Large-scale performance assessment of the BG-Counter 2 used with two different mosquito traps.

BACKGROUND: Mosquitoes are important vectors of pathogens. They are usually collected with CO2-baited traps and subsequently identified by morphology. This procedure is very time-consuming. Automatic counting traps could facilitate timely evaluation of the local risk for mosquito-borne pathogen transmission or decision-making on vector control measures, but the counting accuracy of such devices has rarely been validated in the field. METHODS: The Biogents (BG)-Counter 2 automatically counts mosquitoes by discriminating the size of captured objects directly in the field and transmits the data to a cloud server. To assess the accuracy of this counting device, 27 traps were placed at 19 sampling sites across Germany and used in daily, weekly or bimonthly intervals from April until October 2021. The BG-Counter 2 was attached to a CO2-trap (BG-Pro trap = CO2-Pro) and the same trap was converted to also attract gravid mosquitoes (upside-down BG-Pro trap with a water container beneath = CO2-Pro-gravid). All captured mosquitoes were identified by morphology. The number of females (unfed and gravid), mosquito diversity and the number of identified specimens in relation to the counting data of the BG-Counter were compared between the two trapping devices to evaluate sampling success and counting accuracy. RESULTS: In total 26,714 mosquitoes were collected during 854 trap days. The CO2-Pro-gravid trap captured significantly more mosquitoes per trap day for all specimens, gravid females and non-gravid females, while there was no difference in the mosquito diversity. The linear model with the captured mosquitoes as a response and the counted specimens as a predictor explained only a small degree of the variation within the data (R2 = 0.16), but per individual trap the value could reach up to 0.62 (mean R2 = 0.23). The counting accuracy for the daily samples had a significant positive correlation with sample size, resulting in higher accuracy for the CO2-Pro-gravid trap and higher accuracy for sites and sampling months with high mosquito abundance. CONCLUSIONS: While the accuracy of the BG-Counter 2 is quite low, the device is able to depict mosquito phenology and provide information about local population dynamics.

Clinical and imaging findings associated with preservation of knee joint health over 8 years in individuals aged 65 and over: data from the OAI.

OBJECTIVE: While risk factors for osteoarthritis (OA) are well known, it is not well understood why certain individuals maintain high mobility and joint health throughout their life while others demonstrate OA at older ages. The purpose of this study was to assess which demographic, clinical and MRI quantitative and semi-quantitative factors are associated with preserving healthy knees in older individuals. METHODS: This study analyzed data from the OA Initiative (OAI) cohort of individuals at the age of 65 years or above. Participants without OA at baseline (BL) (Kellgren-Lawrence (KL) ≤ 1) were followed and classified as incident cases (KL ≥ 2 during follow-up; n = 115) and as non-incident (KL ≤ 1 over 96-month; n = 391). Associations between the predictor-variables sex, age, BMI, race, clinical scoring systems, T2 relaxation times and Whole-Organ Magnetic Resonance Imaging-Score (WORMS) readings at BL and the preservation of healthy knees (KL ≤ 1) during a 96-month follow-up period were assessed using logistic regression models. RESULTS: Obesity and presence of pain showed a significant inverse association with maintaining radiographically normal joints in patients aged 65 and above. T2 relaxation times of the lateral femur and tibia as well as the medial femur were also significantly associated with maintaining radiographically normal knee joints. Additionally, absence of lesions of the lateral meniscus and absence of cartilage lesions in the medial and patellofemoral compartments were significantly associated with maintaining healthy knee joints. CONCLUSION: Overall, this study provides protective clinical parameters as well as quantitative and semi-quantitative MR-imaging parameters associated with maintaining radiographically normal knee joints in an older population over 8 years.

Frequency and duration of sensory flicker controls astrocyte and neuron specific transcriptional profiles in 5xFAD mice.

