The dynamic organization of chromatin plays a critical role in regulating muscle cell differentiation. Among the molecular elements influencing chromatin architecture, long noncoding RNAs (lncRNAs) have emerged as important regulators due to their capacity to act as scaffolds, recruiters of chromatin-modifying proteins, or as transcriptional enhancers. This review aims to explore the mechanisms by which lncRNAs influence chromatin structure in the context of skeletal muscle differentiation. We classified the functional roles of lncRNAs into three main strategies: recruitment of epigenetic modifiers, assembly of transcriptional scaffolds, and regulation through enhancer-like activity. We provide specific examples of lncRNAs associated with these mechanisms and discuss their involvement in the control of myogenic gene expression. These findings highlight the complexity and specificity of lncRNA-mediated chromatin remodeling and suggest their potential as targets for therapeutic intervention in muscle-related disorders.
Systemic inflammatory rheumatic disorders are associated with an increased risk of malignancy. The mechanism linking malignancy and rheumatic diseases is complex and multidirectional, and is only partially understood. This review focused on the incidence of neoplastic diseases in patients with the most common systemic rheumatic disorders. Rheumatoid arthritis is associated with a risk of malignancy that is about 10% higher than in the general population, and this is more related to the disease itself than to medication. Systemic lupus erythematosus is associated with an increased risk of neoplasms, particularly haematological malignancies such as non-Hodgkin lymphoma. The risk increases with long-lasting active disease. Systemic sclerosis is associated with an increased risk of lung and liver cancer, as well as malignancies of the haematological system. Men and patients with RNA polymerase III antibodies are at a higher risk. Dermatomyositis and polymyositis are subgroups of idiopathic inflammatory myopathy associated with a high risk of malignancy. Male gender and old age are additional risk factors. Other rheumatic diseases are also thought to be associated with an increased risk of cancer. Currently, the data are insufficient for a clear distinction to be made between subgroups at risk. Most patients with systemic autoimmune disorders are at enhanced risk of malignancy to some degree. The management of these patients should include procedures for the early detection of age- and population-specific malignancies, as well as those which are more prevalent in the patient population suffering from the individual rheumatic disease. It is important to note that an atypical disease course or increased treatment resistance for a rheumatic disorder may indicate that the observed changes are an expression of a paraneoplastic syndrome or that a new neoplasm is modifying the clinical course of an already diagnosed rheumatic disease.
Sarcopenia is an ageing-related disease featured by the loss of skeletal muscle quality and function. Advanced glycation end-products (AGEs) are a complex set of modified proteins or lipids by non-enzymatic glycosylation and oxidation. The formation of AGEs is irreversible, and they accumulate in tissues with increasing age. Currently, AGEs, as a biomarker of ageing, are viewed as a risk factor for sarcopenia. AGE accumulation could cause harmful effects in the human body such as elevated inflammation levels, enhanced oxidative stress, and targeted glycosylation of proteins inside and outside the cells. Several studies have illustrated the pathogenic role of AGEs in sarcopenia, which includes promoting skeletal muscle atrophy, impairing muscle regeneration, disrupting the normal structure of skeletal muscle extracellular matrix, and contributing to neuromuscular junction lesion and vascular disorders. This article reviews studies focused on the pathogenic role of AGEs in sarcopenia and the potential mechanisms of the detrimental effects, aiming to provide new insights into the pathogenesis of sarcopenia and develop novel methods for the prevention and therapy of sarcopenia. Cite this article: Bone Joint Res 2025;14(3):185–198.
