Implications of topological imbalance for representation learning on biomedical knowledge graphs.

Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KGs) that utilize the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE) methods, are promising as they provide a more intuitive representation and are suitable for inference tasks such as predicting missing links. One common application is to produce ranked lists of genes for a given disease, where the rank is based on the perceived likelihood of association between the gene and the disease. It is thus critical that these predictions are not only pertinent but also biologically meaningful. However, KGs can be biased either directly due to the underlying data sources that are integrated or due to modelling choices in the construction of the graph, one consequence of which is that certain entities can get topologically overrepresented. We demonstrate the effect of these inherent structural imbalances, resulting in densely connected entities being highly ranked no matter the context. We provide support for this observation across different datasets, models as well as predictive tasks. Further, we present various graph perturbation experiments which yield more support to the observation that KGE models can be more influenced by the frequency of entities rather than any biological information encoded within the relations. Our results highlight the importance of data modelling choices, and emphasizes the need for practitioners to be mindful of these issues when interpreting model outputs and during KG composition.

A review of biomedical datasets relating to drug discovery: a knowledge graph perspective.

Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graphs (KG) have promise in many tasks, including drug repurposing, drug toxicity prediction and target gene-disease prioritization. In a drug discovery KG, crucial elements including genes, diseases and drugs are represented as entities, while relationships between them indicate an interaction. However, to construct high-quality KGs, suitable data are required. In this review, we detail publicly available sources suitable for use in constructing drug discovery focused KGs. We aim to help guide machine learning and KG practitioners who are interested in applying new techniques to the drug discovery field, but who may be unfamiliar with the relevant data sources. The datasets are selected via strict criteria, categorized according to the primary type of information contained within and are considered based upon what information could be extracted to build a KG. We then present a comparative analysis of existing public drug discovery KGs and an evaluation of selected motivating case studies from the literature. Additionally, we raise numerous and unique challenges and issues associated with the domain and its datasets, while also highlighting key future research directions. We hope this review will motivate KGs use in solving key and emerging questions in the drug discovery domain.

New Associations between Drug-Induced Adverse Events in Animal Models and Humans Reveal Novel Candidate Safety Targets.

To improve our ability to extrapolate preclinical toxicity to humans, there is a need to understand and quantify the concordance of adverse events (AEs) between animal models and clinical studies. In the present work, we discovered 3011 statistically significant associations between preclinical and clinical AEs caused by drugs reported in the PharmaPendium database of which 2952 were new associations between toxicities encoded by different Medical Dictionary for Regulatory Activities terms across species. To find plausible and testable candidate off-target drug activities for the derived associations, we investigated the genetic overlap between the genes linked to both a preclinical and a clinical AE and the protein targets found to interact with one or more drugs causing both AEs. We discuss three associations from the analysis in more detail for which novel candidate off-target drug activities could be identified, namely, the association of preclinical mutagenicity readouts with clinical teratospermia and ovarian failure, the association of preclinical reflexes abnormal with clinical poor-quality sleep, and the association of preclinical psychomotor hyperactivity with clinical drug withdrawal syndrome. Our analysis successfully identified a total of 77% of known safety targets currently tested in in vitro screening panels plus an additional 431 genes which were proposed for investigation as future safety targets for different clinical toxicities. This work provides new translational toxicity relationships beyond AE term-matching, the results of which can be used for risk profiling of future new chemical entities for clinical studies and for the development of future in vitro safety panels.

Comparison of Chemical Structure and Cell Morphology Information for Multitask Bioactivity Predictions.

