Search Results (40)

Background/Objectives: Long-acting insulin glargine (iGlar) has been available as a biosimilar since 2014 in the UK. We reviewed previous prescribing to evaluate if the anticipated cost savings with biosimilars were realized with iGlar. Methods: This study investigated prescribing patterns of long-acting iGlar (100 units/mL) in cartridges and pre-filled pens from 2020 to 2024 across primary care organizations in England, Northern Ireland, Scotland, and Wales. Results: iGlar prescribing declined in all of the four nations. From 2020 to 2024, the total prescribed quantity of biosimilars persistently increased in all countries, reaching 24% in England, 5% in Northern Ireland, 24% in Scotland, and 11% in Wales, all in 2024. Consequently, the proportion of Lantus prescriptions (as quantity) decreased but continued to exceed that of all available iGlar products combined in all countries in all years analyzed. By 2024, Lantus was also priced lower than the most common biosimilar, Abasaglar, across all nations. Conclusions: The introduction of biosimilars does not automatically result in altered prescribing practices, though we show that the most commonly prescribed iGlar was also the least expensive product at the end of the analysis period. At launch and for several years after, biosimilars failed to gain strong utilization, despite cost advantages, highlighting the need for active switching policies and prescriber engagement. Full article

This paper presents an interactive online e-portal development and application using Shiny/R version 4.4.0 programming for personalised varifocal lens surface design and manufacturing in an agile and responsive manner. Varifocal lenses are specialised lenses that provide clear vision at both far and near distances. The user interface (UI) of the e-portal application creates an environment for customers to input their eye prescription data and geometric parameters to visualise the result of the designed freeform varifocal lens surface, which includes interactive 2D contour plots and 3D-rendered diagrams for both left and right eyes simultaneously. The e-portal provides a unified interactive platform where users can simultaneously access both the specialised Copilot demo web for lenses and the main Shiny/R version 4.4.0 programming app, ensuring seamless integration and an efficient process flow. Additionally, the data points of the 3D-designed surface are automatically saved. In order to check the performance of the designed varifocal lens before production, it is remodelled in the COMSOL Multiphysics 6.2 modelling and analysis environment. Ray tracing is built in the environment for the lens design assessment and is then integrated with the lens additive manufacturing (AM) using a Formlabs 3D printer (Digital Fabrication Center (DFC), London, UK). The results are then analysed to further validate the e-portal-driven personalised design and manufacturing approach. Full article

In modern agriculture, plant protection is the key to ensuring crop health and improving yields. Intelligent pesticide prescription spraying (IPPS) technologies monitor, diagnose, and make scientific decisions about pests, diseases, and weeds; formulate personalized and precision control plans; and prevent and control pests through the use of intelligent equipment. This study discusses key IPSS technologies from four perspectives: target information acquisition, information processing, pesticide prescription spraying, and implementation and control. In the target information acquisition section, target identification technologies based on images, remote sensing, acoustic waves, and electronic nose are introduced. In the information processing section, information processing methods such as information pre-processing, feature extraction, pest and disease identification, bioinformatics analysis, and time series data are addressed. In the pesticide prescription spraying section, the impact of pesticide selection, dose calculation, spraying time, and method on the resulting effect and the formulation of prescription pesticide spraying in a certain area are explored. In the implement and control section, vehicle automatic control technology, precision spraying technology, and droplet characteristic control technology and their applications are studied. In addition, this study discusses the future development prospectives of IPPS technologies, including multifunctional target information acquisition systems, decision-support systems based on generative AI, and the development of precision intelligent sprayers. The advancement of these technologies will enhance agricultural productivity in a more efficient, environmentally sustainable manner. Full article

