Abstract Among rheumatic diseases, the prevalence of gout is increasing with population growth and aging. While hyperuricemia remains the major risk factor, gout is now recognized as a curable disease. Several recommendations have been proposed to achieve treatment goals; however, only a small proportion of patients receive adequate management and achieve target serum urate levels. Without proper management, patients often experience gout flares, which can be triggered by multiple risk factors. Recently, tools and criteria have been developed to assess disease activity and define clinical remission in gout. Therefore, this review aims to highlight factors that precipitate gout flares, summarize evidence supporting complete disease control, outline principles and strategies for long‐term management, and provide potential future perspectives.
Immunologic diseases. Allergy, Diseases of the musculoskeletal system
Priyanka P. Brahmachary, Ayten E. Erdogan, Erik P. Myers
et al.
Abstract Purpose Articular chondrocytes synthesize and maintain the avascular and aneural articular cartilage. In vivo these cells are surrounded by a 3D pericellular matrix (PCM) containing predominantly collagen VI. The PCM protects chondrocytes and facilitates mechanotransduction. PCM stiffness is critical in transmitting biomechanical signals to chondrocytes. Various culture systems with different hydrogels are used to encapsulate chondrocytes for 3D culture, but many lack either the PCM or the in vivo stiffness of the cartilage matrix. This study aimed at establishing a culture system to investigate (a) if chondrocytes cultured in alginate will develop a PCM and (b) study mechanotransduction via metabolic changes induced in 3D agarose-embedded chondrocytes upon mechanical stimulation. Methods We cultured primary human and bovine chondrocytes in monolayers or as alginate encapsulated cells in media containing sodium L-ascorbate. PCM expression was analyzed by immunofluorescence and western blots. We further characterized the response of chondrocytes embedded in physiologically stiff agarose to dynamic compression through metabolomic profiling. Results We found that primary human and bovine chondrocytes, when cultured in alginate beads with addition of sodium L-ascorbate for 7 days, had a pronounced PCM, retained their phenotype, and synthesized both collagens VI and II. This novel culture system enables alginate-encapsulated chondrocytes to develop a robust PCM thereby creating a model system to study mechanotransduction in the presence of an endogenous PCM. We also observed distinct compression-induced changes in metabolomic profiles between the monolayer-agarose and alginate-released agarose-embedded chondrocytes indicating physiological changes in cell metabolism. Conclusion These data show that 3D preculture of chondrocytes in alginate before encapsulation in physiologically stiff agarose leads to pronounced development of pericellular matrix that is sustained in the presence of ascorbate. This model can be useful in studying the mechanism by which chondrocytes respond to cyclical compression and other types of loading simulating in vivo physiological conditions.
Abstract Pruritus or itch is a sensation that evolved to protect humans and other vertebrates against environmental irritants, parasitic infestations and disease‐borne insects. However, in the setting of chronic skin inflammation and other medical conditions, pruritus can become pathologic and chronic in nature; indeed, chronic pruritus is defined as itch that persists for greater than 6 weeks and significantly impairs the quality of life of patients. It also poses a major therapeutic challenge. However, recent advances in itch biology have led to the development of novel therapies. This review highlights how new innovations in anti‐pruritic medications are revolutionizing the treatment of chronic pruritus and leading to new paradigms of personalized medicine.
Engin Turkay Yilmaz, Saygin Kamaci, Izzet Bingol
et al.
Abstract Background The primary objective of this study was to examine the incidence of fractures among individuals aged 20–64 years over a 7-year timeframe by utilising an electronic recording system that is integrated with a substantial portion of the Turkish population. Methods De-identified health records were acquired from the nationwide personal health recording system. Four age groups were established: 20–34, 35–44, 45–54, and 55–64 years. Incidence rates were further analysed according to sex and age group. Results A total of 3,286,991 fractures were recorded in the 7-year time period, with male patients accounting for 62.1% of those cases. The overall fracture incidence rate in the Turkish adult population was 1029/100,000. The incidence rate was 727.44/100,000 for women and 1158.86/100,000 for men (p < 0.001). The age group with the highest number of fractures was 20–34 years with 1337.012 (37.3%) fractures. Wrist fractures (17.46%), finger fractures (14.4%), and foot fractures (11.85%) accounted for 43.7% of all fractures. In women, the incidence of fractures, excluding those of the hand and wrist, increased significantly as the age groups increased (p < 0.05). Conclusion Wrist, finger, and foot fractures were found to be the most common fractures among individuals aged 20–64 years. Fracture incidence was highest in men and in the age group of 20–34 years, encompassing individuals who are more active in work and sports.