The understanding of the mechanism-of-action (MoA) of compounds and the prediction of potential drug targets play an important role in small-molecule drug discovery. The aim of this work was to compare chemical and cell morphology information for bioactivity prediction. The comparison was performed using bioactivity data from the ExCAPE database, image data (in the form of CellProfiler features) from the Cell Painting data set (the largest publicly available data set of cell images with ∼30,000 compound perturbations), and extended connectivity fingerprints (ECFPs) using the multitask Bayesian matrix factorization (BMF) approach Macau. We found that the BMF Macau and random forest (RF) performance were overall similar when ECFPs were used as compound descriptors. However, BMF Macau outperformed RF in 159 out of 224 targets (71%) when image data were used as compound information. Using BMF Macau, 100 (corresponding to about 45%) and 90 (about 40%) of the 224 targets were predicted with high predictive performance (AUC > 0.8) with ECFP data and image data as side information, respectively. There were targets better predicted by image data as side information, such as ß-catenin, and others better predicted by fingerprint-based side information, such as proteins belonging to the G-protein-Coupled Receptor 1 family, which could be rationalized from the underlying data distributions in each descriptor domain. In conclusion, both cell morphology changes and chemical structure information contain information about compound bioactivity, which is also partially complementary, and can hence contribute to in silico MoA analysis.

High-content phenotypic assay for proliferation of human iPSC-derived cardiomyocytes identifies L-type calcium channels as targets.

Over 5 million people in the United States suffer from heart failure, due to the limited ability to regenerate functional cardiac tissue. One potential therapeutic strategy is to enhance proliferation of resident cardiomyocytes. However, phenotypic screening for therapeutic agents is challenged by the limited ability of conventional markers to discriminate between cardiomyocyte proliferation and endoreplication (e.g. polyploidy and multinucleation). Here, we developed a novel assay that combines automated live-cell microscopy and image processing algorithms to discriminate between proliferation and endoreplication by quantifying changes in the number of nuclei, changes in the number of cells, binucleation, and nuclear DNA content. We applied this assay to further prioritize hits from a primary screen for DNA synthesis, identifying 30 compounds that enhance proliferation of human induced pluripotent stem cell-derived cardiomyocytes. Among the most active compounds from the phenotypic screen are clinically approved L-type calcium channel blockers from multiple chemical classes whose activities were confirmed across different sources of human induced pluripotent stem cell-derived cardiomyocytes. Identification of compounds that stimulate human cardiomyocyte proliferation may provide new therapeutic strategies for heart failure.

Orthologue chemical space and its influence on target prediction.

Motivation: In silico approaches often fail to utilize bioactivity data available for orthologous targets due to insufficient evidence highlighting the benefit for such an approach. Deeper investigation into orthologue chemical space and its influence toward expanding compound and target coverage is necessary to improve the confidence in this practice. Results: Here we present analysis of the orthologue chemical space in ChEMBL and PubChem and its impact on target prediction. We highlight the number of conflicting bioactivities between human and orthologues is low and annotations are overall compatible. Chemical space analysis shows orthologues are chemically dissimilar to human with high intra-group similarity, suggesting they could effectively extend the chemical space modelled. Based on these observations, we show the benefit of orthologue inclusion in terms of novel target coverage. We also benchmarked predictive models using a time-series split and also using bioactivities from Chemistry Connect and HTS data available at AstraZeneca, showing that orthologue bioactivity inclusion statistically improved performance. Availability and implementation: Orthologue-based bioactivity prediction and the compound training set are available at www.github.com/lhm30/PIDGINv2. Contact: ab454@cam.ac.uk. Supplementary information: Supplementary data are available at Bioinformatics online.

Short- to mid-term outcomes of anatomic MCL reconstruction with Achilles tendon allograft after multiligament knee injury.

