MoTrPAC Study Group, Anna R. Brandt, Jerome Fleg
et al.
SUMMARY The goal of the Molecular Transducers of Physical Activity Consortium (MoTrPAC) is to examine the physiological and molecular basis for health benefits in response to acute and chronic exercise. Prior to COVID-19 suspension, healthy, sedentary participants (N=206, 18-74y) were randomized to endurance exercise (N=80), resistance exercise (N=81), or non-exercise control (N=45) interventions. The prescribed vigorous acute endurance and resistance exercise bouts induced physiological and metabolic perturbations relative to resting homeostasis. The supervised chronic (3d/wk, 12wk) endurance or resistance training programs robustly improved several physiological parameters (i.e., VO 2 peak, muscular strength). Temporal biospecimen (blood, muscle, and adipose) collections and processing coupled to the acute exercise bouts were highly successful. In most cases, over 90% success was achieved for blood, muscle, and adipose samples. Endurance and resistance exercise induced distinct acute and chronic physiological responses, which provide a framework to interrogate the molecular basis for health adaptations to these two popular exercise modalities. Abstract Figure
The omohyoid muscle is frequently encountered during anterior cervical spine surgery, particularly at the C5–C7 levels, yet it has received limited emphasis in spinal literature. Its superior belly often crosses the operative corridor, leading surgeons to choose between preservation, retraction, or sectioning. This narrative review synthesizes anatomical and clinical evidence regarding omohyoid variability, surgical handling, and postoperative outcomes in anterior cervical discectomy and fusion procedures. Anatomical studies reveal substantial muscle variation, including accessory slips and aberrant insertions, which can influence exposure. Emerging clinical data suggest that sectioning the superior belly when obstructive improves visualization and may reduce operative time and blood loss without significantly increasing dysphagia, dysphonia, or cosmetic concerns. However, prospective studies with standardized outcome measures remain limited. Understanding this “forgotten anatomy” may enhance operative planning, optimize exposure, and refine technique in lower cervical approaches.
Oculomics - the concept of predicting systemic diseases, such as cardiovascular disease and dementia, through retinal imaging - has advanced rapidly due to the data efficiency of transformer-based foundation models like RETFound. Image-level mixed sample data augmentations, such as CutMix and MixUp, are frequently used for training transformers, yet these techniques perturb patient-specific attributes, such as medical comorbidity and clinical factors, since they only account for images and labels. To address this limitation, we propose a hierarchical sampling strategy, Oculomix, for mixed sample augmentations. Our method is based on two clinical priors. First (exam level), images acquired from the same patient at the same time point share the same attributes. Second (patient level), images acquired from the same patient at different time points have a soft temporal trend, as morbidity generally increases over time. Guided by these priors, our method constrains the mixing space to the patient and exam levels to better preserve patient-specific characteristics and leverages their hierarchical relationships. The proposed method is validated using ViT models on a five-year prediction of major adverse cardiovascular events (MACE) in a large ethnically diverse population (Alzeye). We show that Oculomix consistently outperforms image-level CutMix and MixUp by up to 3% in AUROC, demonstrating the necessity and value of the proposed method in oculomics.
Benjamin Plotz, Michael Toprover, Robert T. Keenan
et al.
In the past decade, the metabolic syndrome has been recast as a chronic inflammatory disease whose mechanisms involve macrophage and neutrophil activation, initiation of the nod-like receptor protein 3 (NLRP3) inflammasome, and IL-1β secretion. Colchicine, an inhibitor of NLRP3, has been linked to the prevention or amelioration of diseases associated with the metabolic syndrome, including diabetes and cardiovascular disease. Its underlying therapeutic mechanisms extend beyond direct suppression of NLRP3, and include sirtuin and AMP-activated protein kinase (AMPK) pathway regulation, and downregulation of cellular stress signals, which promote atherosclerotic plaque rupture, insulin resistance, and obesity. Colchicine’s proven efficacy in preventing cardiovascular disease is a promising new development recognized by its inclusion in the 2023 American College of Cardiology treatment guidelines. As colchicine’s effects are better understood, along with a clearer understanding of metabolic syndrome’s pathophysiology, promising new applications and uses for this old drug may be on the horizon and are worthy of further investigation. In this review, we discuss colchicine’s pharmacology and explore its established and emerging anti-inflammatory mechanisms, and the role these could play in disrupting the chronic inflammation in metabolic syndrome and associated diseases.
