Emotion Regulation Difficulties and Smoking Behavior among Adults with Chronic Obstructive Pulmonary Diseases.

COPD is one of the leading causes of death in the United States and results in increased healthcare costs and disability. Smoking is the main determinant of COPD development and continued use increases mortality as compared to those who have stopped smoking. Research has indicated that cigarette smoking may play a role in attempts to regulate distressing emotional experiences and thus, may be an important transdiagnostic process underlying continued smoking behavior among adults with COPD. The current study investigated the role of ER difficulties in relation to smoking status and cigarettes smoked per day among adults with COPD. This cross-sectional study included a sample was adults with COPD (N = 320). Participants self-reported current smoking status, daily smoking, and the Difficulties in Emotion Regulation Scale. All analyses were adjusted for age, sex, probable depression, probable anxiety, and dyspnea severity. DERS total scores were associated with greater odds of current smoking. With the exception of impulsivity, all other dimensions of emotion regulation were significantly associated with current smoking. Greater difficulties in emotional awareness were associated with greater cigarettes smoked per day. However, neither the DERS total score nor any other dimensions of emotional regulation were significantly associated with cigarettes smoked per day. The present study provides preliminary data linking ER difficulties to smoking behavior among adults with COPD. If corroborated by future research, these findings suggest that ER might be a potential target for smoking cessation programs among adults with COPD.

Foundation models in ophthalmology.

Foundation models represent a paradigm shift in artificial intelligence (AI), evolving from narrow models designed for specific tasks to versatile, generalisable models adaptable to a myriad of diverse applications. Ophthalmology as a specialty has the potential to act as an exemplar for other medical specialties, offering a blueprint for integrating foundation models broadly into clinical practice. This review hopes to serve as a roadmap for eyecare professionals seeking to better understand foundation models, while equipping readers with the tools to explore the use of foundation models in their own research and practice. We begin by outlining the key concepts and technological advances which have enabled the development of these models, providing an overview of novel training approaches and modern AI architectures. Next, we summarise existing literature on the topic of foundation models in ophthalmology, encompassing progress in vision foundation models, large language models and large multimodal models. Finally, we outline major challenges relating to privacy, bias and clinical validation, and propose key steps forward to maximise the benefit of this powerful technology.

Beyond Pathology- Exploring Salutogenesis as a Novel Approach to Eye Inflammatory Disease Prevention and Management: A Perspective.

The aim of this perspective is to promote the theory of salutogenesis as a novel approach to addressing ophthalmologic inflammatory conditions, illustrating several concepts in which it is based upon and how they can be applied to medical practice. This theory can better contextualize why patients with similar demographics and exposures are not uniform in their clinical presentations. Stressors in daily life can contribute to a state of ill-health and there are various factors that help alleviate their negative impact. These alleviating factors are significantly impaired in people with poor vision, one of the most common presentations of ophthalmologic conditions. Salutogenic principles can guide the treatment of eye conditions to be more respectful of patient autonomy amidst shifting expectations of the doctor-patient relationship. Being able to take ownership of their health and feeling that their cultural beliefs were considered improves compliance and subsequently gives more optimal outcomes. Population-level policy interventions could also utilize salutogenic principles to identify previously overlooked domains that can be addressed. We identified several papers about salutogenesis in an ophthalmological context and acknowledged the relatively few studies on this topic at present and offer directions in which we can explore further in subsequent studies.

A Clinician's Guide to Sharing Data for AI in Ophthalmology.

Data is the cornerstone of using AI models, because their performance directly depends on the diversity, quantity, and quality of the data used for training. Using AI presents unique potential, particularly in medical applications that involve rich data such as ophthalmology, encompassing a variety of imaging methods, medical records, and eye-tracking data. However, sharing medical data comes with challenges because of regulatory issues and privacy concerns. This review explores traditional and nontraditional data sharing methods in medicine, focusing on previous works in ophthalmology. Traditional methods involve direct data transfer, whereas newer approaches prioritize security and privacy by sharing derived datasets, creating secure research environments, or using model-to-data strategies. We examine each method's mechanisms, variations, recent applications in ophthalmology, and their respective advantages and disadvantages. By empowering medical researchers with insights into data sharing methods and considerations, this review aims to assist informed decision-making while upholding ethical standards and patient privacy in medical AI development.

