Investigation of granuloma-targeted host-pathogen interactions identify vaccine correlates of immune protection associated with control of M. tuberculosis replication by David Forrest Ackart, Dr. Faye Lanni, Victoria Mitcham, Dr. G. Brooke Anderson, Dr. Michael Lyons, Dr. Marcela Henao-Tamayo, Dr Brendan Podell

“…While a number of blood transcriptomic signatures have been shown to predict clinical states of TB in human subjects, no correlates exist to indicate immune signatures that control bacterial growth, which would inform improved vaccine design. …”
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Does metaverse improve recommendations quality and customer trust? A user-centric evaluation framework based on the cognitive-affective-behavioural theory by Rabab Ali Abumalloh, Mehrbakhsh Nilashi, Osama Halabi, Raian Ali

“…They can analyse large amounts of data using machine learning algorithms and predictive analytics capabilities to provide highly relevant recommendations to users. …”
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Trends in Russia’s GDP Growth under Environmental Constraints by Z. V. Bragina, A. R. Denisov, N. N. Masyuk, N. V. Steblyanskii

“…Methods used: empirical and statistical analysis, building regression models, algorithmic and predictions, building time trends, etc. …”
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Precision in practice: exploring the impact of ai and machine learning on ultrasound guided regional anaesthesia by Noor Ul Huda Bhatti, Syed Ghazi Ali Kirmani, Maryam Butt

“…It is used to conduct predictive tasks without programming instructions by creating algorithms. …”
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Novel estrogen-related gene variants identified by whole-exome sequencing in pregnancy-associated intrahepatic cholestasis by Hua Lai, Siming Xin, Jinliang Zhang, Yang Hu, Wenjuan Fan, Hong Wan, Bowen Chen, Yang Zou, Xiaoming Zeng, Xianxian Liu

“…These variants exhibited the following characteristics: (1) complete absence in 1,237 controls and all public genomic databases (1000 Genomes, ExAC, and dbSNP); (2) evolutionary conservation of the affected residues, with unanimous pathogenic predictions from all algorithms (PolyPhen-2: damaging; SIFT: deleterious; MutationTaster: disease-causing); (3) molecular modeling demonstrating structural perturbations in critical functional domains, including steroid-binding and redox partner interaction sites. …”
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Federated Learning Based on an Internet of Medical Things Framework for a Secure Brain Tumor Diagnostic System: A Capsule Networks Application by Roman Rodriguez-Aguilar, Jose-Antonio Marmolejo-Saucedo, Utku Köse

“…It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. …”
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Sensor‐Guided Smart Irrigation for Tomato Production: Comparing Low and Optimum Soil Moisture in Greenhouse Environments by Ibrahim Dirlik, Ferhat Uğurlar, Cengiz Kaya

“…Future research should explore the integration of advanced sensors, machine learning algorithms, and predictive models to further optimize irrigation strategies, with an emphasis on scalability and environmental impact. …”
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Multi-omics integration and machine learning uncover molecular basal-like subtype of pancreatic cancer and implicate A2ML1 in promoting tumor epithelial-mesenchymal transition by Jiachen Ge, Jianping Cai, Gaolei Zhang, Deyu Li, Lianyuan Tao

“…Prognostic genes were identified to construct predictive models through various machine learning approaches. …”
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Oil Development Engineering Company, Tehran, Iran by Seyed Ali Mohammad Tajalli, Mazda Moattari, Vahid Naghavi, Mohammad Reza Salehizadeh

“…Leveraging a hybrid approach that combines the Extended Kalman Filter (EKF), Real-Time Transient Modeling (RTTM), and machine learning algorithms, the framework offers accurate insights into the pipeline’s operational status. …”
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Exploration of Biomarkers of Psoriasis through Combined Multiomics Analysis by Lu Xing, Tao Wu, Li Yu, Nian Zhou, Zhao Zhang, Yunjing Pu, Jinnan Wu, Hong Shu

“…This study aims to screen potential diagnostic indicators affected by DNA methylation for psoriasis based on bioinformatics using multiple machine learning algorithms and to preliminarily explore its molecular mechanisms. …”
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Machine learning−derived multivariable predictors of postcardiotomy cardiogenic shock in high-risk cardiac surgery patientsCentral MessagePerspective by Edward G. Soltesz, MD, MPH, Randi J. Parks, PhD, Elise M. Jortberg, MS, Eugene H. Blackstone, MD

