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Indian Ocean temperature anomalies predict long-term global dengue trends

SCIENCE 2024年第6696期384卷 639-646页

作者： Chen, Yuyang Xu, Yiting Wang, Lin Liang, Yilin Li, Naizhe Lourenco, Jose Yang, Yun Lin, Qiushi Wang, Ligui Zhao, He Cazelles, Bernard Song, Hongbin Liu, Ziyan Wang, Zengmiao Brady, Oliver J. Cauchemez, Simon Tian, Huaiyu Beijing Normal Univ Ctr Global Change & Publ Hlth State Key Lab Remote Sensing Sci Beijing Peoples R China China Three Gorges Corp Yangtze Ecoenvironm Engn Res Ctr Wuhan Peoples R China Beijing Normal Univ Sch Natl Safety & Emergency Management Zhuhai Peoples R China Univ Cambridge Dept Genet Cambridge England Univ Catolica Portuguesa Catolica Biomed Res Ctr Catolica Med Sch Lisbon Portugal Ctr Dis Control & Prevent PLA Beijing Peoples R China China Meteorol Adm CMA Earth Syst Modeling & Predict Ctr Beijing Peoples R China Ecole Normale Super Ecoevolutionary Math Inst Biol UMR 8197 F-75005 Paris France Sorbonne Univ Unite Mixte Internationnale 209 Math & Computat Modeling Complex Syst Paris France London Sch Hyg & Trop Med Ctr Math Modelling Infect Dis London England London Sch Hyg & Trop Med Fac Epidemiol & Populat Hlth Dept Infect Dis Epidemiol & Dynam London England Univ Paris Inst Pasteur Math Modelling Infect Dis Unit UMR2000CNRS Paris France

Despite identifying El Ni & ntilde;o events as a factor in dengue dynamics, predicting the oscillation of global dengue epidemics remains challenging. Here, we investigate climate indicators and worldwide dengue incidence from 1990 to 2019 using climate-driven mechanistic models. We identify a distinct indicator, the Indian Ocean basin-wide (IOBW) index, as representing the regional average of sea surface temperature anomalies in the tropical Indian Ocean. IOBW is closely associated with dengue epidemics for both the Northern and Southern hemispheres. The ability of IOBW to predict dengue incidence likely arises as a result of its effect on local temperature anomalies through teleconnections. These findings indicate that the IOBW index can potentially enhance the lead time for dengue forecasts, leading to better-planned and more impactful outbreak responses.

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Deep learning radiomics of dual-modality ultrasound images for hierarchical diagnosis of unexplained cervical lymphadenopathy

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BMC MEDICINE 2022年第1期20卷 269-269页

作者： Zhu, Yangyang Meng, Zheling Fan, Xiao Duan, Yin Jia, Yingying Dong, Tiantian Wang, Yanfang Song, Juan Tian, Jie Wang, Kun Nie, Fang Lanzhou Univ Hosp 2 Ultrasound Med Ctr Lanzhou 730030 Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Gansu Prov Canc Hosp Dept Ultrasound Lanzhou Peoples R China Peoples Hosp Ningxia Hui Autonomous Reg Dept Ultrasound Yinchuan Ningxia Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Med & Engn Beijing Peoples R China Gansu Prov Clin Res Ctr Ultrasonog Lanzhou Peoples R China Gansu Prov Med Engn Res Ctr Intelligence Ultrasou Lanzhou Peoples R China