Background: Current clinical trials are investigating gamma frequency sensory stimulation as a potential therapeutic strategy for Alzheimer's disease, yet we lack a comprehensive picture of the effects of this stimulation on multiple aspects of brain function. While most prior research has focused on gamma frequency sensory stimulation, we previously showed that exposing mice to visual flickering stimulation increased MAPK and NFκB signaling in the visual cortex in a manner dependent on duration and frequency of sensory stimulation exposure. Because these pathways control multiple neuronal and glial functions and are differentially activated based on the duration and frequency of flicker stimulation, we aimed to define the transcriptional effects of different frequencies and durations of flicker stimulation on multiple brain functions. Methods: We exposed 5xFAD mice to different frequencies of audio/visual flicker stimulation (constant light, 10Hz, 20Hz, 40Hz) for durations of 0.5hr, 1hr, or 4hr, then used bulk RNAseq to profile transcriptional changes within the visual cortex and hippocampus tissues. Using weighted gene co-expression network analysis, we identified modules of co-expressed genes controlled by frequency and/or duration of stimulation. Results: Within the visual cortex, we found that all stimulation frequencies caused fast activation of a module of immune genes within 1hr and slower suppression of synaptic genes after 4hrs of stimulation. Interestingly, all frequencies of stimulation led to slow suppression of astrocyte specific gene sets, while activation of neuronal gene sets was frequency and duration specific. In contrast, in the hippocampus, immune and synaptic modules were suppressed based on the frequency of stimulation. Specifically,10Hz activated a module of genes associated with mitochondrial function, metabolism, and synaptic translation while 10Hz rapidly suppressed a module of genes linked to neurotransmitter activity. Conclusion: Collectively, our data indicate that the frequency and duration of flicker stimulation controls immune, neuronal, and metabolic genes in multiple regions of the brain affected by Alzheimer's disease. Flicker stimulation may thus represent a potential therapeutic strategy that can be tuned based on the brain region and the specific cellular process to be modulated.

Time trend analysis of Injury Severity score of adult trauma patients with emergent CT examination.

PURPOSE: Controversy exists about whole-body computed tomography (CT) as a primary screening modality for suspected multiple trauma patients. Therefore, the aim of this study was to analyze time trends of CT examinations for trauma patients in relation to the Injury Severity Score (ISS). METHODS: We retrospectively analyzed 561 adult trauma patients (mean age = 54 years) who were admitted to the trauma room of our hospital, immediately followed by a CT examination, in 2009, 2013 und 2017. Review of electronic patient charts was performed to determine the cause of injury. ISS was either calculated upon hospital charts and CT imaging reports or documented in the TraumaRegister DGU® for trauma patients with ICU treatment or ISS ≥ 16. RESULTS: An increasing number of CT examinations of acute trauma patients were performed at our hospital with 117 patients in 2009 compared to 192 in 2013 and 252 in 2017. Their mean age increased (50 years in 2009, 54 in 2013 and 55 in 2017;p = 0.046), whereas their mean ISS decreased over time (15.2 in 2009 compared to 12.1 in 2013 and 10.6 in 2017;p = 0.001), especially in women (15.1 in 2009, 11.8 in 2013 and 7.4 in 2017;p = 0.001 both), younger age groups (18 to 24 years:15.6 in 2009, 6.5 in 2013 and 8.9 in 2017; p = 0.033 and 25 to 49 years:15.0 in 2009, 11.2 in 2013 and 8.3 in 2017;p = 0.001) as well as motor vehicle collision (MVC) victims (16.2 in 2009, 11.8 in 2013 and 6.1 in 2017; p < 0.001). Trauma patients with a high ISS were especially more likely of older age (OR 1.02,p < 0.001) and with the type of incident being a fall (< 3 m: OR3.84,p < 0.001;>3 m: OR6.22,p < 0.001) compared to MVC. CONCLUSION: Previous studies suggesting a benefit of primary whole-body CT for trauma patients might not reflect the current patient population with decreasing ISS. Especially females, younger age groups and MVC patients might benefit from stricter selection criteria for receiving whole-body CT. Our results also emphasize the importance of prevention of fall or tumble for elderly people.

