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Musculoskeletal calcifications are frequent on radiographs and sometimes problematic. The goal of this article is to help radiologists to make the correct diagnosis when faced with an extraosseous musculoskeletal calcification. One should first differentiate a calcification from an ossification or a foreign body and then locate the calcification correctly. Each location has a specific short differential diagnosis, with minimal further investigation necessary. Intra-tendon calcifications are most frequently associated with hydroxyapatite deposition disease (HADD). In most cases, intra-articular calcifications are caused by calcium pyrophosphate dihydrate (CPPD) crystal deposition disease. Soft tissue calcification can be caused by secondary tumoural calcinosis from renal insufficiency, or collagen vascular diseases and by vascular calcifications, either arterial or venous (phlebolith).Teaching Points• Calcifications have to be differentiated form ossification and foreign body.• A musculoskeletal MRI study must always be correlated with a radiograph.• The clinical manifestations of calcifications may sometimes mimic septic arthritis or sarcoma.• HADD and CPPD crystal deposition have a distinct appearance on radiograph.• Calcinosis is more frequently caused by chronic renal failure and scleroderma.

In the course of the digitization of production facilities, tracking and tracing of assets in the supply chain is becoming increasingly relevant for the manufacturing industry. The collection and use of real-time position data of logistics, tools and load carriers are already standard procedure in entire branches of the industry today. In addition to asset tracking, the technologies used also offer new possibilities for collecting and evaluating position and biometric data of employees. Thus, these technologies can be used for monitoring performance or for tracking worker behaviour, which can lead to additional burdens and stress for employees. In this context, the collection and evaluation of employee data can influence the workplace of the affected employee in the company to his or her disadvantage. The approach of Privacy by Design can help to benefit from all the advantages of these systems, while ensuring that the impact on employee privacy is kept to a minimum. Currently, there is no survey available that reviews tracking and tracing systems supporting this important and emerging field. This work provides a systematic overview from the perspective of the impact on employee privacy. Additionally, this paper identifies and evaluates the techniques used with regard to employee privacy in industrial tracking and tracing systems. This helps to reveal new privacy preserving techniques that are currently underrepresented, therefore enabling new research opportunities in the industrial community.

This study develops and implements a scalable system architecture for dynamic data acquisition and knowledge modeling in industrial contexts. The objective is to efficiently process large datasets to support decision-making and process optimization within Industry 4.0. The architecture integrates modern technologies, such as the ibaPDA system for data acquisition, and employs communication standards like Modbus TCP and OPC UA to ensure broad compatibility with diverse equipment. In addition, it leverages native protocols offered by certain controllers, enabling direct data exchange without the need for conversion layers. A developed prototype demonstrates the practical applicability of the architecture, tested in a real industrial environment with a focus on processing speed, data integrity, and system reliability. The results indicate that the architecture not only meets the requirements for dynamic data acquisition but also enhances knowledge modeling. This leads to more efficient process control and opens new perspectives for managing and analyzing big data in production environments. The study emphasizes the importance of an integrated development approach and highlights the need for interdisciplinary collaboration to address operational challenges. Future extensions may include the implementation of Python interfaces and machine learning algorithms for data simulation, enabling more accurate predictive models. These findings provide valuable insights for industry, software development, data science, and academia, helping to tackle the challenges of Industry 4.0 and drive innovation forward.

