An epilepsy classification based on FFT and fully convolutional neural network nested LSTM.

Background and objective: Epilepsy, which is associated with neuronal damage and functional decline, typically presents patients with numerous challenges in their daily lives. An early diagnosis plays a crucial role in managing the condition and alleviating the patients' suffering. Electroencephalogram (EEG)-based approaches are commonly employed for diagnosing epilepsy due to their effectiveness and non-invasiveness. In this study, a classification method is proposed that use fast Fourier Transform (FFT) extraction in conjunction with convolutional neural networks (CNN) and long short-term memory (LSTM) models. Methods: Most methods use traditional frameworks to classify epilepsy, we propose a new approach to this problem by extracting features from the source data and then feeding them into a network for training and recognition. It preprocesses the source data into training and validation data and then uses CNN and LSTM to classify the style of the data. Results: Upon analyzing a public test dataset, the top-performing features in the fully CNN nested LSTM model for epilepsy classification are FFT features among three types of features. Notably, all conducted experiments yielded high accuracy rates, with values exceeding 96% for accuracy, 93% for sensitivity, and 96% for specificity. These results are further benchmarked against current methodologies, showcasing consistent and robust performance across all trials. Our approach consistently achieves an accuracy rate surpassing 97.00%, with values ranging from 97.95 to 99.83% in individual experiments. Particularly noteworthy is the superior accuracy of our method in the AB versus (vs.) CDE comparison, registering at 99.06%. Conclusion: Our method exhibits precise classification abilities distinguishing between epileptic and non-epileptic individuals, irrespective of whether the participant's eyes are closed or open. Furthermore, our technique shows remarkable performance in effectively categorizing epilepsy type, distinguishing between epileptic ictal and interictal states versus non-epileptic conditions. An inherent advantage of our automated classification approach is its capability to disregard EEG data acquired during states of eye closure or eye-opening. Such innovation holds promise for real-world applications, potentially aiding medical professionals in diagnosing epilepsy more efficiently.

Advanced platelet-rich fibrin promotes the paracrine function and proliferation of adipose-derived stem cells and contributes to micro-autologous fat transplantation by modulating HIF-1α and VEGF.

Background: The micro-autologous fat transplantation (MAFT) technique has demonstrated its feasibility in multiple medical fields, such as facial rejuvenation. Advanced platelet-rich fibrin (APRF), an autologous platelet concentrated on a fibrin membrane without added external factors, has shown significant potential for tissue restoration. However, the role of APRF in the modulation of MAFT remains unclear. Here, we aimed to explore the effect of APRF on MAFT. Methods: Adipose-derived stem cells (ASCs) were isolated from human gastric subcutaneous fat and treated with APRF. ELISA assays measured cytokines. The proliferation of ASCs was analyzed by CCK-8 assays. The levels of hypoxia-inducible factor-1α (HIF-1α), heat shock protein 70 (HSP70), insulin like growth factor 2 (IGF-2), interleukin-6 (IL-6), interleukin-8 (IL-8), and vascular endothelial growth factor (VEGF) were measured by ELISA assays, quantitative reverse transcription-PCR (qRT-PCR), and Western blot analysis. The effect of APRF/HIF-1α/VEGF on MAFT in vivo was analyzed in Balb/c nude mice. The BALB/c mice were subcutaneously co-transplanted with fat, APRF, and control shRNA, HIF-1α shRNA, or VEGF shRNA into the dorsal area. The serum and protein levels of the above cytokines were analyzed by ELISA assays and Western blot analysis. Lipid accumulation was measured by Oil Red O staining. The expression of CD34 was assessed by immunohistochemical staining. Results: APRF continuously secreted multiple cytokines, including epidermal growth factor (EGF), FGF-2, insulin like growth factor 1 (IGF-1), interleukin-1beta (IL-1ß), interleukin-4 (IL-4), platelet-derived growth factor alpha polypeptide b (PDGF-AB), platelet-derived growth factor beta polypeptide b (PDGF-BB), transforming growth factor-beta (TGF-ß), and VEGF. APRF was able to promote the proliferation of ASCs. APRF dose-dependently activated the expression of HIF-1α, HSP70, IGF-2, IL-6, IL-8, and VEGF in ASCs. APRF regulated the paracrine function of ASCs by modulating HIF-1α and VEGF. APRF increased the survival of MAFT by modulating HIF-1α and VEGF in vivo. Conclusions: APRF promotes the paracrine function and proliferation of ASCs and contributes to MAFT by modulating HIF-1α and VEGF. Our findings provide new insights into the mechanism by which APRF regulates MAFT.

Fractional Wavelet-Based Generative Scattering Networks.

Generative adversarial networks and variational autoencoders (VAEs) provide impressive image generation from Gaussian white noise, but both are difficult to train, since they need a generator (or encoder) and a discriminator (or decoder) to be trained simultaneously, which can easily lead to unstable training. To solve or alleviate these synchronous training problems of generative adversarial networks (GANs) and VAEs, researchers recently proposed generative scattering networks (GSNs), which use wavelet scattering networks (ScatNets) as the encoder to obtain features (or ScatNet embeddings) and convolutional neural networks (CNNs) as the decoder to generate an image. The advantage of GSNs is that the parameters of ScatNets do not need to be learned, while the disadvantage of GSNs is that their ability to obtain representations of ScatNets is slightly weaker than that of CNNs. In addition, the dimensionality reduction method of principal component analysis (PCA) can easily lead to overfitting in the training of GSNs and, therefore, affect the quality of generated images in the testing process. To further improve the quality of generated images while keeping the advantages of GSNs, this study proposes generative fractional scattering networks (GFRSNs), which use more expressive fractional wavelet scattering networks (FrScatNets), instead of ScatNets as the encoder to obtain features (or FrScatNet embeddings) and use similar CNNs of GSNs as the decoder to generate an image. Additionally, this study develops a new dimensionality reduction method named feature-map fusion (FMF) instead of performing PCA to better retain the information of FrScatNets,; it also discusses the effect of image fusion on the quality of the generated image. The experimental results obtained on the CIFAR-10 and CelebA datasets show that the proposed GFRSNs can lead to better generated images than the original GSNs on testing datasets. The experimental results of the proposed GFRSNs with deep convolutional GAN (DCGAN), progressive GAN (PGAN), and CycleGAN are also given.