[Artificial intelligence in cardiovascular radiology : Image acquisition, image reconstruction and workflow optimization]. / Künstliche Intelligenz in der kardiovaskulären Radiologie : Bildakquisition, Bildrekonstruktion und Workflowoptimierung.

BACKGROUND: Artificial intelligence (AI) has the potential to fundamentally change radiology workflow. OBJECTIVES: This review article provides an overview of AI applications in cardiovascular radiology with a focus on image acquisition, image reconstruction, and workflow optimization. MATERIALS AND METHODS: First, established applications of AI are presented for cardiovascular computed tomography (CT) and magnetic resonance imaging (MRI). Building on this, we describe the range of applications that are currently being developed and evaluated. The practical benefits, opportunities, and potential risks of artificial intelligence in cardiovascular imaging are critically discussed. The presentation is based on the relevant specialist literature and our own clinical and scientific experience. RESULTS: AI-based techniques for image reconstruction are already commercially available and enable dose reduction in cardiovascular CT and accelerated image acquisition in cardiac MRI. Postprocessing of cardiovascular CT and MRI examinations can already be considerably simplified using established AI-based segmentation algorithms. In contrast, the practical benefits of many AI applications aimed at the diagnosis of cardiovascular diseases are less evident. Potential risks such as automation bias and considerations regarding cost efficiency should also be taken into account. CONCLUSIONS: In a market characterized by great expectations and rapid technical development, it is important to realistically assess the practical benefits of AI applications for your own hospital or practice.

Adherence to a lifestyle intervention - just a question of self-efficacy? Analysis of the AgeWell.de-intervention against cognitive decline.

BACKGROUND: Aim of this study was to detect predictors of better adherence to the AgeWell.de-intervention, a two-year randomized multi-domain lifestyle intervention against cognitive decline. METHODS: Data of 317 intervention group-participants comprising a risk group for dementia (Cardiovascular Risk Factors, Ageing and Dementia (CAIDE) score of ≥ 9; mean age 68.9 years, 49.5% women) from the AgeWell.de intervention study were analysed. Regression models with four blocks of predictors (sociodemographic, cognitive and psychosocial, lifestyle factors and chronic conditions) were run on adherence to the components of nutrition, enhancement of social and physical activity and cognitive training. Adherence to each component was operationalised by assessing the degree of goal achievement per component at up to seven time points during the intervention period, measured using a 5-point Likert scale (mean score of goal achievement). RESULTS: Increasing age was negatively associated with adherence, while higher education positively predicted adherence. Participants with better mental state (Montreal Cognitive Assessment (MoCA)-score > 25) at baseline and higher self-efficacy adhered better. Diabetes and cardiovascular conditions were not associated with adherence, whereas smoking negatively affected adherence. Highest education and quitting smoking in the past were the only predictors associated with all four intervention components. CONCLUSION: Results identified predictors for better and worse adherence. Particularly self-efficacy seems to be of considerable influence on adherence. This should be considered when designing future intervention trials. TRIAL REGISTRATION: German Clinical Trials Register (ref. number: DRKS00013555).

Molecular Analysis of Copy-Back Defective Interfering RNAs of Morbilliviruses.

Copy-back defective interfering RNAs are major contaminants of viral stock preparations of morbilliviruses and other negative strand RNA viruses. They are hybrid molecules of positive sense antigenome and negative sense genome. They possess perfectly complementary ends allowing the formation of extremely stable double-stranded RNA panhandle structures. The presence of the 3'-terminal promoter allows replication of these molecules by the viral polymerase. They thereby negatively interfere with replication of standard genomes. In addition, the double-stranded RNA stem structures are highly immunostimulatory and activate antiviral cell-intrinsic innate immune responses. Thus, copy-back defective interfering RNAs severely affect the virulence and pathogenesis of morbillivirus stocks. We describe two biochemical methods to analyze copy-back defective interfering RNAs in virus-infected samples, or purified viral RNA. First, we present our Northern blotting protocol that allows accurate size determination of defective interfering RNA molecules and estimation of the relative contamination level of virus preparations. Second, we describe a PCR approach to amplify defective interfering RNAs specifically, which allows detailed sequence analysis.

