Burcu Aydemir, Andrew C. Heisler, Lutfiyya N. Muhammad
et al.
Objective Abnormalities in pain regulatory mechanisms are common in patients with rheumatoid arthritis (RA). We investigated whether pain sensitization changes after treatment with a disease‐modifying antirheumatic drug (DMARD) and explored associations between changes in pain sensitization and disease activity. Methods We included 182 participants with active RA initiating/switching DMARD therapy who were observed for 12 weeks. To assess pain sensitization, participants underwent quantitative sensory testing (QST), including pressure pain thresholds (PPTs) at multiple anatomic sites, temporal summation (TS) at the wrist and forearm, and conditioned pain modulation (CPM). RA disease activity was measured using the Disease Activity Score 28 with C‐reactive protein (DAS28‐CRP) and its components. Mean changes in QST measures were examined from baseline to 12 weeks, and associations between QST and disease activity measures were explored using Pearson correlation coefficients and adjusted linear regression analyses. Results PPTs significantly increased (improved) at multiple anatomic sites following 12 weeks of DMARD therapy. No significant changes were observed in TS or CPM. Increased PPTs at multiple anatomic sites were associated with reductions in DAS28‐CRP, swollen joint count, tender joint count, and improvements in patient global assessment. No significant associations were observed between TS, CPM, and disease activity. Conclusion Pain sensitivity improved after 12 weeks of DMARD therapy. These improvements were associated with reductions in disease activity.
Abstract Object Although periprosthetic joint infection (PJI) can affect multiple joints simultaneously, most individuals with multiple joint involvement exhibit PJI in only one joint. Data regarding the metachronous PJI management for these patients are limited. This study aimed to explore the risk factors for metachronous PJI in patients with multiple prosthetic joints, thereby guiding and optimizing clinical practice. Methods The MEDLINE, Web of Science, Cochrane Library, and EMBASE were searched for all clinical studies of metachronous PJI from inception until May 2024. The clinical studies on risk factors for metachronous PJI in patients with multiple prosthetic joints after experiencing a periprosthetic infection were collected, with two authors independently screening the literatures. Newcastle Ottawa scale was used as a quality assessment tool for the included studies, and the meta-analysis was conducted to evaluate the potential risk factors of metachronous PJI. Results A total of 1,544 patients with PJI after multiple joint arthroplasties were reported in 9 studies, including 189 with metachronous PJI. The meta-analysis showed that methicillin-resistant staphylococcus aureus (MRSA; OR, 3.43; 95%CI, 1.71–6.88; p = 0.0005), rheumatoid arthritis (RA; OR, 2.38; 95%CI, 1.06–5.38; p = 0.04), history of steroid use (OR, 2.93; 95%CI, 1.58–5.43; p = 0.0007), and previous or ongoing non-periprosthetic infection (OR, 4.47; 95%CI, 1.45–13.82; p = 0.009) were identified as significant risk factors for metachronous PJI in patients with multiple prosthetic joints. However, there was no significant difference between the metachronous PJI group and non-metachronous group in terms of revision, age, diabetes, and gender. Conclusion Patients with MRSA, RA, history of steroid use, previous or ongoing non-periprosthetic infection are at significantly higher risk for metachronous PJI. Further research is needed to optimize management strategies for preventing metachronous PJI in patients with multiple prostheses after a single joint PJI.
