ABSTRACT Background While the Global Leadership Initiative on Sarcopenia (GLIS) is promising to standardize sarcopenia diagnosis, its operational implementation remains largely undefined. This study aims to operationalize GLIS and evaluate its feasibility, diagnostic concordance and clinical relevance. Methods This three‐stage, multicenter study enrolled 12 116 participants for cut‐off development (mean age 58.7 years, 48.2% men) and 11 241 participants for outcome analysis (mean age 58.4 years, 49.4% men) from a national survey in China. Another 504 patients with chronic kidney disease were included for validation. We proposed the lower limb skeletal muscle mass to five‐time chair stand test ratio (LFR) to assess muscle‐specific strength (MSS). The GLIS conceptual framework was instantiated into six diagnostic criteria combinations using handgrip strength (HGS), appendicular skeletal muscle mass index (ASMI, estimated using a validated formula) and MSS: (1) all three criteria being low (HAM); (2) low HGS plus low ASMI (HA); (3) low MSS (M); (4) low HGS plus low ASMI, or low MSS (HA/M); (5) low HGS or low MSS (H/M); and (6) low ASMI or low MSS (A/M). Intercriteria concordance of these definitions, relevance with functional outcomes and their concordance with the Asian Working Group for Sarcopenia 2019 (AWGS) criteria were evaluated. Results Low MSS cut‐offs were established as < 0.74 for men and < 0.47 for women. Sarcopenia prevalence varied significantly across different definitions: 1055 (8.7%, AWGS), 405 (3.3%, HAM), 619 (5.1%, HA), 2409 (19.9%, M), 2623 (21.6%, HA/M), 3184 (26.3%, H/M) and 3868 (31.9%, A/M). The HA method showed the highest concordance with the AWGS (accuracy = 0.964, κ = 0.722, sensitivity = 1.000, specificity = 0.962). The H/M method demonstrated the strongest correlation with functional outcomes and optimal diagnostic performance (AUCs range from 0.566 to 0.729), with superior discrimination for impaired activities of daily living (ADL), other functional measures and global functional scores (p < 0.05). All methods independently predicted poor functional outcomes. External validation in CKD showed that the H/M method was either superior or comparable to other methods in identifying disabilities (e.g., predicting functional measures, AUC = 0.627, 95% CI = 0.582–0.672). Conclusions This study establishes an operational framework for GLIS using nationally representative data from China and validates its effectiveness in a clinical setting. LFR proves to be a feasible method for assessing MSS. The H/M method effectively captures functional impairment, which may serve as a useful approach for diagnosing sarcopenia. These findings provide actionable benchmarks for sarcopenia research and clinical practice, potentially informing more refined prevention and intervention strategies.
Diseases of the musculoskeletal system, Human anatomy
Heather J. Faust, Margaret H. Chang, A. Helena Jonsson
et al.
Obesity worsens inflammatory arthritis severity, even in non-load–bearing joints, but the mechanism is unknown. Here, we show that there is an immunological mechanism mediated by T cells in adipose tissue. Using an antigen-induced arthritis model with trackable, arthritis-inducing CD8+ OT-I T cells, we found that OT-I T cells home to visceral adipose tissue (VAT) and expand there in the obese high-fat diet (HFD) context. Transplant of VAT from arthritic mice increased arthritis severity in naïve recipient mice and was ameliorated by CD8 T cell depletion. Bulk RNA sequencing identified pro-inflammatory changes to OT-I T cells in VAT characterized by increased IFN α and γ signaling after HFD. Intraperitoneal injection of IFNα, but not IFNγ, expanded CD8 T cell numbers in VAT. HFD-induced expansion of VAT CD8 T cells was ameliorated with global Ifnar1 deletion, and importantly, genetic deletion of Ifnar1 in T cells decreased arthritis severity in obese mice. These results provide a mechanistic explanation of how obesity worsens autoimmunity.
Tharaka Fonseka, Buddhi Wijenayake, Athulya Ratnayake
et al.
