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"Arnold, Corey"
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Integrating remote monitoring into heart failure patients’ care regimen: A pilot study
Around 50% of hospital readmissions due to heart failure are preventable, with lack of adherence to prescribed self-care as a driving factor. Remote tracking and reminders issued by mobile health devices could help to promote self-care, which could potentially reduce these readmissions.
We sought to investigate two factors: (1) feasibility of enrolling heart failure patients in a remote monitoring regimen that uses wireless sensors and patient-reported outcome measures; and (2) their adherence to using the study devices and completing patient-reported outcome measures.
Twenty heart failure patients participated in piloting a remote monitoring regimen. Data collection included: (1) physical activity using wrist-worn activity trackers; (2) body weight using bathroom scales; (3) medication adherence using smart pill bottles; and (4) patient -reported outcomes using patient-reported outcome measures.
We evaluated 150 hospitalized heart failure patients and enrolled 20 individuals. Two factors contributed to 50% (65/130) being excluded from the study: smartphone ownership and patient discharge. Over the course of the study, 60.0% of the subjects wore the activity tracker for at least 70% of the hours, and 45.0% used the scale for more than 70% of the days. The pill bottle was used less than 10% of the days by 55.0% of the subjects.
Our method of recruiting heart failure patients prior to hospital discharge may not be feasible as the enrollment rate was low. Once enrolled, the majority of subjects maintained a high adherence to wearing the activity tracker but low adherence to using the pill bottle and completing the follow-up surveys. Scale usage was fair, but it received positive reviews from most subjects. Given the observed usage and feedback, we suggest mobile health-driven interventions consider including an activity tracker and bathroom scale. We also recommend administering a shorter survey more regularly and through an easier interface.
Journal Article
Harnessing clinical annotations to improve deep learning performance in prostate segmentation
Developing large-scale datasets with research-quality annotations is challenging due to the high cost of refining clinically generated markup into high precision annotations. We evaluated the direct use of a large dataset with only clinically generated annotations in development of high-performance segmentation models for small research-quality challenge datasets.
We used a large retrospective dataset from our institution comprised of 1,620 clinically generated segmentations, and two challenge datasets (PROMISE12: 50 patients, ProstateX-2: 99 patients). We trained a 3D U-Net convolutional neural network (CNN) segmentation model using our entire dataset, and used that model as a template to train models on the challenge datasets. We also trained versions of the template model using ablated proportions of our dataset, and evaluated the relative benefit of those templates for the final models. Finally, we trained a version of the template model using an out-of-domain brain cancer dataset, and evaluated the relevant benefit of that template for the final models. We used five-fold cross-validation (CV) for all training and evaluation across our entire dataset.
Our model achieves state-of-the-art performance on our large dataset (mean overall Dice 0.916, average Hausdorff distance 0.135 across CV folds). Using this model as a pre-trained template for refining on two external datasets significantly enhanced performance (30% and 49% enhancement in Dice scores respectively). Mean overall Dice and mean average Hausdorff distance were 0.912 and 0.15 for the ProstateX-2 dataset, and 0.852 and 0.581 for the PROMISE12 dataset. Using even small quantities of data to train the template enhanced performance, with significant improvements using 5% or more of the data.
We trained a state-of-the-art model using unrefined clinical prostate annotations and found that its use as a template model significantly improved performance in other prostate segmentation tasks, even when trained with only 5% of the original dataset.
Journal Article
Macrophages disseminate pathogen associated molecular patterns through the direct extracellular release of the soluble content of their phagolysosomes
Recognition of pathogen-or-damage-associated molecular patterns is critical to inflammation. However, most pathogen-or-damage-associated molecular patterns exist within intact microbes/cells and are typically part of non-diffusible, stable macromolecules that are not optimally immunostimulatory or available for immune detection. Partial digestion of microbes/cells following phagocytosis potentially generates new diffusible pathogen-or-damage-associated molecular patterns, however, our current understanding of phagosomal biology would have these molecules sequestered and destroyed within phagolysosomes. Here, we show the controlled release of partially-digested, soluble material from phagolysosomes of macrophages through transient, iterative fusion-fission events between mature phagolysosomes and the plasma membrane, a process we term eructophagy. Eructophagy is most active in proinflammatory macrophages and further induced by toll like receptor engagement. Eructophagy is mediated by genes encoding proteins required for autophagy and can activate vicinal cells by release of phagolysosomally-processed, partially-digested pathogen associated molecular patterns. We propose that eructophagy allows macrophages to amplify local inflammation through the processing and dissemination of pathogen-or-damage-associated molecular patterns.
The detection of conserved motifs by pattern recognition receptors is a crucial component of the innate detection of pathogens and danger signals via conserved pattern recognition receptors. Here the authors define a pathway that transfers partially digested material from the phagolysosomal pathway of macrophages to release at the plasma membrane which is associated with enhanced inflammatory potential, by a process they introduce as eructophagy.
Journal Article
Comparing P300 flashing paradigms in online typing with language models
The P300 Speller is a brain-computer interface system that allows victims of motor neuron diseases to regain the ability to communicate by typing characters into a computer by thought. Since the system has a relatively slow typing speed, different stimulus presentation paradigms have been proposed designed to allow users to input information faster by reducing the number of required stimuli or increase signal fidelity. This study compares the typing speeds of the Row-Column, Checkerboard, and Combinatorial Paradigms to examine how their performance compares in online and offline settings. When the different flashing patterns were tested in conjunction with other established optimization techniques such as language models and dynamic stopping, they did not make a significant impact on P300 speller performance. This result could indicate that further performance improvements on the system lie beyond optimizing flashing patterns.