Background Accurate diagnosis of unexplained cervical lymphadenopathy (CLA) using medical images heavily relies on the experience of radiologists, which is even worse for CLA patients in underdeveloped countries and regions, because of lack of expertise and reliable medical history. This study aimed to develop a deep learning (DL) radiomics model based on B-mode and color Doppler ultrasound images for assisting radiologists to improve their diagnoses of the etiology of unexplained CLA. Methods Patients with unexplained CLA who received ultrasound examinations from three hospitals located in underdeveloped areas of China were retrospectively enrolled. They were all pathologically confirmed with reactive hyperplasia, tuberculous lymphadenitis, lymphoma, or metastatic carcinoma. By mimicking the diagnosis logic of radiologists, three DL sub-models were developed to achieve the primary diagnosis of benign and malignant, the secondary diagnosis of reactive hyperplasia and tuberculous lymphadenitis in benign candidates, and of lymphoma and metastatic carcinoma in malignant candidates, respectively. Then, a CLA hierarchical diagnostic model (CLA-HDM) integrating all sub-models was proposed to classify the specific etiology of each unexplained CLA. The assistant effectiveness of CLA-HDM was assessed by comparing six radiologists between without and with using the DL-based classification and heatmap guidance. Results A total of 763 patients with unexplained CLA were enrolled and were split into the training cohort (n=395), internal testing cohort (n=171), and external testing cohorts 1 (n=105) and 2 (n=92). The CLA-HDM for diagnosing four common etiologies of unexplained CLA achieved AUCs of 0.873 (95% CI: 0.838-0.908), 0.837 (95% CI: 0.789-0.889), and 0.840 (95% CI: 0.789-0.898) in the three testing cohorts, respectively, which was systematically more accurate than all the participating radiologists. With its assistance, the accuracy, sensitivity, and specificity of six rad

关键词： Deep learning Cervical lymphadenopathy Ultrasound Reactive hyperplasia Tuberculous lymphadenitis Lymphoma Metastatic carcinoma

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A joint call for actions to advance taxonomy in China

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Zoological Systematics 2022年第3期47卷 188-197页

作者： Chaodong Zhu Arong Luo Ming Bai Michael COrr Zhonge Hou Siqin Ge Jun Chen Yibo Hu Xuming Zhou Gexia Qiao Hongzhi Kong Limin Lu Xiaohua Jin Lei Cai Xinli Wei Ruilin Zhao Wei Miao Qingfeng Wang Zhongli Sha Qiang Lin Meng Qu Jianping Jiang Jiatang Li Jing Che Xuelong Jiang Xiaoyong Chen Lianming Gao Zongxin Ren Chunlei Xiang Shixiao Luo Donghui Wu Dong Liu Yanqiong Peng Tao Su Chenyang Cai Tianqi Zhu Wanzhi Cai Xingyue Liu Hu Li Huaijun Xue Zhen Ye Xuexin Chen Pu Tang Shujun Wei Hong Pang Qiang Xie Feng Zhang Feng Zhang Xianjin Peng Aibing Zhang Taiping Gao Changfa Zhou Chen Shao Libin Ma Zhaoming Wei Yunxia Luan Ziwei Yin Wu Dai Cong Wei Xiaolei Huang Jingxian Liu Xiangsheng Chen Tianci Yi Zhisheng Zhang Zhulidezi Aishan Qin Li Hongying Hu CAS Key Laboratory of Zoological Systematics and Evolution Institute of ZoologyChinese Academy of SciencesBeijing 100101China CAS Key Laboratory of Animal Ecology and Conservation Biology Institute of ZoologyChinese Academy of SciencesBeijing 100101China State Key Laboratory of Systematic and Evolutionary Botany Institute of BotanyChinese Academy of SciencesBeijing 100093China State Key Laboratory of Mycology(SKLM) Institute of MicrobiologyChinese Academy of SciencesBeijing 100101China CAS Key Laboratory of Aquatic Biodiversity and Conservation Institute of HydrobiologyChinese Academy of SciencesWuhan 430072China Wuhan Botanical Garden Chinese Academy of SciencesWuhan 430074China Department of Marine Organism Taxonomy&Phylogeny Institute of OceanologyChinese Academy of SciencesQingdao 266071China CAS Key Laboratory of Tropical Marine Bio-resources and Ecology South China Sea Institute of OceanologyChinese Academy of SciencesGuangzhou 510301China CAS Key Laboratory of Mountain Ecological Restoration and Bioresource Utilization&Ecological Restoration and Biodiversity Conservation Key Laboratory of Sichuan Province Chengdu Institute of BiologyChinese Academy of SciencesChengdu 610041China State Key Laboratory of Genetic Resources and Evolution&Yunnan Key Laboratory of Biodiversity and Ecological Security of Gaoligong Mountain Kunming Institute of ZoologyChinese Academy of SciencesKunming 650221China CAS Key Laboratory for Plant Diversity and Biogeography of East Asia Kunming Institute of BotanyChinese Academy of SciencesKunming 650201China CAS Key Laboratory of Plant Resource Conservation and Sustainable Utilization South China Botanical GardenChinese Academy of SciencesGuangzhou 510650China CAS Key Laboratory of Wetland Ecology and Environment Northeast Institute of Geography and AgroecologyChinese Academy of SciencesChangchun 130102China CAS Key Laboratory of Tropical Forest Ecology Xishuangbanna Tropical Botanical GardenChinese Academy of SciencesMenglunMenglaYunnan 66630

