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1. Lasers in Medicine and Surgery

   Fried, Nathaniel; Seeber, Fred; MacGregor, Tom (July 2021, Lasers in Medicine and Surgery)

   Panayiotou, Chrysanthos; Pedrotti, Leno (Ed.)

   Lasers in Medicine and Surgery was created to provide a fundamental background for technicians on the theory of laser-tissue interactions, describe commonly used lasers in medicine and optical delivery systems, as well as accessories frequently used with lasers. Numerous medical fields have been impacted by the use of lasers, including cosmetic dermatology, ophthalmology, dentistry, urology, and others. Lasers are used for thermal coagulation of tissues as well as ablation and vaporization of both soft and hard tissues. 

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2. Frustration in physiology and molecular medicine

   https://doi.org/10.1016/j.mam.2025.101362  

   Parra, R Gonzalo; Komives, Elizabeth A; Wolynes, Peter G; Ferreiro, Diego U (June 2025, Molecular Aspects of Medicine)
3. Network medicine framework for identifying drug-repurposing opportunities for COVID-19

   https://doi.org/10.1073/pnas.2025581118  

   Morselli Gysi, Deisy; do Valle, Ítalo; Zitnik, Marinka; Ameli, Asher; Gan, Xiao; Varol, Onur; Ghiassian, Susan Dina; Patten, J. J.; Davey, Robert A.; Loscalzo, Joseph; et al (April 2021, Proceedings of the National Academy of Sciences)

   null (Ed.)

   The COVID-19 pandemic has highlighted the need to quickly and reliably prioritize clinically approved compounds for their potential effectiveness for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections. Here, we deployed algorithms relying on artificial intelligence, network diffusion, and network proximity, tasking each of them to rank 6,340 drugs for their expected efficacy against SARS-CoV-2. To test the predictions, we used as ground truth 918 drugs experimentally screened in VeroE6 cells, as well as the list of drugs in clinical trials that capture the medical community’s assessment of drugs with potential COVID-19 efficacy. We find that no single predictive algorithm offers consistently reliable outcomes across all datasets and metrics. This outcome prompted us to develop a multimodal technology that fuses the predictions of all algorithms, finding that a consensus among the different predictive methods consistently exceeds the performance of the best individual pipelines. We screened in human cells the top-ranked drugs, obtaining a 62% success rate, in contrast to the 0.8% hit rate of nonguided screenings. Of the six drugs that reduced viral infection, four could be directly repurposed to treat COVID-19, proposing novel treatments for COVID-19. We also found that 76 of the 77 drugs that successfully reduced viral infection do not bind the proteins targeted by SARS-CoV-2, indicating that these network drugs rely on network-based mechanisms that cannot be identified using docking-based strategies. These advances offer a methodological pathway to identify repurposable drugs for future pathogens and neglected diseases underserved by the costs and extended timeline of de novo drug development. 

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4. Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systems

   Shaier, Sagi; Bennett, Kevin; Hunter, Lawrence; von_der_Wense, Katharina (August 2022, Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (AACL 2023))
5. Neuromorphic applications in medicine

   https://doi.org/10.1088/1741-2552/aceca3  

   Aboumerhi, Khaled; Güemes, Amparo; Liu, Hongtao; Tenore, Francesco; Etienne-Cummings, Ralph (August 2023, Journal of Neural Engineering)

   Abstract In recent years, there has been a growing demand for miniaturization, low power consumption, quick treatments, and non-invasive clinical strategies in the healthcare industry. To meet these demands, healthcare professionals are seeking new technological paradigms that can improve diagnostic accuracy while ensuring patient compliance. Neuromorphic engineering, which uses neural models in hardware and software to replicate brain-like behaviors, can help usher in a new era of medicine by delivering low power, low latency, small footprint, and high bandwidth solutions. This paper provides an overview of recent neuromorphic advancements in medicine, including medical imaging and cancer diagnosis, processing of biosignals for diagnosis, and biomedical interfaces, such as motor, cognitive, and perception prostheses. For each section, we provide examples of how brain-inspired models can successfully compete with conventional artificial intelligence algorithms, demonstrating the potential of neuromorphic engineering to meet demands and improve patient outcomes. Lastly, we discuss current struggles in fitting neuromorphic hardware with non-neuromorphic technologies and propose potential solutions for future bottlenecks in hardware compatibility. 

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