Search Results (29)

As industrial automation evolves towards human-centric, adaptable solutions, collaborative robots must overcome challenges in unstructured, dynamic environments. This paper extends our previous work on developing a digital shadow for industrial robots by introducing a comprehensive framework that bridges the gap between physical systems and their virtual counterparts. The proposed framework advances toward a fully functional digital twin by integrating real-time perception and intuitive human–robot interaction capabilities. The framework is applied to a hospital test lab scenario, where a YuMi robot automates the sorting of microscope slides. The system incorporates a RealSense D435i depth camera for environment perception, Isaac Sim for virtual environment synchronization, and a locally hosted large language model (Mistral 7B) for interpreting user voice commands. These components work together to achieve bi-directional synchronization between the physical and digital environments. The framework was evaluated through 20 test runs under varying conditions. A validation study measured the performance of the perception module, simulation, and language interface, with a 60% overall success rate. Additionally, synchronization accuracy between the simulated and physical robot joint movements reached 98.11%, demonstrating strong alignment between the digital and physical systems. By combining local LLM processing, real-time vision, and robot simulation, the approach enables untrained users to interact with collaborative robots in dynamic settings. The results highlight its potential for improving flexibility and usability in industrial automation. Full article

Introduction: Postoperative atrial fibrillation (POAF) is a common complication after heart surgery, adversely impacting clinical outcomes and healthcare costs. Little is known about the dynamics of nucleotide metabolism associated with the development of POAF at a ventricular level. We conducted a post hoc trial analysis to investigate the changes in ventricular adenine nucleotides and the clinical predictors associated with the development of AF. Methods: Using data from a randomised trial, we analysed ATP/ADP, ATP/AMP, and energy charges in left and right ventricular biopsies of patients who developed AF compared to non-AF patients. A logistic regression model was used to understand the predictors associated with the development of atrial fibrillation in this cohort. Results: We analysed adenine nucleotide levels available in 88 patients who underwent coronary artery bypass grafting (CABG) (n = 65) and aortic valve replacement (AVR) (n = 23), out of which 27 (31%) developed a new onset of AF. Seventeen (43.4%) patients in the CABG group and ten (26.15%) in the AVR group developed AF. The patients who developed postoperative AF had longer cross-clamp times for CABG (p = 0.013) and AVR (p = 0.002). The most significant predictors for AF development were age (p = 0.003) and cross-clamp time (p = 0.012). In patients undergoing CABG who developed AF, we found a significant drop in post-reperfusion ATP/ADP and ATP/AMP ratios compared to pre-reperfusion. This was not significant for the patients who underwent AVR. Furthermore, the patients who underwent CABG and developed AF had higher pre- and post-reperfusion ATP/ADP ratios and energy charges than non-AF patients, suggesting a higher reserve of cardiac nucleotides. Conclusions: The development of postoperative atrial fibrillation is associated with intraoperative changes in the ventricular adenine nucleotide metabolism of patients undergoing CABG. In the clinical analysis, age and cross-clamp time were significant predictors of AF development. Full article

Soil microbes are important for maintaining agricultural ecosystems by promoting nutrient cycling, plant growth, and soil resilience. Microbial-based inoculants, such as bio-inoculants and bioremediation agents, have been identified as suitable means to promote soil health, reduce environmental deterioration, and achieve sustainable agriculture. Bio-inoculants, such as biofertilizers and biopesticides, promote nutrient availability, plant growth, and chemical input dependency reduction. Diverse microbial populations, especially plant growth-promoting bacteria (PGPB), enhance resistance by promoting a symbiotic association with plants and inducing natural resistance against insects. Bioremediation, the second significant microbial intervention, is the use of microorganisms for detoxifying and rehabilitating polluted soils. Methods effectively degrade organic pollutants, immobilize heavy metals, and mitigate the toxic effects of industrial and agricultural pollutants. Recent advances in microbial ecology and biotechnology, such as metagenomics, have transformed the knowledge of microbial soil communities, and tailor-made microbial formulations and monitoring equipment may be developed to maximize their activity. Though promising, environmental heterogeneity, scalability, and lack of field-based evidence constrain their widespread application. Multidimensional applications of microbial solutions in agroecology are explored in this review, with a focus on their potential in maintaining soil health, crop production, and environmental sustainability. It also addresses the application of bioremediation and microbial inoculants in agroecosystems and technological innovations with future research objectives. Microbial innovation to shape the soil microbiome offers a valid tool for addressing global challenges in agriculture, food security, and ecological resilience in the context of climate change. Full article

