The Management of IgG4-Related Disease in Children: A Systematic Review by Evdoxia Sapountzi, Eleni P. Kotanidou, Vasiliki-Rengina Tsinopoulou, Lampros Fotis, Liana Fidani, Assimina Galli-Tsinopoulou

“…<b>Background/Objectives</b>: IgG4-related disease (IgG4-RD) is a multi-organ disease with greatly varying therapeutic approaches and a lack of specific treatment algorithms. This systematic review aimed to determine the therapeutic approaches for pediatric IgG4-RD in real-word practice. …”
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Discovery of novel diagnostic biomarkers of hepatocellular carcinoma associated with immune infiltration by Qiang Liu, Hua Zhang, Heng Xiao, Ao Ren, Ying Cai, Rui Liao, Huarong Yu, Zhongjun Wu, Zuotian Huang

“…Machine learning approaches and big data analyses are viable strategies for identifying HCC diagnostic markers.Materials and methods In this study, we downloaded mRNA expression profiles of HCC from the GEO database and used random forest and machine learning algorithms, such as least absolute shrinkage and selection operator, to screen for reliable diagnostic genes. …”
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Ferroptosis-related hub genes and immune cell dynamics as diagnostic biomarkers in age-related macular degeneration by Jinquan Chen, Zhao Long, Dandan Shi, Qian Zhang, H. Peng

“…Subsequent screening of these 19 genes using LASSO regression, Support Vector Machine (SVM), and Random Forest algorithms identified four hub genes: FADS1, TFAP2A, AKR1C3, and TTPA. …”
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Diagnostic Accuracy of Deep Learning Models in Predicting Glioma Molecular Markers: A Systematic Review and Meta-Analysis by Somayeh Farahani, Marjaneh Hejazi, Sahar Moradizeyveh, Antonio Di Ieva, Emad Fatemizadeh, Sidong Liu

“…<b>Methods:</b> Following PRISMA guidelines, we systematically searched PubMed, Scopus, Ovid, and Web of Science until 27 February 2024 for studies employing DL algorithms to predict gliomas’ molecular markers from MRI sequences. …”
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Immune intrinsic escape signature stratifies prognosis, characterizes the tumor immune microenvironment, and identifies tumorigenic PPP1R8 in glioblastoma multiforme patients by Ran Du, Lijun Jing, Denggang Fu

“…TIME analysis was carried out using multiple deconvolution algorithms. Additionally, functional assays including CCK8, cell cycle, and apoptosis assays were conducted on PPP1R8-silenced U251 cells using CRISPR/Cas9 technologyResultsThirty-six IERGs were associated with GBM outcomes, with 20 linked to poor survival and 16 to better outcomes. …”
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Identifying patterns of high intraoperative blood pressure variability in noncardiac surgery using explainable machine learning: a retrospective cohort study by Zheng Zhang, Jian Wu, Yi Duan, Linwei Liu, Yaru Liu, Jinghan Wang, Li Xiao, Zhifeng Gao

“…We applied four ML algorithms—Extreme Gradient Boosting (XGBoost), Random Forest (RF), Light Gradient Boosting Machine (LightGBM), and Logistic Regression (LR)—to classify patients with or without HIBPV. …”
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Poor Nutritional Status Increases Risk of Postoperative Adverse Events after Surgical Management of Traumatic Ankle Fracture by Sanket Mehta MD, Nicholas Danford MD

“…Patients missing baseline or comorbidity information, dead on arrival, or with a pilon fracture or stress fracture were excluded. Enhanced ICD-9 algorithms were used to identify patients with severe protein-calorie malnutrition and chronic deficiency. …”
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Prediction and Screening of Lead-Free Double Perovskite Photovoltaic Materials Based on Machine Learning by Juan Wang, Yizhe Wang, Xiaoqin Liu, Xinzhong Wang

“…Feature selection was carried out using Pearson correlation and mRMR methods, and 23 key features for bandgap prediction and 18 key features for formation energy prediction were determined. Four algorithms, including gradient-boosting regression (GBR), random forest regression (RFR), LightGBM, and XGBoost, were evaluated, with XGBoost demonstrating the best performance (R² = 0.934 for bandgap, R² = 0.959 for formation energy; MAE = 0.211 eV and 0.013 eV/atom). …”
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Intelligent Prediction Platform for Sepsis Risk Based on Real-Time Dynamic Temporal Features: Design Study by Mingwei Zhang, Ming Zhong, Yunzhang Cheng, Tianyi Zhang

“…Three linear parameters (mean, SD, and endpoint value) were calculated to construct the prediction model using multiple ML algorithms. The interpretability module uses the TreeSHAP (Tree-Based Shapley Additive Explanations) method to enhance model transparency through both individual prediction and global explanations. …”
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Theoretical basis and field experiment of low-temperature gathering and transportation for high water-cut crude trunk pipeline by Yang LYU, Hanwen ZHANG, Luoqian LIU, Fuqiang ZHANG

“…Future studies may incorporate correction coefficients or optimize algorithms within the hydraulic and thermal computational model to enhance the precision of hydraulic and thermal calculations for surface gathering and transportation pipeline networks handling high water-cut crude oil. …”
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Satellite-based detection of deep-convective clouds: the sensitivity of infrared methods and implications for cloud climatology by A. Z. Kotarba, I. Wojciechowska