Orthopedic surgery, Diseases of the musculoskeletal system
Abstract Background The Knee Outcome Survey – Activities of Daily Living Scale (KOS-ADLS) is a patient-reported outcome measure (PROM) developed to assess symptoms and functional limitations in patients with various knee disorders. The aim of this study was to translate and culturally adapt the KOS-ADLS to Danish and to evaluate the psychometric properties of the Danish version (KOS-ADLS-DK) in patients with anterior cruciate ligament (ACL) injury. Methods The KOS-ADLS was translated and culturally adapted to Danish in accordance with recommended guidelines. To evaluate psychometric properties in a test-retest design 115 Danish patients with ACL injury completed KOS-ADLS-DK and other knee specific PROMs at baseline and after 14 days. A sub-population of 79 patients completed the KOS-ADLS-DK before and after 3 months of rehabilitation. Structural validity (factor analysis), Internal consistency (Cronbach`s alpha), construct validity (hypothesis testing), test-retest reliability (Intraclass Correlation Coefficient [ICC]), test-retest agreement (Bland-Altman plots with 95% Limits of Agreement), Standard Error of Measurement (SEM), Smallest Detectable Chance (SDC), responsiveness (construct approach with hypothesis testing) and floor/ceiling effects were assessed. Results No major problems were revealed in the cross-cultural adaptation process. The KOS-ADLS-DK was uni-dimensional and showed a high internal consistency (Chronbach’s alpha = 0.90). Construct validity was not perfect as only five of seven hypotheses were confirmed, but there was a good reliability (ICC = 0.88) and test-retest agreement showed equal distribution of measurement error across the scale and the SEM was 4.9% and SDC was 13.6%. However, hypotheses testing on change scores revealed the KOS-ADLS-DK to be responsive and there were no floor/ceiling effects. Conclusion Overall, the Danish version of KOS-ADLS is considered a valid, reliable and responsive PROM for assessing symptoms and functional limitations in patients with ACL injury but may show some limitations in its construct validity.
Abstract Background Type II odontoid fractures show high incidence and a notable risk of nonunion. Whether regional variation in trabecular density at the odontoid base contributes to this vulnerability remains unclear. Methods We retrospectively analyzed cervical CT scans from 136 adults. Standardized oval regions of interest (ROIs) were placed within cancellous bone while avoiding cortex and sclerosis. Mean Hounsfield unit (HU) values were compared across three predefined regions—the odontoid tip, odontoid base, and C2 vertebral body—with age- and sex-stratified analyses using analysis of variance and multiple-comparison adjustment. Two blinded observers performed the measurements, and reproducibility was quantified with intraclass correlation coefficients (ICCs). Results HU values were highest at the odontoid tip, lowest at the base, and intermediate at the C2 body. On average, HU declined by ~ 51% from tip to base and then partially increased at the body. This regional pattern persisted across age and sex strata, and older individuals showed lower base HU. All primary regional comparisons remained statistically significant after adjustment. Conclusions The odontoid base appears to be a structurally vulnerable zone with reduced trabecular density. This hypodensity may help explain the high incidence and may be associated with nonunion of type II odontoid fractures, particularly in elderly patients; however, biomechanical and clinical studies are required before informing definitive treatment recommendations.
Mark C. Hwang, Seokhun Kim, Shervin Assassi
et al.
ABSTRACT: Objectives: Evaluation of axial spondyloarthritis relies on capturing various inflammatory back pain (IBP) features represented in different criteria. These criteria are likely to be interpreted differently across demographic subpopulations. We examined IBP criteria dimensionality and differential item functioning (DIF) among different age, gender, and race groups using a nationally representative sample. Methods: We utilised the National Health and Nutrition Examination Survey (NHANES) 2009-2010 dataset, a US nationally representative data collection with demographic weighting. The Arthritis Questionnaire (ARQ) within NHANES included questions from published Calin, European Spondyloarthropathy Study Group, and Berlin criteria (8a and 7b). Confirmatory factor analysis (CFA) was performed to assess the dimensionality of IBP criteria. DIF analysis based on Item Response Theory was applied to evaluate differences in criterion performance across age (20-49 years vs >50 years), gender (men vs women), and race (white vs non-white) groups. Results: The study included 1511 patients with complete ARQ IBP questionnaires. CFA indicated that 1-factor models were generally preferred for all IBP criteria. DIF analysis revealed age-related differences in several IBP items, with non-uniform DIF observed except in the Berlin 8a criteria. In contrast, gender differences were noted primarily in the Berlin 8a lower back pain item, and racial differences were minimal. Conclusions: Findings support the predominant use of a single underlying construct in IBP criteria, validating their clinical application. However, demographic factors, especially age and gender, introduce significant variability in responses, necessitating tailored approaches in clinical assessment and further research to confirm these findings across broader populations.