Chondrosarcoma evaluation using hematein-based x-ray staining and high-resolution 3D micro-CT: a feasibility study.

BACKGROUND: Chondrosarcomas are rare malignant bone tumors diagnosed by analyzing radiological images and histology of tissue biopsies and evaluating features such as matrix calcification, cortical destruction, trabecular penetration, and tumor cell entrapment. METHODS: We retrospectively analyzed 16 cartilaginous tumor tissue samples from three patients (51-, 54-, and 70-year-old) diagnosed with a dedifferentiated chondrosarcoma at the femur, a moderately differentiated chondrosarcoma in the pelvis, and a predominantly moderately differentiated chondrosarcoma at the scapula, respectively. We combined a hematein-based x-ray staining with high-resolution three-dimensional (3D) microscopic x-ray computed tomography (micro-CT) for nondestructive 3D tumor assessment and tumor margin evaluation. RESULTS: We detected trabecular entrapment on 3D micro-CT images and followed bone destruction throughout the volume. In addition to staining cell nuclei, hematein-based staining also improved the visualization of the tumor matrix, allowing for the distinction between the tumor and the bone marrow cavity. The hematein-based staining did not interfere with further conventional histology. There was a 5.97 ± 7.17% difference between the relative tumor area measured using micro-CT and histopathology (p = 0.806) (Pearson correlation coefficient r = 0.92, p = 0.009). Signal intensity in the tumor matrix (4.85 ± 2.94) was significantly higher in the stained samples compared to the unstained counterparts (1.92 ± 0.11, p = 0.002). CONCLUSIONS: Using nondestructive 3D micro-CT, the simultaneous visualization of radiological and histopathological features is feasible. RELEVANCE STATEMENT: 3D micro-CT data supports modern radiological and histopathological investigations of human bone tumor specimens. It has the potential for being an integrative part of clinical preoperative diagnostics. KEY POINTS: • Matrix calcifications are a relevant diagnostic feature of bone tumors. • Micro-CT detects all clinically diagnostic relevant features of x-ray-stained chondrosarcoma. • Micro-CT has the potential to be an integrative part of clinical diagnostics.

Influence of Deep Learning Based Image Reconstruction on Quantitative Results of Coronary Artery Calcium Scoring.

RATIONALE AND OBJECTIVES: To assess the impact of deep learning-based imaging reconstruction (DLIR) on quantitative results of coronary artery calcium scoring (CACS) and to evaluate the potential of DLIR for radiation dose reduction in CACS. METHODS: For a retrospective cohort of 100 consecutive patients (mean age 62 ±10 years, 40% female), CACS scans were reconstructed with filtered back projection (FBP), adaptive statistical iterative reconstruction (ASiR-V in 30%, 60% and 90% strength) and DLIR in low, medium and high strength. CACS was quantified semi-automatically and compared between image reconstructions. In a phantom study, a cardiac calcification insert was scanned inside an anthropomorphic thorax phantom at standard dose, 50% dose and 25% dose. FBP reconstructions at standard dose served as the reference standard. RESULTS: In the patient study, DLIR led to a mean underestimation of Agatston score by 3.5, 6.4 and 11.6 points at low, medium and high strength, respectively. This underestimation of Agatston score was less pronounced for DLIR than for ASiR-V. In the phantom study, quantitative CACS results increased with reduced radiation dose and decreased with increasing strength of DLIR. Medium strength DLIR reconstruction at 50% dose reduction and high strength DLIR reconstruction at 75% dose reduction resulted in quantitative CACS results that were comparable to FBP reconstructions at standard dose. CONCLUSION: Compared to FBP as the historical reference standard, DLIR leads to an underestimation of CACS but this underestimation is more moderate than with ASiR-V. DLIR can offset the increase in image noise and calcium score at reduced dose and may thus allow for substantial radiation dose reductions in CACS studies.

