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Precision medicine is the next frontier in pharmaceutical research, aiming to improve the safety and efficacy of therapeutics for patients. The ideal drug delivery system (DDS) should be programmable to provide real-time controlled delivery that is personalised to the patient's needs. However, little progress has been made in this domain. Herein, we combined two cutting-edge technologies, conductive polymers (CPs) and three-dimensional (3D) printing, to demonstrate their potential for achieving programmable controlled release. A DDS was formulated where the CP provided temporal control over drug release. 3D printing was used to ensure dimensional control over the design of the DDS. The CP used in this study is known to be fragile, and thus was blended with thermoplastic polyurethane (TPU) to achieve a conductive elastomer with sound mechanical properties. Rheological and mechanical analyses were performed, where it was revealed that formulation inks with a storage modulus in the order of 103–104 Pa were both extrudable and maintained their structural integrity. Physico-chemical analysis confirmed the presence of the CP functional groups in the 3D printed DDS. Cyclic voltammetry demonstrated that the DDS remained conductive for 100 stimulations. in vitro drug release was performed for 180 min at varying voltages, where a significant difference (p

Predicting a patient’s hemoglobin level or degree of anemia using Electronic Health Records (EHRs) is a non-invasive and rapid approach. However, it presents challenges due to the irregular multivariate time series nature of EHRs, which often contain significant amounts of missing values and irregular time intervals. To address these issues, we introduce HgbNet, a model specifically designed to process irregular EHR data. HgbNet incorporates a NanDense layer with a missing indicator to handle missing values. Inspired by clinicians’ decision-making processes, the model employs three kinds of attention mechanisms to account for both local irregularity and global irregularity. We evaluate the proposed method using two real-world datasets across two use cases. HgbNet outperforms the best baseline results across all test scenarios, achieving an R2 score of $0.867~\pm $ 0.003 and $0.861~\pm $ 0.003 for hemoglobin level prediction, and an F1 score of $0.855~\pm $ 0.005 and $0.843~\pm $ 0.005 for anemia degree prediction under usecase 1 across two datasets. Additionally, we analyze the effect of the length of irregular time intervals on prediction performance and improve HgbNet’s performance at long intervals in usecase 2. These findings highlight the feasibility of estimating hemoglobin levels and anemia degree from EHR data, positioning HgbNet as an effective non-invasive anemia diagnosis solution that could potentially enhance the quality of life for millions of affected individuals worldwide.

In a labor-intensive cell production system, it is important to train operators effectively because their skills are essential for productivity. Our previous study proposed a method to classify these skills according to a “skill index” based on the time required for each task and the allocated operators based on this method. However, in actual workplaces, it is assumed that operators accumulate fatigue due to the repetition of work, which affects the assembly time. In this study, we propose an operator allocation method that considers the effect of fatigue and verify its effectiveness compared with the results of the previous study by computer experiments. In addition, an assembly experiment with a toy is conducted based on the operator allocation method derived from the computer experiments. The experimental results show that the proposed method is effective and indicate that appropriate parameter setting is crucial when applying it in real-world scenarios.

In this study, hybrid particles of AgNPs-loaded eggshell calcium carbonate (AgNPs/eCaCO3) were prepared by co-precipitating the eggshell in the presence of freshly prepared AgNPs with a particle size of 10–30 nm. The hybrid particles were comparatively precipitated at 25°C and 35°C using poly (sodium 4-styrenesulfonate) as a polyelectrolyte. The AgNPs/eCaCO3 particles prepared at 25°C had a spherical morphology with a mean diameter of 3.56 µm, and Brunauer–Emmett–Teller (BET) surface area of 85.08 m2 g−1. On the other hand, the particles prepared at 35°C had a broader size distribution with a mean diameter of 3.19 µm, and a BET surface area of 79.25 m2 g−1. AgNPs-loaded commercial calcium carbonate particles (AgNPs/CaCO3) comparatively prepared at 35°C were perfectly spherical with a mean diameter of 5.61 µm. At preparing temperature of 25°C, the hybrid particles contain AgNPs of 0.78 wt% for AgNPs/eCaCO3 and 3.20 wt% for AgNPs/CaCO3. The AgNPs/eCaCO3 and AgNPs/CaCO3 particles exhibited the same efficiency against bacteria extracted from beef with an average inhibition zone diameter of 7–10 mm according to the modified Kirby–Bauer disc diffusion assay depending on their concentration and beef source. Freshly prepared silver colloids showed comparatively poorer antimicrobial efficiency.

