Validation of the Russian-language version of the ASAS Health Index by A. I. Akulova, A. P. Rebrov, Sh. Erdes, I. Z. Gaydukova

“…The median scores were for: BASDAI, 5.40 [3.20; 6.80]; ASDAS, 3.19 [2.55; 4.15]; BASFI, 5.60 [2.60; 7.50]; BASMI, 4.20 [3.00; 6.60]; ASAS HI, 9.00 [7.00; 12.00]; ASAS EF Items Set, 4.00 [3.00; 7.00]. …”
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A Large-Scale Inter-Comparison and Evaluation of Spatial Feature Engineering Strategies for Forest Aboveground Biomass Estimation Using Landsat Satellite Imagery by John B. Kilbride, Robert E. Kennedy

“…We contextualize this improvement model performance by comparing to AGB models developed with multi-temporal features derived from the LandTrendr and Continuous Change Detection and Classification algorithms. The inclusion of temporal features reduces the model RMSE by 18.41 Mg <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>ha</mi><mrow><mo>−</mo><mn>1</mn></mrow></msup></mrow></semantics></math></inline-formula>. …”
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Detection of Fall Armyworm infestation in maize fields during vegetative growth stages using temporal Sentinel-2 by Tatenda Dzurume, Roshanak Darvishzadeh, Timothy Dube, T.S. Amjath Babu, Mutasim Billah, Syed Nurul Alam, Mustafa Kamal, Md. Harun-Or-Rashid, Badal Chandra Biswas, Md. Ashraf Uddin, Md. Abdul Muyeed, Md. Mostafizur Rahman Shah, Timothy J. Krupnik, Andrew Nelson

“…The results demonstrated that the spectral reflectance from Sentinel-2 bands varied significantly among different classes of FAW infestation, with noticeable differences observed during the early developmental stages of maize (vegetative growth stages 3 to 8). RF identified nine spectral bands and two spectral vegetation indices as important for FAW infestation discrimination. …”
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Patterns of Complementary Medicine Utilization in Patients With Breast Cancer and Colorectal Cancer: A Cross-Sectional Study at a Tertiary Referral Hospital in Yogyakarta, Indonesi... by Juan Adrian Wiranata, Susanna Hilda Hutajulu, Norma Dewi Suryani, Rr. Rayna Adya Harvianti, Ashifa Jasmine, Yufi Kartika Astari, Herindita Puspitaningtyas, Mardiah Suci Hardianti, Yayi Suryo Prabandari

“…The domains of CM used were categorized according to the classification of the National Institutes of Health/National Center for Complementary and Integrative Health.RESULTSOne hundred forty-two patients with BC and 227 patients with CRC (N = 369) were included. …”
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Global 30&thinsp;m seamless data cube (2000–2022) of land surface reflectance generated from Landsat 5, 7, 8, and 9 and MODIS Terra constellations by S. Chen, J. Wang, Q. Liu, X. Liang, R. Liu, P. Qin, J. Yuan, J. Wei, S. Yuan, H. Huang, H. Huang, P. Gong, P. Gong, P. Gong

“…Moreover, experimental results underscore the advantages of employing the SDC for global land cover classification, achieving a sizable improvement in overall accuracy (2.4 %–11.3 %) over that obtained using Landsat composite and interpolated datasets. …”
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Comparative study of National Institute of Health criteria and TNM staging system in predicting the prognosis of gastrointestinal stromal tumours: a retrospective study by Jin-hu Chen, Zhen-rong Yang, Zhi-ming Cai, Tao Lin, Ren Lin, Xin-cheng Su, Rong-bin Kang, Lu Lin, Zai-sheng Ye, Yong-jian Zhou

“…BackgroundIn 2009, the American Joint Commission on Cancer incorporated the gastrointestinal stromal tumours (GISTs) risk classification into the tumour, node, metastasis (TNM) staging system. …”
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Artificial Intelligence and Image Analysis-Assisted Diagnosis for Fibrosis Stage of Metabolic Dysfunction-Associated Steatotic Liver Disease Using Ultrasonography: A Pilot Study by Itsuki Fujii, Naoki Matsumoto, Masahiro Ogawa, Aya Konishi, Masahiro Kaneko, Yukinobu Watanabe, Ryota Masuzaki, Hirofumi Kogure, Norihiro Koizumi, Masahiko Sugitani

