Application of artificial intelligence-assisted Traditional Chinese medicine formula granules in bone and joint degenerative diseases in the field of holistic health by Xian-Quan Zhang, Xian-Quan Zhang, Xian-Quan Zhang, Xing-De Wei, Zhuo-Xu Gu, Guang-Hui Zhou, Chu-Jia Huang, Ming-Hui Luo, Ling-Feng Zeng, Ling-Feng Zeng, Ling-Feng Zeng, Wei-Yi Yang, Wei-Yi Yang

“…It analyzes the application models of AI technology in optimizing TCM formula granules, predicting drug efficacy, and personalized treatment. …”
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Integrated multiomics analysis identifies PHLDA1+ fibroblasts as prognostic biomarkers and mediators of biological functions in pancreatic cancer by Rui Wang, Rui Wang, Guan-Hua Qin, Guan-Hua Qin, Yifei Jiang, Fu-Xiang Chen, Fu-Xiang Chen, Zi-Han Wang, Zi-Han Wang, Lin-Ling Ju, Lin Chen, Da Fu, En-Yu Liu, Su-Qing Zhang, Wei-Hua Cai

“…A 7-gene mCAF-associated risk model was constructed using advanced machine learning algorithms, and the biological significance of PHLDA1 was validated through co-culture experiments and pan-cancer analyses.ResultsOur multiomics analysis revealed that the novel 7-gene model (comprising USP36, KLF5, MT2A, KDM6B, PHLDA1, REL, and DDIT4) accurately predicts patient survival, immunotherapy response, and TME status. …”
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Metabolic profiles in laryngeal cancer defined two distinct molecular subtypes with divergent prognoses by Dan Zheng, Dan Zheng, Xuan Pu, Xuan Pu, XuHui Deng, XuHui Deng, Cui Liu, Cui Liu, SiJun Li, SiJun Li

“…Furthermore, we explored the potentials of several key tumor markers for both diagnosis and prognosis prediction.…”
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Multicenter Development and Prospective Validation of eCARTv5: A Gradient-Boosted Machine-Learning Early Warning Score by Matthew M. Churpek, MD, MPH, PhD, ATSF, Kyle A. Carey, MPH, Ashley Snyder, MPH, Christopher J Winslow, MD, Emily Gilbert, MD, Nirav S Shah, MD, MPH, Brian W. Patterson, MD, MPH, Majid Afshar, MD, MSCR, Alan Weiss, MD, MBA, Devendra N. Amin, MD, Deborah J. Rhodes, MD, Dana P. Edelson, MD, MS

“…All adult patients admitted to the inpatient medical-surgical wards and at 21 hospitals from three health systems for retrospective (2009–2023) and prospective (2023–2024) external validation. PREDICTION MODEL:. Predictor variables (demographics, vital signs, documentation, and laboratory values) were used in a gradient-boosted trees algorithm to predict ICU transfer or death in the next 24 hours. …”
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Transdimensional Inference for Gravitational-wave Astronomy with Bilby by Hui Tong, Nir Guttman, Teagan A. Clarke, Paul D. Lasky, Eric Thrane, Ethan Payne, Rowina Nathan, Ben Farr, Maya Fishbach, Gregory Ashton, Valentina Di Marco

“…The tBilby package allows users to set up transdimensional inference calculations using the existing Bilby architecture with off-the-shelf nested samplers and/or Markov Chain Monte Carlo algorithms. Transdimensional models are particularly helpful when seeking to test theoretically uncertain predictions described by phenomenological models. …”
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Development and evaluation of a soft pneumatic muscle for elbow joint rehabilitation by Mostafa Orban, Mostafa Orban, Mostafa Orban, Kai Guo, Kai Guo, Caijun Luo, Caijun Luo, Hongbo Yang, Hongbo Yang, Karim Badr, Karim Badr, Mahmoud Elsamanty, Mahmoud Elsamanty

“…The algorithm achieved a high predictive accuracy of 99.8% in spatial coordination tracking, indicating the precision of the system in monitoring and controlling the actuator’s motion.…”
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GRAVITY ANOMALIES OF THE CRUST AND UPPER MANTLE FOR CENTRAL AND SOUTH ASIA by V. N. Senachin, A. A. Baranov