Sociodemographic and Visual Outcomes of Juvenile Idiopathic Arthritis Uveitis: IRIS® Registry Study.

Purpose: Understanding sociodemographic factors associated with poor visual outcomes in children with juvenile idiopathic arthritis-associated uveitis may help inform practice patterns. Patients and Methods: Retrospective cohort study on patients <18 years old who were diagnosed with both juvenile idiopathic arthritis and uveitis based on International Classification of Diseases tenth edition codes in the Intelligent Research in Sight Registry through December 2020. Surgical history was extracted using current procedural terminology codes. The primary outcome was incidence of blindness (20/200 or worse) in at least one eye in association with sociodemographic factors. Secondary outcomes included cataract and glaucoma surgery following uveitis diagnosis. Hazard ratios were calculated using multivariable-adjusted Cox proportional hazards models. Results: Median age of juvenile idiopathic arthritis-associated uveitis diagnosis was 11 (Interquartile Range: 8 to 15). In the Cox models adjusting for sociodemographic and insurance factors, the hazard ratios of best corrected visual acuity 20/200 or worse were higher in males compared to females (HR 2.15; 95% CI: 1.45-3.18), in Black or African American patients compared to White patients (2.54; 1.44-4.48), and in Medicaid-insured patients compared to commercially-insured patients (2.23; 1.48-3.37). Conclusion: Sociodemographic factors and insurance coverage were associated with varying levels of risk for poor visual outcomes in children with juvenile idiopathic arthritis-associated uveitis.

Comparing generative and retrieval-based chatbots in answering patient questions regarding age-related macular degeneration and diabetic retinopathy.

BACKGROUND/AIMS: To compare the performance of generative versus retrieval-based chatbots in answering patient inquiries regarding age-related macular degeneration (AMD) and diabetic retinopathy (DR). METHODS: We evaluated four chatbots: generative models (ChatGPT-4, ChatGPT-3.5 and Google Bard) and a retrieval-based model (OcularBERT) in a cross-sectional study. Their response accuracy to 45 questions (15 AMD, 15 DR and 15 others) was evaluated and compared. Three masked retinal specialists graded the responses using a three-point Likert scale: either 2 (good, error-free), 1 (borderline) or 0 (poor with significant inaccuracies). The scores were aggregated, ranging from 0 to 6. Based on majority consensus among the graders, the responses were also classified as 'Good', 'Borderline' or 'Poor' quality. RESULTS: Overall, ChatGPT-4 and ChatGPT-3.5 outperformed the other chatbots, both achieving median scores (IQR) of 6 (1), compared with 4.5 (2) in Google Bard, and 2 (1) in OcularBERT (all p ≤8.4×10-3). Based on the consensus approach, 83.3% of ChatGPT-4's responses and 86.7% of ChatGPT-3.5's were rated as 'Good', surpassing Google Bard (50%) and OcularBERT (10%) (all p ≤1.4×10-2). ChatGPT-4 and ChatGPT-3.5 had no 'Poor' rated responses. Google Bard produced 6.7% Poor responses, and OcularBERT produced 20%. Across question types, ChatGPT-4 outperformed Google Bard only for AMD, and ChatGPT-3.5 outperformed Google Bard for DR and others. CONCLUSION: ChatGPT-4 and ChatGPT-3.5 demonstrated superior performance, followed by Google Bard and OcularBERT. Generative chatbots are potentially capable of answering domain-specific questions outside their original training. Further validation studies are still required prior to real-world implementation.

Misspellings or "miscellings"-Non-verifiable and unknown cell lines in cancer research publications.

