Gout is the most common inflammatory arthritis, and its prevalence is increasing in part due to the rise in chronic kidney disease (CKD). Guidelines for managing gout from the American College of Rheumatology (ACR) and the European Alliance of Associations for Rheumatology (EULAR) provide limited guidance for patients with advanced renal disease, partly due to the exclusion of this group of from clinical trials. This, along with concerns about adverse drug reactions contributes to the undertreatment of gout in advanced CKD. Gout management involves different phases: treatment of acute gout flares, implementing prophylaxis to prevent attacks and urate-lowering therapy (ULT). In this review, we examine the management of gout, with particular attention to recommended adjustments for patients with advanced CKD, those undergoing dialysis, or individuals who have received renal transplants. We review the medications used in the management of gout and suggest adjustments for their selection and dose in patients with advanced CKD. The article discusses colchicine, glucocorticoids, and IL1-β inhibitors for acute gout treatment and provides recommendations for flare prophylaxis. We review the use of xanthine oxidase inhibitors (allopurinol, febuxostat) and pegloticase as urate-lowering therapies for patients with advanced CKD, on dialysis, or with renal transplants. The possible side effects of gout treatments in patients with CKD and the suggested monitoring protocols are discussed. The potential impact of allopurinol, colchicine, and IL1-β inhibitors on cardiovascular disease outcomes are reviewed. Finally, new targets and drugs being explored for treating gout in patients with advanced CKD are discussed.
The training of deep learning models relies on a large amount of labeled data. However, the high cost of medical labeling seriously hinders the development of deep learning in the medical field. Our study proposes a general disease diagnosis approach based on Zero-Shot Learning. The Siamese neural network is used to find similar diseases for the target diseases, and the U-Net segmentation model is used to accurately segment the key lesions of the disease. Finally, based on the ResNet-Agglomerative clustering algorithm, a clustering model is trained on a large number of sample data of similar diseases to obtain a approximate diagnosis of the target disease. Zero-Shot Learning of the target disease is then successfully achieved. To evaluate the validity of the model, we validated our method on a dataset of ophthalmic diseases in CFP modality. The external dataset was used to test its performance, and the accuracy=0.8395, precision=0.8094, recall=0.8463, F1 Score=0.8274, AUC=0.9226, which exceeded the indexes of most Few-Shot Learning and One-Shot Learning models. It proves that our method has great potential and reference value in the medical field, where annotation data is usually scarce and expensive to obtain.
With the rising global burden of chronic diseases and the multimodal and heterogeneous clinical data (medical imaging, free-text recordings, wearable sensor streams, etc.), there is an urgent need for a unified multimodal AI framework that can proactively predict individual health risks. We propose VL-RiskFormer, a hierarchical stacked visual-language multimodal Transformer with a large language model (LLM) inference head embedded in its top layer. The system builds on the dual-stream architecture of existing visual-linguistic models (e.g., PaLM-E, LLaVA) with four key innovations: (i) pre-training with cross-modal comparison and fine-grained alignment of radiological images, fundus maps, and wearable device photos with corresponding clinical narratives using momentum update encoders and debiased InfoNCE losses; (ii) a time fusion block that integrates irregular visit sequences into the causal Transformer decoder through adaptive time interval position coding; (iii) a disease ontology map adapter that injects ICD-10 codes into visual and textual channels in layers and infers comorbid patterns with the help of a graph attention mechanism. On the MIMIC-IV longitudinal cohort, VL-RiskFormer achieved an average AUROC of 0.90 with an expected calibration error of 2.7 percent.
K T Rajashekhar, Kartik Mangsuli, Adarsh Krishna K Bhat
Introduction:
Femoral condyle osteonecrosis of the knee leading to osteoarthritis is a rare entity, which is noticed more commonly in elderly women. Usually, the medial condyle is involved; lateral condyle involvement is extremely rare. Cases like these with occurrence in young individuals are even more rare and need thorough pre-operative evaluation, patient counseling, and intraoperative correlation for proper line of management. Based on the size and stage of the lesion, treatment options vary from medical management to biological therapies to arthroplasty in advanced cases.
Case Report:
A 17-year-old male with a body mass index of 29.6 presented to our outpatient clinic with complaints of pain over the left knee, difficulty in walking, squatting, and sitting cross-legged. He was examined clinically, radiologically, and intraoperatively based on the findings and was diagnosed with osteonecrosis of the lateral femoral condyle femur extending into the trochlea with arthritic changes. After detailed discussion and counseling with the patient and relatives, keeping in mind the patient’s demand and expectations, he underwent robotic-assisted total knee arthroplasty (TKA).