ObjectivesTo evaluate lesion filling and other factors that could affect the clinical outcomes of cementoplasty for pelvic bone metastases.MethodsWe retrospectively reviewed the files of 40 patients treated for 44 pelvic bone metastases, collected the parameters related to patients (pain relief evaluated on a visual analog scale, subsequent fractures, and need for surgery), lesions (size, cortical breach score, fracture, soft-tissue extension), and cementoplasty procedures (number of needles, volume of cement, percentage of lesion filling, cement leaks, residual acetabular roof defect), and performed a statistical analysis.ResultsThe lesions were on average 43.2 mm in diameter and the mean cortical breach score was 2.5 out of 6, with a pathological fracture in 14 lesions. The number of needles inserted was one in 32 out of 44, two in 10 out of 44, and three in 2 out of 44. On average, the volume of cement injected per lesion was 10.3 ml and the filling was 54.8%. Mild or moderate asymptomatic cement leakage occurred in 20 lesions (45.5%). The mean pain score was 84.2 mm before the procedure (with no correlation with lesion size, cortical breach score or fracture) and 45.6 mm at follow-up. The pain relief of 38.6 mm was statistically significant (p < 0.001) and did not correlate with the filling percentage. There were no fractures of the treated lesions at a mean follow-up of 355 days.ConclusionsCementoplasty of pelvic bone metastases appears effective for providing pain relief and may prevent subsequent fractures. We were unable to demonstrate a correlation between the lesion filling and the degree of pain relief.

Agriculture is the dominating land-use in the EU member states covering nearly half of the surface area. Using herbicides to reduce weed competition in agricultural areas can adversely affect Non-Target Terrestrial Plants (NTTP) growing in field margins. According to the EFSA Scientific Opinion on NTTPs an important protection goal is to maintain the biodiversity of plant species in agricultural areas. EFSA recommends to include also non-crop species mentioned in OECD guidelines (OECD 208 and 227) in the testing and to assess not only vegetative but also generative endpoints during the plant life-cycle such as flowering and seed production. The objectives of this study were to evaluate the feasibility of assessing generative endpoints of crop and non-crop species for NTTP regulatory testing under greenhouse conditions and to assess if generative endpoints are more sensitive than vegetative endpoints. The experimental design consisted of one control and four herbicide (Atlantis® WG) application rates, with 6 replicates each. The application rates of the test substance were the maximum field rate and 30%, 10% and 3% of the field rate. Biomass, plant height, flowering, seed production as well as seedling emergence of the F1 generation were assessed. The study shows a feasible approach to assess vegetative and generative endpoints of (non-) crops species under greenhouse conditions on the basis of the OECD guideline 227. The vegetative endpoints plant height and biomass were not more sensitive if assessed during the generative growth stage when compared to the vegetative growth stage of the plants. In contrast to that, the generative endpoint seed production was partly more sensitive in comparison to the vegetative endpoints biomass and plant height. For regulatory NTTP studies, 5 or more test substance rates at non-lethal levels should be tested so to allow the determination of ER10/50 values for vegetative and generative endpoints.

This article has been retracted. Please see the Retraction Notice for more detail: https://doi.org/10.1186/s12889-024-18629-0.

Early Childhood Education and Care (ECEC) places value and benefits on children’s play in nature-based outdoor spaces. However, there is a lack of knowledge regarding teachers’ support of play in environments with rugged terrains and natural materials. Therefore, this systematic review aims to locate, present, and discuss research literature on how teachers in ECEC settings can support children’s play in nature-based outdoor spaces. According to the review, teachers’ support of children’s play was described in the literature as encouraging children’s free and unstructured play through facilitating actions addressing the children’s perspectives and the opportunities offered by the physical environment. Furthermore, teacher support was also described as teacher-led and teacher-guided interactions where teachers and children communicate or collaborate in playful situations. Differences and overlapping elements of types of teacher support are discussed, and implications for researchers, practitioners, and ECEC teacher education are provided.

