Unilateral Biportal Endoscopic Spinal Surgery: Comparing Clinical Outcomes and Learning Curves between Novice and Experienced Surgeons by Shou-En Cheng, Jwo-Luen Pao, Yu-Hung Chen

“…Postoperative outcomes, including pain scores, functional ability, and complications, were compared. …”
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Minimally Invasive Discectomy and Decompression for Lumbar Spine using Tubular Retractor System: Technique, Learning Curve and Outcomes by V A Kumar, Ramanadha Reddy, Vamsi Krishna Yerramneni, Swapnil Kolpakawar, K.S. Vishwa Kumar, Patlolla Pratyusha

“…Conclusion Both MITDS and MITD have a significant learning curve and have a distinct advantage of shorter hospital stay. …”
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Using explainable machine learning to investigate the relationship between childhood maltreatment, positive psychological traits, and CPTSD symptoms by Xiaoxiao Zhou, Zongbao Liang, Guangzhen Zhang

“…Background: The functional impairment resulting from CPTSD symptoms is enduring and far-reaching. …”
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Supervised machine learning and genotype by trait biplot as promising approaches for selection of phytochemically enriched Rhus coriaria genotypes by Hamid Hatami Maleki, Reza Darvishzadeh, Ahmad Alijanpour, Yousef Seyfari

“…Here, the identified phytochemically superior sumac group was effectively distinguished from the inferior sumac group using ISSRs information via supervised machine learning. By using 13 feature selection algorithms, ISSR loci (U823) L1, (U835) L1, (U801) L1, (U816) L2, (U816) L4, (U835) L4, (U854) L1, and (U835) L9 were identified as functional markers which could predict phytochemical response of sumac germplasm. …”
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Prediction of Pt, Ir, Ru, and Rh complexes light absorption in the therapeutic window for phototherapy using machine learning by V. Vigna, T. F. G. G. Cova, A. A. C. C. Pais, E. Sicilia

“…Traditional prediction methods for these light absorption properties, including Time-Dependent Density Functional Theory (TDDFT), are often computationally intensive and time-consuming. …”
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Efficient Explainable Models for Alzheimer’s Disease Classification with Feature Selection and Data Balancing Approach Using Ensemble Learning by Yogita Dubey, Aditya Bhongade, Prachi Palsodkar, Punit Fulzele

“…Clinical data, including a range of cognitive, functional, and demographic variables, play a crucial role in Alzheimer’s disease classification. …”
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Machine learning-assisted multi-dimensional transcriptomic analysis of cytoskeleton-related molecules and their relationship with prognosis in hepatocellular carcinoma by Yuxuan Li, Mingbo Cao, Xiaorui Su, Gaoyuan Yang, Yupeng Ren, Zhiwei He, Zheng Shi, Ziyi Hu, Guirong Liang, Qi Zhang, Zhicheng Yao, Meihai Deng

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Machine learning for classification of pediatric bipolar disorder with and without psychotic symptoms based on thalamic subregional structural volume by Weijia Gao, Kejing Zhang, Qing Jiao, Linyan Su, Dong Cui, Shaojia Lu, Rongwang Yang

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Comparative evaluation of machine learning models for enhancing diagnostic accuracy of otitis media with effusion in children with adenoid hypertrophy by Xiaote Zhang, Qiaoyi Xie, Ganggang Wu

“…SHAP analysis identified the adenoid-to-nasopharyngeal ratio as the predominant diagnostic indicator, followed by tympanometric type and history of recurrent respiratory infections.ConclusionAn RF-based diagnostic model effectively identifies OME in AH children by integrating anatomical, functional, and inflammatory parameters, providing a clinically applicable tool for enhanced diagnostic accuracy and evidence-based management decisions.…”
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Individual differences influencing procedural learning outcomes in virtual reality: a case study on an exterior preflight inspection by Fiona Duruaku, Valerie Sims, Florian G. Jentsch

“…The study aimed to understand how VR functions and to identify who benefits most from its use.MethodsIn the experiment, 79 undergraduate students were trained to conduct an exterior preflight inspection of a passenger aircraft in VR, with varying levels of immersion (desktop PC vs. immersive VR) and interactivity (passive learning vs. active exploration). …”
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Utilizing bioinformatics and machine learning to identify CXCR4 gene-related therapeutic targets in diabetic foot ulcers by Hengyan Zhang, Ye Zhou, Heguo Yan, Changxing Huang, Licong Yang, Yangwen Liu

