Control of the STAT6-BCL6 antagonism by SWAP-70 determines IgE production.

Asthma and allergies are major health concerns in which Ig isotype E plays a pivotal role. Ag-bound IgE drives mast cells and basophils into exocytosis, thereby promoting allergic and potentially anaphylactic reactions. The importance of tightly regulated IgE production is underscored by severe immunological conditions in humans with elevated IgE levels. Cytokines direct IgH class-switching to a particular isotype by initiation of germline transcription (GLT) from isotype-specific intronic (I) promoters. The switch to IgE depends on IL-4, which stimulates GLT of the Iε promoter, but is specifically and strongly impaired in Swap-70(-/-) mice. Although early events in IL-4 signal transduction (i.e., activation of the JAK/STAT6 pathway) do not require SWAP-70, SWAP-70 deficiency results in impaired Iε GLT. The affinity of STAT6 to chromatin is reduced in absence of SWAP-70. Chromatin immunoprecipitation revealed that SWAP-70 binds to Iε and is required for association of STAT6 with Iε. BCL6, known to antagonize STAT6 particularly at Iε, is increased on Iε in absence of SWAP-70. Other promoters bound by BCL6 and STAT6 were found unaffected. We conclude that SWAP-70 controls IgE production through regulation of the antagonistic STAT6 and BCL6 occupancy of Iε. The identification of this mechanism opens new avenues to inhibit allergic reactions triggered by IgE.

Use and Perception of Digital Health Technologies by Surgical Patients in Germany in the Pre-COVID-19 Era: Survey Study.

BACKGROUND: This survey study investigates surgical patients' use and perception of digital health technologies in Germany in the pre-COVID-19 era. OBJECTIVE: The objective of this study was to relate surgical patients' characteristics to the use and perception of several digital health technologies. METHODS: In this single-center, cross-sectional survey study in the outpatient department of a university hospital in Germany, 406 patients completed a questionnaire with the following three domains: general information and use of the internet, smartphones, and general digital health aspects. Analyses were stratified by age group and highest education level achieved. RESULTS: We found significant age-based differences in most of the evaluated aspects. Younger patients were more open to using new technologies in private and medical settings but had more security concerns. Although searching for information on illnesses on the web was common, the overall acceptance of and trust in web-based consultations were rather low, with <50% of patients in each age group reporting acceptance and trust. More people with academic qualifications than without academic qualifications searched for information on the web before visiting physicians (73/121, 60.3% and 100/240, 41.7%, respectively). Patients with academic degrees were also more engaged in health-related information and communication technology use. CONCLUSIONS: These results support the need for eHealth literacy, health literacy, and available digital devices and internet access to support the active, meaningful use of information and communication technologies in health care. Uncertainties and a lack of knowledge exist, especially regarding telemedicine and the use of medical and health apps. This is especially pronounced among older patients and patients with a low education status.

Investigating the utility of VR for spatial understanding in surgical planning: evaluation of head-mounted to desktop display.

Recent technological advances have made Virtual Reality (VR) attractive in both research and real world applications such as training, rehabilitation, and gaming. Although these other fields benefited from VR technology, it remains unclear whether VR contributes to better spatial understanding and training in the context of surgical planning. In this study, we evaluated the use of VR by comparing the recall of spatial information in two learning conditions: a head-mounted display (HMD) and a desktop screen (DT). Specifically, we explored (a) a scene understanding and then (b) a direction estimation task using two 3D models (i.e., a liver and a pyramid). In the scene understanding task, participants had to navigate the rendered the 3D models by means of rotation, zoom and transparency in order to substantially identify the spatial relationships among its internal objects. In the subsequent direction estimation task, participants had to point at a previously identified target object, i.e., internal sphere, on a materialized 3D-printed version of the model using a tracked pointing tool. Results showed that the learning condition (HMD or DT) did not influence participants' memory and confidence ratings of the models. In contrast, the model type, that is, whether the model to be recalled was a liver or a pyramid significantly affected participants' memory about the internal structure of the model. Furthermore, localizing the internal position of the target sphere was also unaffected by participants' previous experience of the model via HMD or DT. Overall, results provide novel insights on the use of VR in a surgical planning scenario and have paramount implications in medical learning by shedding light on the mental model we make to recall spatial structures.

Prediction of laparoscopic procedure duration using unlabeled, multimodal sensor data.

