Prediction of ciprofloxacin resistance in hospitalized patients using machine learning | Communications Medicine – Nature.com
Smith, R. A., Mikanatha, N. M. & Read, A. F. Antibiotic resistance: A primer and call to action. Health Commun 30, 309314 (2015).
Article PubMed Google Scholar
Palumbi, S. R. Humans as the worlds greatest evolutionary force. Science 293, 17861790 (2001).
Article CAS PubMed Google Scholar
Weber, D. J. Collateral damage and what the future might hold. The need to balance prudent antibiotic utilization and stewardship with effective patient management. Int. J. Infect. Dis. 10, S17S24 (2006).
Article CAS Google Scholar
Carrara, E., Pfeffer, I., Zusman, O., Leibovici, L. & Paul, M. Determinants of inappropriate empirical antibiotic treatment: systematic review and meta-analysis. Int. J. Antimicrob. Agents 51, 548553 (2018).
Article CAS PubMed Google Scholar
World Health Organization. Executive summary: the selection and use of essential medicines 2019: report of the 22nd WHO Expert Committee on the selection and use of essential medicines: WHO Headquarters, Geneva, 1-5 April 2019. https://apps.who.int/iris/handle/10665/325773 (2019).
Chowers, M. et al. Estimating the impact of cefuroxime versus cefazolin and amoxicillin/clavulanate use on future collateral resistance: a retrospective comparison. J. Antimicrob. Chemother 77, 19921995 (2022).
Article CAS PubMed Google Scholar
Nathwani, D. et al. Value of hospital antimicrobial stewardship programs [ASPs]: a systematic review. Antimicrob. Resist. Infect. Control 8, 113 (2019).
Article Google Scholar
Tribble, A. C. et al. Appropriateness of antibiotic prescribing in United States childrens hospitals: a national point prevalence survey. Clin. Infect. Dis 71, e226e234 (2020).
Article PubMed Google Scholar
eEML - Electronic Essential Medicines List. https://list.essentialmeds.org/.
Loscalzo, J. et al. Harrisons Principles of Internal Medicine, (Vol. 1 & Vol. 2). (McGraw Hill Professional, 2022).
Sharma, P. C., Jain, A., Jain, S., Pahwa, R. & Yar, M. S. Ciprofloxacin: review on developments in synthetic, analytical, and medicinal aspects. J. Enzyme Inhib. Med. Chem. 25, 577589 (2010).
Article CAS PubMed Google Scholar
Thomson, C. J. The global epidemiology of resistance to ciprofloxacin and the changing nature of antibiotic resistance: a 10 year perspective. J. Antimicrob. Chemother. 43, 3140 (1999).
Article CAS PubMed Google Scholar
Organization, W. H. Global antimicrobial resistance and use surveillance system (GLASS) report: 2021. (2021).
Dalhoff, A. Global fluoroquinolone resistance epidemiology and implictions for clinical use. Interdiscip. Perspect. Infect. Dis. 2012, 976273 (2012).
Article PubMed PubMed Central Google Scholar
Low, M. et al. Association between urinary community-acquired fluoroquinolone-resistant Escherichia coli and neighbourhood antibiotic consumption: a population-based case-control study. Lancet Infect. Dis. 19, 419428 (2019).
Article CAS PubMed Google Scholar
Eliopoulos, G. M., Cosgrove, S. E. & Carmeli, Y. The impact of antimicrobial resistance on health and economic outcomes. Clin. Infect. Dis 36, 14331437 (2003).
Article Google Scholar
Gottesman, B. S., Carmeli, Y., Shitrit, P. & Chowers, M. Impact of quinolone restriction on resistance patterns of Escherichia coli isolated from urine by culture in a community setting. Clin. Infect. Dis. 49, 869875 (2009).
Article CAS PubMed Google Scholar
Anahtar, M. N., Yang, J. H. & Kanjilal, S. Applications of machine learning to the problem of antimicrobial resistance: an emerging model for translational research. J. Clin. Microbiol. 59, e0126020 (2021).
Article CAS PubMed PubMed Central Google Scholar
Rawson, T. M., Ahmad, R., Toumazou, C., Georgiou, P. & Holmes, A. H. Artificial intelligence can improve decision-making in infection management. Nat. Hum. Behav. 3, 543545 (2019).
