The Anti-inflammatory Effect of Chitosan Oligosaccharide on Heart Failure in Mice.

Heart failure is currently one of the leading causes of death worldwide, and the inflammatory factors play an important role in its development. Chitosan oligosaccharide (COS), a low-molecular-weight form of chitosan, has many specific biological activities. In this study, COS effects on heart failure were studied for the first time by performing transverse arch constriction (TAC) surgery in mice, as an animal model of heart failure.

Ultrasonic biomechanics method for vortex and wall motion of left ventricle: a phantom and in vivo study.

Background: The non-invasive quantitative evaluation of left ventricle (LV) function plays a critical role in clinical cardiology. This study proposes a novel ultrasonic biomechanics method by integrating both LV vortex and wall motion to fully assess and understand the LV structure and function. The purpose of this study was to validate the ultrasonic biomechanics method as a quantifiable approach to evaluate LV function.

Clinical outcomes of nicorandil administration in patients with acute ST-segment elevation myocardial infarction undergoing primary percutaneous coronary intervention: a systematic review and meta-analysis of randomized controlled trials.

Background: Primary percutaneous coronary intervention is the treatment of choice in ST-segment elevation myocardial infarction and no-reflow phenomenon is still an unsolved problem.

Methods: We searched PubMed, EmBase, and Cochrane Central Register of Controlled Trials for relevant randomized controlled trials. The primary endpoint was the incidence of major adverse cardiac events and the secondary endpoint was the incidences of no-reflow phenomenon and complete resolution of ST-segment elevation.

Radiomics-Based Features for Prediction of Histological Subtypes in Central Lung Cancer.

Objectives: To evaluate the effectiveness of radiomic features on classifying histological subtypes of central lung cancer in contrast-enhanced CT (CECT) images.

Materials And Methods: A total of 200 patients with radiologically defined central lung cancer were recruited. All patients underwent dual-phase chest CECT, and the histological subtypes (adenocarcinoma (ADC), squamous cell carcinoma (SCC), small cell lung cancer (SCLC)) were confirmed by histopathological samples.

Texture Analysis of Native T1 Images as a Novel Method for Noninvasive Assessment of Uremic Cardiomyopathy.

Background: Noncontrast cardiac T times are increased in dialysis patients which might indicate fibrotic alterations in uremic cardiomyopathy.

Purpose: To explore the application of the texture analysis (TA) of T images in the assessment of myocardial alterations in dialysis patients.

Study Type: Case-control study.

Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging.

Subtle changes in white matter (WM) microstructure have been associated with normal aging and neurodegeneration. To study these associations in more detail, it is highly important that the WM tracts can be accurately and reproducibly characterized from brain diffusion MRI. In addition, to enable analysis of WM tracts in large datasets and in clinical practice it is essential to have methodology that is fast and easy to apply.

The capability of detecting small vessels beyond the conventional MRI sensitivity using iron-based contrast agent enhanced susceptibility weighted imaging.

Imaging brain microvasculature is important in cerebrovascular diseases. However, there is still a lack of non-invasive, non-radiation, and whole-body imaging techniques to investigate them. The aim of this study is to develop an ultra-small superparamagnetic iron oxide (USPIO) enhanced susceptibility weighted imaging (SWI) method for imaging micro-vasculature in both animal (~10 μm in rat) and human brain.

Convolutional neural network enhancement of fast-scan low-dose cone-beam CT images for head and neck radiotherapy.

To improve image quality and CT number accuracy of fast-scan low-dose cone-beam computed tomography (CBCT) through a deep-learning convolutional neural network (CNN) methodology for head-and-neck (HN) radiotherapy. Fifty-five paired CBCT and CT images from HN patients were retrospectively analysed. Among them, 15 patients underwent adaptive replanning during treatment, thus had same-day CT/CBCT pairs.

Structural and functional alterations of the tracheobronchial tree after left upper pulmonary lobectomy for lung cancer.

Background: Pulmonary lobectomy has been a well-established curative treatment method for localized lung cancer. After left upper pulmonary lobectomy, the upward displacement of remaining lower lobe causes the distortion or kink of bronchus, which is associated with intractable cough and breathless. However, the quantitative study on structural and functional alterations of the tracheobronchial tree after lobectomy has not been reported.

Estimation of wave reflection in aorta from radial pulse waveform by artificial neural network: a numerical study.

Background And Objective: Wave reflection in aorta has been shown to have incremental value for predicting cardiovascular events. However, its estimation by wave separation analysis (WSA) is complex.

Methods: In this study, a novel method was proposed based on a cascade artificial neural network (ANN) for wave reflection estimation by the frequency features of radial pressure waveform alone.

Sonoporation of Cells by a Parallel Stable Cavitation Microbubble Array.