Aims: Excessive chondrocyte hypertrophy is a common feature in cartilage degeneration which is susceptible to joint overloading, but the relationship between mechanical overloading and chondrocyte hypertrophy still remains elusive. The aim of our study was to explore the mechanism of mechanical compression-induced chondrocyte hypertrophy. Methods: In this study, the temporomandibular joint (TMJ) degeneration model was built through forced mandibular retrusion (FMR)-induced compression in TMJ. Chondrocytes were also mechanically compressed in vitro. The role of O-GlcNAcylation in mechanical compression-induced chondrocyte hypertrophy manifested through specific activator Thiamet G and inhibitor OSMI-1. Results: Both in vivo and in vitro data revealed that chondrocyte hypertrophic differentiation is promoted by compression. Immunofluorescent and immunoblotting results showed that protein pan-O-GlcNAcylation levels were elevated in these hypertrophic chondrocytes. Pharmacologically inhibiting protein pan-O-GlcNAcylation by OSMI-1 partially mitigated the compression-induced hypertrophic differentiation of chondrocytes. Specifically, runt-related transcription factor 2 (Runx2) and SRY-Box 9 transcription factor (Sox9) were subjected to modification of O-GlcNAcylation under mechanical compression, and pharmacological activation or inhibition of O-GlcNAcylation affected the transcriptional activity of Runx2 but not Sox9. Furthermore, compression-induced protein pan-O-GlcNAcylation in chondrocytes was induced by enhanced expression of glucose transporter 1 (GLUT1), and depletion of GLUT1 by WZB117 dampened the effect of compression on chondrocyte hypertrophy. Conclusion: Our study proposes a novel function of GLUT1-mediated protein O-GlcNAcylation in driving compression-induced hypertrophic differentiation of chondrocytes by O-GlcNAc modification of Runx2, which promoted its transcriptional activity and strengthened the expressions of downstream hypertrophic marker. Cite this article: Bone Joint Res 2025;14(3):209–222.
<b>Background:</b> The overhead squat movement involves various bodily structures, but the interaction with three-dimensional elastic resistance along the kinetic chain approach requires further understanding. <b>Objectives</b>: We aim to describe and compare scapular and trunk kinematics during an overhead squat under different external resistance conditions. <b>Methods</b>: The three-dimensional shoulder and trunk kinematics of 19 male participants were captured at 15-degree intervals, from 30 to 120 degrees, during the overhead squat movement and analyzed by phase. <b>Results</b>: Scapular posterior tilt was significantly affected by resistance during the UNLOAD phase (<i>p</i> = 0.005, η<sup>2</sup>ₚ = 0.26). Significant resistance-by-arm elevation interactions were found for scapular upward rotation during the LOAD phase (<i>p</i> = 0.003, η<sup>2</sup>ₚ = 0.19) and UNLOAD phase <i>(p <</i> 0.001, η<sup>2</sup>ₚ = 0.24); for scapular internal rotation during both the LOAD (<i>p</i> < 0.001, η<sup>2</sup>ₚ = 0.37) and UNLOAD phases (<i>p</i> = 0.006, η<sup>2</sup> = 0.19); and for scapular posterior tilt during both the LOAD (<i>p</i> = 0.003, η<sup>2</sup>ₚ = 0.26) and the UNLOAD phases (<i>p</i> = 0.006, η<sup>2</sup>ₚ = 0.21). Trunk flexion/extension showed a significant effect on resistance during the LOAD phase (<i>p</i> = 0.008, η<sup>2</sup>ₚ = 0.24). <b>Conclusions</b>: Increasing resistance through elastic resistance significantly improves scapular kinematics via the trunk during arm elevation. This underscores the potential of the overhead squat movement as a valuable tool for assessing and treating scapular and trunk dysfunction.
To extend healthy life expectancy in an aging society, it is crucial to prevent various diseases at pre-disease states. Although dynamical network biomarker theory has been developed for pre-disease detection, mathematical frameworks for pre-disease treatment have not been well established. Here I propose a control theory-based approach for pre-disease treatment, named Markov chain sparse control (MCSC), where time evolution of a probability distribution on a Markov chain is described as a discrete-time linear system. By designing a sparse controller, a few candidate states for intervention are identified. The validity of MCSC is demonstrated using numerical simulations and real-data analysis.
Niccolò McConnell, Pardeep Vasudev, Daisuke Yamada
et al.
Low-dose computed tomography (LDCT) imaging employed in lung cancer screening (LCS) programs is increasing in uptake worldwide. LCS programs herald a generational opportunity to simultaneously detect cancer and non-cancer-related early-stage lung disease. Yet these efforts are hampered by a shortage of radiologists to interpret scans at scale. Here, we present TANGERINE, a computationally frugal, open-source vision foundation model for volumetric LDCT analysis. Designed for broad accessibility and rapid adaptation, TANGERINE can be fine-tuned off the shelf for a wide range of disease-specific tasks with limited computational resources and training data. Relative to models trained from scratch, TANGERINE demonstrates fast convergence during fine-tuning, thereby requiring significantly fewer GPU hours, and displays strong label efficiency, achieving comparable or superior performance with a fraction of fine-tuning data. Pretrained using self-supervised learning on over 98,000 thoracic LDCTs, including the UK's largest LCS initiative to date and 27 public datasets, TANGERINE achieves state-of-the-art performance across 14 disease classification tasks, including lung cancer and multiple respiratory diseases, while generalising robustly across diverse clinical centres. By extending a masked autoencoder framework to 3D imaging, TANGERINE offers a scalable solution for LDCT analysis, departing from recent closed, resource-intensive models by combining architectural simplicity, public availability, and modest computational requirements. Its accessible, open-source lightweight design lays the foundation for rapid integration into next-generation medical imaging tools that could transform LCS initiatives, allowing them to pivot from a singular focus on lung cancer detection to comprehensive respiratory disease management in high-risk populations.
