Evolutionary Selection and Constraint on Human Knee Chondrocyte Regulation Impacts Osteoarthritis Risk.

During human evolution, the knee adapted to the biomechanical demands of bipedalism by altering chondrocyte developmental programs. This adaptive process was likely not without deleterious consequences to health. Today, osteoarthritis occurs in 250 million people, with risk variants enriched in non-coding sequences near chondrocyte genes, loci that likely became optimized during knee evolution. We explore this relationship by epigenetically profiling joint chondrocytes, revealing ancient selection and recent constraint and drift on knee regulatory elements, which also overlap osteoarthritis variants that contribute to disease heritability by tending to modify constrained functional sequence. We propose a model whereby genetic violations to regulatory constraint, tolerated during knee development, lead to adult pathology. In support, we discover a causal enhancer variant (rs6060369) present in billions of people at a risk locus (GDF5-UQCC1), showing how it impacts mouse knee-shape and osteoarthritis. Overall, our methods link an evolutionarily novel aspect of human anatomy to its pathogenesis.

Graph Perceiver Network for Lung Tumor and Bronchial Premalignant Lesion Stratification from Histopathology.

Bronchial premalignant lesions (PMLs) precede the development of invasive lung squamous cell carcinoma (LUSC), posing a significant challenge in distinguishing those likely to advance to LUSC from those that might regress without intervention. This study followed a novel computational approach, the Graph Perceiver Network, leveraging hematoxylin and eosin-stained whole slide images to stratify endobronchial biopsies of PMLs across a spectrum from normal to tumor lung tissues. The Graph Perceiver Network outperformed existing frameworks in classification accuracy predicting LUSC, lung adenocarcinoma, and nontumor lung tissue on The Cancer Genome Atlas and Clinical Proteomic Tumor Analysis Consortium datasets containing lung resection tissues while efficiently generating pathologist-aligned, class-specific heatmaps. The network was further tested using endobronchial biopsies from two data cohorts, containing normal to carcinoma in situ histology. It demonstrated a unique capability to differentiate carcinoma in situ lung squamous PMLs based on their progression status to invasive carcinoma. The network may have utility in stratifying PMLs for chemoprevention trials or more aggressive follow-up.

Disease-driven domain generalization for neuroimaging-based assessment of Alzheimer's disease.

Development of deep learning models to evaluate structural brain changes caused by cognitive impairment in MRI scans holds significant translational value. The efficacy of these models often encounters challenges due to variabilities arising from different data generation protocols, imaging equipment, radiological artifacts, and shifts in demographic distributions. Domain generalization (DG) techniques show promise in addressing these challenges by enabling the model to learn from one or more source domains and apply this knowledge to new, unseen target domains. Here we present a framework that utilizes model interpretability to enhance the generalizability of classification models across various cohorts. We used MRI scans and clinical diagnoses from four independent cohorts: Alzheimer's Disease Neuroimaging Initiative (ADNI, n = 1821), the Framingham Heart Study (FHS, n = 304), the Australian Imaging Biomarkers & Lifestyle Study of Ageing (AIBL, n = 661), and the National Alzheimer's Coordinating Center (NACC, n = 4647). With this data, we trained a deep neural network to focus on areas of the brain identified as relevant to the disease for model training. Our approach involved training a classifier to differentiate between structural neurodegeneration in individuals with normal cognition (NC), mild cognitive impairment (MCI), and dementia due to Alzheimer's disease (AD). This was achieved by aligning class-wise attention with a unified visual saliency prior, which was computed offline for each class using all the training data. Our method not only competes with state-of-the-art approaches but also shows improved correlation with postmortem histology. This alignment with the gold standard evidence is a significant step towards validating the effectiveness of DG frameworks, paving the way for their broader application in the field.

Alleviating iatrogenic effects of paclitaxel via antiinflammatory treatment.