Bone marrow mesenchymal stem cells (BMSCs) are multipotent progenitor cells with the capacity to differentiate into various mesenchymal lineages, including osteogenic, chondrogenic, and adipogenic tissues, rendering them promising candidates for regenerative medicine. This review delves into current foundational and preclinical research concerning BMSCs, with a particular emphasis on the use of genetically modified rat-derived BMSCs expressing green fluorescent protein (GFP) to facilitate in vivo cell tracking during tissue repair. It also examines various administration strategies, including intra-articular injections and magnetically guided cell targeting, to evaluate their therapeutic efficacy. Emerging evidence highlights the pivotal role of BMSCs in regenerating musculoskeletal tissues, including muscle, meniscus, and cartilage. Notably, the application of external magnetic fields (EMF) to direct magnetically labeled BMSCs to injury sites has demonstrated encouraging outcomes in cartilage repair. Furthermore, advances in BMSC culture techniques, single-cell genetic analysis, and tissue engineering methodologies may further augment their therapeutic potential. Preclinical and early-phase clinical studies underscore the promise of BMSCs as a minimally invasive therapeutic modality in orthopedic and regenerative medicine. Further research is essential to refine their applications and optimize delivery strategies, such as the use of internal magnetic fields generated by magnetized material implanted in damaged knee cartilage, to ensure long-term efficacy and safety.
Ram Chandra Khatri Chhetri, Hemanta Paudel, Viswaja Kaja
et al.
Giant cell arteritis is the most common primary systemic vasculitis among individuals over 50 years of age. It primarily affects large- and medium-size arteries and is not mediated by antibodies. One of the most recognizable and important symptoms of the disease is headache. The presence of headaches, along with other common cranial manifestations such as vision loss, jaw claudication, and scalp tenderness in the temporal arteries, can assist in diagnosing the condition. We present a complex case involving a 76-year-old male with prolonged headaches, a pituitary macroadenoma, and vestibular schwannoma. Initially, his headaches were attributed to his existing intracranial lesions; however, his symptoms continued to evolve. He continued to have headaches of varying intensity over 2 years, and subsequently developed diffuse scalp tenderness, visual disturbances, and tongue claudication. Input from various medical specialties expanded the differential diagnosis and raised the possibility of giant cell arteritis (GCA). Although the temporal artery biopsy did not reveal the classic giant cells typically associated with the condition, it supported the clinical diagnosis of GCA. Appropriate treatment with high-dose corticosteroids and anti-Interleukin 6 therapy resulted in the rapid resolution of his symptoms. This case emphasizes the importance of recognizing different types of headaches, maintaining a broad differential diagnosis, and thoroughly evaluating all clinical symptoms for timely diagnosis and treatment. It also highlights the significance of a multidisciplinary approach to ensure prompt diagnosis and to prevent irreversible complications, such as permanent vision loss.
Abstract Background Metabolic Syndrome (MetS), as a syndrome characterized by low-grade inflammation and energy metabolism disorders, is considered to be an important systemic risk factor for knee osteoarthritis (KOA). Our previous study showed that the protein level of serum resistin was positively correlated with the degree of metabolic disorder in MetS-OA. However, whether Resistin promotes the progression of KOA synovitis and the underlying mechanisms remain unclear. This study mainly investigateswhether there were metabolism disorder which promote inflammatory and catabolic phenotype in fibroblast-like synoviocytes (FLS) from KOA patients with MetS (MetS-KOA-FLS), and the roles and mechanisim of resistin in MetS-KOA-FLS. Methods Comparative analysis of synovium and FLS from MetS-associated KOA (MetS-KOA) and non-MetS-associated KOA (nMetS-KOA) of females to detect the differences in inflammation, catabolism and glycolipid metabolism. Serum from MetS-KOA stimulated nMetS-KOA-FLS to detect the effect of MetS microenvironment on inflammation, catabolism and glycolipid metabolism of nMetS-KOA-FLS. Resistin stimulated MetS-KOA-FLS to explore the effect of resistin on inflammation and catabolism of MetS-KOA-FLS and its specific mechanism. Results Compared with nMetS-KOA-FLS, MetS-KOA-FLS expressed higher inflammatory related factors, catabolic enzymes, and showed stronger adhesive and invasive ability. Resistin was found to be an important factor in the serum and internal environment of MetS-KOA patients, and it mediated the differences in fatty acid oxidation (FAO) between the two groups. Resistin activated the PKA/CREB pathway through CAP1 and upregulated FAO, promoting the inflammatory and catabolic phenotype of MetS-KOA-FLS. Conclusions This study clarifies the mechanism by which MetS causes synovitis from a metabolic perspective and provides new ideas for further research and treatment of MetS-KOA.
