Adhesive capsulitis, or frozen shoulder, is characterized by pain and progressive restriction of both active and passive shoulder range of motion. The pathophysiology involves an initial inflammatory phase with elevated cytokines, followed by pathological fibrosis, capsular thickening, and contracture involving both intra- and extra-articular structures, including the coracohumeral ligament and rotator cuff interval. Diagnosis is primarily clinical. The traditional three-stage model, freezing, frozen, and thawing, has been challenged by recent evidence showing that spontaneous recovery is uncommon and that many patients do not fully regain shoulder function without active treatment. This paradigm change emphasizes the necessity of early and focused interventions to maximize functional recovery. While physiotherapy remains the mainstay of management, interventional procedures have gained prominence for their ability to reduce pain and facilitate rehabilitation. Interventional options include intra-articular corticosteroid injections, hydrodilatation, and suprascapular nerve blocks. This narrative review summarizes current evidence on interventional procedures for adhesive capsulitis, highlighting their mechanisms, techniques, and comparative efficacy.
Repurposing existing drugs to treat new diseases is a cost-effective alternative to de novo drug development, but there are millions of potential drug-disease combinations to be considered with only a small fraction being viable. In silico predictions of drug-disease associations can be invaluable for reducing the size of the search space. In this work we present a novel network of drugs and the diseases they treat, compiled using a combination of existing textual and machine-readable databases, natural-language processing tools, and hand curation, and analyze it using network-based link prediction methods to identify potential drug-disease combinations. We measure the efficacy of these methods using cross-validation tests and find that several methods, particularly those based on graph embedding and network model fitting, achieve impressive prediction performance, significantly better than previous approaches, with area under the ROC curve above 0.95 and average precision almost a thousand times better than chance.
Terese Geraghty, Alia M. Obeidat, Shingo Ishihara
et al.
Objective Osteoarthritis (OA) is a leading cause of chronic pain, yet OA pain management remains poor. Age is the strongest predictor of OA development, and mechanisms driving OA pain are unclear. We undertook this study to characterize age‐associated changes in knee OA, pain‐related behaviors, and dorsal root ganglion (DRG) molecular phenotypes in mice of both sexes. Methods Male or female C57BL/6 mice 6 or 20 months of age were evaluated for histopathologic knee OA, pain‐related behaviors, and L3–L5 DRG immune characterization via flow cytometry. DRG gene expression in older mice and humans was also examined. Results Male mice at 20 months of age had worse cartilage degeneration than 6‐month‐old mice. Older female mouse knees showed increased cartilage degeneration but to a lesser degree than those of male mice. Older mice of both sexes had worse mechanical allodynia, knee hyperalgesia, and grip strength compared to younger mice. For both sexes, DRGs from older mice showed decreased CD45+ cells and a significant increase in F4/80+ macrophages and CD11c+ dendritic cells. Older male mouse DRGs showed increased expression of Ccl2 and Ccl5 , and older female mouse DRGs showed increased Cxcr4 and Ccl 3 expression compared to 6‐month‐old mouse DRGs, among other differentially expressed genes. Human DRG analysis from 6 individuals >80 years of age revealed elevated CCL2 in men compared to women, whereas CCL3 was higher in DRGs from women. Conclusion We found that aging in male and female mice is accompanied by mild knee OA, mechanical sensitization, and changes to immune cell populations in the DRG, suggesting novel avenues for development of OA therapies. image
Hirofumi Tenshin, Jesus Delgado-Calle, Jolene J. Windle
et al.
Abstract Purpose of Review To describe the contributions of osteocytes to the lesions in Paget’s disease, which are characterized by locally overactive bone resorption and formation. Recent Findings Osteocytes, the most abundant cells in bone, are altered in Paget’s disease lesions, displaying increased size, decreased canalicular length, incomplete differentiation, and less sclerostin expression compared to controls in both patients and mouse models. Pagetic lesions show increased senescent osteocytes that express RANK ligand, which drives osteoclastic bone resorption. Abnormal osteoclasts in Paget’s disease secrete abundant IGF1, which enhances osteocyte senescence, contributing to lesion formation. Summary Recent data suggest that osteocytes contribute to lesion formation in Paget’s disease by responding to high local IGF1 released from abnormal osteoclasts. Here we describe the characteristics of osteocytes in Paget’s disease and their role in bone lesion formation based on recent results with mouse models and supported by patient data.
