Biology subjects

Yip, R.

Publications and source records attributed to Yip, R..

4 recordsLinked to original sources

bioRxiv2026

Systematic Engineering of Intra-Articular Drug Release Profiles Reveals a Key Determinant of Disease-Modifying Efficacy in Post-Traumatic Osteoarthritis

Post-traumatic osteoarthritis (PTOA) is a progressive joint disease for which no disease-modifying osteoarthritis drugs (DMOADs) have been approved. Although injectable drug delivery systems can prolong therapeutic retention within the joint, it remains unclear whether local drug release kinetics influence disease-modifying efficacy. Here, we developed a modular platform of injectable supramolecular hydrogels using biocompatible, generally recognized as safe (GRAS) amphiphilic molecules and systematically engineered a range of degradation and drug release profiles. Using the cathepsin-K inhibitor L-006235 as a model DMOAD, we generated hydrogels with distinct release kinetics and evaluated their therapeutic performance in PTOA. Hydrogels exhibiting slower degradation and more sustained drug release like Sucrose Stearate (SS hydrogel) showed prolonged intra-articular retention and improved therapeutic outcomes. In a destabilization of the medial meniscus (DMM) mouse model, sustained-release formulations significantly reduced cartilage degeneration, preserved aggrecan expression, improved joint histopathology, and enabled effective monthly dosing. In contrast, formulations with faster degradation and release kinetics required more frequent administration to achieve comparable benefits. To our knowledge, this is the first study to establish local drug release kinetics as a critical determinant of disease-modifying efficacy in PTOA. This work provides one of the clearest demonstrations to date that engineering intra-articular release kinetics, rather than merely prolonging residence time, can improve disease-modifying outcomes. Our findings establish local release kinetics as a key design parameter for osteoarthritis therapeutics and highlight the potential of tunable supramolecular hydrogels for long-acting drug delivery.

bioengineering↗

bioRxiv2024

Artificial Intelligence's Contribution to Biomedical Literature Search: Revolutionizing or Complicating?

There is a growing number of articles about conversational AI (i.e., ChatGPT) for generating scientific literature reviews and summaries. Yet, comparative evidence lags its wide adoption by many clinicians and researchers. We explored ChatGPTs utility for literature search from an end-user perspective through the lens of clinicians and biomedical researchers. We quantitatively compared basic versions of ChatGPTs utility against conventional search methods such as Google and PubMed. We further tested whether ChatGPT user-support tools (i.e., plugins, web-browsing function, prompt-engineering, and custom-GPTs) could improve its response across four common and practical literature search scenarios: (1) high-interest topics with an abundance of information, (2) niche topics with limited information, (3) scientific hypothesis generation, and (4) for newly emerging clinical practices questions. Our results demonstrated that basic ChatGPT functions had limitations in consistency, accuracy, and relevancy. User-support tools showed improvements, but the limitations persisted. Interestingly, each literature search scenario posed different challenges: an abundance of secondary information sources in high interest topics, and uncompelling literatures for new/niche topics. This study tested practical examples highlighting both the potential and the pitfalls of integrating conversational AI into literature search processes, and underscores the necessity for rigorous comparative assessments of AI tools in scientific research. Author SummaryAs generative Artificial Intelligence (AI) tools become increasingly functional, the promise of this technology is creating a wave of excitement and anticipation around the globe including the wider scientific and biomedical community. Despite this growing excitement, researchers seeking robust, reliable, reproducible, and peer-reviewed findings have raised concerns about AIs current limitations, particularly in spreading and promoting misinformation. This emphasizes the need for continued discussions on how to appropriately employ AI to streamline the current research practices. We, as members of the scientific community and also end-users of conversational AI tools, seek to explore practical incorporations of AI for streamlining research practices. Here, we probed text-based research tasks--scientific literature mining-- can be outsourced to ChatGPT and to what extent human adjudication might be necessary. We tested different models of ChatGPT as well as augmentations such as plugins and custom GPT under different contexts of biomedical literature searching. Our results show that though at present, ChatGPT does not meet the level of reliability needed for it to be widely adopted for scientific literature searching. However, as conversational AI tools rapidly advance (a trend highlighted by the development of augmentations in this article), we envision a time when ChatGPT can become a great time saver for literature searches and make scientific information easily accessible.

scientific communication and education↗

bioRxiv2023

An immunohistochemical atlas of necroptotic pathway expression

Necroptosis is a lytic form of regulated cell death reported to contribute to inflammatory diseases of the gut, skin and lung, as well as ischemic-reperfusion injuries of the kidney, heart and brain. However, precise identification of the cells and tissues that undergo necroptotic cell death in vivo has proven challenging in the absence of robust protocols for immunohistochemical detection. Here, we provide automated immunohistochemistry protocols to detect core necroptosis regulators - Caspase-8, RIPK1, RIPK3 and MLKL - in formalin-fixed mouse and human tissues. We observed surprising heterogeneity in protein expression within tissues, whereby short-lived immune barrier cells were replete with necroptotic effectors, whereas long-lived cells lacked RIPK3 or MLKL expression. Local changes in the expression of necroptotic effectors occurred in response to insults such as inflammation, dysbiosis or immune challenge, consistent with necroptosis being dysregulated in disease contexts. These methods will facilitate the precise localisation and evaluation of necroptotic signaling in vivo. HighlightsO_LI13 automated immunohistochemistry protocols for detecting the necroptotic pathway C_LIO_LINecroptotic pathway expression is confined to fast-cycling immune barriers C_LIO_LINecroptotic pathway expression changes at sites of immunoinflammatory challenge C_LIO_LIImmunodetection of necrosomes in IBD patients is a putative new diagnostic tool C_LI

cell biology↗

bioRxiv2019

Multiplexed measurement of protein biomarkers in high-frequency longitudinal dried blood spot (DBS) samples: characterization of inflammatory responses

A detailed understanding of changes in blood protein biomarkers occuring in individuals over time would enable truly personalized approaches to health and disease monitoring. Such measurements could reveal smaller, earlier departures from normal baseline levels of biomarkers thus allowing better disease detection and treatment monitoring. Current practice, however, generally involves infrequent, sporadic biomarker testing, and this undersampling likely fails to capture important biological phenomena. Here we report the use of a robust multiplex immuno-mass spectrometric method (SISCAPA) to measure a panel of clinically-relevant proteins in a unique collection of 1,522 dried blood spots collected longitudinally by 8 individuals over periods of up to 9 years, with daily sampling during some intervals. Analytical workflow CVs of 2-6% for most assays were achieved by normalizing DBS plasma volume using a set of 3 minimally varying proteins, facilitating temporal analysis of both high- and low-amplitude biomarker changes compared to personalized baselines. The biomarkers included a panel of 9 positive and 5 negative acute phase response (inflammatory) proteins, allowing longitudinal analysis of inflammation markers associated with major and minor infections, influenza vaccination, recovery from hip-replacement surgery and Crohns disease. The results illustrate complex time-dependent \"biomarker trajectories\" on multiple timescales and provide a basis for detailed personalized models of inflammation dynamics. The striking stability of most biomarker protein levels over time, combined with the convenience of self-sampling and low cost of multiplexed measurements using mass spectrometry, provide a new window into the temporal dynamics of disease processes. The extensive results obtained using this high throughput approach offer a new source of precision biomarker big data amenable to machine learning approaches and application to more personalized health monitoring.

systems biology↗