“…We integrated the genes screened by three machine learning models (LASSO, SVM, and Random Forest), and CXCR4 was identified as a key gene with potential therapeutic value in DFUs. …”
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A Novel Framework for Saraiki Script Recognition Using Advanced Machine Learning Models (YOLOv8 and CNN) by Hafiz Muhammad Raza Ur Rehman, Syed Arfan Haider, Hiba Faisal, Kook-Yeol Yoo, M. Z. Jhandir, Gyu Sang Choi

“…Linguistics’ fundamental goal is to comprehend how language functions in composition and context. On the other hand, machine learning is the study of how to teach machines to learn and predict using data instead of explicit programming. …”
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Assessing dynamic brain activity during verbal associative learning using MEG/fMRI co-processing by Sangeeta Nair, Jerzy P. Szaflarski, Yingying Wang, Diana Pizarro, Jeffrey F. Killen, Jane B. Allendorfer

“…Methods: 24 healthy participants were recruited to perform a paired-associate verbal learning task during fMRI and MEG scans. FMRI data were processed within Group ICA fMRI Toolbox. …”
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Machine learning-based prediction model for lung ischemia-reperfusion injury: insights from disulfidptosis-related genes by Yanpeng Zhang, Yanpeng Zhang, Yanpeng Zhang, Jingyang Sun, Jingyang Sun, Jingyang Sun, Yihan Lin, Yihan Lin, Yihan Lin, Rongxuan Jiang, Rongxuan Jiang, Rongxuan Jiang, Niuniu Dong, Niuniu Dong, Niuniu Dong, Huanhuan Dong, Huanhuan Dong, Huanhuan Dong, Peng Li, Peng Li, Peng Li, Jinteng Feng, Jinteng Feng, Jinteng Feng, Zijiang Zhu, Guangjian Zhang, Guangjian Zhang, Guangjian Zhang

“…Differential expressed analysis and functional enrichment analysis were conducted to identify key genes associated with lung transplant IRI. …”
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Immuno-transcriptomic analysis based on machine learning identifies immunity signature genes of chronic rhinosinusitis with nasal polyps by Zhaonan Xu, Qing Hao, Bingrui Yan, Qiuying Li, Xuan Kan, Qin Wu, Hongtian Yi, Xianji Shen, Lingmei Qu, Peng Wang, Yanan Sun

“…Through machine learning, we further identified five candidate genes, CXCR1, CCL13, CCR3, PPBP, and MMP9. …”
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Does the surgeon’s learning curve impact pentafecta outcomes in radical prostatectomy? a systematic review and meta-analysis by Jose Arnaldo Shiomi Da Cruz, Breno Cordeiro Porto, Bruno Damico Terada, Felipe Giraldo Alvarez Gonçalves, Soraya Hussein Orra, Juan Victor Nabhan Martinez, Carlo Camargo Passerotti, Rodrigo A. S. Sardenberg, Kenneth Nunes Tavares De Almeida, Everson Luiz De Almeida Artifon, Jose Pinhata Otoch

“…However, the impact of the learning curve of the surgeon has on the pentafecta is unclear. …”
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CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer’s disease by Jingyuan Liu, Xiaojie Yu, Hidenao Fukuyama, Toshiya Murai, Jinglong Wu, Qi Li, Zhilin Zhang

“…The architecture first extracts balanced multiscale features from structural MRI (sMRI) data and functional MRI (fMRI) data using a cross-scale pyramid module. …”
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Effect of discontinuing antipsychotic medications on the risk of hospitalization in long-term care: a machine learning-based analysis by Mikko Nuutinen, Riikka-Leena Leskelä, Daniela Fialova, Ira Haavisto, Harriet Finne-Soveri, Jokke Häsä, Johanna Edgren, Hein van Hout, Daniel E. da Cunha Leme, John P. Hirdes, Graziano Onder, Rosa Liperoti

“…The use of restraints, age, and functional impairment were relevant variables in all ITE models in influencing the predicted ITE. …”
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A system for generating chatbots to support learning in the field of exact sciences using generative artificial intelligence models by Oleksandr Prokhorov, Dmytro Shymko, Olena Kuzminska, Andrey Chukhray, Oleksii Shatalov, Oleksandr Kholodniak

“…Chatbots such as ChatGPT can promote interactive learning, allowing students to explore complex scientific concepts through personalized support and real-time feedback. …”
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Data Mining–Based Model for Computer-Aided Diagnosis of Autism and Gelotophobia: Mixed Methods Deep Learning Approach by Mohamed Eldawansy, Hazem El Bakry, Samaa M Shohieb

“…This comorbidity can significantly impair the quality of life, particularly in adolescents with high-functioning ASD, where the prevalence reaches 41.98%. …”
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