Development of a pan-tau multivalent nanobody that binds tau aggregation motifs and recognizes pathological tau aggregates.

Alzheimer's disease and other tauopathies are characterized by the misfolding and aggregation of the tau protein into oligomeric and fibrillar structures. Antibodies against tau play an increasingly important role in studying these neurodegenerative diseases and the generation of tools to diagnose and treat them. The development of antibodies that recognize tau protein aggregates, however, is hindered by complex immunization and antibody selection strategies and limitations to antigen presentation. Here, we have taken a facile approach to identify single-domain antibodies, or nanobodies, that bind to many forms of tau by screening a synthetic yeast surface display nanobody library against monomeric tau and creating multivalent versions of our lead nanobody, MT3.1, to increase its avidity for tau aggregates. We demonstrate that MT3.1 binds to tau monomer, oligomers, and fibrils, as well as pathogenic tau from a tauopathy mouse model, despite being identified through screens against monomeric tau. Through epitope mapping, we discovered binding epitopes of MT3.1 contain the key motif VQIXXK which drives tau aggregation. We show that our bivalent and tetravalent versions of MT3.1 have greatly improved binding ability to tau oligomers and fibrils compared to monovalent MT3.1. Our results demonstrate the utility of our nanobody screening and multivalent design approach in developing nanobodies that bind amyloidogenic protein aggregates. This approach can be extended to the generation of multivalent nanobodies that target other amyloid proteins and has the potential to advance the research and treatment of neurodegenerative diseases.

Development and Optimization of a Multiplex Real-Time RT-PCR to Detect SARS-CoV-2 in Human Samples.

PCR and its variants (RT-PCR and qRT-PCR) are valuable and innovative molecular techniques for studying nucleic acids. qPCR has proven to be highly sensitive, efficient, and reproducible, generating reliable results that are easy to analyze. During the COVID-19 pandemic, qPCR became the gold standard technique for detecting the SARS-CoV-2 virus that allowed to confirm the infection event, and those asymptomatic ones, and thus save millions of lives. In-house multiplex qPCR tests were developed worldwide to detect different viral targets and ensure results, follow the infections, and favor the containment of a pandemic. Here, we present the detailed fundamentals of the qPCR technique based on fluorogenic probes and processes to develop and optimize a successful multiplex RT-qPCR test for detecting SARS-CoV-2 that could be used to diagnose COVID-19 accurately.

Standardization of a CT Protocol for Imaging Patients with Suspected COVID-19-A RACOON Project.

CT protocols that diagnose COVID-19 vary in regard to the associated radiation exposure and the desired image quality (IQ). This study aims to evaluate CT protocols of hospitals participating in the RACOON (Radiological Cooperative Network) project, consolidating CT protocols to provide recommendations and strategies for future pandemics. In this retrospective study, CT acquisitions of COVID-19 patients scanned between March 2020 and October 2020 (RACOON phase 1) were included, and all non-contrast protocols were evaluated. For this purpose, CT protocol parameters, IQ ratings, radiation exposure (CTDIvol), and central patient diameters were sampled. Eventually, the data from 14 sites and 534 CT acquisitions were analyzed. IQ was rated good for 81% of the evaluated examinations. Motion, beam-hardening artefacts, or image noise were reasons for a suboptimal IQ. The tube potential ranged between 80 and 140 kVp, with the majority between 100 and 120 kVp. CTDIvol was 3.7 ± 3.4 mGy. Most healthcare facilities included did not have a specific non-contrast CT protocol. Furthermore, CT protocols for chest imaging varied in their settings and radiation exposure. In future, it will be necessary to make recommendations regarding the required IQ and protocol parameters for the majority of CT scanners to enable comparable IQ as well as radiation exposure for different sites but identical diagnostic questions.

