Deep molecular profiling of synovial biopsies in the STRAP trial identifies signatures predictive of treatment response to biologic therapies in rheumatoid arthritis by Myles J. Lewis, Cankut Çubuk, Anna E. A. Surace, Elisabetta Sciacca, Rachel Lau, Katriona Goldmann, Giovanni Giorli, Liliane Fossati-Jimack, Alessandra Nerviani, Felice Rivellese, Costantino Pitzalis, the STRAP collaborative group

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Artificial intelligence in primary aldosteronism: current achievements and future challenges by Yisi Xu, Benjin Liu, Xuqi Huang, Xudong Guo, Ning Suo, Shaobo Jiang, Hanbo Wang

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CRYPTO-RESISTANT METHODS AND RANDOM NUMBER GENERATORS IN INTERNET OF THINGS (IOT) DEVICES by Petro Klimushyn, Tetiana Solianyk, Oleksandr Mozhaiev, Yurii Gnusov, Oleksandr Manzhai, Vitaliy Svitlychny

“…The analysis of technologies and circuit solutions allowed to draw the following conclusions: protection of IoT solutions includes: security of IoT network nodes and their connection to the cloud using secure protocols, ensuring confidentiality, authenticity and integrity of IoT data by cryptographic methods, attack analysis and network cryptographic stability; the initial basis for the protection of IoT solutions is the true randomness of the formed RNG sequences and used in algorithms for cryptographic transformation of information to protect it; feature of IoT devices is their heterogeneity and geographical distribution, limited computing resources and power supply, small size; The most effective (reduce power consumption and increase the generation rate) for use in IoT devices are RNG exclusively on a digital basis, which implements a three-stage process: the initial digital circuit, normalizer and random number flow generator; Autonomous Boolean networks (ABN) allow to create RNG with unique characteristics: the received numbers are really random, high speed – the number can be received in one measure, the minimum power consumption, miniature, high (up to 3 GHz) throughput of Boolean chaos; a promising area of ABN development is the use of optical logic valves for the construction of optical ABN with a bandwidth of up to 14 GHz; the classification of known classes of RNG attacks includes: direct cryptanalytic attacks, attacks based on input data, attacks based on the disclosure of the internal state of RNG, correlation attacks and special attacks; statistical test packages to evaluate RNG sequences have some limitations or shortcomings and do not replace cryptanalysis; Comparison of cryptoaccelerators with cryptographic transformation software shows their significant advantages: for AES block encryption algorithm, speeds increase by 10-20 times in 8/16-bit cryptoaccelerators and 150 times in 32-bit, growth hashing of SHA-256 in 32-bit cryptoaccelerators more than 100 times, and for the NMAS algorithm - up to 500 times. …”
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Characteristics of gut and lung microbiota in patients with lung masses and their relationship with clinical features by Yanping Yang, Jiacheng Shen, Sulan Wei, Maosong Ye, Xing Zhao, Jian Zhou, Lin Tong, Jie Hu, Yuanlin Song, Shengdi Wu, Nuo Xu

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Combining Super-Resolution Imaging and Shear Wave Elastography for Enhanced Risk Assessment of Moderate-to-Severe Renal Fibrosis in Chronic Kidney Disease Patients by Huang X, Zhang Y, Hu Y, Pan J, Huang X, Zhang J, Pu H, Chen Y, Deng Q, Zhou Q

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A case study on the application of a data-driven (XGBoost) approach on the environmental and socio-economic perspectives of agricultural groundwater management by Sheng-Wei Wang, Yen-Yu Chen, Shu-Han Hsu, Yu-Hsuan Kao, Masaomi Kimura, Li-chiu Chang, Tzi-Wen Pan, Chuen-Fa Ni

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Machine learning modeling for the risk of acute kidney injury in inpatients receiving amikacin and etimicin by Pei Zhang, Qiong Chen, Jiahui Lao, Juan Shi, Jia Cao, Xiao Li, Xin Huang

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Prediction of 1-year post-operative mortality in elderly patients with fragility hip fractures in China: evaluation of risk prediction models by Qiyuan Lu, Mengmeng Chen, Houfu Ling

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Multi video stream collaborative adaptive offloading scheme based on equilibrium game theory by Shijun Yuan

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The association between cystatin C and hypertension risk in diabetes patients: A multi-cohort cross-sectional study by Ye Kuang, Jia Wang, Yang Wang, Chuanmei Peng, Pei He, Yong Ji, Jinrong Tian, Yong Yuan, Lei Feng