PURPOSE: Multiple techniques have been described in the literature for reconstruction of the medial collateral ligament. The purpose of this study is to describe functional outcome, range of motion, and knee stability following anatomic MCL reconstruction utilizing an Achilles tendon bone allograft after multiligament knee injury. METHODS: A comprehensive search of a single-hospital multiligament knee injury (MLKI) procedural database was conducted to identify all patients that underwent reconstruction of the MCL utilizing an Achilles tendon bone allograft and with 2-year clinical follow-up. Medical charts were retrospectively reviewed to determine each patient's knee dislocation (KD) grade, final range of motion, stability on clinical examination, and the incidence of complications and reoperations. KOOS, IKDC, and Marx scores were also collected. RESULTS: Thirty-two knees in 32 patients (21 males and 11 females) with a mean age of 30 years (range 15-51) were followed for an average of 40 months (range 28-87 months) following MCL reconstruction with Achilles tendon bone allograft. For patients with multiligament knee injuries, there were 14 KD-I (11 ACL/MCL; 3 MCL/PCL; 1 MCL/ACL/LCL; 1 MCL/PCL/LCL), 12 KD 3-M, and 3 KD-IV. One patient underwent isolated revision MCL reconstruction. At final follow-up, clinically significant valgus laxity was observed in only 1 patient (3%). All patients were able to achieve full extension of the knee and the average flexion was 121.1 ± 19.6. The average IKDC score was 67.6 ± 19.9 (range 27.7-98.9), the average KOOS score 77.1 ± 16.8 (range 31-100). The average Marx score was 4.9 (range 0-16, SD 5.2). Thirty-one of 32 (96%) patients reported being satisfied with results of the surgery. Knee dislocation grades were significantly correlated with post-operative outcome measures. CONCLUSION: In a series utilizing a modified Marx Achilles tendon, MCL reconstruction in the setting of MLKI demonstrated satisfactory clinical and functional outcomes, as well as patient satisfaction at short- to mid-term follow-up. Furthermore, knee dislocation grades were demonstrated to correlate with post-operative IKDC, KOOS, and Marx scores. LEVEL OF EVIDENCE: Type IV.

Kinase Inhibition Leads to Hormesis in a Dual Phosphorylation-Dephosphorylation Cycle.

Many antimicrobial and anti-tumour drugs elicit hormetic responses characterised by low-dose stimulation and high-dose inhibition. While this can have profound consequences for human health, with low drug concentrations actually stimulating pathogen or tumour growth, the mechanistic understanding behind such responses is still lacking. We propose a novel, simple but general mechanism that could give rise to hormesis in systems where an inhibitor acts on an enzyme. At its core is one of the basic building blocks in intracellular signalling, the dual phosphorylation-dephosphorylation motif, found in diverse regulatory processes including control of cell proliferation and programmed cell death. Our analytically-derived conditions for observing hormesis provide clues as to why this mechanism has not been previously identified. Current mathematical models regularly make simplifying assumptions that lack empirical support but inadvertently preclude the observation of hormesis. In addition, due to the inherent population heterogeneities, the presence of hormesis is likely to be masked in empirical population-level studies. Therefore, examining hormetic responses at single-cell level coupled with improved mathematical models could substantially enhance detection and mechanistic understanding of hormesis.

Soft Tissue Neoplasms Causing Apparent Venous Thoracic Outlet Syndrome.

Venous thoracic outlet syndrome (vTOS) usually results from compression of the subclavian vein classically as a result of narrowing of the costoclavicular space. We report 2 rare cases of soft tissue neoplasms resulting in apparent vTOS. The first case is a 46-year-old female with a 2-year history of intermittent unilateral shoulder pain, who was initially diagnosed with intervertebral disk herniation. Cervical fusion was performed; however, her symptoms progressed and she additionally developed paresthesias and venous congestion. Computed tomography (CT) angiogram demonstrated a 13-cm-encapsulated mass within the subscapularis muscle compressing the axillary vein. Radiological findings suggested lipoma. She subsequently underwent complete resection via a transaxillary approach with extension along the lateral border of the latissimus. Final pathology confirmed an intramuscular lipoma. The second case is a 21-year-old female who presented with acute onset of unilateral chest wall pain, palpable nodularity, and venous congestion. CT chest showed pulmonary embolism and an anterior chest wall mass. An initial attempt at resection was aborted due to proximity of the mass to the subclavian vein. The mass enlarged on serial imaging, measuring 3.8 cm in greatest dimension. Additionally, tumor thrombus was seen, and a subsequent ultrasound-guided biopsy was positive for high-grade synovial sarcoma. Positron emission tomography scan showed a pulmonary nodule that was resected thoracoscopically with pathology confirming metastatic synovial sarcoma. Subsequently, she underwent neoadjuvant chemoradiation followed by successful resection of the chest wall mass. An extended infraclavicular approach with a secondary transaxillary incision was utilized to achieve adequate exposure and margins. Final pathology was consistent with preoperative biopsy. Venous reconstruction was not needed. Although rare, an extrinsic mass as a cause of apparent TOS should be in the differential diagnosis. Surgical approach is based on tumor type, location, and proximity to the neurovascular bundle.