Hepato-pancreato-biliary (HPB) disorders represent a global public health challenge due to their high morbidity and mortality. Although large language models (LLMs) have shown promising performance in general medical question-answering tasks, the current evaluation benchmarks are mostly derived from standardized examinations or manually designed questions, lacking HPB coverage and clinical cases. To address these issues, we systematically eatablish an HPB disease evaluation benchmark comprising 3,535 closed-ended multiple-choice questions and 337 open-ended real diagnosis cases, which encompasses all the 33 main categories and 465 subcategories of HPB diseases defined in the International Statistical Classification of Diseases, 10th Revision (ICD-10). The multiple-choice questions are curated from public datasets and synthesized data, and the clinical cases are collected from prestigious medical journals, case-sharing platforms, and collaborating hospitals. By evalauting commercial and open-source general and medical LLMs on our established benchmark, namely ClinBench-HBP, we find that while commercial LLMs perform competently on medical exam questions, they exhibit substantial performance degradation on HPB diagnosis tasks, especially on complex, inpatient clinical cases. Those medical LLMs also show limited generalizability to HPB diseases. Our results reveal the critical limitations of current LLMs in the domain of HPB diseases, underscoring the imperative need for future medical LLMs to handle real, complex clinical diagnostics rather than simple medical exam questions. The benchmark will be released at https://clinbench-hpb.github.io.
In this study, we explored the use of spectrograms to represent handwriting signals for assessing neurodegenerative diseases, including 42 healthy controls (CTL), 35 subjects with Parkinson's Disease (PD), 21 with Alzheimer's Disease (AD), and 15 with Parkinson's Disease Mimics (PDM). We applied CNN and CNN-BLSTM models for binary classification using both multi-channel fixed-size and frame-based spectrograms. Our results showed that handwriting tasks and spectrogram channel combinations significantly impacted classification performance. The highest F1-score (89.8%) was achieved for AD vs. CTL, while PD vs. CTL reached 74.5%, and PD vs. PDM scored 77.97%. CNN consistently outperformed CNN-BLSTM. Different sliding window lengths were tested for constructing frame-based spectrograms. A 1-second window worked best for AD, longer windows improved PD classification, and window length had little effect on PD vs. PDM.
Umakanta Maharana, Sarthak Verma, Avarna Agarwal
et al.
Large language models (LLMs) offer a promising pre-screening tool, improving early disease detection and providing enhanced healthcare access for underprivileged communities. The early diagnosis of various diseases continues to be a significant challenge in healthcare, primarily due to the nonspecific nature of early symptoms, the shortage of expert medical practitioners, and the need for prolonged clinical evaluations, all of which can delay treatment and adversely affect patient outcomes. With impressive accuracy in prediction across a range of diseases, LLMs have the potential to revolutionize clinical pre-screening and decision-making for various medical conditions. In this work, we study the diagnostic capability of LLMs for Rheumatoid Arthritis (RA) with real world patients data. Patient data was collected alongside diagnoses from medical experts, and the performance of LLMs was evaluated in comparison to expert diagnoses for RA disease prediction. We notice an interesting pattern in disease diagnosis and find an unexpected \textit{misalignment between prediction and explanation}. We conduct a series of multi-round analyses using different LLM agents. The best-performing model accurately predicts rheumatoid arthritis (RA) diseases approximately 95\% of the time. However, when medical experts evaluated the reasoning generated by the model, they found that nearly 68\% of the reasoning was incorrect. This study highlights a clear misalignment between LLMs high prediction accuracy and its flawed reasoning, raising important questions about relying on LLM explanations in clinical settings. \textbf{LLMs provide incorrect reasoning to arrive at the correct answer for RA disease diagnosis.}
Samantha Min Er Yew, Xiaofeng Lei, Jocelyn Hui Lin Goh
et al.
