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Background & Aims: A “microbrachytherapy” was developed as treatment option for inoperable tumours by direct intratumoral injection of radioactive holmium-166 (166Ho) microspheres (MS). 166Ho emits β-radiation which potentially enables a high, ablative, radioactive-absorbed dose on the tumour tissue while sparing surrounding tissues. Materials & Methods: Safety and efficacy of 166Ho microbrachytherapy were evaluated in a prospective cohort study of 13 cats with inoperable oral squamous cell carcinoma without evidence of distant metastasis. Results: Local response rate was 55%, including complete response or partial response (downstaging) enabling subsequent marginal resection. Median survival time was 113 days overall, and 296 days for patients with local response. Side effects were minimal. Tumour volume was a significant predictor of response. Discussion: Response rate may be further improved by optimizing the intratumoral spatial distribution of 166Ho MS. Conclusion: 166Ho microbrachytherapy has potential as a minimally invasive, single procedure radio-ablation treatment of unresectable tumours with minimal morbidity. [ABSTRACT FROM AUTHOR]

This article investigates the usability of a navigation support tool, which guides learners by generating advice on the next best step to take in a self-study e-learning course. The article draws on log data and responses from online questionnaires to provide insights into learners' evaluation of the tool, their adherence to the advice and their expectations of self-efficacy. The theoretical underpinnings of the work are described together with the experimental set-up. Results show that more than half of the learners in the experimental group adhered to the advice and held the opinion that the advice stimulated them to proceed with the course. Learners expressed a need to know what the advice was based on which can be seen as an essential element in future development of the tool. [ABSTRACT FROM AUTHOR]

Besides genetic alterations, the cellular environment also determines disease onset and progression. When different cell types contribute to disease outcome, this imposes environmental challenges as different cell types likely differ in their extracellular dependencies. Hsa-microRNA-31-5p (miR-31) is highly expressed in keratinocytes of psoriatic skin, and we show that expression in keratinocytes is induced by limited glucose availability and enables increased survival under limiting glucose conditions by increasing glutamine metabolism. In addition, miR-31 expression results in not only secretion of specific metabolites (aspartate and glutamate) but also secretion of immunomodulatory factors. We show that this miR-31-induced secretory phenotype is sufficient to induce Th17 cell differentiation, a hallmark of psoriasis. Inhibitors of miR31-induced metabolic rewiring and metabolic crosstalk with immune cells alleviate psoriasis pathology in a mouse model of psoriasis. Together our data illustrate an emerging concept of metabolic interaction across cell compartments that characterizes disease development, which can be employed to design effective treatment options for disease, as shown here for psoriasis., (© 2023 The Authors. Published under the terms of the CC BY 4.0 license.)

BACKGROUND The correct interpretation of the ECG is pivotal for the accurate diagnosis of many cardiac abnormalities, and conventional computerized interpretation has not been able to reach physician-level accuracy in detecting (acute) cardiac abnormalities. This study aims to develop and validate a deep neural network for comprehensive automated ECG triage in daily practice. METHODS AND RESULTS We developed a 37-layer convolutional residual deep neural network on a data set of free-text physician-annotated 12-lead ECGs. The deep neural network was trained on a data set with 336.835 recordings from 142.040 patients and validated on an independent validation data set (n=984), annotated by a panel of 5 cardiologists electrophysiologists. The 12-lead ECGs were acquired in all noncardiology departments of the University Medical Center Utrecht. The algorithm learned to classify these ECGs into the following 4 triage categories: normal, abnormal not acute, subacute, and acute. Discriminative performance is presented with overall and category-specific concordance statistics, polytomous discrimination indexes, sensitivities, specificities, and positive and negative predictive values. The patients in the validation data set had a mean age of 60.4 years and 54.3% were men. The deep neural network showed excellent overall discrimination with an overall concordance statistic of 0.93 (95% CI, 0.92-0.95) and a polytomous discriminatory index of 0.83 (95% CI, 0.79-0.87). CONCLUSIONS This study demonstrates that an end-to-end deep neural network can be accurately trained on unstructured free-text physician annotations and used to consistently triage 12-lead ECGs. When further fine-tuned with other clinical outcomes and externally validated in clinical practice, the demonstrated deep learning-based ECG interpretation can potentially improve time to treatment and decrease healthcare burden.

Background: Different disease stages of arrhythmogenic right ventricular cardiomyopathy (ARVC) can be identified by right ventricle (RV) longitudinal deformation (strain) patterns. This requires assessment of the onset of shortening, (systolic) peak strain, and postsystolic index, which is time-consuming and prone to inter- and intra-observer variability. The aim of this study was to design and validate an algorithm to automatically classify RV deformation patterns., Methods: We developed an algorithm based on specific local characteristics from the strain curves to detect the parameters required for classification. Determination of the onset of shortening by the algorithm was compared to manual determination by an experienced operator in a dataset containing 186 RV strain curves from 26 subjects carrying a pathogenic plakophilin-2 (PKP2) mutation and 36 healthy subjects. Classification agreement between operator and algorithm was solely based on differences in onset shortening, as the remaining parameters required for classification of RV deformation patterns could be directly obtained from the strain curves., Results: The median difference between the onset of shortening determined by the experienced operator and by the automatic detector was 5.3 ms [inter-quartile range (IQR) 2.7-8.6 ms]. 96% of the differences were within 1 time frame. Both methods correlated significantly with ρ = 0.97 (P < .001). For 26 PKP2 mutation carriers, there was 100% agreement in classification between the algorithm and experienced operator., Conclusion: The determination of the onset of shortening by the experienced operator was comparable to the algorithm. Our computer algorithm seems a promising method for the automatic classification of RV deformation patterns. The algorithm is publicly available at the MathWorks File Exchange., (© 2020 The Authors. Echocardiography published by Wiley Periodicals LLC.)