Wolbachia strains wMel and wAlbB differentially affect Aedes aegypti traits related to fecundity.

Two Wolbachia strains, wMel and wAlbB, have been transinfected into Aedes aegypti mosquitoes for population replacement with the aim of reducing dengue transmission. Epidemiological data from various endemic sites suggest a pronounced decrease in dengue transmission after implementing this strategy. In this study, we investigated the impact of the Wolbachia strains wMel and wAlbB on Ae. aegypti fitness in a common genetic background. We found that Ae. aegypti females infected with the wMel strain exhibited several significant differences compared with those infected with the wAlbB strain. Specifically, wMel-infected females laid significantly fewer eggs, ingested a lower amount of blood, had a reduced egg production rate, and exhibited a decreased Wolbachia density at a later age compared with mosquitoes infected with the wAlbB strain. Conversely, the wAlbB strain showed only mild negative effects when compared with Wolbachia-uninfected specimens. These differential effects on Ae. aegypti fitness following infection with either wMel or wAlbB may have important implications for the success of population replacement strategies in invading native Ae. aegypti populations in endemic settings. Further research is needed to better understand the underlying mechanisms responsible for these differences in fitness effects and their potential impact on the long-term efficacy of Wolbachia-based dengue control programs.IMPORTANCEThe transmission of arboviruses such as dengue, Zika, and chikungunya is on the rise globally. Among the most promising strategies to reduce arbovirus burden is the release of one out of two strains of Wolbachia-infected Aedes aegypti: wMel and wAlbB. One critical aspect of whether this approach will succeed involves the fitness cost of either Wolbachia strains on mosquito life history traits. For instance, we found that wMel-infected Ae. aegypti females laid significantly fewer eggs, ingested a lower amount of blood, had a reduced egg production rate, and exhibited a decreased Wolbachia density at a later age compared with mosquitoes infected with the wAlbB strain. Conversely, the wAlbB strain showed only mild negative effects when compared with Wolbachia-uninfected specimens. These differential effects on mosquito fitness following infection with either wMel or wAlbB may have important implications for the success of population replacement strategies in invading native Ae. aegypti populations.

When evidence changes: Communicating uncertainty protects against a loss of trust.

Scientific findings can be overturned when new evidence arises. This study examines how communicating and explaining uncertainty around scientific findings affect trust in the communicator when findings change. In an online experiment (N = 800, convenience sample), participants read a fictitious statement from a public health authority announcing that there was no link between a new COVID-19 vaccine and heart muscle inflammation. The authority communicated (1) no uncertainty, (2) uncertainty without giving a reason, (3) uncertainty due to imprecision, or (4) uncertainty due to incomplete accounting of patients. Participants were then informed that the authority's statement was no longer correct as new data showed a link between the vaccine and heart muscle inflammation. Participants rated the authority's trustworthiness before and after the evidence update. Our findings indicate that communicating uncertainty buffers against a loss of trust when evidence changes. Moreover, explaining uncertainty does not appear to harm trust.

Multi-modal assessment of a cardiac stem cell therapy reveals distinct modulation of regional scar properties.

