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Workflow evaluation of environmental contamination with hazardous drugs during compounding and administration in an UK hospital
Full text linkIntroduction: Exposure of healthcare workers to hazardous drugs may result in adverse health effects underscoring the importance of validating working procedures and safety precautions to minimise the risk. The objective was to monitor environmental contamination caused by the hazardous drug workflow: from drug vials, compounding process, to patient administration. Methods: Surface wipe samples were collected from potentially contaminated surfaces in the compounding department and in the administration department. The outside of drug vials, compounded syringes, bags, elastomeric pumps, and gloves used by the nurses for administration were also monitored. Stationary air samples were collected near the isolators and above the bench top. Personal air samples were collected from pharmacy technicians, pharmacists, and nurses. Monitoring was performed in three trials during two-months. Samples were analysed for cyclophosphamide, 5-fluorouracil, docetaxel, and paclitaxel using liquid chromatography tandem mass spectrometry. Results: Contamination was mainly found for 5-fluorouracil and cyclophosphamide on isolator surfaces, bench top, trays, and compounded products. Lower levels of contamination were measured in the administration department on trays, trolley arms and gloves of the nurses. Paclitaxel and docetaxel were incidentally detected. Air contamination was found for paclitaxel in the compounding department in one trial, and 5-fluorouracil was detected once in front of an isolator. Docetaxel was found in one air sample of a nurse. Conclusions: Contamination was mainly found for 5-fluorouracil and cyclophosphamide on the products compounded in the isolators. Contamination was further spread along the workflow towards the administration department causing surfaces in between being contaminated too
Automated monitoring of alcoholic fermentation: trends and challenges
No full textThe progress of fermentation, an important step in spirit production, needs to be monitored regularly to detect possible faults. Automated monitoring of fermentation, however, is often limited to only a few parameters of the mash such as, and mainly, its temperature. With the advance of sensor technology and data analytics, various solutions to automated fermentation monitoring emerged, mainly for the beer and wine industry, however, these are not yet critically evaluated and compared. Thus, scientific articles on automated monitoring of alcoholic fermentation are reviewed and evaluated here according to the type of sensors used, the type of fermented material, and the reproducibility and feasibility of the presented solutions. Possible data analytics methods to utilize are introduced and their pros and cons are discussed. A critical evaluation from scientific and industrial perspectives is provided with prospects for the distilling industry where mashes of various states of matter, inhomogeneity and viscosity can appear. Key findings and conclusions of this review are: Electronic nose and electronic tongue biosensors are a promising direction in the area. A publicly available database on recorded data from e-nose and e-tongue as well as other sensors on fermentation monitoring is needed but still missing. Current solutions on automated fermentation monitoring are rather isolated studies, conducted in laboratories, yet to be evaluated and tested in industrial environments. The use of machine learning techniques in these studies, in general, does not comply with the well-established standards in data science and artificial intelligence
INTERCEPT: a feasibility study of a digital intervention by nurse prescribers for secondary prevention of cardiovascular disease
No full textBackground: Patients with coronary disease are commonly diagnosed, revascularised and discharged from hospital within 24-72 hours and only about one third access cardiac rehabilitation often with delays of weeks before joining such programmes. Purpose: To bridge this care gap we developed the INTERCEPT digital model for secondary prevention. It comprises an I-App on a smart device, linked to wearables, and a Health Professional Portal overseen by a nurse prescriber in hospital to monitor and manage patients virtually. It was developed by co-design with patients, health professionals and a software company. The I-App encompasses lifestyle, risk factor control to targets and cardioprotective medications. Methods: We conducted a feasibility study in the CCU/CTU at a University Hospital in patients with an acute coronary syndrome or elective revascularisation. The primary objective was to examine the feasibility of patient recruitment, engagement and usage from the I-App analytical data and acceptability of this form of preventive care among patients through qualitative interviews. Results: 40 patients were recruited: mean age of 61.9 years (13% female); 56% STEMI/NSTEMI, 18% unstable angina, 26% elective PTCA/CABG. 25% were smoking, Mediterranean diet score 6.5 (SD 1.8), 87% BMI > 25 Kg/m2 and 68% low or moderate (IPAQ) physical activity. HADS: 27% anxiety >8 and 14% depression score >8. 