Henry B.G. Baird, Cody C. Ashy, Prudhvi Kodali
et al.
Purpose To evaluate and synthesize the available literature related to platelet‐rich plasma (PRP) treatment of knee pathologies and to provide recommendations to inform future research in the field. Methods PubMed, CINAHL, and Scopus databases were queried on October 6, 2023. All identified citations were collated and uploaded into Covidence for screening and data extraction. Studies were included if they were human studies published in English with adult cohorts that received PRP as a procedural injection or surgical augmentation for knee pathologies with patient‐reported outcome measures (PROMs) and level of evidence Levels I‐IV. Results Our search yielded 2,615 studies, of which 155 studies from 2006 to 2023 met the inclusion criteria. Median follow‐up was 9 months (±11.2 months). Most studies (75.5%) characterized the leukocyte content of PRP, although most studies (86%) did not use a comprehensive classification scheme. In addition, most studies were from Asia (50%) and Europe (32%) and were from a single center (96%). In terms of treatment, 74% of studies examined PRP as a procedural injection, whereas 26% examined PRP as an augmentation. Most studies (68%) examined treatment of knee osteoarthritis. Many studies (83%) documented significant improvements in PROMs, including 93% of Level III/IV evidence studies and 72% of Level I/II evidence studies, although most studies (70%) failed to include minimal clinically important difference values. The visual analog scale was the most‐used PROM (58% of studies), whereas the Short Form Health Survey 36‐item was the least‐used PROM (5% of studies). Conclusions Most published investigations of knee PRP are performed in Asia, investigate procedural injection for osteoarthritis, and show significant outcome improvements. In addition, this review highlights the need for better classification of PRP formulations. Level of Evidence Level IV, scoping review of Level I‐IV studies.
A. A. Kargaltsev, M. A. Makarov, S. A. Makarov
et al.
Introduction. Hip-spine syndrome (HSS) is a combination of coexisting hip osteoarthritis (OA) and degenerative lumbar spine stenosis (LSS). Main difficulties in treating patients with HSS are in early diagnostics and in choosing right surgery, because mistakes lead to pain maintenance. Existing diagnostic algorithms show right surgery choosing failure in 15–20%. We present results of examination patients with HSS in our survey.The aim – to present clinical and instrumental results of examination of patients with hip-spine syndrome.Materials and methods. We have examined 378 patients with typical pain pattern (buttocks, low back spine, groin and lateral hip) and difficulties in pain source definition. We performed hip X-rays and low back spine MRI.Due to results we divided patients into three groups – patients with HSS (n=100), with hip OA (n=172) and patients with LSS (n=106). We used Harris Hip Score, Oswestry Disability Index and Visual Analogue Score to determine hip and lumbar spine functional status and pain level.Results. Patients with HSS had higher (p<0.05) pain levels (76.5±9.1 mm) than patients with hip (68.3±7.9 mm) or lumbar spine pathology (67.4±7.9 mm). Harris Hip Score in patients wirh HSS (52.7±8.1 points) was same as in patients with hip OA (55.5±9.1 points), Oswestry Disability Index in these patients (44.2±7.6%) was same as in patients with LSS (43.2±7.8%).Conclusion. High pain level and low Harris Hip Score and Oswestry index, along with clinical examination, on first visit can help suspect hip-spine syndrome and recommend both hip and lumbar spine imaging.
