Hasil untuk "Diseases of the musculoskeletal system"

Yutaka Fujita, Tomoharu Mochizuki, Shigeru Takagi et al.

Abstract Purpose To evaluate and compare clinical and radiological outcomes of surgical strategies for osteochondritis dissecans (OCD) lesions across ICRS grades, and to investigate the impact of recipient hole drilling (RHD) as a biological augmentation to osteochondral grafting. Methods This retrospective study included 60 knees (ICRS grades 1–4) treated surgically from 2001 to 2022. Stable lesions (grade 1) were managed with either retrograde or antegrade drilling with bioabsorbable pin fixation. For unstable lesions (grades 2–3), in situ osteochondral autograft transfer (OATS) or detach-and-fix techniques were performed. Detached lesions (grade 4) underwent either mosaicplasty or fragment fixation. RHD was introduced in select OATS and mosaicplasty cases to stimulate subchondral bone remodeling. Outcomes were assessed using the Lysholm and Tegner scores and MOCART-based MRI evaluations. Statistical comparisons were performed across groups. Results Antegrade drilling resulted in significantly higher Lysholm, Tegner, and MOCART scores than retrograde drilling for ICRS grade 1 lesions. In situ OATS for ICRS 2–3 lesions showed superior clinical and radiological outcomes compared to detach-and-fix, with no reoperations. For ICRS 4 lesions, both mosaicplasty and fragment fixation yielded favorable outcomes, though only Tegner scores showed a significant difference. RHD tended to improve cartilage repair quality in OATS and mosaicplasty but did not reach statistical significance. Conclusions Antegrade drilling and in situ OATS were superior to retrograde drilling and detach-and-fix, respectively. No statistically significant advantage was found between mosaicplasty and fragment fixation. RHD may enhance subchondral integration and healing, supporting its further evaluation. Clinical relevance Optimizing OCD treatment requires surgical strategies that achieve both mechanical stability and biological activation. Integrating these principles may enhance repair quality and long-term joint preservation. Level of evidence Level III, retrospective comparative study.

Volodymyr Sydorskyi, Igor Krashenyi, Oleksii Yakubenko

Melanoma detection is vital for early diagnosis and effective treatment. While deep learning models on dermoscopic images have shown promise, they require specialized equipment, limiting their use in broader clinical settings. This study introduces a multi-modal melanoma detection system using conventional photo images, making it more accessible and versatile. Our system integrates image data with tabular metadata, such as patient demographics and lesion characteristics, to improve detection accuracy. It employs a multi-modal neural network combining image and metadata processing and supports a two-step model for cases with or without metadata. A three-stage pipeline further refines predictions by boosting algorithms and enhancing performance. To address the challenges of a highly imbalanced dataset, specific techniques were implemented to ensure robust training. An ablation study evaluated recent vision architectures, boosting algorithms, and loss functions, achieving a peak Partial ROC AUC of 0.18068 (0.2 maximum) and top-15 retrieval sensitivity of 0.78371. Results demonstrate that integrating photo images with metadata in a structured, multi-stage pipeline yields significant performance improvements. This system advances melanoma detection by providing a scalable, equipment-independent solution suitable for diverse healthcare environments, bridging the gap between specialized and general clinical practices.

Peter M. van der Kraan

Objective: Osteoarthritis (OA) is a progressive joint disease habitually linked to ageing, characterized by the gradual breakdown of cartilage leading to pain and reduced mobility. Historically viewed as mainly a “wear and tear” condition, new insights suggest that OA may be part of an evolutionary, age-related biological process rather than mainly driven by mechanical damage. Design: This conceptual paper discusses the model of antagonistic pleiotropy that proposes that certain genes beneficial early in life may contribute to diseases in the context of OA. Results: Findings indicate that OA is connected to biological and not to chronological age supporting the idea that OA is not merely a wear and tear process. Chondrocyte hypertrophy, essential in endochondral bone formation at a (pre)reproductive age, is stimulated by a displaced and wrongly timed endochondral ossification quasi-program in age-related OA. Age-related chondrocyte hypertrophic differentiation in articular cartilage is likely driven by loss of loading-induced TGF-β signaling. Conclusion: Comprehending OA within this evolutionary and biological frame provides a solid alternative to the theory of “wear and tear”, offering insights into further understanding, prevention and disease management.

