Predicting cardiovascular disease and all-cause mortality using the lymphocyte-to-monocyte ratio: Insights from explainable machine learning models by Jichao Wu, Die Huang, Jiefang Li, Jingxing Yi, Yu Lei, Jun Yin

“…Conclusion: The study concludes that LMR is a simple and practical indicator for predicting cardiovascular disease and its mortality. …”
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Development of an m6A-Related lncRNAs Signature Predicts Tumor Stemness and Prognosis for Low-Grade Glioma Patients by Dahua Xu, Peihu Li, Chunrui Zhang, Yutong Shen, Jiale Cai, Qingchen Wei, Meng Cao, Zhizhou Xu, Deng Wu, Hong Wang, Xiaoman Bi, Bo Wang, Kongning Li

“…Based on the expression of LINC02984, PFKP-DT, and CRNDE, a risk model and nomogram were constructed; they successfully predicted the survival of patients and were extended to external datasets. …”
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Predictive model for abdominal liposuction volume in patients with obesity using machine learning in a longitudinal multi-center study in Korea by Hyunji Sang, Jaeyu Park, Soeun Kim, Myeongcheol Lee, Hojae Lee, Sun-Ho Lee, Dong Keon Yon, Sang Youl Rhee

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Deafferentation-Induced Plasticity of Visual Callosal Connections: Predicting Critical Periods and Analyzing Cortical Abnormalities Using Diffusion Tensor Imaging by Jaime F. Olavarria, Andrew S. Bock, Lindsey A. Leigland, Christopher D. Kroenke

“…We observed that the relationship between the timing of the critical period and the DTI-characterized developmental trajectory is strikingly similar in rodents and ferrets, which opens the possibility of using cortical DTI trajectories for predicting the critical period in species, such as humans, in which this period likely occurs prenatally. …”
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Performance in organic chemistry at either a four-year university or community college predicts academic performance in an accelerated school of pharmacy by Troy J. Smith, Jason Ross, Charles R. Breese, Melissa A. Burmeister

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Forest stand height predicted from ICESat-2 ATLAS data for forest inventory and comparison to airborne laser scanning metrics by Lang Mait, Tampuu Tauri, Trofimov Heido

“…The study analysed 2019–2022 summertime canopy height predictions (HICESat) given in ICESat-2 ATLAS dataset ATL08 for hemiboreal forests growing on an area of 40,000 km2 in Estonia around 25.6° E, 58.8° N. …”
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Secondary Circulating Prostate Cells Predict Biochemical Failure in Prostate Cancer Patients after Radical Prostatectomy and without Evidence of Disease by Nigel P. Murray, Eduardo Reyes, Nelson Orellana, Cynthia Fuentealba, Leonardo Bádinez, Ruben Olivares, José Porcell, Ricardo Dueñas

“…The presence of circulating prostate cells (CPCs) in men without evidence of BF may be useful to predict patients at risk for BF. We describe the frequency of CPCs detected after RP, relation with clinicopathological parameters, and association with biochemical failure. …”
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Risk factors and early prediction of cardiorenal syndrome type 3 among acute kidney injury patients: a cohort study by Hui Lin, Xiaoyu Guo, Mengzhu Wang, Xiaole Su, Xi Qiao

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Value of triglyceride glucose-body mass index in predicting nonalcoholic fatty liver disease in individuals with type 2 diabetes mellitus by Xiaoyi Qian, Wenwen Wu, Boyang Chen, Simin Zhang, Chunmei Xiao, Long Chen, Jun Chen, Jun Chen, Lingli Ke, Meian He, Xiulou Li, Xiulou Li, Xiulou Li

“…The magnitude of risk prediction and predictive efficacy of TyG-BMI for T2DM with NAFLD remains unclear.ObjectiveTo examine the association of TyG-BMI with NAFLD in T2DM patients and assess the effectiveness of screening using the TyG-BMI index.MethodsWe conducted a retrospective analysis of clinical data from 602 T2DM patients at an enterprise health lodge from September 2021 to November 2022. …”
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Immunization of cattle with a Rhipicephalus microplus chitinase peptide containing predicted B-cell epitopes reduces tick biological fitness by María Martina Esperanza Perez-Soria, Daniel Gustavo López-Díaz, Rafael Jiménez-Ocampo, Gabriela Aguilar-Tipacamú, Massaro W. Ueti, Juan Mosqueda, Ala Tabor