Understanding the relationship between population dynamics and disease-specific mortality is central to evidence-based health policy. This study introduces two novel metrics, PoPDivergence and PoPStat, one to quantify the difference between population pyramids and the other to assess the strength and nature of their association with the mortality of a given disease. PoPDivergence, based on Kullback-Leibler divergence, measures deviations between a countrys population pyramid and a reference pyramid. PoPStat is the correlation between these deviations and the log form of disease-specific mortality rates. The reference population is selected by a brute-force optimization that maximizes this correlation. Utilizing mortality data from the Global Burden of Disease 2021 and population statistics from the United Nations, we applied these metrics to 371 diseases across 204 countries. Results reveal that PoPStat outperforms traditional indicators such as median age, GDP per capita, and Human Development Index in explaining the mortality of most diseases. Noncommunicable diseases (NCDs) like neurological disorders and cancers, communicable diseases (CDs) like neglected tropical diseases, and maternal and neonatal diseases were tightly bound to the underlying demographic attributes whereas NCDs like diabetes, CDs like respiratory infections and injuries including self-harm and interpersonal violence were weakly associated with population pyramid shapes. Notably, except for diabetes, the NCD mortality burden was shared by constrictive population pyramids, while mortality of communicable diseases, maternal and neonatal causes and injuries were largely borne by expansive pyramids. Therefore, PoPStat provides insights into demographic determinants of health and empirical support for models on epidemiological transition. Code and scripts: https://github.com/Buddhi19/DevisingPoPStat.git
Afsana Ahsan Jeny, Masum Shah Junayed, Md Robel Mia
et al.
Facial acne is a common disease, especially among adolescents, negatively affecting both physically and psychologically. Classifying acne is vital to providing the appropriate treatment. Traditional visual inspection or expert scanning is time-consuming and difficult to differentiate acne types. This paper introduces an automated expert system for acne recognition and classification. The proposed method employs a machine learning-based technique to classify and evaluate six types of acne diseases to facilitate the diagnosis of dermatologists. The pre-processing phase includes contrast improvement, smoothing filter, and RGB to L*a*b color conversion to eliminate noise and improve the classification accuracy. Then, a clustering-based segmentation method, k-means clustering, is applied for segmenting the disease-affected regions that pass through the feature extraction step. Characteristics of these disease-affected regions are extracted based on a combination of gray-level co-occurrence matrix (GLCM) and Statistical features. Finally, five different machine learning classifiers are employed to classify acne diseases. Experimental results show that the Random Forest (RF) achieves the highest accuracy of 98.50%, which is promising compared to the state-of-the-art methods.
In Bangladesh, tomatoes are a staple vegetable, prized for their versatility in various culinary applications. However, the cultivation of tomatoes is often hindered by a range of diseases that can significantly reduce crop yields and quality. Early detection of these diseases is crucial for implementing timely interventions and ensuring the sustainability of tomato production. Traditional manual inspection methods, while effective, are labor-intensive and prone to human error. To address these challenges, this research paper sought to develop an automated disease detection system using Convolutional Neural Networks (CNNs). A comprehensive dataset of tomato leaves was collected from the Brahmanbaria district, preprocessed to enhance image quality, and then applied to various deep learning models. Comparative performance analysis was conducted between YOLOv5, MobileNetV2, ResNet18, and our custom CNN model. In our study, the Custom CNN model achieved an impressive accuracy of 95.2%, significantly outperforming the other models, which achieved an accuracy of 77%, 89.38% and 71.88% respectively. While other models showed solid performance, our Custom CNN demonstrated superior results specifically tailored for the task of tomato leaf disease detection. These findings highlight the strong potential of deep learning techniques for improving early disease detection in tomato crops. By leveraging these advanced technologies, farmers can gain valuable insights to detect diseases at an early stage, allowing for more effective management practices. This approach not only promises to boost tomato yields but also contributes to the sustainability and resilience of the agricultural sector, helping to mitigate the impact of plant diseases on crop production.
Existing language learning tools, even those powered by Large Language Models (LLMs), often lack support for polyglot learners to build linguistic connections across vocabularies in multiple languages, provide limited customization for individual learning paces or needs, and suffer from detrimental cognitive offloading. To address these limitations, we design Do-It-Yourself Multilingual Knowledge Graph (DIY-MKG), an open-source system that supports polyglot language learning. DIY-MKG allows the user to build personalized vocabulary knowledge graphs, which are constructed by selective expansion with related words suggested by an LLM. The system further enhances learning through rich annotation capabilities and an adaptive review module that leverages LLMs for dynamic, personalized quiz generation. In addition, DIY-MKG allows users to flag incorrect quiz questions, simultaneously increasing user engagement and providing a feedback loop for prompt refinement. Our evaluation of LLM-based components in DIY-MKG shows that vocabulary expansion is reliable and fair across multiple languages, and that the generated quizzes are highly accurate, validating the robustness of DIY-MKG.
