Contrast-enhanced ultrasound can differentiate the level of glioma infiltration and correlate it with biological behavior: a study based on local pathology.

Purpose: The objective of this study is to assess the diagnostic efficacy of contrast-enhanced ultrasound (CEUS) in determining the level of glioma infiltration and to investigate its correlation with pathological markers.

Methods: A prospective study involving 16 adult glioma patients was conducted. Preoperative US-(Magnetic Resonance)MR fusion imaging was utilized for tumor infiltration localization, while CEUS was employed to assess hemodynamic alterations.

In vivo ultrasound localization microscopy for high-density microbubbles.

Ultrasound Localization Microscopy (ULM) surpasses the constraints imposed by acoustic diffraction, achieving sub-wavelength resolution visualization of microvasculature through the precise localization of minute microbubbles (MBs). Nonetheless, the analysis of densely populated regions with overlapping MB point spread responses introduces significant localization errors, limiting the use of technique to low-concentration conditions. This raises a trade-off issue between localization efficiency and MB density.

A transformer-guided cross-modality adaptive feature fusion framework for esophageal gross tumor volume segmentation.

Background And Objective: Accurate segmentation of esophageal gross tumor volume (GTV) indirectly enhances the efficacy of radiotherapy for patients with esophagus cancer. In this domain, learning-based methods have been employed to fuse cross-modality positron emission tomography (PET) and computed tomography (CT) images, aiming to improve segmentation accuracy. This fusion is essential as it combines functional metabolic information from PET with anatomical information from CT, providing complementary information.

Condition control training-based ConVMLP-ResU-Net for semantic segmentation of esophageal cancer in F-FDG PET/CT images.

The precise delineation of esophageal gross tumor volume (GTV) on medical images can promote the radiotherapy effect of esophagus cancer. This work is intended to explore effective learning-based methods to tackle the challenging auto-segmentation problem of esophageal GTV. By employing the progressive hierarchical reasoning mechanism (PHRM), we devised a simple yet effective two-stage deep framework, ConVMLP-ResU-Net.

Automatic segmentation of esophageal gross tumor volume in F-FDG PET/CT images via GloD-LoATUNet.

Background And Objective: For esophageal squamous cell carcinoma, radiotherapy is one of the primary treatments. During the planning before radiotherapy, the intractable task is to precisely delineate the esophageal gross tumor volume (GTV) on medical images. In current clinical practice, the manual delineation suffers from high intra- and inter-rater variability, while also exhausting the oncologists on a treadmill.

Automatic Seizure Identification from EEG Signals Based on Brain Connectivity Learning.

Epilepsy is a neurological disorder caused by brain dysfunction, which could cause uncontrolled behavior, loss of consciousness and other hazards. Electroencephalography (EEG) is an indispensable auxiliary tool for clinical diagnosis. Great progress has been made by current seizure identification methods.

Multi-view cross-subject seizure detection with information bottleneck attribution.

Significant progress has been witnessed in within-subject seizure detection from electroencephalography (EEG) signals. Consequently, more and more works have been shifted from within-subject seizure detection to cross-subject scenarios. However, the progress is hindered by inter-patient variations caused by gender, seizure type, etc.

EEG-Based Seizure detection using linear graph convolution network with focal loss.

Background And Objectives: Epilepsy is a clinical phenomenon caused by sudden abnormal and excessive discharge of brain neurons. It affects around 70 million people all over the world. Seizure detection from Electroencephalography (EEG) has achieved rapid development.

Graph Attention Network with Focal Loss for Seizure Detection on Electroencephalography Signals.

Automatic seizure detection from electroencephalogram (EEG) plays a vital role in accelerating epilepsy diagnosis. Previous researches on seizure detection mainly focused on extracting time-domain and frequency-domain features from single electrodes, while paying little attention to the positional correlations between different EEG channels of the same subject. Moreover, data imbalance is common in seizure detection scenarios where the duration of nonseizure periods is much longer than the duration of seizures.

Amino acid substitutions in VP2, VP1, and 2C attenuate a Coxsackievirus A16 in mice.

Coxsackievirus A16 (CVA16) is one of the major etiological agents of hand, foot and mouth disease (HFMD), a common acute infectious disease affecting infants and young children. Severe symptoms of the central nervous system may develop and even lead to death. Here, a plaque-purified CVA16 strain, L731-P1 (P1), was serially passaged in Vero cells for six times and passage 6 (P6) stock became highly attenuated in newborn mice.

Increased Excess Intracellular Cyclic di-AMP Levels Impair Growth and Virulence of Bacillus anthracis.

Cyclic di-AMP (c-di-AMP) is a recently identified bacterial second messenger that regulates biological processes. In this study, we found that inactivation of two c-di-AMP phosphodiesterases (PDEs), GdpP and PgpH, resulted in accumulation of 3.8-fold higher c-di-AMP levels than in the parental strain Sterne in and inhibited bacterial growth.

The surface-exposed loop region of norovirus GII.3 VP1 plays an essential role in binding histo-blood group antigens.

Trypsin digestion promotes disassembly of GII.3 NoV virus-like particles (VLPs) and binding of VLPs to salivary histo-blood group antigens (HBGAs), but it is not clear which specific regions or residues mediate viral attachment to HBGAs. An earlier study indicated that arginine residues in the predicted surface-exposed loop region are susceptible to trypsin digestion.

A Reverse Genetics System for Cypovirus Based on a Bacmid Expressing T7 RNA Polymerase.

cypovirus (DpCPV), belonging to the genus within the family , is considered the most destructive pest of pine forests worldwide. DpCPV has a genome consisting of 10 linear double-stranded RNA segments. To establish a reverse genetics system, we cloned cDNAs encoding the 10 genomic segments of DpCPV into three reverse genetics vectors in which each segment was transcribed under the control of a T7 RNA polymerase promoter and terminator tagged with a hepatitis delta virus ribozyme sequence.