Discussion:
Knee osteonecrosis is a debilitating, progressive degenerative disease characterized by subchondral bone ischemia. It can lead to localized necrosis, tissue death, and progressive joint destruction. For this reason, it is essential to diagnose and treat this disease early to avoid subchondral collapse, chondral damage, and end-stage osteoarthritis, where the only management option is TKA.
Conclusion:
Osteonecrosis of the knee in young patients, particularly when there is extensive articular involvement and associated osteoarthritic changes, is quite challenging to treat. Although joint preservation is typically preferred in younger individuals, TKA may be the only viable option in advanced stages to restore function and quality of life. Robotic-assisted TKA allows for precise implant positioning and optimal alignment and thus enhances the functional outcome. Individualized treatment planning, thorough pre-operative evaluation, and comprehensive patient counseling are essential to achieving successful outcomes in such rare and complex cases.
Orthopedic surgery, Diseases of the musculoskeletal system
Hisanori Gamada, Toru Funayama, Yusuke Setojima
et al.
Abstract Background Minimally invasive posterior fixation surgery for pyogenic spondylitis is known to reduce invasiveness and complication rates; however, the outcomes of concomitant insertion of pedicle screws (PS) into the infected vertebrae via the posterior approach are undetermined. This study aimed to assess the safety and efficacy of PS insertion into infected vertebrae in minimally invasive posterior fixation for thoracolumbar pyogenic spondylitis. Methods This multicenter retrospective cohort study included 70 patients undergoing minimally invasive posterior fixation for thoracolumbar pyogenic spondylitis across nine institutions. Patients were categorized into insertion and skip groups based on PS insertion into infected vertebrae, and surgical data and postoperative outcomes, particularly unplanned reoperations due to complications, were compared. Results The mean age of the 70 patients was 72.8 years. The insertion group (n = 36) had shorter operative times (146 versus 195 min, p = 0.032) and a reduced range of fixation (5.4 versus 6.9 vertebrae, p = 0.0009) compared to the skip group (n = 34). Unplanned reoperations occurred in 24% (n = 17) due to surgical site infections (SSI) or implant failure; the incidence was comparable between the groups. Poor infection control necessitating additional anterior surgery was reported in four patients in the skip group. Conclusions PS insertion into infected vertebrae during minimally invasive posterior fixation reduces the operative time and range of fixation without increasing the occurrence of unplanned reoperations due to SSI or implant failure. Judicious PS insertion in patients with minimal bone destruction in thoracolumbar pyogenic spondylitis can minimize surgical invasiveness.
Kevin A. Wu, Devika Shenoy, Elizabeth Sachs
et al.
Abstract Background Orthopedic procedures often require removing bone or pathological tissue, with traditional methods involving instruments like curettes and rongeurs. However, these methods can be time-consuming and lead to increased blood loss. To mitigate these side effects, vacuum-assisted tools have been developed to aid in tissue removal. These devices enable surgeons to suction tissue without discarding it, potentially improving outcomes in conditions such as osteomyelitis or tumor removal while enabling collection of the material for downstream applications. Despite limited research, vacuum-assisted devices show promise beyond bone marrow harvesting. This study assesses infection and clearance rates, estimated blood loss, and total procedure time associated with the use of vacuum-assisted tissue removal, with a goal to understand if these devices can be used for tissue removal across a variety of pathologic conditions. Methods A retrospective cohort study was conducted on patients undergoing orthopedic procedures with the Avitus® Bone Harvester repurposed from its original design from December 1, 2021, to July 1, 2023. Procedures were categorized into oncology, and debridement for infection cases. Infection cases were further categorized into those secondary to trauma and those involving primary infections (osteomyelitis and periprosthetic joint infection). Clinical variables, including demographics, intraoperative details, complications, and follow-up, were reviewed. Statistical analysis included descriptive statistics computed with R Studio. Results The study included 44 patients, with debridement for infection cases being the most common (primary infection: 45.5%; infection secondary to trauma: 18.1%), followed by oncology cases (36.4%). In all oncology cases, a definitive diagnosis was established using the device, and no post-operative infections were reported. The infection clearance rate was 85.0% for primary infection cases and 50.0% for cases of infection following trauma. Across the entire cohort, the average blood loss was 314.52 mL (sd: 486.74), and the average total procedure time was 160.93 min (sd: 91.07). The overall reoperation rate was 47.7%, with an unplanned reoperation rate of 11.4%. Conclusion The vacuum-assisted bone harvester was effectively utilized in a wide range of debridement and curettage procedures across diverse orthopedic surgeries. In oncology cases, the device enabled effective tissue removal with comparable recurrence rates, demonstrating its potential to minimize contamination while preserving tissue for accurate diagnoses. Additionally, a high rate of osteomyelitis eradication was observed in debridement for primary infection cases (85%). Despite the relatively high reoperation rate of 47.7%, it is crucial to interpret this figure within the context of the varied reasons for reoperation. Many of these reoperations were planned as part of a staged approach to treatment or were unrelated to the device's performance. It is crucial to acknowledge that isolating the device's contribution to these results can be difficult. The utilization of the device should be guided by considerations of cost-effectiveness and patient-specific risk factors.
