Hydrogel microspheres for spatiotemporally controlled delivery of RNA and silencing gene expression within scaffold-free tissue engineered constructs.

Delivery systems for controlled release of RNA interference (RNAi) molecules, including small interfering (siRNA) and microRNA (miRNA), have the potential to direct stem cell differentiation for regenerative musculoskeletal applications. To date, localized RNA delivery platforms in this area have focused predominantly on bulk scaffold-based approaches, which can interfere with cell-cell interactions important for recapitulating some native musculoskeletal developmental and healing processes in tissue regeneration strategies. In contrast, scaffold-free, high density human mesenchymal stem cell (hMSC) aggregates may provide an avenue for creating a more biomimetic microenvironment. Here, photocrosslinkable dextran microspheres (MS) encapsulating siRNA-micelles were prepared via an aqueous emulsion method and incorporated within hMSC aggregates for localized and sustained delivery of bioactive siRNA. siRNA-micelles released from MS in a sustained fashion over the course of 28 days, and the released siRNA retained its ability to transfect cells for gene silencing. Incorporation of fluorescently labeled siRNA (siGLO)-laden MS within hMSC aggregates exhibited tunable siGLO delivery and uptake by stem cells. Incorporation of MS loaded with siRNA targeting green fluorescent protein (siGFP) within GFP-hMSC aggregates provided sustained presentation of siGFP within the constructs and prolonged GFP silencing for up to 15 days. This platform system enables sustained gene silencing within stem cell aggregates and thus shows great potential in tissue regeneration applications. STATEMENT OF SIGNIFICANCE: This work presents a new strategy to deliver RNA-nanocomplexes from photocrosslinked dextran microspheres for tunable presentation of bioactive RNA. These microspheres were embedded within scaffold-free, human mesenchymal stem cell (hMSC) aggregates for sustained gene silencing within three-dimensional cell constructs while maintaining cell viability. Unlike exogenous delivery of RNA within culture medium that suffers from diffusion limitations and potential need for repeated transfections, this strategy provides local and sustained RNA presentation from the microspheres to cells in the constructs. This system has the potential to inhibit translation of hMSC differentiation antagonists and drive hMSC differentiation toward desired specific lineages, and is an important step in the engineering of high-density stem cell systems with incorporated instructive genetic cues for application in tissue regeneration.

Deep learning prediction of likelihood of ICU admission and mortality in COVID-19 patients using clinical variables.

BACKGROUND: This study aimed to develop a deep-learning model and a risk-score system using clinical variables to predict intensive care unit (ICU) admission and in-hospital mortality in COVID-19 patients. METHODS: This retrospective study consisted of 5,766 persons-under-investigation for COVID-19 between 7 February 2020 and 4 May 2020. Demographics, chronic comorbidities, vital signs, symptoms and laboratory tests at admission were collected. A deep neural network model and a risk-score system were constructed to predict ICU admission and in-hospital mortality. Prediction performance used the receiver operating characteristic area under the curve (AUC). RESULTS: The top ICU predictors were procalcitonin, lactate dehydrogenase, C-reactive protein, ferritin and oxygen saturation. The top mortality predictors were age, lactate dehydrogenase, procalcitonin, cardiac troponin, C-reactive protein and oxygen saturation. Age and troponin were unique top predictors for mortality but not ICU admission. The deep-learning model predicted ICU admission and mortality with an AUC of 0.780 (95% CI [0.760-0.785]) and 0.844 (95% CI [0.839-0.848]), respectively. The corresponding risk scores yielded an AUC of 0.728 (95% CI [0.726-0.729]) and 0.848 (95% CI [0.847-0.849]), respectively. CONCLUSIONS: Deep learning and the resultant risk score have the potential to provide frontline physicians with quantitative tools to stratify patients more effectively in time-sensitive and resource-constrained circumstances.

Deep-learning artificial intelligence analysis of clinical variables predicts mortality in COVID-19 patients.