Andrea Buonsenso, Domenico Di Fonza, Gloria Di Claudio et al.

<b>Objectives</b>: The lat pulldown machine is one of the most versatile pieces of equipment for back strengthening, allowing variations in grip and load. However, there are significant gaps in the literature regarding the relationship between exercise modality and specific muscle activation. <b>Methods</b>: This study examined the electromyographic (EMG) activity of major back muscles during seven lat pulldown exercise variants that differed in grip type, width, and trunk inclination. Forty male subjects, with at least 5 years of resistance training experience, performed five repetitions of lat pulldown exercise using 70% of their repetition maximum. Prior to the surface EMG analysis, maximal voluntary contraction (MVC) tests were performed for each muscle group analysed, specifically the latissimus dorsi, posterior deltoid, brachial biceps, middle and lower trapezium, and infraspinatus. The normalised root mean square of the EMG (NrmsEMG) activity for each muscle was recorded during full, concentric, and eccentric movements. <b>Results</b>: Multivariate analysis of variance (MANOVA) showed no significant difference in the NrmsEMG muscle activation across the different lat pulldown exercise variations (all <i>p</i> > 0.05). A significant difference was found in the posterior deltoid where the wide-pronated grip with a 30° trunk inclination showed greater EMG activation compared to the wide pronated grip (<i>p</i> = 0.011) and wide neutral grip (<i>p</i> = 0.017). <b>Conclusions</b>: These findings suggest that grip variations may not significantly alter latissimus dorsi recruitment, challenging the assumption that grip effectiveness targets this muscle. The results highlight the need for individualised approaches to exercise selection, given the variability in muscle activation patterns observed.

Jun Young Park, Tae Kang Kim, Byung Woo Cho et al.

Abstract Background Trochanteric fractures in the older population are challenging to treat due to osteoporotic bone and high risk of fixation failure. Cement augmentation (CA) of cephalomedullary fixation has been proposed to enhance implant anchorage and reduce complications. This study compared mechanical failure rates and clinical outcomes between cement-augmented and conventional cephalomedullary nailing in osteoporotic trochanteric fractures. Methods We performed a retrospective comparative study of patients with trochanteric fractures treated with either a CA or non-CA cephalomedullary nail from February 2022 to July 2023. To minimize selection bias, 1:2 propensity score matching was applied to our initial 143 consecutive cases (28 CA, 115 non-CA), yielding 28 augmented and 56 conventional cases. The primary outcome was the rate of implant cut-out. Secondary outcomes were excessive telescopic sliding, 1-year mortality, and patient-reported outcome measures using Harris Hip Score (HHS) and EuroQol 5-Dimension (Eq. 5D) at the final follow up. Results After matching, the CA group had no instances of cut-out (0/28), compared to 1 case (1/56, 1.8%) in the non-CA group (p = 1.00), though the study was underpowered for this rare outcome, with mean follow-up periods of 19.2 ± 18.3 weeks and 23.0 ± 22.5 weeks, respectively. Excessive sliding of the proximal screw occurred in 3 patients (10.7%) with CA versus 7 (12.5%) without (p = 1.00) during the same follow-up period, while 1-year mortality was similar between groups (CA 25.0% vs. non-CA 19.6%, p = 0.78). Final follow-up HHS and Eq. 5D scores were similar between the CA and non-CA groups. No cement-related complications, such as leakage or thermal injury, were observed in this cohort. Conclusions Cement augmentation of a cephalomedullary nail demonstrated comparable mechanical outcomes in terms of cut-out and excessive sliding, as well as similar 1-year mortality and functional outcomes to conventional fixation. This process with a cephalomedullary nail appears to be a safe and effective option for older patients with a trochanteric fracture and shows no postoperative complications. Further large prospective studies are needed to identify patients expected to benefit most from CA. Trial registration not applicable.

Charles Poh Thean Ang, Kunalan Ganthel, Jade Pei Yuik Ho et al.

