CD34 and FSHR Expression to Differentiate Multiple Subtypes of Benign and Malignant Renal Neoplasms by Giancarlo Marra, Didier Meseure, Marine Lefèvre, Andre Nicolas, Laetitia Lesage, Nicolae Ghinea, Marco Moschini, Caio Pasquali, Petr Macek, Claudia Filippini, Paolo Gontero, Rafael Sanchez-Salas, Xavier Cathelineau

“…The correlation amongst levels of staining in tumor tissues and distance from the capsule were overall weak (Spearman coefficient CD34 to 0.0644; FSHR-0.16322).ConclusionCD34 and FSHR are differentially expressed across renal tumor subtypes and between tumor and surrounding tissues. …”
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Characterizing Seasonal Variation of the Atmospheric Mixing Layer Height Using Machine Learning Approaches by Yufei Chu, Guo Lin, Min Deng, Hanqing Guo, Jun A. Zhang

“…As machine learning becomes more integrated into atmospheric science, XGBoost has gained popularity for its ability to assess the relative contributions of influencing factors in the atmospheric boundary layer height. …”
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Study of the diagnostic and prognostic value of parameters of cytokine profile, oxidative stress and cytogenetic abnormalities in patients with tick-borne infections by E. N. Ilyinskikh, O. V. Voronkova, K. V. Samoylov, A. V. Semenova, R. R. Hasanova, Yu. I. Bulankov, V. N. Nekrasov, E. I. Petrova, G. N. Kolganova

“…The parameters showed from average to excellent predictive ability as well as high positive and negative predictive values.Conclusion. …”
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Change in Fractional Vegetation Cover and Its Prediction during the Growing Season Based on Machine Learning in Southwest China by Xiehui Li, Yuting Liu, Lei Wang

“…Over the 21 years, the FVC spatial distribution in SWC generally showed a high east and low west pattern, with extremely low FVC in the western plateau of Tibet and higher FVC in parts of eastern Sichuan, Chongqing, Guizhou, and Yunnan. The determination coefficient <i>R</i>² scores from tenfold cross-validation for the four ML models indicated that LightGBM had the strongest predictive ability whereas RR had the weakest. …”
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Remote sensing monitoring of wheat aphids by combining HJ satellite images with least squares twin support vector machine model by HU Gensheng, WU Wentian, LUO Juhua, HUANG Wenjiang, LIANG Dong, HUANG Linsheng

“…The LSTSVM has a good processing ability for large scale unbalanced data and has stronger robustness than the traditional support vector machine (SVM) . …”
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Analysis and Prediction of Deformation of Shield Tunnel Under the Influence of Random Damages Based on Deep Learning by Xiaokai Niu, Yuqiang Pan, Wei Li, Zhitian Xie, Wei Song, Chengping Zhang

“…The surrogate model achieved a correlation coefficient (R²) exceeding 0.95 and an RMSE below 0.016 mm, confirming its ability to accurately predict the deformation of tunnel segments across different damage conditions. …”
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Oxford Shoulder Instability Score: cross-cultural adaptation into Spanish and analysis of its methodological quality by Rocio Aldon-Villegas, Gema Chamorro-Moriana, Fernando Espuny-Ruiz, Maria-Luisa Benitez-Lugo

“…Cronbach’s alpha and intraclass correlation coefficient were both 0.93 (IC95%: 0.91–0.94). Standard error of measurement was 0.70, and the percentage of error and smallest detectable change were 1.46% and 1.94, respectively. …”
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The role of eco-digital learning in enhancing the impact of IoT, blockchain, and artificial intelligence on green supply chain for SME internationalization by Faisol, Hestin Sri Widiawati, Risky Aswi Ramadhani, Bambang Agus Sumantri

“…For blockchain, eco-digital learning enhances its positive impact (p = 0.0277, T-statistic = 2.2085) by strengthening the organization’s ability to leverage transparency and sustainability. …”
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Comparison of Empirical and Deep Learning Models for Solar Wind Speed Prediction by Seungwoo Ahn, Jihyeon Son, Yong-Jae Moon, Hyun-Jin Jeong

“…In particular, it achieves a high success ratio of 0.82, emphasizing the model’s stable performance and ability to minimize false alarms. These results show that our deep learning model has strong potential for practical application as a reliable tool for fast solar wind forecasting with its high accuracy and stability.…”
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TMN-Net: A Hybrid 2.5D Multi-Branch Transformer Network for Coronary Artery Segmentation in Cardiac Diagnosis by Bo Zhao, Jianjun Peng, Kai Zhang, Yongyan Fan, Ce Chen, Yang Zhang

