Topological Spectra and Entropy of Chromatin Loop Networks.

The 3D folding of a mammalian gene can be studied by a polymer model, where the chromatin fiber is represented by a semiflexible polymer which interacts with multivalent proteins, representing complexes of DNA-binding transcription factors and RNA polymerases. This physical model leads to the natural emergence of clusters of proteins and binding sites, accompanied by the folding of chromatin into a set of topologies, each associated with a different network of loops. Here, we combine numerics and analytics to first classify these networks and then find their relative importance or statistical weight, when the properties of the underlying polymer are those relevant to chromatin. Unlike polymer networks previously studied, our chromatin networks have finite average distances between successive binding sites, and this leads to giant differences between the weights of topologies with the same number of edges and nodes but different wiring. These weights strongly favor rosettelike structures with a local cloud of loops with respect to more complicated nonlocal topologies. Our results suggest that genes should overwhelmingly fold into a small fraction of all possible 3D topologies, which can be robustly characterized by the framework we propose here.

Combinatorics and topological weights of chromatin loop networks.

Polymer physics models suggest that chromatin spontaneously folds into loop networks with transcription units (TUs), such as enhancers and promoters, as anchors. Here we use combinatoric arguments to enumerate the emergent chromatin loop networks, both in the case where TUs are labeled and where they are unlabeled. We then combine these mathematical results with those of computer simulations aimed at finding the inter-TU energy required to form a target loop network. We show that different topologies are vastly different in terms of both their combinatorial weight and energy of formation. We explain the latter result qualitatively by computing the topological weight of a given network-i.e., its partition function in statistical mechanics language-in the approximation where excluded volume interactions are neglected. Our results show that networks featuring local loops are statistically more likely with respect to networks including more nonlocal contacts. We suggest our classification of loop networks, together with our estimate of the combinatorial and topological weight of each network, will be relevant to catalog three-dimensional structures of chromatin fibers around eukaryotic genes, and to estimate their relative frequency in both simulations and experiments.

Cardiac power output ratio: Novel survival predictor after percutaneous ventricular assist device in cardiogenic shock.

AIMS: Currently, there is limited data on prognostic indicators after insertion of percutaneous ventricular assist device (PVAD) in the treatment of cardiogenic shock (CS). This study evaluated the prognostic role of cardiac power output (CPO) ratio, defined as CPO at 24 h divided by early CPO (30 min to 2 h), in CS patients after PVAD. METHODS AND RESULTS: Consecutive CS patients from the QEH-PVAD Registry were followed up for survival at 90 days after PVAD. Among 121 consecutive patients, 98 underwent right heart catheterization after PVAD, with CPO ratio available in 68 patients. The CPO ratio and 24-h CPO, but not the early CPO post PVAD, were significantly associated with 90-day survival, with corresponding area under curve in ROC analysis of 0.816, 0.740, and 0.469, respectively. In multivariate analysis, only the CPO ratio and lactate level at 24 h remained as independent survival predictors. The CPO ratio was not associated with age, sex, and body size. Patients with lower CPO ratio had significantly lower coronary perfusion pressure, worse right heart indices, and higher pulmonary vascular resistance. A lower CPO ratio was also significantly associated with mechanical ventilation and higher creatine kinase levels in myocardial infarction patients. CONCLUSION: In post-PVAD patients, the CPO ratio outperformed the absolute CPO values and other haemodynamic metrics in predicting survival at 90 days. Such a proportional change of CPO over time, likely reflecting native heart function recovery, may help to guide management of CS patients post-PVAD.

Use of an Artificial Intelligence-Generated Vascular Severity Score Improved Plus Disease Diagnosis in Retinopathy of Prematurity.