BACKGROUND: Paclitaxel (PTX) is touted as an essential medicine due to its extensive use as a chemotherapeutic agent for various cancers and an antiproliferative agent for endovascular applications. Emerging studies in cardio-oncology implicate various vascular complications of chemotherapeutic agents. METHODS: We evaluated the inflammatory response induced by the systemic administration of PTX. The investigation included RNAseq analysis of primary human endothelial cells (ECs) treated with PTX to identify transcriptional changes in pro-inflammatory mediators. Additionally, we used dexamethasone (DEX), a well-known antiinflammatory compound, to assess its effectiveness in counteracting these PTX-induced changes. Further, we studied the effects of PTX on monocyte chemoattractant protein-1 (MCP-1) levels in the media of ECs. The study also extended to in vivo analysis, where a group of mice was injected with PTX and subsequently harvested at different times to assess the immediate and delayed effects of PTX on inflammatory mediators in blood and aortic ECs. RESULTS: Our RNAseq analysis revealed that PTX treatment led to significant transcriptional perturbations in pro-inflammatory mediators such as MCP-1 and CD137 within primary human ECs. These changes were effectively abrogated when DEX was administered. In vitro experiments showed a marked increase in MCP-1 levels in EC media following PTX treatment, which returned to baseline upon treatment with DEX. In vivo, we observed a threefold increase in MCP-1 levels in blood and aortic ECs 12 h post-PTX administration. Similar trends were noted for CD137 and other downstream mediators like tissue factor, vascular cell adhesion molecule 1, and E-selectin in aortic ECs. CONCLUSION: Our findings illustrate that PTX exposure induces an upregulation of atherothrombotic mediators, which can be alleviated with concurrent administration of DEX. Considering these observations, further long-term investigations should focus on understanding the systemic implications associated with PTX-based therapies and explore the clinical relevance of DEX in mitigating such risks.

Survival analysis on subchondral bone length for total knee replacement.

OBJECTIVE: Use subchondral bone length (SBL), a new MRI-derived measure that reflects the extent of cartilage loss and bone flattening, to predict the risk of progression to total knee replacement (TKR). METHODS: We employed baseline MRI data from the Osteoarthritis Initiative (OAI), focusing on 760 men and 1214 women with bone marrow lesions (BMLs) and joint space narrowing (JSN) scores, to predict the progression to TKR. To minimize bias from analyzing both knees of a participant, only the knee with a higher Kellgren-Lawrence (KL) grade was considered, given its greater potential need for TKR. We utilized the Kaplan-Meier survival curves and Cox proportional hazards models, incorporating raw and normalized values of SBL, JSN, and BML as predictors. The study included subgroup analyses for different demographics and clinical characteristics, using models for raw and normalized SBL (merged, femoral, tibial), BML (merged, femoral, tibial), and JSN (medial and lateral compartments). Model performance was evaluated using the time-dependent area under the curve (AUC), Brier score, and Concordance index to gauge accuracy, calibration, and discriminatory power. Knee joint and region-level analyses were conducted to determine the effectiveness of SBL, JSN, and BML in predicting TKR risk. RESULTS: The SBL model, incorporating data from both the femur and tibia, demonstrated a predictive capacity for TKR that closely matched the performance of the BML score and the JSN grade. The Concordance index of the SBL model was 0.764, closely mirroring the BML's 0.759 and slightly below JSN's 0.788. The Brier score for the SBL model stood at 0.069, showing comparability with BML's 0.073 and a minor difference from JSN's 0.067. Regarding the AUC, the SBL model achieved 0.803, nearly identical to BML's 0.802 and slightly lower than JSN's 0.827. CONCLUSION: SBL's capacity to predict the risk of progression to TKR highlights its potential as an effective imaging biomarker for knee osteoarthritis.

DREAMER: a computational framework to evaluate readiness of datasets for machine learning.