58% had a BP <130/80 mmHg, 21% had a LDL-C <1.4 mmol/l and 29% of those with diabetes had a HbA1c <53. The commonest reason for non-enrolment was not owning a smart phone followed by early transfer back to the referring hospital and no interest in technology or research. For the patients recruited retention of I-App usage was 100%. Total number of I-App views was 25,965 (average 7.2 times per user per day) over 3 months. The most frequently viewed pages were: ‘My numbers’ (weight, waist, cholesterol, blood glucose, HbA1c); ‘My devices’ (heart rate, blood pressure, steps, active minutes); ‘My health tracker’ (healthy eating, physical activity, mood); ‘Medications’(reminders); and ‘Goals’. Qualitative interviews with 11 patients revealed the I-App as a trusted source of information, education and inspiration providing: (i) information in their hands and recording daily activities and medications; (ii) remote monitoring by the nurse providing reassurance, confidence and actions; (iii) self-management through prompts motivating goal setting and tracking progress. Conclusion: The INTERCEPT programme bridged the gap in preventive care but not having a smart phone and rapid discharge limited recruitment. Patients require immediate support following discharge to address lifestyle, risk factors and medication adherence all provided by the INTERCEPT digital intervention. The I-App information was trusted, provided reassurance and encouraged self-management. We now plan to test INTERCEPT in a cluster randomised controlled trial to evaluate clinical and cost effectiveness.INTERCEPT App and Porta
Multifunctional ultra-low voltage sweat-activated battery using piezo-ionic hydrogel
No full textOptimising ultra-low voltage with high capacity in batteries presents a challenge for emerging applications, such as wearable technology. In this study, we developed a multifunctional ultra-low voltage, sweat-activated fabric battery (SFB) using a biomaterial-based piezo-ionic hydrogel from water hyacinth carboxymethyl cellulose, mitigating risks of high-power and toxic materials near human skin. The SFB's multi-layer active material enhances conductivity and reduces resistance, enabling a 1 cm² device to discharge for 10 + h below 0.4 V, with an areal capacity of 4.1 mAh cm⁻² at 400 μA cm⁻². Furthermore, SFB with piezo-ionic hydrogel, when affixed to the elbow, generates a peak current of 115 nA cm⁻² as the elbow is fully flexed. Consequently, SFB can be utilised for energy storage, along with force and bending sensing. This study opens new avenues in advancing research on ultra-low voltage batteries for wearable and biomedical devices
xMagNet: Dynamic magnification-aware fusion with uncertainty quantification for robust breast cancer histopathology
No full textHistopathology image analysis faces challenges due to magnification variability, limiting robust tumor categorization. Existing deep learning models prioritize accuracy but neglect explainability, ethical biases, and real-world deployment. This study proposes xMagNet, a hybrid Transformer-Convolutional Neural Network (CNN) framework that synergizes technical rigor, clinical transparency, and ethical fairness for multi-magnification breast cancer diagnostics. xMagNet integrates a hybrid encoder combining Vision Transformers (ViT) for global tissue modeling at low magnifications (4x - 10x) and Separable Dilation Convolutions (SDC) for localized nuclear texture extraction at high magnifications (20x - 40x). Magnification-Aware Gating (MAG) dynamically balances ViT and SDC features via temperature-scaled sigmoid activation. A multi-task decoder employs Thresholded Grad-CAM (top 10% gradients) for explainable decision-making and Point-wise Reformation Blocks (PRB) for boundary preservation. Federated learning (FL) with momentum-enhanced aggregation and Sinkhorn divergence regularization ensures scanner/stain-invariant training across six institutions (Hamamatsu/Leica, H&E/IHC). Uncertainty-quantified predictions (Monte Carlo dropout) and adversarial debiasing mitigate demographic leakage. xMagNet achieves 97.8% F1-score for tumor segmentation on Camelyon16 and 93% Gleason AUC on PANDA, with 96.5% pathologist concordance via Grad-CAM. At 40x magnification, it detects micro-metastases with 94% sensitivity (vs. UNet++’s 89% and ResUNet’s 91%). Computational efficiency includes sub-second inference (0.42 sec/slide) and 2.3 x faster convergence than HoVer-Net. Ethical auditing reveals 3% fairness gaps () and 73% domain shift reduction (MMD: 0.12 vs. FedAvg’s 0.45), validated on 15,000 whole-slide images (WSIs) from TCGA-BRCA, Camelyon16, and PANDA datasets. xMagNet bridges critical gaps in multi-magnification histopathology by harmonizing technical robustness (MAG fusion, bounded gradients) with clinical utility (HER2+/ER+ subtyping, Gleason grading) and ethical scalability. By achieving high accuracy, rapid inference, and equitable deployment, it advances AI-driven diagnostics toward trustworthy, deployable systems for breast, prostate, and metastatic cancer imaging. Code available at: xMagNet
Arts in Criminal Justice and Corrections: International Perspectives on Methods, Journeys and Challenges, Edited by Amanda Gardner and Laura Caulfield