Journal Article
Mutation of FOXC1 and PITX2 induces cerebral small-vessel disease
Patients with cerebral small-vessel disease (CSVD) exhibit perturbed end-artery function and have an increased risk for stroke and age-related cognitive decline. Here, we used targeted genome-wide association (GWA) analysis and defined a CSVD locus adjacent to the forkhead transcription factor FOXC1. Moreover, we determined that the linked SNPs influence FOXC1 transcript levels and demonstrated that patients as young as 1 year of age with altered FOXC1 function exhibit CSVD. MRI analysis of patients with missense and nonsense mutations as well as FOXC1-encompassing segmental duplication and deletion revealed white matter hyperintensities, dilated perivascular spaces, and lacunar infarction. In a zebrafish model, overexpression or morpholino-induced suppression of foxc1 induced cerebral hemorrhage. Inhibition of foxc1 perturbed platelet-derived growth factor (Pdgf) signaling, impairing neural crest migration and the recruitment of mural cells, which are essential for vascular stability. GWA analysis also linked the FOXC1-interacting transcription factor PITX2 to CSVD, and both patients with PITX2 mutations and murine Pitx2-/- mutants displayed brain vascular phenotypes. Together, these results extend the genetic etiology of stroke and demonstrate an increasing developmental basis for human cerebrovascular disease.
Journal Article
From Bench-to-Bedside: How Artificial Intelligence is Changing Thyroid Nodule Diagnostics, a Systematic Review
Abstract
Context
Use of artificial intelligence (AI) to predict clinical outcomes in thyroid nodule diagnostics has grown exponentially over the past decade. The greatest challenge is in understanding the best model to apply to one's own patient population, and how to operationalize such a model in practice.
Evidence Acquisition
A literature search of PubMed and IEEE Xplore was conducted for English-language publications between January 1, 2015 and January 1, 2023, studying diagnostic tests on suspected thyroid nodules that used AI. We excluded articles without prospective or external validation, nonprimary literature, duplicates, focused on nonnodular thyroid conditions, not using AI, and those incidentally using AI in support of an experimental diagnostic outside standard clinical practice. Quality was graded by Oxford level of evidence.
Evidence Synthesis
A total of 61 studies were identified; all performed external validation, 16 studies were prospective, and 33 compared a model to physician prediction of ground truth. Statistical validation was reported in 50 papers. A diagnostic pipeline was abstracted, yielding 5 high-level outcomes: (1) nodule localization, (2) ultrasound (US) risk score, (3) molecular status, (4) malignancy, and (5) long-term prognosis. Seven prospective studies validated a single commercial AI; strengths included automating nodule feature assessment from US and assisting the physician in predicting malignancy risk, while weaknesses included automated margin prediction and interobserver variability.
Conclusion
Models predominantly used US images to predict malignancy. Of 4 Food and Drug Administration–approved products, only S-Detect was extensively validated. Implementing an AI model locally requires data sanitization and revalidation to ensure appropriate clinical performance.
Journal Article
Using Large-scale Social Media Analytics to Understand Patient Perspectives About Urinary Tract Infections: Thematic Analysis
Current qualitative literature about the experiences of women dealing with urinary tract infections (UTIs) is limited to patients recruited from tertiary centers and medical clinics. However, traditional focus groups and interviews may limit what patients share. Using digital ethnography, we analyzed free-range conversations of an online community.
This study aimed to investigate and characterize the patient perspectives of women dealing with UTIs using digital ethnography.
A data-mining service was used to identify online posts. A thematic analysis was conducted on a subset of the identified posts. Additionally, a latent Dirichlet allocation (LDA) probabilistic topic modeling method was applied to review the entire data set using a semiautomatic approach. Each identified topic was generated as a discrete distribution over the words in the collection, which can be thought of as a word cloud. We also performed a thematic analysis of the word cloud topic model results.
A total of 83,589 posts by 53,460 users from 859 websites were identified. Our hand-coding inductive analysis yielded the following 7 themes: quality-of-life impact, knowledge acquisition, support of the online community, health care utilization, risk factors and prevention, antibiotic treatment, and alternative therapies. Using the LDA topic model method, 105 themes were identified and consolidated into 9 categories. Of the LDA-derived themes, 25.7% (27/105) were related to online community support, and 22% (23/105) focused on UTI risk factors and prevention strategies.
Our large-scale social media analysis supports the importance and reproducibility of using online data to comprehend women's UTI experience. This inductive thematic analysis highlights patient behavior, self-empowerment, and online media utilization by women to address their health concerns in a safe, anonymous way.
Journal Article
Stroke-associated intergenic variants modulate a human FOXF2 transcriptional enhancer
SNPs associated with human stroke risk have been identified in the intergenic region between Forkhead family transcription factors FOXF2 and FOXQ1, but we lack amechanism for the association. FoxF2 is expressed in vascular mural pericytes and is important for maintaining pericyte number and stabilizing small vessels in zebrafish. The stroke-associated SNPs are located in a previously unknown transcriptional enhancer for FOXF2, functional in human cells and zebrafish. We identify critical enhancer regions for FOXF2 gene expression, including binding sites occupied by transcription factors ETS1, RBPJ, and CTCF. rs74564934, a stroke-associated SNP adjacent to the ETS1 binding site, decreases enhancer function, as does mutation of RPBJ sites. rs74564934 is significantly associated with the increased risk of any stroke, ischemic stroke, small vessel stroke, and elevated white matter hyperintensity burden in humans. Foxf2 has a conserved function cross-species and is expressed in vascular mural pericytes of the vessel wall. Thus, stroke-associated SNPs modulate enhancer activity and expression of a regulator of vascular stabilization, FOXF2, thereby modulating stroke risk.
Journal Article