BACKGROUND: The initial idea of functional tissue replacement has shifted to the concept that injected cells positively modulate myocardial healing by a non-specific immune response of the transplanted cells within the target tissue. This alleged local modification of the scar requires assessment of regional properties of the left ventricular wall in addition to commonly applied measures of global morphological and functional parameters. Hence, we aimed at investigating the effect of cardiac cell therapy with cardiovascular progenitor cells, so-called cardiac induced cells, on both global and regional properties of the left ventricle by a multimodal imaging approach in a mouse model. METHODS: Myocardial infarction was induced in mice by ligation of the left anterior descending artery, the therapy group received an intramyocardial injection of 1 × 106 cardiac induced cells suspended in matrigel, the control group received matrigel only. [18F]FDG positron emission tomography imaging was performed after 17 days, to assess regional glucose metabolism. Three weeks after myocardial infarction, cardiac magnetic resonance imaging was performed for morphological and functional assessment of the left ventricle. Following these measurements, hearts were excised for histological examinations. RESULTS: Cell therapy had no significant effect on global morphological parameters. Similarly, there was no difference in scar size and capillary density between therapy and control group. However, there was a significant improvement in contractile function of the left ventricle - left ventricular ejection fraction, stroke volume and cardiac output. Regional analysis of the left ventricle identified changes of wall properties in the scar area as the putative mechanism. Cell therapy reduced the thinning of the scar and significantly improved its radial contractility. Furthermore, the metabolic defect, assessed by [18F]FDG, was significantly reduced by the cell therapy. CONCLUSION: Our data support the relevance of extending the assessment of global left ventricular parameters by a structured regional wall analysis for the evaluation of therapies targeting at modulation of healing myocardium. This approach will enable a deeper understanding of mechanisms underlying the effect of experimental regenerative therapies, thus paving the way for a successful translation into clinical application.

Spectral computed tomography angiography using a gadolinium-based contrast agent for imaging of pathologies of the aorta.

OBJECTIVES: Especially patients with aortic aneurysms and multiple computed tomography angiographies (CTA) might show medical conditions which oppose the use of iodine-based contrast agents. CTA using monoenergetic reconstructions from dual layer CT and gadolinium (Gd-)based contrast agents might be a feasible alternative in these patients. Therefore, the purpose of this study was to evaluate the feasibility of clinical spectral CTA with a Gd-based contrast agent in patients with aortic aneurysms. METHODS: Twenty-one consecutive scans in 15 patients with and without endovascular aneurysm repair showing contraindications for iodine-based contrast agents were examined using clinical routine doses (0.2 mmol/kg) of Gd-based contrast agent with spectral CT. Monoenergetic reconstructions of the spectral data set were computed. RESULTS: There was a significant increase in the intravascular attenuation of the aorta between pre- and post-contrast images for the MonoE40 images in the thoracic and the abdominal aorta (p < 0.001 for both). Additionally, the ratio between pre- and post-contrast images was significantly higher in the MonoE40 images as compared to the conventional images with a factor of 6.5 ± 4.5 vs. 2.4 ± 0.5 in the thoracic aorta (p = 0.003) and 4.1 ± 1.8 vs. 1.9 ± 0.5 in the abdominal aorta (p < 0.001). CONCLUSIONS: To conclude, our study showed that Gd-CTA is a valid and reliable alternative for diagnostic imaging of the aorta for clinical applications. Monoenergetic reconstructions of computed tomography angiographies using gadolinium based contrast agents may be a useful alternative in patients with aortic aneurysms and contraindications for iodine based contrast agents.

A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images.

Accurate species identification is crucial to assess the medical relevance of a mosquito specimen, but requires intensive experience of the observers and well-equipped laboratories. In this proof-of-concept study, we developed a convolutional neural network (CNN) to identify seven Aedes species by wing images, only. While previous studies used images of the whole mosquito body, the nearly two-dimensional wings may facilitate standardized image capture and reduce the complexity of the CNN implementation. Mosquitoes were sampled from different sites in Germany. Their wings were mounted and photographed with a professional stereomicroscope. The data set consisted of 1155 wing images from seven Aedes species as well as 554 wings from different non-Aedes mosquitoes. A CNN was trained to differentiate between Aedes and non-Aedes mosquitoes and to classify the seven Aedes species based on grayscale and RGB images. Image processing, data augmentation, training, validation and testing were conducted in python using deep-learning framework PyTorch. Our best-performing CNN configuration achieved a macro F1 score of 99% to discriminate Aedes from non-Aedes mosquito species. The mean macro F1 score to predict the Aedes species was 90% for grayscale images and 91% for RGB images. In conclusion, wing images are sufficient to identify mosquito species by CNNs.