Fahud Ahmmed, Md. Zaheer Raihan, Kamnur Nahar
et al.
Skin diseases can arise from infections, allergies, genetic factors, autoimmune disorders, hormonal imbalances, or environmental triggers such as sun damage and pollution. Some skin diseases, such as Actinic Keratosis and Psoriasis, can be fatal if not treated in time. Early identification is crucial, but the diagnostic methods for these conditions are often expensive and not widely accessible. In this study, we propose a novel and efficient method for diagnosing skin diseases using deep learning techniques. This approach employs a modified VGG16 Convolutional Neural Network (CNN) model. The model includes several convolutional layers and utilizes ImageNet weights with modified top layers. The top layer is updated with fully connected layers and a final softmax activation layer to classify skin diseases. The dataset used, titled "Skin Disease Dataset," is publicly available. While the VGG16 architecture does not include data augmentation by default, preprocessing techniques such as rotation, shifting, and zooming were applied to augment the data prior to model training. The proposed methodology achieved 90.67% accuracy using the modified VGG16 model, demonstrating its reliability in classifying skin diseases. The promising results highlight the potential of this approach for real-world applications.
Symptom Checkers (SCs) provide medical information tailored to user symptoms. A critical challenge in SC development is preventing unexpected performance degradation for individual diseases, especially rare diseases, when updating algorithms. This risk stems from the lack of practical pre-deployment evaluation methods. For rare diseases, obtaining sufficient evaluation data from user feedback is difficult. To evaluate the impact of algorithm updates on the diagnostic performance for individual rare diseases before deployment, this study proposes and validates a novel Synthetic Vignette Simulation Approach. This approach aims to enable this essential evaluation efficiently and at a low cost. To estimate the impact of algorithm updates, we generated synthetic vignettes from disease-phenotype annotations in the Human Phenotype Ontology (HPO), a publicly available knowledge base for rare diseases curated by experts. Using these vignettes, we simulated SC interviews to predict changes in diagnostic performance. The effectiveness of this approach was validated retrospectively by comparing the predicted changes with actual performance metrics using the R-squared ($R^2$) coefficient. Our experiment, covering eight past algorithm updates for rare diseases, showed that the proposed method accurately predicted performance changes for diseases with phenotype frequency information in HPO (n=5). For these updates, we found a strong correlation for both Recall@8 change ($R^2$ = 0.83,$p$ = 0.031) and Precision@8 change ($R^2$ = 0.78,$p$ = 0.047). Our proposed method enables the pre-deployment evaluation of SC algorithm changes for individual rare diseases. This evaluation is based on a publicly available medical knowledge database created by experts, ensuring transparency and explainability for stakeholders. Additionally, SC developers can efficiently improve diagnostic performance at a low cost.
Zilal Eiz AlDin, John Wu, Jeffrey Paul Fung
et al.
Despite rare diseases affecting 1 in 10 Americans, their differential diagnosis remains challenging. Due to their impressive recall abilities, large language models (LLMs) have been recently explored for differential diagnosis. Existing approaches to evaluating LLM-based rare disease diagnosis suffer from two critical limitations: they rely on idealized clinical case studies that fail to capture real-world clinical complexity, or they use ICD codes as disease labels, which significantly undercounts rare diseases since many lack direct mappings to comprehensive rare disease databases like Orphanet. To address these limitations, we explore MIMIC-RD, a rare disease differential diagnosis benchmark constructed by directly mapping clinical text entities to Orphanet. Our methodology involved an initial LLM-based mining process followed by validation from four medical annotators to confirm identified entities were genuine rare diseases. We evaluated various models on our dataset of 145 patients and found that current state-of-the-art LLMs perform poorly on rare disease differential diagnosis, highlighting the substantial gap between existing capabilities and clinical needs. From our findings, we outline several future steps towards improving differential diagnosis of rare diseases.