Taxonomy plays an important role in understanding the origin, evolution, and ecological functionality of biodiversity. There are large number of unknown species yet to be described by taxonomists, which together with their ecosystem services cannot be effectively protected prior to description. Despite this, taxonomy has been increasingly underrated insufficient funds and permanent positions to retain young talents. Further, the impact factordriven evaluation systems in China exacerbate this downward trend, so alternative evaluation metrics are urgently necessary. When the current generation of outstanding taxonomists retires,there will be too few remaining taxonomists left to train the next generation. In light of these challenges, all co-authors worked together on this paper to analyze the current situation of taxonomy and put out a joint call for immediate actions to advance taxonomy in China.

关键词： Endangered taxonomists morphology-based taxonomy integrative taxonomy capacity building systematics

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Optimization of ODAP-Urea-based dual-modality PSMA targeting probes for sequential PET-CT and optical imaging

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BIOORGANIC & MEDICINAL CHEMISTRY 2022年 66卷 116810页

作者： Li, Yuan Duan, Xiaojiang Xu, Hongchuang Zhang, Jingming Zhou, Haoxi Zhang, Xiaojun Zhang, Jinming Yang, Zhi Hu, Zhenhua Zhang, Ning Tian, Jie Yang, Xing Peking Univ Hlth Sci Ctr Inst Med Technol Beijing 100191 Peoples R China Peking Univ First Hosp Dept Nucl Med Beijing 100034 Peoples R China NMPA Key Lab Res & Evaluat Radiopharmaceut Beijing 100142 Peoples R China Chinese Peoples Liberat Army Gen Hosp Dept Nucl Med First Med Ctr 1 Beijing 100853 Peoples R China Peking Univ Canc Hosp & Inst Dept Nucl Med Key Lab Carcinogenesis & Translat Res Minist Educ Beijing 100142 Peoples R China Chinese Acad Sci Inst Automat CAS Key Lab Mol Imaging State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Peking Univ First Hosp Translat Canc Res Ctr Beijing 100034 Peoples R China Xidian Univ Engn Res Ctr Mol & Neuro Imaging Sch Life Sci & Technol Minist Educ Xian 710071 Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Engn Med Beijing 100191 Peoples R China

