AI-Driven Predictive Modeling for Lung Cancer Detection and Management Using Synthetic Data Augmentation and Random Forest Classifier by Nisreen Innab, Asma Aldrees, Dina Abdulaziz AlHammadi, Abeer Hakeem, Muhammad Umer, Shtwai Alsubai, Silvia Trelova, Imran Ashraf

“…Extensive experimental evaluation for this method included testing nine classification algorithms. The implementation of different classifiers employed data balancing methods including SMOTE and borderline-SMOTE along with SMOTE ENN and unbalanced data configurations. …”
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Integrated bioinformatics identifies ferroptosis biomarkers and therapeutic targets in idiopathic pulmonary arterial hypertension by Yuhao Zhang, Tao Qian, Wei Jiang, Haoyong Yuan, Ting Lu, Ni Yin, Zhongshi Wu, Can Huang

“…GO and KEGG analyses were performed to explore biological functions and potential pathways. LASSO and SVM-RFE algorithms were used to identify optimal gene biomarkers for IPAH. …”
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Radiomics in pediatric brain tumors: from images to insights by Pranjal Rai, Sabha Ahmed, Abhishek Mahajan

“…Pediatric brain tumors, which differ significantly from adult tumors in biology and imaging appearance, present unique diagnostic and prognostic challenges. …”
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Development and validation of a prognostic staging system for primary plasma cell leukemia by Mengru Tian, Gang An, Weijun Fu, Wenqiang Yan, Lu Li, Chunyan Sun, Zhenyu Li, Lijuan Chen, Aijun Liao, Guangxun Gao, Xiaoqi Qin, Mengyao Li, Chunrui Li, Hua Xue, Li Gao, Yi Wang, Aili He, Fan Zhou, Dongmei Guo, Yujun Dong, Zhihong Fang, Xiaoxia Chu, Jianqing Mi, Chengcheng Fu, Hui Zeng, Shuling Hou, Xiaotao Wang, Hua Wang, Yongqiang Wei, Xinyue Liang, Xingcheng Yi, Yue Sun, Lugui Qiu, Yun Dai, Fengyan Jin

“…Moreover, patients with different stages had highly diverse genomic and transcriptomic aberrations. …”
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Study on consistency between liver fat fraction quantification based on photon-counting CT and MRI proton density fat fraction by CAI Xinxin, DENG Rong, XU Xinxin, XU Zhihan, CHANG Rui, DONG Haipeng, LIN Huimin, YAN Fuhua

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Enhancement in dynamic range for dual-rotating retarder Mueller matrix polarimetry by Jiongying Lv, Shuan Yao, Nan Zeng, Caizhong Guan, Honghui He

“…In this study, we propose a novel method to generate high dynamic range (HDR) MM images based on a multi-exposure fusion algorithm. By employing an optimal exposure selection strategy for transmission imaging and a multi-exposure weighted averaging strategy for backscattering imaging, the method expands the dynamic range while accurately preserving the polarization information of the samples. …”
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Evaluation of the Effectiveness of Pulsed Ultraviolet Devices in Medical Premises of the Emergency Department of a Hospital Under Conditions of Intensive Patient Flow by B. L. Kurilin, N. E. Drozdova, A. Yu. Perminov, E. V. Kislukhina, Ya. V. Kulikova, N. S. Fomenko, A. V. Shapoval, V. I. Drozdova, A. R. Samarin

“…Sklifosovsky Research Institute for Emergency Medicine of the Moscow Health Department (hereinafter referred to as the Institute), the number of visits to the admission and diagnostic departments has more than doubled over the past 15 years. …”
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The role of advanced machine learning in COVID-19 medical imaging: A technical review by Abdul Muiz Fayyaz, Said Jadid Abdulkadir, Shahab Ul Hassan, Safwan Mahmood Al-Selwi, Ebrahim Hamid Sumiea, Lareib Fatima Talib

“…Medical images such as CXR, ultrasound, and CT scans are crucial for the rapid and accurate diagnosis of COVID-19. However, the diagnostic process is often challenging, time-consuming, and prone to human error. …”
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IKZF1 as a potential therapeutic target for dendritic cell-mediated immunotherapy in IgA nephropathy by Fei Peng, Chunjia Sheng, Jiayi He, Yena Zhou, Yilun Qu, Shuwei Duan, Yinghua Zhao, Jikai Xia, Jie Wu, Guangyan Cai, Lingling Wu, Chuyue Zhang, Xiangmei Chen

“…Receiver operating characteristic (ROC) curve analysis and machine learning algorithms were employed to screen for DC-related diagnostic biomarkers from the dataset. …”
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A Multi-Branch Convolution and Dynamic Weighting Method for Bearing Fault Diagnosis Based on Acoustic–Vibration Information Fusion by Xianming Sun, Yuhang Yang, Changzheng Chen, Miao Tian, Shengnan Du, Zhengqi Wang

