Enhancing ovarian cancer prognosis with an artificial intelligence-derived model: Multi-omics integration and therapeutic implications by You Wu, Kunyu Wang, Yan Song, Bin Li

“…The AIDPI model was developed and refined using univariate Cox regression analysis and an ensemble of machine learning algorithms. Functional analysis, immunoprofiling, and the role of the MFAP4 gene were investigated to elucidate the biological mechanisms underlying the model. …”
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Multimodal personalization of transcranial direct current stimulation for modulation of sensorimotor integration by Jan-Ole Radecke, Alexander Kühn, Tim Erdbrügger, Yvonne Buschermöhle, Sogand Rashidi, Hannah Stöckler, Benjamin Sack, Stefan Borgwardt, Till R. Schneider, Joachim Gross, Carsten H. Wolters, Andreas Sprenger, Rebekka Lencer

“…Here, we applied personalized tDCS explicitly targeting individual V5 in healthy human participants using algorithmic optimization informed by functional magnetic resonance imaging and combined electro- and magnetoencephalography. …”
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MRI-based machine learning analysis of perivascular spaces and their link to sleep disturbances, dementia, and mental distress in young adults with long-time mobile phone use by Li Li, Yalan Wu, Jiaojiao Wu, Bin Li, Rui Hua, Feng Shi, Lizhou Chen, Yeke Wu

“…This study investigated computational MRI-visible EPVSs and their association with sleep disturbance, dementia, and mental distress in young adults with LTMPU.MethodsThis retrospective study included 82 LTMPU patients who underwent MRI scans and assessments using six clinical scales: Montreal Cognitive Assessment (MoCA), Pittsburgh Sleep Quality Index (PSQI), Insomnia Severity Index (ISI), Epworth Sleepiness Scale (ESS), Hamilton Anxiety (HAM-A), and Hamilton Depression (HAM-D). Deep learning algorithms segmented EPVSs lesions, extracting quantitative metrics (count, volume, mean length, and mean curvature) across 17 brain subregions. …”
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Classification of SERS spectra for agrochemical detection using a neural network with engineered features by Mateo Frausto-Avila, Monserrat Ochoa-Elias, Jose Pablo Manriquez-Amavizca, María del Carmen González-López, Gonzalo Ramírez-García, Mario Alan Quiroz-Juárez

“…Compared to other machine-learning algorithms, our approach offers reduced computational complexity while maintaining or exceeding the accuracy of more complex models. …”
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AI-based thematic exploration to understand patients with myasthenia gravis by serological subtype by Louis Jackson, Caroline Brethenoux, Alyssa DeLuca, Jacqueline Pesa, Zia Choudhry, Patrick Furey, Rosario Alvarez, Laura Gonzalez, Alex Lorenzo, Raghav Govindarajan, Ashley E. L. Anderson

“…Advanced search techniques and AI-powered algorithms were used to extract/organize data by topics into a large, unstructured dataset. …”
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SbD4Skin by EosCloud: Integrating multi-view molecular representation for predicting skin sensitization, irritation, and acute dermal toxicity by Nikoletta-Maria Koutroumpa, Dimitra-Danai Varsou, Panagiotis D. Kolokathis, Maria Antoniou, Konstantinos D. Papavasileiou, Eleni Papadopoulou, Anastasios G. Papadiamantis, Andreas Tsoumanis, Georgia Melagraki, Milica Velimirovic, Antreas Afantitis

“…Different molecular representations for skin toxicity-related endpoints were first evaluated using three machine learning algorithms (Random Forest, Support Vector Machine, and k-Nearest Neighbors), then combined into a unified input space for training a fully connected neural network (FCNN). …”
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Multi-task aquatic toxicity prediction model based on multi-level features fusion by Xin Yang, Jianqiang Sun, Bingyu Jin, Yuer Lu, Jinyan Cheng, Jiaju Jiang, Qi Zhao, Jianwei Shuai