Article PubMed Google Scholar
Yelin, I. et al. Personal clinical history predicts antibiotic resistance of urinary tract infections. Nat. Med. 25, 11431152 (2019).
Article CAS PubMed PubMed Central Google Scholar
Feretzakis, G. et al. Using machine learning techniques to aid empirical antibiotic therapy decisions in the intensive care unit of a general hospital in Greece. Antibiotics 9, 50 (2020).
Article CAS PubMed PubMed Central Google Scholar
Dan, S. et al. Prediction of fluoroquinolone resistance in gram-negative bacteria causing bloodstream infections. Antimicrob. Agents Chemother. 60, 22652272 (2016).
Article CAS PubMed PubMed Central Google Scholar
Dickstein, Y., Geffen, Y., Andreassen, S., Leibovici, L. & Paul, M. Predicting antibiotic resistance in urinary tract infection patients with prior urine cultures. Antimicrob. Agents Chemother. 60, 47174721 (2016).
Article CAS PubMed PubMed Central Google Scholar
Binuya, M. A. E., Engelhardt, E. G., Schats, W., Schmidt, M. K. & Steyerberg, E. W. Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review. BMC Med. Res. Methodol. 22, 114 (2022).
Article Google Scholar
Staffa, S. J. & Zurakowski, D. Statistical development and validation of clinical prediction models. Anesthesiology 135, 396405 (2021).
Article PubMed Google Scholar
de Hond, A. A. et al. Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review. Npj Digit. Med. 5, 113 (2022).
Google Scholar
Debray, T. P. et al. A new framework to enhance the interpretation of external validation studies of clinical prediction models. J. Clin. Epidemiol. 68, 279289 (2015).
Article PubMed Google Scholar
Eilers, P. H. C., Boer, J. M., van Ommen G. J. & van Houwelingen, H. C. Classification of microarray data with penalized logistic regression. in Microarrays: Optical Technologies and Informatics vol. 4266 187198 (International Society for Optics and Photonics, 2001).
Friedman, J., Hastie, T. & Tibshirani, R. The Elements of Statistical Learning. vol. 1 (Springer series in statistics New York, 2001).
Bergstra, J. & Bengio, Y. Random search for hyper-parameter optimization. J. Mach. Learn. Res. 13, 281305 (2012).
Google Scholar
Sill, J., Takcs, G., Mackey, L. & Lin, D. Feature-weighted linear stacking. ArXiv Prepr. arXiv:0911.0460 (2009).
Van der Laan, M. J., Polley, E. C. & Hubbard, A. E. Super learner. Stat. Appl. Genet. Mol. Biol. 6 (2007).
Lundberg, S. M. & Lee, S.-I. A Unified Approach to Interpreting Model Predictions. in Advances in Neural Information Processing Systems 30 (eds. Guyon, I. et al.) 47654774 (Curran Associates, Inc., 2017).
Vickers, A. J. & Elkin, E. B. Decision curve analysis: a novel method for evaluating prediction models. Med. Decis. Mak. 26, 565574 (2006).
Article Google Scholar
Kerr, K. F., Brown, M. D., Zhu, K. & Janes, H. Assessing the clinical impact of risk prediction models with decision curves: guidance for correct interpretation and appropriate use. J. Clin. Oncol. 34, 2534 (2016).
Article PubMed PubMed Central Google Scholar
Python Software Foundation. Python programming language. https://www.python.org/.
NumPy Developers. NumPy: Scientific computing with Python. https://numpy.org/doc/stable/.
Pandas Developers. Pandas: Powerful data structures for data analysis and manipulation. https://pandas.pydata.org/.
Scikit-learn developers. Scikit-learn: Machine learning in Python. https://scikit-learn.org/stable/.
XGBoost: Scalable, distributed gradient boosting. https://xgboost.readthedocs.io/en/latest/.
TensorFlow Developers. TensorFlow: An end-to-end open source machine learning platform. https://www.tensorflow.org/.
Matplotlib: A comprehensive library for static, animated, and interactive visualizations in Python. https://matplotlib.org/stable/.
SHAP Developers. SHAP: A unified approach to explain the output of any machine learning model. https://shap.readthedocs.io/en/latest/.
Gallini, A. et al. Influence of fluoroquinolone consumption in inpatients and outpatients on ciprofloxacin-resistant Escherichia coli in a university hospital. J. Antimicrob. Chemother. 65, 26502657 (2010).
