Rational design of novel phenol ether derivatives as non-covalent proteasome inhibitors through 3D-QSAR, molecular docking and ADMET prediction by Miao Yuan, Hanwen Ji, Fengxin Sun, Qiang Chen, Ping Cheng

“…At the same time, the pharmacokinetic (PK) prediction, drug-likeness, and synthesis prediction were made for the screened novel drugs. …”
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The trend of Epstein-Barr virus DNA loads and CD8+ T lymphocyte numbers can predict the prognosis of pediatric liver transplant recipients with PTLD by Wang Kedi, Xu Dongjiang, Gao Yan, Ma Kaihui, Zhao Wen

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Commentary on “A systematic review and meta-analysis comparing objectively measured and estimated fitness to predict all-cause and cardiovascular disease mortality in adults” by Barbara E. Ainsworth, Zhenghua Cai

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Ensemble Methods with Voting Protocols Exhibit Superior Performance for Predicting Cancer Clinical Endpoints and Providing More Complete Coverage of Disease-Related Genes by Runyu Jing, Yu Liang, Yi Ran, Shengzhong Feng, Yanjie Wei, Li He

“…In genetic data modeling, the use of a limited number of samples for modeling and predicting, especially well below the attribute number, is difficult due to the enormous number of genes detected by a sequencing platform. …”
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Predicting the Development of Gastric Neoplasms in a Healthcare Cohort by Combining Helicobacter pylori Antibodies and Serum Pepsinogen: A 5-Year Longitudinal Study by Min-Sun Kwak, Goh Eun Chung, Su Jin Chung, Seung Joo Kang, Jong In Yang, Joo Sung Kim

“…We evaluated whether the combination of serum HP antibody and pepsinogen (PG), which is indicative of gastric atrophy, could serve as a predictive marker for the development of gastric neoplasms in a Korean population. …”
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Development and validation of an interpretable machine learning model for predicting left atrial thrombus or spontaneous echo contrast in non-valvular atrial fibrillation patients by Chaoqun Huang, Shangzhi Shu, Miaomiao Zhou, Zhenming Sun, Shuyan Li

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Uric acid to albumin ratio is a novel predictive marker for all-cause and cardiovascular death in diabetic patients: a prospective cohort study by Shengnan Chen, Shengnan Chen, Ming Zhang, Shouye Hu, Xiaolong Shao, Lin Liu, Zhi Yang, Kai Nan

“…Age and UAR had good predictive value for 1-, 3-, and 5-year all-cause death in diabetic patients, and the combination of UAR and age had the highest predictive value. …”
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Does the 2D Frontal Plane Projection Angle Predict Frontal Plane Knee Moments during Stepping, Landing, and Change of Direction Tasks? by Rachel K Straub, Christopher M Powers

“…However, the predictive ability of the FPPA was weak to moderate.…”
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A successful case study of using HCl and viscoelastic diverting acid systems for carbonate matrix acidizing in an oil well with optimized predictive model by Javad Mahdavi Kalatehno, Ehsan Khamehchi, Mostafa Keihanikamal, Fatemeh Yousefmarzi, Matin Dargi, Peyman Daneshfar

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P Wave Duration/P Wave Voltage Ratio Plays a Promising Role in the Prediction of Atrial Fibrillation: A New Player in the Game by E. Karacop, A. Enhos, N. Bakhshaliyev, R. Ozdemir

“…Their negative and positive predictive values were 78.7% and 68.6%, respectively. …”
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Enhancing intracranial aneurysm rupture risk prediction with a novel multivariable logistic regression model incorporating high-resolution vessel wall imaging by Zihang Wang, Zihang Wang, Chang Yan, Chang Yan, Wenqing Yuan, Wenqing Yuan, Shuangyan Jiang, Shuangyan Jiang, Yongxiang Jiang, Ting Chen

“…The model revealed strong calibration and good agreement between predicted and observed rupture probabilities.ConclusionThe multivariate model based on HR-VWI, which incorporates aneurysm and parent artery features, provides a more accurate prediction of IA rupture risk than conventional scoring systems, offering a valuable tool for clinical decision-making.…”
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Artificial Neural Networks as a Tool for High-Accuracy Prediction of In-Cylinder Pressure and Equivalent Flame Radius in Hydrogen-Fueled Internal Combustion Engines by Federico Ricci, Massimiliano Avana, Francesco Mariani