Meng-Chieh Lin, Guan-Yu Chen, Hsin-Hsien Yu
et al.
Aims: Osteosarcoma is the most common primary bone malignancy among children and adolescents. We investigated whether benzamil, an amiloride analogue and sodium-calcium exchange blocker, may exhibit therapeutic potential for osteosarcoma in vitro. Methods: MG63 and U2OS cells were treated with benzamil for 24 hours. Cell viability was evaluated with the MTS/PMS assay, colony formation assay, and flow cytometry (forward/side scatter). Chromosome condensation, the terminal deoxynucleotidyl transferase dUTP nick end labelling (TUNEL) assay, cleavage of poly-ADP ribose polymerase (PARP) and caspase-7, and FITC annexin V/PI double staining were monitored as indicators of apoptosis. Intracellular calcium was detected by flow cytometry with Fluo-4 AM. The phosphorylation and activation of focal adhesion kinase (FAK) and signal transducer and activator of transcription 3 (STAT3) were measured by western blot. The expression levels of X-linked inhibitor of apoptosis protein (XIAP), B-cell lymphoma 2 (Bcl-2), B-cell lymphoma-extra large (Bcl-xL), SOD1, and SOD2 were also assessed by western blot. Mitochondrial status was assessed with tetramethylrhodamine, ethyl ester (TMRE), and intracellular adenosine triphosphate (ATP) was measured with BioTracker ATP-Red Live Cell Dye. Total cellular integrin levels were evaluated by western blot, and the expression of cell surface integrins was assessed using fluorescent-labelled antibodies and flow cytometry. Results: Benzamil suppressed growth of osteosarcoma cells by inducing apoptosis. Benzamil reduced the expression of cell surface integrins α5, αV, and β1 in MG63 cells, while it only reduced the expression of αV in U2OS cells. Benzamil suppressed the phosphorylation and activation of FAK and STAT3. In addition, mitochondrial function and ATP production were compromised by benzamil. The levels of anti-apoptotic proteins XIAP, Bcl-2, and Bcl-xL were reduced by benzamil. Correspondingly, benzamil potentiated cisplatin- and methotrexate-induced apoptosis in osteosarcoma cells. Conclusion: Benzamil exerts anti-osteosarcoma activity by inducing apoptosis. In terms of mechanism, benzamil appears to inhibit integrin/FAK/STAT3 signalling, which triggers mitochondrial dysfunction and ATP depletion. Cite this article: Bone Joint Res 2024;13(4):157–168.
Mohamed G. Hassan, Allison L. Horenberg, Ariella Coler-Reilly
et al.
AbstractPurpose of ReviewThis review examines the diverse functional relationships that exist between the peripheral nervous system (PNS) and bone, including key advances over the past century that inform our efforts to translate these discoveries for skeletal repair.Recent FindingsThe innervation of the bone during development, homeostasis, and regeneration is highly patterned. Consistent with this, there have been nearly 100 studies over the past century that have used denervation approaches to isolate the effects of the different branches of the PNS on the bone. Overall, a common theme of balance emerges whereby an orchestration of both local and systemic neural functions must align to promote optimal skeletal repair while limiting negative consequences such as pain.SummaryAn improved understanding of the functional bidirectional pathways linking the PNS and bone has important implications for skeletal development and regeneration. Clinical advances over the next century will necessitate a rigorous identification of the mechanisms underlying these effects that is cautious not to oversimplify the in vivo condition in diverse states of health and disease.
Camryn B. Petit, Jed A. Diekfuss, Shayla M. Warren
et al.