BACKGROUND: Machine learning (ML) has emerged as the predominant computational paradigm for analyzing large-scale datasets across diverse domains. The assessment of dataset quality stands as a pivotal precursor to the successful deployment of ML models. In this study, we introduce DREAMER (Data REAdiness for MachinE learning Research), an algorithmic framework leveraging supervised and unsupervised machine learning techniques to autonomously evaluate the suitability of tabular datasets for ML model development. DREAMER is openly accessible as a tool on GitHub and Docker, facilitating its adoption and further refinement within the research community.. RESULTS: The proposed model in this study was applied to three distinct tabular datasets, resulting in notable enhancements in their quality with respect to readiness for ML tasks, as assessed through established data quality metrics. Our findings demonstrate the efficacy of the framework in substantially augmenting the original dataset quality, achieved through the elimination of extraneous features and rows. This refinement yielded improved accuracy across both supervised and unsupervised learning methodologies. CONCLUSION: Our software presents an automated framework for data readiness, aimed at enhancing the integrity of raw datasets to facilitate robust utilization within ML pipelines. Through our proposed framework, we streamline the original dataset, resulting in enhanced accuracy and efficiency within the associated ML algorithms.

Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models.

INTRODUCTION: Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials. METHODS: We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases. RESULTS: Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years. DISCUSSION: The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment. HIGHLIGHTS: Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease.

Hepatic proinflammatory myeloid phenotypes are a hallmark of Ebola virus Kikwit pathogenesis in rhesus monkeys.

The liver is an early systemic target of Ebola virus (EBOV), but characterization beyond routine histopathology and viral antigen distribution is limited. We hypothesized Ebola virus disease (EVD) systemic proinflammatory responses would be reflected in temporally altered liver myeloid phenotypes. We utilized multiplex fluorescent immunohistochemistry (mfIHC), multispectral whole slide imaging, and image analysis to quantify molecular phenotypes of myeloid cells in the liver of rhesus macaques (Macaca mulatta; n = 21) infected with EBOV Kikwit. Liver samples included uninfected controls (n = 3), 3 days postinoculation (DPI; n = 3), 4 DPI (n = 3), 5 DPI (n = 3), 6 DPI (n = 3), and terminal disease (6-8 DPI; n = 6). Alterations in hepatic macrophages occurred at ≥ 5 DPI characterized by a 1.4-fold increase in CD68+ immunoreactivity and a transition from primarily CD14-CD16+ to CD14+CD16- macrophages, with a 2.1-fold decrease in CD163 expression in terminal animals compared with uninfected controls. An increase in the neutrophil chemoattractant and alarmin S100A9 occurred within hepatic myeloid cells at 5 DPI, followed by rapid neutrophil influx at ≥ 6 DPI. An acute rise in the antiviral myxovirus resistance protein 1 (MxA) occurred at ≥ 4 DPI, with a predilection for enhanced expression in uninfected cells. Distinctive expression of major histocompatibility complex (MHC) class II was observed in hepatocytes during terminal disease. Results illustrate that EBOV causes macrophage phenotype alterations as well as neutrophil influx and prominent activation of interferon host responses in the liver. Results offer insight into potential therapeutic strategies to prevent and/or modulate the host proinflammatory response to normalize hepatic myeloid functionality.

Novel Payloads to Mitigate Maladaptive Inward Arterial Remodeling in Drug-Coated Balloon Therapy.

Drug-coated balloon therapy is a minimally invasive endovascular approach to treat obstructive arterial disease, with increasing utilization in the peripheral circulation due to improved outcomes as compared to alternative interventional modalities. Broader clinical use of drug-coated balloons is limited by an incomplete understanding of device- and patient-specific determinants of treatment efficacy, including late outcomes that are mediated by postinterventional maladaptive inward arterial remodeling. To address this knowledge gap, we propose a predictive mathematical model of pressure-mediated femoral artery remodeling following drug-coated balloon deployment, with account of drug-based modulation of resident vascular cell phenotype and common patient comorbidities, namely, hypertension and endothelial cell dysfunction. Our results elucidate how postinterventional arterial remodeling outcomes are altered by the delivery of a traditional anti-proliferative drug, as well as by codelivery with an anti-contractile drug. Our findings suggest that codelivery of anti-proliferative and anti-contractile drugs could improve patient outcomes following drug-coated balloon therapy, motivating further consideration of novel payloads in next-generation devices.

Gait, physical activity and tibiofemoral cartilage damage: a longitudinal machine learning analysis in the Multicenter Osteoarthritis Study.

