Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis by Fei Zuo, Lei Zhong, Jie Min, Jinyu Zhang, Longping Yao

“…Using these features, four machine learning (ML) algorithms were developed. The optimal model was visualized and clarified using SHapley Additive exPlanations (SHAP) and presented as a nomogram. …”
Get full text

Automated and code-free development of a risk calculator using ChatGPT-4 for predicting diabetic retinopathy and macular edema without retinal imaging by Eun Young Choi, Joon Yul Choi, Tae Keun Yoo

“…The performance of the ChatGPT-4 developed models was comparable to those created using various machine-learning tools. Conclusion By utilizing ChatGPT-4 with code-free prompts, we overcame the technical barriers associated with using coding skills for developing prediction models, making it feasible to build a risk calculator for DR and DME prediction. …”
Get full text

Response characteristics and particle contributions of epoxy resin composite embedded with Al particles under different loading modes by Zhenhui He, Enling Tang, Wenjin Yao, Ruizhi Wang

“…In this study, the universal testing machine and split hopkinson pressure bar were used to study the mechanical response and load distribution of Al particles embedded in epoxy resin composites with different volume fractions under various loading modes by combining theoretical derivation and digital image technology. …”
Get full text

Risk prediction models for dysphagia after radiotherapy among patients with head and neck cancer: a systematic review and meta-analysis by You Pu, Jin Yang, Lian Shui, Qianlong Tang, Xianqin Zhang, Guangguo Liu

“…Of these models, most were constructed based on logistic regression, while only two studies used machine learning methods. The area under the receiver operating characteristic curve (AUC) reported values for these models ranged from 0.57 to 0.909, with 13 studies having a combined AUC value of 0.78 (95% CI: 0.74-0.81). …”
Get full text

Investigating the Effect of Coffee Husk Ash as Partial Replacement of Cement in Concrete C30. by Niwainamani, Laban

“…The results of the study concluded that, up to 5% replacement of OPC by CHA achieved advanced compressive strength at all test ages that is. 3, 7, 14, and 28 days of age using compressive test machine. Recommend efficient curing at early ages as the OPC - CHA showed early strength gain and checking the chemical composition, state of CHA using advanced methods such as X-Ray Diffrnction (XRD) Analysis.…”
Get full text

Investigating the Effect of Heavy Metals in Water Used for Concrete Production: A Case Study of Kabale. by Bwengye, Felix

“…In accordance with (BS 1881: Part: 108 1983) Compressive strength values for each specimen for each water sample were determined by means of a universal compression test machine and all specimens for water samples were tested at 14 and 28 days and the results were obtained. …”
Get full text

Retrospective study on the effect of adding heparin sodium saline at different time points on improving severe coagulation in dialyzers (不同时间点追加肝素钠盐水对透析器重度凝血改善作用的回顾性研究)... by LI Linyang (李林洋), WANG Xinhui (王新慧), LI Hao (李昊), HUANG Yanping (黄砚萍), YU Renhuan (余仁欢)

“…The treatment group consisted of 6 patients who completed 4 hours of normal dialysis treatment, while the control group consisted of 4 patients who completed normal treatment, and 2 patients who returned to the machine early due to severe coagulation in the dialyzer and vascular system. …”
Get full text

Prenatal exposure to a mixture of organophosphate ester and organophosphorus pesticides in relation to child neurodevelopment in the Shanghai Birth Cohort by Hui Wang, Dezheng Fu, Xiaoning Liu, Xiaochen Chang, Siyu Guo, Xiaomeng Cheng, Ying Tian, Jinjun Ran, Jun Zhang, Shengju Yin

“…We utilized multivariable linear regression and Bayesian kernel machine regression (BKMR) to estimate associations with individual and mixture component, respectively. …”
Get full text

Phytocompounds from Indonesia Medicinal Herbs as Potential Apelin Receptor Agonist for Heart Failure Therapy: An In-silico Approach by Muhamad Rizqy Fadhillah, Wawaimuli Arozal, Muhammad Habiburrahman, Somasundaram Arumugam, Heri Wibowo, Suci Widya Primadhani, Aryo Tedjo, Surya Dwira, Nurul Gusti Khatimah

“…This study investigates bioactive phytochemicals from ten Indonesian medicinal herbs using computer-aided drug design (CADD) to predict ligand-receptor interactions via molecular docking and bioactivity prediction through machine learning. The selected herbs include Andrographis paniculata, Centella asiatica, Zingiber officinale, Curcuma longa, Curcuma domestica, Morinda citrifolia, Guazuma ulmifolia, Orthosiphon stamineus, Moringa oleifera, and Garcinia mangostana. …”
Get full text

Effect of Massage on the TLR4 Signalling Pathway in Rats with Neuropathic Pain by Qian Wang, Jing Lin, Peng Yang, Yingye Liang, Dongming Lu, Kailong Wang, Wei Gan, Jianping Fu, Zhenbao Gan, Mingchen Ma, Pingting Wu, Fengshi He, Jun Pang, Hongliang Tang

“…The rats in the massage group underwent massage using a massage simulation machine once a day for 14 d in succession; the hind limbs of the rats in the sham massage group were gently touched with a cloth bag once a day for 14 continuous days. …”
Get full text

MFBTFF-Net: A Novel Multi-Frequency Brightness Temperature Feature Fusion Network for Global Lunar Surface Oxides Abundance Estimation With Chang'e-2 Lunar Microwave Sounder... by Yu Li, Zifeng Yuan, Sarah Mazhar, Zhiguo Meng, Yuanzhi Zhang, Jinsong Ping, Ferdinando Nunziata

