Evaluating the value of machine learning models for predicting hematoma expansion in acute spontaneous intracerebral hemorrhage based on CT imaging features of hematomas and surrou... by Tianyu Yang, Tianyu Yang, Zhen Zhao, Yan Gu, Shengkai Yang, Yonggang Zhang, Lei Li, Ting Wang, Zhongchang Miao

“…Independent risk factors were identified through univariate analysis and least absolute shrinkage and selection operator (LASSO) regression. Machine learning algorithms were applied to construct predictive models for hematoma expansion. …”
Get full text

A novel multiscale feature enhancement network using learnable density map for red clustered pepper yield estimation by Chenming Cheng, Chenming Cheng, Jin Lei, Jin Lei, Zicui Zhu, Lijian Lu, Lijian Lu, Zhi Wang, Zhi Wang, Jiali Tao, Jiali Tao, Xinyan Qin, Xinyan Qin

“…The proposed method provides an robust algorithmic support for efficient and intelligent yield estimation in RCP.…”
Get full text

Gender Disparities in Artificial Intelligence–Generated Images of Hospital Leadership in the United States by Mia Gisselbaek, MD, Joana Berger-Estilita, MD, PhD, Laurens Minsart, MD, Ekin Köselerli, MD, Arnout Devos, PhD, Francisco Maio Matos, PhD, Odmara L. Barreto Chang, MD, PhD, Peter Dieckmann, PhD, Melanie Suppan, MD, Sarah Saxena, MD, PhD

“…Ethical AI practices, including diverse training data sets and fairness-aware algorithms, are essential to ensure equitable representation in health care leadership.…”
Get full text

Automated classification of oral potentially malignant disorders and oral squamous cell carcinoma using a convolutional neural network framework: a cross-sectional studyResearch in... by Cristina Saldivia-Siracusa, Eduardo Santos Carlos de Souza, Arnaldo Vitor Barros da Silva, Anna Luíza Damaceno Araújo, Caíque Mariano Pedroso, Tarcília Aparecida da Silva, Maria Sissa Pereira Sant'Ana, Felipe Paiva Fonseca, Hélder Antônio Rebelo Pontes, Marcos G. Quiles, Marcio Ajudarte Lopes, Pablo Agustin Vargas, Syed Ali Khurram, Alexander T. Pearson, Mark W. Lingen, Luiz Paulo Kowalski, Keith D. Hunter, André Carlos Ponce de Leon Ferreira de Carvalho, Alan Roger Santos-Silva

“…Transfer learning strategies enhanced performance for both algorithms, and the greatest model achieved mean accuracy, precision, recall, F1-score and AUROC of 0.799, 0.837, 0.756, 0.794 and 0.863 during internal testing, respectively. …”
Get full text

Neuromorphic, physics-informed spiking neural network for molecular dynamics by Vuong Van Pham, Temoor Muther, Amirmasoud Kalantari Dahaghi

“…It also leverages the enhanced representation of real biological neural systems through spiking neural network integration with molecular dynamic physical principles, offering greater efficiency compared to conventional AI algorithms. NP-SNN integrates three core components: (1) embedding MD principles directly into the training, (2) employing best practices for training physics-informed ML systems, and (3) utilizing a highly advanced and efficient SNN architecture. …”
Get full text

A novel gene signature for predicting outcome in colorectal cancer patients based on tumor cell-endothelial cell interaction via single-cell sequencing and machine learning by Lina Pang, Qingxia Sun, Wenyue Wang, Mingjie Song, Ying Wu, Xin Shi, Xiaonan Shi

“…Prognostic signatures were developed using various machine learning algorithms based on marker genes linked to the identified cell subpopulations. …”
Get full text

Identification and validation of glycolysis-related diagnostic signatures in diabetic nephropathy: a study based on integrative machine learning and single-cell sequence by Xiaoyin Wu, Xiaoyin Wu, Buyu Guo, Buyu Guo, Xingyu Chang, Xingyu Chang, Yuxuan Yang, Yuxuan Yang, Qianqian Liu, Qianqian Liu, Jiahui Liu, Jiahui Liu, Yichen Yang, Yichen Yang, Kang Zhang, Yumei Ma, Songbo Fu, Songbo Fu, Songbo Fu

