Organs to Cells and Cells to Organoids: The Evolution of in vitro Central Nervous System Modelling

With 100 billion neurons and 100 trillion synapses, the human brain is not just the most complex organ in the human body, but has also been described as “the most complex thing in the universe.” The limited availability of human living brain tissue for the study of neurogenesis, neural processes and neurological disorders has resulted in more than a century-long strive from researchers worldwide to model the central nervous system (CNS) and dissect both its striking physiology and enigmatic pathophysiology. The invaluable knowledge gained with the use of animal models and post mortem human tissue remains limited to cross-species similarities and structural features, respectively. The advent of human induced pluripotent stem cell (hiPSC) and 3-D organoid technologies has revolutionised the approach to the study of human brain and CNS in vitro, presenting great potential for disease modelling and translational adoption in drug screening and regenerative medicine, also contributing beneficially to clinical research. We have surveyed more than 100 years of research in CNS modelling and provide in this review an historical excursus of its evolution, from early neural tissue explants and organotypic cultures, to 2-D patient-derived cell monolayers, to the latest development of 3-D cerebral organoids. We have generated a comprehensive summary of CNS modelling techniques and approaches, protocol refinements throughout the course of decades and developments in the study of specific neuropathologies. Current limitations and caveats such as clonal variation, developmental stage, validation of pluripotency and chromosomal stability, functional assessment, reproducibility, accuracy and scalability of these models are also discussed

Evaluation of prognostic risk models for postoperative pulmonary complications in adult patients undergoing major abdominal surgery: a systematic review and international external validation cohort study

Background Stratifying risk of postoperative pulmonary complications after major abdominal surgery allows clinicians to modify risk through targeted interventions and enhanced monitoring. In this study, we aimed to identify and validate prognostic models against a new consensus definition of postoperative pulmonary complications. Methods We did a systematic review and international external validation cohort study. The systematic review was done in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. We searched MEDLINE and Embase on March 1, 2020, for articles published in English that reported on risk prediction models for postoperative pulmonary complications following abdominal surgery. External validation of existing models was done within a prospective international cohort study of adult patients (≥18 years) undergoing major abdominal surgery. Data were collected between Jan 1, 2019, and April 30, 2019, in the UK, Ireland, and Australia. Discriminative ability and prognostic accuracy summary statistics were compared between models for the 30-day postoperative pulmonary complication rate as defined by the Standardised Endpoints in Perioperative Medicine Core Outcome Measures in Perioperative and Anaesthetic Care (StEP-COMPAC). Model performance was compared using the area under the receiver operating characteristic curve (AUROCC). Findings In total, we identified 2903 records from our literature search; of which, 2514 (86·6%) unique records were screened, 121 (4·8%) of 2514 full texts were assessed for eligibility, and 29 unique prognostic models were identified. Nine (31·0%) of 29 models had score development reported only, 19 (65·5%) had undergone internal validation, and only four (13·8%) had been externally validated. Data to validate six eligible models were collected in the international external validation cohort study. Data from 11 591 patients were available, with an overall postoperative pulmonary complication rate of 7·8% (n=903). None of the six models showed good discrimination (defined as AUROCC ≥0·70) for identifying postoperative pulmonary complications, with the Assess Respiratory Risk in Surgical Patients in Catalonia score showing the best discrimination (AUROCC 0·700 [95% CI 0·683–0·717]). Interpretation In the pre-COVID-19 pandemic data, variability in the risk of pulmonary complications (StEP-COMPAC definition) following major abdominal surgery was poorly described by existing prognostication tools. To improve surgical safety during the COVID-19 pandemic recovery and beyond, novel risk stratification tools are required. Funding British Journal of Surgery Society