Internetnutzung in der VR China

Die Zahl der regelmäßigen Internetnutzer in der VR China wird auf 22,5 Mill., also etwa 1,2 % der Bevölkerung, geschätzt. Dies zeigt eine deutliche Verlangsamung des Internetwachstums gegenüber den Raten der vergangenen Jahre, in denen sich die Internetnutzerzahlen in China alle sechs Monate verdoppelten.China (Volksrepublik), Internet

Ability of 18F-FDG Positron Emission Tomography Radiomics and Machine Learning in Predicting KRAS Mutation Status in Therapy-Naive Lung Adenocarcinoma.

OBJECTIVE Considering the essential role of KRAS mutation in NSCLC and the limited experience of PET radiomic features in KRAS mutation, a prediction model was built in our current analysis. Our model aims to evaluate the status of KRAS mutants in lung adenocarcinoma by combining PET radiomics and machine learning. METHOD Patients were retrospectively selected from our database and screened from the NSCLC radiogenomic dataset from TCIA. The dataset was randomly divided into three subgroups. Two open-source software programs, 3D Slicer and Python, were used to segment lung tumours and extract radiomic features from 18F-FDG-PET images. Feature selection was performed by the Mann-Whitney U test, Spearman's rank correlation coefficient, and RFE. Logistic regression was used to build the prediction models. AUCs from ROCs were used to compare the predictive abilities of the models. Calibration plots were obtained to examine the agreements of observed and predictive values in the validation and testing groups. DCA curves were performed to check the clinical impact of the best model. Finally, a nomogram was obtained to present the selected model. RESULTS One hundred and nineteen patients with lung adenocarcinoma were included in our study. The whole group was divided into three datasets: a training set (n = 96), a validation set (n = 11), and a testing set (n = 12). In total, 1781 radiomic features were extracted from PET images. One hundred sixty-three predictive models were established according to each original feature group and their combinations. After model comparison and selection, one model, including wHLH_fo_IR, wHLH_glrlm_SRHGLE, wHLH_glszm_SAHGLE, and smoking habits, was validated with the highest predictive value. The model obtained AUCs of 0.731 (95% CI: 0.619~0.843), 0.750 (95% CI: 0.248~1.000), and 0.750 (95% CI: 0.448~1.000) in the training set, the validation set and the testing set, respectively. Results from calibration plots in validation and testing groups indicated that there was no departure between observed and predictive values in the two datasets (p = 0.377 and 0.861, respectively). CONCLUSIONS Our model combining 18F-FDG-PET radiomics and machine learning indicated a good predictive ability of KRAS status in lung adenocarcinoma. It may be a helpful non-invasive method to screen the KRAS mutation status of heterogenous lung adenocarcinoma before selected biopsy sampling

Jump starting e-learning: The impact of COVID-19 on perceived learning success: A real-time case study

The global upheaval caused by COVID-19 in 2020 has by many been considered as the biggest accelerator for digitalization in various areas, and Higher Education Institutions (HEI) are no exception to this. Universities had to abruptly shift to digital formats also termed e-learning. Some universities have been better prepared for online teaching than others, but for most within the educational community it has been a new experience to study or teach most if not all courses online without any further physical touch points. Due to the semester schedule, lecturers and students at Munich Business School had to adapt during an on-going semester and switch their way of teaching and learning more or less overnight, from offline teaching only to online teaching only. This situation has offered a unique opportunity to add to the literature on effective learning as well as e-learning by comparing exclusively offline and online learning environments across the same student population and measuring their impact on perceived learning success. This unique situation has further allowed for the development of a new model to explain possible drivers of perceived learning success under such circumstances. While an unexpected upheaval like COVID-19 may not happen in this exact same scenario again, new pandemics and global crises can easily impact future teaching environments in a similar manner, at least with regards to the limited time available to make such a switch. Consequently, the model presented here encourages further research and, if sufficiently validated in future research, has the power to provide insights into how to design an effective learning environment in HEI

Analyse von Mitarbeiterglück anhand eines Quintuple-Bottom-Line-Modells am Beispiel der Generation Y in Deutschland

Ein zunehmend wichtiger Aspekt, um die Bindung und Rekrutierung von talentierten Arbeitskräften zu gewährleisten, ist das Thema „Mitarbeiterglück“. Arbeitgeber sind gezwungen zu verstehen, welche Faktoren einen Einfluss auf das Glück ihrer Mitarbeiter haben. Aus diesem Grund befasst sich dieser Beitrag mit der Entwicklung eines innovativen Quintuple-Bottom-Line-Modells – einem holistischen Modell, das zum einen die Faktoren beinhaltet, die zu hedonistischem Glück führen und zum anderen auch diejenigen Faktoren berücksichtigt, die zu eudämonistischem Glück bei der Arbeit beitragen. Eine Kurzstudie auf der Basis des vorgeschlagenen Modells weist im Ergebnis auf die besondere Bedeutung eudämonistischer Faktorenfür das Mitarbeiterglück der Generation Y in Deutschland hin. As the labor market is tightening and happiness at work is more and more becoming an important factor for individuals, companies are forced to respond to this issue to attract the talented workforce. Therefore, an innovative Quintuple Bottom Line model was developed which contains thirty-five factors that impact the employees’ hedonic and eudaimonic happiness and thus allows to analyze what the strongest influencing factors on happiness at work are. Keywords: quintuple bottom line modell, human research management, dimension, compan

