Quantitative image analysis of the extracellular matrix of esophageal squamous cell carcinoma and high grade dysplasia via two-photon microscopy by Kausalya Neelavara Makkithaya, Wei-Chung Chen, Chun-Chieh Wu, Ming-Chi Chen, Wei-Hsun Wang, Jackson Rodrigues, Ming-Tsang Wu, Nirmal Mazumder, I-Chen Wu, Guan-Yu Zhuo

“…Thus, distinguishing between the two may require careful examination by a pathologist and consideration of clinical findings. …”
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Saliva-derived transcriptomic signature for gastric cancer detection using machine learning and leveraging publicly available datasets by Catarina Lopes, Andreia Brandão, Manuel R. Teixeira, Mário Dinis-Ribeiro, Carina Pereira

“…Leveraging transcriptomic data from the Gene Expression Omnibus (GEO), we constructed and validated predictive models through machine learning algorithms within the tidymodels framework. Tissue-based models were validated on independent tissue datasets, and subsequently applied to saliva. …”
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Validating laboratory predictions of soil rewetting respiration pulses using field data by X. Li, X. Li, M. Pallandt, M. Pallandt, D. Naidu, D. Naidu, J. Rousk, G. Hugelius, G. Hugelius, S. Manzoni, S. Manzoni

“…<p>Improved understanding of the mechanisms driving heterotrophic CO<span class="inline-formula">₂</span> emissions after rewetting of a dry soil may improve projections of future soil carbon fate. …”
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Artificial intelligence assisted risk prediction in organ transplantation: a UK Live-Donor Kidney Transplant Outcome Prediction tool by Hatem Ali, Arun Shroff, Tibor Fülöp, Miklos Z. Molnar, Adnan Sharif, Bernard Burke, Sunil Shroff, David Briggs, Nithya Krishnan

“…We set out to apply artificial intelligence (AI) algorithms to create a highly predictive risk stratification indicator, applicable to the UK’s transplant selection process.Methodology: Pre-transplant characteristics from 12,661 live-donor kidney transplants (performed between 2007 and 2022) from the United Kingdom Transplant Registry database were analyzed. …”
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Anti-masking Posts on Instagram: Content Analysis During the COVID-19 Pandemic by Emma K. Quinn, Robert T. Duffy, Kristian Larsen, Maria Dalton, Cheryl E. Peters

“…Social media algorithms promote similar misinformation or authority-questioning beliefs to users who view related content. …”
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Explainable machine learning model for predicting compressive strength of CO2-cured concrete by Jia Chu, Bingbing Guo, Taotao Zhong, Qinghao Guan, Yan Wang, Ditao Niu

“…However, higher CO2 pressures may decrease the strength due to the increased risk of microcrack propagation caused by the synergistic effects of excessive pressure and considerable heat from the carbonation reaction.…”
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New Insights into the Role of the Immune Microenvironment in Breast Carcinoma by Luis de la Cruz-Merino, Antonio Barco-Sánchez, Fernando Henao Carrasco, Esteban Nogales Fernández, Ana Vallejo Benítez, Javier Brugal Molina, Antonio Martínez Peinado, Ana Grueso López, Manuel Ruiz Borrego, Manuel Codes Manuel de Villena, Víctor Sánchez-Margalet, Adoración Nieto-García, Emilio Alba Conejo, Noelia Casares Lagar, José Ibáñez Martínez

“…Infiltration by tumor infiltrating lymphocytes (TIL) and their subtypes, tumor-associated macrophages (TAM) and myeloid-derived suppressive cells (MDSC) seem bona fide prognostic and even predictive biomarkers, that will eventually be incorporated into diagnostic and therapeutic algorithms of breast cancer. In addition, the complex interaction of costimulatory and coinhibitory molecules on the immune synapse and the different signals that they may exert represent another exciting field to explore. …”
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Predicting postoperative trauma-induced coagulopathy in patients with severe injuries by machine learning by Xiaohui Du, Wei Wang, Bo Xu, Jiang Zheng, Victor W. Xia, Yi Guo, Shuai Feng, Qingxiang Mao, Hong Fu

“…Utilizing machine learning may enhance clinical decision-making and improve management strategies for postoperative coagulation issues.…”
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An MRI-based fusion model for preoperative prediction of perineural invasion status in patients with intrahepatic cholangiocarcinoma by Zuochao Qi, Hao Yuan, Qingshan Li, Pengyu Chen, Dongxiao Li, Kunlun Chen, Bo Meng, Peigang Ning, Haibo Yu, Deyu Li

“…Feature selection was performed, and a radiomics model was constructed using machine learning algorithms, followed by SHapley Additive exPlanations (SHAP) visualization. …”
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Machine learning-based Diagnostic model for determining the etiology of pleural effusion using Age, ADA and LDH by Qing-Yu Chen, Shu-Min Yin, Ming-Ming Shao, Feng-Shuang Yi, Huan-Zhong Shi