Reproducible laboratory research relies on correctly identified reagents. We have previously described gene research papers with wrongly identified nucleotide sequence(s), including papers studying miR-145. Manually verifying reagent identities in 36 recent miR-145 papers found that 56% and 17% of papers described misidentified nucleotide sequences and cell lines, respectively. We also found 5 cell line identifiers in miR-145 papers with misidentified nucleotide sequences and cell lines, and 18 cell line identifiers published elsewhere, that did not represent indexed human cell lines. These 23 identifiers were described as non-verifiable (NV), as their identities were unclear. Studying 420 papers that mentioned 8 NV identifier(s) found 235 papers (56%) that referred to 7 identifiers (BGC-803, BSG-803, BSG-823, GSE-1, HGC-7901, HGC-803, and MGC-823) as independent cell lines. We could not find any publications describing how these cell lines were established. Six cell lines were sourced from cell line repositories with externally accessible online catalogs, but these cell lines were not indexed as claimed. Some papers also stated that short tandem repeat (STR) profiles had been generated for three cell lines, yet no STR profiles could be identified. In summary, as NV cell lines represent new challenges to research integrity and reproducibility, further investigations are required to clarify their status and identities.

The role of emotion dysregulation in self-management behaviors among adults with type 2 diabetes.

Suboptimal disease self-management among adults with type 2 diabetes is associated with greater risk of diabetes related health complications and mortality. Emotional distress has been linked with poor diabetes self-management; however, few studies have examined the role of emotion dysregulation in diabetes management. The purpose of this study was to examine the relations between different facets of emotion dysregulation and diabetes self-management behaviors among a sample of 373 adults with type 2 diabetes. Separate median regression and binary logistic regression models were used to examine the association of emotion dysregulation facets and each diabetes self-care behavior (i.e., medication nonadherence, diet, exercise, self-monitoring of blood glucose (SMBG), foot care, and smoking). Generally, greater difficulties in emotion regulation were associated with poorer self-management behaviors. However, several facets of emotion dysregulation were linked with better self-management behaviors. Addressing emotion dysregulation among adults with type 2 diabetes has the potential to improve diabetes related self-management.

Convolutional neural network-based classification of glaucoma using optic radiation tissue properties.

BACKGROUND: Sensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections. METHODS: We analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models. RESULTS: We showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration. CONCLUSIONS: Our findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.

In this study, we explored the relationship between glaucoma, the most common cause of blindness, and changes within the brain. We used data from diffusion MRI, a measurement method which assesses the properties of brain connections. We examined 905 individuals with glaucoma alongside 5292 healthy people. We refined the test cohort to be closely matched in age, sex, ethnicity, and socioeconomic backgrounds. The use of deep learning neural networks allowed accurate detection of glaucoma by focusing on the tissue properties of the optic radiations, a major brain pathway that transmits visual information, rather than other brain pathways used for comparison. Our work provides additional evidence that brain connections may age differently based on varying sensory inputs.

Higher odds of periodontitis in systemic lupus erythematosus compared to controls and rheumatoid arthritis: a systematic review, meta-analysis and network meta-analysis.

Introduction: Periodontitis as a comorbidity in systemic lupus erythematosus (SLE) is still not well recognized in the dental and rheumatology communities. A meta-analysis and network meta-analysis were thus performed to compare the (i) prevalence of periodontitis in SLE patients compared to those with rheumatoid arthritis (RA) and (ii) odds of developing periodontitis in controls, RA, and SLE. Methods: Pooled prevalence of and odds ratio (OR) for periodontitis were compared using meta-analysis and network meta-analysis (NMA). Results: Forty-three observational studies involving 7,800 SLE patients, 49,388 RA patients, and 766,323 controls were included in this meta-analysis. The pooled prevalence of periodontitis in SLE patients (67.0%, 95% confidence interval [CI] 57.0-77.0%) was comparable to that of RA (65%, 95% CI 55.0-75.0%) (p>0.05). Compared to controls, patients with SLE (OR=2.64, 95% CI 1.24-5.62, p<0.01) and RA (OR=1.81, 95% CI 1.25-2.64, p<0.01) were more likely to have periodontitis. Indirect comparisons through the NMA demonstrated that the odds of having periodontitis in SLE was 1.49 times higher compared to RA (OR=1.49, 95% CI 1.09-2.05, p<0.05). Discussion: Given that RA is the autoimmune disease classically associated with periodontal disease, the higher odds of having periodontitis in SLE are striking. These results highlight the importance of addressing the dental health needs of patients with SLE. Systematic review registration: https://www.crd.york.ac.uk/PROSPERO/ identifier CRD42021272876.

