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Cancer cells typically exhibit altered ion channel expression and membrane potential, wich plays an important role in various processes such as proliferation or apoptosis Hence, therapeutic strategies that modulate ion channel activity or shift the membrane potential prove to be promising for cancer therapy. Since temperature significantly affects ion channel function, deviation from normal body temperture might provide akey factor in electrophysiological progression. In this pilot study, we focused on a first insight into the temperature-induced modulation of A549 cells using an automated patch clamp system. Measurements of 14 cells at hypo-, normo- and hypertermia were accomplished to investigate the temperature-dependent electophysiological behavior. The results revealed that both, temperatures below and above normal body temperature affect the current and potential of A549 cells. In hyperthermia the potential tends to more hyperpolarized values, while conversely, in hypothermic conditions we observer mainly a lower current reponse and thus a depolarization of the membrane potential.

In this paper, we present a control system theory based modeling approach for the voltage-gated ion-channel Kv10.1 at different temperatures. Kv10.1 is expressed in the central nervous system and the brain of healthy humans. The study of the activity of this ion channel is important, because its activity is associated with the occurrence of cancer in different organs or tissues. According to systems and control theory, the voltage-gated channel Kv10.1 was assumed to be a linear time-invariant system and, as such, exhibits dynamic behavior. The experimental results show that Kv10.1 operates as a first-order model based on the input voltage step protocol and the measured macroscopic ion current output.

Capacitive extracellular stimulation is a common method in implanted stimulation electrodes. The basis for investigating the transmission of stimuli from an electrode to adhered cells are in vitro experiments using calcium imaging or patch clamp measurements. Computational spatial models are used to simulate the mechanism of signal transmission at the cell-electrode interface but require high computing power. In this work, the Stern model to characterize the electrochemical double-layer (EDL) formation and a modified two-domain model are combined into a holistic equivalent circuit modelling capacitive cell stimulation. The described parameters can be directly associated with physicochemical effects. A simulation of the involved control and measurement systems allows the validation of the model with in vitro patch clamp recordings. The relationship of the cell’s distance to the electrode and efficacy of signal transmission could be observed. With this concept we aim to convert different complex approaches into a simple model and thus give an overview of the mechanisms of stimulation. We want to facilitate the interpretation of measured signals especially in voltage clamp measurements.

BACKGROUND ChatGPT, an artificial intelligence chatbot developed by OpenAI has ushered in a new era of possibilities, offering exciting opportunities for natural language processing in healthcare; it marks a clear paradigm shift in academic research, patient care, and clinical communication. However, a hurried and uninformed implementation of ChatGPT in healthcare settings can have unforeseen results. OBJECTIVE The objective of this study is to discuss and provide a comprehensive summary of the existing literature on ChatGPT based on insights derived from different research teams across diverse disciplines, focusing on the main applications in medicine as conceived by authors who have published on the subject. Furthermore, we seek to address potential concerns associated with the integration of ChatGPT in the medical field, while simultaneously opening up unexplored paths. METHODS The corresponding author selected a group of authors through an initial screening of papers published on ChatGPT from various databases. Based on the replies and their level of cooperation, a dozen authors were chosen for the study and included based on their cooperation and geographic representation. We conducted a comprehensive search across databases, including Web of Science, PubMed, and Scopus. The study followed the PRISMA guidelines for screening and selection. Analysis was performed on the selected articles (n=223), categorizing them based on article type, specialty, and specific role discussed. Descriptive statistics were calculated, and bibliometric analyses were conducted using VOSviewer. RESULTS We identified 223 peer-reviewed articles. Writing assistance emerged as the most widely discussed role of ChatGPT. The rest of the roles included: general use (16%, n=35), education tool (17%, n=37), solution and discussion of clinical vignettes (15%, n=34), valuing and predicting future impact (7%, n=15), information source (5%, n=12), and as a discussion forum about ethical issues (6%, n=14). To detect any difference in usage across continents, a chi-square test for distribution of categorical variables was carried out and yielded a p-value of P=0.56, in article type. Regarding specialty and the role of ChatGPT, the p-values were P=0.86 and P=0.87, respectively. We found no significant association between a specific geographic location and the distribution of any of the variables. CONCLUSIONS This study provides a comprehensive overview of the literature on ChatGPT, revealing its diverse range of applications as observed across different disciplines. By examining the concerns surrounding the integration of ChatGPT in medicine, we underscore the importance of responsible implementation and highlight the need for ongoing research and development in this area. Ultimately, this work opens new perspectives for the detection of unexplored paths and the realization of ChatGPT's potential in enhancing various domains, including medicine.