In the general domain, large multimodal models (LMMs) have achieved significant advancements, yet challenges persist in applying them to specific fields, especially agriculture. As the backbone of the global economy, agriculture confronts numerous challenges, with pests and diseases being particularly concerning due to their complexity, variability, rapid spread, and high resistance. This paper specifically addresses these issues. We construct the first multimodal instruction-following dataset in the agricultural domain, covering over 221 types of pests and diseases with approximately 400,000 data entries. This dataset aims to explore and address the unique challenges in pest and disease control. Based on this dataset, we propose a knowledge-infused training method to develop Agri-LLaVA, an agricultural multimodal conversation system. To accelerate progress in this field and inspire more researchers to engage, we design a diverse and challenging evaluation benchmark for agricultural pests and diseases. Experimental results demonstrate that Agri-LLaVA excels in agricultural multimodal conversation and visual understanding, providing new insights and approaches to address agricultural pests and diseases. By open-sourcing our dataset and model, we aim to promote research and development in LMMs within the agricultural domain and make significant contributions to tackle the challenges of agricultural pests and diseases. All resources can be found at https://github.com/Kki2Eve/Agri-LLaVA.
Saurav Bharadwaj, Akshita Midha, Shikha Sharma
et al.
Citrus diseases pose threats to citrus farming and result in economic losses worldwide. Nucleic acid and serology-based methods of detection and, immunochromatographic assays are commonly used but these laboratory tests are laborious, expensive and might be subjected to cross-reaction and contamination. Modern optical spectroscopic techniques offer a promising alternative as they are label-free, sensitive, rapid, non-destructive, and demonstrate the potential for incorporation into an autonomous system for disease detection in citrus orchards. Nevertheless, the majority of optical spectroscopic methods for citrus disease detection are still in the trial phases and, require additional efforts to be established as efficient and commercially viable methods. The review presents an overview of fundamental working principles, the state of the art, and explains the applications and limitations of the optical spectroscopy technique including the spectroscopic imaging approach (hyperspectral imaging) in the identification of diseases in citrus plants. The review highlights (1) the technical specifications of optical spectroscopic tools that can potentially be utilized in field measurements, (2) their applications in screening citrus diseases through leaf spectroscopy, and (3) discusses their benefits and limitations, including future insights into label-free identification of citrus diseases. Moreover, the role of artificial intelligence is reviewed as potential effective tools for spectral analysis, enabling more accurate detection of infected citrus leaves even before the appearance of visual symptoms by leveraging compositional, morphological, and chemometric characteristics of the plant leaves. The review aims to encourage stakeholders to enhance the development and commercialization of field-based, label-free optical tools for the rapid and early-stage screening of citrus diseases in plants.
Continuous-time multistate models are widely used for analyzing interval-censored data on disease progression over time. Sometimes, diseases manifest differently and what appears to be a coherent collection of symptoms is the expression of multiple distinct disease subtypes. To address this complexity, we propose a mixture hidden Markov model, where the observation process encompasses states representing common symptomatic stages across these diseases, and each underlying process corresponds to a distinct disease subtype. Our method models both the overall and the type-specific disease incidence/prevalence accounting for sampling conditions and exactly observed death times. Additionally, it can utilize partially available disease-type information, which offers insights into the pathway through specific hidden states in the disease process, to aid in the estimation. We present both a frequentist and a Bayesian way to obtain the estimates. The finite sample performance is evaluated through simulation studies. We demonstrate our method using the Nun Study and model the development and progression of dementia, encompassing both Alzheimer's disease (AD) and non-AD dementia.
Zeki Akyildiz, Yılmaz Yüksel, Yaşar Birgonül
et al.