Article CAS PubMed Google Scholar
Wang, T. et al. Predicting Antimicrobial Resistance in the Intensive Care Unit. ArXiv Prepr. ArXiv211103575 (2021).
Wojcik, G. et al. Understanding the complexities of antibiotic prescribing behaviour in acute hospitals: a systematic review and meta-ethnography. Arch. Public Health 79, 119 (2021).
Article Google Scholar
Diamant, M. et al. A game theoretic approach reveals that discretizing clinical information can reduce antibiotic misuse. Nat. Commun. 12, 113 (2021).
Article Google Scholar
Shapley, L. S. A value for n-person games. Contrib. Theory Games 2, 307317 (1953).
Google Scholar
Kumar, I. E., Venkatasubramanian, S., Scheidegger, C. & Friedler, S. Problems with Shapley-value-based explanations as feature importance measures. in International Conference on Machine Learning 54915500 (PMLR, 2020).
Chen, M. et al. Physician and Medical Student Attitudes Toward Clinical Artificial Intelligence: A Systematic Review with Cross-Sectional Survey. Available SSRN 4128867.
Mulder, M. et al. Risk factors for resistance to ciprofloxacin in community-acquired urinary tract infections due to Escherichia coli in an elderly population. J. Antimicrob. Chemother. 72, 281289 (2016).
Article PubMed Google Scholar
Arslan, H., Azap, . K., Ergnl, . & Timurkaynak, F. On behalf of the Urinary Tract Infection Study Group Risk factors for ciprofloxacin resistance among Escherichia coli strains isolated from community-acquired urinary tract infections in Turkey. J. Antimicrob. Chemother. 56, 914918 (2005).
Article CAS PubMed Google Scholar
Beckley, A. M. & Wright, E. S. Identification of antibiotic pairs that evade concurrent resistance via a retrospective analysis of antimicrobial susceptibility test results. Lancet Microbe 2, e545e554 (2021).
Article CAS PubMed PubMed Central Google Scholar
Cherny, S. S., Chowers, M. & Obolski, U. Patterns of antibiotic cross-resistance by bacterial sample source: a retrospective cohort study. medRxiv (2022).
Cherny, S. S. et al. Revealing antibiotic cross-resistance patterns in hospitalized patients through Bayesian network modelling. J. Antimicrob. Chemother 76, 239248 (2021).
Article CAS PubMed Google Scholar
Lewin-Epstein, O., Baruch, S., Hadany, L., Stein, G. & Obolski, U. Predicting antibiotic resistance in hospitalized patients by applying machine learning to electronic medical records. medRxiv 2020.06.03.20120535 https://doi.org/10.1101/2020.06.03.20120535. (2020)
Chatterjee, A. et al. Quantifying drivers of antibiotic resistance in humans: a systematic review. Lancet Infect. Dis. 18, e368e378 (2018).
Article CAS PubMed Google Scholar
Truong, W. R., Hidayat, L., Bolaris, M. A., Nguyen, L. & Yamaki, J. The antibiogram: Key considerations for its development and utilization. JAC-Antimicrob. Resist. 3, dlab060 (2021).
Article PubMed PubMed Central Google Scholar
Oonsivilai, M. et al. Using machine learning to guide targeted and locally-tailored empiric antibiotic prescribing in a childrens hospital in Cambodia. Wellcome Open Res. 3, 131 (2018).
Article PubMed PubMed Central Google Scholar
Bell, B. G., Schellevis, F., Stobberingh, E., Goossens, H. & Pringle, M. A systematic review and meta-analysis of the effects of antibiotic consumption on antibiotic resistance. BMC Infect. Dis. 14, 125 (2014).
Article Google Scholar
Baraz, A., Chowers, M., Nevo, D. & Obolski, U. Stable temporal relationships as a first step towards causal inference: an application to antibiotic resistance. medRxiv (2022).
Fasugba, O., Gardner, A., Mitchell, B. G. & Mnatzaganian, G. Ciprofloxacin resistance in community-and hospital-acquired Escherichia coli urinary tract infections: a systematic review and meta-analysis of observational studies. BMC Infect. Dis. 15, 116 (2015).