Background: RETFound, a self-supervised, retina-specific foundation model (FM), showed potential in downstream applications. However, its comparative performance with traditional deep learning (DL) models remains incompletely understood. This study aimed to evaluate RETFound against three ImageNet-pretrained supervised DL models (ResNet50, ViT-base, SwinV2) in detecting ocular and systemic diseases. Methods: We fine-tuned/trained RETFound and three DL models on full datasets, 50%, 20%, and fixed sample sizes (400, 200, 100 images, with half comprising disease cases; for each DR severity class, 100 and 50 cases were used. Fine-tuned models were tested internally using the SEED (53,090 images) and APTOS-2019 (3,672 images) datasets and externally validated on population-based (BES, CIEMS, SP2, UKBB) and open-source datasets (ODIR-5k, PAPILA, GAMMA, IDRiD, MESSIDOR-2). Model performance was compared using area under the receiver operating characteristic curve (AUC) and Z-tests with Bonferroni correction (P<0.05/3). Interpretation: Traditional DL models are mostly comparable to RETFound for ocular disease detection with large datasets. However, RETFound is superior in systemic disease detection with smaller datasets. These findings offer valuable insights into the respective merits and limitation of traditional models and FMs.
Gout is common in people with chronic kidney disease and in general is sub-optimally managed. Lack of evidence due to the exclusion of people with chronic kidney disease from the majority of clinical trials, concerns about adverse effects and conflicting gout management guidelines all contribute to suboptimal management. Herein we review the evidence for the pharmacological treatment of gout, both flares and long-term urate-lowering, in people with concomitant chronic kidney disease.
Maurizio De Pellegrin, Dario Fracassetti, Lorenzo Marcucci
et al.
This paper provides a review of the years of experience of hip sonography since the first ultrasound (US) course in Italy in 1987. Clinical and US findings were correlated in 1,000 newborns examined consecutively in a study in 1991. Developmental dysplasia of the hip (DDH) was present even in the absence of clinical signs, including the Ortolani sign. The percentage of US diagnosis of DDH in newborns was 2.8%, while instability according to the Ortolani test was present in 0.75%. After recommendations from the American Academy of Pediatrics against universal US screening, early diagnosis decreased from 74.4% in the period 1992–2002 (43,418 hips examined) to 52.7% in 2013–2014 (5,598 hips examined). In order to answer the question of whether early treatment of DDH has better outcomes, the acetabulum maturation was studied in 93 type III hips. The statistical analysis showed a strong dependency (P < 0.001) between the alpha-angle gain and the age at which treatment was started. The first 2 weeks of life is the optimum time for early diagnosis and treatment; after 6 weeks of life, treatment is less effective and the results are less predictable. Furthermore, the role of the labrum and its morphological changes was analyzed in 86 unstable dysplastic hips (13 type D, 49 type III and 24 type IV) in patients with an average age of 53 days (range 1–134 days) at DDH diagnosis and the beginning of treatment. The labrum was never inverted and underwent a statistically significant increase in echogenicity and dimensions with a frequency of 97% and 96% respectively, suggesting the labrum’s stabilizing role. Abnormal findings such as in achondroplasia, cleidocranial dysplasia, other rare osteochondrodysplasias and in coxa vara are underlined. Uncommon findings such as incomplete acetabular bony rim and eccentric position of the femoral head nucleus are also described.
Rheumatoid arthritis (RA) is a prevalent chronic disease that is associated with numerous comorbidities. Accurate assessment of these coexisting conditions, as reported by clinicians, is critical for an improved understanding of the impact of the disease and patient care. This perspective aims to evaluate the utility of real-world data (RWD) for enhancing the understanding of comorbidities in RA and to assess its potential in reshaping clinical management. RWD approaches, specifically the use of structured databases or data extracted from electronic health records, offer promising alternatives to overcome the limitations of traditional methodologies. Structured databases provide a systematic approach to data analysis, utilizing diagnosis codes to study large patient cohorts, revealing the prevalence of conditions, and demonstrating the potential for long-term disease trend analysis. Meanwhile, natural language processing (NLP) and artificial intelligence (AI) image analysis can bridge the gap between structured and unstructured data, by extracting meaningful information from unstructured fields such as free text or imaging. NLP has proven effective in the identification of RA patients and research outcomes, while AI image analysis has enabled the discovery of hidden findings in cardiovascular assessments, establishing a basis for the assessment of comorbidities in RA. However, while the benefits of using RWD are substantial, challenges remain. Ensuring comprehensive data capture, managing missing data, and improving data detection are key areas requiring attention. The involvement of clinicians and researchers in rheumatology is crucial in unlocking the potential of RWD studies, offering the promise of significant improvements in disease characterization and patient health outcomes.