Abstract Background Achieving soft tissue balance is challenging in valgus arthritic knee because of a combination of anomalies in the soft tissues and bones. It has been stipulated that contemporary robotic systems are more precise than traditional instrumentation. Its advantage lies in the soft tissue algorithms. Presently, there is paucity of information on the use of robotic-assisted TKA in addressing moderate and severe valgus deformities. The aim of this series is to demonstrate the utility of robotic-assisted surgery in TKA for arthritic knees with moderate and severe valgus deformities, including accuracy, soft tissue releases performed and level of constraint required. Methods This is a single surgeon series of 14 cases of moderate and severe valgus osteoarthritic knees who underwent robotic-assisted TKA, utilizing the robotic surgical assistant ROSA® System. Results All patients were restored to within 3° of the planned alignment. 8 patients were implanted with cruciate retaining implants, 2 had ultracongruent implants, 3 had posterior stabilized implants, and 1 had a constrained posterior stabilized implant. No patients required release of the popliteus tendon or origin of the lateral collateral ligament. No perioperative complications were encountered in all cases. All patients reported an improvement in the Forgotten Joint Score of > 10.8 at 1year follow up with a 100% satisfaction rate. Conclusion In this case series, the utilization of robotic assisted the surgeon to achieve a final limb alignment to within 3° of the planned alignment with minimal use of constrained prostheses and good patient-reported outcomes in moderate and severe valgus deformities.

Rajnish Kumar, Suriya Prakash Muthukrishnan, Lalan Kumar et al.

Accurate EMG-driven musculoskeletal (MSK) modeling is critical for biomechanics, rehabilitation, and assistive technology. However, most models calibrate parameters under a single load, ignoring the fact that tasks with similar kinematics may differ in mechanical demand. This study introduces a load-aware calibration framework to improve joint torque prediction accuracy and generalizability. Surface EMG and joint kinematics were recorded from eleven participants during elbow flexion-extension under 0, 2, and 4\,kg loads. We evaluated three calibration strategies (load-specific, global, cross-load) and three optimization frameworks (simulated annealing (SA), particle swarm optimization (PSO), and hybrid PSO-pattern search (PSO-PS)). Results indicate that load-specific calibration significantly improves performance, with lower RMSE and higher correlation ($r > 0.75$). Parameters related to muscle force, fiber length, and activation dynamics showed high load sensitivity. PSO-based methods yielded more consistent and physiologically plausible estimates than simulated annealing. The proposed framework enables MSK models to distinguish between visually similar but mechanically distinct tasks, supporting robust subject-specific modeling for clinical and real-world applications.

Daniel Alazard, Francesco Sanfedino, Ervan Kassarian

This paper presents causal block-diagram models to represent the equations of motion of multi-body systems in a very compact and simple closed form. Both the forward dynamics (from the forces and torques imposed at the various degrees-of-freedom to the motions of these degrees-of-freedom) or the inverse dynamics (from the motions imposed at the degrees-of-freedom to the resulting forces and torques) can be considered and described by a block diagram model. This work extends the Two-Input Two-Output Port (TITOP) theory by including all non-linear terms and uniform or gravitational acceleration fields. Connection among different blocks is possible through the definition of the motion vector. The model of a system composed of a floating base, rigid bodies, revolute and prismatic joints, working under gravity is developed to illustrate the methodology. The proposed model is validated by simulation and cross-checking with a model built using an alternative modeling tool on a scenario where the nonlinear terms are determining.

Yuri Gardinazzi, Roger Gonzaléz March, Suprabhath Kalahasti et al.

Comorbidity networks, which capture disease-disease co-occurrence usually based on electronic health records, reveal structured patterns in how diseases cluster and progress across individuals. However, how these networks evolve across different age groups and how this evolution relates to properties like disease prevalence and mortality remains understudied. To address these issues, we used publicly available comorbidity networks extracted from a comprehensive dataset of 45 million Austrian hospital stays from 1997 to 2014, covering 8.9 million patients. These networks grow and become denser with age. We identified groups of diseases that exhibit similar patterns of structural centrality throughout the lifespan, revealing three dominant age-related components with peaks in early childhood, midlife, and late life. To uncover the drivers of this structural change, we examined the relationship between prevalence and degree. This allowed us to identify conditions that were disproportionately connected to other diseases. Using betweenness centrality in combination with mortality data, we further identified high-mortality bridging diseases. Several diseases show high connectivity relative to their prevalence, such as iron deficiency anemia (D50) in children, nicotine dependence (F17), and lipoprotein metabolism disorders (E78) in adults. We also highlight structurally central diseases with high mortality that emerge at different life stages, including cancers (C group), liver cirrhosis (K74), subarachnoid hemorrhage (I60), and chronic kidney disease (N18). These findings underscore the importance of targeting age-specific, network-central conditions with high mortality for prevention and integrated care.