The Anthropocene, the strongly debated potential new unit of the Geological Time Scale, describes the intensified anthropogenic influence on the environment and geological processes, and its traces in geological archives. Regional studies characterizing the growth of human impact, the Anthropocene transformation, are scarce, especially for urban or per-urban environments.In this project, we investigate the anthropogenic impact of the metropolis Vienna on its peri-urban environment and the proposed beginning of the Anthropocene epoch in the 1950s CE by applying sedimentological and geochemical methods. In previous studies (Wagreich et al. 2022), the authors were able to successfully detect the human influence in urban sedimentary archives of Vienna (anthropogenic coarse sediments) using artificial isotopes and anthropogenic trace metals. For our project, we extend the study area from Vienna to the city of Hainburg to investigate Vienna’s anthropogenic impact in both anthropogenic and natural sediments downstream the Danube river. In this area, direct human intervention in the environment, such as ground excavations, backfill and damming, is highly variable, from locally strong (e.g., hydro-power dams, airport constructions), to not existing (National Park Donau-Auen), thus offering a suitable location to trace and quantify the extent of anthropogenic impact.Within petrographic facies, sedimentological and geochemical markers are applied to characterize the anthropogenic strata in this area: The archive of fine-grained natural Danube deposits, i.e. erosional profiles and sediment cores, is analysed for trace metals, artificial radiogenic isotopes, and microplastics with the aim (i) to disentangle the anthropogenic fingerprint of Vienna from the sediment, (ii) to identify and evaluate the proposed Anthropocene geological boundary around 1950 CE, and (iii) to evaluate a potential correlative stratigraphic reference section section/ point for the Anthropocene downstream of Vienna. Finally, the Carnuntum-Vienna Anthropocene field lab offers the opportunity to integrate environmental systems modelling with an Anthropocene equation approach for the temporal and spatial growth of the anthropogenic layers (iv).

The project »From Romans to the Anthropocene, from Carnuntum to Vienna: An Urban Anthropocene Field Lab« (WWTF ESR20-027) focuses on the urban transformation from the Roman legionary camps of Carnuntum and Vindobona to Vienna's periurban areas. Combining historical and geoarchaeological methods, we investigate the diverging development of the two sites and their manifold relations over time. The Danube river crosses the mountain ranges of the Wienerwald and Malé Karpaty, forming the eastern and western limits of the central Vienna Basin. For many centuries, the river was both a barrier and a transportation route. The floodplains and river terraces along the Danube served as concentration areas and battlegrounds during countless conflicts between central Europe and its enemies. The legionary camps of Carnuntum and Vindobona were built during the first century AD at the rim of glacial river terraces, next to the shortest passages across the Danube. During late antiquity, the former provincial capital Carnuntum lost importance. However, Vindobona became first a local center and later the capital of the Austrian rulers. After a sudden Turkish siege in 1529, the fortifications of Vienna were strengthened and maintained until the middle of the 19th century.Urban development of the region over the centuries was limited not only by permanent military threats. North of the Danube, agriculture was always restricted climatically by aridity and sand drift. Most settlements on the riverbanks of the Danube and its tributaries were affected frequently by floods and erosion. Many villages vanished completely as a consequence of such natural hazards. Since some decades, natural river dynamics have been restricted technically, and many areas under cultivation are now irrigated artificially. The analysis of the sedimentary record downstream of Vienna clearly shows the anthropogenic impact on sedimentation processes. Contrary to Carnuntum, Vienna could evolve from a legionary camp step by step into a capital and a strong fortress. The demolition of the city walls after the middle of the 19th century, the following long period of peace, and, in particular, the consequent river engineering, were preconditions for the development into a modern metropolis.