“…While traditional simulation tools such as GT-POWER are widely utilized for these purposes, recent advancements in artificial intelligence provide new opportunities for achieving faster and more accurate predictions. This study presents a comparative evaluation of the predictive capabilities of GT-POWER and an artificial neural network model in estimating in-cylinder pressure, with a particular focus on improvements in computational efficiency. …”
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Habitat suitability mapping and landscape connectivity analysis to predict African swine fever spread in wild boar populations: A focus on Northern Italy. by Giulia Faustini, Marie Soret, Alexandre Defossez, Jaime Bosch, Annamaria Conte, Annelise Tran

“…The distribution of ASF positive wild boars along the major corridors predicted by the model suggests the obtained maps as valuable support to decision-makers to improve ASF surveillance and carcass early detection, aiming for eradication. …”
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Significance of Baseline and Changes of Tumor Markers and Neutrophil-to-Lymphocyte Ratio in Predicting Overall Survival for Patients with Advanced Pancreatic Adenocarcinoma: A Retrospective Analysis by Makoto Kadokura, Keisuke Tanaka, Fumitake Amemiya, Shinichi Takano, Mitsuharu Fukasawa, Nobuyuki Enomoto

“…Conclusion: Baseline and change in tumor markers and NLR are useful in predicting overall survival in APC patients undergoing chemotherapy.…”
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An MRI radiomics model for predicting a prostate-specific antigen response following abiraterone treatment in patients with metastatic castration-resistant prostate cancer by Yi Wu, Xiang Liu, Shaoxian Chen, Fen Fang, Feng Shi, Yuwei Xia, Zehong Yang, Daiying Lin

“…ObjectiveTo establish a combined radiomics-clinical model for the early prediction of a prostate-specific antigen(PSA) response in patients with metastatic castration-resistant prostate cancer(mCRPC) after treatment with abiraterone acetate(AA).MethodsThe data of a total of 60 mCRPC patients from two hospitals were retrospectively analyzed and randomized into a training group(n=48) or a validation group(n=12). …”
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Building a near-infrared (NIR) soil spectral dataset and predictive machine learning models using a handheld NIR spectrophotometerZenodo by Colleen Partida, Jose Lucas Safanelli, Sadia Mannan Mitu, Mohammad Omar Faruk Murad, Yufeng Ge, Richard Ferguson, Keith Shepherd, Jonathan Sanderman

“…All scanning was performed on dried and sieved (<2 mm) soil samples. Machine learning predictive models were developed for soil organic carbon (SOC), pH, bulk density (BD), carbonate (CaCO3), exchangeable potassium (Ex. …”
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Prediction of microvascular obstruction from angio-based microvascular resistance and available clinical data in percutaneous coronary intervention: an explainable machine learning model by Zhe Zhang, Yang Dai, Peng Xue, Xue Bao, Xinbo Bai, Shiyang Qiao, Yuan Gao, Xuemei Guo, Yanan Xue, Qing Dai, Biao Xu, Lina Kang

“…Although we observed the inconsistency between AMR and MVO but the ML-based construction of MVO prediction model is feasible, which brings the possibility of timely prediction of patients with MVO and timely imposition of interventions during PPCI.…”
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Identification of specific risk factors and predictive analytics for cardio-cerebral arterial stenosis: a comparative study utilizing framingham risk stratification insights by Gege Zhang, Sijie Dong, Fanfan Feng, Weihao Kan, Taozhen Shi, Hongmei Ding, Ruiguo Dong

“…This study aimed to investigate the prevalence of ICAS and COAS among ischemic stroke patients across different risk strata and to construct a predictive model for assessing atherosclerosis risk. …”
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Value of the systemic immunoinflammatory index, nutritional risk index, and triglyceride-glucose index in predicting the condition and prognosis of patients with hypertriglyceridemia-associated acute pancreatitis by Yuan Zhen Wang, Ya Ling Yun, Ting Ye, Wen Tun Yao, Yu Feng Guo, Li Ya Huang

“…The ROC curve analysis showed that the AUC value of the combined SII, NRI, and TyG index for predicting SAP occurrence was 0.705 (95%CI:0.632 ~ 0.778).ConclusionThe SII, NRI, and TyG index are related to the severity of HTGAP, and a combination of the three can better predict the occurrence of SAP.…”
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Prediction and unsupervised clustering of fertility intention among migrant workers based on machine learning: a cross-sectional survey from Henan, China by Xinghong Guo, Mingze Ma, Yiyang Chen, Zhaoyang Fang, Jingru Liu, Shuming Yan, Yifei Feng, Xinya Cheng, Jian Wu, Beizhu Ye

“…Result Out of 18,806 participants, only 1057 had fertility intention. We constructed a predictive model for fertility intention based on XGBoost, with an AUC of 0.83. …”
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