“…Experimental results demonstrate that our method significantly outperforms other algorithms in terms of convergence speed and final performance. …”
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Are Artificial Intelligence Models Listening Like Cardiologists? Bridging the Gap Between Artificial Intelligence and Clinical Reasoning in Heart-Sound Classification Using Explain... by Sami Alrabie, Ahmed Barnawi

“…Although this approach is fast, non-invasive, and cost-effective, its diagnostic accuracy still mainly depends on the clinician’s expertise, making it particularly challenging to detect rare or complex conditions. …”
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Enhanced quantitation of pathological α-synuclein in patient biospecimens by RT-QuIC seed amplification assays. by Ankit Srivastava, Qinlu Wang, Christina D Orrù, Manel Fernandez, Yaroslau Compta, Bernardino Ghetti, Gianluigi Zanusso, Wen-Quan Zou, Byron Caughey, Catherine A A Beauchemin

“…In some scenarios, analysis of the data using Poisson and midSIN algorithms provided more consistent and statistically significant discrimination of different seed concentrations. …”
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Species delimitation and microalgal cryptic diversity analysis of the genus Micractinium (Chlorophyta) by E. S. Krivina, A. D. Temraleeva, Yu. S. Bukin

“…Phylogenetic analysis showed that the genus Micractinium is characterized by high cryptic diversity. The algorithms used for species delimitation had different results on the number of potentially species-level clusters allocated. …”
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Intracranial atherosclerosis: structure, clinical aspects and risk factors by Marine M. Tanashyan, Andrey S. Mazur, Anton A. Raskurazhev, Anna N. Berliand, Marina V. Dreval, Polina I. Kuznetsova, Оlga V. Lagoda

“…Active research is underway to determine the prevalence of intracranial atherosclerosis across different ethnic groups, the role of modifiable and non-modifiable risk factors in its pathogenesis, and the advances in diagnostic algorithms for patients with extra-/intracranial atherosclerosis. …”
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EPIGENETIC REGULATION OF GENE EXPRESSION IN HEAD AND NECK SQUAMOUS CELL CARCINOMA: THERAPEUTIC PERSPECTIVES by R. B. Samsonov, Z. A. Radzhabova, Yu. V. Cheburkin, V. A. Klyuge, E. V. Tkachenko, A. V. Malek

“…Unfortunately, optimization of diagnostic algorithms and wide implementation of instrumental diagnostics (ultrasound examination, computed tomography, fiber endoscopy) do not improve the situation. …”
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Pain sensitisation and joint inflammation in patients with active rheumatoid arthritis by Sam Norton, Bruce W Kirkham, James Galloway, Khaldoun Chaabo, Toby Garrood, Zoe Rutter-Locher, Estee Chan, Alex Vincent

“…We were unable to develop algorithms to identify different groups.Conclusion The unexpected group −FM−PD group may have peripheral nociplastic pain, not commonly recognised in rheumatology. …”
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The role of artificial intelligence in breast cancer screening as a supportive tool for radiologists by Agata Król, Katarzyna Kwaterska, Karol Kutyłowski, Paweł Łuckiewicz

“…The evaluation has been focused on the degree of AI participation in diagnostic process and its impact on screening or human performance. …”
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Transparent memory tests with even repeating addresses for storage devices by V. N. Yarmolik, I. M. Mrozek, V. A. Levantsevich, D. V. Demenkovets

“…The basis of new metrics is the distance D(A(j), pA) determined by the difference between the indices of repeated addresses A(j) in the sequence pA. …”
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Deep Learning Approaches for Retinal Disease Identification in Fundus Imaging: A Comprehensive Overview by Ismael Abdulkareem Ali, Sozan Abdullah Mahmood

“…Therefore, deep learning diagnostic techniques are widely used for identifying eye diseases and play a crucial role in diagnosing vision-related problems. …”
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Causes of resistance to PARP inhibitors and ways to overcome it. Case report of aggressive <i>BRCA</i>-related breast cancer by A. I. Stukan, A. Yu. Goryainova, S. V. Sharov, O. A. Goncharova, Z. K. Khachmamuk, V. V. Durov

“…However, in clinical practice, despite the proven antitumor efficacy of drugs, acquired resistance to PARP inhibitors leads to difficulties in selecting further therapy due unknown resistance mechanisms and absence of algorithm of action. Despite the various mechanisms of resistance to PARP inhibitors, the choice of subsequent combination therapy after the detection of resistance to PARP inhibitors should be based on an understanding of these mechanisms and the existence of heterogeneous metastatic process. …”
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