Cytokine-mediated inflammation is increasingly recognized for playing a vital role in the pathophysiology of a wide range of brain disorders, including neurodegenerative, psychiatric, and neurodevelopmental problems. Pro-inflammatory cytokines such as interleukin-1 (IL-1), tumor necrosis factor-alpha (TNF-α), and interleukin-6 (IL-6) cause neuroinflammation, alter brain function, and accelerate disease development. Despite progress in understanding these pathways, effective medicines targeting brain inflammation are still limited. Traditional anti-inflammatory and immunomodulatory drugs are effective in peripheral inflammatory illnesses. Still, they face substantial hurdles when applied to the central nervous system (CNS), such as the blood–brain barrier (BBB) and unwanted systemic effects. This review highlights the developing treatment techniques for modifying cytokine-driven neuroinflammation, focusing on advances that selectively target critical cytokines involved in brain pathology. Novel approaches, including cytokine-specific inhibitors, antibody-based therapeutics, gene- and RNA-based interventions, and sophisticated drug delivery systems like nanoparticles, show promise with respect to lowering neuroinflammation with greater specificity and safety. Furthermore, developments in biomarker discoveries and neuroimaging techniques are improving our ability to monitor inflammatory responses, allowing for more accurate and personalized treatment regimens. Preclinical and clinical trial data demonstrate the therapeutic potential of these tailored techniques. However, significant challenges remain, such as improving delivery across the BBB and reducing off-target effects. As research advances, the creation of personalized, cytokine-centered therapeutics has the potential to alter the therapy landscape for brain illnesses, giving patients hope for better results and a higher quality of life. Full article

Background: Malignant pleural effusion (MPE) affects up to 15% of patients with malignancy, and the prevalence is increasing. Non-expandable lung (NEL) complicates MPE in up to 30% of cases. However, it is not known if patients with malignant pleural effusion and NEL are more symptomatic in activities of daily living compared to patients with MPE with expandable lung. Methods: This was an observational study on consecutively recruited patients with MPE from our pleural clinic. Before thoracentesis, patients completed patient-reported outcomes on cancer symptoms (ESAS), health-related quality of life (5Q-5D-5L), and dyspnoea scores. Following thoracentesis, patients scored dyspnoea relief and symptoms during thoracentesis. Data on focused lung ultrasound and pleural effusion biochemistry were collected. The non-expandable lung diagnosis was made by pleural experts based on radiological and clinical information. Results: We recruited 43 patients, including 12 with NEL (28%). The NEL cohort resembled those from previous studies concerning ultrasonography, pleural fluid biochemistry, and fewer cases with high volume thoracentesis. Patients with and without NEL were comparable concerning baseline demography. The 5Q-5D-5L utility scores were 0.836 (0.691–0.906) and 0.806 (0.409–0.866), respectively, for patients with and without NEL. We observed no between-group differences in symptom burden or health-related quality of life. Conclusion: While the presence of NEL affects the clinical management of recurrent MPE, the presence of NEL seems not to affect patients’ overall symptom burden in patients with MPE. Full article

Bone healing occurs through three consecutive and interdependent phases. While the acute inflammatory response is vital to fracture healing, chronic and systemic inflammation negatively affect the healing process. The bone tissue relies heavily on the immune system for its normal physiology and turnover. The interactions are more pronounced in injury states, such as fractures and autoimmune disorders. Recently, the field of osteoimmunology, the study of the molecular interplay of the immune and skeletal systems, has gained much-needed attention to develop new therapeutic strategies to accelerate fracture healing and prevent the complications of fracture healing. This review provides an overview of the process of fracture healing and discusses the role of immune cells, their interplay with the released cytokines, and the current state of the art in the field of osteoimmunology. Full article

Background: Non-expandable lung (NEL) has severe implications for patient symptoms and impaired lung function, as well as crucial implications for the management of malignant pleural effusion (MPE). Indwelling pleural catheters have shown good symptom relief for patients with NEL; hence, identifying patients early in their disease is vital. With the inability of the lung to achieve pleural apposition following thoracentesis and the formation of a hydropneumothorax, traditionally, chest X-ray and clinical symptoms have been used to make the diagnosis following thoracentesis. It is our aim to investigate whether ultrasound measurement of lung movement during respiration can predict NEL before thoracentesis, thereby aiding clinicians in their planning for the optimal treatment of affected patients. Methods: A total of 49 patients were consecutively included in a single-centre trial performed at a pleural clinic. Patients underwent protocolled ultrasound assessment pre-thoracentesis with measurements of lung and diaphragm movement and shear wave elastography measurements of the pleura and pleural effusion at the planned site of thoracentesis. Results: M-mode measurements of lung movement provided the best diagnostic ROC-curve results, with an AUC of 0.81. Internal validity showed good results utilising the calibration belt test and Brier test. Conclusion: M-mode measurement of lung movement shows promise in diagnosing NEL before thoracentesis in patients with known or suspected MPE. A validation cohort is needed to confirm the results. Full article