Modern disease classification often overlooks molecular commonalities hidden beneath divergent clinical presentations. This study introduces a transcriptomics-driven framework for discovering disease relationships by analyzing over 1300 disease-condition pairs using GenoMAS, a fully automated agentic AI system. Beyond identifying robust gene-level overlaps, we develop a novel pathway-based similarity framework that integrates multi-database enrichment analysis to quantify functional convergence across diseases. The resulting disease similarity network reveals both known comorbidities and previously undocumented cross-category links. By examining shared biological pathways, we explore potential molecular mechanisms underlying these connections-offering functional hypotheses that go beyond symptom-based taxonomies. We further show how background conditions such as obesity and hypertension modulate transcriptomic similarity, and identify therapeutic repurposing opportunities for rare diseases like autism spectrum disorder based on their molecular proximity to better-characterized conditions. In addition, this work demonstrates how biologically grounded agentic AI can scale transcriptomic analysis while enabling mechanistic interpretation across complex disease landscapes. All results are publicly accessible at github.com/KeeeeChen/Pathway_Similarity_Network.
Dmitrii Seletkov, Sophie Starck, Ayhan Can Erdur
et al.
Reliable preclinical disease risk assessment is essential to move public healthcare from reactive treatment to proactive identification and prevention. However, image-based risk prediction algorithms often consider one condition at a time and depend on hand-crafted features obtained through segmentation tools. We propose a whole-body self-supervised representation learning method for the preclinical disease risk assessment under a competing risk modeling. This approach outperforms whole-body radiomics in multiple diseases, including cardiovascular disease (CVD), type 2 diabetes (T2D), chronic obstructive pulmonary disease (COPD), and chronic kidney disease (CKD). Simulating a preclinical screening scenario and subsequently combining with cardiac MRI, it sharpens further the prediction for CVD subgroups: ischemic heart disease (IHD), hypertensive diseases (HD), and stroke. The results indicate the translational potential of whole-body representations as a standalone screening modality and as part of a multi-modal framework within clinical workflows for early personalized risk stratification. The code is available at https://github.com/yayapa/WBRLforCR/
Kosar Asna Ashari, Nima Parvaneh, Kayvan Mirnia
et al.
Abstract Background Gain of function (GOF) mutations in NOD-like receptor family CARD-containing 4 protein (NLRC4) gene induce a wide spectrum of autoinflammatory phenotypes. Currently, we categorize them into four groups: familial cold autoinflammatory syndrome (FCAS)4, autoinflammatory infantile enterocolitis (AIFEC), NLRC4-macrophage associated syndrome (MAS), and neonatal-onset multisystem inflammatory disease (NOMID). The rarity and complexity of the disease necessitate the description of new cases and a reexamination of our understanding of the condition. Case presentations We present three patients with NLRC4-GOF mutations and AIFEC phenotypes. The first patient is an infant girl with periodic fever, seizure, high inflammatory markers, and an episode of macrophage associated syndrome (MAS). History of recurrent fever episodes since childhood was reported in mother and maternal grandmother. A heterozygous mutation was found in CARD domain of NLRC4: c.A91C: p.Asn31His. The second patient is an adolescent boy with periodic fever, diarrhea, aphthous stomatitis, seizure, and central nervous system (CNS) vasculitis. A heterozygous mutation was found in NLRC4 gene: c.1202T > C. p. Val401Ala. The third patient is a child with chronic diarrhea and elevated inflammatory markers. We found a heterozygous mutation in NLRC4 gene: c.390delG: p.S132Afs*21. All mutations have been reported for the first time as NLRC4 mutations associated with autoinflammation. We introduced novel mutations in the CARD domain and between CARD and NBD domain in the first and third cases, respectively. All three children are under remission following treatment. Conclusions NLRC4-GOF mutations can be associated with autoinflammation with diverse symptoms. Given the rarity of the disease and the possibility of new mutations being identified, the existence of a phenotype/genotype correlation has yet to be thoroughly investigated. The variety in manifestations and severity spectrum mandates a variety of treatments. Adalimumab has shown favorable outcomes in our AIFEC cases.
