PhyloFunc: phylogeny-informed functional distance as a new ecological metric for metaproteomic data analysis by Luman Wang, Caitlin M. A. Simopoulos, Joeselle M. Serrana, Zhibin Ning, Yutong Li, Boyan Sun, Jinhui Yuan, Daniel Figeys, Leyuan Li

“…PCoA and machine learning-based classification algorithms revealed higher sensitivity of PhyloFunc in microbiome responses to paracetamol. …”
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Prevalence and genetic profiles of isoniazid resistance in tuberculosis patients: A multicountry analysis of cross-sectional data. by Anna S Dean, Matteo Zignol, Andrea Maurizio Cabibbe, Dennis Falzon, Philippe Glaziou, Daniela Maria Cirillo, Claudio U Köser, Lice Y Gonzalez-Angulo, Olga Tosas-Auget, Nazir Ismail, Sabira Tahseen, Maria Cecilia G Ama, Alena Skrahina, Natavan Alikhanova, S M Mostofa Kamal, Katherine Floyd

“…Many patients with Hr-TB would be missed by current diagnostic algorithms driven by rifampicin testing, highlighting the need for new rapid molecular technologies to ensure access to appropriate treatment and care. …”
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Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research by Parveen Kumar, Benu Chaudhary, Preeti Arya, Rupali Chauhan, Sushma Devi, Punit B. Parejiya, Madan Mohan Gupta

“…This technique includes data collection, effective data usage system development, conclusion illustration, and arrangements. Analysis algorithms that are learning to mimic human cognitive activities are the most widespread application of AI. …”
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Leveraging Digital Twins for Stratification of Patients with Breast Cancer and Treatment Optimization in Geriatric Oncology: Multivariate Clustering Analysis by Pierre Heudel, Mashal Ahmed, Felix Renard, Arnaud Attye

“…Manifold learning and machine learning algorithms were applied to uncover complex data relationships and develop predictive models. …”
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Ways to Reduce In-Hospital Mortality in Patients with Cardiogenic Shock in Acute Coronary Syndrome by G. V. Artamonova, V. Yu. Kheraskov, E. V. Grigoryev, O. V. Kushch, D. V. Kryuchkov, L. S. Barbarash

“…The management is based on the principle of continuity of care, by applying the well-defined activity algorithms through valid information exchange and risk stratification for poor outcomes of ACS. …”
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Optimizing skin cancer screening with convolutional neural networks in smart healthcare systems. by Ali Raza, Akhtar Ali, Sami Ullah, Yasir Nadeem Anjum, Basit Rehman

“…Skin cancer if detected early and treated in time can be controlled and its deadly impacts arrested completely. …”
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Artificial Intelligence as a Problem of Modern Sociology by V. A. Glukhikh, S. M. Eliseev, N. P. Kirsanova

“…In order to correctly and accurately define the problem of artificial intelligence in the social sciences, it is necessary to carefully analyze the opinions of the experts in the exact sciences, in which artificial intelligence is understood as algorithms or models created by human, and which perform certain tasks and help them manage specific processes in various spheres of society.…”
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Genetically distinct within-host subpopulations of hepatitis C virus persist after Direct-Acting Antiviral treatment failure. by Lele Zhao, Matthew Hall, Prahalad Giridhar, Mahan Ghafari, Steven Kemp, Haiting Chai, Paul Klenerman, Eleanor Barnes, M Azim Ansari, Katrina Lythgoe

“…Analysis of viral genetic data has previously revealed distinct within-host population structures in both untreated and interferon-treated chronic hepatitis C virus (HCV) infections. …”
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Identification of M2 macrophage-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches by Jiayi Zhang, Zhixiang Jia, Jiahui Zhang, Xiaohui Mu, Limei Ai

“…Using the Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF) algorithms, we screened for seven potential diagnostic biomarkers with strong diagnostic capabilities: SMAD3, IL7R, IL18, FAS, CD5, CCR7, and CSF1R. …”
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Predicting anemia management in dialysis patients using open-source machine learning libraries by Takahiro Inoue, Norio Hanafusa, Yuki Kawaguchi, Ken Tsuchiya

“…Performance metrics were compared across models, including XGBoost and LightGBM, to identify the most accurate algorithms. Results LightGBM and XGBoost outperformed logistic regression in predicting ESA and iron dosage changes, achieving high accuracy (e.g., area under the curve (AUC) = 0.86 for iron dosing). …”
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The Role of Pharmacometrics in Advancing the Therapies for Autoimmune Diseases by Artur Świerczek, Dominika Batko, Elżbieta Wyska