A new method based on the Recurrence Quantification Analysis (RQA) of the heart rate (HR) offers an objective, efficient alternative to traditional methods for Aerobic Threshold (AerT) identification that have practical limitations due to the complexity of equipment and interpretation. This study aims to validate the RQA-based method’s applicability across varied demographics, exercise protocols, and health status. Data from 123 cardiopulmonary exercise tests were analyzed, and participants were categorized into four groups: athletes, young athletes, obese individuals, and cardiac patients. Each participant’s AerT was assessed using both traditional ventilatory equivalent methods and the automatic RQA-based method. Ordinary Least Products (OLP) regression analysis revealed strong correlations (r > 0.77) between the RQA-based and traditional methods in both oxygen consumption (VO₂) and HR at the AerT. Mean percentage differences in HR were below 2.5%, and the Technical Error for HR at AerT was under 8%. The study validates the RQA-based method, directly applied to HR time series, as a reliable tool for the automatic detection of the AerT, demonstrating its accuracy across diverse age groups and fitness levels. These findings suggest a versatile, cost-effective, non-invasive, and objective tool for personalized exercise prescription and health risk stratification, thereby fulfilling the study’s goal of broadening the method’s applicability. Full article

A precision agriculture approach that uses drones for crop protection and variable rate application has become the main method of rice weed control, but it suffers from excessive spraying issues, which can pollute soil and water environments and harm ecosystems. This study proposes a method to generate variable spray prescription maps based on the actual distribution of weeds in rice fields and utilize DJI plant protection UAVs to perform automatic variable spraying operations according to the prescription maps, achieving precise pesticide application. We first construct the YOLOv8n DT model by transferring the “knowledge features” learned by the larger YOLOv8l model with strong feature extraction capabilities to the smaller YOLOv8n model through knowledge distillation. We use this model to identify weeds in the field and generate an actual distribution map of rice field weeds based on the recognition results. The number of weeds in each experimental plot is counted, and the specific amount of pesticide for each plot is determined based on the amount of weeds and the spraying strategy proposed in this study. Variable spray prescription maps are then generated accordingly. DJI plant protection UAVs are used to perform automatic variable spraying operations based on prescription maps. Water-sensitive papers are used to collect droplets during the automatic variable operation process of UAVs, and the variable spraying effect is evaluated through droplet analysis. YOLOv8n-DT improved the accuracy of the model by 3.1% while keeping the model parameters constant, and the accuracy of identifying weeds in rice fields reached 0.82, which is close to the accuracy of the teacher network. Compared to the traditional extensive spraying method, the approach in this study saves approximately 15.28% of herbicides. This study demonstrates a complete workflow from UAV image acquisition to the evaluation of the variable spraying effect of plant protection UAVs. The method proposed in this research may provide an effective solution to balance the use of chemical herbicides and protect ecological safety. Full article

The twenty-first century has witnessed an extensive evolution in translation practice thanks to the accelerated progress in machine translation tools and software. With the increased scalability and availability of machine translation software empowered by artificial intelligence, translation students and practitioners have continued to show an unwavering reliance on automatic translation systems. Academically, there is little recognition of the need to develop machine translation skillsets amongst translation learners in English/Arabic translation programs. This study provides a systematic review of machine translation postediting with reference to English/Arabic machine translation. Using the Preferred Reporting Items for Systematic Review and Meta-Analysis, the paper reviewed 60 studies conducted since the beginning of the twenty-first century and classified them by different metrics to identify relevant trends and research gaps. The results showed that research on the topic has been primarily prescriptive, concentrating on evaluating and developing machine translation software while neglecting aspects related to translators’ skillsets and competencies. The paper highlights the significance of postediting as an important digital literacy to be developed among Arabic translation students and the need to bridge the existing research and pedagogic gap in MT education. Full article

Background: Observational studies of intermittent hemodialysis therapy have reported that the excess decrease in K⁺ concentration in plasma (KP) during treatment is associated with the destabilization of cardiac function. Elucidating the mechanism by which the decrease in KP impairs myocardial excitation is indispensable for a deeper understanding of prescription design. Methods: In this study, by using an electrophysiological mathematical model, we investigated the relationship between KP dynamics and cardiomyocyte excitability for the first time. Results: The excess decrease in KP during treatment destabilized cardiomyocyte excitability through the following events: (1) a decrease in KP led to the prolongation of the depolarization phase of ventricular cells due to the reduced potassium efflux rate of the Kr channel, temporarily enhancing contraction force; (2) an excess decrease in KP activated the transport of K⁺ and Na⁺ through the funny channel in sinoatrial nodal cells, disrupting automaticity; (3) the excess decrease in KP also resulted in a significant decrease in the resting membrane potential of ventricular cells, causing contractile dysfunction. Avoiding an excess decrease in KP during treatment contributed to the maintenance of cardiomyocyte excitability. Conclusions: The results of these mathematical analyses showed that it is necessary to implement personal prescription or optimal control of K⁺ concentration in dialysis fluid based on predialysis KP from the perspective of regulatory science in dialysis treatment. Full article