Cai Lin, Yunyi Ding, Kai Lin et al.

Recent advances in medical diagnostics have highlighted the importance of wearable technologies for continuous and real-time physiological monitoring. In this study, we introduce a flexible, self-powered triboelectric nanogenerator (MB-TENG) engineered from commercially available medical elastic bandages for biomechanical sensing during rehabilitation and gait analysis. Leveraging the porous and skin-friendly properties of the bandage combined with a PTFE film, the MB-TENG delivers robust electrical performance, achieving a peak open-circuit voltage (VOC) of 122~V, a short-circuit current (ISC) of 25~$μ$A, and a transferred charge (QSC) of 110~nC, while maintaining long-term stability across 40{,}000 mechanical cycles. Its inherent self-adhesive property allows for multi-layer assembly without extra bonding agents, and mechanical stretching enhances output, enabling dual configurability. A stacked design further improves the power capacity, supporting applications in wearable medical electronics. The MB-TENG device seamlessly conforms to joint surfaces and foot regions, providing accurate detection of motion states and abnormal gait patterns. These features underscore the MB-TENG's potential as a low-cost, scalable platform for personalized rehabilitation, injury monitoring, and early musculoskeletal diagnosis.

Yukun Ma, Dingyan Zhao, Xing Yu et al.

Noa Martonovich, Sharon Reisfeld, Yaniv Yonai et al.

Pyomyositis, accompanied by aseptic arthritis, has been previously documented in several publications. However, none of the authors in the mentioned case reports offered a pathophysiological explanation for this unusual phenomenon or proposed a treatment protocol. We present a case of a healthy, 70-year-old male who was presented to the emergency department 4 days after tripping over a pile of wooden planks and getting stabbed by a nail to his thigh. The right thigh was swollen. Unproportional pain was produced by a light touch to the thigh. A laboratory test and a CT scan were obtained. The working diagnosis was pyomyositis of the thigh and septic arthritis of the ipsilateral knee. The patient underwent urgent debridement and irrigation of his right thigh. An arthroscopic knee lavage was performed as well. Intraoperative cultures from the thigh revealed the growth of Streptococcus pyogenes and Staphylococcus aureus. Cultures from synovial fluid were sterile; thus, septic arthritis was very unlikely. The source of the knee effusion might have been an aseptic inflammatory response due to the proximity of the thigh infection. Anatomically, the quadriceps muscle inserts on the patella, and its tendon fuses with the knee capsule, creating a direct fascial track from the thigh to the knee. The inflammatory response surrounding the infection may have followed this track, creating a domino effect, affecting adjacent capillaries within the joint capsule, and causing plasma leakage into the synovial space, leading to joint effusion. Our suggested treatment is addressing the primary infection with antibiotics and considering adding anti-inflammatory therapy, given our suspicion that this process has an inflammatory component.

Verner Marijančić, Stanislav Peharec, Gordana Starčević-Klasan et al.