South Africa was the most affected country in Africa by the coronavirus disease 2019 (COVID-19) pandemic, where over 4 million confirmed cases of COVID-19 and over 102,000 deaths have been recorded since 2019. Aside from clinical methods, artificial intelligence (AI)-based solutions such as machine learning (ML) models have been employed in treating COVID-19 cases. However, limited application of AI for COVID-19 in Africa has been reported in the literature. This study aimed to investigate the performance and interpretability of several ML algorithms, including deep multilayer perceptron (Deep MLP), support vector machine (SVM) and Extreme gradient boosting trees (XGBoost) for predicting COVID-19 mortality risk with an emphasis on the effect of cross-validation (CV) and principal component analysis (PCA) on the results. For this purpose, a dataset with 154 features from 490 COVID-19 patients admitted into the intensive care unit (ICU) of Tygerberg Hospital in Cape Town, South Africa, during the first wave of COVID-19 in 2020 was retrospectively analysed. Our results show that Deep MLP had the best overall performance (F1 = 0.92; area under the curve (AUC) = 0.94) when CV and the synthetic minority oversampling technique (SMOTE) were applied without PCA. By using the Shapley Additive exPlanations (SHAP) model to interpret the mortality risk predictions, we identified the Length of stay (LOS) in the hospital, LOS in the ICU, Time to ICU from admission, days discharged alive or death, D-dimer (blood clotting factor), and blood pH as the six most critical variables for mortality risk prediction. Also, Age at admission, Pf ratio (PaO2/FiO2 ratio), troponin T (TropT), ferritin, ventilation, C-reactive protein (CRP), and symptoms of acute respiratory distress syndrome (ARDS) were associated with the severity and fatality of COVID-19 cases. The study reveals how ML could assist medical practitioners in making informed decisions on handling critically ill COVID-19 patients with comorbidities. It also offers insight into the combined effect of CV, PCA, and SMOTE on the performance of ML models for COVID-19 mortality risk prediction, which has been little explored.

Percutaneous approaches for pelvic bone procedures (bone biopsies, tumour ablation and cementoplasty) are multiple and less well systematised than for the spine or extremities. Among the different imaging techniques that can be used for guidance, computed tomography (CT) scan is the modality of choice because of the complex pelvic anatomy. In specific cases, such as cementoplasty where real-time evaluation is a determinant, a combination of CT and fluoroscopy is highly recommended. The objective of this article is to propose a systematic approach for image-guided pelvic bone procedures, as well as to provide some technical tips. We illustrate the article with multiple examples, and diagrams of the approaches and important structures to avoid to perform these procedures safely.Teaching Points• Pelvic bone procedures are safe to perform if anatomical landmarks are recognised.• The safest approach varies depending on the pelvic level.• CT is the modality of choice for guiding pelvic percutaneous procedures.• Fluoroscopy is recommended when real-time monitoring is mandatory.• MRI can also be used for guiding pelvic percutaneous procedures.

A growing body of evidence suggest that the children's physical activity (PA) level in early childhood education and care (ECEC) settings are insufficient. Since most children attend ECEC settings for many hours on most days of the week, and these institutions reach children across the socioeconomic spectrum, the ECEC settings may serve as an ideal avenue for increasing physical activity level, reduce sedentary time and enhance the overall health of young children. This paper investigates the effectiveness of the \"Active Kindergarten - Active Children\" study to increase children's PA level and reduce sedentary time within the ECEC setting. Accelerometers were used to asses PA and sedentary time. A total of 116 three to four-year olds took part in a randomised controlled trial in 11 ECEC settings. Participants were cluster-randomised, by ECEC setting, to either a 12 week staff-led and expert-supported intervention or a waiting list control group. The intervention group increased time spent in moderate- and vigorous intensity PA by 10 min/day (95% CI = 3, 18; P = 0.01), took 1909 more steps per day (95% CI = 1130, 2688; P < 0.01) and reduced sedentary time with 14 min/day (95% CI = - 27, - 1; P = 0.04) compared to the control group. The intervention group had a 2.4 higher odds (95% CI = 1.05, 5.7; P = 0.04) of meeting the PA recommendations compared to the control group at follow-up. Our results show that a flexible staff-led and expert-supported multicomponent PA intervention can increase total PA level, moderate- and vigorous intensity PA and reduce time spent sedentary in three to four-year old children during their stay in ECEC settings. The trial was retrospectively registered on September 19, 2020 and available online at ClinicalTrials.gov: No. NCT04555746 .