Rare diseases represent the long tail of medical imaging, where AI models often fail due to the scarcity of representative training data. In clinical workflows, radiologists frequently consult case reports and literature when confronted with unfamiliar findings. Following this line of reasoning, we introduce RADAR, Retrieval Augmented Diagnostic Reasoning Agents, an agentic system for rare disease detection in brain MRI. Our approach uses AI agents with access to external medical knowledge by embedding both case reports and literature using sentence transformers and indexing them with FAISS to enable efficient similarity search. The agent retrieves clinically relevant evidence to guide diagnostic decision making on unseen diseases, without the need of additional training. Designed as a model-agnostic reasoning module, RADAR can be seamlessly integrated with diverse large language models, consistently improving their rare pathology recognition and interpretability. On the NOVA dataset comprising 280 distinct rare diseases, RADAR achieves up to a 10.2% performance gain, with the strongest improvements observed for open source models such as DeepSeek. Beyond accuracy, the retrieved examples provide interpretable, literature grounded explanations, highlighting retrieval-augmented reasoning as a powerful paradigm for low-prevalence conditions in medical imaging.
Chronic painful intervertebral disc (IVD) degeneration (i.e., discogenic pain) is a major source of global disability needing improved knowledge on multiple-tissue interactions and how they progress in order improve treatment strategies. This study used an in vivo rat annulus fibrosus (AF) injury-driven discogenic pain model to investigate the acute and chronic changes in IVD degeneration and spinal inflammation, as well as sensitization, inflammation, and remodeling in dorsal root ganglion (DRG) and spinal cord (SC) dorsal horn. AF injury induced moderate IVD degeneration with acute and broad spinal inflammation that progressed to DRG to SC changes within days and weeks, respectively. Specifically, AF injury elevated macrophages in the spine (CD68) and DRGs (Iba1) that peaked at 3 days post-injury, and increased microglia (Iba1) in SC that peaked at 2 weeks post-injury. AF injury also triggered glial responses with elevated GFAP in DRGs and SC at least 8 weeks post-injury. Spinal CD68 and SC neuropeptide Substance P both remained elevated at 8 weeks, suggesting that slow and incomplete IVD healing provides a chronic source of inflammation with continued SC sensitization. We conclude that AF injury-driven IVD degeneration induces acute spinal, DRG, and SC inflammatory crosstalk with sustained glial responses in both DRGs and SC, leading to chronic SC sensitization and neural plasticity. The known association of these markers with neuropathic pain suggests that therapeutic strategies for discogenic pain need to target both spinal and nervous systems, with early strategies managing acute inflammatory processes, and late strategies targeting chronic IVD inflammation, SC sensitization, and remodeling.
Calcium pyrophosphate deposition disease (CPPD) is a cause of inflammatory arthropathy that increases in prevalence with increasing age, presents in acute and chronic forms, and is characterized by the finding of positively birefringent crystals on polarized microscopy of synovial fluid. This review finds that although strides are being made in CPPD diagnosis and classification, CPPD remains a poorly understood, unrecognized, and debilitating disease. As a consequence, treatment options usually lack supportive evidence and there has been little progress in novel drug development for the condition. This article aims to discuss the updated evidence on treatment options for CPPD and identifies promising future areas for improvement.