Plant disease is a critical factor affecting agricultural production. Traditional manual recognition methods face significant drawbacks, including low accuracy, high costs, and inefficiency. Deep learning techniques have demonstrated significant benefits in identifying plant diseases, but they still face challenges such as inference delays and high energy consumption. Deep learning algorithms are difficult to run on resource-limited embedded devices. Offloading these models to cloud servers is confronted with the restriction of communication bandwidth, and all of these factors will influence the inference's efficiency. We propose a collaborative inference framework for recognizing plant diseases between edge devices and cloud servers to enhance inference speed. The DNN model for plant disease recognition is pruned through deep reinforcement learning to improve the inference speed and reduce energy consumption. Then the optimal split point is determined by a greedy strategy to achieve the best collaborated inference acceleration. Finally, the system for collaborative inference acceleration in plant disease recognition has been implemented using Gradio to facilitate friendly human-machine interaction. Experiments indicate that the proposed collaborative inference framework significantly increases inference speed while maintaining acceptable recognition accuracy, offering a novel solution for rapidly diagnosing and preventing plant diseases.
Md. Jalal Uddin Chowdhury, Zumana Islam Mou, Rezwana Afrin
et al.
A very crucial part of Bangladeshi people's employment, GDP contribution, and mainly livelihood is agriculture. It plays a vital role in decreasing poverty and ensuring food security. Plant diseases are a serious stumbling block in agricultural production in Bangladesh. At times, humans can't detect the disease from an infected leaf with the naked eye. Using inorganic chemicals or pesticides in plants when it's too late leads in vain most of the time, deposing all the previous labor. The deep-learning technique of leaf-based image classification, which has shown impressive results, can make the work of recognizing and classifying all diseases trouble-less and more precise. In this paper, we've mainly proposed a better model for the detection of leaf diseases. Our proposed paper includes the collection of data on three different kinds of crops: bell peppers, tomatoes, and potatoes. For training and testing the proposed CNN model, the plant leaf disease dataset collected from Kaggle is used, which has 17,430 images. The images are labeled with 14 separate classes of damage. The developed CNN model performs efficiently and could successfully detect and classify the tested diseases. The proposed CNN model may have great potency in crop disease management.
Abstract Objective To investigate the reduction quality and hip function after internal fixation of acetabular double column fractures combined with posterior wall fractures using the Stoppa approach compared to the ilioinguinal approach. Methods A total of 94 patients admitted to our hospital from October 2016 to October 2021 were included. All patients were diagnosed with acetabular double-column fractures combined with posterior wall fractures. Based on the random number table method, patients were divided into the Stoppa group (treated via the Stoppa approach) and the control group (treated via the conventional ilioinguinal approach), with 47 cases in each group. Postoperative reduction quality and hip function were recorded and compared between the groups. Multivariate logistic regression analysis was performed to identify risk factors affecting surgical outcomes. Results The Stoppa group demonstrated significantly less intraoperative bleeding, shorter operation time, reduced postoperative drainage volume, shorter drainage tube retention time, and shorter hospital stay compared to the control group (P < 0.05). Postoperatively, the Stoppa group had a significantly lower VAS score (2.90 ± 0.72) and higher Harris scores than those in the control group (5.62 ± 1.18) (P < 0.05). The excellent and good rate of fracture reduction in the Stoppa group was significantly higher (P < 0.05). ROM in hip extension and flexion, internal-external rotation, and Merle-d’Aubigne-Postel scores were also significantly higher in the Stoppa group (P < 0.05). The quality of life score post-operation was higher in the Stoppa group (P < 0.05). Univariate analysis indicated that timing of surgery, fracture type, presence of bone fragments in the joint, cartilage surface injury, surgical approach, and heterotopic ossification significantly influenced surgical outcomes (P < 0.05). Multivariate analysis showed that surgery timing (1–2 weeks) and heterotopic ossification were risk factors, while the Stoppa approach was associated with improved outcomes across all measures (P < 0.05). Conclusion Compared to the ilioinguinal approach, the Stoppa approach provides patients with acetabular double column fractures and posterior wall involvement with significantly reduced operation time and blood loss, shortens hospitalization duration, reduces postoperative pain, enhances reduction quality, and improves hip joint function.
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.
Fracture non-union remains a significant clinical challenge despite considerable advances in diagnostic imaging and treatment modalities. Unpredictable healing, repeated interventions, and prolonged disability contribute to high patient morbidity and increased healthcare costs. Early and reliable prediction of non-union is therefore essential for timely intervention. This review discusses traditional radiographic assessment using the Radiologic Union Scale for the Tibia (RUST), its inherent limitations, and the emerging role of artificial intelligence (AI) and deep learning in fracture analysis. In addition, we review recent studies—including Bayesian classifiers and simulation models—that integrate AI for early prediction of non-union, and we provide an updated summary table of key studies.
