Development and validation of interpretable machine learning models for predicting AKI risk in patients treated with PD-1/PD-L1: a retrospective study by Wentong Liu, Kaiyue Ji, Qianwen Tang, Weiqi Xia, Wei Zhang, Lina Shao, Jiana Shi, Yukun Li, Ping Huang, Xiaolan Ye

“…Previous research has been constrained by significant limitations, underscoring the necessity for AKI risk prediction models for patients treated with PD-1/PD-L1 inhibitors. This study aimed to develop and validate an interpretable machine learning (ML) model for early AKI prediction in patients undergoing PD-1/PD-L1 inhibitor therapy using a retrospective cohort design. …”
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Why Has It Been So Difficult to Transform Evaluation Practices of Learning in the Educational Context? A Critical Essay on a Pedagogical Pathology Still to Be Treated by Mario Hernández-Nodarse

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A Retrospective Machine Learning Analysis to Predict 3-Month Nonunion of Unstable Distal Clavicle Fracture Patients Treated with Open Reduction and Internal Fixation by Ma C, Lu W, Liang L, Huang K, Zou J

“…Changke Ma,1,* Wei Lu,2,3,* Limei Liang,4,* Kaizong Huang,3 Jianjun Zou3,5 1Department of Orthopaedics, Nanjing Luhe People’s Hospital, Yangzhou University, Nanjing, People’s Republic of China; 2School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, People’s Republic of China; 3Department of Pharmacology, Nanjing First Hospital, Nanjing Medical University, Nanjing, People’s Republic of China; 4Department of Rehabilitation, Nanjing Luhe People’s Hospital, Yangzhou University, Nanjing, People’s Republic of China; 5Department of Pharmacy, Nanjing First Hospital, China Pharmaceutical University, Nanjing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Kaizong Huang, Email kzhuang@nju.edu.cn Jianjun Zou, Email zoujianjun100@126.comBackground: This retrospective study aims to predict the risk of 3-month nonunion in patients with unstable distal clavicle fractures (UDCFs) treated with open reduction and internal fixation (ORIF) using machine learning (ML) methods. …”
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Construction and evaluation of a height prediction model for children with growth disorders treated with recombinant human growth hormone by Feng Zhu, Anle Wu, Lingling Chen, Ya Xia, Xiaoju Luo, Jieqian Zhu, Lina Huang, Yu Zhang

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Pretreatment CT-Based Machine Learning Radiomics Model Predicts Response in Inoperable Stage III NSCLC Treated with Concurrent Radiochemotherapy Plus PD-1 Inhibitors by Ya Li Bachelor, Min Zhang Bachelor, Yong Hu MM, Bo Du Bachelor, Youlong Mo Bachelor, Tianchu He MM, Mingdan Zhao Bachelor, Benlan Li Bachelor, Ji Xia Bachelor, Zhongjun Huang Bachelor, Fangyang Lu MD, Zhen Huang Bachelor, Bing Lu MD, Jie Peng MD

“…Third, radiological models were built using six machine learning algorithms: logistic regression (LR), discriminant analysis (DA), neural network (NN), random forest (RF), support vector machine (SVM) and K-Nearest Neighbour (KNN). …”
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Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy by Haihong Zhu, Xuan Luo, Zhixian Sun, Yonglin Hua, Yuxin Xiao, Huilong Li, Xiaowu Ma, Wenliang Tan, Zhiqin Xie, Ziyu Zhang, Chenwei Tang, Hongkai Zhuang, Weikai Xu, Yajin Chen, Changzhen Shang

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Machine Learning-Based Prediction of Unconfined Compressive Strength of Sands Treated by Microbially-Induced Calcite Precipitation (MICP): A Gradient Boosting Approach and Correlation Analysis by Saeed Talamkhani

“…The current study applies a soft-computing approach based on the gradient boosting method to predict the unconfined compressive strength (UCS) of sands treated with microbially-induced calcite precipitation (MICP). …”
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Deep learning-based CT radiomics predicts prognosis of unresectable hepatocellular carcinoma treated with TACE-HAIC combined with PD-1 inhibitors and tyrosine kinase inhibitors by Linan Yin, Ruibao Liu, Wei Li, Shijie Li, Xunbo Hou

“…Abstract Objective To develop and validate a computed tomography (CT)-based deep learning radiomics model to predict treatment response and progression-free survival (PFS) in patients with unresectable hepatocellular carcinoma (uHCC) treated with transarterial chemoembolization (TACE)-hepatic arterial infusion chemotherapy (HAIC) combined with PD-1 inhibitors and tyrosine kinase inhibitors (TKIs). …”
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A multi-centred pilot randomised controlled trial of learning through play plus culturally adapted cognitive behaviour therapy for treating postnatal depression in Nigerian women by Dung Ezekiel Jidong, Dung Ezekiel Jidong, Tarela Juliet Ike, Maigari Yusuf Taru, Juliet Yop Pwajok, Charles Nnaemeka Nwoga, John Ezekiel Jidong, Christopher Francis, Shadrack Bitrus Mwankon, Karick Haruna, Zubairu Dagona, Nusrat Husain, Nusrat Husain