<b>Background/Aim:</b> University students are a particularly vulnerable population, as they spend increasing amounts of time sitting, which poses a major threat to their musculoskeletal health and posture. The aim of this cross-sectional study was to investigate gender differences in the relationships between physical activity (PA) and sedentary behavior, spinal curvatures and mobility, the endurance and balance of the trunk muscles, and the possible presence of non-specific low back pain (NS-LBP) in young adults aged 18–25 years. <b>Methods:</b> A total of 139 students completed all required tests. <b>Results:</b> Male students engaged in significantly more PA related to recreation, sports and leisure and were significantly more likely to be hyperkyphotic than female students. The more the male students participated in sports, the more pronounced the thoracic kyphosis. Female students had significantly more pronounced lumbar lordosis and anterior pelvic tilt that correlated with lumbar lordosis. Female students generally had significantly higher trunk extensor endurance and more balanced trunk musculature than males. NS-LBP correlated with PA in female students who generally had higher levels of NS-LBP than male students, with a statistically significant difference between those who practiced the most PA. <b>Conclusions:</b> Our results suggest that female students practice less PA and have pronounced lordosis and trunk extensor endurance, in contrast to males who practice more PA and have pronounced trunk flexor endurance and hyperkyphosis. Our findings suggest that more PA should be encouraged but implemented with caution and as an individualized gender-specific approach to prevent postural deformities and chronic musculoskeletal disorders, including NS-LBP.

Alexy Hernandez, Leslie Zapata Leiva, Maria Mutka et al.

Abstract Background Current treatment for localized scleroderma (LS) has been shown to halt disease activity, but little is still known about patient experiences with these treatments, nor is there consensus about optimal measurement strategies for future clinical trials. Objective Conduct a scoping review of the literature for the types of outcomes and measures (i.e. clinician-, patient-, and caregiver-reported) utilized in published treatment studies of LS. Methods Online databases were searched for articles related to the evaluation of treatment efficacy in LS with a special focus on pediatrics. Results Of the 168 studies, the most common outcomes used were cutaneous disease activity and damage measured via clinician-reported assessments. The most frequently cited measure was the Localized Scleroderma Cutaneous Assessment Tool (LoSCAT). Few patient-reported outcome measures (PROMs) were used. Limitations Some studies only vaguely reported the measures utilized, and the review yielded a low number of clinical trials. Conclusion In addition to evaluating disease activity with clinician-reported measures, the field could obtain critical knowledge on the patient experience by including high-quality PROMs of symptoms and functioning. More clinical trials using a variety of outcomes and measures are necessary to determine the most suitable course of treatment for LS patients.

A. Nogueira, Joaquim Barreto, F. Moura et al.

Background The escalating prevalence of type 2 diabetes (T2DM) poses an unparalleled economic catastrophe to developing countries. Cardiovascular diseases remain the primary source of costs among individuals with T2DM, incurring expenses for medications, hospitalizations, and surgical interventions. Compelling evidence suggests that the risk of cardiovascular outcomes can be reduced by three classes of glucose-lowering therapies (GLT), including SGLT2i, GLP-1A, and pioglitazone. However, an evidence-based and cost-effective protocol is still unavailable for many countries. The objective of the current study is to compare the effectiveness and cost-effectiveness of GLT in individuals with T2DM in Brazil. Methods We employed Bayesian Networks to calculate the incremental cost-effectiveness ratios (ICER), expressed in international dollars (Int$) per disease-adjusted life years [DALYs] averted. To determine the effectiveness of GLT, we conducted a systematic review with network meta-analysis (NMA) to provide insights for our model. Additionally, we obtained cardiovascular outcome incidence data from two real-world cohorts comprising 851 and 1337 patients in primary and secondary prevention, respectively. Our cost analysis took into account the perspective of the Brazilian public health system, and all values were converted to Int$. Results In the NMA, SGLT2i [HR: 0.81 (95% CI 0.69–0.96)], GLP-1A [HR: 0.79 (95% CI 0.67–0.94)], and pioglitazone [HR: 0.73 (95% CI 0.59–0.91)] demonstrated reduced relative risks of non-fatal cardiovascular events. In the context of primary prevention, pioglitazone yielded 0.2339 DALYs averted, with an ICER of Int$7,082 (95% CI 4,521–10,770) per DALY averted when compared to standard care. SGLT2i and GLP-1A also increased effectiveness, resulting in 0.261 and 0.259 DALYs averted, respectively, but with higher ICERs of Int$12,061 (95% CI: 7,227–18,121) and Int$29,119 (95% CI: 23,811–35,367) per DALY averted. In the secondary prevention scenario, all three classes of treatments were deemed cost-effective at a maximum willingness-to-pay threshold of Int$26,700. Notably, pioglitazone consistently exhibited the highest probability of being cost-effective in both scenarios. Conclusions In Brazil, pioglitazone presented a higher probability of being cost-effective both in primary and secondary prevention, followed by SGLT2i and GLP-1A. Our findings support the use of cost-effectiveness models to build optimized and hierarchical therapeutic strategy in the management of T2DM. Trial registration CRD42020194415.

