Prediction for children with autism spectrum disorder based on digital behavioral features during free play.

Background: Play is an indispensable and meaningful activity in children's daily life. Research has shown that autistic children often exhibit differences in play development. The core traits of autism, such as distinct patterns in social interaction and communication, focused interests, and repetitive behaviors, frequently manifest in their play.

A2HTL: An Automated Hybrid Transformer-Based Learning for Predicting Survival of Esophageal Cancer Using CT Images.

Esophageal cancer is a common malignant tumor, precisely predicting survival of esophageal cancer is crucial for personalized treatment. However, current region of interest (ROI) based methodologies not only necessitate prior medical knowledge for tumor delineation, but may also cause the model to be overly sensitive to ROI. To address these challenges, we develop an automated Hybrid Transformer based learning that integrates a Hybrid Transformer size-aware U-Net with a ranked survival prediction network to enable automatic survival prediction for esophageal cancer.

Collagen type Ⅰ-loaded methacrylamide hyaluronic acid hydrogel microneedles alleviate stress urinary incontinence in mice: A novel treatment and prevention strategy.

Stress urinary incontinence (SUI), a chronic disease with widespread effects and an overall prevalence of up to 46% in adult women, is associated with a heavy disease burden. The clinical treatment for mild to moderate SUI is conservative, such as electrical stimulation and Kegel exercises, but the therapeutic effect is unsatisfactory, so it is imperative to seek new treatment modalities. Hydrogel microneedles (MNs) have been widely used in transdermal drug delivery because of their minimally invasive and highly biocompatible characteristics.

DARC: Deep adaptive regularized clustering for histopathological image classification.

In recent years, deep learning as a state-of-the-art machine learning technique has made great success in histopathological image classification. However, most of deep learning approaches rely heavily on the substantial task-specific annotations, which require experienced pathologists' manual labelling. As a result, they are laborious and time-consuming, and many unlabeled pathological images are difficult to use without experts' annotations.

MLDRL: Multi-loss disentangled representation learning for predicting esophageal cancer response to neoadjuvant chemoradiotherapy using longitudinal CT images.

Accurate prediction of pathological complete response (pCR) after neoadjuvant chemoradiotherapy (nCRT) is essential for clinical precision treatment. However, the existing methods of predicting pCR in esophageal cancer are based on the single stage data, which limits the performance of these methods. Effective fusion of the longitudinal data has the potential to improve the performance of pCR prediction, thanks to the combination of complementary information.

COVID-19 mortality prediction in the intensive care unit with deep learning based on longitudinal chest X-rays and clinical data.

Objectives: We aimed to develop deep learning models using longitudinal chest X-rays (CXRs) and clinical data to predict in-hospital mortality of COVID-19 patients in the intensive care unit (ICU).

Methods: Six hundred fifty-four patients (212 deceased, 442 alive, 5645 total CXRs) were identified across two institutions. Imaging and clinical data from one institution were used to train five longitudinal transformer-based networks applying five-fold cross-validation.

A Fully Automated Multimodal MRI-Based Multi-Task Learning for Glioma Segmentation and IDH Genotyping.

The accurate prediction of isocitrate dehydrogenase (IDH) mutation and glioma segmentation are important tasks for computer-aided diagnosis using preoperative multimodal magnetic resonance imaging (MRI). The two tasks are ongoing challenges due to the significant inter-tumor and intra-tumor heterogeneity. The existing methods to address them are mostly based on single-task approaches without considering the correlation between the two tasks.

M2 Macrophage-derived exosomal miR-501 contributes to pubococcygeal muscle regeneration.

Pubococcygeal muscle injury can lead to stress urinary incontinence (SUI). M2 macrophages play a crucial role in myoblast differentiation during injured muscle regeneration. However, the underlying mechanism remains unclear.

Automated Diagnosis of COVID-19 Using Deep Supervised Autoencoder With Multi-View Features From CT Images.

Accurate and rapid diagnosis of coronavirus disease 2019 (COVID-19) from chest CT scans is of great importance and urgency during the worldwide outbreak. However, radiologists have to distinguish COVID-19 pneumonia from other pneumonia in a large number of CT scans, which is tedious and inefficient. Thus, it is urgently and clinically needed to develop an efficient and accurate diagnostic tool to help radiologists to fulfill the difficult task.

Multimodal Disentangled Variational Autoencoder With Game Theoretic Interpretability for Glioma Grading.

Effective fusion of multimodal magnetic resonance imaging (MRI) is of great significance to boost the accuracy of glioma grading thanks to the complementary information provided by different imaging modalities. However, how to extract the common and distinctive information from MRI to achieve complementarity is still an open problem in information fusion research. In this study, we propose a deep neural network model termed as multimodal disentangled variational autoencoder (MMD-VAE) for glioma grading based on radiomics features extracted from preoperative multimodal MRI images.

Prediction of Glioma Grade Using Intratumoral and Peritumoral Radiomic Features From Multiparametric MRI Images.

The accurate prediction of glioma grade before surgery is essential for treatment planning and prognosis. Since the gold standard (i.e.

Lectin Enhances Oncolytic Vaccinia Virus Replication to Suppress In Vivo Hepatocellular Carcinoma Growth.

Lectins play diverse roles in physiological processes as biological recognition molecules. In this report, a gene encoding Lectin (TTL) was inserted into an oncolytic vaccinia virus (oncoVV) vector to form oncoVV-TTL, which showed significant antitumor activity in a hepatocellular carcinoma mouse model. Furthermore, TTL enhanced oncoVV replication through suppressing antiviral factors expression such as interferon-inducible protein 16 (IFI16), mitochondrial antiviral signaling protein (MAVS) and interferon-beta (IFN-β).

Sialic Acid-Binding Lectin Reduces the Oncolytic Vaccinia Virus Induced Toxicity in a Glioblastoma Mouse Model.

Although oncolytic viruses provide attractive vehicles for cancer treatment, their adverse effects are largely ignored. In this work, rat C6 glioblastoma cells were subcutaneously xenografted into mice, and a thymidine kinase-deficient oncolytic vaccinia virus (oncoVV) induced severe toxicity in this model. However, oncoVV-HddSBL, in which a gene encoding sialic acid-binding lectin (HddSBL) was inserted into oncoVV, significantly prolonged the survival of mice as compared to the control virus.

CD47-retargeted oncolytic adenovirus armed with melanoma differentiation-associated gene-7/interleukin-24 suppresses in vivo leukemia cell growth.

Our previous studies have suggested that harboring a soluble coxsackie-adenovirus receptor-ligand (sCAR-ligand) fusion protein expression cassette in the viral genome may provide a universal method to redirect oncolytic adenoviruses to various membrane receptors on cancer cells resisting to serotype 5 adenovirus infection. We report here a novel oncolytic adenovirus vector redirected to CD47+ leukemia cells though carrying a sCAR-4N1 expression cassette in the viral genome, forming Ad.4N1, in which 4N1 represents the C-terminal CD47-binding domain of thrombospondin-1.