Here is the original post:
Prediction of ciprofloxacin resistance in hospitalized patients using machine learning | Communications Medicine - Nature.com
- A 3X Leader for the Agentic Era: DataRobot Named a Leader Again in the Gartner Magic Quadrant for Data Science and Machine Learning Platforms -... - June 24th, 2026 [June 24th, 2026]
- A 3X Leader for the Agentic Era: DataRobot Named a Leader Again in the Gartner Magic Quadrant for Data Science and Machine Learning Platforms - Yahoo... - June 24th, 2026 [June 24th, 2026]
- Undergrads gain hands-on machine learning experience in summer program - The Pennsylvania State University - June 24th, 2026 [June 24th, 2026]
- Python and Machine Learning: Why the Two Skills Are Increasingly Inseparable - BNO News - June 24th, 2026 [June 24th, 2026]
- Domino Data Lab Named a Visionary for the Third Consecutive Year in the 2026 Gartner Magic Quadrant for AI Platforms for Data Science and Machine... - June 24th, 2026 [June 24th, 2026]
- Machine Learning Boosts Smart Thermochromic Window Efficiency - Bioengineer.org - June 24th, 2026 [June 24th, 2026]
- A.I. VS HUMAN ROAST BATTLE to Pit Machine Learning Against Live Rapper in SF - BroadwayWorld - June 16th, 2026 [June 16th, 2026]
- Machine learning gives the U.S. a 1% chance of winning the World Cup final in its own backyard - Fortune - June 16th, 2026 [June 16th, 2026]
- Machine Learning Reveals Genes That Help Yeasts Resist Stress - Department of Energy (.gov) - June 16th, 2026 [June 16th, 2026]
- Machine Learning Reveals AED Impact on LGG Prognosis - Bioengineer.org - June 16th, 2026 [June 16th, 2026]
- Introducing the Third Generation of Apples Foundation Models - Apple Machine Learning Research - June 12th, 2026 [June 12th, 2026]
- Machine learning model predicts T2D risk up to 10 years before onset - Managed Healthcare Executive - June 12th, 2026 [June 12th, 2026]
- GPU as a Service Market to Reach USD 14.4 Billion by 2033 at 16.0% CAGR, Fueled by Generative AI, Machine Learning, and Cloud Infrastructure Expansion... - June 12th, 2026 [June 12th, 2026]
- Machine learning-guided design of mechanoadaptive bioglues for multitissue trauma and first-aid applications - Nature - June 12th, 2026 [June 12th, 2026]
- OUCRU scientists are using machine learning to forecast the next dengue outbreak - tropicalmedicine.ox.ac.uk - June 12th, 2026 [June 12th, 2026]
- IIT Roorkee invites applications for 11th Batch of Data Science, Machine Learning & Generative AI Programme - Elets Technomedia - June 12th, 2026 [June 12th, 2026]
- RAG Is Not Machine Learning, and the ML Toolkit Solves the Wrong Problem - Towards Data Science - June 3rd, 2026 [June 3rd, 2026]
- A reality check on the AI jobs hysteria - Machine Learning Week US - June 3rd, 2026 [June 3rd, 2026]
- STMicroelectronics Releases Vibration Sensor With Integrated Machine Learning for Industrial Monitoring - geneonline.com - June 3rd, 2026 [June 3rd, 2026]
- NAVER LABS Europe is offering a 2026 Research Internship in Large Language Models, focusing on AI Alignment, Controlled Generation, and Machine... - May 29th, 2026 [May 29th, 2026]
- Q&A: A Machine-Learning-Based Tool to Enhance Clinical Care of Patients With Multiple Sclerosis - Physician's Weekly - May 29th, 2026 [May 29th, 2026]
- Evaluating the Diagnostic Performance of AI and Machine Learning in Sickle Cell Disease Detection: A Systematic Review - Cureus - May 29th, 2026 [May 29th, 2026]
- HTC-19 Update: Artificial Intelligence and Machine Learning - Chromatography Online - May 29th, 2026 [May 29th, 2026]
- Multimodal phenotypic classification of generalized anxiety and panic using structural MRI data and psychosocial factors: machine learning results... - May 29th, 2026 [May 29th, 2026]
- Machine Learning Personalizes Depression Treatment with the Help of Wearable Technology - UC San Diego Today - May 27th, 2026 [May 27th, 2026]
- How Machine Learning Makes Complex Knowledge Useable in Real-World Conditions - Supply & Demand Chain Executive - May 25th, 2026 [May 25th, 2026]