Alexandra Lauren Rice, Sarah Gillespie, Nikhil Sai
et al.
Background In order to apply current treatment recommendations for psoriatic arthritis (PsA), a complete assessment of psoriatic disease domains must be completed by the clinician. This includes a musculoskeletal examination (including tender and swollen joints, dactylitis, enthesitis, and axial disease) as well as skin and nail examination. Documentation in the clinician’s note serves as a proxy for disease assessment. Objective To explore differences in documentation of psoriatic domains between PsA specialist and general rheumatologists at 2 academic centers. Methods We identified PsA patients seen by either general rheumatologists or by PsA combined clinic specialist providers at 2 established PPACMAN (Psoriasis and Psoriatic Arthritis Clinics Multicenter Advancement Network) sites. Records were assessed for the presence (and extent) of documentation for musculoskeletal and cutaneous PsA domains. We also examined accuracy of ICD coded diagnoses to understand the extent to which discrete data from the electronic medical record can be used to evaluate completeness of assessment. Results PsA combined clinic specialist providers documented disease domains significantly more consistently compared to generalists, including tender and swollen joint counts ( P < 0.001), assessment of spondyloarthritis ( P = 0.017), and presence/extent of skin involvement ( P < 0.001). Additionally, PsA specialists more consistently coded for both psoriasis (PsO) and PsA. Conclusions In this multicenter, retrospective study, compared to generalists, PsA combined-clinic specialist providers more thoroughly documented both musculoskeletal and cutaneous psoriatic disease domains and ICD coding of PsO for patients, highlighting gaps in assessment and documentation. These findings underscore the need for improved training in psoriatic disease assessment and simplified modalities for documentation.
Michelle L. Robinette, Lachelle D. Weeks, Ryan J. Kramer
et al.
ObjectiveGiant cell arteritis (GCA) is an age‐related vasculitis. Prior studies have identified an association between GCA and hematologic malignancies (HMs). How the presence of somatic mutations that drive the development of HMs, or clonal hematopoiesis (CH), may influence clinical outcomes in GCA is not well understood.MethodsTo examine an association between CH and GCA, we analyzed sequenced exomes of 470,960 UK Biobank (UKB) participants for the presence of CH and used multivariable Cox regression. To examine the clinical phenotype of GCA in patients with and without somatic mutations across the spectrum of CH to HM, we performed targeted sequencing of blood samples and electronic health record review on 114 patients with GCA seen at our institution. We then examined associations between specific clonal mutations and GCA disease manifestations.ResultsUKB participants with CH had a 1.48‐fold increased risk of incident GCA compared to UKB participants without CH. GCA risk was highest among individuals with cytopenia (hazard ratio [HR] 2.98, P = 0.00178) and with TET2 mutation (HR 2.02, P = 0.00116). Mutations were detected in 27.2% of our institutional GCA cohort, three of whom had HM at GCA diagnosis. TET2 mutations were associated with vision loss in patients with GCA (odds ratio 4.33, P = 0.047).ConclusionsCH increases risk for development of GCA in a genotype‐specific manner, with the greatest risk being conferred by the presence of mutations in TET2. Somatic TET2 mutations likewise increase the risk of GCA‐associated vision loss. Integration of somatic genetic testing in GCA diagnostics may be warranted in the future.
Marco Angelozzi, Anirudha Karvande, Véronique Lefebvre
AbstractPivotal in many ways for human health, the control of adult bone mass is governed by complex, incompletely understood crosstalk namely between mesenchymal stem cells, osteoblasts and osteoclasts. The SOX4, SOX11 and SOX12 (SOXC) transcription factors were previously shown to control many developmental processes, including skeletogenesis, and SOX4 was linked to osteoporosis, but how SOXC control adult bone mass remains unknown. Using SOXC loss- and gain-of-function mouse models, we show here that SOXC redundantly promote prepubertal cortical bone mass strengthening whereas only SOX4 mitigates adult trabecular bone mass accrual in early adulthood and subsequent maintenance. SOX4 favors bone resorption over formation by lowering osteoblastogenesis and increasing osteoclastogenesis. Single-cell transcriptomics reveals its prevalent expression in Lepr+ mesenchymal cells and ability to upregulate genes for prominent anti-osteoblastogenic and pro-osteoclastogenic factors, including interferon signaling-related chemokines, contributing to these adult stem cells’ secretome. SOXC, with SOX4 predominantly, are thus key regulators of adult bone mass.