Objectives: In this synopsis, we identify and highlight research papers representing noteworthy developments in signals, sensors, and imaging informatics in 2021. Methods: A broad literature search was conducted on PubMed and Scopus databases. We combined Medical Subject Heading (MeSH) terms and keywords to construct particular queries for sensors, signals, and imaging informatics. Except for the sensor section, we only consider papers that have been published in journals providing at least three articles in the query response. Using a three-point Likert scale (1=not include, 2=maybe include, and 3=include), we reviewed the titles and abstracts of all database returns. Only those papers which reached two times three points were further considered for full paper review using the same Likert scale. Again, we only considered works with two times three points and provided these for external reviews. Based on the external reviews, we selected three best papers, as it happens that the three highest ranked papers represent works from all three parts of this section: sensors, signals, and imaging informatics. Results: The search for papers was executed in January 2022. After removing duplicates and conference proceedings, the query returned a set of 88, 376, and 871 papers for sensors, signals, and imaging informatics, respectively. For signals and images, we filtered out journals that had less than three papers in the query results, reducing the number of papers to 215 and 512, respectively. From this total of 815 papers, the section co-editors identified 35 candidate papers with two times three Likert points, from which nine candidate best papers were nominated after full paper assessment. At least three external reviewers then rated the remaining papers and the three best-ranked papers were selected using the composite rating of all external reviewers. By accident, these three papers represent each of the three fields of sensor, signal, and imaging informatics. They were approved by consensus of the International Medical Informatics Association (IMIA) Yearbook editorial board. Deep and machine learning techniques are still a dominant topic as well as concepts beyond the state-of-the-art. Conclusions: Sensors, signals, and imaging informatics is a dynamic field of intense research. Current research focuses on creating and processing heterogeneous sensor data towards meaningful decision support in clinical settings.

Nongenetic optical control of neurons is a powerful technique to study and manipulate the function of the nervous system. This research has benchmarked the performance of organic electrolytic photocapacitor (OEPC) optoelectronic stimulators at the level of single mammalian cells: human embryonic kidney (HEK) cells with heterologously expressed voltage-gated K+ channels and hippocampal primary neurons. OEPCs act as extracellular stimulation electrodes driven by deep red light. The electrophysiological recordings show that millisecond light stimulation of OEPC shifts conductance-voltage plots of voltage-gated K+ channels by ≈30 mV. Models are described both for understanding the experimental findings at the level of K+ channel kinetics in HEK cells, as well as elucidating interpretation of membrane electrophysiology obtained during stimulation with an electrically floating extracellular photoelectrode. A time-dependent increase in voltage-gated channel conductivity in response to OEPC stimulation is demonstrated. These findings are then carried on to cultured primary hippocampal neurons. It is found that millisecond time-scale optical stimuli trigger repetitive action potentials in these neurons. The findings demonstrate that OEPC devices enable the manipulation of neuronal signaling activities with millisecond precision. OEPCs can therefore be integrated into novel in vitro electrophysiology protocols, and the findings can inspire in vivo applications.

For cardiac defibrillator testing and design purposes, the range and limits of the human TTI is of high interest. Potential influencing factors regarding the electronic configurations, the electrode/tissue interface and patient characteristics were identified and analyzed. A literature survey based on 71 selected articles was used to review and assess human TTI and the influencing factors found. The human TTI extended from 12 to 212 Ω in the literature selected. Excluding outliers and pediatric measurements, the mean TTI recordings ranged from 51 to 112 Ω with an average TTI of 76.7 Ω under normal distribution. The wide range of human impedance can be attributed to 12 different influencing factors, including shock waveforms and protocols, coupling devices, electrode size and pressure, electrode position, patient age, gender, body dimensions, respiration and lung volume, blood hemoglobin saturation and different pathologies. The coupling device, electrode size and electrode pressure have the greatest influence on TTI.

High throughput technologies in genomics enable the analysis of small alterations in gene expression levels. Patterns of such deviations are an important starting point for the discovery and verification of new biomarker candidates. Identifying such patterns is a challenging task that requires sophisticated machine learning approaches. Currently, there are a variety of classification models, and a common approach is to compare the performance and select the best one for a given classification problem. Since the association between the features of a data set and the performance of a particular classification method is still not fully understood, the main contribution of this work is to provide a new methodology for predicting the prediction results of different classifiers in the field of biomarker discovery. We propose here a three-steps computational workflow that includes an analysis of the data set characteristics, the calculation of the classification accuracy and, finally, the prediction of the resulting classification error. The experiments were carried out on synthetic and microarray datasets. Using this method, we showed that the predictability strongly depends on the discriminatory ability of the features, e.g., sets of genes, in two or multi-class datasets. If a dataset has a certain discriminatory ability, this method enables prediction of the classification performance before applying a learning model. Thus, our results contribute to a better understanding of the relationship between dataset characteristics and the corresponding performance of a machine learning method, and suggest the optimal classification method for a given dataset based on its discriminatory ability.