The COVID-19 pandemic changed healthcare practices and social media played a significant role in those changes. While social media and online practice communities allow collaboration and engagement, education and knowledge dissemination, research and publication, promotion, and the potential for improved clinical care, their use also involves perils and pitfalls. The literature suggests that rheumatologists use innovative social media platforms for both professional and social purposes. Similarly, many patients with rheumatic disease use social media for education and communication. This review outlined the background of social media platforms, the reasons for their use, and associated risks. This review further discussed the need to better understand the benefits of social media and online communities as well as the potential negative effects that could impact the practice of rheumatology.
Mahmoud Mohamed Abousayed, Hossam Salah El-Din Taha, Raafat Elsayed Farag
et al.
Background:
Because of improvements in initial tumor identification and treatment, as well as longer life expectancies, more people are receiving diagnoses for spinal metastases.
OBJECTIVE:
The aim of this study was to assess early functional outcomes and quality of life (QOL) after surgical management of patients with spinal metastases.
PATIENTS AND METHODS:
In this prospective cohort study, a total of 33 patients with thoracic and lumbar spine metastases who underwent surgical management between November 2021 and August 2023 were followed up for 1 year or until death. Oswestry Disability Index and the Eastern Cooperative Oncology Group Performance Status were used for the functional outcome; QOL was assessed using European Quality of Life 5-Dimensions (EuroQOL-5D). Scores were recorded preoperatively, 4 weeks postoperatively, and 6 and 12 months postoperatively.
Results:
The mean age was 52.12 ± 13.4 years (range: 23–70 years), 22 (66.7%) were females, and 11 (33.3%) were males. Patients were divided into three groups according to the revised Katagiri score: 12 (36.4%) patients were at low risk (0–3), 18 (54.5%) patients were at intermediate risk (4–6), and 3 (9.1%) patients were at high risk (7–10). The mean survival was 5.44 ± 3.46 months (range 1–13), and there was no perioperative death (within 1 month postoperative). Sixteen (48.5%) patients survived for more than 1 year and 17 (51.5%) patients died from different causes related to the natural history of tumor metastasis.
Conclusion:
Following surgical treatment of the spinal metastases, improvements in QoL and functional results were seen in the short-term. For patients with a projected life expectancy of longer than 3 months, surgery is a good alternative.
Asish Bera, Debotosh Bhattacharjee, Ondrej Krejcar
Crop yield production could be enhanced for agricultural growth if various plant nutrition deficiencies, and diseases are identified and detected at early stages. The deep learning methods have proven its superior performances in the automated detection of plant diseases and nutrition deficiencies from visual symptoms in leaves. This article proposes a new deep learning method for plant nutrition deficiencies and disease classification using a graph convolutional network (GNN), added upon a base convolutional neural network (CNN). Sometimes, a global feature descriptor might fail to capture the vital region of a diseased leaf, which causes inaccurate classification of disease. To address this issue, regional feature learning is crucial for a holistic feature aggregation. In this work, region-based feature summarization at multi-scales is explored using spatial pyramidal pooling for discriminative feature representation. A GCN is developed to capacitate learning of finer details for classifying plant diseases and insufficiency of nutrients. The proposed method, called Plant Nutrition Deficiency and Disease Network (PND-Net), is evaluated on two public datasets for nutrition deficiency, and two for disease classification using four CNNs. The best classification performances are: (a) 90.00% Banana and 90.54% Coffee nutrition deficiency; and (b) 96.18% Potato diseases and 84.30% on PlantDoc datasets using Xception backbone. Furthermore, additional experiments have been carried out for generalization, and the proposed method has achieved state-of-the-art performances on two public datasets, namely the Breast Cancer Histopathology Image Classification (BreakHis 40X: 95.50%, and BreakHis 100X: 96.79% accuracy) and Single cells in Pap smear images for cervical cancer classification (SIPaKMeD: 99.18% accuracy). Also, PND-Net achieves improved performances using five-fold cross validation.