Three-dimensional (3D) printing is an advanced pharmaceutical manufacturing technology, and concerted efforts are underway to establish its applicability to various industries. However, for any technology to achieve widespread adoption, robustness and reliability are critical factors. Machine vision (MV), a subset of artificial intelligence (AI), has emerged as a powerful tool to replace human inspection with unprecedented speed and accuracy. Previous studies have demonstrated the potential of MV in pharmaceutical processes. However, training models using real images proves to be both costly and time consuming. In this study, we present an alternative approach, where synthetic images were used to train models to classify the quality of dosage forms. We generated 200 photorealistic virtual images that replicated 3D-printed dosage forms, where seven machine learning techniques (MLTs) were used to perform image classification. By exploring various MV pipelines, including image resizing and transformation, we achieved remarkable classification accuracies of 80.8%, 74.3%, and 75.5% for capsules, tablets, and films, respectively, for classifying stereolithography (SLA)-printed dosage forms. Additionally, we subjected the MLTs to rigorous stress tests, evaluating their scalability to classify over 3000 images and their ability to handle irrelevant images, where accuracies of 66.5% (capsules), 72.0% (tablets), and 70.9% (films) were obtained. Moreover, model confidence was also measured, and Brier scores ranged from 0.20 to 0.40. Our results demonstrate promising proof of concept that virtual images exhibit great potential for image classification of SLA-printed dosage forms. By using photorealistic virtual images, which are faster and cheaper to generate, we pave the way for accelerated, reliable, and sustainable AI model development to enhance the quality control of 3D-printed medicines.

Actions that transform our energy system are the cornerstone of decarbonizing our economy but have been hindered by the ineffective interface between researchers and decision-makers in Canada. This paper begins by arguing for a more holistic perspective on energy system decarbonization modelling and exploring how insights can aid evidence-based decision making. We then respond with the development of a modelling platform that includes three core pillars: (1) a toolbox of models that together represent the integrated energy system, (2) a dataset containing the inputs required to populate those models, and (3) a visualization suite to analyze and communicate their outputs. The Spine Toolbox is leveraged to process these three components in an efficient workflow. Taken together, the platform promotes the usability of model results by fostering consistency, transparency, and timeliness. Furthermore, the epistemic limitations of energy systems modelling and implications for platform and model design, and engaging extended peer communities, are discussed. Our hope is that this platform can be a foundational resource that facilitates collaboration between energy system and decarbonization researchers, modelling teams and decision-makers, ultimately enabling the effective application of evidence-based policy.

Electrospraying (ES) is a state-of-the-art processing technique with the promise of achieving key nanotechnology and contemporary manufacturing needs. As a versatile technique, ES can produce particles with different sizes, morphologies, and porosities by tuning a list of experiment parameters. However, this level of precision demands an exhaustive trial-and-error approach, at high costs and heavily relies on processing expertise. The present study demonstrates how machine learning (ML) can expedite the optimization process by accurately predicting particle diameter, for both nano- and micron-sized particles. This was achieved by constructing an informative electrospraying database containing 445 records from the literature, followed by the development of predictive ML models. Feature engineering techniques were explored, where ultimately it was found that solvent physiochemical properties as the molecular representation and data with imputation provided models the highest performance. The top two models were XGBoost and Random Forest (RF), which yielded root-mean-squared errors (RMSE) of 3.91 μm and 6.19 μm evaluated by 5-fold cross-validation (CV), respectively. These models were experimentally validated in-house with different combinations of experiment parameters, where RMSE between the predicted and actual particle size was found to be 1.30 μm for the XGBoost model and 1.62 μm for the RF model. Therefore, it was concluded that data generated by the ES literature, in addition to being both cost- and material-free, can yield high-performing ML models for predicting particle size. The ML models were also consulted to determine the key processing parameters that govern particle size, where it was concluded that the models learnt similar attributes identified by scaling laws.

Abstract Background Glomus tumors are soft tissue neoplasms comprised of glomus cells, vasculature, and smooth muscle cells, which occur commonly in a single subungual area of the digits, and their main clinical features include severe paroxysmal pain, localized tenderness, and cold hypersensitivity. Case presentation A 47-year-old Japanese man had suffered from chronic progressive paroxysmal shooting pain in his right leg since childhood. He avoided putting weight on his right foot whenever he walked. The frequency of paroxysmal pain and the number of tender points both gradually increased with age, and his right leg gradually atrophied. Magnetic resonance imaging of the lower extremity demonstrated multiple gadolinium-enhanced nodules that corresponded with his tender points. Excisional biopsy relieved his pain and provided a histopathological diagnosis of glomus tumors. Conclusion This case suggests that small glomus tumors located in deep tissue may cause disuse atrophy because of their long delay before diagnosis. Clinicians should consider the potential for glomus tumors when patients exhibit unilateral lower limb muscular atrophy with pain.