PURPOSE: To evaluate whether providing clinicians with an artificial intelligence (AI)-based vascular severity score (VSS) improves consistency in the diagnosis of plus disease in retinopathy of prematurity (ROP). DESIGN: Multireader diagnostic accuracy imaging study. PARTICIPANTS: Eleven ROP experts, 9 of whom had been in practice for 10 years or more. METHODS: RetCam (Natus Medical Incorporated) fundus images were obtained from premature infants during routine ROP screening as part of the Imaging and Informatics in ROP study between January 2012 and July 2020. From all available examinations, a subset of 150 eye examinations from 110 infants were selected for grading. An AI-based VSS was assigned to each set of images using the i-ROP DL system (Siloam Vision). The clinicians were asked to diagnose plus disease for each examination and to assign an estimated VSS (range, 1-9) at baseline, and then again 1 month later with AI-based VSS assistance. A reference standard diagnosis (RSD) was assigned to each eye examination from the Imaging and Informatics in ROP study based on 3 masked expert labels and the ophthalmoscopic diagnosis. MAIN OUTCOME MEASURES: Mean linearly weighted κ value for plus disease diagnosis compared with RSD. Area under the receiver operating characteristic curve (AUC) and area under the precision-recall curve (AUPR) for labels 1 through 9 compared with RSD for plus disease. RESULTS: Expert agreement improved significantly, from substantial (κ value, 0.69 [0.59, 0.75]) to near perfect (κ value, 0.81 [0.71, 0.86]), when AI-based VSS was integrated. Additionally, a significant improvement in plus disease discrimination was achieved as measured by mean AUC (from 0.94 [95% confidence interval (CI), 0.92-0.96] to 0.98 [95% CI, 0.96-0.99]; difference, 0.04 [95% CI, 0.01-0.06]) and AUPR (from 0.86 [95% CI, 0.81-0.90] to 0.95 [95% CI, 0.91-0.97]; difference, 0.09 [95% CI, 0.03-0.14]). CONCLUSIONS: Providing ROP clinicians with an AI-based measurement of vascular severity in ROP was associated with both improved plus disease diagnosis and improved continuous severity labeling as compared with an RSD for plus disease. If implemented in practice, AI-based VSS could reduce interobserver variability and could standardize treatment for infants with ROP. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Panretinal handheld OCT angiography for pediatric retinal imaging.

Comprehensive visualization of retina morphology is essential in the diagnosis and management of retinal diseases in pediatric populations. Conventional imaging techniques often face challenges in effectively capturing the peripheral retina, primarily due to the limitations in current optical designs, which lack the necessary field of view to characterize the far periphery. To address this gap, our study introduces a novel ultra-widefield optical coherence tomography angiography (OCTA) system. This system, specifically tailored for pediatric applications, incorporates an ultrahigh-speed 800 kHz swept-source laser. The system's innovative design achieves a 140° field of view while maintaining excellent optical performance. Over the last 15 months, we have conducted 379 eye examinations on 96 babies using this system. It demonstrates marked efficacy in the diagnosis of retinopathy of prematurity, providing detailed and comprehensive peripheral retinal angiography. The capabilities of the ultra-widefield handheld OCTA system in enhancing the clarity and thoroughness of retina vascularization assessments have significantly improved the precision of diagnoses and the customization of treatment strategies. Our findings underscore the system's potential to advance pediatric ophthalmology and broaden the scope of retinal imaging.

Retrograde closure of post-infarct ventricular septal rupture to avoid moderator band entanglement.

A 63-year-old man presented with delayed inferior ST-elevation myocardial infarction complicated with ventricular septal rupture (VSR). He developed cardiogenic shock and respiratory failure requiring intubation and Impella (Abiomed) support.

Isolated Retinal Neovascularization in Retinopathy of Prematurity: Clinical Associations and Prognostic Implications.