“…The expression levels of diagnostic signatures were verified in vitro.ResultsThrough the 108 combinations of machine learning algorithms, we selected 12 diagnostic signatures, including CD163, CYBB, ELF3, FCN1, PROM1, GPR65, LCN2, LTF, S100A4, SOX4, TGFB1 and TNFAIP8. …”
Get full text

Trends, outcomes and knowledge gaps in mobile apps for reproductive endocrinology and infertility: a scoping review protocol by Alba Regina de Abreu Lima, Emerson Roberto dos Santos, Aline Russomano de Gouvêa, Natália Almeida de Arnaldo Silva Rodriguez Castro, João Daniel de Souza Menezes, Matheus Querino da Silva, Helena Landin Gonçalves Cristóvão, Cíntia Canato Martins, Jéssica Gisleine de Oliveira, Patrícia da Silva Fucuta, Alexandre Lins Werneck, Gerardo Maria de Araújo Filho, Heloisa Cristina Caldas, Vânia Maria Sabadoto Brienze, Júlio César André, Antônio Hélio Oliani

“…Despite promising advancements such as the development of apps with sophisticated algorithms for ovulation prediction and comprehensive platforms offering integrated fertility education and emotional support, there remain gaps in the literature regarding the comprehensive evaluation of mobile apps for reproductive endocrinology and infertility. …”
Get full text

Correlation Between Clinical Indicators and Liver Pathology in Children with Chronic Hepatitis B by Chenyang Huang, Ying Lu, Ziwei Wang, Qiyu Jiang, Yi Dong, Lili Cao, Jianguo Yan, Zhiqiang Xu, Fuchuan Wang, Yinjie Gao, Junliang Fu, Min Zhang, Fu-Sheng Wang

“…Our findings highlight the value of integrating age and key biochemical markers into non-invasive diagnostic algorithms for the early detection and management of liver pathology in children.…”
Get full text

A mobile app to improve adherence to colorectal cancer screening and post polypectomy surveillance guidelines by María A. Casas, Francisco Schlottmann, Leandro Steinberg, Xavier Bessa, Anna Serradesanferm, Angels Pozo, Sonia Torres, Antoni Castells, Francesc Balaguer, Jaume Grau, Lisandro Pereyra, María Pellisé

“…Methods A mobile app including ergonomic algorithms integrating all pertinent guideline information was created by a group of experts. …”
Get full text

D-pinitol modulates the anti-emetic effects of aprepitant, domperidone, and ondansetron in chicks by Md. Elit Rahman, Md. Anisur Rahman, Salehin Sheikh, Md. Jannatul Islam Polash, Sozoni Khatun, Mst. Sonia Akter Bristi, Md. Showkoth Akbor, Mst. Farjanamul Haque, Mehedi Hasan Bappi, Tohidul Islam Tanim, Siddique Akber Ansari, Irfan Aamer Ansari, Elaine Cristina Pereira Lucetti, Carolina Bandeira Domiciano, Henrique D.M. Coutinho, Muhammad Torequl Islam

“…Additionally, A variety of computational algorithms were used to visualise ligand–receptor interactions and quantify the binding affinities of DPL and other ligands towards the dopamine receptors (D2 and D3), muscarinic acetylcholine receptors (M1–M5), and serotonin receptor (5HT3). …”
Get full text

Potential applications of gene expression profiles obtained from circulating extracellular vesicles in breast cancer by Aritra Gupta, Siddharth Bhardwaj, Sayan Ghorai, Rosina Ahmed, Sanjit Agarwal, Geetashree Mukherjee, Kartiki V. Desai

“…Computational deconvolution algorithms for cell signatures identified immune cells such as Th1 and memory T-cells, endothelial cells, and osteoblasts from the stromal compartment as significant. …”
Get full text

Bipolar disorders and creativity: the roles of ambition, effort-based decision-making, and exploration by Samson Tse, Sheri L. Johnson, Chong Ho Yu, Winnie Yuen, Iris Lo, Luke Clark, Luke Clark, Erin E. Michalak, Erin E. Michalak, Manon Ironside, Kiana Modavi

“…However, decision tree algorithms revealed multivariate contributors to creative accomplishments. …”
Get full text

