Machine learning for synchronous bone metastasis risk prediction in high grade lung neuroendocrine carcinoma by Bo Lan, Zongyun He, Zhe Chen, Haibing Tao, Tao Liu, Jin Yang

“…This study aimed to use multiple machine learning algorithms to exploring the significant factors associated with synchronous BM in these patients. …”
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SEARCH FOR CLINICAL PREDICTORS OF PULMONARY HYPERTENSION IN PATIENTS WITH SYSTEMIC SCLEROSIS by N. A. Klyaus, M. A. Simakova, A. L. Maslyansky, O. M. Moiseeva

“…The scope of investigations necessary to detect PH, the complexity of its diagnostic algorithms for routine use, as well as the impossibility to apply the existing algorithms for all PH variants increase the relevance of searching for novel PH predictors in patients with SSc.Objective: to reveal the relationship of capillary structural changes to the clinical and immunological subtype of SSc, disease activity, and risk for PH.Subjects and methods. …”
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Teaching Internal Medicine Residents to Critically Appraise the Role of Race in Pulmonary Function Testing by Ananya Bhatia-Lin, Nirav Bhakta, Neha Deshpande, Laura Granados, Rosemary Adamson

“…We conducted pre- and posttest surveys; the posttest survey was designed using the Kirkpatrick model to assess reaction, learning, and anticipated behavioral change. Mean score differences were evaluated for level 2 questions using Cohen's d effect size. …”
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Advances in Light Field Spatial Super-Resolution: A Comprehensive Literature Survey by Wenqi Lyu, Hao Sheng, Wei Ke, Xiao Ma

“…We present a systematic review of 17 mainstream light field spatial super-resolution techniques, evaluating their performance across seven public datasets. …”
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Mapping Vegetation Dynamics in Wyoming: A Multi-Temporal Analysis using Landsat NDVI and Clustering by N. Kuppala, C. Navneet Krishna, V. V. Sajith Variyar, R. Sivanpillai

“…GMM assigns data points probabilistically, assuming data are generated from a mixture of Gaussian distributions, while AP identifies clusters by finding representative exemplars without needing a predefined number of clusters. To evaluate the performance of these clustering algorithms, we assigned the clusters into six classes based on vegetation growth patterns observed over the five-year period. …”
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A Machine Learning Approach for Predicting Maternal Health Risks in Lower-Middle-Income Countries Using Sparse Data and Vital Signs by Avnish Malde, Vishnunarayan Girishan Prabhu, Dishant Banga, Michael Hsieh, Chaithanya Renduchintala, Ronald Pirrallo

“…The models’ performance were evaluated using regular, random and stratified sampling techniques. …”
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Towards sustainable agriculture in Iran using a machine learning-driven crop mapping framework by Iman Khosravi

“…The Ministry of Agriculture-Jihad (MAJ) and the Iranian Space Agency (ISA) aim to accurately estimate the cultivated area of strategic crops and evaluate their annual yield through meticulous crop mapping. …”
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From Words to Ratings: Machine Learning and NLP for Wine Reviews by Iliana Ilieva, Margarita Terziyska, Teofana Dimitrova

“…The descriptions were transformed into numerical representations using a pre-trained language model (BERT), after which algorithms were used to predict style categories and ratings. …”
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Investigating Similarities Across Decentralized Finance (DeFi) Services by Junliang Luo, Stefan Kitzler, Pietro Saggese

“… We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. …”
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Cross-sectional and longitudinal Biomarker extraction and analysis for multicentre FLAIR brain MRI by J. DiGregorio, A. Gibicar, H. Khosravani, P. Jabehdar Maralani, J.-C. Tardif, P.N. Tyrrell, A.R. Moody, A. Khademi

“…Biomarkers were extracted from two dementia cohorts (4356 vol, 162 233 images) and one vascular disease cohort (869 vol, 42 850 images) using deep learning-based segmentation algorithms designed specifically for FLAIR. Biomarkers from all cohorts were summarized using descriptive statistics, correlation analysis, and ANCOVA to assess differences in diagnostic labels while accounting for demographic and acquisition factors. …”
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Lossy DICOM conversion may affect AI performance by Robin Sebastian Mayer, Fabian Fliedner, Ingvild Frøberg Mathisen, Anna Laib, Julia Bein, Marco Eichelberg, Peter J. Wild, Nadine Flinner