OBJECTIVE: Isolated retinal neovascularization (IRNV) is a common finding in patients with stage 2 and 3 retinopathy of prematurity (ROP). This study aimed to further classify the clinical course and significance of these lesions (previously described as "popcorn" based on clinical appearance) in patients with ROP as visualized with ultrawidefield OCT (UWF-OCT). DESIGN: Single center, retrospective case series. PARTICIPANTS: Images were collected from 136 babies in the Oregon Health and Science University neonatal intensive care unit. METHODS: A prototype UWF-OCT device captured en face scans (>140°), which were reviewed for the presence of IRNV along with standard zone, stage, and plus classification. In a cross-sectional analysis we compared demographics and the clinical course of eyes with and without IRNV. Longitudinally, we compared ROP severity using a clinician-assigned vascular severity score (VSS) and compared the risk of progression among eyes with and without IRNV using multivariable logistic regression. MAIN OUTCOME MEASURES: Differences in clinical demographics and disease progression between patients with and without IRNV. RESULTS: Of the 136 patients, 60 developed stage 2 or worse ROP during their disease course, 22 of whom had IRNV visualized on UWF-OCT (37%). On average, patients with IRNV had lower birth weights (BWs) (660.1 vs. 916.8 g, P = 0.001), gestational age (GA) (24.9 vs. 26.1 weeks, P = 0.01), and were more likely to present with ROP in zone I (63.4% vs. 15.8%, P < 0.001). They were also more likely to progress to stage 3 (68.2% vs. 13.2%, P < 0.001) and receive treatment (54.5% vs. 15.8%, P = 0.002). Eyes with IRNV had a higher peak VSS (5.61 vs. 3.73, P < 0.001) and averaged a higher VSS throughout their disease course. On multivariable logistic regression, IRNV was independently associated with progression to stage 3 (P = 0.02) and requiring treatment (P = 0.03), controlling for GA, BW, and initial zone 1 disease. CONCLUSIONS: In this single center study, we found that IRNV occurs in higher risk babies and was an independent risk factor for ROP progression and treatment. These findings may have implications for OCT-based ROP classifications in the future. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Examination of pain comorbid diagnoses in the inpatient rehabilitation population across all impairment groups.

OBJECTIVE: Pain is common in inpatient rehabilitation patients; however, the prevalence of pain diagnoses in this population is not well-defined. This study examines comorbid pain diagnoses in inpatient rehabilitation patients across impairment groups. DESIGN: Adult inpatient rehabilitation patients discharged from January 2016 through December 2019 were identified in the Uniform Data System for Medical Rehabilitation® database using a literature-established framework containing ICD-10-CM pain diagnoses. Demographic data, clinical data, and pain diagnoses were compared across the 17 rehabilitation impairment groups. RESULTS: Of 1,925,002 patients identified, 1,347,239 (70.0%) had at least one ICD-10 pain diagnosis. Over half of all patients in each impairment group had at least one pain diagnosis. The most common pain diagnoses were limb/extremity and joint pain, with variation between impairment groups. Female sex and being in the arthritis, major multiple trauma, and pain syndrome impairment groups were associated with a greater odds of a pain diagnosis. CONCLUSION: Over half of all patients in each rehabilitation impairment group have a pain diagnosis, which varies between impairment groups. Due to the high prevalence of pain diagnoses, a new focus on pain management in inpatient rehabilitation patients is needed. Rehabilitation outcomes may also be affected by pain.

Multinational External Validation of Autonomous Retinopathy of Prematurity Screening.

Importance: Retinopathy of prematurity (ROP) is a leading cause of blindness in children, with significant disparities in outcomes between high-income and low-income countries, due in part to insufficient access to ROP screening. Objective: To evaluate how well autonomous artificial intelligence (AI)-based ROP screening can detect more-than-mild ROP (mtmROP) and type 1 ROP. Design, Setting, and Participants: This diagnostic study evaluated the performance of an AI algorithm, trained and calibrated using 2530 examinations from 843 infants in the Imaging and Informatics in Retinopathy of Prematurity (i-ROP) study, on 2 external datasets (6245 examinations from 1545 infants in the Stanford University Network for Diagnosis of ROP [SUNDROP] and 5635 examinations from 2699 infants in the Aravind Eye Care Systems [AECS] telemedicine programs). Data were taken from 11 and 48 neonatal care units in the US and India, respectively. Data were collected from January 2012 to July 2021, and data were analyzed from July to December 2023. Exposures: An imaging processing pipeline was created using deep learning to autonomously identify mtmROP and type 1 ROP in eye examinations performed via telemedicine. Main Outcomes and Measures: The area under the receiver operating characteristics curve (AUROC) as well as sensitivity and specificity for detection of mtmROP and type 1 ROP at the eye examination and patient levels. Results: The prevalence of mtmROP and type 1 ROP were 5.9% (91 of 1545) and 1.2% (18 of 1545), respectively, in the SUNDROP dataset and 6.2% (168 of 2699) and 2.5% (68 of 2699) in the AECS dataset. Examination-level AUROCs for mtmROP and type 1 ROP were 0.896 and 0.985, respectively, in the SUNDROP dataset and 0.920 and 0.982 in the AECS dataset. At the cross-sectional examination level, mtmROP detection had high sensitivity (SUNDROP: mtmROP, 83.5%; 95% CI, 76.6-87.7; type 1 ROP, 82.2%; 95% CI, 81.2-83.1; AECS: mtmROP, 80.8%; 95% CI, 76.2-84.9; type 1 ROP, 87.8%; 95% CI, 86.8-88.7). At the patient level, all infants who developed type 1 ROP screened positive (SUNDROP: 100%; 95% CI, 81.4-100; AECS: 100%; 95% CI, 94.7-100) prior to diagnosis. Conclusions and Relevance: Where and when ROP telemedicine programs can be implemented, autonomous ROP screening may be an effective force multiplier for secondary prevention of ROP.