Prostate-specific membrane antigen (PSMA) is emerging as a promising target to specifically image prostate cancer. Dual-modality probe combining radionuclide imaging and near-infrared fluorescence navigation targeting PSMA would enable both the preoperative staging and intraoperative detection of the tumor lesions. To overcome one of the key barriers for achieving high contrast imaging at both early and late time points, we optimized the pharmacokinetics of dual-modality probes based on oxalyldiaminopropionic acid-urea (ODAPUrea) PSMA inhibitors recently developed. Four dual-modality probes with variable hydrophilicity were synthesized and evaluated. They displayed good optical properties (lambda em max = 835 nm, QY = 0.67%-1.50%), high affinity to PSMA (Ki = 2.09 +/- 1.71-4.15 +/- 2.20 nM) and PSMA specific cellular uptake (0.48 +/- 0.01% - 0.64 +/- 0.04% IA/105 LNCaP cells) upon labeled with 68Ga. In vivo studies showed that [68Ga]Ga-P3 exhibited an optimum pharmacokinetic property with high specific tumor uptake (SUVmax = 1.88 +/- 0.36, at 1 h) in medium level PSMA expressing 22Rv1 tumor model and high tumor-to-muscle ratio (12.56 +/- 2.63, at 1 h). Specific fluorescence imaging could also be achieved with high contrast for later time points (tumor-to-background ratio = 11.63 +/- 4.16 at 24 h). This study demonstrates that ODAP-Urea-based P3 has the potential for PET imaging and intraoperative optical imaging of prostate cancer.

关键词： Prostate cancer Prostate-specific membrane antigen (PSMA) Dual-modality imaging PET imaging Optical imaging

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Analyzing and optimizing yield formation of tomato introgression lines using plant model

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EUPHYTICA 2021年第6期217卷 100-100页

作者： Kang, Mengzhen Wang, Xiujuan Qi, Rui Jia, Zhi-Qi de Reffye, Philippe Huang, San-Wen Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100949 Peoples R China Chinese Acad Sci Beijing Engn Res Ctr Intelligent Syst & Technol Inst Automat Beijing 100190 Peoples R China Amadeus 485 Route Pin Montard F-06410 Biot France Coll Hort Henan Agr Univ Zhengzhou 450002 Peoples R China Univ Montpellier CNRS AMAP CIRADINRAIRD F-34000 Montpellier France Chinese Acad Agr Sci Agr Genomes Inst Shenzhen Shenzhen 518124 Peoples R China

Generally, the relation between quantitative trait loci (QTLs) and yield is empirical, and their roles in source-sink dynamics are unclear. A tomato introgression line (IL) population (S. pennellii ILs) was applied to analyze the effect of chromosome segment from wild cultivar on numerous yield-related phenotypes, including plant yield, the weight of vegetative part, the number and weight of individual fruits. A functional-structural plant model was applied to analyze the difference in yield formation of tomato ILs. Measurements on organ biomass were performed at four stages during the growth period of plants. Source and sink parameters were estimated from the experimental measurements of different organs for each IL, discovering how the final yield is linked to the fruit number, size and expansion process. The correlation and distribution of source-sink parameters for ILs were analyzed. The sink parameters were optimized to find a better combination of ILs to improve the yield using Particle Swarm Optimisation (PSO) algorithm. Optimization results indicate a potential yield increase of 35% for the control M82. This model-assisted analysis provides a promising approach to deeper insight in phenotypic data.

关键词： Greenlab model Yield formation Parameter estimation Tomato introgression line Optimization

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The role of three-dimensional printed models of skull in anatomy education: a randomized controlled trail

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SCIENTIFIC REPORTS 2017年第1期7卷 575-575页

作者： Chen, Shi Pan, Zhouxian Wu, Yanyan Gu, Zhaoqi Li, Man Liang, Ze Zhu, Huijuan Yao, Yong Shui, Wuyang Shen, Zhen Zhao, Jun Pan, Hui Chinese Acad Med Sci Peking Union Med Coll Hosp Minist Hlth Dept EndocrinolEndocrine Key Lab Beijing 100730 Peoples R China CAMS Peking Union Med Coll Beijing 100730 Peoples R China PUMC Beijing 100730 Peoples R China CAMS PUMCH Natl Virtual Simulat Lab Educ Ctr Med Sci Beijing 100730 Peoples R China CAMS PUMCH Year Program Clin Med 8 Beijing 100730 Peoples R China CAMS PUMCH Dept Neurosurg Beijing 100730 Peoples R China Beijing Normal Univ Coll Informat Sci & Technol Beijing 100875 Peoples R China Chinese Acad Sci CASIA Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Chinese Acad Sci Cloud Comp Ctr Dongguan 523808 Peoples R China CAMS PUMCH Dept Educ Beijing 100730 Peoples R China