“…Furthermore, in comparison with previous algorithms, ATFPGT-multi outperforms comparative methods, emphasizing that our approach exhibits higher accuracy and reliability in predicting aquatic toxicity. …”
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Potential Modulatory Roles of Gut Microbiota and Metabolites in the Associations of Macronutrient‐to‐Physical Activity Ratios With Dyslipidemia by Menghan Wang, Guoqing Ma, Yunfeng Li, Junqi Li, Jiawen Xie, Juan He, Chen He, Yifei He, Kaizhen Jia, Xinran Feng, Tian Tian, Hongbao Li, Xia Liao, Xin Liu

“…Gut microbial genera and fecal metabolites were profiled through 16S rRNA sequencing and untargeted LC–MS metabolomics, respectively. Machine‐learning algorithms were applied to identify gut microbiome features of macronutrient‐to‐PA ratios and to construct microbiome risk score. …”
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Construction of a risk model associated with tryptophan metabolism and identification of related molecular subtypes in laryngeal squamous cell carcinoma by Feng Liu, Yanchao Qin, Wei Luo, XianHui Ruan, Lifang Lu, Bowei Feng, Jianfei Yu

“…Next, differentially expressed TMRGs (DE-TMRGs) was obtained in key model and DEGs, and prognostic genes were identifde through multiple algorithms. Five prognostic genes, namely SERPINA1, TMC8, RENBP, SDS and FAM107A were finally identified. …”
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Machine Learning Models in the Detection of MB2 Canal Orifice in CBCT Images by Shishir Shetty, Meliz Yuvali, Ilker Ozsahin, Saad Al-Bayatti, Sangeetha Narasimhan, Mohammed Alsaegh, Hiba Al-Daghestani, Raghavendra Shetty, Renita Castelino, Leena R David, Dilber Uzun Ozsahin

“…Conclusion: The success rates (AUC, precision, recall) of ML algorithms in the detection of MB2 were remarkable in our study. …”
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An Optimal Internet of Things-Driven Intelligent Decision-Making System for Real-Time Fishpond Water Quality Monitoring and Species Survival by Saima Kanwal, Muhammad Abdullah, Sahil Kumar, Saqib Arshad, Muhammad Shahroz, Dawei Zhang, Dileep Kumar

“…Advanced machine learning techniques, with feature transformation and balancing, were applied to preprocess the dataset, which includes water quality metrics and species-specific parameters. Multiple algorithms were trained and evaluated, with the Decision Tree classifier emerging as the optimal model, achieving remarkable performance metrics: 99.8% accuracy, precision, recall, and F1-score, a 99.6% Matthews Correlation Coefficient (MCC), and the highest Area Under the Curve (AUC) score for multi-class classification. …”
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Early detection of retinal and choroidal microvascular impairments in diabetic patients with myopia by Yufei Wu, Yufei Wu, Jiahui Jiang, Xiaoyu Deng, Xixi Zhang, Jinger Lu, Zian Xu, Yitian Zhao, Zai-Long Chi, Zai-Long Chi, Qinkang Lu, Qinkang Lu

“…The AI-driven analysis revealed that decreased CVI and CT were significantly associated with age and spherical equivalent (SE), highlighting the utility of automated algorithms in identifying early microvascular impairments.ConclusionDiabetic patients with high myopia exhibited significantly lower CVI compared to those with diabetic retinopathy, indicating that CVI monitoring could facilitate risk stratification of diabetic retinopathy progression. …”
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A deep neural network framework for estimating coastal salinity from SMAP brightness temperature data by Yidi Wei, Qing Xu, Qing Xu, Xiaobin Yin, Xiaobin Yin, Yan Li, Yan Li, Kaiguo Fan

“…The framework leverages machine learning interpretability tools (Shapley Additive Explanations, SHAP) to optimize input feature selection and employs a grid search strategy for hyperparameter tuning.Results and discussionSystematic validation against independent in-situ measurements demonstrates that the baseline DNN model constructed for the entire region and time period outperforms conventional algorithms including K-Nearest Neighbors, Random Forest, and XGBoost and the standard SMAP SSS product, achieving a reduction of 36.0%, 33.4%, 40.1%, and 23.2%, respectively in root mean square error (RMSE). …”
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Clinical Manifestation and Errors in the Diagnosis of Classical Paroxysmal Nocturnal Hemoglobinuria: A clinical case series of 150 patients by AD Kulagin, OU Klimova, AV Dobronravov, MO Ivanova, TA Rudakova, EV Babenko, VA Dobronravov, BV Afanas’ev