“…Four peptides containing predicted B-cell epitopes were designed. The immunogenicity of each peptide was assessed by inoculating 2 cattle, 4 times at 21 days intervals, and the antibody response was verified by indirect ELISA. …”
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Evaluation of hesperidin as a potential larvicide against Culex pipiens with computational prediction of its mode of action via molecular docking by Abdullah Haikal, Mahmoud Kamal, Eslam M. Hosni, Yhiya Amen

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Ultrasonic radiomics-based nomogram for preoperative prediction of residual tumor in advanced epithelial ovarian cancer: a multicenter retrospective study by Shanshan Li, Qiuping Ding, Lijuan Li, Yuwei Liu, Hanyu Zou, Yushuang Wang, Xiangyu Wang, Bingqing Deng, Qingxiu Ai

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Prediction model study focusing on eHealth in the management of urinary incontinence: the Personalised Advantage Index as a decision-making aid by Huibert Burger, Henk van der Worp, Marco H Blanker, Marjolein Y Berger, Anne Martina Maria Loohuis, Nienke Wessels, Janny Dekker, Alec GGA Malmberg

“…Objective To develop a prediction model and illustrate the practical potential of personalisation of treatment decisions between app-based treatment and care as usual for urinary incontinence (UI).Design A prediction model study using data from a pragmatic, randomised controlled, non-inferiority trial.Setting Dutch primary care from 2015, with social media included from 2017. …”
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Development and validation of a hyperlipidemia risk prediction model for middle-aged and older adult Chinese using 2015 CHARLS data by Li-xiang Zhang, Shan-Bing Hou, Fang-fang Zhao, Ting-ting Wang, Ying Jiang, Xiao-juan Zhou, Jiao-yu Cao

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ZleepNet: A Deep Convolutional Neural Network Model for Predicting Sleep Apnea Using SpO2 Signal by Hnin Thiri Chaw, Thossaporn Kamolphiwong, Sinchai Kamolphiwong, Krongthong Tawaranurak, Rattachai Wongtanawijit

“…The proposed model includes three convolutional layers, which include ReLu activation function, 2 dense layers, and one dropout layer for predicting sleep apnea. In this proposed model, the use of signals for detecting the sleep apnea can be reduced from 25 sensors to 1 sensor. …”
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Early characterization and prediction of glioblastoma and brain metastasis treatment efficacy using medical imaging-based radiomics and artificial intelligence algorithms by Noémie N. Moreau, Noémie N. Moreau, Samuel Valable, Cyril Jaudet, Loïse Dessoude, Leleu Thomas, Romain Hérault, Romain Modzelewski, Romain Modzelewski, Dinu Stefan, Juliette Thariat, Juliette Thariat, Alexis Lechervy, Aurélien Corroyer-Dulmont, Aurélien Corroyer-Dulmont

“…This review will focus on 52 published research studies between 2013 and 2024 on (1) the early characterization of treatment efficacy with biomarker imaging and radiomic-based solutions, (2) predictive solutions with radiomic and artificial intelligence-based solutions, (3) interest in other biomarkers, and (4) the importance of the prediction of new treatment modalities’ efficacy.…”
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Daily Prediction Model of Photovoltaic Power Generation Using a Hybrid Architecture of Recurrent Neural Networks and Shallow Neural Networks by Wilson Castillo-Rojas, Juan Bekios-Calfa, César Hernández

“…It is concluded that the proposed and implemented models are functional and capable of predicting with a high level of accuracy the photovoltaic energy production of the solar plant, based on historical data on photovoltaic energy production.…”
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Predictions of State and County Top Civil Servants Regarding the Abolition of County State Administration Offices: Two Sides of the Same Coin? by Iva Lopižić, Romea Manojlović Toman

Subjects: “…civil servants’ predictions, abolition of county state administration offices, administrative decentralization, factors of cognitive biases…”
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Prediction of suitable habitat for tundra swans in the Nanji wetland Nature Reserve of Poyang Lake under different water level conditions by Tianwei Fang, Jin Jin, Chao Chen, Li Zhang, Yankuo Li, Chaoyang Li

“…It is important to predict the habitat of the migratory bird Cygnus columbianus (tundra swan) at different water levels when generating wetland conservation strategies. …”
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Modeling dose uncertainty in cone-beam computed tomography: Predictive approach for deep learning-based synthetic computed tomography generation by Cédric Hémon, Lucía Cubero, Valentin Boussot, Romane-Alize Martin, Blanche Texier, Joël Castelli, Renaud de Crevoisier, Anaïs Barateau, Caroline Lafond, Jean-Claude Nunes

“…Conclusions:: Our proposed methods effectively predict uncertainty maps that aid in evaluating sCT quality. …”
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