Objective: The large number of clinical variables associated with coronavirus disease 2019 (COVID-19) infection makes it challenging for frontline physicians to effectively triage COVID-19 patients during the pandemic. This study aimed to develop an efficient deep-learning artificial intelligence algorithm to identify top clinical variable predictors and derive a risk stratification score system to help clinicians triage COVID-19 patients. Methods: This retrospective study consisted of 181 hospitalized patients with confirmed COVID-19 infection from January 29, 2020 to March 21, 2020 from a major hospital in Wuhan, China. The primary outcome was mortality. Demographics, comorbidities, vital signs, symptoms, and laboratory tests were collected at initial presentation, totaling 78 clinical variables. A deep-learning algorithm and a risk stratification score system were developed to predict mortality. Data were split into 85% training and 15% testing. Prediction performance was compared with those using COVID-19 severity score, CURB-65 score, and pneumonia severity index (PSI). Results: Of the 181 COVID-19 patients, 39 expired and 142 survived. Five top predictors of mortality were D-dimer, O2 Index, neutrophil:lymphocyte ratio, C-reactive protein, and lactate dehydrogenase. The top 5 predictors and the resultant risk score yielded, respectively, an area under curve (AUC) of 0.968 (95% CI = 0.87-1.0) and 0.954 (95% CI = 0.80-0.99) for the testing dataset. Our models outperformed COVID-19 severity score (AUC = 0.756), CURB-65 score (AUC = 0.671), and PSI (AUC = 0.838). The mortality rates for our risk stratification scores (0-5) were 0%, 0%, 6.7%, 18.2%, 67.7%, and 83.3%, respectively. Conclusions: Deep-learning prediction model and the resultant risk stratification score may prove useful in clinical decisionmaking under time-sensitive and resource-constrained environment.

Dual non-viral gene delivery from microparticles within 3D high-density stem cell constructs for enhanced bone tissue engineering.

High-density mesenchymal stem cell (MSC) aggregates can be guided to form bone-like tissue via endochondral ossification in vitro when culture media is supplemented with proteins, such as growth factors (GFs), to first guide the formation of a cartilage template, followed by culture with hypertrophic factors. Recent reports have recapitulated these results through the controlled spatiotemporal delivery of chondrogenic transforming growth factor-ß1 (TGF-ß1) and chondrogenic and osteogenic bone morphogenetic protein-2 (BMP-2) from microparticles embedded within human MSC aggregates to avoid diffusion limitations and the lengthy, costly in vitro culture necessary with repeat exogenous supplementation. However, since GFs have limited stability, localized gene delivery is a promising alternative to the use of proteins. Here, mineral-coated hydroxyapatite microparticles (MCM) capable of localized delivery of Lipofectamine-plasmid DNA (pDNA) nanocomplexes encoding for TGF-ß1 (pTGF-ß1) and BMP-2 (pBMP-2) were incorporated, alone or in combination, within MSC aggregates from three healthy porcine donors to induce sustained production of these transgenes. Three donor populations were investigated in this work due to the noted MSC donor-to-donor variability in differentiation capacity documented in the literature. Delivery of pBMP-2 within Donor 1 aggregates promoted chondrogenesis at week 2, followed by an enhanced osteogenic phenotype at week 4. Donor 2 and 3 aggregates did not promote robust glycosaminoglycan (GAG) production at week 2, but by week 4, Donor 2 aggregates with pTGF-ß1/pBMP-2 and Donor 3 aggregates with both unloaded MCM and pBMP-2 enhanced osteogenesis compared to controls. These results demonstrate the ability to promote osteogenesis in stem cell aggregates through controlled, non-viral gene delivery within the cell masses. These findings also indicate the need to screen donor MSC regenerative potential in response to gene transfer prior to clinical application. Taken together, this work demonstrates a promising gene therapy approach to control stem cell fate in biomimetic 3D condensations for treatment of bone defects.