Incidence and long-term follow-up of isolated posterior cruciate ligament tears.

PURPOSE: Isolated posterior cruciate ligament (PCL) tears are an uncommon injury. The goals of this study are to (1) determine the population-based incidence of isolated PCL tears, (2) compare the occurrence of secondary meniscal tears or arthritis in patients with PCL deficiency to patients without PCL tears, and (3) evaluate factors associated with long-term sequelae among patients with PCL deficiency. METHODS: This retrospective study included a population-based incidence cohort of 48 patients with new-onset, isolated PCL tears between 1990 and 2010, as well as an age and sex-matched cohort of individuals without PCL tears. A chart review was performed to collect information related to the initial injury, treatment, and outcomes. Subjects were retrospectively followed to determine the development of subsequent meniscal tears, arthritis, or total knee arthroplasty (TKA). RESULTS: The age- and sex-adjusted annual incidence of isolated, complete PCL tears was 1.8 (95 % CI 1.3, 2.3) per 100,000. During a mean 12.2-year follow-up, patients with isolated PCL tears had a significantly higher likelihood (HR 6.2, 95 % CI 1.8, 21.2) of symptomatic arthritis compared to individuals without PCL tears. The likelihood of subsequent meniscal tears (HR 2.1, 95 % CI 0.4, 10.7) and TKA (HR 3.2, 95 % CI 0.5, 19.6) was more frequent among patients with PCL tears compared to subjects without PCL tears. Older age at injury was significantly associated with future arthritis (P = 0.003) and TKA (P = 0.02). CONCLUSION: Isolated PCL tears remain a rare injury with an estimated annual incidence of 2 per 100,000 persons. Patients with isolated PCL tears have a significantly higher risk of symptomatic arthritis than patients without PCL tears. Older age at injury is associated with a higher risk of arthritis and the need for TKA. The results of this study can be used to educate patients about the natural history of isolated PCL tears and provide a baseline of expectations for the future development of arthritis and subsequent meniscal injury following isolated PCL injury. LEVEL OF EVIDENCE: Retrospective comparative study, Level III.

Preoperative Radiographic Evaluation of Patients With Pelvic Discontinuity.

BACKGROUND: Pelvic discontinuity (PD) is a rare but devastating mechanism of failure in total hip arthroplasty. Radiographic findings have been described for the identification of PD. However, no study has specifically examined radiographic parameters and the utility of specific views in the preoperative identification of PD. METHODS: We performed a retrospective review of 133 patients who underwent acetabular revision for PD. Preoperative radiographic studies were reviewed including anteroposterior pelvis (AP; n = 133), true lateral hip (n = 132), Judet (n = 47), false profile (n = 4), and computed tomography scans (n = 14). Radiographs were read by the senior authors to identify the following parameters suggestive of PD: visible fracture line, medial migration of the inferior hemipelvis, and obturator ring asymmetry. RESULTS: Using only the AP view, the fracture line was visible in 116 (87%), medial migration of the inferior hemipelvis in 126 (95%), and obturator ring asymmetry in 114 (86%). A fracture line was visualized in 65 of 132 hips (49%) evaluated with laterals, 36 of 47 hips (77%) evaluated with Judet views, 3 of 4 (75%) evaluated with a false profile view, and 10 of 14 (71%) evaluated with computed tomography. CONCLUSION: Preoperative evaluation with a combination of an AP pelvis radiograph, plus a true lateral radiograph of the hip, plus Judet films in combination with the criteria for discontinuity defined in this article, allowed for identification of PD in a 100% of patients.