Abstract Background Turkish Super League teams need more information about the external load. Considering the specific country and the coaches’ philosophies, the purpose of this study was to compare the external match load of a Turkish Super League team considering the different playing positions and in-season periods. Methodology A longitudinal study design was employed by observing 29 official matches of the same team. A total of fifteen players, consisting of five defenders, five midfielders, and five forwards, were analyzed using the Sentio Sports Optical Tracking System. The following outcomes were extracted in each match: total distance (TD), walking [from 0 to 7.2 km/h], jogging [from 7.2 to 14.4 km/h], running [from 14.4 to 20 km/h], high speed running (HSR) [> 20 km/h], metabolic power (MP), maximum deceleration (Decmax) [Dec < -3 m/s2], total deceleration distance (Dectotal), maximum acceleration (Accmax) [Acc > 3 m/s2], and total acceleration distance (Acctotal). Statistical analysis consisted of ANOVA and Bonferroni correction post hoc tests. Results The main results showed that all variables were similar between periods of the season (p > 0.05) except for walking (p = 0.021 witha large effect size), which revealed lower values in the mid-season period. When analyzing periods of the season considering playing positions, there were several significant results for TD, walking, jogging, running, HSR, Dectotal and Acctotal (p < 0.05, with small to large effects). A tendency for higher values was noted in the mid and end-season. Considering the playing positions comparisons, midfielders showed higher values than defenders for TD, MP, Dectotal and Acctotal (p < 0.05 with large effect for all). Midfielders also showed higher values than forwards for TD, jogging, Dectotal and Acctotal (p < 0.05 with a large effect for all). Conclusions The present study emphasizes the significance of analyzing data based on minute, playing position, and season period. Findings reveal that defenders consistently displayed the lowest values in all external load measures during matches compared to other positions throughout the season. Furthermore, midfielders demonstrated a higher activity profile during the initial and middle stages of the season compared to other positions, with a slight tendency to decrease load towards the end of the season.
BEATRIZ FOGAROLLI AFONSO, ARTHUR FELIPE LAUF MELOTTI, ITALO BARCELLOS DE SOUZA
et al.
ABSTRACT Introduction: Low back pain is defined as pain, muscle spasm, or stiffness between the L1 and L5 vertebrae, below the lower margin of the twelfth rib and above the upper gluteal fold, and may or may not be associated with pain radiating to the lower limbs. Objective: To determine the prevalence of low back pain in spine surgeons. Method: A non-randomized quantitative cross-sectional clinical study was carried out in a sample of 95 spine surgeons in Brazil, with the application of the Oswestry and visual analog pain scales, in addition to a structured questionnaire for the characterization of the participants. Results: Among the studied population, 69.5% were orthopedists, 30.5% were neurosurgeons, and the mean age of the sample was 46 years (±10.6), with neurosurgeons being older than orthopedists. Regarding BMI, the majority (77.8%) were overweight or obese, and seventy-six percent performed physical activity. The prevalence of low back pain was 58.9%. No relevant differences were found in the time spent weekly in surgeries between those who had low back pain and those who did not (p = 0.364). Mean pain intensity was 2.0 (SD = 2.2), statistically (p = 0.025) higher in orthopedists (2.3) when compared to neurosurgeons (1.3). Regarding the ODI score, 98.2% of the surgeons had a minimal disability (0-20%) for daily activities. Conclusion: The prevalence of low back pain in spine surgeons is high and is associated with mild inability to perform daily activities. Level Of Evidence IV; Non-Randomized Quantitative Cross-Sectional Clinical Study.
Orthopedic surgery, Diseases of the musculoskeletal system
Konrad Furmańczyk, Wojciech Niemiro, Mariola Chrzanowska
et al.
We propose a new graphical model to describe the comorbidity of allergic diseases. We present our model in two versions. First, we introduce a generative model that correctly reflects the variables' causal relationship. Then we propose an approximation of the generative model by another misspecified model that is computationally more efficient and easily interpretable. We will focus on the misspecified version, which we consider more practical. We include in the model two directed graphs, one graph of known dependency between the main binary variables (diseases), and a second graph of the dependence between the occurrence of the diseases and their symptoms. In the model, we also consider additional auxiliary variables. The proposed model is evaluated on a cross-sectional multicentre study in Poland on the ECAP database (www.ecap.pl). An assessment of the stability of the proposed model was obtained using bootstrap and jackknife techniques.