Hidden Flaws Behind Expert-Level Accuracy of Multimodal GPT-4 Vision in Medicine.

Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study extends the current scope by conducting a comprehensive analysis of GPT-4V's rationales of image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges - an imaging quiz designed to test the knowledge and diagnostic capabilities of medical professionals. Evaluation results confirmed that GPT-4V performs comparatively to human physicians regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy. However, we discovered that GPT-4V frequently presents flawed rationales in cases where it makes the correct final choices (35.5%), most prominent in image comprehension (27.2%). Regardless of GPT-4V's high accuracy in multi-choice questions, our findings emphasize the necessity for further in-depth evaluations of its rationales before integrating such multimodal AI models into clinical workflows.

Genome-wide association identifies novel ROP risk loci in a multiethnic cohort.

We conducted a genome-wide association study (GWAS) in a multiethnic cohort of 920 at-risk infants for retinopathy of prematurity (ROP), a major cause of childhood blindness, identifying 1 locus at genome-wide significance level (p < 5×10-8) and 9 with significance of p < 5×10-6 for ROP ≥ stage 3. The most significant locus, rs2058019, reached genome-wide significance within the full multiethnic cohort (p = 4.96×10-9); Hispanic and European Ancestry infants driving the association. The lead single nucleotide polymorphism (SNP) falls in an intronic region within the Glioma-associated oncogene family zinc finger 3 (GLI3) gene. Relevance for GLI3 and other top-associated genes to human ocular disease was substantiated through in-silico extension analyses, genetic risk score analysis and expression profiling in human donor eye tissues. Thus, we identify a novel locus at GLI3 with relevance to retinal biology, supporting genetic susceptibilities for ROP risk with possible variability by race and ethnicity.

Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data.

OBJECTIVE: Surgical outcome prediction is challenging but necessary for postoperative management. Current machine learning models utilize pre- and post-op data, excluding intraoperative information in surgical notes. Current models also usually predict binary outcomes even when surgeries have multiple outcomes that require different postoperative management. This study addresses these gaps by incorporating intraoperative information into multimodal models for multiclass glaucoma surgery outcome prediction. MATERIALS AND METHODS: We developed and evaluated multimodal deep learning models for multiclass glaucoma trabeculectomy surgery outcomes using both structured EHR data and free-text operative notes. We compare those to baseline models that use structured EHR data exclusively, or neural network models that leverage only operative notes. RESULTS: The multimodal neural network had the highest performance with a macro AUROC of 0.750 and F1 score of 0.583. It outperformed the baseline machine learning model with structured EHR data alone (macro AUROC of 0.712 and F1 score of 0.486). Additionally, the multimodal model achieved the highest recall (0.692) for hypotony surgical failure, while the surgical success group had the highest precision (0.884) and F1 score (0.775). DISCUSSION: This study shows that operative notes are an important source of predictive information. The multimodal predictive model combining perioperative notes and structured pre- and post-op EHR data outperformed other models. Multiclass surgical outcome prediction can provide valuable insights for clinical decision-making. CONCLUSIONS: Our results show the potential of deep learning models to enhance clinical decision-making for postoperative management. They can be applied to other specialties to improve surgical outcome predictions.

Oxygenation Fluctuations Associated with Severe Retinopathy of Prematurity: Insights from a Multimodal Deep Learning Approach.