The shape of the proximal femur influences acetabular wear patterns over time.

BACKGROUND: Femoroacetabular impingement has been proposed as a cause of early osteoarthritis, but it is not known how this develops over time or whether the shape of the proximal femur influences this risk. QUESTIONS/PURPOSES: (1) Which areas of the acetabulum are worn more frequently by individuals with a cam deformity of the proximal femur? (2) Do observed acetabular wear patterns differ based on the etiology of the cam deformity? (3) Do wear patterns of individuals with a cam deformity differ based on an individual's age? METHODS: We examined 645 corresponding femora and acetabuli from the Hamann-Todd Osteological Collection and determined the offset and alpha angle using photographs; 370 specimens met inclusion criteria and were examined for signs of wear and the locations of wear were recorded. Specimens were separated into eight subgroups based on age either younger than 40 years or older than 60 years, alpha angle greater or less than 55°, and degree of anterior head-neck offset. We compared the prevalence of wear between groups in each location. RESULTS: Individuals with abnormal geometry of the proximal femur demonstrated different wear patterns from individuals with normal geometry. There were few differences in wear patterns identified based on the etiology of the femoral deformity. Abnormal femoral geometry was associated with more frequent wear primarily at the anterosuperior acetabulum for individuals younger than 40 years of age and globally for individuals older than 60 years of age. CONCLUSION: Femoral geometry appears to influence the pattern of acetabular wear occurring over time.

Small Gene Networks Delineate Immune Cell States and Characterize Immunotherapy Response in Melanoma.

Single-cell technologies have elucidated mechanisms responsible for immune checkpoint inhibitor (ICI) response, but are not amenable to a clinical diagnostic setting. In contrast, bulk RNA sequencing (RNA-seq) is now routine for research and clinical applications. Our workflow uses transcription factor (TF)-directed coexpression networks (regulons) inferred from single-cell RNA-seq data to deconvolute immune functional states from bulk RNA-seq data. Regulons preserve the phenotypic variation in CD45+ immune cells from metastatic melanoma samples (n = 19, discovery dataset) treated with ICIs, despite reducing dimensionality by >100-fold. Four cell states, termed exhausted T cells, monocyte lineage cells, memory T cells, and B cells were associated with therapy response, and were characterized by differentially active and cell state-specific regulons. Clustering of bulk RNA-seq melanoma samples from four independent studies (n = 209, validation dataset) according to regulon-inferred scores identified four groups with significantly different response outcomes (P < 0.001). An intercellular link was established between exhausted T cells and monocyte lineage cells, whereby their cell numbers were correlated, and exhausted T cells predicted prognosis as a function of monocyte lineage cell number. The ligand-receptor expression analysis suggested that monocyte lineage cells drive exhausted T cells into terminal exhaustion through programs that regulate antigen presentation, chronic inflammation, and negative costimulation. Together, our results demonstrate how regulon-based characterization of cell states provide robust and functionally informative markers that can deconvolve bulk RNA-seq data to identify ICI responders.

Automatic extraction of angiogenesis bioprocess from text.

MOTIVATION: Understanding key biological processes (bioprocesses) and their relationships with constituent biological entities and pharmaceutical agents is crucial for drug design and discovery. One way to harvest such information is searching the literature. However, bioprocesses are difficult to capture because they may occur in text in a variety of textual expressions. Moreover, a bioprocess is often composed of a series of bioevents, where a bioevent denotes changes to one or a group of cells involved in the bioprocess. Such bioevents are often used to refer to bioprocesses in text, which current techniques, relying solely on specialized lexicons, struggle to find. RESULTS: This article presents a range of methods for finding bioprocess terms and events. To facilitate the study, we built a gold standard corpus in which terms and events related to angiogenesis, a key biological process of the growth of new blood vessels, were annotated. Statistics of the annotated corpus revealed that over 36% of the text expressions that referred to angiogenesis appeared as events. The proposed methods respectively employed domain-specific vocabularies, a manually annotated corpus and unstructured domain-specific documents. Evaluation results showed that, while a supervised machine-learning model yielded the best precision, recall and F1 scores, the other methods achieved reasonable performance and less cost to develop. AVAILABILITY: The angiogenesis vocabularies, gold standard corpus, annotation guidelines and software described in this article are available at http://text0.mib.man.ac.uk/~mbassxw2/angiogenesis/ CONTACT: xinglong.wang@gmail.com.