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Identification of a PANoptosis-related long noncoding rna risk signature for prognosis and immunology in colon adenocarcinoma by Yuekai Cui, Jie Mei, Shengsheng Zhao, Bingzi Zhu, Jianhua Lu, Hongzheng Li, Binglong Bai, Weijian Sun, Wenyu Jin, Xueqiong Zhu, Shangrui Rao, Yongdong Yi

“…Long noncoding RNAs (lncRNAs) play crucial roles in PCD. However, the predictive value of PANoptosis-related lncRNAs (PRlncRNAs) for colon adenocarcinoma (COAD) has not been established. …”
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A validated heart-specific model for splice-disrupting variants in childhood heart disease by Robert Lesurf, Jeroen Breckpot, Jade Bouwmeester, Nour Hanafi, Anjali Jain, Yijing Liang, Tanya Papaz, Jane Lougheed, Tapas Mondal, Mahmoud Alsalehi, Luis Altamirano-Diaz, Erwin Oechslin, Enrique Audain, Gregor Dombrowsky, Alex V. Postma, Odilia I. Woudstra, Berto J. Bouma, Marc-Phillip Hitz, Connie R. Bezzina, Gillian M. Blue, David S. Winlaw, Seema Mital

“…Recent computational algorithms such as SpliceAI have shown an ability to predict such variants, but are not specific to cardiac-expressed genes and transcriptional isoforms. …”
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Constitutive modeling and workability characterization of pre-deformed AZ31 magnesium alloy during hot shear-compression deformation by Junsong Jin, Fangtao Chai, Jinchuan Long, Chang Gao, Shaolei Wang, Pan Zeng, Xuefeng Tang, Pan Gong, Mao Zhang, Lei Deng, Xinyun Wang

“…The developed hot processing map can precisely predict microstructure evolution. The optimal pre-deformation amount is determined to be 2 %, with a recommended hot processing window defined as 300–400 °C and 0.07–7 s−1.…”
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Whole genome sequencing improves tissue-of-origin diagnosis and treatment options for cancer of unknown primary by Richard J. Rebello, Atara Posner, Ruining Dong, Owen W. J. Prall, Tharani Sivakumaran, Camilla B. Mitchell, Aidan Flynn, Alex Caneborg, Catherine Mitchell, Sehrish Kanwal, Clare Fedele, Samantha Webb, Krista Fisher, Hui-Li Wong, Shiva Balachander, Wenying Zhu, Shannon Nicolson, Voula Dimitriadis, Nicholas Wilcken, Anna DeFazio, Bo Gao, Madhu Singh, Ian M. Collins, Christopher Steer, Mark Warren, Narayan Karanth, Huiling Xu, Andrew Fellowes, Rodney J. Hicks, Kym Pham Stewart, Charles Shale, Peter Priestley, Sarah-Jane Dawson, Joseph H. A. Vissers, Stephen B. Fox, Penelope Schofield, David Bowtell, Oliver Hofmann, Sean M. Grimmond, Linda Mileshkin, Richard W. Tothill

“…Curated WGTS features and a CUP prediction algorithm (CUPPA) trained on WGTS data of known cancer types informs TOO in 71% of cases otherwise undiagnosed by clinicopathology review. …”
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Feasibility of GPT-3.5 versus Machine Learning for Automated Surgical Decision-Making Determination: A Multicenter Study on Suspected Appendicitis by Sebastian Sanduleanu, Koray Ersahin, Johannes Bremm, Narmin Talibova, Tim Damer, Merve Erdogan, Jonathan Kottlors, Lukas Goertz, Christiane Bruns, David Maintz, Nuran Abdullayev

“…Overall agreement between the GPT-3.5 output and the reference standard was assessed by means of inter-observer kappa values as well as accuracy, sensitivity, specificity, and positive and negative predictive values with the “Caret” and “irr” packages. …”
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Transcranial High-Frequency Terahertz Stimulation Alleviates Anxiety-like Behavior in Mice via a Noninvasive Approach by Pan Wang, Chaoyang Tan, Wenyu Peng, Zekun Yan, Wenrui Jiang, Han Zhao, Huaxing Si, Jingchen Jia, Chunkui Zhang, Jian Wang, Yuchen Tian, Kun Chen, Yuefan Yang, Zhenyu Wu, Kangning Xie, Yuanming Wu, Mingming Zhang, Tao Chen

