Biology, biography, and the translational gap

Long COVID and Medicine's Two Cultures

Medicine has separated the two cultures of biological science and social science in research, even though they are intimately connected in the lives of our patients. To understand the cause, progression, and treatment of long COVID , biology and biography, the patient's lived experience, must be studied together.

Biosocial medicine: Biology, biography, and the tailored care of the patient

Biosocial Medicine, with its emphasis on the full integration of the person's biology and biography, proposes a strategy for clinical research and the practice of medicine that is transformative for the care of individual patients. In this paper, we argue that Biology is one component of what makes a person unique, but it does not do so alone. Biography, the lived experience of the person, integrates with biology to create a unique signature for each individual and is the foundational concept on which Biosocial Medicine is based. Biosocial Medicine starts with the premise that the individual patient is the focus of clinical care, and that average results for "ideal" patients in population level research cannot substitute for the "real" patient for whom clinical decisions are needed. The paper begins with a description of the case-based method of clinical reasoning, considers the strengths and limitations of Randomized Controlled Trials and Evidence Based Medicine, reviews the increasing focus on precision medicine and then explores the neglected role of biography as part of a new approach to the tailored care of patients. After a review of the analytical challenges in Biosocial Medicine, the paper concludes by linking the physician's commitment to understanding the patient's biography as a critical element in developing trust with the patient.

Evaluation of Preclinical Models for the Testing of Bone Tissue-Engineered Constructs

Autologous bone grafting is the clinical gold standard for the treatment of large bone defects, but it can only be obtained in limited amounts and is associated with donor site morbidity. These challenges might be overcome by tissue engineering (TE). Although promising results have been reported, translation into clinics often fails. Lack of reproducibility in preclinical studies may be one of the reasons. We evaluated preclinical models for testing of novel TE strategies, as well as the perception of researchers and clinicians toward the models. Therefore, a review of publications on preclinical models of the past 10 years was performed. A survey addressed to both clinicians and scientists was conducted to assess the clinical need for bone tissue engineering (BTE) constructs and researchers were asked about their satisfaction with the currently available preclinical models. A literature review revealed 169 articles on in vivo studies in the field of BTE, including 26 studies utilizing large animal models and 143 studies in small animals, with rabbits and rats presenting the most commonly used species. Only a few studies used skeletally mature animals, which is in large contrast to the patients targeted. The localization of the bone defects varied, but the vast majority (60%) were segmental bone defects with various fixation techniques. Results of 70 surveys confirmed a great clinical need for TE constructs and positive perceptions of all participants toward its future clinical application. Nevertheless, the need for optimization of preclinical models and limitations when it comes to translation of results to the clinical situation were indicated. No clear trends were detected with regards to the preclinical model, leading to most satisfying results despite the trend that scientists rated generally large animal models higher than small animal models. Results of the literature review and the survey reveal the lack of standardized methods. Despite the affirmed clinical need as well as a very positive perception of clinicians toward the use of TE, results indicate a critical need to optimize preclinical models and, in particular, improve translational aspects of the models. A consensus in the field on a limited number of well-standardized models should be reached.