Purpose: Retinopathy of prematurity (ROP) is one of the leading causes of blindness in children. Although the role of oxygen in the pathophysiology of ROP is well established, a precise understanding of the dynamic relationship between oxygen exposure ROP incidence and severity is lacking. The purpose of this study was to evaluate the correlation between time-dependent oxygen variables and the onset of ROP. Design: Retrospective cohort study. Participants: Two hundred thirty infants who were born at a single academic center and met the inclusion criteria were included. Infants are mainly born between January 2011 and October 2022. Methods: Patient data were extracted from electronic health records (EHRs), with sufficient time-dependent oxygen data. Clinical outcomes for ROP were recorded as none/mild or moderate/severe (defined as type II or worse). Mixed-effects linear models were used to compare the 2 groups in terms of dynamic oxygen variables, such as daily average and the coefficient of variation (COV) fraction of inspired oxygen (FiO2). Support vector machine (SVM) and long-short-term memory (LSTM)-based multimodal models were trained with fivefold cross-validation to predict which infants would develop moderate/severe ROP. Gestational age (GA), birth weight, and time-dependent oxygen variables were used to develop predictive models. Main Outcome Measures: Model cross-validation performance was evaluated by computing the mean area under the receiver operating characteristic (AUROC) curve, precision, recall, and F1 score. Results: We found that both daily average and COV of FiO2 were associated with more severe ROP (adjusted P < 0.001). With fivefold cross-validation, the multimodal LSTM models had higher performance than the best static models (SVM using GA and 3 average FiO2 features) and SVM models trained on GA alone (mean AUROC = 0.89 ± 0.04 vs. 0.86 ± 0.05 vs. 0.83 ± 0.04). Conclusions: The development of severe ROP might not only be influenced by oxygen exposure but also by its fluctuation, which provides direction for future study of pathophysiological factors associated with severe ROP development. Additionally, we demonstrated that multimodal neural networks can be a method to extract useful information from time-series data, which may be a valuable methodology for the investigation of other diseases using EHR data. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Automated expert-level scleral spur detection and quantitative biometric analysis on the ANTERION anterior segment OCT system.

AIM: To perform an independent validation of deep learning (DL) algorithms for automated scleral spur detection and measurement of scleral spur-based biometric parameters in anterior segment optical coherence tomography (AS-OCT) images. METHODS: Patients receiving routine eye care underwent AS-OCT imaging using the ANTERION OCT system (Heidelberg Engineering, Heidelberg, Germany). Scleral spur locations were marked by three human graders (reference, expert and novice) and predicted using DL algorithms developed by Heidelberg Engineering that prioritise a false positive rate <4% (FPR4) or true positive rate >95% (TPR95). Performance of human graders and DL algorithms were evaluated based on agreement of scleral spur locations and biometric measurements with the reference grader. RESULTS: 1308 AS-OCT images were obtained from 117 participants. Median differences in scleral spur locations from reference locations were significantly smaller (p<0.001) for the FPR4 (52.6±48.6 µm) and TPR95 (55.5±50.6 µm) algorithms compared with the expert (61.1±65.7 µm) and novice (79.4±74.9 µm) graders. Intergrader reproducibility of biometric measurements was excellent overall for all four (intraclass correlation coefficient range 0.918-0.997). Intergrader reproducibility of the expert grader (0.567-0.965) and DL algorithms (0.746-0.979) exceeded that of the novice grader (0.146-0.929) for images with narrow angles defined by OCT measurement of angle opening distance 500 µm anterior to the scleral spur (AOD500)<150 µm. CONCLUSIONS: DL algorithms on the ANTERION approximate expert-level measurement of scleral spur-based biometric parameters in an independent patient population. These algorithms could enhance clinical utility of AS-OCT imaging, especially for evaluating patients with angle closure and performing intraocular lens calculations.

Motility induced phase separation of deformable cells.

Using a multi-phase field model, we examine how particle deformability, which is a proxy for cell stiffness, affects motility induced phase separation (MIPS). We show that purely repulsive deformable, i.e., squishy, cells phase separate more effectively than their rigid counterparts. This can be understood as due to the fact that deformability increases the effective duration of collisions. In addition, the dense regions become increasingly disordered as deformability increases. Our results contextualize the applicability of MIPS to biological systems and have implications for how cells in biological systems may self-organize.