Three-dimensional (3D) printed models represent educational tools of high quality compared with traditional teaching aids. Colored skull models were produced by 3D printing technology. A randomized controlled trial (RCT) was conducted to compare the learning efficiency of 3D printed skulls with that of cadaveric skulls and atlas. Seventy-nine medical students, who never studied anatomy, were randomized into three groups by drawing lots, using 3D printed skulls, cadaveric skulls, and atlas, respectively, to study the anatomical structures in skull through an introductory lecture and small group discussions. All students completed identical tests, which composed of a theory test and a lab test, before and after a lecture. Pre-test scores showed no differences between the three groups. In post-test, the 3D group was better than the other two groups in total score (cadaver: 29.5 [IQR: 25-33], 3D: 31.5 [IQR: 29-36], atlas: 27.75 [IQR: 24.125-32];p = 0.044) and scores of lab test (cadaver: 14 [IQR: 10.5-18], 3D: 16.5 [IQR: 14.375-21.625], atlas: 14.5 [IQR: 10-18.125];p = 0.049). Scores involving theory test, however, showed no difference between the three groups. In this RCT, an inexpensive, precise and rapidly-produced skull model had advantages in assisting anatomy study, especially in structure recognition, compared with traditional education materials.

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A deep-learning-based prognostic nomogram integrating microscopic digital pathology and macroscopic magnetic resonance images in nasopharyngeal carcinoma: a multi-cohort study

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THERAPEUTIC ADVANCES IN MEDICAL ONCOLOGY 2020年第12期12卷 1758835920971416页

作者： Zhang, Fan Zhong, Lian-Zhen Zhao, Xun Dong, Di Yao, Ji-Jin Wang, Si-Yang Liu, Ye Zhu, Ding Wang, Yin Wang, Guo-Jie Wang, Yi-Ming Li, Dan Wei, Jiang Tian, Jie Shan, Hong Guilin Med Univ Dept Radiat Oncol Affiliated Hosp Guilin 541000 Guangxi Provinc Peoples R China Chinese Acad Sci CAS Key Lab Mol Imaging Beijing Key Lab Mol Imaging State Key Lab Management & Control Complex SystI Beijing Peoples R China Beihang Univ Beijing Adv Innovat Ctr Big Data Based Precis Med Sch Med Beijing Peoples R China Sun Yat Sen Univ Guangdong Prov Key Lab Biomed Imaging Affiliated Hosp 5 Zhuhai 519000 Guangdong Peoples R China Sun Yat Sen Univ Dept Intervent Med Affiliated Hosp 5 52 Meihua East Rd Zhuhai 519000 Guangdong Peoples R China Sun Yat Sen Univ Dept Head & Neck Oncol Affiliated Hosp 5 Ctr Canc Zhuhai Guangdong Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing Peoples R China Sun Yat Sen Univ Dept Pathol Affiliated Hosp 5 Zhuhai Guangdong Peoples R China Sun Yat Sen Univ Dept Radiol Affiliated Hosp 5 Zhuhai Guangdong Peoples R China