“…These diseases are difficult to treat due to variability in drug PK among individuals, patient responses to treatment, and the side effects of long-term immunosuppressive therapies. …”
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Identification of effective subdominant anti-HIV-1 CD8+ T cells within entire post-infection and post-vaccination immune responses. by Gemma Hancock, Hongbing Yang, Elisabeth Yorke, Emma Wainwright, Victoria Bourne, Alyse Frisbee, Tamika L Payne, Mark Berrong, Guido Ferrari, Denis Chopera, Tomas Hanke, Beatriz Mothe, Christian Brander, M Juliana McElrath, Andrew McMichael, Nilu Goonetilleke, Georgia D Tomaras, Nicole Frahm, Lucy Dorrell

“…These vulnerable and so-called "beneficial" regions were of low entropy overall, yet several were not predicted by stringent conservation algorithms. Consistent with this, stronger inhibition of clade-matched than mismatched viruses was observed in the majority of subjects, indicating better targeting of clade-specific than conserved epitopes. …”
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Improving ACS prediction in T2DM patients by addressing false records in electronic medical records using propensity score by David Seung U Lee, Jung-Hyun Won, Howard Lee

“…By utilizing various ML algorithms, we developed and validated ACS prediction models on 80% and 20% of the dataset, respectively. …”
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Burden of chronic spontaneous urticaria in Italy through healthcare resource utilization and direct costs: a retrospective analysis of real-world using administrative healthcare da... by Giulia Ronconi, Letizia Dondi, Silvia Calabria, Leonardo Dondi, Irene Dell’Anno, Lucia Casoli, Diletta Valsecchi, Ornella Bonavita, Eustachio Nettis, Nello Martini, Carlo Piccinni

“…At least one CSU exacerbation occurred in 81.2% patients; of them, only one third were treated with omalizumab on average 10.9 months following exacerbation. …”
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Longitudinal CE-MRI-based Siamese network with machine learning to predict tumor response in HCC after DEB-TACE by Nan Wei, René Michael Mathy, De-Hua Chang, Philipp Mayer, Jakob Liermann, Christoph Springfeld, Michael T Dill, Thomas Longerich, Georg Lurje, Hans-Ulrich Kauczor, Mark O. Wielpütz, Osman Öcal

“…This study aims to develop and validate a predictive model that integrates deep learning and machine learning algorithms on longitudinal contrast-enhanced MRI (CE-MRI) to predict treatment response in HCC patients undergoing DEB-TACE. …”
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PREDICTIVE MODELS FOR EARLY DETECTION OF PARKINSON’S DISEASE: A MACHINE LEARNING APPROACH by S. Jeyantha Jafna Juliet, D. Jasmine David, J. S. Raj Kumar, Angelin Jeba P., R. Golden Nancy, M. Selvarathi, T. Jemima Jebaseeli

“…To properly control and treat PD, the condition must be identified as soon as possible. …”
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A Combined Approach Using T2*-Weighted Dynamic Susceptibility Contrast MRI Perfusion Parameters and Radiomics to Differentiate Between Radionecrosis and Glioma Progression: A Proof... by José Pablo Martínez Barbero, Francisco Javier Pérez García, David López Cornejo, Marta García Cerezo, Paula María Jiménez Gutiérrez, Luis Balderas, Miguel Lastra, Antonio Arauzo-Azofra, José M. Benítez, Antonio Jesús Láinez Ramos-Bossini

“…Differentiating tumor progression from radionecrosis in patients with treated brain glioma represents a significant clinical challenge due to overlapping imaging features. …”
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Geospatial impact evaluation of a low-cost agricultural intervention for enhancing environmental resilience by Pratap Khattri, Rachel Sayers, Kunwar K. Singh, Ryan Slapikas, Chet Bahadur Tamang, Dinee Tamang, Brad Sagara, Ariel BenYishay

“…Our results show that sugarcane adoption increased significantly in both treated and spillover communities, highlighting its potential as a sustainable agricultural practice. …”
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A real-world pharmacovigilance study of Sorafenib based on the FDA Adverse Event Reporting System by Dongdong Zhang, Dongdong Zhang, Dongdong Zhang, Ying Cai, Ying Cai, Ying Cai, Yixin Sun, Peiji Zeng, Wei Wang, Wenhui Wang, Xiaohua Jiang, Yifan Lian, Yifan Lian, Yifan Lian

“…AimsThe primary objective of this study was to closely monitor and identify adverse events (AEs) associated with Sorafenib, a pharmacological therapeutic agent used to treat hepatocellular carcinoma, renal cell carcinoma, and thyroid cancer. …”
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STING is significantly increased in high-grade glioma with high risk of recurrence by Meishi Zhong, Manmei Long, Chenjie Han, Saiyan Ji, Qingyuan Yang

“…Then, a relapse predictive risk-scoring model was established using the least absolute shrinkage and selection operator regression algorithms. The scores based on the expression of ATRX and STING significantly predict the recurrence for glioma patients, which further predict the survival for specific subgroups, characterized with high expression of RAD51 and wild-type TERT. …”
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