Development of a three-species gut microbiome diagnostic model for acute pancreatitis and its association with systemic inflammation: a prospective cross-sectional study by Yuanyuan Gou, Long Yao, Wenli Yang, Qian Chen, Yuetao Wen, Jie Cao

“…Notably, Veillonella parvula and Fusobacterium periodonticum abundances correlated positively with white blood cell count (r = 0.40, p = 0.018) and procalcitonin (r = 0.41, p = 0.015). Functional prediction revealed elevated microbial pathways linked to cell membrane transport and infectious disease risk in AP. …”
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Mapping the air temperature in China from time-normalized MODIS land surface temperature data via zone-based stacking ensemble models by Yan Xin, Yongming Xu, Xudong Tong, Yaping Mo, Yonghong Liu, Shanyou Zhu

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AI-based virtual immunocytochemistry for rapid and robust fine needle aspiration biopsy diagnosis by Irfan Ahmed, Wei Zhang, Pikting Cheung, Vardhan Basnet, Zulfiqar Ali, May PY Tse, Fraser Hill, Tom Tak Lam Chan, Haibo Hu, Xinyue Li, Condon Lau

“…In total, the geometrical features of 8.48 million segmented cells (4.24 million pairs) were translated into a tabular format and paired based on the Euclidean cell matching algorithm. This approach facilitated the prediction of cell labels, achieving sensitivity and specificity of 0.98 and 0.97 (0.94 and 0.99), respectively for CD3 (PAX5). …”
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Integrative machine learning and molecular simulation approaches identify GSK3β inhibitors for neurodegenerative disease therapy by Hassan H. Alhassan

“…Among all models, the Random Forest (RF) algorithm had the best prediction accuracy, with a value of 0.6832 on the test set and 0.7432 on the training set, and was employed to screen the target library of 11,032 phytochemicals. …”
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Using Deep Learning Techniques to Enhance Blood Cell Detection in Patients with Leukemia by Mahwish Ilyas, Muhammad Bilal, Nadia Malik, Hikmat Ullah Khan, Muhammad Ramzan, Anam Naz

“…This research study proposes an automatic diagnostics prediction of leukemia by analyzing blood images according to the shape of the blast cells using digital image processing and machine learning. …”
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Predictors for recurrence of chronic osteomyelitis following long bones cavity defect replacement with synthetic materials by Alexander V. Afanasyev, Svetlana A. Bozhkova, Vasily A. Artyukh

“…Aim of the study: to determine significant risk factors of infection recurrence during one- and two-stage treatment of a cavity defect with synthetic materials in patients with chronic osteomyelitis and to create an assessment scale for prediction of the treatment outcome based on this data. …”
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On-Chip Age Estimation Using Machine Learning by Turki Alnuayri, Saqib Khursheed, Daniele Rossi

“…It outperforms the state-of-the-art IC age prediction models even when learning and validating the model with aging and PV.…”
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BedEye: A Bed Exit and Bedside Fall Warning System Based on Skeleton Recognition Technology for Elderly Patients by Liang-Bi Chen, Wan-Jung Chang, Tzu-Chin Yang

“…Moreover, we develop an algorithm for determining bed posture using the posture of patients leaving the bed. …”
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Transparent and Robust Artificial Intelligence-Driven Electrocardiogram Model for Left Ventricular Systolic Dysfunction by Min Sung Lee, Jong-Hwan Jang, Sora Kang, Ga In Han, Ah-Hyun Yoo, Yong-Yeon Jo, Jeong Min Son, Joon-myoung Kwon, Sooyeon Lee, Ji Sung Lee, Hak Seung Lee, Kyung-Hee Kim

“…The AiTiALVSD model, based on a deep learning algorithm, was evaluated against echocardiographic ejection fraction values. …”
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Estimating the Risk of Lower Extremity Complications in Adults Newly Diagnosed With Diabetic Polyneuropathy: Retrospective Cohort Study by Alyce S Adams, Catherine Lee, Gabriel Escobar, Elizabeth A Bayliss, Brian Callaghan, Michael Horberg, Julie A Schmittdiel, Connie Trinacty, Lisa K Gilliam, Eileen Kim, Nima S Hejazi, Lin Ma, Romain Neugebauer

“…ObjectiveThis study aimed to develop an electronic medical record–based machine learning algorithm that would predict lower extremity complications. …”
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Combined mechanistic and machine learning method for construction of oil reservoir permeability map consistent with well test measurements by Evgenii Kanin, Alsu Garipova, Sergei Boronin, Vladimir Vanovskiy, Albert Vainshtein, Andrey Afanasyev, Andrei Osiptsov, Evgeny Burnaev

“…This inverse optimization problem is addressed by repeatedly solving forward problems, where an artificial neural network (ANN) predicts the integral permeability of the formation surrounding a well and skin factor. …”
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Identification and validation of endoplasmic reticulum autophagy-related potential biomarkers in periodontitis by Ruyue Wang, Jinyue Hu, Qing Sun, Shuixiang Guo, Gege Zhang, Ao Lu, Shuo Liu, Xue Yang, Lina Wang