OBJECTIVE: To (1) develop and evaluate a machine learning model incorporating gait and physical activity to predict medial tibiofemoral cartilage worsening over 2 years in individuals without advanced knee osteoarthritis and (2) identify influential predictors in the model and quantify their effect on cartilage worsening. DESIGN: An ensemble machine learning model was developed to predict worsened cartilage MRI Osteoarthritis Knee Score at follow-up from gait, physical activity, clinical and demographic data from the Multicenter Osteoarthritis Study. Model performance was evaluated in repeated cross-validations. The top 10 predictors of the outcome across 100 held-out test sets were identified by a variable importance measure. Their effect on the outcome was quantified by g-computation. RESULTS: Of 947 legs in the analysis, 14% experienced medial cartilage worsening at follow-up. The median (2.5-97.5th percentile) area under the receiver operating characteristic curve across the 100 held-out test sets was 0.73 (0.65-0.79). Baseline cartilage damage, higher Kellgren-Lawrence grade, greater pain during walking, higher lateral ground reaction force impulse, greater time spent lying and lower vertical ground reaction force unloading rate were associated with greater risk of cartilage worsening. Similar results were found for the subset of knees with baseline cartilage damage. CONCLUSIONS: A machine learning approach incorporating gait, physical activity and clinical/demographic features showed good performance for predicting cartilage worsening over 2 years. While identifying potential intervention targets from the model is challenging, lateral ground reaction force impulse, time spent lying and vertical ground reaction force unloading rate should be investigated further as potential early intervention targets to reduce medial tibiofemoral cartilage worsening.

Deep-Learning-Driven Quantification of Interstitial Fibrosis in Digitized Kidney Biopsies.

Interstitial fibrosis and tubular atrophy (IFTA) on a renal biopsy are strong indicators of disease chronicity and prognosis. Techniques that are typically used for IFTA grading remain manual, leading to variability among pathologists. Accurate IFTA estimation using computational techniques can reduce this variability and provide quantitative assessment. Using trichrome-stained whole-slide images (WSIs) processed from human renal biopsies, we developed a deep-learning framework that captured finer pathologic structures at high resolution and overall context at the WSI level to predict IFTA grade. WSIs (n = 67) were obtained from The Ohio State University Wexner Medical Center. Five nephropathologists independently reviewed them and provided fibrosis scores that were converted to IFTA grades: ≤10% (none or minimal), 11% to 25% (mild), 26% to 50% (moderate), and >50% (severe). The model was developed by associating the WSIs with the IFTA grade determined by majority voting (reference estimate). Model performance was evaluated on WSIs (n = 28) obtained from the Kidney Precision Medicine Project. There was good agreement on the IFTA grading between the pathologists and the reference estimate (κ = 0.622 ± 0.071). The accuracy of the deep-learning model was 71.8% ± 5.3% on The Ohio State University Wexner Medical Center and 65.0% ± 4.2% on Kidney Precision Medicine Project data sets. Our approach to analyzing microscopic- and WSI-level changes in renal biopsies attempts to mimic the pathologist and provides a regional and contextual estimation of IFTA. Such methods can assist clinicopathologic diagnosis.

Smartphone-Based Neuropsychological Assessment in Parkinson's Disease: Feasibility, Validity, and Contextually Driven Variability in Cognition.

OBJECTIVES: The prevalence of neurodegenerative disorders demands methods of accessible assessment that reliably captures cognition in daily life contexts. We investigated the feasibility of smartphone cognitive assessment in people with Parkinson's disease (PD), who may have cognitive impairment in addition to motor-related problems that limit attending in-person clinics. We examined how daily-life factors predicted smartphone cognitive performance and examined the convergent validity of smartphone assessment with traditional neuropsychological tests. METHODS: Twenty-seven nondemented individuals with mild-moderate PD attended one in-lab session and responded to smartphone notifications over 10 days. The smartphone app queried participants 5x/day about their location, mood, alertness, exercise, and medication state and administered mobile games of working memory and executive function. RESULTS: Response rate to prompts was high, demonstrating feasibility of the approach. Between-subject reliability was high on both cognitive games. Within-subject variability was higher for working memory than executive function. Strong convergent validity was seen between traditional tests and smartphone working memory but not executive function, reflecting the latter's ceiling effects. Participants performed better on mobile working memory tasks when at home and after recent exercise. Less self-reported daytime sleepiness and lower PD symptom burden predicted a stronger association between later time of day and higher smartphone test performance. CONCLUSIONS: These findings support feasibility and validity of repeat smartphone assessments of cognition and provide preliminary evidence of the effects of context on cognitive variability in PD. Further development of this accessible assessment method could increase sensitivity and specificity regarding daily cognitive dysfunction for PD and other clinical populations.