Retrospective analysis of COVID-19 clinical and laboratory data: Constructing a multivariable model across different comorbidities by Mahdieh Shokrollahi Barough, Mohammad Darzi, Masoud Yunesian, Danesh Amini Panah, Yekta Ghane, Sam Mottahedan, Sohrab Sakinehpour, Tahereh Kowsarirad, Zahra Hosseini-Farjam, Mohammad Reza Amirzargar, Samaneh Dehghani, Fahimeh Shahriyary, Mohammad Mahdi Kabiri, Marzieh Nojomi, Neda Saraygord-Afshari, Seyedeh Ghazal Mostofi, Zeynab Yassin, Nazanin Mojtabavi

“…Clinical and laboratory data, along with comorbidity information, were collected and analyzed using advanced coding, data alignment, and regression analyses. Machine learning algorithms were employed to identify relevant features and calculate predictive probability scores. …”
Get full text

A Comparison of Recent Global Time-Series Land Cover Products by Peilin Li, Yan Wang, Chisheng Wang, Lin Tian, Meijiao Lin, Siyao Xu, Chuanhua Zhu

“…This study highlights challenges in dynamic datasets, including classification system discrepancies, resolution effects, and reference data limitations, and suggests that future advancements should focus on improving classification algorithms, refining sampling methods, and developing assessment systems that incorporate high-precision, real-time validation data.…”
Get full text

Performance of externally validated machine learning models based on histopathology images for the diagnosis, classification, prognosis, or treatment outcome prediction in female b... by Ricardo Gonzalez, Peyman Nejat, Ashirbani Saha, Clinton J.V. Campbell, Andrew P. Norgan, Cynthia Lokker

“…Most studies used Convolutional Neural Networks and one used logistic regression algorithms. For diagnostic/classification models, the most common performance metrics reported in the EV were accuracy and area under the curve, which were greater than 87% and 90%, respectively, using pathologists' annotations/diagnoses as ground truth. …”
Get full text

The 3-level Wells score combined with D-dimer can accurately diagnose acute pulmonary embolism in hospitalized patients with acute exacerbation of COPD: A multicentre cohort study by Xiaojing Jiao, Yixiao zhang, Tuguang Kuang, Juanni Gong, Yadong Yuan, Guohua Zhen, Jifeng Li, Suqiao Yang, Jianguo He, Yuanhua Yang

“…Conclusions: The 3-level Wells score combined with a D-dimer cut-off value of 690.12 ng/mL performed better than other clinical scoring algorithms for assessing clinical probability of APE in patients with AECOPD.…”
Get full text

Long-term prognosis of 47 pediatric patients with Blau syndrome in China by Xinwei Shi, Jianghong Deng, Junmei Zhang, Xiaozhen Zhao, Yinan Zhao, Li Li, Fengqiao Gao, Weiying Kuang, Jiang Wang, Xiaohua Tan, Chao Li, Shipeng Li, Caifeng Li

“…A Bayesian network was constructed to integrate prediction algorithms of genetic mutations and clinical manifestations, exploring the complex relationship between genotype and phenotype through R (Version 4.4.1, R Core Development Team). …”
Get full text

Discovery of Potent Dengue Virus NS2B-NS3 Protease Inhibitors Among Glycyrrhizic Acid Conjugates with Amino Acids and Dipeptides Esters by Yu-Feng Lin, Hsueh-Chou Lai, Chen-Sheng Lin, Ping-Yi Hung, Ju-Ying Kan, Shih-Wen Chiu, Chih-Hao Lu, Svetlana F. Petrova, Lidia Baltina, Cheng-Wen Lin

“…We utilized docking algorithms to evaluate the interactions of these GL derivatives with key residues (His51, Asp75, Ser135, and Gly153) within 10 Å of the DENV-2 NS2B-NS3 protease binding pocket (PDB ID: 2FOM). …”
Get full text

MRI radiomics model for predicting tumor immune microenvironment types and efficacy of anti-PD-1/PD-L1 therapy in hepatocellular carcinoma by Rui Zhang, Wei Peng, Yao Wang, Yunping Jiang, Junli Wang, Siying Zhang, Zhi Li, Yushu Shi, Feng Chen, Zhan Feng, Wenbo Xiao

“…In Cohort 1, after feature selection, clinical, intratumoral radiomics, peritumoral radiomics, combined radiomics, and clinical-radiomics models were established using machine learning algorithms. In cohort 2, the clinical-radiomics model’s predictive ability for ICIs efficacy was assessed. …”
Get full text