Background: To explore the prognostic value of radiomics-based and digital pathology-based imaging biomarkers from macroscopic magnetic resonance imaging (MRI) and microscopic whole-slide images for patients with nasopharyngeal carcinoma (NPC). Methods: We recruited 220 NPC patients and divided them into training (n = 132), internal test (n = 44), and external test (n = 44) cohorts. The primary endpoint was failure-free survival (FFS). Radiomic features were extracted from pretreatment MRI and selected and integrated into a radiomic signature. The histopathological signature was extracted from whole-slide images of biopsy specimens using an end-to-end deep-learning method. Incorporating two signatures and independent clinical factors, a multi-scale nomogram was constructed. We also tested the correlation between the key imaging features and genetic alternations in an independent cohort of 16 patients (biological test cohort). Results: Both radiomic and histopathologic signatures presented significant associations with treatment failure in the three cohorts (C-index: 0.689-0.779, all p < 0.050). The multi-scale nomogram showed a consistent significant improvement for predicting treatment failure compared with the clinical model in the training (C-index: 0.817 versus 0.730, p < 0.050), internal test (C-index: 0.828 versus 0.602, p < 0.050) and external test (C-index: 0.834 versus 0.679, p < 0.050) cohorts. Furthermore, patients were stratified successfully into two groups with distinguishable prognosis (log-rank p < 0.0010) using our nomogram. We also found that two texture features were related to the genetic alternations of chromatin remodeling pathways in another independent cohort. Conclusion: The multi-scale imaging features showed a complementary value in prognostic prediction and may improve individualized treatment in NPC.

关键词： digital pathology multi-scale features nasopharyngeal carcinoma radiomics survival analysis

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Milestones in Autonomous Driving and Intelligent Vehicles Part I: control, Computing System Design, Communication, HD Map, Testing, and Human Behaviors

arXiv

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arXiv 2023年

作者： Chen, Long Li, Yuchen Huang, Chao Xing, Yang Tian, Daxin Li, Li Hu, Zhongxu Teng, Siyu Lv, Chen Wang, Jinjun Cao, Dongpu Zheng, Nanning Wang, Fei-Yue The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Waytous Ltd. China BNU-HKBU United International College Zhuhai519087 China Hong Kong Baptist University Kowloon999077 Hong Kong The Department of Industrial and System Engineering Hong Kong Polytechnical University Hong Kong The School of Aerospace Transport and Manufacturing Cranfield University United Kingdom The School of Transportation Science and Engineering Beihang University China The Department of Automation Tsinghua University China The Department of Mechanical and Aerospace Engineering Nanyang Technological University Singapore The College of Artificial Intelligence Xi'an Jiaotong University China The School of Mechanical Engineering Tsinghua University China

Interest in autonomous driving (AD) and intelligent vehicles (IVs) is growing at a rapid pace due to the convenience, safety, and economic benefits. Although a number of surveys have reviewed research achievements in this field, they are still limited in specific tasks and lack systematic summaries and research directions in the future. Our work is divided into 3 independent articles and the first part is a Survey of Surveys (SoS) for total technologies of AD and IVs that involves the history, summarizes the milestones, and provides the perspectives, ethics, and future research directions. This is the second part (Part I for this technical survey) to review the development of control, computing system design, communication, High Definition map (HD map), testing, and human behaviors in IVs. In addition, the third part (Part II for this technical survey) is to review the perception and planning sections. The objective of this paper is to involve all the sections of AD, summarize the latest technical milestones, and guide abecedarians to quickly understand the development of AD and IVs. Combining the SoS and Part II, we anticipate that this work will bring novel and diverse insights to researchers and abecedarians, and serve as a bridge between past and future. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children

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BMC MEDICAL IMAGING 2019年第1期19卷 63-63页