- How Airbnbs machine-learning tools aim to prevent Memorial Day weekend parties in Las Vegas - FOX5 Vegas - May 25th, 2026 [May 25th, 2026]
- Artificial Intelligence and Machine Learning in Hospital Quality Management, Patient Safety, and Accreditation Readiness: A Systematic Review and... - May 25th, 2026 [May 25th, 2026]
- Machine learning accelerates analysis of fusion materials - Technology Org - May 25th, 2026 [May 25th, 2026]
- Dr. Kaveh Heidary Presents Innovations in AI, Machine Learning and Multispectral Imaging - aamu.edu - May 25th, 2026 [May 25th, 2026]
- Comparison of Prognostic Performance Between a Machine Learning Model and Manually Measured Grey-White-Matter Ratio on Early Brain Computed Tomography... - May 25th, 2026 [May 25th, 2026]
- Machine learning proves that graphene is hydrophobic - Phys.org - May 13th, 2026 [May 13th, 2026]
- Machine learning algorithm predicts AMD stock price on May 31, 2026 - Finbold - May 13th, 2026 [May 13th, 2026]
- Genetic association and machine learning improve the prediction of type 1 diabetes risk - Nature - May 1st, 2026 [May 1st, 2026]
- What Can We Expect From Machine Learning Predictions in Daily Clinical Neurology? - Neurology Live - May 1st, 2026 [May 1st, 2026]
- How Spam Filters Paved the Way for Adversarial Machine Learning - 150sec - May 1st, 2026 [May 1st, 2026]
- Real-Time Estimation of Numerical Rating Scale (NRS) Scores Using Machine Learning-Based Facial Expression Analysis: A Proof-of-Concept Study - Cureus - May 1st, 2026 [May 1st, 2026]
- Heriot-Watt researcher warns gen AI in machine learning carries serious and underestimated risks - EdTech Innovation Hub - May 1st, 2026 [May 1st, 2026]
- HS-SPME/GCMS and Machine Learning Enable Volatile Fingerprinting and Classification of Commercial Vinegars - Chromatography Online - April 12th, 2026 [April 12th, 2026]
- Role of Artificial Intelligence and Machine Learning in Diagnosing Knee Lesions: Where Are We Now? - Cureus - April 12th, 2026 [April 12th, 2026]
- CMML2AML: machine-learning discovery of co-mutations and specific single mutations predictive of blast transformation in chronic myelomonocytic... - April 12th, 2026 [April 12th, 2026]
- Machine-learning-based reconstruction of Ming-dynasty defensive corridors in Yuxian - Nature - April 12th, 2026 [April 12th, 2026]
- Have you published a disruptive paper? New machine-learning tool helps you check - Physics World - April 12th, 2026 [April 12th, 2026]
- Microsoft is automatically updating Windows 11 24H2 to 25H2 using machine learning - TweakTown - April 5th, 2026 [April 5th, 2026]
- Inside the Magic of Machine Learning That Powers Enemy AI in Arc Raiders - 80 Level - April 3rd, 2026 [April 3rd, 2026]
- We analyzed Philly street scenes and identified signs of gentrification using machine learning trained on longtime residents observations - The... - April 3rd, 2026 [April 3rd, 2026]
- Boston University To Apply Machine Learning To Alzheimers Biomarker And Cognitive Data - Quantum Zeitgeist - April 3rd, 2026 [April 3rd, 2026]
- Sony buys machine-learning company to help "enhance gameplay visuals, improve rendering techniques, and unlock new levels of visual... - April 3rd, 2026 [April 3rd, 2026]
- The Machine Learning Stack Is Being Rebuilt From Scratch Here's What Developers Need to Know in 2026 - HackerNoon - April 3rd, 2026 [April 3rd, 2026]
- Closing the Revenue Gap: Leveraging Machine Learning to Solve the $260 Billion Denial Crisis - vocal.media - April 3rd, 2026 [April 3rd, 2026]
- Machine Learning for Pharmaceuticals Set to Witness Rapid - openPR.com - April 3rd, 2026 [April 3rd, 2026]
- You Must Address These 4 Concerns To Deploy Predictive AI - Machine Learning Week US - March 30th, 2026 [March 30th, 2026]
- Google and the rise of space-based machine learning - Latitude Media - March 30th, 2026 [March 30th, 2026]