Cardiovascular Magnetic Resonance Reference Ranges From the Healthy Hearts Consortium.

BACKGROUND: The absence of population-stratified cardiovascular magnetic resonance (CMR) reference ranges from large cohorts is a major shortcoming for clinical care. OBJECTIVES: This paper provides age-, sex-, and ethnicity-specific CMR reference ranges for atrial and ventricular metrics from the Healthy Hearts Consortium, an international collaborative comprising 9,088 CMR studies from verified healthy individuals, covering the complete adult age spectrum across both sexes, and with the highest ethnic diversity reported to date. METHODS: CMR studies were analyzed using certified software with batch processing capability (cvi42, version 5.14 prototype, Circle Cardiovascular Imaging) by 2 expert readers. Three segmentation methods (smooth, papillary, anatomic) were used to contour the endocardial and epicardial borders of the ventricles and atria from long- and short-axis cine series. Clinically established ventricular and atrial metrics were extracted and stratified by age, sex, and ethnicity. Variations by segmentation method, scanner vendor, and magnet strength were examined. Reference ranges are reported as 95% prediction intervals. RESULTS: The sample included 4,452 (49.0%) men and 4,636 (51.0%) women with average age of 61.1 ± 12.9 years (range: 18-83 years). Among these, 7,424 (81.7%) were from White, 510 (5.6%) South Asian, 478 (5.3%) mixed/other, 341 (3.7%) Black, and 335 (3.7%) Chinese ethnicities. Images were acquired using 1.5-T (n = 8,779; 96.6%) and 3.0-T (n = 309; 3.4%) scanners from Siemens (n = 8,299; 91.3%), Philips (n = 498; 5.5%), and GE (n = 291, 3.2%). CONCLUSIONS: This work represents a resource with healthy CMR-derived volumetric reference ranges ready for clinical implementation.

The Effect of Substrate Properties on Cellular Behavior and Nanoparticle Uptake in Human Fibroblasts and Epithelial Cells.

The delivery of nanomedicines into cells holds enormous therapeutic potential; however little is known regarding how the extracellular matrix (ECM) can influence cell-nanoparticle (NP) interactions. Changes in ECM organization and composition occur in several pathophysiological states, including fibrosis and tumorigenesis, and may contribute to disease progression. We show that the physical characteristics of cellular substrates, that more closely resemble the ECM in vivo, can influence cell behavior and the subsequent uptake of NPs. Electrospinning was used to create two different substrates made of soft polyurethane (PU) with aligned and non-aligned nanofibers to recapitulate the ECM in two different states. To investigate the impact of cell-substrate interaction, A549 lung epithelial cells and MRC-5 lung fibroblasts were cultured on soft PU membranes with different alignments and compared against stiff tissue culture plastic (TCP)/glass. Both cell types could attach and grow on both PU membranes with no signs of cytotoxicity but with increased cytokine release compared with cells on the TCP. The uptake of silica NPs increased more than three-fold in fibroblasts but not in epithelial cells cultured on both membranes. This study demonstrates that cell-matrix interaction is substrate and cell-type dependent and highlights the importance of considering the ECM and tissue mechanical properties when designing NPs for effective cell targeting and treatment.

Large language models as tax attorneys: a case study in legal capabilities emergence.