“…Mice were subjected to acute restraint stress to induce anxiety and then clustered into anxiety-susceptible and anxiety-resilient groups using the K-means algorithm. We developed an anxiety phenotype prediction classifier utilizing the naïve Bayes algorithm to accurately categorize mice. …”
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Emergency Department Blood Pressure Management in Type B Aortic Dissection: An Analysis with Machine Learning by Nelson Chen, Jessica V. Downing, Jacob Epstein, Samira Mudd, Angie Chan, Sneha Kuppireddy, Roya Tehrani, Isha Vashee, Emily Hart, Emily Esposito, Rose Chasm, Quincy K. Tran

“…We used random forest (RF) algorithms, a machine-learning tool that uses clusters of decision trees to predict a categorical outcome, to identify predictors of achieving HR and SBP goals prior to ED departure, defined as the time point at which patients left the referring ED to come to our institution. …”
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Integrative analysis of T cell-associated markers in Ewing sarcoma reveals prognostic signatures and immune dynamics by Umair Ali Khan Saddozai, Umair Ali Khan Saddozai, Chenxu Liu, Chenxu Liu, Fei Yan, Zhendong Lu, Muhammad Babar Khawar, Muhammad Babar Khawar, Muhammad Usman Akbar, Saadullah Khattak, Haibo Sun, Haibo Sun, Ping Yang

“…The prognostic model demonstrated high predictive accuracy for 1-, 3-, and 5-year survival (AUC: 0.85, 0.82, 0.78). …”
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Exploring the relationship between sepsis and Golgi apparatus dysfunction: bioinformatics insights and diagnostic marker discovery by Wanli Ma, Xinyi Liu, Ran Yu, Jiannan Song, Lina Hou, Ying Guo, Hongwei Wu, Dandan Feng, Qi Zhou, Haibo Li

“…A diagnostic model constructed using five pivotal genes (B3GNT5, FUT11, MAN1C1, ST6GAL1, and C1GALT1C1) exhibited predictive accuracy, with AUC values exceeding 0.96 for all genes. …”
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Analysis of factors influencing clinical pregnancy rates in frozen-thawed embryo transfer cycles by Junqiang Wang, Zexing Yang, Ying Chen, Ying Chen, Fengchen Gao, Wenxiu Zhao, Shuxuan Cao, Yixi Li, Limei He, Limei He

“…The random forest model identified seven variables with the highest predictive value: female age, number of high-quality blastocysts, anti-Müllerian hormone (AMH) level, embryo stage at transfer, endometrial thickness on the day of transfer, number of high-quality cleavage-stage embryos, and pre-transfer endometrial thickness. …”
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Detecting Changes in Soil Fertility Properties Using Multispectral UAV Images and Machine Learning in Central Peru by Lucia Enriquez, Kevin Ortega, Dennis Ccopi, Claudia Rios, Julio Urquizo, Solanch Patricio, Lidiana Alejandro, Manuel Oliva-Cruz, Elgar Barboza, Samuel Pizarro

“…A UAV-captured image was used to predict the spatial distribution of soil parameters, generating fourteen spectral indices and a digital surface model (DSM) from 103 soil plots across 49.83 hectares. …”
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TELEPROM Psoriasis: Enhancing patient-centered care and health-related quality of life (HRQoL) in moderate-to-severe plaque psoriasis by Gabriel Mercadal-Orfila, Gabriel Mercadal-Orfila, Piedad López Sánchez, Aranzazu Pou Alonso, Olatz Ibarra-Barrueta, Emilio Monte-Boquet, Joaquin Borrás Blasco, Nuria Padullés Zamora, Patricia Sanmartin-Fenollera, Cristina Capilla Montes, M. Ángeles Bernabéu Martínez, Salvador Herrera-Pérez

“…Machine learning models, particularly Random Forest (AUC = 0.98) and Support Vector Machine (AUC = 0.96), effectively predicted patient engagement. DLQI scores significantly decreased from 9.33 ± 7.75 at baseline to 4.34 ± 5.86 at 6 months. …”
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Exploring the Global and Regional Factors Influencing the Density of <i>Trachurus japonicus</i> in the South China Sea by Mingshuai Sun, Yaquan Li, Zuozhi Chen, Youwei Xu, Yutao Yang, Yan Zhang, Yalan Peng, Haoda Zhou

“…This elucidation of the distinct biological and physical pathways linking these diverse factors leading to <i>T. japonicus</i> density provides a significantly improved foundation for predicting distribution patterns globally and offers concrete scientific insights for sustainable fishery management strategies.…”
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