Patient-Specific, Mechanistic Models of Tumor Growth Incorporating Artificial Intelligence and Big Data.

Despite the remarkable advances in cancer diagnosis, treatment, and management over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires the integration of patient-specific information with an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous yet practical mathematical theory of tumor initiation, development, invasion, and response to therapy. We begin this review with an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on big data and artificial intelligence. We then present illustrative examples of mathematical models manifesting their utility and discuss the limitations of stand-alone mechanistic and data-driven models. We then discuss the potential of mechanistic models for not only predicting but also optimizing response to therapy on a patient-specific basis. We describe current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.

Patient-specific, mechanistic models of tumor growth incorporating artificial intelligence and big data.

Despite the remarkable advances in cancer diagnosis, treatment, and management that have occurred over the past decade, malignant tumors remain a major public health problem. Further progress in combating cancer may be enabled by personalizing the delivery of therapies according to the predicted response for each individual patient. The design of personalized therapies requires patient-specific information integrated into an appropriate mathematical model of tumor response. A fundamental barrier to realizing this paradigm is the current lack of a rigorous, yet practical, mathematical theory of tumor initiation, development, invasion, and response to therapy. In this review, we begin by providing an overview of different approaches to modeling tumor growth and treatment, including mechanistic as well as data-driven models based on ``big data" and artificial intelligence. Next, we present illustrative examples of mathematical models manifesting their utility and discussing the limitations of stand-alone mechanistic and data-driven models. We further discuss the potential of mechanistic models for not only predicting, but also optimizing response to therapy on a patient-specific basis. We then discuss current efforts and future possibilities to integrate mechanistic and data-driven models. We conclude by proposing five fundamental challenges that must be addressed to fully realize personalized care for cancer patients driven by computational models.

Detergent-Free Decellularization Preserves the Mechanical and Biological Integrity of Murine Tendon.

Tissue decellularization has demonstrated widespread applications across numerous organ systems for tissue engineering and regenerative medicine applications. Decellularized tissues are expected to retain structural and/or compositional features of the natural extracellular matrix (ECM), enabling investigation of biochemical factors and cell-ECM interactions that drive tissue homeostasis, healing, and disease. However, the dense collagenous tendon matrix has limited the efficacy of traditional decellularization strategies without the aid of harsh chemical detergents and/or physical agitation that disrupt tissue integrity and denature proteins involved in regulating cell behavior. In this study, we adapted and established the advantages of a detergent-free decellularization method that relies on latrunculin B actin destabilization, alternating hypertonic-hypotonic salt and water incubations, nuclease-assisted elimination of cellular material, and protease inhibitor supplementation under aseptic conditions. Our method maintained the collagen molecular structure (i.e., minimal extent of denaturation), while adequately removing cells and preserving bulk mechanical properties. Furthermore, we demonstrated that decellularized tendon ECM-derived coatings isolated from different mouse strains, injury states (i.e., naive and acutely injured/"provisional"), and anatomical sites harness distinct biochemical cues and robustly maintain tendon cell viability in vitro. Together, our work provides a simple and scalable decellularization method to facilitate mechanistic studies that will expand our fundamental understanding of tendon ECM and cell biology. Impact statement In this study, we present a decellularization method for tendon that does not rely on any detergent or physical processing techniques. We assessed the impact of detergent-free decellularization using tissue, cellular, and molecular level analyses and validated the preservation of gross fiber architecture, collagen molecular structure, and extracellular matrix (ECM)-associated biological cues that are essential for studying physiological cell-ECM interactions. Finally, we demonstrated the applicability of this method for healthy and injured tendon environments, across mouse strains, and for different types of tendons, illustrating the utility of this approach for isolating the contributions of biochemical cues within unique tendon ECM microenvironments.

Innate tissue properties drive improved tendon healing in MRL/MpJ and harness cues that enhance behavior of canonical healing cells.

Development of tendon therapeutics has been hindered by the lack of informative adult mammalian models of regeneration. Murphy Roth's Large (MRL/MpJ) mice exhibit improved healing following acute tendon injuries, but the driver of this regenerative healing response remains unknown. The tissue-specific attributes of this healing response, despite a shared systemic environment within the mouse, support the hypothesis of a tissue-driven mechanism for scarless healing. Our objective was to investigate the potential of MRL/MpJ tendon extracellular matrix (ECM)-derived coatings to regulate scar-mediated healing. We found that deviations in the composition of key structural proteins within MRL/MpJ vs C57Bl/6 tendons occur synergistically to mediate the improvements in structure and mechanics following a 1-mm midsubstance injury. Improvement in mechanical properties of healing MRL/MpJ vs C57Bl/6 tendons that were isolated from systemic contributions via organ culture, highlighted the innate tendon environment as the driver of scarless healing. Finally, we established that decellularized coatings derived from early-deposited MRL/MpJ tendon provisional extracellular matrix (provisional-ECM), can modulate canonical healing B6 tendon cell behavior by inducing morphological changes and increasing proliferation in vitro. This study supports that the unique compositional cues in MRL/MpJ provisional-ECM have the therapeutic capability to motivate canonically healing cells toward improved behavior; enhancing our ability to develop effective therapeutics.

Retention of high tactile acuity throughout the life span in blindness.

Previous studies of tactile acuity on the fingertip, using passive touch, have demonstrated an age-related decline in spatial resolution for both sighted and blind subjects. We have reexamined this age dependence with two newly designed tactile-acuity charts that require active exploration of the test symbols. One chart used dot patterns similar to braille, and the other used embossed Landolt rings. Groups of blind braille readers and sighted subjects ranging from 12 to 85 years old were tested in two experiments. We replicated previous findings for sighted subjects by showing an age-related decrease in tactile acuity by nearly 1% per year. Surprisingly, the blind subjects retained high acuity into old age, showing no age-related decline. For the blind subjects, tactile acuity did not correlate with braille reading speed, the amount of daily reading, or the age at which braille was learned. We conclude that when measured with active touch, blind subjects retain high tactile acuity into old age, unlike their aging sighted peers. We propose that blind people's use of active touch in daily activities, not specifically braille reading, results in preservation of tactile acuity across the life span.