Brittany D. Partain, Evelyn Bracho-Sanchez, Shaheen A. Farhadi
et al.
Abstract Objective Osteoarthritis (OA) is driven by low-grade inflammation, and controlling local inflammation may offer symptomatic relief. Here, we developed an indoleamine 2,3-dioxygenase and galectin-3 fusion protein (IDO-Gal3), where IDO increases the production of local anti-inflammatory metabolites and Gal3 binds carbohydrates to extend IDO’s joint residence time. In this study, we evaluated IDO-Gal3’s ability to alter OA-associated inflammation and pain-related behaviors in a rat model of established knee OA. Methods Joint residence was first evaluated with an analog Gal3 fusion protein (NanoLuc™ and Gal3, NL-Gal3) that produces luminescence from furimazine. OA was induced in male Lewis rats via a medial collateral ligament and medial meniscus transection (MCLT + MMT). At 8 weeks, NL or NL-Gal3 were injected intra-articularly ( n = 8 per group), and bioluminescence was tracked for 4 weeks. Next, IDO-Gal3s’s ability to modulate OA pain and inflammation was assessed. Again, OA was induced via MCLT + MMT in male Lewis rats, with IDO-Gal3 or saline injected into OA-affected knees at 8 weeks post-surgery ( n = 7 per group). Gait and tactile sensitivity were then assessed weekly. At 12 weeks, intra-articular levels of IL6, CCL2, and CTXII were assessed. Results The Gal3 fusion increased joint residence in OA and contralateral knees ( p < 0.0001). In OA-affected animals, both saline and IDO-Gal3 improved tactile sensitivity ( p = 0.008), but IDO-Gal3 also increased walking velocities ( p ≤ 0.033) and improved vertical ground reaction forces ( p ≤ 0.04). Finally, IDO-Gal3 decreased intra-articular IL6 levels within the OA-affected joint ( p = 0.0025). Conclusion Intra-articular IDO-Gal3 delivery provided long-term modulation of joint inflammation and pain-related behaviors in rats with established OA.
Abstract Introduction Glucocorticoids (GC) are currently recommended as a bridging therapy in combination with conventional synthetic disease-modifying anti-rheumatic drugs (csDMARD) for the treatment of rheumatoid arthritis (RA) and should be tapered as rapidly as clinically feasible. We aimed to explore potential predictors for GC discontinuation in patients commencing GC with concomitant csDMARD. Methods We used data from a longitudinal real-world cohort. RA patients who newly started GC concomitantly with csDMARD were included. All patients were divided into four groups, according to degree of change in disease activity at 3 months from baseline (group 1: worsening or no decrease; group 2: 0–24.9% decrease; group 3: 25.0–49.9% decrease; group 4: ≥ 50.0% decrease). Cox regression was used to estimate hazard risk (HR) with 95% confidence interval (CI). Results In total, 124 out of 207 RA patients discontinued GC at the rheumatologist's discretion and 79.1% (91/115) of them successfully stopping GC without flare within 6 months after GC withdrawal. Increasing age (HR 0.99, 95% CI 0.98–1.00, p = 0.043) and concomitant nonsteroidal anti-inflammatory drugs use at GC initiation (HR 0.47, 95% CI 0.25–0.88, p = 0.018) were independently associated with GC withdrawal failure. Moreover, the degrees of disease activity improvement at 3 months significantly predicted the possibility of subsequent GC discontinuation (fully adjusted HR 1.35–1.47, p < 0.01), with 2.38–3.59 times higher in group 4 than group 1. Switching the outcome to successfully stopping GC without short-term flare yielded similar findings. Conclusions The degrees of disease activity improvement at 3 months independently predicted the subsequent GC withdrawal. These findings suggest the importance of dynamic treatment strategies with a closer look at disease activity during GC tapering and discontinuation.