The binding of drugs to nucleic acids, proteins, lipids, amino acids, and other biological receptors is necessary for the transportation of drugs. However, various side effects may also originate if the bound drug molecules are not dissociated from the carrier, especially with the aid of non-toxic agents. The sequestration of small drug molecules bound to biomolecules is thus central to counter issues related to drug overdose and drug detoxification. In this article, we aim to present several methods used for the dissociation of small drug molecules bound to different biological and biomimicking assemblies under in vitro experimental conditions. To this effect, the application of various molecular assemblies, like micelles, mixed micelles, molecular containers, like β-cyclodextrin, cucurbit[7]uril hydrate, etc., has been discussed. Herein, we also try to shed light on the driving forces underlying such sequestration processes through spectroscopic and calorimetric techniques. Full article

The current gold standard technique for the treatment of anterior cruciate ligament (ACL) injury is reconstruction with a tendon autograft. These treatments have a relatively high failure and re-rupture rate and are associated with early-onset osteoarthritis, developing within two decades of injury. Furthermore, both autografting and allografting come with several drawbacks. Tissue engineering and additive manufacturing present exciting new opportunities to explore 3D scaffolds as graft substitutes. We previously showed that 3D-printed scaffolds using low-cost equipment are suitable for tissue engineering approaches to regenerative medicine. Here, we hypothesize that Lay-Fomm 60, a commercially available nanoporous elastomer, may be a viable tissue engineering candidate for an ACL graft substitute. We first printed nanoporous thermoplastic elastomer scaffolds using low-cost desktop 3D printers and determined the mechanical and morphological properties. We then tested the impact of different surface coatings on primary human ACL fibroblast adhesion, growth, and ligamentous matrix deposition in vitro. Our data suggest that poly-L-lysine-coated Lay-Fomm 60 scaffolds increased ligament fibroblast activity and matrix formation when compared to uncoated scaffolds but did not have a significant effect on cell attachment and proliferation. Therefore, uncoated 3D printed Lay-Fomm 60 scaffolds may be viable standalone scaffolds and warrant further research as ligament tissue engineering and reconstruction grafts. Full article

Background: Immune checkpoint inhibitors (ICI) are increasingly the mainstay of oncology treatment. Immune-related adverse events (irAEs) from ICI therapy differ from cytotoxic adverse events. Cutaneous irAEs are one of the most common irAEs and require careful attention to optimize the quality of life for oncology patients. Patient and Methods: These are two cases of patients with advanced solid-tumour malignancies treated with PD-1 inhibitor therapy. Results: Both patients developed multiple pruritic hyperkeratotic lesions, which were initially diagnosed as squamous cell carcinoma from skin biopsies. The presentation as squamous cell carcinoma was atypical and, upon further pathology review, the lesions were more in keeping with a lichenoid immune reaction stemming from the immune checkpoint blockade. With the use of oral or topical steroids and immunomodulators, the lesions resolved. Conclusions: These cases emphasize that patients on PD-1 inhibitor therapy who develop lesions resembling squamous cell carcinoma on initial pathology may require an additional pathology review to assess for immune-mediated reactions, allowing appropriate immunosuppressive therapy to be initiated. Full article

The present study explores the efficacy of Machine Learning and Artificial Neural Networks in age assessment using the root length of the second and third molar teeth. A dataset of 1000 panoramic radiographs with intact second and third molars ranging from 12 to 25 years was archived. The length of the mesial and distal roots was measured using ImageJ software. The dataset was classified in three ways based on the age distribution: 2–Class, 3–Class, and 5–Class. We used Support Vector Machine (SVM), Random Forest (RF), and Logistic Regression models to train, test, and analyze the root length measurements. The mesial root of the third molar on the right side was a good predictor of age. The SVM showed the highest accuracy of 86.4% for 2–class, 66% for 3–class, and 42.8% for 5–Class. The RF showed the highest accuracy of 47.6% for 5–Class. Overall the present study demonstrated that the Deep Learning model (fully connected model) performed better than the Machine Learning models, and the mesial root length of the right third molar was a good predictor of age. Additionally, a combination of different root lengths could be informative while building a Machine Learning model. Full article