PURPOSE: The course of surgical procedures is often unpredictable, making it difficult to estimate the duration of procedures beforehand. This uncertainty makes scheduling surgical procedures a difficult task. A context-aware method that analyses the workflow of an intervention online and automatically predicts the remaining duration would alleviate these problems. As basis for such an estimate, information regarding the current state of the intervention is a requirement. METHODS: Today, the operating room contains a diverse range of sensors. During laparoscopic interventions, the endoscopic video stream is an ideal source of such information. Extracting quantitative information from the video is challenging though, due to its high dimensionality. Other surgical devices (e.g., insufflator, lights, etc.) provide data streams which are, in contrast to the video stream, more compact and easier to quantify. Though whether such streams offer sufficient information for estimating the duration of surgery is uncertain. In this paper, we propose and compare methods, based on convolutional neural networks, for continuously predicting the duration of laparoscopic interventions based on unlabeled data, such as from endoscopic image and surgical device streams. RESULTS: The methods are evaluated on 80 recorded laparoscopic interventions of various types, for which surgical device data and the endoscopic video streams are available. Here the combined method performs best with an overall average error of 37% and an average halftime error of approximately 28%. CONCLUSION: In this paper, we present, to our knowledge, the first approach for online procedure duration prediction using unlabeled endoscopic video data and surgical device data in a laparoscopic setting. Furthermore, we show that a method incorporating both vision and device data performs better than methods based only on vision, while methods only based on tool usage and surgical device data perform poorly, showing the importance of the visual channel.

Active learning using deep Bayesian networks for surgical workflow analysis.

PURPOSE: For many applications in the field of computer-assisted surgery, such as providing the position of a tumor, specifying the most probable tool required next by the surgeon or determining the remaining duration of surgery, methods for surgical workflow analysis are a prerequisite. Often machine learning-based approaches serve as basis for analyzing the surgical workflow. In general, machine learning algorithms, such as convolutional neural networks (CNN), require large amounts of labeled data. While data is often available in abundance, many tasks in surgical workflow analysis need annotations by domain experts, making it difficult to obtain a sufficient amount of annotations. METHODS: The aim of using active learning to train a machine learning model is to reduce the annotation effort. Active learning methods determine which unlabeled data points would provide the most information according to some metric, such as prediction uncertainty. Experts will then be asked to only annotate these data points. The model is then retrained with the new data and used to select further data for annotation. Recently, active learning has been applied to CNN by means of deep Bayesian networks (DBN). These networks make it possible to assign uncertainties to predictions. In this paper, we present a DBN-based active learning approach adapted for image-based surgical workflow analysis task. Furthermore, by using a recurrent architecture, we extend this network to video-based surgical workflow analysis. To decide which data points should be labeled next, we explore and compare different metrics for expressing uncertainty. RESULTS: We evaluate these approaches and compare different metrics on the Cholec80 dataset by performing instrument presence detection and surgical phase segmentation. Here we are able to show that using a DBN-based active learning approach for selecting what data points to annotate next can significantly outperform a baseline based on randomly selecting data points. In particular, metrics such as entropy and variation ratio perform consistently on the different tasks. CONCLUSION: We show that using DBN-based active learning strategies make it possible to selectively annotate data, thereby reducing the required amount of labeled training in surgical workflow-related tasks.

Analysis of circulating plasmacytoid dendritic cells during the course of sepsis.

BACKGROUND: Sepsis is characterized as a biphasic immune reaction in response to invading micro-organisms causing a life-threatening condition. This reaction is triggered by the activation of many different immune cells causing a dramatic inflammatory response often followed by immunosuppression. The balance of the immune response in this complex interplay of pro- and anti-inflammatory processes is crucial for the course of sepsis and host survival. For a better understanding of the involved mechanisms, a precise knowledge of participating immune cells in a timely manner is necessary. METHODS: We analyzed circulating plasmacytoid dendritic cells (pDCs) by using multicolor, flow cytometric analysis in septic patients over 28 days. In addition, we assessed disease severity, organ failure, and outcome in these septic patients. RESULTS: The numbers of circulating pDCs started to increase at day 1 after the onset of sepsis and were greatly increased from day 4 after sepsis onset. At days 7 and 14, the numbers of circulating pDCs peaked and returned to normal values at day 28 after the onset of sepsis. These changes were accompanied by increased expression of CD11b, which is known as crucial factor for transendothelial migration. In addition, the circulating pDCs in nonsurvivors showed greatly decreased values compared with survivors over the course of sepsis. CONCLUSION: The results presented here support the concept that circulating pDCs might have an important role in the immune response during sepsis and might function as an early predictive biomarker for the outcome of sepsis.