“…The detection depth of spectral sensors and the drilled depths of the returned samples are not consistent, lowering the reliability of the results. Moreover, existing machine/deep learning models may not be suitable for processing the data acquired in lunar exploration. …”
Get full text

Association between mixed exposure of non-persistent pesticides and liver fibrosis in the general US population: NHANES 2013–2016 by Shuge Shu, Yuan Li, Xiangyu Yu, Xinting Chen, Ummara Abdullah, Yongquan Yu

“…Survey-weighted linear/logistic regression and Bayesian kernel machine regression (BKMR) were used to detected the independent and combined associations between non-persistent pesticides and liver fibrosis, respectively. …”
Get full text

Zipper Pattern: An Investigation into Psychotic Criminal Detection Using EEG Signals by Gulay Tasci, Prabal Datta Barua, Dahiru Tanko, Tugce Keles, Suat Tas, Ilknur Sercek, Suheda Kaya, Kubra Yildirim, Yunus Talu, Burak Tasci, Filiz Ozsoy, Nida Gonen, Irem Tasci, Sengul Dogan, Turker Tuncer

“…<b>Background:</b> Electroencephalography (EEG) signal-based machine learning models are among the most cost-effective methods for information retrieval. …”
Get full text

Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triang... by Bankole Olatosi, Jiajia Zhang, Xiaoming Li, Chen Liang, Jihong Liu, Peiyin Hung, Shan Qiao, Berry A Campbell, Myriam E Torres, Neset Hikmet

“…Non-Hispanic black and Hispanic pregnant women appear to have disproportionate SARS-CoV-2 infection and death rates.Methods and analysis We will use the socioecological framework and employ a concurrent triangulation, mixed-methods study design to achieve three specific aims: (1) examine the impacts of the COVID-19 pandemic on racial/ethnic disparities in severe maternal morbidity and mortality (SMMM); (2) explore how social contexts (eg, racial/ethnic residential segregation) have contributed to the widening of racial/ethnic disparities in SMMM during the pandemic and identify distinct mediating pathways through maternity care and mental health; and (3) determine the role of social contextual factors on racial/ethnic disparities in pregnancy-related morbidities using machine learning algorithms. We will leverage an existing South Carolina COVID-19 Cohort by creating a pregnancy cohort that links COVID-19 testing data, electronic health records (EHRs), vital records data, healthcare utilisation data and billing data for all births in South Carolina (SC) between 2018 and 2021 (&gt;200 000 births). …”
Get full text

Identification and preliminary validation of biomarkers associated with mitochondrial and programmed cell death in pre-eclampsia by Rong Lin, Rong Lin, XiaoYing Weng, XiaoYing Weng, Liang Lin, Liang Lin, XuYang Hu, XuYang Hu, ZhiYan Liu, ZhiYan Liu, Jing Zheng, Jing Zheng, FenFang Shen, FenFang Shen, Rui Li, Rui Li

“…Hub genes were determined using support vector machine, least absolute shrinkage and selection operator, and Boruta based on consistent expression profiles. …”
Get full text

Associations between brominated flame retardants exposure and non-alcoholic fatty liver disease: Mediation analysis in the NHANES by Yulan Cheng, Jingyi Su, Xiangdong Wang, Ruiyao Huang, Zixuan Zhao, Kai Tian, Tianxiang Gu, Xiaoke Wang, Lin Chen, Xinyuan Zhao

“…The quantile-based g-computation (QGC), weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) were applied to evaluate the overall correlation of BFRs mixtures with NAFLD and identify significant compounds. …”
Get full text

Associations of early life per- and polyfluoroalkyl substances (PFAS) exposure with body mass index and risk of overweight or obesity at age 2–18 years: Mixture analysis in the pro... by Zeyu Li, Guoying Wang, Joseph M. Braun, Xiumei Hong, Giehae Choi, Shawn P. O’Leary, Chang Ho Yu, Colleen Pearson, William G. Adams, Zhihua (Tina) Fan, Jessie P. Buckley, Xiaobin Wang

“…The exposure-outcome associations were evaluated with linear and modified Poisson mixed-effects regression for individual PFAS and Bayesian kernel machine regression and quantile-based g-computation models for PFAS mixture. …”
Get full text

Privacy-preserving approach for IoT networks using statistical learning with optimization algorithm on high-dimensional big data environment by Fatma S. Alrayes, Mohammed Maray, Asma Alshuhail, Khaled Mohamad Almustafa, Abdulbasit A. Darem, Ali M. Al-Sharafi, Shoayee Dlaim Alotaibi

“…In the period of big data, statistical learning has seen fast progressions in methodological practical and innovation applications. Privacy-preserving machine learning (ML) training in the development of aggregation permits a demander to firmly train ML techniques with the delicate data of IoT collected from IoT devices. …”
Get full text

Spatiotemporal Variation and Driving Factors of Carbon Sequestration Rate in Terrestrial Ecosystems of Ningxia, China by Yi Zhang, Chunxiao Cheng, Zhihui Wang, Hongxin Hai, Lulu Miao

“…Based on ground observation data and multimodal datasets, the optimal machine learning model (EXT) was used to invert a 30 m high-resolution vegetation and soil carbon density dataset for Ningxia from 2000 to 2023. …”
Get full text

Severity Assessment of COVID-19 Using a CT-Based Radiomics Model by Zhigao Xu, Lili Zhao, Guoqiang Yang, Ying Ren, Jinlong Wu, Yuwei Xia, Xuhong Yang, Milan Cao, Guojiang Zhang, Taisong Peng, Jiafeng Zhao, Hui Yang, Jinfeng Hu, Jiangfeng Du

“…On this basis, a support vector machine (SVM) classifier was trained to build this model. …”
Get full text