The destructive impact of invasive apple snail (Pomacea canaliculata) on young rice seedlings has garnered global attention, particularly in warm regions where rice production occurs. The preventative application of insecticide, particularly in areas with young rice seedlings and water depths exceeding 4 cm, has proven effective in mitigating this damage. In line with this recommendation, our study investigates the efficacy of site-specific drone-based insecticide applications to mitigate snail damage in rice paddies. These site-specific drone applications were strategically executed as directed by a highly accurate prescription map indicating the required insecticide quantity at specific locations. The prescription map was automatically generated through an advanced data processing program that used the aerial images acquired by a Real-Time Kinematic (RTK)-Unmanned Aerial Vehicle (UAV) as the input. Criteria were established to select the treatment locations; a value of below 4 cm from the top 95% percentile in the histogram of ground elevation data was used as a threshold to identify areas with a high-density of snail damage. The results demonstrated reductions in both the rates of rice damage and chemical usage following site-specific drone applications compared with the control fields. The findings in this study contribute to the advancement of effective site-specific pest control in precision agriculture. Full article

Remote sensing, the Internet, the Internet of Things (IoT), artificial intelligence, and other technologies have become the core elements of modern agriculture and smart farming. Agricultural production and management modes guided by data and services have become a cutting-edge carrier of agricultural information monitoring, which promotes the transformation of the intelligent computing of remote sensing big data and agricultural intensive management from theory to practical applications. In this paper, the main research objective is to construct a new high-frequency agricultural production monitoring and intensive sharing service and management mode, based on the three dimensions of space, time, and attributes, that includes crop recognition, growth monitoring, yield estimation, crop disease or pest monitoring, variable-rate prescription, agricultural machinery operation, and other automatic agricultural intelligent computing applications. The platforms supported by this mode include a data management and agricultural information production subsystem, an agricultural monitoring and macro-management subsystem (province and county scales), and two mobile terminal applications (APPs). Taking Shandong as the study area of the application case, the technical framework of the system and its mobile terminals were systematically elaborated at the province and county levels, which represented macro-management and precise control of agricultural production, respectively. The automatic intelligent computing mode of satellite–air–ground spatiotemporal collaboration that we proposed fully couples data obtained from satellites, unmanned aerial vehicles (UAVs), and IoT technologies, which can provide the accurate and timely monitoring of agricultural conditions and real-time guidance for agricultural machinery scheduling throughout the entire process of agricultural cultivation, planting, management, and harvest; the area accuracy of all obtained agricultural information products is above 90%. This paper demonstrates the necessity of customizable product and service research in agricultural intelligent computing, and the proposed practical mode can provide support for governments to participate in agricultural macro-management and decision making, which is of great significance for smart farming development and food security. Full article

Background: This study aimed to develop a time-efficient method of acquiring simultaneous, dual-slice MR spectroscopic imaging (MRSI) for the evaluation of brain metabolism. Methods: Adaptive Hadamard-encoded pulses were developed and integrated with atlas-based automatic prescription. The excitation profiles were evaluated via simulation, phantom and volunteer experiments. The feasibility of γ-aminobutyric acid (GABA)-edited dual-slice MRSI was also assessed. Results: The signal between slices in the dual-band MRSI was less than 1% of the slice profiles. Data from a homemade phantom containing separate, interfacing compartments of creatine and acetate solutions demonstrated ~0.4% acetate signal contamination relative to the amplitude in the excited creatine compartment. The normalized signal-to-noise ratios from atlas-based acquisitions in volunteers were found to be comparable between dual-slice, Hadamard-encoded MRSI and 3D acquisitions. The mean and standard deviation of the coefficients of variation for NAA/Cho from the repeated volunteer scans were 8.2% ± 0.8% and 10.1% ± 3.7% in the top and bottom slices, respectively. GABA-edited, dual-slice MRSI demonstrated simultaneous detection of signals from GABA and coedited macromolecules (GABA+) from both superior grey and deep grey regions of volunteers. Conclusion: This study demonstrated a fully automated dual-slice MRSI acquisition using atlas-based automatic prescription and adaptive Hadamard-encoded pulses. Full article