Automated detection of mild cognitive impairment and dementia from voice recordings: A natural language processing approach.

INTRODUCTION: Automated computational assessment of neuropsychological tests would enable widespread, cost-effective screening for dementia. METHODS: A novel natural language processing approach is developed and validated to identify different stages of dementia based on automated transcription of digital voice recordings of subjects' neuropsychological tests conducted by the Framingham Heart Study (n = 1084). Transcribed sentences from the test were encoded into quantitative data and several models were trained and tested using these data and the participants' demographic characteristics. RESULTS: Average area under the curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal cognition from Dementia, Normal or Mild Cognitive Impairment (MCI) from Dementia, and Normal from MCI, respectively. DISCUSSION: The proposed approach offers a fully automated identification of MCI and dementia based on a recorded neuropsychological test, providing an opportunity to develop a remote screening tool that could be adapted easily to any language.

Relationship Between Serum Severe Acute Respiratory Syndrome Coronavirus 2 Nucleic Acid and Organ Damage in Coronavirus 2019 Patients: A Cohort Study.

BACKGROUND: Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread worldwide and has the ability to damage multiple organs. However, information on serum SARS-CoV-2 nucleic acid (RNAemia) in patients affected by coronavirus disease 2019 (COVID-19) is limited. METHODS: Patients who were admitted to Zhongnan Hospital of Wuhan University with laboratory-confirmed COVID-19 were tested for SARS-COV-2 RNA in serum from 28 January 2020 to 9 February 2020. Demographic data, laboratory and radiological findings, comorbidities, and outcomes data were collected and analyzed. RESULTS: Eighty-five patients were included in the analysis. The viral load of throat swabs was significantly higher than of serum samples. The highest detection of SARS-CoV-2 RNA in serum samples was between 11 and 15 days after symptom onset. Analysis to compare patients with and without RNAemia provided evidence that computed tomography and some laboratory biomarkers (total protein, blood urea nitrogen, lactate dehydrogenase, hypersensitive troponin I, and D-dimer) were abnormal and that the extent of these abnormalities was generally higher in patients with RNAemia than in patients without RNAemia. Organ damage (respiratory failure, cardiac damage, renal damage, and coagulopathy) was more common in patients with RNAemia than in patients without RNAemia. Patients with vs without RNAemia had shorter durations from serum testing SARS-CoV-2 RNA. The mortality rate was higher among patients with vs without RNAemia. CONCLUSIONS: In this study, we provide evidence to support that SARS-CoV-2 may have an important role in multiple organ damage. Our evidence suggests that RNAemia has a significant association with higher risk of in-hospital mortality.

Development and validation of an interpretable deep learning framework for Alzheimer's disease classification.