“…Qualitative evaluations and attention heatmaps highlight the model&#x2019;s ability to preserve vessel continuity, capture distal branches, and ensure accurate boundary delineation. …”
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Remote Epilepsy Monitoring: Signal Quality and Reliability Comparative Analysis by Theeban Raj Shivaraja, Rabani Remli, Wan Asyraf Wan Zaidi, Kalaivani Chellappan

“…Signal quality, component analysis, and reliability were evaluated using error metrics, time-frequency analysis, Bland-Altman plots, repeatability, Pearson Correlation Coefficient (PCC) and also EEG characteristics analysis of individual channels. …”
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DYNAMICS OF CHANGES IN THE ELECTROMYOGRAPHIC INDICATORS OF MASTICATING MUSCLES DURING THE PROSTHETIC REHABILITATION OF PATIENTS WITH PATHOLOGICAL ABRASION OF TEETH WHEN USING REMOV... by H.M. Balia, V.S. Kuz, O.V. Shemetov, I.M. Martynenko, I.O. Kuz

“…The difficulties of prosthetic rehabilitation of this category of patients are caused by combined morpho-functional disorders. Therefore, the ability to assess the condition of the masticatory muscles is quite important for the further development of the doctor's actions and the preparation of a treatment plan. …”
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Proposed SmartBarrel System for Monitoring and Assessment of Wine Fermentation Processes Using IoT Nose and Tongue Devices by Sotirios Kontogiannis, Meropi Tsoumani, George Kokkonis, Christos Pikridas, Yorgos Kotseridis

“…SmartBarrel experimental results validate the SmartBarrel’s ability to monitor fermentation parameters. Additionally, the implemented models show that the V-LSTM model outperforms existing neural network classifiers and regression models, reducing RMSE loss by at least 45%. …”
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The Psychodiagnostic Potential of the General Health Questionnaire and its Ukrainian-Language Adaptation by V. I. Barko, O. O. Yevdokimova, O. M. Smirnova, V. V. Barko

“…Thus, for all four scales of the questionnaire, the Cronbach’s α coefficient is greater than 0.70. The correlation coefficients between the results of the first survey and the scores obtained during the retest range from 0.87 to 0.90 (p < 0.01). …”
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Detection of Bipolar Disorder and Schizophrenia Employing Bayesian-Optimized Grad-CAM-Driven Deep Learning by Osman Tayfun Bişkin, Cemre Candemir, Mustafa Alper Selver

“…Deep learning (DL) has emerged as a transformative tool in neuroimaging analysis, offering the ability to automatically extract intricate features from large datasets. …”
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Bioinformatic screen with clinical validation for the identification of novel stool based mRNA biomarkers for the detection of colorectal lesions including advanced adenoma by Houcong Liu, Loren Hansen, Changpu Song, Haijiu Lin, Dan Chen, Zhufang Chen, Hekai Zhou, Xiao Yang, Wenying Pan, Jihui Du

“…The Pearson correlation coefficient between tissue and stool expression was 0.57 (p-value = 0.007). …”
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Functionalized-AgNPs for Long-Term Stability and Its Applicability in the Detection of Manganese Ions by Van-Tuan Hoang, Mai Mai, Le Thi Tam, Ngoc Phan Vu, Nguyen Tien Khi, Phuong Dinh Tam, Tran Quang Huy, Anh-Tuan Le, Ngo Xuan Dinh, Vinh-Hoang Tran

“…The formed bond led to improving maintenance ability of the electrostatic repulsion layer among independent nanoparticles. …”
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Automated sparse feature selection in high-dimensional proteomics data via 1-bit compressed sensing and K-Medoids clustering by FuDong Wen, Yue Su, Dan Liu, YuPeng Wang, MeiNa Liu

“…Unlike conventional methods relying on manual thresholds, ST-CS automates feature selection by dynamically partitioning coefficient magnitudes into discriminative biomarkers and noise. …”
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Seasonal childhood anaemia in West Africa is associated with the haptoglobin 2-2 genotype. by Sarah H Atkinson, Kirk Rockett, Giorgio Sirugo, Philip A Bejon, Anthony Fulford, Maria A O'Connell, Robin Bailey, Dominic P Kwiatkowski, Andrew M Prentice

“…<h4>Conclusions</h4>The finding that haptoglobin 2-2 genotype is a risk factor for anaemia in children in a malaria-endemic area may reflect the reduced ability of the Hp2-2 polymer to scavenge free haemoglobin-iron following malaria-induced haemolysis. …”
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THE EVALUATION OF CURRENT RESEARCH ON THE STRENGTH OF DENTAL RESTORATIVE COMPOSITE MATERIALS by V.S. Kukhta, О.S. Kyrmanov

“…The advantages of this method include the ability to obtain information about the destruction in the early stages, its high accuracy and sensitivitys. …”
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