Jung-Han Kim, Young-Kyoung Min, Dae-Yoo Kim et al.

Abstract Background Capsular contracture is a well-known etiology in the primary stiff shoulder; thus capsular contracture and resultant decreased joint volume could lead to postoperative stiffness, which is a commonly reported morbidity after arthroscopic rotator cuff repair (ARCR). The purpose of this study was (1) to quantify the joint volume (total joint volume and each quadrant compartmental volume) using computed tomography arthrography (CTA) and (2) to demonstrate the relationship between joint volume and postoperative range of motion (ROM) after ARCR. Materials and methods Eighty-three patients (60 ± 5.11 years, men = 26, women = 57) who had undergone ARCR between January 2015 to December 2020 due to small to medium full-thickness tear and followed by CTA 6 months postoperatively were retrospectively reviewed. An image reconstruction program (3D Slicer, version 4.11.2 software) was used to calculate the joint volume (total joint volume and quadrant compartment joint volumes; anteroinferior, anterosuperior, posterosuperior and posteroinferior). For shoulder ROM, data including scaption (Sc), external rotation on side (ERs), external rotation at 90° (ER90), and internal rotation on back (IRb) were collected 6 months postoperatively. An evaluation of the correlation between joint volume and each shoulder motion was performed. Results There were moderate correlations between the total joint volume and each motion (Sc: Pearson coefficient, 0.32, p = 0.0047; ERs: Pearson coefficient, 0.24, p = 0.0296; ER90: Pearson coefficient, 0.33, p = 0.0023; IRb: Pearson coefficient, 0.23, p = 0.0336). Among the quadrant compartments, the anteroinferior (Sc: Pearson coefficient, 0.26, p = 0.0199; ERs: Pearson coefficient, 0.23, p = 0.0336; ER90: Pearson coefficient, 0.25, p = 0.0246; IRb: Pearson coefficient, 0.26, p = 0.0168) and posterosuperior (Sc: Pearson coefficient, 0.24, p = 0.029; ER90: Pearson coefficient, 0.29, p = 0.008; IRb: Pearson coefficient, 0.22, p = 0.0491) and posteroinferior (Sc: Pearson coefficient, 0.30, p = 0.0064; ER90: Pearson coefficient, 0.29, p = 0.0072) showed moderate correlations with each shoulder motion. Conclusion Total joint volume, anteroinferior compartment joint volume, posterosuperior compartment joint volume and posteroinferior compartment joint volume were related to postoperative ROM after ARCR. Perioperative methods to increase the joint volume, especially the anteroinferior, posterosuperior and posteroinferior parts of the capsule may prevent postoperative stiffness after ARCR. Level of Evidence Level III; Retrospective Case-Control Study.

T. V. Afanasieva, A. P. Kuzlyakin, A. V. Komolov

Cardiovascular diseases are widespread among patients with chronic noncommunicable diseases and are one of the leading causes of death, including in the working age. The article presents the relevance of the development and application of patient-oriented systems, in which machine learning (ML) is a promising technology that allows predicting cardiovascular diseases. Automated machine learning (AutoML) makes it possible to simplify and speed up the process of developing AI/ML applications, which is key in the development of patient-oriented systems by application users, in particular medical specialists. The authors propose a framework for the application of automatic machine learning and three scenarios that allowed for data combining five data sets of cardiovascular disease indicators from the UCI Machine Learning Repository to investigate the effectiveness in detecting this class of diseases. The study investigated one AutoML model that used and optimized the hyperparameters of thirteen basic ML models (KNeighborsUnif, KNeighborsDist, LightGBMXT, LightGBM, RandomForestGini, RandomForestEntr, CatBoost, ExtraTreesGini, ExtraTreesEntr, NeuralNetFastA, XGBoost, NeuralNetTorch, LightGBMLarge) and included the most accurate models in the weighted ensemble. The results of the study showed that the structure of the AutoML model for detecting cardiovascular diseases depends not only on the efficiency and accuracy of the basic models used, but also on the scenarios for preprocessing the initial data, in particular, on the technique of data normalization. The comparative analysis showed that the accuracy of the AutoML model in detecting cardiovascular disease varied in the range from 87.41% to 92.3%, and the maximum accuracy was obtained when normalizing the source data into binary values, and the minimum was obtained when using the built-in AutoML technique.