Purpose To characterize the secondary anterior cruciate ligament (ACL) injury rates after primary allograft anterior cruciate ligament reconstruction (ACLR) and to identify the age cut‐score at which the risk of allograft failure decreases. Methods All patients who underwent primary ACLR within a single orthopaedic department between January 2005 and April 2020 were contacted at a minimum of 2 years post‐ACLR to complete a survey regarding complications experienced post‐surgery, activity level, and perceptions of knee health. Patients were excluded for incidence of previous ACLR (ipsilateral or contralateral) and/or age younger than 14 years. Relative proportions were calculated, binary regression analysis was performed, and receiver operating characteristic analysis was used to identify the threshold age for maximal sensitivity and specificity to predict high risk of allograft failure, defined as undergoing revision ACLR. Results Of the 939 surveys completed, 398 patients underwent primary allograft ACLR (mean age 39.5 years; range 16.0‐66.1 years; 54.3% female). The secondary ACL injury rate was 11.6% (5.8% ipsilateral revision ACLR, 5.8% contralateral ACL injury). Male and female patients had similar revision (5.5% male, 6.0% female, P = .82) and contralateral ACL injury rates (6.6% male, 5.1% female, P = .52). Receiver operating characteristic analysis indicated that age ≤34 years was threshold for differentiating high risk of allograft failure (area under the curve 0.65, 95% confidence interval 0.55‐0.76; P = .014). Patients aged ≤34 years had a greater secondary injury rate than patients >34 years (20.4% (10.2% revision ACLR, 10.2% contralateral ACL injury) versus 6.9% (3.5% revision ACLR, 3.5% contralateral ACL injury; P < .001). Binary regression analysis demonstrated that decreasing age was associated with increased risk of graft failure (χ 2 = 7.9, P = .02.). Conclusions Allograft ACLR showed similar failure rates between sexes but displayed suboptimal graft failure outcomes in younger and active patients. By age 34 years, the increased revision risk for younger patients diminished. Level of Evidence Level IV, therapeutic case series.
Rathnam K Venkat, Xiaosong Wang, Naomi J Patel
et al.
Abstract Objective We investigated the baseline DMARD use and post-acute sequelae of COVID-19 (PASC) risk among patients with systemic autoimmune rheumatic diseases (SARDs). Methods Patients with SARDs and confirmed COVID-19 infection at Mass General Brigham completed a survey ≥28 days after a positive PCR/antigen test to prospectively investigate their COVID-19 courses. We investigated DMARD use at COVID-19 onset and PASC risk. PASC was defined as any COVID-19 symptom that persisted for ≥28 days. We used logistic regression to estimate the odds ratios (OR) for PASC by DMARD class. We also used restricted mean survival time to determine the difference in symptom-free days by DMARD class in the 28-day period after infection. Results We analysed 510 patients with SARDs and COVID-19 from 11 March 2021 to 17 June 2023; 202 (40%) developed PASC. CD20 inhibitor (CD20i) users had significantly higher odds of developing PASC vs conventional synthetic DMARD (csDMARD) users [adjusted OR (aOR) 2.69 (95% CI 1.23, 5.88)]. IL-12/23, IL-17A or IL-23 inhibitor (IL-12/23i, IL-17Ai, IL-23i) users also had significantly higher odds of PASC [aOR 3.03 (95% CI 1.08, 8.49)]. CD20i users had significantly fewer symptom-free days vs csDMARD users [aOR −4.12 (95% CI −7.29, −0.94)]. Conclusion CD20i users had significantly higher odds of PASC and fewer symptom-free days over the 28 days following COVID-19 diagnosis compared with csDMARD users. Further research is needed to investigate whether PASC risk in CD20i users may be due to prolonged infection or other immune mechanisms. The association of IL-12/23i, IL-17Ai and IL-23i with PASC calls for additional study.
Stiffness and actomyosin contractility are intrinsic mechanical properties of animal cells required for the shaping of tissues. However, whether tissue stem cells (SCs) and progenitors located within SC niche have different mechanical properties that modulate their size and function remains unclear. Here, we show that hair follicle SCs in the bulge are stiff with high actomyosin contractility and resistant to size change, whereas hair germ (HG) progenitors are soft and periodically enlarge and contract during quiescence. During activation of hair follicle growth, HGs reduce contraction and more frequently enlarge, a process that is associated with weakening of the actomyosin network, nuclear YAP accumulation, and cell cycle reentry. Induction of miR-205 , a novel regulator of the actomyosin cytoskeleton, reduces actomyosin contractility and activates hair regeneration in young and old mice. This study reveals the control of tissue SC size and activities by spatiotemporally compartmentalized mechanical properties and demonstrates the possibility to stimulate tissue regeneration by fine-tuning cell mechanics.