As a significant agricultural country, Bangladesh utilizes its fertile land for guava cultivation and dedicated labor to boost its economic development. In a nation like Bangladesh, enhancing guava production and agricultural practices plays a crucial role in its economy. Anthracnose and fruit fly infection can lower the quality and productivity of guava, a crucial tropical fruit. Expert systems that detect diseases early can reduce losses and safeguard the harvest. Images of guava fruits classified into the Healthy, Fruit Flies, and Anthracnose classes are included in the Guava Fruit Disease Dataset 2024 (GFDD24), which comes from plantations in Rajshahi and Pabna, Bangladesh. This study aims to create models using CNN alongside traditional machine learning techniques that can effectively identify guava diseases in locally cultivated varieties in Bangladesh. In order to achieve the highest classification accuracy of approximately 99.99% for the guava dataset, we propose utilizing ensemble models that combine CNNML with Gradient Boosting Machine. In general, the CNN-ML cascade framework exhibits strong, high-accuracy guava disease detection that is appropriate for real-time agricultural monitoring systems.
Janet Pope,1 Axel Finckh,2 Lucia Silva-Fernández,3 Peter Mandl,4 Haiyun Fan,5 Jose L Rivas,6 Monica Valderrama,6 Maria Montoro6 1Division of Rheumatology, University of Western Ontario, London, Ontario, Canada; 2Division of Rheumatology, University Hospital of Geneva, Geneva, Switzerland; 3Rheumatology Department, A Coruña University Hospital Complex, A Coruña, Spain; 4Division of Rheumatology, Medical University of Vienna, Vienna, Austria; 5Pfizer Inc, Collegeville, PA, USA; 6Pfizer SLU, Madrid, SpainCorrespondence: Maria Montoro, Pfizer SLU, Madrid, Spain, Email maria.montoro@pfizer.comPurpose: To evaluate the characteristics, efficacy, and retention of tofacitinib monotherapy in patients with rheumatoid arthritis using data from randomized controlled trials (RCTs) and real-world data (RWD).Patients and Methods: Three patient groups receiving tofacitinib 5 mg twice daily (BID) monotherapy were defined for post hoc RCT/long-term extension (LTE) analyses: (1) disease-modifying antirheumatic drug (DMARD)-inadequate responder patients from phase 3/3b/4 RCTs; (2) methotrexate-naïve patients from a phase 3 RCT; and (3) index study patients continuing in an LTE study. Outcomes included low disease activity (LDA)/remission rates defined by Clinical Disease Activity Index (CDAI); Disease Activity Score in 28 joints (DAS28-4), erythrocyte sedimentation rate; DAS28-4, C-reactive protein (DAS28-4[CRP]); and rates of/time to discontinuation due to lack of efficacy/adverse events. RWD were identified by non-systematic literature searches of PubMed, Embase, and American College of Rheumatology/European Alliance of Associations for Rheumatology congress abstracts (2012– 2022).Results: RCT/LTE analyses included 1000/498 patients receiving tofacitinib 5 mg BID monotherapy. Baseline disease activity was high; patients tended to receive concomitant glucocorticoids; most were biologic DMARD-naïve. CDAI LDA rates were 32.2– 62.2% for Groups 1/2 (months 3– 12) and 64.0– 70.7% for Group 3 (months 12– 72). In Groups 1, 2, and 3, 4.0%, 15.6%, and 27.7% of patients, respectively, discontinued tofacitinib monotherapy due to lack of efficacy/adverse events. From 11 RWD publications, 16.6– 66.1% received tofacitinib monotherapy. Consistent with clinical data, tofacitinib monotherapy effectiveness (month 6 CDAI LDA, 30.2%; month 3 DAS28-4[CRP] remission, 53.4%) and persistence were observed in RWD, with retention comparable to tofacitinib combination therapy.Conclusion: Tofacitinib monotherapy demonstrated clinically significant responses/persistence in RCT/LTE analyses, with effectiveness observed and persistence comparable to combination therapy in RWD.Trial Registration: NCT00814307, NCT02187055, NCT01039688, NCT00413699, NCT00661661 (ClinicalTrials.gov).Keywords: autoimmune, JAK inhibitor, clinical practice, long-term, efficacy, retention
Darlyn Buenaño Vera, Byron Oviedo, Washington Chiriboga Casanova
et al.