Better understanding of Large Language Models' (LLMs) legal analysis abilities can contribute to improving the efficiency of legal services, governing artificial intelligence and leveraging LLMs to identify inconsistencies in law. This paper explores LLM capabilities in applying tax law. We choose this area of law because it has a structure that allows us to set up automated validation pipelines across thousands of examples, requires logical reasoning and maths skills, and enables us to test LLM capabilities in a manner relevant to real-world economic lives of citizens and companies. Our experiments demonstrate emerging legal understanding capabilities, with improved performance in each subsequent OpenAI model release. We experiment with retrieving and using the relevant legal authority to assess the impact of providing additional legal context to LLMs. Few-shot prompting, presenting examples of question-answer pairs, is also found to significantly enhance the performance of the most advanced model, GPT-4. The findings indicate that LLMs, particularly when combined with prompting enhancements and the correct legal texts, can perform at high levels of accuracy but not yet at expert tax lawyer levels. As LLMs continue to advance, their ability to reason about law autonomously could have significant implications for the legal profession and AI governance. This article is part of the theme issue 'A complexity science approach to law and governance'.

Self-Supervised Learning for Improved Optical Coherence Tomography Detection of Macular Telangiectasia Type 2.

Importance: Deep learning image analysis often depends on large, labeled datasets, which are difficult to obtain for rare diseases. Objective: To develop a self-supervised approach for automated classification of macular telangiectasia type 2 (MacTel) on optical coherence tomography (OCT) with limited labeled data. Design, Setting, and Participants: This was a retrospective comparative study. OCT images from May 2014 to May 2019 were collected by the Lowy Medical Research Institute, La Jolla, California, and the University of Washington, Seattle, from January 2016 to October 2022. Clinical diagnoses of patients with and without MacTel were confirmed by retina specialists. Data were analyzed from January to September 2023. Exposures: Two convolutional neural networks were pretrained using the Bootstrap Your Own Latent algorithm on unlabeled training data and fine-tuned with labeled training data to predict MacTel (self-supervised method). ResNet18 and ResNet50 models were also trained using all labeled data (supervised method). Main Outcomes and Measures: The ground truth yes vs no MacTel diagnosis is determined by retinal specialists based on spectral-domain OCT. The models' predictions were compared against human graders using accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), area under precision recall curve (AUPRC), and area under the receiver operating characteristic curve (AUROC). Uniform manifold approximation and projection was performed for dimension reduction and GradCAM visualizations for supervised and self-supervised methods. Results: A total of 2636 OCT scans from 780 patients with MacTel and 131 patients without MacTel were included from the MacTel Project (mean [SD] age, 60.8 [11.7] years; 63.8% female), and another 2564 from 1769 patients without MacTel from the University of Washington (mean [SD] age, 61.2 [18.1] years; 53.4% female). The self-supervised approach fine-tuned on 100% of the labeled training data with ResNet50 as the feature extractor performed the best, achieving an AUPRC of 0.971 (95% CI, 0.969-0.972), an AUROC of 0.970 (95% CI, 0.970-0.973), accuracy of 0.898%, sensitivity of 0.898, specificity of 0.949, PPV of 0.935, and NPV of 0.919. With only 419 OCT volumes (185 MacTel patients in 10% of labeled training dataset), the ResNet18 self-supervised model achieved comparable performance, with an AUPRC of 0.958 (95% CI, 0.957-0.960), an AUROC of 0.966 (95% CI, 0.964-0.967), and accuracy, sensitivity, specificity, PPV, and NPV of 90.2%, 0.884, 0.916, 0.896, and 0.906, respectively. The self-supervised models showed better agreement with the more experienced human expert graders. Conclusions and Relevance: The findings suggest that self-supervised learning may improve the accuracy of automated MacTel vs non-MacTel binary classification on OCT with limited labeled training data, and these approaches may be applicable to other rare diseases, although further research is warranted.

Non-operative Management of Acute Knee Injuries.

PURPOSE OF REVIEW: Acute knee injuries are commonly encountered in both the clinical and sideline setting and may be treated operatively or non-operatively. This article describes an evidence-based approach to non-operative acute knee injury. This includes history, physical exam, imaging, and initial management. In addition, the non-operative management of three such injuries-ligament injury, meniscus injury, and patellar dislocation injury-will be discussed via a case-based practical approach. RECENT FINDINGS: Aside from grade III ACL tears, most acute knee ligament injuries, especially in the absence of other concurrent injuries, can be treated non-operatively. There is new evidence that acute traumatic meniscus tears in those younger than 40 can be successfully treated non-operatively and can do equally, as well as those that undergo surgery, at 1 year out from injury. Based on the current literature, a short period of knee bracing in extension with progression to weightbearing to tolerance is recommended after initial patellar dislocation. Many of the most common acute knee injuries, including MCL tears, meniscus tears, and patellar dislocations, can be managed non-operatively. A detailed systemic approach to initial evaluation, including pertinent history, physical exam, and appropriate imaging, is essential and complementary to the subsequent non-operative treatment algorithm.