Abstract Purpose Comminuted coronal shear fractures of the distal humerus represent rare injuries and are difficult to treat, especially comminuted capitellum and trochlear fractures (Dubberley Type III). The on-table reconstruction technique of comminuted articular fractures may be an option, although it has not been reported in the coronal shear fracture of the distal humerus. The aim of the present case series is to determine the functional and radiological outcomes of on-table reconstructed Dubberley III fractures. Methods A retrospective review was conducted of 10 patients with Dubberley type III fractures in coronal shear fractures of the capitellum and trochlea who underwent an ‘on-table’ reconstruction technique between January 2009 and October 2019. All patients were evaluated using the disabilities of the arm, shoulder, and hand (DASH) score, American Shoulder and Elbow Surgeons(ASES) score, Mayo Elbow Score Performance Index (MEPI) score and at least 4 years later. Results All cases achieved union. At the final follow-up, the mean range of elbow motion was 11.5°of flexion contracture and 131.9° of further flexion. The mean DASH score was 21.2 (5.7) points (range 13.3–32.5). The mean ASES score was 88.6 ± 7.4 (range, 77 to 100). The mean MEPI score was 87 (10) points (range 70–100). In complication, partial osteonecrosis of capitellum is developed in one patient. One patient had heterotopic ossification without functional impairment. Conclusion The on-table reconstruction technique can be a reliable option in the surgical treatment of complex distal humerus fractures. This technique allows anatomical reduction of comminuted capitellum and trochlea, with a low risk of avascular necrosis over 4 years of follow-up. Level of evidence Level IV, retrospective case series.
Juan Valentín Fernández de la Gala hablará de Los médicos de Macondo, una investigación que aborda la Medicina en la obra del Premio Nobel colombiano. Además, liderará un taller de narrativa dirigido a los asistentes al Congreso Panamericano.
Medicine (General), Diseases of the musculoskeletal system
Objective: The aim of this study was to compare the magnitude and the predictors of the placebo response in an internet versus onsite randomised controlled trials (RCTs) in people with hand osteoarthritis (HOA). Method: This study is a post-hoc analysis based on one internet RCT (RADIANT) and previously published onsite RCTs for HOA identified through a rigorous searching and selection strategy. The magnitude of the placebo response in the two different types of RCTs were compared using heterogeneity statistics and forest plots visualisation. Classic placebo predictors as well as a combined model, defined with data from onsite RCTs, were tested to predict the placebo response. Results: We analysed the dataset from RADIANT and fourteen previously published onsite RCTs. None of the analyses showed a significant difference between the placebo response for the internet versus onsite RCTs. The “classic” placebo predictors combined in a multivariate predictive model correlated significantly with the placebo response measured in RADIANT study. Conclusion: Despite the absence of face-to-face interactions with the study personnel, there is no evidence that either the magnitude or the predictors of the placebo response of this internet RCT differ from those of onsite RCTs. This analysis is considered as a first step towards evaluating the difference between these designs and strengthens the argument that internet RCTs remain an acceptable alternative way to assess the efficacy of an active treatment in comparison to a placebo.
Md Aziz Hosen Foysal, Foyez Ahmed, Md Zahurul Haque
Plant diseases significantly impact agricultural productivity, resulting in economic losses and food insecurity. Prompt and accurate detection is crucial for the efficient management and mitigation of plant diseases. This study investigates advanced techniques in plant disease detection, emphasizing the integration of image processing, machine learning, deep learning methods, and mobile technologies. High-resolution images of plant leaves were captured and analyzed using convolutional neural networks (CNNs) to detect symptoms of various diseases, such as blight, mildew, and rust. This study explores 14 classes of plants and diagnoses 26 unique plant diseases. We focus on common diseases affecting various crops. The model was trained on a diverse dataset encompassing multiple crops and disease types, achieving 98.14% accuracy in disease diagnosis. Finally integrated this model into mobile apps for real-time disease diagnosis.
In modern healthcare, addressing the complexities of accurate disease prediction and personalized recommendations is both crucial and challenging. This research introduces MLtoGAI, which integrates Semantic Web technology with Machine Learning (ML) to enhance disease prediction and offer user-friendly explanations through ChatGPT. The system comprises three key components: a reusable disease ontology that incorporates detailed knowledge about various diseases, a diagnostic classification model that uses patient symptoms to detect specific diseases accurately, and the integration of Semantic Web Rule Language (SWRL) with ontology and ChatGPT to generate clear, personalized health advice. This approach significantly improves prediction accuracy and ensures results that are easy to understand, addressing the complexity of diseases and diverse symptoms. The MLtoGAI system demonstrates substantial advancements in accuracy and user satisfaction, contributing to developing more intelligent and accessible healthcare solutions. This innovative approach combines the strengths of ML algorithms with the ability to provide transparent, human-understandable explanations through ChatGPT, achieving significant improvements in prediction accuracy and user comprehension. By leveraging semantic technology and explainable AI, the system enhances the accuracy of disease prediction and ensures that the recommendations are relevant and easily understood by individual patients. Our research highlights the potential of integrating advanced technologies to overcome existing challenges in medical diagnostics, paving the way for future developments in intelligent healthcare systems. Additionally, the system is validated using 200 synthetic patient data records, ensuring robust performance and reliability.