- Researchers use machine learning and social network theory to identify formation patterns in digital forums - techxplore.com - March 30th, 2026 [March 30th, 2026]
- Mayo Clinic Study Uses Wearables and Machine Learning to Predict COPD Rehab Participation - HIT Consultant - March 30th, 2026 [March 30th, 2026]
- Machine learning at the edge in retail: constraints and gains - IoT News - March 26th, 2026 [March 26th, 2026]
- AI agents are flashy, but machine learning still pays the bills - TechRadar - March 26th, 2026 [March 26th, 2026]
- Single-cell imaging and machine learning reveal hidden coordination in algae's response to light stress - Phys.org - March 26th, 2026 [March 26th, 2026]
- Machine learning analysis of CT scans - National Institutes of Health (.gov) - March 22nd, 2026 [March 22nd, 2026]
- TransUnion Machine Learning Fraud Tools Tested Against Weak Share Price Momentum - simplywall.st - March 22nd, 2026 [March 22nd, 2026]
- Machine learning could help predict how people with depression respond to treatment - Medical Xpress - March 22nd, 2026 [March 22nd, 2026]
- KR approves machine learning-based fuel reduction methodology - Smart Maritime Network - March 22nd, 2026 [March 22nd, 2026]
- Available solar energy in Andalusia will increase through the end of the century, machine learning model finds - Tech Xplore - March 22nd, 2026 [March 22nd, 2026]
- How Machine Learning Is Reshaping Environmental Policy and Water Governance - Devdiscourse - March 22nd, 2026 [March 22nd, 2026]
- Chemistry student uses machine learning to transform gene therapy production - The University of North Carolina at Chapel Hill - March 13th, 2026 [March 13th, 2026]
- AI and Machine Learning - City of Brownsville to build smart city safety solution - Smart Cities World - March 13th, 2026 [March 13th, 2026]
- AI and Machine Learning - London borough overhauls public safety infrastructure - Smart Cities World - March 13th, 2026 [March 13th, 2026]
- Titan Technology Corp. Responds to Alberta Innovates RFP AI, Machine Learning and Automation Services - TradingView - March 13th, 2026 [March 13th, 2026]
- Vietnam FPT's AI automation solution secures new machine learning patent on overseas market - VnExpress International - March 13th, 2026 [March 13th, 2026]
- AI Healthcare Technology: The Power of Machine Learning Diagnosis in Modern Medicine - Tech Times - March 13th, 2026 [March 13th, 2026]
- Future Perspectives: Key Trends Shaping the Machine Learning Market in Financial Services Until 2030 - openPR.com - March 13th, 2026 [March 13th, 2026]
- How to Build an Autonomous Machine Learning Research Loop in Google Colab Using Andrej Karpathys AutoResearch Framework for Hyperparameter Discovery... - March 13th, 2026 [March 13th, 2026]
- The Arc in Arc Raiders have multiple "brains," and they all love pursuing you because Embark gives them "rewards" in real-time via... - March 13th, 2026 [March 13th, 2026]
- OnPoint AI to Present its Augmented Reality and Machine Learning Surgical Platform at the 2026 Canaccord Genuity Musculoskeletal Conference - Yahoo... - February 27th, 2026 [February 27th, 2026]
- TD Bank continues to develop AI, machine learning tools - Auto Finance News - February 27th, 2026 [February 27th, 2026]
- AI and Machine Learning - Tech companies team to scale private 5G and physical AI - Smart Cities World - February 27th, 2026 [February 27th, 2026]
- AI and Machine Learning in Dating Apps: Smarter Matchmaking Algorithms - Programming Insider - February 27th, 2026 [February 27th, 2026]
- Machine-Learning App Helps Anesthesiologists Navigate Critical Surgical Equipment in Real Time - Carle Illinois College of Medicine - February 24th, 2026 [February 24th, 2026]
- Fractal Launches PiEvolve, an Evolutionary Agentic Engine for Autonomous Machine Learning and Scientific Discovery - Yahoo Finance - February 24th, 2026 [February 24th, 2026]
- How Brain Data and Machine Learning Could Transform the Aging Industry - gritdaily.com - February 24th, 2026 [February 24th, 2026]