Marcelo V. W. Zibetti, Rajiv G. Menon, Hector L. de Moura
et al.
Osteoarthritis (OA) is a widely occurring degenerative joint disease that is severely debilitating and causes significant socioeconomic burdens to society. Magnetic resonance imaging (MRI) is the preferred imaging modality for the morphological evaluation of cartilage due to its excellent soft tissue contrast and high spatial resolution. However, its utilization typically involves subjective qualitative assessment of cartilage. Compositional MRI, which refers to the quantitative characterization of cartilage using a variety of MRI methods, can provide important information regarding underlying compositional and ultrastructural changes that occur during early OA. Cartilage compositional MRI could serve as early imaging biomarkers for the objective evaluation of cartilage and help drive diagnostics, disease characterization, and response to novel therapies. This review will summarize current and ongoing state‐of‐the‐art cartilage compositional MRI techniques and highlight emerging methods for cartilage compositional MRI including MR fingerprinting, compressed sensing, multiexponential relaxometry, improved and robust radio‐frequency pulse sequences, and deep learning‐based acquisition, reconstruction, and segmentation. The review will also briefly discuss the current challenges and future directions for adopting these emerging cartilage compositional MRI techniques for use in clinical practice and translational OA research studies.Evidence Level2Technical EfficacyStage 2.
Shogo Makino, Kento Kawaharazuka, Masaya Kawamura
et al.
Human can not only support their body during standing or walking, but also support them by hand, so that they can dangle a bar and others. But most humanoid robots support their body only in the foot and they use their hand just to manipulate objects because their hands are too weak to support their body. Strong hands are supposed to enable humanoid robots to act in much broader scene. Therefore, we developed new life-size five-fingered hand that can support the body of life-size humanoid robot. It is tendon-driven and underactuated hand and actuators in forearms produce large gripping force. This hand has flexible joints using machined springs, which can be designed integrally with the attachment. Thus, it has both structural strength and impact resistance in spite of small size. As other characteristics, this hand has force sensors to measure external force and the fingers can be flexed along objects though the number of actuators to flex fingers is less than that of fingers. We installed the developed hand on musculoskeletal humanoid "Kengoro" and achieved two self-weight supporting motions: push-up motion and dangling motion.
Cathal Mills, Tarek Alrefae, William S. Hart
et al.
During infectious disease outbreaks, estimates of time-varying pathogen transmissibility, such as the instantaneous reproduction number R(t) or epidemic growth rate r(t), are used to inform decision-making by public health authorities. For directly transmitted infectious diseases, the renewal equation framework is a widely used method for measuring time-varying transmissibility. The framework uses information on the typical time elapsing between an infection and the offspring infections (quantified by the generation time distribution), and R(t), to describe the rate at which currently infected individuals generate new infections. For diseases with transmission cycles involving hosts and vectors, however, renewal equation models have been far less used. This is likely due to difficulties in mechanistically defining generation times that can capture the complexity of multi-stage, human-vector relationships. Here, using dengue as an example, we provide general renewal equations that are derived from first principles using age-structured systems of coupled partial differential equations across human and vector sub-populations. Our framework tracks the multi-stage transmission cycle over calendar time and across stage-specific ages, resulting in governing renewal equations that quantify how the rate at which new infections are generated from existing infections depends on stage-specific processes. The framework provides a foundation on which to base inferential frameworks for estimating R(t) and r(t) for infectious diseases with multiple stages in the transmission cycle
Medical question answer (QA) assistants respond to lay users' health-related queries by synthesizing information from multiple sources using natural language processing and related techniques. They can serve as vital tools to alleviate issues of misinformation, information overload, and complexity of medical language, thus addressing lay users' information needs while reducing the burden on healthcare professionals. QA systems, the engines of such assistants, have typically used either language models (LMs) or knowledge graphs (KG), though the approaches could be complementary. LM-based QA systems excel at understanding complex questions and providing well-formed answers, but are prone to factual mistakes. KG-based QA systems, which represent facts well, are mostly limited to answering short-answer questions with pre-created templates. While a few studies have jointly used LM and KG approaches for text-based QA, this was done to answer multiple-choice questions. Extant QA systems also have limitations in terms of automation and performance. We address these challenges by designing a novel, automated disease QA system which effectively utilizes both LM and KG techniques through a joint-reasoning approach to answer disease-related questions appropriate for lay users. Our evaluation of the system using a range of quality metrics demonstrates its efficacy over benchmark systems, including the popular ChatGPT.