Evaluating the long-term biological stability of cytokine biomarkers in ocular fluid samples.

PURPOSE: The quality of biological fluid samples is vital for optimal preanalytical procedures and a requirement for effective translational biomarker research. This study aims to determine the effects of storage duration and freeze-thawing on the levels of various cytokines in the human aqueous humour and vitreous samples. METHODS AND ANALYSIS: Human ocular aqueous humour and vitreous samples were obtained from 25 eyes and stored at -80°C for analysis. All samples were assayed for 27 cytokine biomarker concentrations (pg/mL) using a multiplex assay. Four sample storage durations following sample collection were evaluated (1 week, 3 months, 9 months and 15 months). Additionally, samples underwent up to three freeze-thaw cycles within the study period. RESULTS: Among the 27 cytokine biomarkers, concentrations of four cytokines (Interleukin (IL)-2, IL-10, IL-12 and platelet-derived growth factor-BB) were significantly decreased by storage duration at all time points, as early as 3 months following sample collection (range of 9%-37% decline between 1 week and 15 months, p<0.001). Freeze-thawing of up to three cycles did not significantly impact the cytokine biomarker concentrations in aqueous humour or vitreous. Separability of patient-specific cytokine biomarker profiles in the principal component analysis remained relatively the same over the 15 months of storage duration. CONCLUSION: The findings from this study suggest that several intraocular cytokine biomarkers in human aqueous humour and vitreous samples may be susceptible to degradation with long-term storage, as early as 3 months after collection. The overall patient-specific cytokine biomarker profiles are more stable than concentrations of individual cytokines. Future studies should focus on developing guidelines for optimal and standardised sample handling methods to ensure correct research findings about intraocular biomarkers are translated into clinical practice.

Developing and Validating a Prediction Model For Death or Critical Illness in Hospitalized Adults, an Opportunity for Human-Computer Collaboration.

Hospital early warning systems that use machine learning (ML) to predict clinical deterioration are increasingly being used to aid clinical decision-making. However, it is not known how ML predictions complement physician and nurse judgment. Our objective was to train and validate a ML model to predict patient deterioration and compare model predictions with real-world physician and nurse predictions. DESIGN: Retrospective and prospective cohort study. SETTING: Academic tertiary care hospital. PATIENTS: Adult general internal medicine hospitalizations. MEASUREMENTS AND MAIN RESULTS: We developed and validated a neural network model to predict in-hospital death and ICU admission in 23,528 hospitalizations between April 2011 and April 2019. We then compared model predictions with 3,374 prospectively collected predictions from nurses, residents, and attending physicians about their own patients in 960 hospitalizations between April 30, and August 28, 2019. ML model predictions achieved clinician-level accuracy for predicting ICU admission or death (ML median F1 score 0.32 [interquartile range (IQR) 0.30-0.34], AUC 0.77 [IQ 0.76-0.78]; clinicians median F1-score 0.33 [IQR 0.30-0.35], AUC 0.64 [IQR 0.63-0.66]). ML predictions were more accurate than clinicians for ICU admission. Of all ICU admissions and deaths, 36% occurred in hospitalizations where the model and clinicians disagreed. Combining human and model predictions detected 49% of clinical deterioration events, improving sensitivity by 16% compared with clinicians alone and 24% compared with the model alone while maintaining a positive predictive value of 33%, thus keeping false alarms at a clinically acceptable level. CONCLUSIONS: ML models can complement clinician judgment to predict clinical deterioration in hospital. These findings demonstrate important opportunities for human-computer collaboration to improve prognostication and personalized medicine in hospital.

Clinical Artificial Intelligence: Design Principles and Fallacies.

Clinical artificial intelligence (AI)/machine learning (ML) is anticipated to offer new abilities in clinical decision support, diagnostic reasoning, precision medicine, clinical operational support, and clinical research, but careful concern is needed to ensure these technologies work effectively in the clinic. Here, we detail the clinical ML/AI design process, identifying several key questions and detailing several common forms of issues that arise with ML tools, as motivated by real-world examples, such that clinicians and researchers can better anticipate and correct for such issues in their own use of ML/AI techniques.

Robotic fluidic coupling and interrogation of multiple vascularized organ chips.