The early identification of diseases in cocoa pods is an important task to guarantee the production of high-quality cocoa. The use of artificial intelligence techniques such as machine learning, computer vision and deep learning are promising solutions to help identify and classify diseases in cocoa pods. In this paper we introduce the development and evaluation of a deep learning computational model applied to the identification of diseases in cocoa pods, focusing on "monilia" and "black pod" diseases. An exhaustive review of state-of-the-art of computational models was carried out, based on scientific articles related to the identification of plant diseases using computer vision and deep learning techniques. As a result of the search, EfficientDet-Lite4, an efficient and lightweight model for object detection, was selected. A dataset, including images of both healthy and diseased cocoa pods, has been utilized to train the model to detect and pinpoint disease manifestations with considerable accuracy. Significant enhancements in the model training and evaluation demonstrate the capability of recognizing and classifying diseases through image analysis. Furthermore, the functionalities of the model were integrated into an Android native mobile with an user-friendly interface, allowing to younger or inexperienced farmers a fast and accuracy identification of health status of cocoa pods
Branko Mitic, Philipp Seeböck, Jennifer Straub
et al.
Fast detection of emerging diseases is important for containing their spread and treating patients effectively. Local anomalies are relevant, but often novel diseases involve familiar disease patterns in new spatial distributions. Therefore, established local anomaly detection approaches may fail to identify them as new. Here, we present a novel approach to detect the emergence of new disease phenotypes exhibiting distinct patterns of the spatial distribution of lesions. We first identify anomalies in lung CT data, and then compare their distribution in a continually acquired new patient cohorts with historic patient population observed over a long prior period. We evaluate how accumulated evidence collected in the stream of patients is able to detect the onset of an emerging disease. In a gram-matrix based representation derived from the intermediate layers of a three-dimensional convolutional neural network, newly emerging clusters indicate emerging diseases.
Alexander I. Tyukavin, Alexander V. Solomennikov, Sergei Z. Umarov
This lesson outlines the latest data on bone structure and physiology, as well as those related to the mechanisms of bone development and regeneration. The most common diseases of musculoskeletal system are caused by genetic and metabolic disorders, infectious agents, andmechanical stress, all of which are discussed. Innovative technologies for diagnosing osteoporosis (X-ray absorptiometry, quantitative computed tomography (QCT), ultrasonic bone densitometry, etc.) are shown. The joints diseases (arthropathies) followed by inflammation are highlighted. The basic principles of conservative and surgical treatment of arthrosis are explored. Different types of bone injuries caused by mechanical stress are thoroughly described as well as the latest technologies and equipment used in modern trauma centers. Basic orthopedic aids for disabled patients are listed. The main directions for improving the diagnosis and conservative treatment of diseases of the musculoskeletal system, as well as methods for prosthetics of large joints are described.
Margaret H. Chang, Alexandra V. Bocharnikov, Siobhan M. Case
et al.
ObjectiveInflammatory arthritides exhibit hallmark patterns of affected and spared joints, but in each individual, arthritis affects only a subset of all possible sites. The purpose of this study was to identify patient‐specific patterns of joint flare to distinguish local from systemic drivers of disease chronicity.MethodsPatients with juvenile idiopathic arthritis followed without interruption from disease onset into adulthood were identified across 2 large academic centers. Joints inflamed at each visit were established by medical record review. Flare was defined as physician‐confirmed joint inflammation following documented inactive disease.ResultsAmong 222 adults with JIA, 95 had complete serial joint examinations dating from disease onset in childhood. Mean follow‐up was 12.5 years (interquartile range 7.9–16.7 years). Ninety (95%) of 95 patients achieved inactive disease, after which 81% (73 patients) experienced at least 1 flare. Among 940 joints affected in 253 flares, 74% had been involved previously. In flares affecting easily observed large joint pairs where only 1 side had been involved before (n = 53), the original joint was affected in 83% and the contralateral joint in 17% (P < 0.0001 versus random laterality). However, disease extended to at least 1 new joint in ~40% of flares, a risk that remained stable even decades after disease onset, and was greatest in flares that occurred while patients were not receiving medication (54% versus 36% of flares occurring with therapy; odds ratio 2.09, P = 0.015).ConclusionArthritis flares preferentially affect previously inflamed joints but carry an ongoing risk of disease extension. These findings confirm joint‐specific memory and suggest that prevention of new joint accumulation should be an important target for arthritis therapy.