A Knowledge Graph-Enhanced Tensor Factorisation Model for Discovering Drug Targets.

The drug discovery and development process is a long and expensive one, costing over 1 billion USD on average per drug and taking 10-15 years. To reduce the high levels of attrition throughout the process, there has been a growing interest in applying machine learning methodologies to various stages of drug discovery and development in the recent decade, especially at the earliest stage - identification of druggable disease genes. In this paper, we have developed a new tensor factorisation model to predict potential drug targets (genes or proteins) for treating diseases. We created a three-dimensional data tensor consisting of 1,048 gene targets, 860 diseases and 230,011 evidence attributes and clinical outcomes connecting them, using data extracted from the Open Targets and PharmaProjects databases. We enriched the data with gene target representations learned from a drug discovery-oriented knowledge graph and applied our proposed method to predict the clinical outcomes for unseen gene target and disease pairs. We designed three evaluation strategies to measure the prediction performance and benchmarked several commonly used machine learning classifiers together with Bayesian matrix and tensor factorisation methods. The result shows that incorporating knowledge graph embeddings significantly improves the prediction accuracy and that training tensor factorisation alongside a dense neural network outperforms all other baselines. In summary, our framework combines two actively studied machine learning approaches to disease target identification, namely tensor factorisation and knowledge graph representation learning, which could be a promising avenue for further exploration in data-driven drug discovery.

Cell Morphological Profiling Enables High-Throughput Screening for PROteolysis TArgeting Chimera (PROTAC) Phenotypic Signature.

PROteolysis TArgeting Chimeras (PROTACs) use the ubiquitin-proteasome system to degrade a protein of interest for therapeutic benefit. Advances made in targeted protein degradation technology have been remarkable, with several molecules having moved into clinical studies. However, robust routes to assess and better understand the safety risks of PROTACs need to be identified, which is an essential step toward delivering efficacious and safe compounds to patients. In this work, we used Cell Painting, an unbiased high-content imaging method, to identify phenotypic signatures of PROTACs. Chemical clustering and model prediction allowed the identification of a mitotoxicity signature that could not be expected by screening the individual PROTAC components. The data highlighted the benefit of unbiased phenotypic methods for identifying toxic signatures and the potential to impact drug design.

Signaling Dynamics Regulating Crosstalks between T-Cell Activation and Immune Checkpoints.

Immune checkpoint inhibitors (ICIs) targeting cytotoxic T lymphocyte-associated protein-4 (CTLA-4) and programmed cell death protein-1 (PD-1) have been hailed as major advances in cancer therapeutics; however, in many cancers response rates remain low. Extensive research efforts are underway to improve the efficacy of ICIs. The signaling pathways regulated by immune checkpoints (ICs) may be an important lever as they interfere with T-cell activation when activated by ICIs. Here, we review the current understanding of T-cell receptor signaling and their intersection with IC signaling pathways. As these signaling processes are highly dynamic and controlled by intricate spatiotemporal mechanisms, we focus on aspects of kinetic regulation that are modulated by ICs. Recent advances in computational modeling and experimental methods that can resolve spatiotemporal dynamics provide insights that reveal molecular mechanisms and new potential approaches for improving the design and application of ICIs.

Probabilistic Random Forest improves bioactivity predictions close to the classification threshold by taking into account experimental uncertainty.