“…Clinical samples examined by RT-qPCR verified that these ER-phagy-related signature genes in periodontitis may better predict the development of periodontitis.ER-phagy is closely related to the pathological process of periodontitis. …”
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Physics-informed modeling of splitting tensile strength of recycled aggregate concrete using advanced machine learning by Kennedy C. Onyelowe, Viroon Kamchoom, Shadi Hanandeh, S. Anandha Kumar, Rolando Fabián Zabala Vizuete, Rodney Orlando Santillán Murillo, Susana Monserrat Zurita Polo, Rolando Marcel Torres Castillo, Ahmed M. Ebid, Paul Awoyera, Krishna Prakash Arunachalam

“…Five advanced machine learning techniques created using the “Weka Data Mining” software version 3.8.6 were applied to predict the Fsp and the Hoffman & Gardener method and performance metrics were also used to evaluate the sensitivity and performance of the variables and ML models, respectively. …”
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microRNA-183–5p induces cell density-dependent apoptosis through the regulation of Presenilin 2 by Yuki Yabuuchi, Yosuke Matsuno, Kai Yazaki, Wei Zhen Ting, Kengo Nishino, Sosuke Matsumura, Kenya Kuramoto, Kazufumi Yoshida, Masashi Matsuyama, Takumi Kiwamoto, Yuko Morishima, Nobuyuki Hizawa

“…PSEN2 regulated the development of apoptosis, which is accompanied by increased Bcl-2 expression, decreased Bax expression, and activated PI3K/Akt pathway. PSEN2 is predicted to be targeted by microRNA-183–5p (miR-183–5p) by several algorithms. …”
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Spatio-Temporal Habitat Dynamics of Migratory Small Yellow Croaker (<i>Larimichthys polyactis</i>) in Hangzhou Bay, China by Xiangyu Long, Dong Wang, Pengbo Song, Mengwen Han, Rijin Jiang, Yongdong Zhou

“…We evaluated the performance of eleven modeling algorithms to identify the most accurate model for predicting small yellow croaker distributions. …”
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Critical load exceedances for North America and Europe using an ensemble of models and an investigation of causes of environmental impact estimate variability: an AQMEII4 study by P. A. Makar, P. Cheung, C. Hogrefe, A. Akingunola, U. Alyuz, J. O. Bash, M. D. Bell, R. Bellasio, R. Bianconi, T. Butler, H. Cathcart, O. E. Clifton, O. E. Clifton, A. Hodzic, I. Kioutsioukis, R. Kranenburg, A. Lupascu, A. Lupascu, J. A. Lynch, K. Momoh, J. L. Perez-Camanyo, J. Pleim, Y.-H. Ryu, R. San Jose, D. Schwede, D. Schwede, T. Scheuschner, M. W. Shephard, R. S. Sokhi, S. Galmarini

“…If model–measurement fusion is only applied to some but not all of the fields contributing to the total deposition of sulfur or nitrogen, the corrections may result in greater variability between models or less accurate results for an ensemble of models, for those cases where an unobserved or unused observed component contributes significantly to predicted total deposition.</p> <p>Based on these results, an increased process-research focus is therefore recommended for the following model processes and for observations which may assist in model evaluation and improvement: multiphase hydrometeor scavenging combined with updated particle dry-deposition, cuticle, and soil deposition pathway algorithms for acidifying gases, base cation chemistry and emissions, and NH<span class="inline-formula">₃</span> bidirectional fluxes. …”
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Applied AMT machine learning and multi-objective optimization for enhanced performance and reduced environmental impact of sunflower oil biodiesel in compression ignition engine by Ali A. Al-jabiri, Hyder H. Balla, Mudhaffar S. Al-zuhairy, Hussein Alahmer, Ahmed Al-Manea, Raed Al-Rbaihat, Ali Alahmer

“…The AMT models demonstrate high prediction accuracy, with coefficient of determination (R2) values exceeding 0.98.…”
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Quantifying the chemical composition of weathering products of Hainan basalts with reflectance spectroscopy and its implications for Mars by Xing Wu, JiaCheng Liu, WeiChao Sun, Yang Liu, Joseph Michalski, Wei Tan, XiaoRong Qin, YongLiao Zou

“…We established a quantitative spectral model by combining the genetic algorithm and partial least squares regression (GA-PLSR) to predict the chemical properties (SiO2, Al2O3, Fe2O3) and index of laterization (IOL). …”
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NETosis-based prognostic model reveals immune modulation in clear cell renal cell carcinoma using single-cell and bulk RNA sequencing by Zijie Yu, Zihao Xu, Xi Zhang, Wenchuan Shao, Da Zhong, Xinghan Yan, Tingfei Jiang, Yichun Wang, Ninghong Song

“…Netosis and TME scores exhibited a high degree of predictive power for patient survival, as illustrated by Kaplan-Meier (KM) curves. …”
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Differences in Characteristics of Peripartum Patients Who Did and Did Not Require an Upgrade to the Intensive Care Unit: A Propensity Score Matching Study by Jennifer A. Walker, Jerry M. Yang, Saad Pirzada, Manahel Zahid, Samantha Asuncion, Amanda Tuchler, Gillian Cooper, Allison Lankford, Emad Elsamadicy, Quincy K. Tran

“…The Sequential Organ Failure Assessment score at Maternal Fetal Ward admission was most predictive, followed by the Acute Physiology and Chronic Health Evaluation II (APACHE II) score, and lactate dehydrogenase levels. …”
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