Rheumatoid arthritis (RA) is an inflammatory arthritis that affects synovial joints, and it is not surprising that the temporomandibular joint (TMJ), a synovial joint, is also affected. However, TMJ is rarely the first affected joint in the course of RA. Often, RA patients come to the physician with more focus on complaints in other peripheral joints. Therefore, asking TMJ complaints and symptoms, and TMJ examination in RA patients is often neglected by doctors too, because they focus more on other joints. This neglect may cause serious damage to the joints and cause disability. Examination of TMJs, which is a crucial component of vital activities such as nutrition and speech, should be added to the routine. Also, further studies may be focused on adding TMJ assessment to disease activity scales and health assessment questionnaires.
Most people around the globe are dying due to heart disease. The main reason behind the rapid increase in the death rate due to heart disease is that there is no infrastructure developed for the healthcare department that can provide a secure way of data storage and transmission. Due to redundancy in the patient data, it is difficult for cardiac Professionals to predict the disease early on. This rapid increase in the death rate due to heart disease can be controlled by monitoring and eliminating some of the key attributes in the early stages such as blood pressure, cholesterol level, body weight, and addiction to smoking. Patient data can be monitored by cardiac Professionals (Cp) by using the advanced framework in the healthcare departments. Blockchain is the world's most reliable provider. The use of advanced systems in the healthcare departments providing new ways of dealing with diseases has been developed as well. In this article Machine Learning (ML) algorithm known as a sine-cosine weighted k-nearest neighbor (SCA-WKNN) is used for predicting the Hearth disease with the maximum accuracy among the existing approaches. Blockchain technology has been used in the research to secure the data throughout the session and can give more accurate results using this technology. The performance of the system can be improved by using this algorithm and the dataset proposed has been improved by using different resources as well.
Md. Hamjajul Ashmafee, Tasnim Ahmed, Sabbir Ahmed
et al.
Correct identification and categorization of plant diseases are crucial for ensuring the safety of the global food supply and the overall financial success of stakeholders. In this regard, a wide range of solutions has been made available by introducing deep learning-based classification systems for different staple crops. Despite being one of the most important commercial crops in many parts of the globe, research proposing a smart solution for automatically classifying apple leaf diseases remains relatively unexplored. This study presents a technique for identifying apple leaf diseases based on transfer learning. The system extracts features using a pretrained EfficientNetV2S architecture and passes to a classifier block for effective prediction. The class imbalance issues are tackled by utilizing runtime data augmentation. The effect of various hyperparameters, such as input resolution, learning rate, number of epochs, etc., has been investigated carefully. The competence of the proposed pipeline has been evaluated on the apple leaf disease subset from the publicly available `PlantVillage' dataset, where it achieved an accuracy of 99.21%, outperforming the existing works.
This paper introduces the first quantum computing framework for Stochastic Quantum Power Flow (SQPF) analysis in power systems. The proposed method leverages quantum states to encode power flow distributions, enabling the use of Quantum Monte Carlo (QMC) sampling to efficiently assess the probability of line overloads. Our approach significantly reduces the required sample size compared to traditional Monte Carlo methods, making it particularly suited for risk assessments in scenarios involving high uncertainty, such as renewable energy integration. We validate the method on two test systems, demonstrating the computational advantage of quantum algorithms in reducing sample complexity while maintaining accuracy. This work represents a foundational step toward scalable quantum power flow analysis, with potential applications in future power system operations and planning. The results show promising computational speedups, underscoring the potential of quantum computing in addressing the increasing uncertainty in modern power grids.
There is rising evidence of the health benefit associated with specific dietary interventions. Current food-disease databases focus on associations and treatment relationships but haven't provided a reasonable assessment of the strength of the relationship, and lack of attention on food nutrition. There is an unmet need for a large database that can guide dietary therapy. We fill the gap with NutriFD, a scoring network based on associations and therapeutic relationships between foods and diseases. NutriFD integrates 9 databases including foods, nutrients, diseases, genes, miRNAs, compounds, disease ontology and their relationships. To our best knowledge, this database is the only one that can score the associations and therapeutic relationships of everyday foods and diseases by weighting inference scores of food compounds to diseases. In addition, NutriFD demonstrates the predictive nature of nutrients on the therapeutic relationships between foods and diseases through machine learning models, laying the foundation for a mechanistic understanding of food therapy.