“…The LTP+CaCBT is a 12-session (90 minutes each) intervention to treat postnatal depression, and this was compared with the Enhanced Treatment As Usual (ETAU). …”
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Data analysis-based framework for the design and assessment of chemical process plants: a case study in amine gas-treating systems by Rahul Gupta, Gladys Navas, Daniela Galatro

“…Our case study, a gas treating unit, illustrates its implementation compared with traditional process guidelines. …”
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Retrospective study of onychomycosis patients treated with ciclopirox 8% HPCH and oral antifungals applying artificial intelligence to electronic health records by Gastón Roustan, José Luis López Estébaranz, Pablo De La Cueva, Savana Research Group, Francesca Pajuelo, María Luisa Tamarit, Aida Valmaseda, Jordi Galván

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Risk prediction of QTc prolongation occurrence in cancer patients treated with commonly used oral tyrosine kinase inhibitors: machine learning modeling or conventional statistical analysis better? by Hsiang-Wen Lin, Tien-Chao Lin, Chien-Ning Hsu, Tzu-Pei Yeh, Yu-Chieh Chen, Liang-Chih Liu, Chen-Yuan Lin

“…Both univariate logistic regression (LR) and supervised machine learning (ML) approaches were used for variable selection. …”
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Three-Dimensional Semantic Segmentation of Palatal Rugae and Maxillary Teeth and Motion Evaluation of Orthodontically Treated Teeth Using Convolutional Neural Networks by Abdul Rehman El Bsat, Elie Shammas, Daniel Asmar, Kinan G. Zeno, Anthony T. Macari, Joseph G. Ghafari

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Artificial intelligence MacHIne learning for the detection and treatment of atrial fibrillation guidelines in the emergency department setting (AIM HIGHER): Assessing a machine learning clinical decision support tool to detect and treat non‐valvular atrial fibrillation in the emergency department by Kim Schwab, Dacloc Nguyen, GilAnthony Ungab, Gregory Feld, Alan S. Maisel, Martin Than, Laura Joyce, W. Frank Peacock

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Mobile applications as an aid in treating obese and overweight people by Marika Gutowska, Wiktoria Belcarz, Paulina Fijałek, Karolina Kalinowska, Jan Karczmarz, Agnieszka Kosińska, Joanna Orzechowska, Michał Orzechowski, Aleksandra Paprocka, Urszula Świrk

“…Early app engagement and self reported dietary adherence are predictors of weight loss. By using machine learning and an app it is possible to identify and predict weight loss trajectories. …”
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Habitat radiomics analysis for progression free survival and immune-related adverse reaction prediction in non-small cell lung cancer treated by immunotherapy by Yuemin Wu, Wei Zhang, Xiao Liang, Pengpeng Zhang, Mengzhe Zhang, Yuqin Jiang, Yanan Cui, Yi Chen, Wenxin Zhou, Qi Liang, Jiali Dai, Chen Zhang, Jiali Xu, Jun Li, Tongfu Yu, Zhihong Zhang, Renhua Guo

“…Conclusion By leveraging machine learning to predict the outcomes of ICIs, we can move closer to achieving tailored ICIs for lung cancer. …”
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Introduction and Utilization of “Spray-On Skin” at an Urban, Level-1 Trauma Center: Learning Curve Analysis of 125 Consecutive Patients with Full-Thickness Wounds and Defects, Treated with Autologous Skin Cell Suspension by C. Scott Hultman, MD, MBA, Riley Shegos, MS (co-first author), Sarah Miller, MS (co-first author), Carrie McGroarty, PA-C, Cori Rogers, MD

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Treating the individual: moving towards personalised eating disorder care by Emma Bryant, Peta Marks, Kristi Griffiths, Stephanie Boulet, Melissa Pehlivan, Sarah Barakat, Stephen Touyz, Sarah Maguire

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Comment on “Predictive Value of Advanced Lung Cancer Inflammation Index and Development of a Nomogram for Prognosis in Patients with Cervical Cancer Treated with Radiothe... by Yu Q, Jin X

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Inspecting the potential of thymol as a therapeutic agent for treating Alzheimer's disease by Muhammad Waleed Imran, Sanwal Sardar Nawaz, Muhammad Moaz

“…Thymol can also alleviate protein kinase C activity as a memory-related protein.4 thymol has proved to reduce the cognitive defects and was also found to alleviate learning and memory impairment in rat models.5,6 Although thymol has a lot of beneficial effects against cognitive impairment caused by Alzheimer’s, all of the experiments have been done on rat models; hence thymol’s therapeutic potentials in modulating and preventing Alzheimer in humans need to be fully explored. …”
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