blood contributed to amyloid pathology and cognitive deficits as a risk factor in 5XFAD mice. Conclusions Our findings highlight that the cystatin F dimer plays a crucial role in regulating Aβ metabolism via its peripheral clearance pathway, providing us with a potential biomarker for diagnosis and potential target for therapeutic intervention.

Published
2024
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21. Monolithic 3D integration of 2D materials-based electronics towards ultimate edge computing solutions

Author

Kang, Ji-Hoon, Shin, Heechang, Kim, Ki Seok, Song, Min-Kyu, Lee, Doyoon, Meng, Yuan, Choi, Chanyeol, Suh, Jun Min, Kim, Beom Jin, Kim, Hyunseok, Hoang, Anh Tuan, Park, Bo-In, Zhou, Guanyu, Sundaram, Suresh, Vuong, Phuong, Shin, Jiho, Choe, Jinyeong, Xu, Zhihao, Younas, Rehan, Kim, Justin S., Han, Sangmoon, Lee, Sangho, Kim, Sun Ok, Kang, Beomseok, Seo, Seungju, Ahn, Hyojung, Seo, Seunghwan, Reidy, Kate, Park, Eugene, Mun, Sungchul, Park, Min-Chul, Lee, Suyoun, Kim, Hyung-Jun, Kum, Hyun S., Lin, Peng, Hinkle, Christopher, Ougazzaden, Abdallah, Ahn, Jong-Hyun, Kim, Jeehwan, and Bae, Sang-Hoon

Published
2023
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28. Fungal diversity notes 1611–1716: taxonomic and phylogenetic contributions on fungal genera and species emphasis in south China

Author

Senanayake, Indunil C., Rossi, Walter, Leonardi, Marco, Weir, Alex, McHugh, Mark, Rajeshkumar, Kunhiraman C., Verma, Rajnish K., Karunarathna, Samantha C., Tibpromma, Saowaluck, Ashtekar, Nikhil, Ashtamoorthy, Sreejith K., Raveendran, Sanjay, Kour, Gurmeet, Singh, Aishwarya, De la Peña-Lastra, Saúl, Mateos, Antonio, Kolařík, Miroslav, Antonín, Vladimír, Ševčíková, Hana, Esteve-Raventós, Fernando, Larsson, Ellen, Pancorbo, Fermín, Moreno, Gabriel, Altés, Alberto, Turégano, Yolanda, Du, Tian-Ye, Lu, Li, Li, Qi-Rui, Kang, Ji-Chuan, Gunaseelan, Sugantha, Kezo, Kezhocuyi, Kaliyaperumal, Malarvizhi, Fu, Jizhen, Samarakoon, Milan C., Gafforov, Yusufjon, Teshaboeva, Shakhnoza, Kunjan, Pradeep C., Chamaparambath, Arya, Flakus, Adam, Etayo, Javier, Rodriguez-Flakus, Pamela, Zhurbenko, Mikhail P., de Silva, Nimali I., Tennakoon, Danushka S., Latha, K. P. Deepna, Manimohan, Patinjareveettil, Raj, K. N. Anil, Calabon, Mark S., Ahmadpour, Abdollah, Heidarian, Zeinab, Alavi, Zahra, Alavi, Fatemeh, Ghosta, Youbert, Azizi, Razmig, Luo, Mei, Zhao, Min-Ping, Kularathnage, Nuwan D., Hua, Li, Yang, Yun-Hui, Liao, Chun-Fang, Zhao, Hai-Jun, Lestari, Anis S., Jayasiri, Subashini C., Yu, Feng-Ming, Lei, Lei, Liu, Jian-Wei, Karimi, Omid, Tang, Song-Ming, Sun, Ya-Ru, Wang, Yong, Zeng, Ming, Htet, Zin H., Linaldeddu, Benedetto T., Alves, Artur, Phillips, Alan J. L., Bregant, Carlo, Montecchio, Lucio, De Kesel, André, Hustad, Vincent P., Miller, Andrew N., Fedosova, Anna G., Kučera, Viktor, Raza, Mubashar, Hussain, Muzammil, Chen, Yan-Peng, Thiyagaraja, Vinodhini, Gomdola, Deecksha, Rathnayaka, Achala R., Dissanayake, Asha J., Suwannarach, Nakarin, Hongsanan, Sinang, Maharachchikumbura, Sajeewa S. N., Dissanayake, Lakmali S., Wijayawardene, Nalin N., Phookamsak, Rungtiwa, Lumyong, Saisamorn, Jones, E. B. Gareth, Yapa, Neelamanie, Wanasinghe, Dhanushka N., Xie, Ning, Doilom, Mingkwan, Manawasinghe, Ishara S., Liu, Jian-Kui (Jack), Zhao, Qi, Xu, Biao, Hyde, Kevin D., and Song, Jiage

Published
2023
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41. Risk adjustment model for tuberculosis compared to non-tuberculosis mycobacterium or latent tuberculosis infection: Center for Personalized Precision Medicine of Tuberculosis (cPMTb) cohort database

Author

Seo, Woo Jung, Koo, Hyeon-Kyoung, Kang, Ji Yeon, Kang, Jieun, Park, So Hee, Kang, Hyung Koo, Park, Hye Kyeong, Lee, Sung-Soon, Choi, Sangbong, Jang, Tae Won, Shin, Kyeong-Cheol, Oh, Jee Youn, Choi, Joon Young, Min, Jinsoo, Choi, Young-Kyung, Shin, Jae-Gook, and Cho, Yong-Soon

Published
2023
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49. Quantitative and qualitative interpretation of community partitions by map overlaying and calculating the distribution of related geographical features

Author

Haitao Zhang, Kang Ji, Huixian Shen, Rui Song, Yuan Liu Jin, and Xin Yang Yu

Subjects
spatial interaction network, community partitions, geographical features, statistical distribution, Geology, QE1-996.5
Abstract

Applying community detection algorithms in spatial interaction networks constructed from modern human communication records is an essential means of evaluating urban territorial subdivisions. Previous studies have usually involved qualitative rather than quantitative interpretations of community detection results. This article proposes a method of quantitatively and qualitatively interpreting community partition results by map overlaying the spatial regions corresponding to the detected communities with the related geographical features and by calculating the distribution of the geographical features contained in the regions and the entropy value of each distribution. The interpretation of the communities detected from the spatial interaction networks is carried out from the perspective of multi-temporal and multi-spatial scales and multi-geographical features. Extensive experiments were conducted with Milan, Italy, as the study area. The spatial interaction records reflected by telephone calls, land use, and point of interest (POI) data were used as the experimental data. Experimental results demonstrated the effectiveness of our method, and the specific results include: (1) Qualitative interpretation of multi-spatial resolution scale communities detected from the long-term aggregated spatial interaction network. The cohesiveness, homogeneity, and heterogeneity of the detected communities were qualitatively interpreted by the spatial distribution patterns of the land use dataset and the POI dataset. (2) Quantitative interpretation of multi-spatial resolution scale communities detected from the long-term aggregated spatial interaction network. The low spatial resolution scale community partitions and the high spatial resolution scale community partitions were interpreted through the statistical distribution of the land use dataset and the POI dataset, respectively. (3) Qualitative interpretation of the stable and active regions discovered from the community time series. Regardless of the community partitions’ spatial resolution scales, the stable and active regions were distinguished with the statistical distributions of the land use dataset and the POI dataset.

Published
2023
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