Pumpkin is a vital crop cultivated globally, and its productivity is crucial for food security, especially in developing regions. Accurate and timely detection of pumpkin leaf diseases is essential to mitigate significant losses in yield and quality. Traditional methods of disease identification rely heavily on subjective judgment by farmers or experts, which can lead to inefficiencies and missed opportunities for intervention. Recent advancements in machine learning and deep learning offer promising solutions for automating and improving the accuracy of plant disease detection. This paper presents a comprehensive analysis of state-of-the-art Convolutional Neural Network (CNN) models for classifying diseases in pumpkin plant leaves. Using a publicly available dataset of 2000 highresolution images, we evaluate the performance of several CNN architectures, including ResNet, DenseNet, and EfficientNet, in recognizing five classes: healthy leaves and four common diseases downy mildew, powdery mildew, mosaic disease, and bacterial leaf spot. We fine-tuned these pretrained models and conducted hyperparameter optimization experiments. ResNet-34, DenseNet-121, and EfficientNet-B7 were identified as top-performing models, each excelling in different classes of leaf diseases. Our analysis revealed DenseNet-121 as the optimal model when considering both accuracy and computational complexity achieving an overall accuracy of 86%. This study underscores the potential of CNNs in automating disease diagnosis for pumpkin plants, offering valuable insights that can contribute to enhancing agricultural productivity and minimizing economic losses.
In response to the COVID-19 pandemic, the integration of interpretable machine learning techniques has garnered significant attention, offering transparent and understandable insights crucial for informed clinical decision making. This literature review delves into the applications of interpretable machine learning in predicting the prognosis of respiratory diseases, particularly focusing on COVID-19 and its implications for future research and clinical practice. We reviewed various machine learning models that are not only capable of incorporating existing clinical domain knowledge but also have the learning capability to explore new information from the data. These models and experiences not only aid in managing the current crisis but also hold promise for addressing future disease outbreaks. By harnessing interpretable machine learning, healthcare systems can enhance their preparedness and response capabilities, thereby improving patient outcomes and mitigating the impact of respiratory diseases in the years to come.
Plant diseases pose significant threats to agriculture. It necessitates proper diagnosis and effective treatment to safeguard crop yields. To automate the diagnosis process, image segmentation is usually adopted for precisely identifying diseased regions, thereby advancing precision agriculture. Developing robust image segmentation models for plant diseases demands high-quality annotations across numerous images. However, existing plant disease datasets typically lack segmentation labels and are often confined to controlled laboratory settings, which do not adequately reflect the complexity of natural environments. Motivated by this fact, we established PlantSeg, a large-scale segmentation dataset for plant diseases. PlantSeg distinguishes itself from existing datasets in three key aspects. (1) Annotation type: Unlike the majority of existing datasets that only contain class labels or bounding boxes, each image in PlantSeg includes detailed and high-quality segmentation masks, associated with plant types and disease names. (2) Image source: Unlike typical datasets that contain images from laboratory settings, PlantSeg primarily comprises in-the-wild plant disease images. This choice enhances the practical applicability, as the trained models can be applied for integrated disease management. (3) Scale: PlantSeg is extensive, featuring 11,400 images with disease segmentation masks and an additional 8,000 healthy plant images categorized by plant type. Extensive technical experiments validate the high quality of PlantSeg's annotations. This dataset not only allows researchers to evaluate their image classification methods but also provides a critical foundation for developing and benchmarking advanced plant disease segmentation algorithms.