Pediatrics, Diseases of the musculoskeletal system
Aims: cAMP response element binding protein (CREB1) is involved in the progression of osteoarthritis (OA). However, available findings about the role of CREB1 in OA are inconsistent. 666-15 is a potent and selective CREB1 inhibitor, but its role in OA is unclear. This study aimed to investigate the precise role of CREB1 in OA, and whether 666-15 exerts an anti-OA effect. Methods: CREB1 activity and expression of a disintegrin and metalloproteinase with thrombospondin motifs 4 (ADAMTS4) in cells and tissues were measured by immunoblotting and immunohistochemical (IHC) staining. The effect of 666-15 on chondrocyte viability and apoptosis was examined by cell counting kit-8 (CCK-8) assay, JC-10, and terminal deoxynucleotidyl transferase-mediated dUTP nick end-labelling (TUNEL) staining. The effect of 666-15 on the microstructure of subchondral bone, and the synthesis and catabolism of cartilage, in anterior cruciate ligament transection mice were detected by micro-CT, safranin O and fast green (S/F), immunohistochemical staining, and enzyme-linked immunosorbent assay (ELISA). Results: CREB1 was hyperactive in osteoarthritic articular cartilage, interleukin (IL)-1β-treated cartilage explants, and IL-1β- or carbonyl cyanide 3-chlorophenylhydrazone (CCCP)-treated chondrocytes. 666-15 enhanced cell viability of OA-like chondrocytes and alleviated IL-1β- or CCCP-induced chondrocyte injury through inhibition of mitochondrial dysfunction-associated apoptosis. Moreover, inhibition of CREB1 by 666-15 suppressed expression of ADAMTS4. Additionally, 666-15 alleviated joint degeneration in an ACLT mouse model. Conclusion: Hyperactive CREB1 played a critical role in OA development, and 666-15 exerted anti-IL-1β or anti-CCCP effects in vitro as well as joint-protective effects in vivo. 666-15 may therefore be used as a promising anti-OA drug. Cite this article: Bone Joint Res 2024;13(1):4–18.
Abstract Background To investigate the effect of the position of the femoral endobutton on knee function in the posterior cruciate ligament reconstruction (PCLR). Methods A retrospective study was conducted to analyze 96 patients who underwent single-bundle reconstruction of posterior cruciate ligament (PCL) in our hospital from January 2020 to June 2023. The patients were divided into three groups according to the position of the endobutton on the lateral knee radiography. The first group is the position of endobutton in front of the lateral cortex of the femur, the second group is the position of endobutton in back of the lateral cortex of the femur, and the third group is the position of endobutton behind the femur. Knee Lysholm score, International Knee Documentation Committee (IKDC) Evaluation, Tegner Activity Scale, knee flexion angle and postoperative complication rate of the three groups were compared to evaluate whether there were statistical differences among the three groups. Results There were 39 patients in group 1, 46 patients in group 2, and 11 patients in group 3. The mean Lysholm Knee Scores of the group 1 was 91.77, 92.17 in group 2, and 90.36 in group 3. The mean IKDC Evaluation of group 1 was 90.48, 92.41 in group 2, and 93.00 in group 3. Tegner Activity Level was 5.69 in group 1, 5.72 in group 2, and 5.45 in group 3. The mean flexion degree was found as 125° in patients in group1, 127° in group 2, and 122° in group 3. There was no statistically significant difference between Lysholm Knee Scores (p = 0.434), IKDC (p = 0.068), Tegner Activity Level (p = 0.797), and knee flexion angle (p = 0.135). There was also no significant difference in the incidence of complications among the three groups (p > 0.05). Conclusion There were not statistically differences in clinical functional results when comparing patients’ endobutton location on femur. This indicates that it does not need to adjust the orientation of the exit hole of the femur whether it is forward or backward during the PCLR.