Alzheimer's disease is the primary cause of dementia worldwide, with an increasing morbidity burden that may outstrip diagnosis and management capacity as the population ages. Current methods integrate patient history, neuropsychological testing and MRI to identify likely cases, yet effective practices remain variably applied and lacking in sensitivity and specificity. Here we report an interpretable deep learning strategy that delineates unique Alzheimer's disease signatures from multimodal inputs of MRI, age, gender, and Mini-Mental State Examination score. Our framework linked a fully convolutional network, which constructs high resolution maps of disease probability from local brain structure to a multilayer perceptron and generates precise, intuitive visualization of individual Alzheimer's disease risk en route to accurate diagnosis. The model was trained using clinically diagnosed Alzheimer's disease and cognitively normal subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset (n = 417) and validated on three independent cohorts: the Australian Imaging, Biomarker and Lifestyle Flagship Study of Ageing (AIBL) (n = 382), the Framingham Heart Study (n = 102), and the National Alzheimer's Coordinating Center (NACC) (n = 582). Performance of the model that used the multimodal inputs was consistent across datasets, with mean area under curve values of 0.996, 0.974, 0.876 and 0.954 for the ADNI study, AIBL, Framingham Heart Study and NACC datasets, respectively. Moreover, our approach exceeded the diagnostic performance of a multi-institutional team of practicing neurologists (n = 11), and high-risk cerebral regions predicted by the model closely tracked post-mortem histopathological findings. This framework provides a clinically adaptable strategy for using routinely available imaging techniques such as MRI to generate nuanced neuroimaging signatures for Alzheimer's disease diagnosis, as well as a generalizable approach for linking deep learning to pathophysiological processes in human disease.

Temporal and tissue-specific activation of aryl hydrocarbon receptor in discrete mouse models of kidney disease.

Emerging evidence in animal models of chronic kidney disease (CKD) implicates Aryl Hydrocarbon Receptor (AHR) signaling as a mediator of uremic toxicity. However, details about its tissue-specific and time-dependent activation in response to various renal pathologies remain poorly defined. Here, a comprehensive analysis of AHR induction was conducted in response to discrete models of kidney diseases using a transgenic mouse line expressing the AHR responsive-promoter tethered to a ß-galactosidase reporter gene. Following validation using a canonical AHR ligand (a dioxin derivative), the transgenic mice were subjected to adenine-induced and ischemia/reperfusion-induced injury models representing CKD and acute kidney injury (AKI), respectively, in humans. Indoxyl sulfate was artificially increased in mice through the drinking water and by inhibiting its excretion into the urine. Adenine-fed mice showed a distinct and significant increase in ß-galactosidase in the proximal and distal renal tubules, cardiac myocytes, hepatocytes, and microvasculature in the cerebral cortex. The pattern of ß-galactosidase increase coincided with the changes in serum indoxyl sulfate levels. Machine-learning-based image quantification revealed positive correlations between indoxyl sulfate levels and ß-galactosidase expression in various tissues. This pattern of ß-galactosidase expression was recapitulated in the indoxyl sulfate-specific model. The ischemia/reperfusion injury model showed increase in ß-galactosidase in renal tubules that persisted despite reduction in serum indoxyl sulfate and blood urea nitrogen levels. Thus, our results demonstrate a relationship between AHR activation in various tissues of mice with CKD or AKI and the levels of indoxyl sulfate. This study demonstrates the use of a reporter gene mouse to probe tissue-specific manifestations of uremia in translationally relevant animal models and provide hypothesis-generating insights into the mechanism of uremic toxicity that warrant further investigation.

Surface Modification Using Ultraviolet-Ozone Treatment Enhances Acute Drug Transfer in Drug-Coated Balloon Therapy.

Endovascular deployment of drug-coated balloons (DCB) is an emerging strategy for the revascularization of arterial disease. Randomized clinical trials have demonstrated DCB effectiveness, but a recent meta-analysis reported increased mortality risk in humans with use of DCBs containing the common antiproliferative drug paclitaxel. While many factors could have contributed to adverse outcomes, current DCB designs have poor drug delivery efficiency, risk of systemic toxicity, and limited potential to retain therapeutic drug concentrations within the arterial wall following the procedure. Our study focuses on developing a strategy to enhance acute drug transfer from the balloon to the arterial wall over the short procedural window (∼30-120 s). We employed ultraviolet-ozone plasma (UVO) treatment to increase the hydrophilicity of a prototypical balloon material (Nylon-12) and subsequently applied a urea-paclitaxel coating previously shown to undergo favorable adhesive interactions with the arterial wall under simulated ex-vivo deployment. A series of assays were performed to characterize our experimental DCBs in terms of UVO-induced alterations in balloon surface hydrophobicity, formed coating microstructure, coating stability, and acute drug transfer to the arterial wall. Obtained results suggest that the UVO-based surface modification of angioplasty balloons is a promising design strategy and highlights the critical role of coating microstructure in determining the drug transfer efficiency in DCB therapy.

