Heat treatment in health and disease: How water-filtered infrared-A (wIRA) irradiation affects key cellular mechanisms in myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) patients compared to healthy donors.

Heat treatment or hyperthermia is a promising therapy for many diseases, especially cancer, and can be traced back thousands of years. Despite its long history, little is known about the cellular and molecular effects of heat on human cells. Therefore, we investigated the impact of water-filtered infrared-A (wIRA) irradiation (39 °C, 60 min) on key cellular mechanisms, namely autophagy, mitochondrial function and mRNA expression, in human fibroblasts and peripheral blood mononuclear cells (PBMCs) from healthy donors and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) patients. Our results show an induction of autophagy in healthy fibroblasts and PBMCs from healthy donors and ME/CFS patients. ME/CFS patients have higher mitochondrial function compared to healthy donors. The wIRA treatment leads to a slight reduction in mitochondrial function in PBMCs from ME/CFS patients, thereby approaching the level of mitochondrial function of healthy donors. Furthermore, an activation of the mRNA expression of the autophagy-related genes MAP1LC3B and SIRT1 as well as for HSPA1, which codes for a heat shock protein, can be observed. These results confirm an impact of heat treatment in human cells on key cellular mechanisms, namely autophagy and mitochondrial function, in health and disease, and provide hope for a potential treatment option for ME/CFS patients.

Deep learning-enabled detection of hypoxic-ischemic encephalopathy after cardiac arrest in CT scans: a comparative study of 2D and 3D approaches.

Objective: To establish a deep learning model for the detection of hypoxic-ischemic encephalopathy (HIE) features on CT scans and to compare various networks to determine the best input data format. Methods: 168 head CT scans of patients after cardiac arrest were retrospectively identified and classified into two categories: 88 (52.4%) with radiological evidence of severe HIE and 80 (47.6%) without signs of HIE. These images were randomly divided into a training and a test set, and five deep learning models based on based on Densely Connected Convolutional Networks (DenseNet121) were trained and validated using different image input formats (2D and 3D images). Results: All optimized stacked 2D and 3D networks could detect signs of HIE. The networks based on the data as 2D image data stacks provided the best results (S100: AUC: 94%, ACC: 79%, S50: AUC: 93%, ACC: 79%). We provide visual explainability data for the decision making of our AI model using Gradient-weighted Class Activation Mapping. Conclusion: Our proof-of-concept deep learning model can accurately identify signs of HIE on CT images. Comparing different 2D- and 3D-based approaches, most promising results were achieved by 2D image stack models. After further clinical validation, a deep learning model of HIE detection based on CT images could be implemented in clinical routine and thus aid clinicians in characterizing imaging data and predicting outcome.

Brownian translation and rotation from the ballistic to the diffusive limit and derivation of the physical properties of dust agglomerates.

We analyzed the translational and rotational Brownian motion of aggregates of micrometer-sized silica spheres under microgravity conditions and in rarefied gas. The experimental data was collected in the form of high-speed recordings using a long-distance microscope as part of the ICAPS (Interactions in Cosmic and Atmospheric Particle Systems) experiment on board of the sounding rocket flight Texus-56. Our data analysis shows that the translational Brownian motion can be used to determine the mass and translational response time of each individual dust aggregate. The rotational Brownian motion additionally provides the moment of inertia and the rotational response time. A shallow positive correlation between mass and response time was found as predicted for aggregate structures with low fractal dimensions. Translational and rotational response times are roughly in agreement. Using the mass and the moment of inertia of each aggregate, we determined the fractal dimension of the aggregate ensemble. Slight deviations from the pure Gaussian one-dimensional displacement statistics were found in the ballistic limit for both the translational and rotational Brownian motion.

Two-Stage Deep Learning Model for Automated Segmentation and Classification of Splenomegaly.

Splenomegaly is a common cross-sectional imaging finding with a variety of differential diagnoses. This study aimed to evaluate whether a deep learning model could automatically segment the spleen and identify the cause of splenomegaly in patients with cirrhotic portal hypertension versus patients with lymphoma disease. This retrospective study included 149 patients with splenomegaly on computed tomography (CT) images (77 patients with cirrhotic portal hypertension, 72 patients with lymphoma) who underwent a CT scan between October 2020 and July 2021. The dataset was divided into a training (n = 99), a validation (n = 25) and a test cohort (n = 25). In the first stage, the spleen was automatically segmented using a modified U-Net architecture. In the second stage, the CT images were classified into two groups using a 3D DenseNet to discriminate between the causes of splenomegaly, first using the whole abdominal CT, and second using only the spleen segmentation mask. The classification performances were evaluated using the area under the receiver operating characteristic curve (AUC), accuracy (ACC), sensitivity (SEN), and specificity (SPE). Occlusion sensitivity maps were applied to the whole abdominal CT images, to illustrate which regions were important for the prediction. When trained on the whole abdominal CT volume, the DenseNet was able to differentiate between the lymphoma and liver cirrhosis in the test cohort with an AUC of 0.88 and an ACC of 0.88. When the model was trained on the spleen segmentation mask, the performance decreased (AUC = 0.81, ACC = 0.76). Our model was able to accurately segment splenomegaly and recognize the underlying cause. Training on whole abdomen scans outperformed training using the segmentation mask. Nonetheless, considering the performance, a broader and more general application to differentiate other causes for splenomegaly is also conceivable.