Studying human factors has gained a lot of interest in recommender systems research recently. User experience plays a vital role in tourism recommender systems since user satisfaction is the main factor that guarantees the success of such recommender systems. In this work, we have designed and implemented a destination recommender system in which the recommendations adapt instantly based on the user preferences. The recommendations can be explored on a world map with additional information. This interface addresses common visualization challenges in recommender systems, such as transparency, justification, controllability, explorability, the cold-start problem, and context awareness. We have conducted a user study to evaluate different aspects of this recommender system from the users' perspective.
This research paper presents AMaizeD: An End to End Pipeline for Automatic Maize Disease Detection, an automated framework for early detection of diseases in maize crops using multispectral imagery obtained from drones. A custom hand-collected dataset focusing specifically on maize crops was meticulously gathered by expert researchers and agronomists. The dataset encompasses a diverse range of maize varieties, cultivation practices, and environmental conditions, capturing various stages of maize growth and disease progression. By leveraging multispectral imagery, the framework benefits from improved spectral resolution and increased sensitivity to subtle changes in plant health. The proposed framework employs a combination of convolutional neural networks (CNNs) as feature extractors and segmentation techniques to identify both the maize plants and their associated diseases. Experimental results demonstrate the effectiveness of the framework in detecting a range of maize diseases, including powdery mildew, anthracnose, and leaf blight. The framework achieves state-of-the-art performance on the custom hand-collected dataset and contributes to the field of automated disease detection in agriculture, offering a practical solution for early identification of diseases in maize crops advanced machine learning techniques and deep learning architectures.
Knowledge Graph (KG) plays a crucial role in Medical Report Generation (MRG) because it reveals the relations among diseases and thus can be utilized to guide the generation process. However, constructing a comprehensive KG is labor-intensive and its applications on the MRG process are under-explored. In this study, we establish a complete KG on chest X-ray imaging that includes 137 types of diseases and abnormalities. Based on this KG, we find that the current MRG data sets exhibit a long-tailed problem in disease distribution. To mitigate this problem, we introduce a novel augmentation strategy that enhances the representation of disease types in the tail-end of the distribution. We further design a two-stage MRG approach, where a classifier is first trained to detect whether the input images exhibit any abnormalities. The classified images are then independently fed into two transformer-based generators, namely, ``disease-specific generator" and ``disease-free generator" to generate the corresponding reports. To enhance the clinical evaluation of whether the generated reports correctly describe the diseases appearing in the input image, we propose diverse sensitivity (DS), a new metric that checks whether generated diseases match ground truth and measures the diversity of all generated diseases. Results show that the proposed two-stage generation framework and augmentation strategies improve DS by a considerable margin, indicating a notable reduction in the long-tailed problem associated with under-represented diseases.
Samantha Muscat, Anne E. C. Nichols, Emma Gira
et al.
Abstract During tendon healing, macrophages are thought to be a key mediator of scar tissue formation, which prevents successful functional restoration of the tendon. However, macrophages are critical for successful tendon healing as they aid in wound debridement, extracellular matrix deposition, and promote fibroblast proliferation. Recent work has sought to better define the multi‐faceted functions of macrophages using depletion studies, while other studies have identified a tendon resident macrophage population. To begin to delineate the functions of tendon‐resident versus circulation‐derived macrophages, we examined the tendon healing phenotype in Chemokine Receptor 2 (CCR2) reporter (CCR2 GFP/+ ), and knockout mice. CCR2 is a chemokine receptor primarily found on the surface of circulating bone marrow‐derived monocytes, with CCR2 being an important mediator of macrophage recruitment to wound environments. Surprisingly, CCR2 GFP/+ cells were present in the tendon during adult homeostasis, and single‐cell RNA sequencing identified these cells as tendon‐resident macrophages and T cells. During both homeostasis and healing, CCR2 knockout resulted in a substantial decrease in CCR2 GFP+ cells and pan‐macrophages. Additionally, loss of CCR2 resulted in reduced numbers of myofibroblasts and impeded functional recovery during late healing. This study highlights the heterogeneity of tendon‐resident and recruited immune cells and their contributions following injury, and establishes an important role for CCR2 in modulating both the adult tendon cell environment and tendon healing process.