Artificial intelligence support in MR imaging of incidental renal masses: an early health technology assessment.

OBJECTIVE: This study analyzes the potential cost-effectiveness of integrating an artificial intelligence (AI)-assisted system into the differentiation of incidental renal lesions as benign or malignant on MR images during follow-up. MATERIALS AND METHODS: For estimation of quality-adjusted life years (QALYs) and lifetime costs, a decision model was created, including the MRI strategy and MRI + AI strategy. Model input parameters were derived from recent literature. Willingness to pay (WTP) was set to $100,000/QALY. Costs of $0 for the AI were assumed in the base-case scenario. Model uncertainty and costs of the AI system were assessed using deterministic and probabilistic sensitivity analysis. RESULTS: Average total costs were at $8054 for the MRI strategy and $7939 for additional use of an AI-based algorithm. The model yielded a cumulative effectiveness of 8.76 QALYs for the MRI strategy and of 8.77 for the MRI + AI strategy. The economically dominant strategy was MRI + AI. Deterministic and probabilistic sensitivity analysis showed high robustness of the model with the incremental cost-effectiveness ratio (ICER), which represents the incremental cost associated with one additional QALY gained, remaining below the WTP for variation of the input parameters. If increasing costs for the algorithm, the ICER of $0/QALY was exceeded at $115, and the defined WTP was exceeded at $667 for the use of the AI. CONCLUSIONS: This analysis, rooted in assumptions, suggests that the additional use of an AI-based algorithm may be a potentially cost-effective alternative in the differentiation of incidental renal lesions using MRI and needs to be confirmed in the future. CLINICAL RELEVANCE STATEMENT: These results hint at AI's the potential impact on diagnosing renal masses. While the current study urges careful interpretation, ongoing research is essential to confirm and seamlessly integrate AI into clinical practice, ensuring its efficacy in routine diagnostics. KEY POINTS: • This is a model-based study using data from literature where AI has been applied in the diagnostic workup of incidental renal lesions. • MRI + AI has the potential to be a cost-effective alternative in the differentiation of incidental renal lesions. • The additional use of AI can reduce costs in the diagnostic workup of incidental renal lesions.

Automated assessment of cardiac pathologies on cardiac MRI using T1-mapping and late gadolinium phase sensitive inversion recovery sequences with deep learning.

BACKGROUND: A deep learning (DL) model that automatically detects cardiac pathologies on cardiac MRI may help streamline the diagnostic workflow. To develop a DL model to detect cardiac pathologies on cardiac MRI T1-mapping and late gadolinium phase sensitive inversion recovery (PSIR) sequences were used. METHODS: Subjects in this study were either diagnosed with cardiac pathology (n = 137) including acute and chronic myocardial infarction, myocarditis, dilated cardiomyopathy, and hypertrophic cardiomyopathy or classified as normal (n = 63). Cardiac MR imaging included T1-mapping and PSIR sequences. Subjects were split 65/15/20% for training, validation, and hold-out testing. The DL models were based on an ImageNet pretrained DenseNet-161 and implemented using PyTorch and fastai. Data augmentation with random rotation and mixup was applied. Categorical cross entropy was used as the loss function with a cyclic learning rate (1e-3). DL models for both sequences were developed separately using similar training parameters. The final model was chosen based on its performance on the validation set. Gradient-weighted class activation maps (Grad-CAMs) visualized the decision-making process of the DL model. RESULTS: The DL model achieved a sensitivity, specificity, and accuracy of 100%, 38%, and 88% on PSIR images and 78%, 54%, and 70% on T1-mapping images. Grad-CAMs demonstrated that the DL model focused its attention on myocardium and cardiac pathology when evaluating MR images. CONCLUSIONS: The developed DL models were able to reliably detect cardiac pathologies on cardiac MR images. The diagnostic performance of T1 mapping alone is particularly of note since it does not require a contrast agent and can be acquired quickly.