Safety occurrence reports can contain valuable information on how incidents occur, revealing knowledge that can assist safety practitioners. This paper presents and discusses a literature review exploring how Natural Language Processing (NLP) has been applied to occurrence reports within safety-critical industries, informing further research on the topic and highlighting common challenges. Some of the uses of NLP include the ability for occurrence reports to be automatically classified against categories, and entities such as causes and consequences to be extracted from the text as well as the semantic searching of occurrence databases. The review revealed that machine learning models form the dominant method when applying NLP, although rule-based algorithms still provide a viable option for some entity extraction tasks. Recent advances in deep learning models such as Bidirectional Transformers for Language Understanding are now achieving a high accuracy while eliminating the need to substantially pre-process text. The construction of safety-themed datasets would be of benefit for the application of NLP to occurrence reporting, as this would allow the fine-tuning of current language models to safety tasks. An interesting approach is the use of topic modelling, which represents a shift away from the prescriptive classification taxonomies, splitting data into “topics”. Where many papers focus on the computational accuracy of models, they would also benefit from real-world trials to further inform usefulness. It is anticipated that NLP will soon become a mainstream tool used by safety practitioners to efficiently process and gain knowledge from safety-related text. Full article

Bronchial asthma has a high socio-economic impact in Western countries. Low adherence to prescribed inhalation treatments contributes to poor asthma control and the higher utilization of healthcare resources. Although adolescents usually do not comply with long-term inhaled treatments prescribed on a regular basis, the related economic consequences still are poorly investigated in Italy. Aim: A 12-month estimation of the economic impact of non-adherence to inhalation treatments in adolescents with mild-to-moderate atopic asthma. Methods: Non-smoking adolescents aged 12–19 years, without any significant comorbidity, prescribed with inhaled cortico-steroids (ICS) or ICS/long-acting beta(2)-adrenergics (LABA) via dry powder inhalers (DPIs) on a regular basis were automatically selected from the institutional database. Spirometric lung function, clinical outcomes, and pharmacological information were collected. The adolescents’ adherence to their prescribed regimen was calculated monthly. Adolescents were divided in two sub-groups based on their adherence to prescriptions: ≤70% (not adherent) or >70% (adherent), and statistically compared (Wilcoxon test, assuming p < 0.05). Results: Overall, 155 adolescents fulfilled the inclusion criteria (males, 49.0%; mean age, 15.6 years ± 2.9 SD; mean BMI, 19.1 ± 1.3 SD). Mean values of lung function were: FEV1 = 84.9% pred. ± 14.8 SD, FEV1/FVC = 87.9 ± 12.5 SD; MMEF = 74.8% pred. ± 15.1 SD and V25 = 68.4% pred. ± 14.9 SD. ICS had been prescribed in 57.4% of subjects and ICS/LABA in 42.6%. Mean adherence to original prescriptions was 46.6% ± 9.2 SD in non-adherent and 80.3% ± 6.6 SD in adherent adolescents, respectively (p < 0.001). The mean rates of hospitalizations, exacerbations, and GP visits; the average duration of absenteeism; the frequency of systemic steroids and antibiotics courses needed over the study period were significantly and substantially lower in adolescents adherent to prescriptions (all p < 0.001). The mean total annual extra cost calculated in the two sub-groups was EUR 705.8 ± 420.9 SD in non-adherent adolescents and EUR 192.1 ± 68.1 SD in adherent adolescents, respectively (p < 0.001), which was 3.7 times higher than in non-adherent adolescents. Conclusions: In adolescents, the clinical control of mild-to-moderate atopic asthma is directly and strictly related to the degree of adherence to prescribed inhalation therapies. All clinical and economic outcomes prove dramatically poor when adherence is low, and treatable asthma can be frequently mistaken for refractory asthma in these cases. Adolescents’ non-adherence impacts the burden of the disease quite substantially. Much more effective strategies centered specifically on adolescents’ asthma are needed. Full article