Particle filter de-noising of voxel-specific time-activity-curves in personalized 177Lu therapy

Background: Currently, there is a high interest in Lu-177 targeted radionuclide therapies, which could be attributed to favorable results obtained from Lu-177 compounds targeting neuro-endocrine and prostate tumors. SPECT based dosimetry could be used for deriving dose values for individual voxels, as is the standard in external-beam radiation-therapy (EBRT). For this a time-activity-curve (TAC) at voxel resolution and also a voxel-wise modeling of radiation energy deposition are necessary. But a voxel-wise determination of TACs is problematic, since several confounding factors exist, such as e.g. poor count-statistics or registration inaccuracies, which add noise to the observed activity states. A particle filter (PF) is a class of methods which applies regularization based on a model of the temporal evolution of activity states. The aim of this study is to introduce the application of PFs for de-noising of per-voxel time-activity curves. Methods: We applied a PF for de-noising the TACs of 26 patients, who underwent Lu-177-DOTATOC or -PSMA therapy. The TACs were obtained from fiilly-quantitative, serial SPECT (/CT) data, acquired at 4 h, 24 h, 48 h, 72 h p.i. The model used in the PF was a mono-exponential decay and its free parameters were determined based on objective criteria. The time-integrated activities (TIA) resulting from the PF (PFF) were compared to the results of a mono-exponential fit (SF) of individual voxels in several volumes of interest (kidneys, spleen, tumors). Additionally, an organ-averaged TIA was derived from whole-organ VOIs and subsequent curve-fitting. This whole-organ TIA was also compared to the whole-organ TIAs obtained from summation of the voxel-wise TIAs from PFF and SF. Results: The number of particles was set to 1000. Optimal values for noise of observations and noise of the model were 0.25 and 0.5, respectively The deviation of whole-organ TIAs from conventional organ-based dosimetry and the summation of the voxel-wise TIAs was substantial for SF (kidneys -22.3%, spleen -49.6%, tumor -60.0%), as well as for PFF (kidneys -37.1%, spleen -57.9%, tumor -70.9%). The distribution of voxel-wise half-lives resulting from the PFF method was considerably closer to the organ-averaged value, and the number of implausibly long half-lives (>physical HL) was reduced. Conclusion: The PFF leads to voxel-wise half-lives, which are more plausible than those resulting from SF. However, one has to admit that voxel-wise fitting generally leads to considerable deviations from the organ-averaged TIA as obtained by conventional whole-organ evaluation. Unfortunately, we did not have ground-truth TIA of our patient data and proper ground-truth could even be impossible to obtain. Nevertheless, there are strong indicators that particle filtering can be used for reducing voxel-wise TAC noise

Genetic diversity of calcareous grassland plant species depends on historical landscape configuration

Abstract Background Habitat fragmentation is considered to be a main reason for decreasing genetic diversity of plant species. However, the results of many fragmentation studies are inconsistent. This may be due to the influence of habitat conditions, having an indirect effect on genetic variation via reproduction. Consequently we took a comparative approach to analyse the impact of habitat fragmentation and habitat conditions on the genetic diversity of calcareous grassland species in this study. We selected five typical grassland species (Primula veris, Dianthus carthusianorum, Medicago falcata, Polygala comosa and Salvia pratensis) occurring in 18 fragments of calcareous grasslands in south eastern Germany. We sampled 1286 individuals in 87 populations and analysed genetic diversity using amplified fragment length polymorphisms. Additionally, we collected data concerning habitat fragmentation (historical and present landscape structure) and habitat conditions (vegetation structure, soil conditions) of the selected study sites. The whole data set was analysed using Bayesian multiple regressions. Results Our investigation indicated a habitat loss of nearly 80% and increasing isolation between grasslands since 1830. Bayesian analysis revealed a significant impact of the historical landscape structure, whereas habitat conditions played no important role for the present-day genetic variation of the studied plant species. Conclusions Our study indicates that the historical landscape structure may be more important for genetic diversity than present habitat conditions. Populations persisting in abandoned grassland fragments may contribute significantly to the species’ variability even under deteriorating habitat conditions. Therefore, these populations should be included in approaches to preserve the genetic variation of calcareous grassland species

Three-dimensional Monte Carlo-based voxel-wise tumor dosimetry in patients with neuroendocrine tumors who underwent 177Lu-DOTATOC therapy

