Diagnosis of invasive encapsulated follicular variant papillary thyroid carcinoma by protein-based machine learning by Truong Phan-Xuan Nguyen, Minh-Khang Le, Sittiruk Roytrakul, Shanop Shuangshoti, Nakarin Kitkumthorn, Somboon Keelawat

“…In the recent World Health Organization Classification of Endocrine and Neuroendocrine Tumors (5th edition), the invasive encapsulated follicular variant of papillary thyroid carcinoma was reclassified as its own entity. …”
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Comparing the Rates of Further Resection After Intraoperative MRI Visualisation of Residual Tumour Between Brain Tumour Subtypes: A 17-Year Single-Centre Experience by Daniel Madani, R. Dineth Fonseka, Sihyong Jake Kim, Patrick Tang, Krishna Muralidharan, Nicholas Chang, Johnny Wong

“…Patients were identified using SurgiNet and were grouped according to their histopathological diagnosis in accordance with the WHO 2021 classification. The primary outcome was the rate of reoperation due to iMRI visualisation of residual tumours. …”
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Depicting Soybean Diversity via Complementary Application of Three Marker Types by Vesna Perić, Natalija Kravić, Marijenka Tabaković, Snežana Mladenović Drinić, Valentina Nikolić, Marijana Simić, Ana Nikolić

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Integrating radiological and clinical data for clinically significant prostate cancer detection with machine learning techniques by Luis Mariano Esteban, Ángel Borque-Fernando, Maria Etelvina Escorihuela, Javier Esteban-Escaño, Jose María Abascal, Pol Servian, Juan Morote

“…In terms of clinical utility, for a 10% missclassification of CsPCa, XGBoost can avoid 41.77% of unnecessary biopsies, followed closely by random forest (41.67%) and neural networks (41.46%), while logistic regression has a lower rate of 40.62%. …”
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Two novel entomopathogenic fungal species of Lecanicillium isolated from soil in China by Tongyi Liu, Wei Chen, Yanhong Tang, Fen Liu, Liyan Yao, Qiongbo Hu, Ke Zhang, Qunfang Weng

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Risk factors for loss to follow-up in patients with gout: A Korean prospective cohort study. by Hyunsue Do, Chang-Nam Son, Hyo Jin Choi, Ji Hyoun Kim, Min Jung Kim, Kichul Shin, Sang-Hyon Kim, Byoongyong Choi, You-Jung Ha, Joong Kyong Ahn, Hyun-Ok Kim, Sung Won Lee, Chang Hoon Lee, Ran Song, Kyeong Min Son, Seung-Geun Lee, Ki Won Moon

“…<h4>Results</h4>Among 269 patients, 125 (46.5%) were classified as LTFU. Patients not lost to follow-up experienced more frequent gout attacks (P = 0.020) and expressed greater concerns about future flares (P = 0.034). …”
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Cross-sectional study exploring barriers to adverse drug reactions reporting in community pharmacy settings in Dhaka, Bangladesh by Tahir Mehmood Khan, Mohammad Nurul Amin, Syed Masudur Rahman Dewan, Mohammad Safiqul Islam, Mizanur Rahman Moghal, Long Chiau Ming

“…In addition to these, a majority (141, 69.46%) were not confident about the classification of ADRs (RII=0.889) and were afraid of legal liabilities associated with reporting ADRs (RII=0.806). …”
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Deformation Law of the Diaphragm Wall during Deep Foundation Pit Construction on Lake and Sea Soft Soil in the Yangtze River Delta by Yuan Mei, Dongbo Zhou, Xueyan Wang, Liangjie Zhao, Jinxin Shen, Shumin Zhang, Yuanying Liu

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Accelerating biopharmaceutical cell line selection with label-free multimodal nonlinear optical microscopy and machine learning by Jindou Shi, Alexander Ho, Corey E. Snyder, Eric J. Chaney, Janet E. Sorrells, Aneesh Alex, Remben Talaban, Darold R. Spillman, Marina Marjanovic, Minh Doan, Gary Finka, Steve R. Hood, Stephen A. Boppart