Enterococci cause infections with various localizations, the most common being urinary infections. The purpose of the study was to identify the profile of the antimicrobial resistance of enterococci species (AMRE) isolated from patients hospitalized in three hospitals in Romania. We evaluated AMRE retrospectively (2019–2021) in various biological samples. The microbiological diagnosis was sustained by classical methods of bacteria culture and automatic identification. The sensitivity testing was performed by the Kirby–Bauer method, and the antibiotic minimum inhibitory concentration was tested by the automated Vitek system. We analyzed 86 strains of Enterococcus spp., identifying the following species: 47.7% E. faecalis, 47.7% E. faecium, 3.55% E. gallinarum, and 1% E. hirae. Most of the bacterial strains were isolated from urocultures (38.4%) and hemocultures (32.6%). Overall, the rate of vancomycin resistance was 5.8% for E. faecalis and 15.1%. for E. faecium. The prevalence of multidrug-resistant (MDR) strains was found to be 100% in E. gallinarum, 75.6% in E. faecium, and 21.9% in E. faecalis. The results confirm the high level of AMRE, which creates difficulties with adequate antibiotic prescriptions. The continuous monitoring of AMRE is essential for updating the local diagnostic and treatment protocols for enterococcal infections. Full article

Foot pain is a common musculoskeletal disorder. Orthotic insoles are widely used in patients with foot pain. Inexperienced clinicians have difficulty prescribing orthotic insoles appropriately by considering various factors associated with the alteration of foot alignment. We attempted to develop deep-learning algorithms that can automatically prescribe orthotic insoles to patients with foot pain and assess their accuracy. In total, 838 patients were included in this study; 70% (n = 586) and 30% (n = 252) were used as the training and validation sets, respectively. The resting calcaneal stance position and data related to pelvic elevation, pelvic tilt, and pelvic rotation were used as input data for developing the deep-learning algorithms for insole prescription. The target data were the foot posture index for the modified root technique and the necessity of heel lift, entire lift, and lateral wedge, medial wedge, and calcaneocuboid arch supports. In the results, regarding the foot posture index for the modified root technique, for the left foot, the mean absolute error (MAE) and root mean square error (RMSE) of the validation dataset for the developed model were 1.408 and 3.365, respectively. For the right foot, the MAE and RMSE of the validation dataset for the developed model were 1.601 and 3.549, respectively. The accuracies for heel lift, entire lift, and lateral wedge, medial wedge, and calcaneocuboid arch supports were 89.7%, 94.8%, 72.2%, 98.4%, and 79.8%, respectively. The micro-average area under the receiver operating characteristic curves for heel lift, entire lift, and lateral wedge, medial wedge, and calcaneocuboid arch supports were 0.949, 0.941, 0.826, 0.792, and 0.827, respectively. In conclusion, our deep-learning models automatically prescribed orthotic insoles in patients with foot pain and showed outstanding to acceptable accuracy. Full article

Patient compliance with prescribed medication regimens is critical for maintaining health and managing disease and illness. To encourage patient compliance, multiple aids, like automatic pill dispensers, pill organizers, and various reminder applications, have been developed to help people adhere to their medication regimens. However, when utilizing these aids, the user or patient must manually enter their medication information and schedule. This process is time-consuming and often prone to error. For example, elderly patients may have difficulty reading medication information on the bottle due to decreased eyesight, leading them to enter medication information incorrectly. This study explored methods for extracting pertinent information from cylindrically distorted prescription drug labels using Machine Learning and Computer Vision techniques. This study found that Deep Convolutional Neural Networks (DCNN) performed better than other techniques in identifying label key points under different lighting conditions and various backgrounds. This method achieved a percentage of Correct Key points PCK @ 0.03 of 97%. These key points were then used to correct the cylindrical distortion. Next, the multiple dewarped label images were stitched together and processed by an Optical Character Recognition (OCR) engine. Pertinent information, such as patient name, drug name, drug strength, and directions of use, were extracted from the recognized text using Natural Language Processing (NLP) techniques. The system created in this study can be used to improve patient health and compliance by creating an accurate medication schedule. Full article