作者： Wang, Bei Li, Min Ma, He Han, Fangfang Wang, Yan Zhao, Shunying Liu, Zhimin Yu, Tong Tian, Jie Dong, Di Peng, Yun Capital Med Univ Beijing Childrens Hosp Natl Ctr Childrens Hlth Dept Radiol 56 Nanlishi Rd Beijing 100045 Peoples R China Northeastern Univ Sino Dutch Biomed & Informat Engn Sch 3-11 Wenhua Rd Shenyang Liaoning Peoples R China Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst CAS Key Lab Mol Imaging 95 Zhongguancun East Rd Beijing 100190 Peoples R China Capital Med Univ Beijing Childrens Hosp Natl Ctr Childrens Hlth Dept Resp Med 56 Nanlishi Rd Beijing 100045 Peoples R China Beihang Univ Sch Med Beijing Adv Innovat Ctr Big Data Based Precis Med 37 Xueyuan Rd Beijing 100191 Peoples R China Univ Chinese Acad Sci 19 Yuquan Rd Beijing Peoples R China

Background To investigate the value of predictive nomogram in optimizing computed tomography (CT)-based differential diagnosis of primary progressive pulmonary tuberculosis (TB) from community-acquired pneumonia (CAP) in children. Methods This retrospective study included 53 patients with clinically confirmed pulmonary TB and 62 patients with CAP. Patients were grouped at random according to a 3:1 ratio (primary cohort n = 86, validation cohort n = 29). A total of 970 radiomic features were extracted from CT images and key features were screened out to build radiomic signatures using the least absolute shrinkage and selection operator algorithm. A predictive nomogram was developed based on the signatures and clinical factors, and its performance was assessed by the receiver operating characteristic curve, calibration curve, and decision curve analysis. Results Initially, 5 and 6 key features were selected to establish a radiomic signature from the pulmonary consolidation region (RS1) and a signature from lymph node region (RS2), respectively. A predictive nomogram was built combining RS1, RS2, and a clinical factor (duration of fever). Its classification performance (AUC = 0.971, 95% confidence interval [CI]: 0.912-1) was better than the senior radiologist's clinical judgment (AUC = 0.791, 95% CI: 0.636-0.946), the clinical factor (AUC = 0.832, 95% CI: 0.677-0.987), and the combination of RS1 and RS2 (AUC = 0.957, 95% CI: 0.889-1). The calibration curves indicated a good consistency of the nomogram. Decision curve analysis demonstrated that the nomogram was useful in clinical settings. Conclusions A CT-based predictive nomogram was proposed and could be conveniently used to differentiate pulmonary TB from CAP in children.

关键词： Child Tuberculosis Pulmonary Pneumonia Radiomics Nomogram

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Mathematical model of brain tumour growth with drug resistance

arXiv

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arXiv 2020年

作者： Trobia, José Tian, Kun Batista, Antonio M. Grebogi, Celso Ren, Hai-Peng Santos, Moises S. Protachevicz, Paulo R. Borges, Fernando S. Szezech, José D. Viana, Ricardo L. Caldas, Iberê L. Iarosz, Kelly C. Department of Mathematics and Statistics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Graduate in Science Program - Physics State University of Ponta Grossa Ponta GrossaPR84030-900 Brazil Shaanxi Key Lab of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’anPR710048 China Institute of Physics University of São Paulo São PauloSP05508-900 Brazil Institute for Complex Systems and Mathematical Biology University of Aberdeen AberdeenAB24 3UE United Kingdom Xi’an Technological University Xi’anPR710021 China Center for Mathematics Computation and Cognition Federal University of ABC São Bernardo do CampoSP09606-045 Brazil Department of Physics Federal University of Paraná CuritibaPR80060-000 Brazil Faculdade de Telêmaco Borba FATEB Telêmaco BorbaPR84266-010 Brazil

Brain tumours are masses of abnormal cells that can grow in an uncontrolled way in the brain. There are different types of malignant brain tumours. Gliomas are malignant brain tumours that grow from glial cells and are identified as astrocytoma, oligodendroglioma, and ependymoma. We study a mathematical model that describes glia-neuron interaction, glioma, and chemotherapeutic agent. In this work, we consider drug sensitive and resistant glioma cells. We show that continuous and pulsed chemotherapy can kill glioma cells with a minimal loss of neurons. © 2020, CC BY.

关键词： Tumors

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