“…So, if DICOM images are intended for a diagnostic use, all processes and algorithms must be (re-)evaluated with the converted files, as images are not identical. …”
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Transbronchial lung cryobiopsy for diagnosing interstitial lung disease: a cross-sectional, comparative study by Min Yu, Xuefeng Xu, Xiaoyan Xin, Shangwen Yang, Yihua Wang, Yonglong Xiao, Shenyun Shi, Xuebing Feng

“…Conclusions This study indicated that the clinical applicability of TBLC in ILD diagnostic algorithms, with a diagnostic yield of 85.0% and an acceptable safety profile.…”
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Machine learning-based prognostic prediction for acute ischemic stroke using whole-brain and infarct multi-PLD ASL radiomics by Zhenyu Wang, Chaojun Jiang, Xianxian Zhang, Tianchi Mu, Qingqing Li, Shu Wang, Congsong Dong, Yuan Shen, Zhenyu Dai, Fei Chen

“…Additionally, no significant differences were observed between the combined radiomics models and the clinical models across the five algorithms. …”
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Synergistic detection of E. coli using ultrathin film of functionalized graphene with impedance spectroscopy and machine learning by Amrit Kumar, Shweta Mishra, R. K. Gupta, V. Manjuladevi

“…Machine learning (ML) algorithms applied to raw impedance data improved detection precision and reliability, enabling automated and accurate analysis. …”
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Architectural Gaps in Generative AI: Quantifying Cognitive Risks for Safety Applications by He Wen, Pingfan Hu

“…<b>Conclusions</b>: These findings suggest that such distances represent not only algorithmic abstraction but also potential safety risks when generative AI is deployed in practice. …”
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Obstructive sleep apnea severity and pathophysiological traits in overlap syndrome: Insights from the SNOOzzzE cohort by Janna Raphelson, Ana Sanchez‐Azofra, Jeremy E. Orr, Gabriela Parra, Lana McGinnis, Alexis Salinas, Steven Luu, Scott A. Sands, Ali Azarbarzin, Robert L. Owens, Jose M. Marin, Atul Malhotra, Christopher N. Schmickl

“…OSA severity and mechanisms differ in OVS versus OSA‐only. Future research should seek to evaluate these differences for their prognostic ability.…”
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Machine Learning-Driven Prediction of Brain Age for Alzheimer’s Risk: APOE4 Genotype and Gender Effects by Carter Woods, Xin Xing, Subash Khanal, Ai-Ling Lin

“…Additionally, we introduced two novel metrics, brain age difference (BAD) and integrated difference (ID), to evaluate the models’ performances and analyze the influences of the APOE4 genotype and gender on brain aging. …”
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Machine Learning-Based Detection of Archeological Sites Using Satellite and Meteorological Data: A Case Study of Funnel Beaker Culture Tombs in Poland by Krystian Kozioł, Natalia Borowiec, Urszula Marmol, Mateusz Rzeszutek, Celso Augusto Guimarães Santos, Jerzy Czerniec

“…Key indices—including the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Moisture Index (NDMI), and Normalized Archeological Index (NAI)—were analyzed alongside meteorological variables such as wind speed, temperature, humidity, and total precipitation. …”
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Reducing the Effects of City Heat Islands Through Cool Pavement Materials (Case Study: District 8 of Tabriz Metropolis) by Asghar Abedini, Mojtaba Azmoun, Kiarash Azarkish, Sina Moshtaghi

“…Now, in this research, after examining the influencing indicators and determining the heat islands, the impact of surface materials as one of the effective components on urban heat islands compared to other components is comprehensively evaluated. After analyzing the 15 main factors determined for the 8th area of ​​Tabriz and field observations of the main materials used in the area, which were mostly cement and clay, and determining the albedo, thermal radiation emission rate, heat capacity and solar reflectance of the colors in the area, we came to the conclusion that with More or less the entire region is one of the most effective indicators of heat islands, the main difference in urban temperature difference can be seen in the type of pavement materials used and its color in the designated heat islands. …”
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Identification of SnRNA U6 as an endogenous reference gene for normalization of MiRNA expression data in COVID-19 patients by Íris Terezinha Santos de Santana Silva, Hllytchaikra Ferraz Fehlberg, Fabrício Barbosa Ferreira, Murillo Ferreira da Silva, Pérola Rodrigues dos Santos, Vinicius Moreira Porto, João Carlos Teixeira Dias, George Rego Albuquerque, Ana Paula Melo Mariano, Sandra Rocha Gadelha, Carla Martins Kaneto

“…Abstract SARS-CoV-2, the virus responsible for COVID-19, exhibits structural differences compared to other coronaviruses that impact transmission dynamics and host response. …”
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