Correction to: Assessment of knee pain from MR imaging using a convolutional Siamese network.

The original version of this article, published on 13 February 2020, unfortunately contained a mistake.

Assessment of knee pain from MR imaging using a convolutional Siamese network.

OBJECTIVES: It remains difficult to characterize the source of pain in knee joints either using radiographs or magnetic resonance imaging (MRI). We sought to determine if advanced machine learning methods such as deep neural networks could distinguish knees with pain from those without it and identify the structural features that are associated with knee pain. METHODS: We constructed a convolutional Siamese network to associate MRI scans obtained on subjects from the Osteoarthritis Initiative (OAI) with frequent unilateral knee pain comparing the knee with frequent pain to the contralateral knee without pain. The Siamese network architecture enabled pairwise learning of information from two-dimensional (2D) sagittal intermediate-weighted turbo spin echo slices obtained from similar locations on both knees. Class activation mapping (CAM) was utilized to create saliency maps, which highlighted the regions most associated with knee pain. The MRI scans and the CAMs of each subject were reviewed by an expert radiologist to identify the presence of abnormalities within the model-predicted regions of high association. RESULTS: Using 10-fold cross-validation, our model achieved an area under curve (AUC) value of 0.808. When individuals whose knee WOMAC pain scores were not discordant were excluded, model performance increased to 0.853. The radiologist review revealed that about 86% of the cases that were predicted correctly had effusion-synovitis within the regions that were most associated with pain. CONCLUSIONS: This study demonstrates a proof of principle that deep learning can be applied to assess knee pain from MRI scans. KEY POINTS: • Our article is the first to leverage a deep learning framework to associate MR images of the knee with knee pain. • We developed a convolutional Siamese network that had the ability to fuse information from multiple two-dimensional (2D) MRI slices from the knee with pain and the contralateral knee of the same individual without pain to predict unilateral knee pain. • Our model achieved an area under curve (AUC) value of 0.808. When individuals who had WOMAC pain scores that were not discordant for knees (pain discordance < 3) were excluded, model performance increased to 0.853.

c-Cbl Expression Correlates with Human Colorectal Cancer Survival and Its Wnt/ß-Catenin Suppressor Function Is Regulated by Tyr371 Phosphorylation.

The proto-oncogene ß-catenin drives colorectal cancer (CRC) tumorigenesis. Casitas B-lineage lymphoma (c-Cbl) inhibits CRC tumor growth through targeting nuclear ß-catenin by a poorly understood mechanism. In addition, the role of c-Cbl in human CRC remains largely underexplored. Using a novel quantitative histopathologic technique, we demonstrate that patients with high c-Cbl-expressing tumors had significantly better median survival (3.7 years) compared with low c-Cbl-expressing tumors (1.8 years; P = 0.0026) and were more than twice as likely to be alive at 3 years compared with low c-Cbl tumors (P = 0.0171). Our data further demonstrate that c-Cbl regulation of nuclear ß-catenin requires phosphorylation of c-Cbl Tyr371 because its mutation compromises its ability to target ß-catenin. The tyrosine 371 (Y371H) mutant interacted with but failed to ubiquitinate nuclear ß-catenin. The nuclear localization of the c-Cbl-Y371H mutant contributed to its dominant negative effect on nuclear ß-catenin. The biological importance of c-Cbl-Y371H was demonstrated in various systems, including a transgenic Wnt-8 zebrafish model. c-Cbl-Y371H mutant showed augmented Wnt/ß-catenin signaling, increased Wnt target genes, angiogenesis, and CRC tumor growth. This study demonstrates a strong link between c-Cbl and overall survival of patients with CRC and provides new insights into a possible role of Tyr371 phosphorylation in Wnt/ß-catenin regulation, which has important implications in tumor growth and angiogenesis in CRC.