Organ chips can recapitulate organ-level (patho)physiology, yet pharmacokinetic and pharmacodynamic analyses require multi-organ systems linked by vascular perfusion. Here, we describe an 'interrogator' that employs liquid-handling robotics, custom software and an integrated mobile microscope for the automated culture, perfusion, medium addition, fluidic linking, sample collection and in situ microscopy imaging of up to ten organ chips inside a standard tissue-culture incubator. The robotic interrogator maintained the viability and organ-specific functions of eight vascularized, two-channel organ chips (intestine, liver, kidney, heart, lung, skin, blood-brain barrier and brain) for 3 weeks in culture when intermittently fluidically coupled via a common blood substitute through their reservoirs of medium and endothelium-lined vascular channels. We used the robotic interrogator and a physiological multicompartmental reduced-order model of the experimental system to quantitatively predict the distribution of an inulin tracer perfused through the multi-organ human-body-on-chips. The automated culture system enables the imaging of cells in the organ chips and the repeated sampling of both the vascular and interstitial compartments without compromising fluidic coupling.

Author Correction: A complex human gut microbiome cultured in an anaerobic intestine-on-a-chip.

In the version of this Article originally published, the authors mistakenly cited Fig. 5d in the sentence beginning 'Importantly, the microbiome cultured in these primary Intestine Chips...'; the correct citation is Supplementary Table 2. This has now been amended.

A complex human gut microbiome cultured in an anaerobic intestine-on-a-chip.

The diverse bacterial populations that comprise the commensal microbiome of the human intestine play a central role in health and disease. A method that sustains complex microbial communities in direct contact with living human intestinal cells and their overlying mucus layer in vitro would thus enable the investigation of host-microbiome interactions. Here, we show the extended coculture of living human intestinal epithelium with stable communities of aerobic and anaerobic human gut microbiota, using a microfluidic intestine-on-a-chip that permits the control and real-time assessment of physiologically relevant oxygen gradients. When compared to aerobic coculture conditions, the establishment of a transluminal hypoxia gradient in the chip increased intestinal barrier function and sustained a physiologically relevant level of microbial diversity, consisting of over 200 unique operational taxonomic units from 11 different genera and an abundance of obligate anaerobic bacteria, with ratios of Firmicutes and Bacteroidetes similar to those observed in human faeces. The intestine-on-a-chip may serve as a discovery tool for the development of microbiome-related therapeutics, probiotics and nutraceuticals.

Scalable Fabrication of Stretchable, Dual Channel, Microfluidic Organ Chips.

A significant number of lead compounds fail in the pharmaceutical pipeline because animal studies often fail to predict clinical responses in human patients. Human Organ-on-a-Chip (Organ Chip) microfluidic cell culture devices, which provide an experimental in vitro platform to assess efficacy, toxicity, and pharmacokinetic (PK) profiles in humans, may be better predictors of therapeutic efficacy and safety in the clinic compared to animal studies. These devices may be used to model the function of virtually any organ type and can be fluidically linked through common endothelium-lined microchannels to perform in vitro studies on human organ-level and whole body-level physiology without having to conduct experiments on people. These Organ Chips consist of two perfused microfluidic channels separated by a permeable elastomeric membrane with organ-specific parenchymal cells on one side and microvascular endothelium on the other, which can be cyclically stretched to provide organ-specific mechanical cues (e.g., breathing motions in lung). This protocol details the fabrication of flexible, dual channel, Organ Chips through casting of parts using 3D printed molds, enabling combination of multiple casting and post-processing steps. Porous poly (dimethyl siloxane) (PDMS) membranes are cast with micrometer sized through-holes using silicon pillar arrays under compression. Fabrication and assembly of Organ Chips involves equipment and steps that can be implemented outside of a traditional cleanroom. This protocol provides researchers with access to Organ Chip technology for in vitro organ- and body-level studies in drug discovery, safety and efficacy testing, as well as mechanistic studies of fundamental biological processes.

Simulation of Anastomosis in Coronary Artery Bypass Surgery.

Developing cardiac surgical skills and experience takes years of practice. Cardiac trainees need to develop technical proficiency in order to enhance quality of care and patient safety. Simulation-based models are common resources for teaching procedural skills in both undergraduate and postgraduate medical education. Suitable and accessible educational platforms can play a progressively important role in the training process for young surgeons in the area of cardiac surgery. Coronary artery bypass graft (CABG) surgery consists of a wide range of pathologic anatomies and surgical techniques. In this paper we introduce a novel, synthetic, biomimetic platform that allows for the realistic practice of the CABG surgery. The prototype uses a polyvinyl alcohol hydrogel which has been designed to mimic the geometric properties of vasculature. The proposed models look and feel like human tissue and possess somewhat consistent mechanical properties. In this study, we apply the platform to simulate a case of autogenous saphenous vein bypass grafting of a patient. An autogenous saphenous vein graft is placed from the aorta to the left anterior descending coronary artery. The standard procedures of the coronary artery bypass surgery were successfully simulated. Using the proposed technology, other complicated surgeries such as end to end, side to end, and sequential anastomoses can be simulated such that these models lend themselves very well to various types of anastomoses.