Measurements of protein-ligand interactions have reproducibility limits due to experimental errors. Any model based on such assays will consequentially have such unavoidable errors influencing their performance which should ideally be factored into modelling and output predictions, such as the actual standard deviation of experimental measurements (σ) or the associated comparability of activity values between the aggregated heterogenous activity units (i.e., Ki versus IC50 values) during dataset assimilation. However, experimental errors are usually a neglected aspect of model generation. In order to improve upon the current state-of-the-art, we herein present a novel approach toward predicting protein-ligand interactions using a Probabilistic Random Forest (PRF) classifier. The PRF algorithm was applied toward in silico protein target prediction across ~ 550 tasks from ChEMBL and PubChem. Predictions were evaluated by taking into account various scenarios of experimental standard deviations in both training and test sets and performance was assessed using fivefold stratified shuffled splits for validation. The largest benefit in incorporating the experimental deviation in PRF was observed for data points close to the binary threshold boundary, when such information was not considered in any way in the original RF algorithm. For example, in cases when σ ranged between 0.4-0.6 log units and when ideal probability estimates between 0.4-0.6, the PRF outperformed RF with a median absolute error margin of ~ 17%. In comparison, the baseline RF outperformed PRF for cases with high confidence to belong to the active class (far from the binary decision threshold), although the RF models gave errors smaller than the experimental uncertainty, which could indicate that they were overtrained and/or over-confident. Finally, the PRF models trained with putative inactives decreased the performance compared to PRF models without putative inactives and this could be because putative inactives were not assigned an experimental pXC50 value, and therefore they were considered inactives with a low uncertainty (which in practice might not be true). In conclusion, PRF can be useful for target prediction models in particular for data where class boundaries overlap with the measurement uncertainty, and where a substantial part of the training data is located close to the classification threshold.

Discovery of retinoic acid receptor agonists as proliferators of cardiac progenitor cells through a phenotypic screening approach.

Identification of small molecules with the potential to selectively proliferate cardiac progenitor cells (CPCs) will aid our understanding of the signaling pathways and mechanisms involved and could ultimately provide tools for regenerative therapies for the treatment of post-MI cardiac dysfunction. We have used an in vitro human induced pluripotent stem cell-derived CPC model to screen a 10,000-compound library containing molecules representing different target classes and compounds reported to modulate the phenotype of stem or primary cells. The primary readout of this phenotypic screen was proliferation as measured by nuclear count. We identified retinoic acid receptor (RAR) agonists as potent proliferators of CPCs. The CPCs retained their progenitor phenotype following proliferation and the identified RAR agonists did not proliferate human cardiac fibroblasts, the major cell type in the heart. In addition, the RAR agonists were able to proliferate an independent source of CPCs, HuES6. The RAR agonists had a time-of-differentiation-dependent effect on the HuES6-derived CPCs. At 4 days of differentiation, treatment with retinoic acid induced differentiation of the CPCs to atrial cells. However, after 5 days of differentiation treatment with RAR agonists led to an inhibition of terminal differentiation to cardiomyocytes and enhanced the proliferation of the cells. RAR agonists, at least transiently, enhance the proliferation of human CPCs, at the expense of terminal cardiac differentiation. How this mechanism translates in vivo to activate endogenous CPCs and whether enhancing proliferation of these rare progenitor cells is sufficient to enhance cardiac repair remains to be investigated.

Safety and Efficacy of Intraoperative Computer-Navigated Versus Non-Navigated Shoulder Arthroplasty at a Tertiary Referral.

Emerging technologies in shoulder arthroplasty, such as 3-dimensional planning software and real-time intraoperative navigation, are now available for surgeons to perform more accurate placement of the glenoid component without malposition or perforation. Using these tools, the surgeon can visualize the version, inclination, and containment of the implant and determine whether augmented components would be necessary. This review provides an updated investigation of the present literature to elucidate the role of computer navigation in modern shoulder arthroplasty.