Developing a Continuous Severity Scale for Macular Telangiectasia Type 2 Using Deep Learning and Implications for Disease Grading.

PURPOSE: Deep learning (DL) models have achieved state-of-the-art medical diagnosis classification accuracy. Current models are limited by discrete diagnosis labels, but could yield more information with diagnosis in a continuous scale. We developed a novel continuous severity scaling system for macular telangiectasia (MacTel) type 2 by combining a DL classification model with uniform manifold approximation and projection (UMAP). DESIGN: We used a DL network to learn a feature representation of MacTel severity from discrete severity labels and applied UMAP to embed this feature representation into 2 dimensions, thereby creating a continuous MacTel severity scale. PARTICIPANTS: A total of 2003 OCT volumes were analyzed from 1089 MacTel Project participants. METHODS: We trained a multiview DL classifier using multiple B-scans from OCT volumes to learn a previously published discrete 7-step MacTel severity scale. The classifiers' last feature layer was extracted as input for UMAP, which embedded these features into a continuous 2-dimensional manifold. The DL classifier was assessed in terms of test accuracy. Rank correlation for the continuous UMAP scale against the previously published scale was calculated. Additionally, the UMAP scale was assessed in the κ agreement against 5 clinical experts on 100 pairs of patient volumes. For each pair of patient volumes, clinical experts were asked to select the volume with more severe MacTel disease and to compare them against the UMAP scale. MAIN OUTCOME MEASURES: Classification accuracy for the DL classifier and κ agreement versus clinical experts for UMAP. RESULTS: The multiview DL classifier achieved top 1 accuracy of 63.3% (186/294) on held-out test OCT volumes. The UMAP metric showed a clear continuous gradation of MacTel severity with a Spearman rank correlation of 0.84 with the previously published scale. Furthermore, the continuous UMAP metric achieved κ agreements of 0.56 to 0.63 with 5 clinical experts, which was comparable with interobserver κ values. CONCLUSIONS: Our UMAP embedding generated a continuous MacTel severity scale, without requiring continuous training labels. This technique can be applied to other diseases and may lead to more accurate diagnosis, improved understanding of disease progression, and key imaging features for pathologic characteristics. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Psychometric Properties of the Short Scale Anxiety Sensitivity Index Among Adults with Chronic Respiratory Disease.

Approximately one-third of adults with chronic respiratory disease (CRD) have comorbid depressive and anxiety disorders; yet these disorders are often unrecognized in this patient population. Transdiagnostic processes such as anxiety sensitivity (AS) are useful for identifying mechanisms underlying psychological and heath conditions. The Short-Scale AS Index (SSASI) is a brief self-report measure of AS which has potential clinical utility among CRD populations to evaluate psychological distress and inform comprehensive care. The present study investigated the psychometric properties of the SSASI among adults with CRDs. Participants were recruited from a web-based panel of adults with CRDs (n = 768; 49.3% female; 57.8% White) including adults with asthma only (n = 230), COPD only (n = 321), or co-occurring asthma and COPD (n = 217). Participants completed a battery of self-report questionnaires assessing psychological and medical symptoms. Analyses were conducted to examine the factor structure and measurement invariance across CRD groups. Convergent validity and criterion validity of the SSASI were assessed within each group. Results supported partial measurement invariance across CRD groups. The SSASI demonstrated high reliability, convergent validity, and criterion validity with each CRD group. Findings from this study and existing work indicate that the SSASI is an effective and economical assessment tool for identifying patients CRD who may benefit from psychological interventions to reduce AS.