No full textIt was the first day of my study I had planned as part of my PhD research. I had prepared all the materials the night before, took an early train to ensure I was on time, went through all the checks at the prison gate, set up my room and waited for the first participant to come in for their interview. I welcomed him with ease, introduced myself and went through the consent form. I turned on the audio recorder. ‘It’s just like the fucking police’. My heart sank. I don’t think I told anyone about how big a misgiving this interaction felt, not until much later when I had a couple of research projects under my belt. This memory came back to me as I read Amanda Gardner and Laura Caulfield’s edited collection, ‘Arts in Criminal Justice and Corrections’. The most distinctive element of the collection is that it is not a traditional edited collection of studies, rather it is a collection of essays about how the researchers have developed their methods of research over their careers (and there are careers of various lengths and kinds represented throughout the text). Many of the contributors acknowledge the messiness of undertaking research including the feelings of unease when something is not said quite right, when an ethics approval gets revoked or the tensions felt when navigating stakeholders and funders
Animal geolocation with convolution algorithms in Julia and R via Wahoo.jl
Full text linkAnimal geolocation is the core of movement ecology. In aquatic ecosystems, electronic tagging and tracking technologies, such as passive acoustic telemetry systems and biologging sensors, are widely deployed. However, statistical estimation of individual locations from these datasets can be challenging and computationally expensive.Here, we introduce Wahoo.jl, a Julia package that fits state-space models to animal-tracking data via convolution algorithms. Wahoo.jl supports passive acoustic telemetry (detection/non-detection) and biologging (i.e. depth) datasets; implements grid-based filtering, smoothing and sampling of trajectories; and exploits GPU acceleration.Using simulations, we illustrate how to use Wahoo.jl from Julia and R to reconstruct movements for an example individual tagged with an acoustic transmitter and an archival depth tag. We also provide validation and sensitivity analyses.Wahoo.jl fills a key gap in the animal-tracking toolbox. The package provides an accessible, flexible and performant interface for an inference methodology that reliably handles multimodal inference problems that challenge other approaches. We discuss the approach's pros and cons and provide guidance to readers on when to reach for Wahoo.jl
High Isolation and High Gain of MIMO Antennas with FSS for 5G mm-wave Applications
No full textThis paper presents a comprehensive study on the gain enhancement and higher Isolation of a 28 GHz MIMO antenna system for 5 G communications using Frequency Selective Surfaces (FSS). The antenna under study is designed with dimensions of 2.86λ × 2.86λ × 0.047λ; the ground patches have a dimension of 7.6 × 11 mm2, the substrate is a Rogers RT885 (εr = 2.2, height = 0.51 mm), and operating at 28 GHz. A 4-element MIMO antenna array is formed by orthogonally placed elements in order to optimise spatial diversity. To improve isolation between the antenna elements, a mutual coupling reduction technique is employed. Furthermore, a 36-element FSS array is strategically placed above the MIMO antenna to significantly boost the gain. The initial gain performance without the FSS is 4.2 dBi at 28 GHz, by placing the FSS surface 7 mm above the MIMO antenna, this increases the gain to 8.4 dBi, resulting in a gain enhancement of approximately 3.6 dBi. Simulation results, obtained using CST Microwave Studio, demonstrate notable improvements in the antenna gain. The integration of FSS proved to be an effective solution for meeting the stringent performance requirements of 5 G communication systems, particularly in high-frequency mmWave applications. The present work aims to enhance gain, get high efficiency, improve the isolation between the ports, good value of ECC
Carbapenemase genes in the healthcare environment: does it matter?
No full text[Abstract unavailable.
MD-EGAN: Evolutionary GAN with dynamic latent sampling and relative adaptive discriminator for improved performance
No full textTraining instability, mode collapse, vanishing gradients, and high computational cost are significant challenges in generative adversarial networks (GANs), particularly in evolutionary-based GANs. To address these issues, we propose the multi-distribution evolutionary GAN (MD-EGAN), a method aimed at improving training stability, enhancing sample diversity, and accelerating convergence. MD-EGAN leverages multiple latent space priors—including Gaussian, Uniform, Poisson, and Truncated Gaussian distributions paired with a relative adaptive discriminator (RAD) that provides dynamic and comparative feedback. By exploring diverse latent distributions and RAD feedback, MD-EGAN enables more robust population-based generator evolution. Experimental evaluations on the CIFAR-10 and STL-10 demonstrate that MD-EGAN outperforms several baseline GANs models in both image quality and diversity. Specifically, MD-EGAN achieves inception score (IS) = 8.92, and Fréchet inception distance (FID) = 10.08 on CIFAR-10 and IS = 10.31, and FID = 21.93 on STL-10. Meanwhile, MD-EGAN reduces convergence time by up to 43.16 % when compared with cooperative dual evolutionary GAN, demonstrating significant improvement in computational efficiency. These results validate the effectiveness of multi-distribution latent modeling and relative feedback in addressing key limitations of GAN training, leading to improved generative performance
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