“…The accuracy of the classification of the liver surface as smooth or rough via ResNet 50 was 84.6%. …”
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Influence of frozen storage and flavoring substances on the nonvolatile metabolite profile of raw beef: Correlation of lipids and lipid-like molecules with flavor profiles by Sam Al-Dalali, Zhigui He, Miying Du, Hui Sun, Dong Zhao, Cong Li, Peijun Li, Baocai Xu

“…There were 3 categories of metabolites under superclass classification, accounting for 77.93 % of the total metabolites, including lipids and lipid-like compounds (502 species, 33.87 %), organic acids and derivatives (459 species, 30.97 %), and organoheterocyclic compounds (194, 13.09 %). …”
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Clinical manifestations of damage to the cardiovascular system and predictors of poor prognosis of Takayasu arteritis in Kyrgyz patients by G. M. Koilubaeva, A. M. Bolotbekova, M. T. Beishenkulov, O. N. Egorova, G. M. Tarasova, A. M. Lila, G. Suiunbay kyzy, A. T. Turatbekova, A. A. Okunova, M. A. Chukubaev, Z. E. Turdukulov, D. A. Usupbaeva

“…Myocarditis (8.5%) and coronary syndrome (3.8%) were significantly less frequently diagnosed. …”
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Analysis of locus estimates of allelic diversity of STR markers in a sample of breeding bulls by V. M. Kuznetsov

“…The nonparametric Mann-Whitney-Wilcoxon test showed statistically significant differences in the medians of the Eth3 locus with the Bm2113 locus, the Tgla126 locus with the Eth3, Inra23, Tgla122, Eth225, Bm2113, Bm1824, Eth10 loci. …”
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Prevalence Of Diabetes Mellitus And Its Associated Factors Among Tuberculosis Cases in Taluk Tuberculosis Unit, India - A Hospital-based : Exploratory Cross-sectional Study by Md Alinawaz Khan, Ashok Dorle

“…Out of total, 81,331cases were notified from the state of Karnataka. Whereas  9.3% of the Indian population are known cases of diabetes mellitus. …”
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Machine learning based on alcohol drinking-gut microbiota-liver axis in predicting the occurrence of early-stage hepatocellular carcinoma by Yi Yang, Zhiyuan Bo, Jingxian Wang, Bo Chen, Qing Su, Yiran Lian, Yimo Guo, Jinhuan Yang, Chongming Zheng, Juejin Wang, Hao Zeng, Junxi Zhou, Yaqing Chen, Gang Chen, Yi Wang

“…Eight ML algorithms including Classification and Regression Tree (CART), Gradient Boosting Machine (GBM), K-Nearest Neighbor (KNN), Logistic Regression (LR), Neural Network (NN), Random Forest (RF), Support Vector Machine (SVM), and eXtreme Gradient Boosting (XGBoost) were applied. …”
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Intravenous Thrombolysis in Cervical Artery Dissection–Related Stroke: A Nationwide Study by Liqi Shu, Elizabeth Lee, Thalia S. Field, Xiaofan Guo, Nils Henninger, Zafer Keser, Muhib Khan, Eva A. Mistry, Thanh N. Nguyen, James E. Siegler, Lukas Strelecky, Daniel M. Mandel, Christoph Stretz, Karen L. Furie, Shadi Yaghi

“…Methods We included adult patients with concurrent CeAD and AIS diagnoses (International Classification of Diseases, Tenth Revision [ICD‐10], codes) hospitalized from 2016 to 2019. …”
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Predictive model based on blood cell analysis and coagulation function indicators for neuroblastic tumors staging diagnosis by Yanzi Zhang, Yanzi Zhang, Lihong Zhang, Lihong Zhang, Mengmeng Chen, Mengmeng Chen, Qin Dong, Qin Dong, Chong Hu, Chong Hu, Juan Wang, Juan Wang, Jiao Meng, Jiao Meng, Xin Lv, Xin Lv