“…The results of the research showed the need for multidisciplinary approach and strict adherence to diagnostic algorithms of PNH in the risk groups, according to current recommendations.…”
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Development and validation of a population pharmacokinetic model to guide perioperative tacrolimus dosing after lung transplantation by Todd A. Miano, PharmD, PhD, Athena F. Zuppa, MD, MSCE, Rui Feng, PhD, Stephen Griffiths, BS, Laurel Kalman, BA, Michelle Oyster, MS, Edward Cantu, MD, MSCE, Wei Yang, PhD, Joshua M. Diamond, MD, MSCE, Jason D. Christie, MD, MSCE, Marc H. Scheetz, PharmD, MSC, Michael G.S. Shashaty, MD, MSCE

“…Population PK models that include lung-transplant–specific covariates may enable precision dosing algorithms that account for this highly dynamic clearance. …”
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Language task-based fMRI analysis using machine learning and deep learning by Elaine Kuan, Elaine Kuan, Elaine Kuan, Viktor Vegh, Viktor Vegh, Viktor Vegh, John Phamnguyen, John Phamnguyen, John Phamnguyen, Kieran O’Brien, Amanda Hammond, David Reutens, David Reutens, David Reutens, David Reutens

“…Their analysis necessitates the use of alternative methods such as machine learning (ML) and deep learning (DL) because task regressors may be difficult to define in these paradigms.MethodsUsing task-based language fMRI as a starting point, this study investigates the use of different categories of ML and DL algorithms to identify brain regions subserving language. …”
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Optimized AI and IoT-Driven Framework for Intelligent Water Resource Management by Mahmoud Badee Rokaya Mahmoud, Dalia Ismaeil Ibrahim Hemdan, Samah Hazzaa Alajmani, Raneem Yousif Alyami, Ghada Elmarhomy, Hassan Hashim, El-Sayed Atlam

“…The architecture combines the ensemble-learning algorithms (XGBoost, LightGBM), hybrid AIs (XGBoost + Autoencoder), and metaheuristic feature selection (GA, PSO, SA) for making intelligent decisions. …”
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The interaction between temperature and precipitation on the potential distribution range of Betula ermanii in the alpine treeline ecotone on the Changbai Mountain by Yu Cong, Yongfeng Gu, Wen J. Wang, Lei Wang, Zhenshan Xue, Yingyi Chen, Yinghua Jin, Jiawei Xu, Mai-He Li, Hong S. He, Ming Jiang

“…Hence, we used a GF-2 satellite image, along with bioclimatic and topographic variables, to develop an ensemble suitable habitat model based on the species distribution modeling algorithms in Biomod2. We investigated the distribution of suitable habitats for B. ermanii under three climate change scenarios (i.e., low (SSP126), moderate (SSP370) and extreme (SSP585) future emission trajectories) between two consecutive time periods (i.e., current–2055, and 2055–2085). …”
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High-expression of BCL10 inhibits cell-mediated immunity within the tumor immune microenvironment by Jinyi Gu, Jinyi Gu, Changshun Chen, Changshun Chen, Yuanjing Chen, Wei Lv, Fei Li, Puyuan Zhu, Pu He, Yunjie Du, Huiling Liu, Bingdong Zhu

“…The correlation between BCL10 expression and infiltrated immune cells was analyzed by immune algorithms such as xCELL, CIBERSORT, QUANTISEQ and MCPcounter using TCGA and GTEx databases. …”
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Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids by Xin-Ru Wen, Jia-Wei Tang, Jie Chen, Hui-Min Chen, Muhammad Usman, Quan Yuan, Yu-Rong Tang, Yu-Dong Zhang, Hui-Jin Chen, Liang Wang

“…This study aims to develop a novel method for BV detection by integrating surface-enhanced Raman scattering (SERS) with machine learning (ML) algorithms. Vaginal fluid samples were classified as BV positive or BV negative using the BVBlue Test and clinical microscopy, followed by SERS spectral acquisition to construct the data set. …”
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