Md Abrar Jahin, Subrata Talapatra

This research delves into Musculoskeletal Disorder (MSD) risk factors, using a blend of Natural Language Processing (NLP) and mode-based ranking. The aim is to refine understanding, classification, and prioritization for focused prevention and treatment. Eight NLP models are evaluated, combining pre-trained transformers, cosine similarity, and distance metrics to categorize factors into personal, biomechanical, workplace, psychological, and organizational classes. BERT with cosine similarity achieves 28% accuracy; sentence transformer with Euclidean, Bray-Curtis, and Minkowski distances scores 100%. With 10-fold cross-validation, statistical tests ensure robust results. Survey data and mode-based ranking determine severity hierarchy, aligning with the literature. "Working posture" is the most severe, highlighting posture's role. Survey insights emphasize "Job insecurity," "Effort reward imbalance," and "Poor employee facility" as significant contributors. Rankings offer actionable insights for MSD prevention. The study suggests targeted interventions, workplace improvements, and future research directions. This integrated NLP and ranking approach enhances MSD comprehension and informs occupational health strategies.

Achyut Tiwari, Aryan Chugh, Aman Sharma

Heart disease is the major cause of non-communicable and silent death worldwide. Heart diseases or cardiovascular diseases are classified into four types: coronary heart disease, heart failure, congenital heart disease, and cardiomyopathy. It is vital to diagnose heart disease early and accurately in order to avoid further injury and save patients' lives. As a result, we need a system that can predict cardiovascular disease before it becomes a critical situation. Machine learning has piqued the interest of researchers in the field of medical sciences. For heart disease prediction, researchers implement a variety of machine learning methods and approaches. In this work, to the best of our knowledge, we have used the dataset from IEEE Data Port which is one of the online available largest datasets for cardiovascular diseases individuals. The dataset isa combination of Hungarian, Cleveland, Long Beach VA, Switzerland & Statlog datasets with important features such as Maximum Heart Rate Achieved, Serum Cholesterol, Chest Pain Type, Fasting blood sugar, and so on. To assess the efficacy and strength of the developed model, several performance measures are used, such as ROC, AUC curve, specificity, F1-score, sensitivity, MCC, and accuracy. In this study, we have proposed a framework with a stacked ensemble classifier using several machine learning algorithms including ExtraTrees Classifier, Random Forest, XGBoost, and so on. Our proposed framework attained an accuracy of 92.34% which is higher than the existing literature.

Yagya Raj Pandeya, Samin Karki, Ishan Dangol et al.

We have developed a comprehensive computer system to assist farmers who practice traditional farming methods and have limited access to agricultural experts for addressing crop diseases. Our system utilizes artificial intelligence (AI) to identify and provide remedies for vegetable diseases. To ensure ease of use, we have created a mobile application that offers a user-friendly interface, allowing farmers to inquire about vegetable diseases and receive suitable solutions in their local language. The developed system can be utilized by any farmer with a basic understanding of a smartphone. Specifically, we have designed an AI-enabled mobile application for identifying and suggesting remedies for vegetable diseases, focusing on tomato diseases to benefit the local farming community in Nepal. Our system employs state-of-the-art object detection methodology, namely You Only Look Once (YOLO), to detect tomato diseases. The detected information is then relayed to the mobile application, which provides remedy suggestions guided by domain experts. In order to train our system effectively, we curated a dataset consisting of ten classes of tomato diseases. We utilized various data augmentation methods to address overfitting and trained a YOLOv5 object detector. The proposed method achieved a mean average precision of 0.76 and offers an efficient mobile interface for interacting with the AI system. While our system is currently in the development phase, we are actively working towards enhancing its robustness and real-time usability by accumulating more training samples.