Orthopedic surgery, Diseases of the musculoskeletal system
Many uncontrollable disease outbreaks of the past exposed several vulnerabilities in the healthcare systems worldwide. While advancements in technology assisted in the rapid creation of the vaccinations, there needs to be a pressing focus on the prevention and prediction of such massive outbreaks. Early detection and intervention of an outbreak can drastically reduce its impact on public health while also making the healthcare system more resilient. The complexity of disease transmission dynamics, influence of various directly and indirectly related factors and limitations of traditional approaches are the main bottlenecks in taking preventive actions. Specifically, this paper implements deep learning algorithms and LLM's to predict the severity of infectious disease outbreaks. Utilizing the historic data of several diseases that have spread in India and the climatic data spanning the past decade, the insights from our research aim to assist in creating a robust predictive system for any outbreaks in the future.
Selestine Melchane, Youssef Elmir, Farid Kacimi
et al.
Artificial Intelligence (AI) and infectious diseases prediction have recently experienced a common development and advancement. Machine learning (ML) apparition, along with deep learning (DL) emergence, extended many approaches against diseases apparition and their spread. And despite their outstanding results in predicting infectious diseases, conflicts appeared regarding the types of data used and how they can be studied, analyzed, and exploited using various emerging methods. This has led to some ongoing discussions in the field. This research aims not only to provide an overview of what has been accomplished, but also to highlight the difficulties related to the types of data used, and the learning methods applied for each research objective. It categorizes these contributions into three areas: predictions using Public Health Data to prevent the spread of a transmissible disease within a region; predictions using Patients' Medical Data to detect whether a person is infected by a transmissible disease; and predictions using both Public and patient medical data to estimate the extent of disease spread in a population. The paper also critically assesses the potential of AI and outlines its limitations in infectious disease management.
The "density limit" is one of the fundamental bounds on tokamak operating space, and is commonly estimated via the empirical Greenwald scaling. This limit has garnered renewed interest in recent years as it has become clear that ITER and many tokamak pilot plant concepts must operate near or above the Greenwald limit to achieve their objectives. Evidence has also grown that the Greenwald scaling - in its remarkable simplicity - may not capture the full complexity of the density limit. In this study, we assemble a multi-machine database to quantify the effectiveness of the Greenwald limit as a predictor of the L-mode density limit and compare it with data-driven approaches. We find that a boundary in the plasma edge involving dimensionless collisionality and pressure, $ν_{*\rm, edge}^{\rm limit} = 3.5 β_{T,{\rm edge}}^{-0.40}$, achieves significantly higher accuracy (false positive rate of 2.3% at a true positive rate of 95%) of predicting density limit disruptions than the Greenwald limit (false positive rate of 13.4% at a true positive rate of 95%) across a multi-machine dataset including metal- and carbon-wall tokamaks (AUG, C-Mod, DIII-D, and TCV). This two-parameter boundary succeeds at predicting L-mode density limits by robustly identifying the radiative state preceding the terminal MHD instability. This boundary can be applied for density limit avoidance in current devices and in ITER, where it can be measured and responded to in real time.
Objective: We aimed to delineate a novel soluble Biglycan Neo-epitope-BGN262 in saliva from young reference and osteoarthritic horses in conjunction with the influence of short-term training exercise, riding surface hardness, circadian rhythm, and feeding on its soluble levels. Design: A custom-made inhibition ELISA was used for the quantification of BGN262 in saliva. Cohort 1: A cross-sectional study comprising reference (N = 19) and OA horses (N = 9) with radiographically classified subchondral bone sclerosis. Receiver operating characteristic curve analysis was performed to evaluate the robustness of BGN262. Cohorts 2 (N = 5) & 3 (N = 7): Longitudinal studies of sampling during a short-term training exercise (sand-fibre) and a cross-over design of short-training exercise on 2 different riding arenas (sand and sand-fibre), respectively. Capillary western immunoassay was used to determine the BGN262 molecular size in a selection of saliva samples collected from cohort 1. Results: Cohort 1: Salivary BGN262 levels were significantly higher in the OA group. The Receiver operating characteristic curve analysis showed an area under the curve of 0.8304 [0.6386 to 1.022], indicating a good separation from the reference group. Cohorts 2 & 3: Salivary BGN262 levels significantly changed during the exercise on sand and sand-fibre arena, with a trend towards higher levels for sand-fibre. The size of the BGN262 fragment determined by Capillary western assay was 18 kDa. Conclusions: The data presented show saliva BGN262 levels as a novel biomarker in evaluating the influence of exercise, and interaction with riding arenas alongside assessing osteoarthritis severity.