“…Patients were stratified into localized (INSS 1-2, Group 1) and advanced (INSS 3-4, Group 2) stages according to the INSS classification, with mature ganglioneuroma serving as the control group. …”
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Developing Machine-Learning Models to Predict Bacteremia in Febrile Adults Presenting to the Emergency Department: A Retrospective Cohort Study from a Large Center by Chia-Ming Fu, Ike Ngo, Pak Sheung Lau, Yaroslav Ivanchuk, Fan-Ya Chou, Chih-Hung Wang, Chien-Yu Lin, Chu-Lin Tsai, Shey-Ying Chen, Tsung-Chien Lu, Hung-Yu Wei

“…Bacteremia was identified in 5,831 (12.1%) and 3,824 (11.9%) cases of the training/validation set and test set, respectively. …”
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Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects by Maryam Yeganegi, Mahsa Danaei, Sepideh Azizi, Fatemeh Jayervand, Reza Bahrami, Seyed Alireza Dastgheib, Heewa Rashnavadi, Ali Masoudi, Amirmasoud Shiri, Kazem Aghili, Mahood Noorishadkam, Hossein Neamatzadeh

“…SVM models have demonstrated 71.50% accuracy on training datasets and 68.57% on testing datasets for NTD classification, while advanced deep learning (DL) methods report patient-level prediction accuracy of 94.5% and an area under the receiver operating characteristic curve (AUROC) of 99.3%. …”
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Anorexia nervosa—the frequency of occurrence in Polish youth, the connection with stress, auto-aggressive behaviors and the abuse of psychoactive substances by Michał Górski, Renata Polaniak, Beata Całyniuk, Jagoda Garbicz-Kata, Marta Buczkowska, Joanna Fojcik, Joanna Fojcik, Justyna Nowak, Joanna Domagalska

“…In the statistical analysis, a significance level of α = 0.05 was adopted.ResultsThe diagnostic criteria for anorexia nervosa were met by 13.3% of respondents. 37.1% of respondents admitted to engaging in self-injurious behavior, and 13.9% reported having suicidal thoughts. …”
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Application of Machine Learning in the Prediction of the Acute Aortic Dissection Risk Complicated by Mesenteric Malperfusion Based on Initial Laboratory Results by Zhechuan Jin, Jiale Dong, Jian Yang, Chengxiang Li, Zequan Li, Zhaofei Ye, Yuyu Li, Ping Li, Yulin Li, Zhili Ji

“…Subsequently, six machine learning algorithms were used to develop and validate an MMP risk identification model: logistic regression (LR), support vector classification (SVC), random forest (RF), extreme gradient boosting (XGBoost), naive Bayes (NB), and multilayer perceptron (MLP). …”
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Distinct and content-specific neural representations of self- and other-produced actions in joint piano performance by Natalie Kohler, Natalie Kohler, Anna M. Czepiel, Anna M. Czepiel, Örjan de Manzano, Örjan de Manzano, Giacomo Novembre, Peter E. Keller, Peter E. Keller, Arno Villringer, Daniela Sammler, Daniela Sammler

“…Expert pianists played familiar right-hand melodies in a 3 T MR-scanner, in duet with a partner who played the corresponding left-hand basslines in an adjacent room. …”
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Genotyping by sequencing reveals the genetic diversity and population structure of Peruvian highland maize races by Carlos I. Arbizu, Carlos I. Arbizu, Isamar Bazo-Soto, Joel Flores, Rodomiro Ortiz, Raul Blas, Pedro J. García-Mendoza, Ricardo Sevilla, José Crossa, Alexander Grobman

“…Peruvian maize exhibits abundant morphological diversity, with landraces cultivated from sea level (sl) up to 3,500 m above sl. Previous research based on morphological descriptors, defined at least 52 Peruvian maize races, but its genetic diversity and population structure remains largely unknown. …”
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