Background Patients with advanced neuroendocrine tumors (NETs) of the midgut are suitable candidates for Lu-177-DOTATOC therapy. Integrated SPECT/CT systems have the potential to help improve the accuracy of patient-specific tumor dosimetry. Dose estimations to target organs are generally performed using the Medical Internal Radiation Dose scheme. We present a novel Monte Carlo-based voxel-wise dosimetry approach to determine organ- and tumor-specific total tumor doses (TTD). Methods A cohort of 14 patients with histologically confirmed metastasized NETs of the midgut (11 men, 3 women, 62.3 +/- 11.0 years of age) underwent a total of 39 cycles of Lu-177-DOTATOC therapy (mean 2.8 cycles, SD +/- 1 cycle). After the first cycle of therapy, regions of interest were defined manually on the SPECT/CT images for the kidneys, the spleen, and all 198 tracer-positive tumor lesions in the field of view. Four SPECT images, taken at 4 h, 24 h, 48 h and 72 h after injection of the radiopharmaceutical, were used to determine their effective half-lives in the structures of interest. The absorbed doses were calculated by a three-dimensional dosimetry method based on Monte Carlo simulations. TTD was calculated as the sum of all products of single tumor doses with single tumor volumes divided by the sum of all tumor volumes. Results The average dose values per cycle were 3.41 +/- 1.28 Gy (1.91-6.22 Gy) for the kidneys, 4.40 +/- 2.90 Gy (1.14-11.22 Gy) for the spleen, and 9.70 +/- 8.96 Gy (1.47-39.49 Gy) for all Lu-177-DOTATOC-positive tumor lesions. Low- and intermediate-grade tumors (G 1-2) absorbed a higher TTD compared to high-grade tumors (G 3) (signed-rank test, p = < 0.05). The pre-therapeutic chromogranin A (CgA) value and the TTD correlated significantly (Pearson correlation: = 0.67, p = 0.01). Higher TTD resulted in a significant decrease of CgA after therapy. Conclusion These results suggest that Monte Carlo-based voxel-wise dosimetry is a very promising tool for predicting the absorbed TTD based on histological and clinical parameters

Estimation of [177Lu]PSMA-617 tumor uptake based on voxel-wise 3D Monte Carlo tumor dosimetry in patients with metastasized castration resistant prostate cancer

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Targeting of fibroblast activation protein in rheumatoid arthritis patients:imaging and ex vivo photodynamic therapy

OBJECTIVE: Activated synovial fibroblasts are key effector cells in rheumatoid arthritis (RA). Selectively depleting these based upon their expression of fibroblast activation protein (FAP) is an attractive therapeutic approach. Here we introduce FAP imaging of inflamed joints using [68Ga]Ga-FAPI-04 in an RA patient, and aim to assess feasibility of anti-FAP targeted photodynamic therapy (FAP-tPDT) ex vivo using 28H1-IRDye700DX on RA synovial explants. METHODS: Remnant synovial tissue from RA patients was processed into 6 mm biopsies and, from several patients, into primary fibroblast cell cultures. Both were treated using FAP-tPDT. Cell viability was measured in fibroblast cultures and biopsies were evaluated for histological markers of cell damage. Selectivity of the effect of FAP-tPDT was assessed using flowcytometry on primary fibroblasts and co-cultured macrophages. Additionally, one RA patient intravenously received [68Ga]Ga-FAPI-04 and was scanned using PET/CT imaging. RESULTS: In the RA patient,FAPI-04 PET imaging showed high accumulation of the tracer in arthritic joints with very low background signal. In vitro, FAP-tPDT induced cell death in primary RA synovial fibroblasts in a light dose dependent manner. An upregulation of cell damage markers was observed in the synovial biopsies after FAP-tPDT. No significant effects of FAP-tPDT were noted on macrophages after FAP-tPDT of neighbouring fibroblasts. CONCLUSION: In this study the feasibility of selective FAP-tPDT in synovium of rheumatoid arthritis patients ex vivo is demonstrated. Furthermore, this study provides the first indication that FAP-targeted PET/CT can be used to image arthritic joints, an important step towards application of FAP-tPDT as a targeted locoregional therapy for RA

Imaging in inflammatory arthritis: progress towards precision medicine

International audienceImaging techniques such as ultrasonography and MRI have gained ground in the diagnosis and management of inflammatory arthritis, as these imaging modalities allow a sensitive assessment of musculoskeletal inflammation and damage. However, these techniques cannot discriminate between disease subsets and are currently unable to deliver an accurate prediction of disease progression and therapeutic response in individual patients. This major shortcoming of today’s technology hinders a targeted and personalized patient management approach. Technological advances in the areas of high-resolution imaging (for example, high-resolution peripheral quantitative computed tomography and ultra-high field MRI), functional and molecular-based imaging (such as chemical exchange saturation transfer MRI, positron emission tomography, fluorescence optical imaging, optoacoustic imaging and contrast-enhanced ultrasonography) and artificial intelligence-based data analysis could help to tackle these challenges. These new imaging approaches offer detailed anatomical delineation and an in vivo and non-invasive evaluation of the immunometabolic status of inflammatory reactions, thereby facilitating an in-depth characterization of inflammation. By means of these developments, the aim of earlier diagnosis, enhanced monitoring and, ultimately, a personalized treatment strategy looms closer