“…Objective: To develop a model for preoperatively predicting postcardiotomy cardiogenic shock (PCCS) in patients with poor left ventricular (LV) function undergoing cardiac surgery. …”
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Machine learning developed LKB1-AMPK signaling related signature for prognosis and drug sensitivity in hepatocellular carcinoma by Wang Li, Xiaoyi Zhu, Jieying Fang

“…The optimal LRS developed using the Lasso algorithm served as a significant risk factor for HCC patients. …”
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Performance evaluation and multi-objective optimization of EDM parameters for Ti6Al4V using different tool electrodes by Manoj Jagdale, Masuk Abdullah, Nitin Ambhore, Atul Kulkarni, Rakesh Chaudhari, Jay Vora, József Menyhárt

“…A minor adequate deviance was observed between the TLBO algorithm’s predicted and actual findings. Scanning electron microscopy (SEM) analysis was conducted to evaluate surface morphology. …”
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Mapping forest types along ecological gradient in Pakistan by Naveed Ahmad, Syed Ghias Ali

“…This study suggests that mapping of forest types through machine learning algorithms may be improved by incorporating other explanatory variables such as microclimate, soil types, nutrients, anthropogenic, demographic factors and spectral indices.…”
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Supervised machine learning and genotype by trait biplot as promising approaches for selection of phytochemically enriched Rhus coriaria genotypes by Hamid Hatami Maleki, Reza Darvishzadeh, Ahmad Alijanpour, Yousef Seyfari

“…By using 13 feature selection algorithms, ISSR loci (U823) L1, (U835) L1, (U801) L1, (U816) L2, (U816) L4, (U835) L4, (U854) L1, and (U835) L9 were identified as functional markers which could predict phytochemical response of sumac germplasm. …”
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Design and Development of Diabetes Management System Using Machine Learning by Robert A. Sowah, Adelaide A. Bampoe-Addo, Stephen K. Armoo, Firibu K. Saalia, Francis Gatsi, Baffour Sarkodie-Mensah

“…The model achieved specified goals by predicting with high accuracy, labels of new images. …”
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Machine learning unveils key Redox signatures for enhanced breast Cancer therapy by Tao Wang, Shu Wang, Zhuolin Li, Jie Xie, Kuiying Du, Jing Hou

“…We employed a comprehensive approach by combining ten distinct machine learning algorithms across 108 different combinations to scrutinize 88 pre-existing signatures of breast cancer. …”
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MISHTI study evaluating hepatic fibrosis in type 2 diabetes patients in India by Debasis Datta, Krishna G. Seshadri, Samit Ghosal

“…Future research should focus on refining diagnostic algorithms to better address the burden of significant fibrosis in at-risk populations.…”
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The Inversion of SPAD Value in Pear Tree Leaves by Integrating Unmanned Aerial Vehicle Spectral Information and Textural Features by Ning Yan, Yasen Qin, Haotian Wang, Qi Wang, Fangyu Hu, Yuwei Wu, Xuedong Zhang, Xu Li

“…The results showed the following: (1) both vegetation indices and textural features were significantly correlated with SPAD values, which were important indicators for estimating the SPAD values of pear leaves; (2) combining vegetation indices and textural features significantly improved the accuracy of SPAD value estimation compared with a single feature type; (3) the four machine learning algorithms demonstrated good predictive ability, and the OIA model outperformed the single model, with the model based on the OIA inversion model combining vegetation indices and textural features having the best accuracy, with <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></semantics></math></inline-formula> values of 0.931 and 0.877 for the training and validation sets, respectively. …”
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Assessing current and future areas of ecological suitability for Lutzomyia shannoni in North America by Sydney DeWinter, Grace K. Nichol, Christopher Fernandez-Prada, Amy L. Greer, J. Scott Weese, Katie M. Clow

“…The objectives of this study were to predict the current and future ecological suitability of regions across North America for Lu. shannoni and to identify variables driving ecological suitability. …”
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