“…The dataset was divided into training and test set with a ratio of 7:3 with 6 machine learning algorithms implemented to diagnosis pleural effusion. …”
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Diagnosis on Ultrasound Images for Developmental Dysplasia of the Hip with a Deep Learning-Based Model Focusing on Signal Heterogeneity in the Bone Region by Hirokazu Shimizu, Ken Enda, Hidenori Koyano, Takuya Ogawa, Daisuke Takahashi, Shinya Tanaka, Norimasa Iwasaki, Tomohiro Shimizu

“…Existing automatic diagnostic models lack precision due to noise, but 3D technology may enhance it. This study aimed to create and assess a deep-learning-based model for automated DDH diagnosis by using 3D transformation technology on two-dimensional ultrasound images. …”
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Thermal ablation in the management of oligometastatic colorectal cancer by Thierry De Baere, Lambros Tselikas, Alexandre Delpla, Charles Roux, Eloi Varin, Adrian Kobe, Steven Yevich, Frederic Deschamps

“…As ablation techniques and technology improve, the role of percutaneous thermal ablation may expand even into surgically resectable disease. …”
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Identification and validation of the nicotine metabolism-related signature of bladder cancer by bioinformatics and machine learning by Yating Zhan, Min Weng, Yangyang Guo, Dingfeng Lv, Feng Zhao, Zejun Yan, Junhui Jiang, Yanyi Xiao, Lili Yao

“…Integrative machine learning combination based on 10 machine learning algorithms was used for the construction of robust signature. …”
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Ensemble learning techniques reveals multidimensional EEG feature alterations in pediatric schizophrenia by Ying Mao, Ying Mao, Fang Wang, Shan Wang, Zhaowei Wang, Gang Li, Xuchen Qi, Xuchen Qi, Yu Sun, Yu Sun

“…In summary, this study not only showcases the potential of advanced ensemble learning algorithms in precisely identifying pediatric SCZ, but also provides new insights into the altered brain functions in pediatric SCZ patients, which may benefit the future development of automatic diagnosis systems.…”
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Clustering Electrophysiological Predisposition to Binge Drinking: An Unsupervised Machine Learning Analysis by Marcos Uceta, Alberto del Cerro‐León, Danylyna Shpakivska‐Bilán, Luis M. García‐Moreno, Fernando Maestú, Luis Fernando Antón‐Toro

“…Recent studies have changed their scope into finding predisposition factors that may lead adolescents into this kind of patterns of consumption. …”
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Pan-Cancer Analysis of Disulfidptosis-Related Genes Affecting Prognosis and Tumor Microenvironment by Jingyang SUN, Rongxuan JIANG, Liren HOU, Huanhuan DONG, Yihan LIN, Niuniu DONG, Guangjian ZHANG, Yanpeng ZHANG

“…ConclusionPan-cancer analysis of DRGs indicates that DRGs may serve as important biomarkers for the diagnosis and prognosis of renal clear-cell carcinoma, lung adenocarcinoma, and hepatocellular carcinoma.…”
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Applications of molecular dynamics in nanomaterial design and characterization - A review by Md. Aminul Islam, S M Maksudur Rahman, Juhi Jannat Mim, Safiullah Khan, Fardin Khan, Md. Ahadul Islam Patwary, Nayem Hossain

“…Recent advances, such as improved multiscale modeling and computational algorithms and real-time simulations, shed light onto MD processes of revolutionizing nanotechnology. …”
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Two-dimensional differential form of distributed Xinanjiang model by J. Zhao, Z. Liang, V. P. Singh, V. P. Singh, V. P. Singh, T. Wen, Y. Hu, B. Li, J. Wang

“…Our results suggested that (a) numerical errors in the existing distributed Xinanjiang model are significant and may be exacerbated by a potential terrain amplification effect, which could be effectively controlled by the fully coupled numerical framework within the TDD-XAJ model; (b) the 2D slope concentration methods showed enhanced terrain capture ability and eliminated the reliance on the flow direction algorithms used in 1D methods; and (c) the TDD-XAJ model exhibited improved simulation capabilities compared to the existing model when applied in the Tunxi watershed, particularly for flood volume. …”
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THE EFFECTS OF LIQUIRITIGEN ON THE LUKEMIA INHIBITORY FACTOR RECEPTOR IN GRANULOSA CELLS OF INFERTILE PATIENTS by Emel Usta, Sibel Köktürk, Sibel Doğan, Ayşe Altun, Sibel Bulgurcuoğlu Kuran, Feride Özdemir

“…These results suggest that LQ may be used as an alternative medium agent in IVF units. …”
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Novel genes involved in vascular dysfunction of the middle temporal gyrus in Alzheimer’s disease: transcriptomics combined with machine learning analysis by Meiling Wang, Aojie He, Yubing Kang, Zhaojun Wang, Yahui He, Kahleong Lim, Chengwu Zhang, Li Lu

“…These genes were downregulated in the middle temporal gyrus of patients with Alzheimer’s disease and may be used to target the VEGF pathway. Alzheimer’s disease mouse models demonstrated consistent altered expression of these genes in the middle temporal gyrus. …”
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