“…Analyzing 5210 DM patients from three cohorts, this study identified serum cystatin C (CysC) as an independent risk factor for DM + HTN through univariate and multivariate logistic regression. …”
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Exploring the Landscape of Operating Room Scheduling: A Bibliometric Analysis of Recent Advancements and Future Prospects by Md Al Amin, Majed Hadid, Adel Elomri, Rabah Ismaen, Ismail Dergaa, Hind Alashi, Amal Jobran Al-Hajaji, Moustafa Alkhalil, Omar M Aboumarzouk, Abdelfatteh EL Omri

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Machine learning as a tool for diagnostic and prognostic research in coronary artery disease by B. I. Geltser, M. M. Tsivanyuk, K. I. Shakhgeldyan, V. Yu. Rublev

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GIS Analysis Model Integration and Service Composition Prospects by L. Ding, P. Cai, W. Huang, H. Zhang, F. Ding, W. Zhao, D. Tang, Z. Wang

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Identifying Strong Connectivity in Urban Road Networks Considering Traffic Constraints: An Analysis of Road Networks With Different Patterns by Ruru Xing, ZePeng Yang, Xinghua Zhang, Yiwen Liang, Tao Yang, Fei Wang

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Novel nocturnal insect pest monitoring for sustainable crop protection using ensemble augmented deep learning classification by Hui Zhao, Bo Fu, Fernando Auat Cheein, Matthew Butler, W. Edwin Harris, Tom W. Pope, Joe M. Roberts

“…The proposed framework strategically integrates a lightweight network model with adaptive ensemble augmentation mechanisms to comprehensively address three core data challenges: (1) chromatic variance under varying illumination conditions, (2) partial occlusion from pest aggregation, and (3) morphological deformation during specimen collection. …”
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Oral contrast-enhanced ultrasonographic features and radiomics analysis to predict NIH risk stratification for gastrointestinal stromal tumors by Fan Yang, Fan Yang, Fan Yang, Fan Yang, Chun-wei Liu, Dai Zhang, Dai Zhang, Dai Zhang, Dai Zhang, Hai-Ling Wang, Hai-Ling Wang, Hai-Ling Wang, Hai-Ling Wang, Xi Wei, Xi Wei, Xi Wei, Xi Wei, Mo Yang, Mo Yang, Mo Yang

“…The patient dataset was randomly divided into a training set and a validation set at a ratio of 7:3. Leveraging the XGBoost (XGB) algorithm within the Scikit-learn (Sklearn) machine-learning library, three distinct predictive models were developed: a clinical ultrasound imaging model (US model), an ultrasonographic radiomics model (US radiomics model), and a combined model integrating both clinical, ultrasound, and radiomics features. …”
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Enhanced cardiovascular risk prediction in the Western Pacific: A machine learning approach tailored to the Malaysian population. by Sazzli Kasim, Putri Nur Fatin Amir Rudin, Sorayya Malek, Nurulain Ibrahim, Xue Ning Kiew, Nafiza Mat Nasir, Khairul Shafiq Ibrahim, Raja Ezman Raja Shariff

“…Ensemble model were also created using three commonly used meta learners, including RF, Generalized Linear Model (GLM), and Gradient Boosting Model (GBM). …”
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A manganese metabolism-related gene signature stratifies prognosis and immunotherapy efficacy in kidney cancer by Yang Liu, Hao Ye, Ruoxuan Zhang, Xiaolong Liu, Ranlu Liu

“…Through integrated bioinformatics approaches, including differential expression analysis, univariate Cox regression, and three machine learning algorithms (Boruta, GBM, and RFS), we identified prognosis-related MMCG. …”
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Artificial Intelligence-Based Prediction of Bloodstream Infections Using Standard Hematological and Biochemical Markers by Ferhat DEMİRCİ, Murat AKŞİT, Aylin DEMİRCİ

“…Basophil count, while ranked highest by SHAP, showed low sensitivity, highlighting the difference between algorithmic weight and bedside utility. Conclusion: These findings support the integration of routine, readily available laboratory data into an explainable AI framework to accurately predict culture positivity. …”
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A recurrence model for non-puerperal mastitis patients based on machine learning. by Gaosha Li, Qian Yu, Feng Dong, Zhaoxia Wu, Xijing Fan, Lingling Zhang, Ying Yu

“…<h4>Results</h4>The logistic regression model emerged as the optimal model for predicting recurrence of NPM with machine learning, primarily utilizing three variables: FIB, bacterial infection, and CD4+ T cell count. …”
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