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Radiomics for differentiating adenocarcinoma and squamous cell carcinoma in non-small cell lung cancer beyond nodule morphology in chest CT by Minmini Selvam, Abjasree Sadanandan, Anupama Chandrasekharan, Sidharth Ramesh, Arunan Murali, Ganapathy Krishnamurthi

“…The workflow comprised manual nodule segmentation, regions of interest creation, preprocessing data, feature extraction, and nodule classification using machine learning algorithms. The dataset comprised 46 adenocarcinoma and 28 SCC cases. …”
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Data linkage of German statutory health insurance claims data and care needs assessments preceding a population-based cohort study on nursing home admission by Karin Wolf-Ostermann, Dominik Domhoff, Kathrin Seibert, Susanne Stiefler, Dirk Peschke

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Dream Enactment Behaviour in Post-Traumatic Stress Disorder by Cresta Asah, Hinuga Sandahl, Lone Baandrup, Jessica Carlsson, Poul Jennum

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The Adsorption of Pentachlorophenol from Aqueous Solutions onto Exchanged Al-MCM-41 Materials by Kheira Marouf-Khelifa, Amine Khelifa, Ahmed Belhakem, Reda Marouf, Fatiha Abdelmalek, Ahmed Addou

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Deep Learning-Based Speech Emotion Recognition Using Multi-Level Fusion of Concurrent Features by Samuel, Kakuba, Alwin, Poulose, Dong, Seog Han, Senior Member, Ieee

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Most prevalent jobs of young master’s degree graduates by detailed field of study by Marc Frenette, Tomasz Handler

“…Note that the intention is to show the five most prevalent jobs based on the five-digit 2021 National Occupational Classification (NOC 2021) code for each six-digit 2021 Classification of Instructional Programs (CIP 2021) code, but some results had to be suppressed. …”
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Impact of Chronic Statins Use on the Development of Esophagitis in Patients with Gastroesophageal Reflux Disease by Tawfik Khoury, Amir Mari, Hana Amara, Mohamed Jabaren, Abdulla Watad, Wiliam Nseir, Wisam Sbeit, Mahmud Mahamid

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Clinical validation of explainable AI for fetal growth scans through multi-level, cross-institutional prospective end-user evaluation by Zahra Bashir, Manxi Lin, Aasa Feragen, Kamil Mikolaj, Caroline Taksøe-Vester, Anders Nymark Christensen, Morten B. S. Svendsen, Mette Hvilshøj Fabricius, Lisbeth Andreasen, Mads Nielsen, Martin Grønnebæk Tolsgaard

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The Use of Machine Learning to Create a Risk Score to Predict Survival in Patients with Hepatocellular Carcinoma: A TCGA Cohort Analysis by Samer Tohme, Hamza O Yazdani, Amaan Rahman, Sanah Handu, Sidrah Khan, Tanner Wilson, David A Geller, Richard L Simmons, Michele Molinari, Christof Kaltenmeier

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Distribution of Microbes and Drug Susceptibility in Patients with Diabetic Foot Infections in Southwest China by Mingxia Wu, Hang Pan, Weiling Leng, Xiaotian Lei, Liu Chen, Ziwen Liang

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Burden of Myasthenia Gravis in the Czech Republic: Analysis of the Nationwide Patient Registry by Stanislav Voháňka, Aleš Tichopád, Magda Horáková, Jana Junkerová, Michala Jakubíková, Jiří Piťha, Michaela Týblová, Daniela Vlažná, Katarína Breciková, Jacek Cudny, Petr Hájek

“…We identified 70 patients (5.0%) with refractory MG, of whom 58.6% were female. The MGFA classifications in those with refractory vs. non-refractory disease was as follows: IIa 21.8% vs 23.2%, IIb 45.3% vs 33.6%, and IIIb 14.1% vs 4.6%, respectively. …”
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