“…<p>Reliable deep-convective cloud (DCC) climatology relies heavily on accurate detection. Infrared-based algorithms play a critical role, as they are the only ones that can be applied to the 6.7 <span class="inline-formula">µ</span>m water vapour (WV) absorption band and the 11 <span class="inline-formula">µ</span>m infrared (IR) window band. …”
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“Voice is the New Blood”: a discourse analysis of voice AI health-tech start-up websites by Alden Blatter, Hortense Gallois, Emily Evangelista, Yael Bensoussan, Bridge2AI-Voice Consortium, Jean-Christophe Bélisle-Pipon, Yael E. Bensoussan, Olivier Elemento, Jean-Christophe Bélisle-Pipon, David Dorr, Satrajit Ghosh, Alistair Johnson, Phillip Payne, Maria E. Powell, Anaïs Rameau, Vardit Ravitsky, Alexandros Sigaras

“…However, descriptions of voice datasets used to train algorithms vary widely and are often absent, reflecting broader gaps in acoustic and ethical standards for voice data collection and insufficient incentives for start-ups to disclose key data details.DiscussionStart-ups play a crucial role in the research, development, and marketization of voice AI health-tech, prefacing the integration of this new technology into healthcare systems. …”
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PyNET-Q&#x00D7;Q: An Efficient PyNET Variant for Q&#x00D7;Q Bayer Pattern Demosaicing in CMOS Image Sensors by Minhyeok Cho, Haechang Lee, Hyunwoo Je, Kijeong Kim, Dongil Ryu, Albert No

“…Experiments using <inline-formula> <tex-math notation="LaTeX">$\text{Q}\times \text{Q}$ </tex-math></inline-formula> images captured by a prototype <inline-formula> <tex-math notation="LaTeX">$\text{Q}\times \text{Q}$ </tex-math></inline-formula> camera sensor show that PyNET-<inline-formula> <tex-math notation="LaTeX">$\text{Q}\times \text{Q}$ </tex-math></inline-formula> outperforms existing conventional algorithms in terms of texture and edge reconstruction, despite its significantly reduced parameter count. …”
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Repeatability, reproducibility, and agreement for three optical coherence tomography instruments by Jiali Zhang, Muhan Zhong, Shuqi Fan, Xindan Chang, Xue Li, Hao Chen, Jinhua Bao, Yingying Huang

“…Built-in software was used to calculate RT-auto and ChT-auto, and custom-designed algorithms were used to calculate ChT-custom to assess interanalysis and intermachine agreements. …”
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Machine learning-based prediction method for open-pit mining truck speed distribution in manned operation by Changyou XU, Gang CHEN, Qiuxia ZHANG, Bo WANG, Hongwang ZHANG, Hongrui LI, Weiwei QIN, Muyang LI

“…Finally, machine learning algorithms such as random forest and XGBoost are used to predict vehicle speed based on onboard data and weather sensor data. …”
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THE AUGMENTED REALITY FUNCTIONS TO SUPPORT THE STEAM EDUCATION AT GENERAL EDUCATION INSTITUTIONS by Наталія Сороко

“…Our further research is the creation of algorithms for the use of AR for lessons and projects using the STEAM approach, which will improve the quality of education at general education institutions.…”
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Alfalfa stem count estimation using remote sensing imagery and machine learning on Google Earth Engine by Hazhir Bahrami, Karem Chokmani, Saeid Homayouni, Viacheslav I. Adamchuk, Md Saifuzzaman, Rami Albasha, Maxime Leduc

“…This study aims to propose a framework for estimating alfalfa stem density using satellite imagery and machine learning (ML) algorithms, which can lead to winter mortality detection early in the spring and provide a better understanding of potential total dry matter. …”
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Machine Learning for the estimation of foliar nitrogen content in pineapple crops using multispectral images and Internet of Things (IoT) platforms by Jorge Enrique Chaparro, José Edinson Aedo, Felipe Lumbreras Ruiz

“…These strategies generated more robust and accurate models, with the multilayer perceptron regressor (MLP regressor) and extreme gradient boosting (XGBoost) algorithms standing out. On the first sampling date, XGBoost achieved an R2 of 86.98 %, being the highest. …”
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Automated diagnosis of respiratory diseases from lung ultrasound videos ensuring XAI: an innovative hybrid model approach by Arefin Ittesafun Abian, Mohaimenul Azam Khan Raiaan, Asif Karim, Sami Azam, Nur Mohammad Fahad, Niusha Shafiabady, Kheng Cher Yeo, Friso De Boer

“…The DSS facilitates clinical marker analysis, and the validation by using the proposed algorithms highlights its impact on a reliable diagnosis outcome.DiscussionOur proposed methodology could assist radiologists in accurately detecting and comprehending the patterns of respiratory disorders.…”
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Estimation of Subtropical Forest Aboveground Biomass Using Active and Passive Sentinel Data with Canopy Height by Yi Wu, Yu Chen, Chunhong Tian, Ting Yun, Mingyang Li

“…This study utilizes four machine learning algorithms, namely Multivariate Stepwise Regression (MSR), K-Nearest Neighbors (k-NN), Artificial Neural Network (ANN), and Random Forest (RF), to estimate forest aboveground biomass (AGB) in Chenzhou City, Hunan Province, China. …”
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