Background and Objectives: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. Materials and Methods: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images’ slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. Results: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. Conclusions: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance. Full article

Background: Wearable device technology has recently been involved in the healthcare industry substantially. India is the world’s third largest market for wearable devices and is projected to expand at a compound annual growth rate of ~26.33%. However, there is a paucity of literature analyzing the factors determining the acceptance of wearable healthcare device technology among low-middle-income countries. Methods: This cross-sectional, web-based survey aims to analyze the perceptions affecting the adoption and usage of wearable devices among the Indian population aged 16 years and above. Results: A total of 495 responses were obtained. In all, 50.3% were aged between 25–50 years and 51.3% belonged to the lower-income group. While 62.2% of the participants reported using wearable devices for managing their health, 29.3% were using them daily. technology and task fitness (TTF) showed a significant positive correlation with connectivity (r = 0.716), health care (r = 0.780), communication (r = 0.637), infotainment (r = 0.598), perceived usefulness (PU) (r = 0.792), and perceived ease of use (PEOU) (r = 0.800). Behavioral intention (BI) to use wearable devices positively correlated with PEOU (r = 0.644) and PU (r = 0.711). All factors affecting the use of wearable devices studied had higher mean scores among participants who were already using wearable devices. Male respondents had significantly higher mean scores for BI (p = 0.034) and PEOU (p = 0.009). Respondents older than 25 years of age had higher mean scores for BI (p = 0.027) and Infotainment (p = 0.032). Conclusions: This study found a significant correlation with the adoption and acceptance of wearable devices for healthcare management in the Indian context. Full article

Medical imaging is a complex process that capitulates images created by X-rays, ultrasound imaging, angiography, etc. During the imaging process, it also captures image noise during image acquisition, some of which are extremely corrosive, creating a disturbance that results in image degradation. The proposed work addresses the challenge to eliminate the corrosive Gaussian additive white noise from computed tomography (CT) images while preserving the fine details. The proposed approach is synthesized by amalgamating the concept of method noise with a deep learning-based framework of a convolutional neural network (CNN). The corrupted images are obtained by explicit addition of Gaussian additive white noise at multiple noise variance levels (σ = 10, 15, 20, 25). The denoised images obtained are then evaluated according to their visual quality and quantitative metrics, such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). These metrics for denoised CT images are then compared with their respective values for the reference CT image. The average PSNR value of the proposed method is 25.82, the average SSIM value is 0.85, and the average computational time is 2.8760. To better understand the proposed approach’s effectiveness, an intensity profile of denoised and original medical images is plotted and compared. To further test the performance of the proposed methodology, the results obtained are also compared with that of other non-traditional methods. The critical analysis of the results shows the commendable efficiency of the proposed methodology in denoising the medical CT images corrupted by Gaussian noise. This approach can be utilized in multiple pragmatic areas of application in the field of medical image processing. Full article

Most cultivated potatoes are tetraploid, and the tuber is the main economic part that is consumed due to its calorific and nutritional values. Recent trends in climate change led to the frequent occurrence of heat and drought stress in major potato-growing regions worldwide. The optimum temperature for tuber production is 15–20 °C. High-temperature and water-deficient conditions during the growing season result in several morphological, physiological, biochemical, and molecular alterations. The morphological changes under stress conditions may affect the process of stolon formation, tuberization, and bulking, ultimately affecting the tuber yield. This condition also affects the physiological responses, including an imbalance in the allocation of photoassimilates, respiration, water use efficiency, transpiration, carbon partitioning, and the source–sink relationship. The biochemical responses under stress conditions involve maintaining ionic homeostasis, synthesizing heat shock proteins, achieving osmolyte balance, and generating reactive oxygen species, ultimately affecting various biochemical pathways. Different networks that include both gene regulation and transcription factors are involved at the molecular level due to the combination of hot and water-deficient conditions. This article attempts to present an integrative content of physio-biochemical and molecular responses under the combined effects of heat and drought, prominent factors in climate change. Taking into account all of these aspects and responses, there is an immediate need for comprehensive screening of germplasm and the application of appropriate approaches and tactics to produce potato cultivars that perform well under drought and in heat-affected areas. Full article