Sonoporation is a targeted drug delivery technique that employs cavitation microbubbles to generate transient pores in the cell membrane, allowing foreign substances to enter cells by passing through the pores. Due to the broad size distribution of microbubbles, cavitation events appear to be a random process, making it difficult to achieve controllable and efficient sonoporation. In this work a technique is reported using a microfluidic device that enables in parallel modulation of membrane permeability by an oscillating microbubble array.

Robust heart-rate estimation from facial videos using Project_ICA.

Objective: Remote photoplethysmography (rPPG) can achieve non-contact measurement of heart rate (HR) from a continuous video sequence by scanning the skin surface. However, practical applications are still limited by factors such as non-rigid facial motion and head movement. In this work, a detailed system framework for remotely estimating heart rate from facial videos under various movement conditions is described.

Identifying non-muscle-invasive and muscle-invasive bladder cancer based on blood serum surface-enhanced Raman spectroscopy.

The assessment of the muscle invasion of bladder cancer typically plays a crucial role in therapeutic decision-making and has significant impacts on the recurrence rate and survival rate. Although histopathology is sufficiently accurate and usually served as the gold standard for bladder cancer diagnosis, it is invasive, time-consuming, and requires intensive sample preparation by a well-trained pathologist to achieve an optimal diagnosis. Therefore, a fast and noninvasive method to accurately identify non-muscle-invasive bladder cancer (NMIBC) and muscle-invasive bladder cancer (MIBC) is in demand.

Hemodynamic effects of enhanced external counterpulsation on cerebral arteries: a multiscale study.

Background: Enhanced external counterpulsation (EECP) is an effective method for treating patients with cerebral ischemic stroke, while hemodynamics is the major contributing factor in the treatment of EECP. Different counterpulsation modes have the potential to lead to different acute and long-term hemodynamic changes, resulting in different treatment effects. However, various questions about appropriate counterpulsation modes for optimizing hemodynamic effects remain unanswered in clinical treatment.

Correction to: Non-contrast MRI for breast screening: preliminary study on detectability of benign and malignant lesions in women with dense breasts.

In the original version of the article, the image of Figure 2 was erroneously duplicated as Figure 4. The correct version of Figure 4 is given below. The original article has been corrected.

Computed tomography-based predictive nomogram for differentiating primary progressive pulmonary tuberculosis from community-acquired pneumonia in children.

Background: To investigate the value of predictive nomogram in optimizing computed tomography (CT)-based differential diagnosis of primary progressive pulmonary tuberculosis (TB) from community-acquired pneumonia (CAP) in children.

Methods: This retrospective study included 53 patients with clinically confirmed pulmonary TB and 62 patients with CAP. Patients were grouped at random according to a 3:1 ratio (primary cohort n = 86, validation cohort n = 29).

Non-contrast MRI for breast screening: preliminary study on detectability of benign and malignant lesions in women with dense breasts.

Purpose: The importance of breast cancer screening has long been known. Unfortunately, there is no imaging modality for screening women with dense breasts that is both sensitive and without concerns regarding potential side effects. The purpose of this study is to explore the possibility of combined diffusion-weighted imaging and turbo inversion recovery magnitude MRI (DWI + TIRM) to overcome the difficulty of detection sensitivity and safety.

Morphology-based classification of mycobacteria-infected macrophages with convolutional neural network: reveal EsxA-induced morphologic changes indistinguishable by naked eyes.

EsxA is an essential virulence factor for Mycobacterium tuberculosis (Mtb) pathogenesis as well as an important biomarker for Mtb detection. In this study, we use light microscopy and deep learning-based image analysis to classify the morphologic changes of macrophages infected by Mycobacterium marinum (Mm), a surrogate model for Mtb. Macrophages were infected either with the mCherry-expressing Mm wild type strain (Mm(WT)), or a mutant strain with deletion of the esxA-esxB operon (Mm(ΔEsxA:B)).

Use of Indicator Dilution Principle to Evaluate Accuracy of Arterial Input Function Measured With Low-Dose Ultrafast Prostate Dynamic Contrast-Enhanced MRI.

Accurately measuring arterial input function (AIF) is essential for quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI). We used the indicator dilution principle to evaluate the accuracy of AIF measured directly from an artery following a low-dose contrast media ultrafast DCE-MRI. In total, 15 patients with biopsy-confirmed localized prostate cancers were recruited.

Connectome-Based Biomarkers Predict Subclinical Depression and Identify Abnormal Brain Connections With the Lateral Habenula and Thalamus.

Subclinical depression (SD) has been considered as the precursor to major depressive disorder. Accurate prediction of SD and identification of its etiological origin are urgent. Bursts within the lateral habenula (LHb) drive depression in rats, but whether dysfunctional LHb is associated with SD in human is unknown.

Deep CNN models for pulmonary nodule classification: Model modification, model integration, and transfer learning.

Background: Deep learning has made spectacular achievements in analysing natural images, but it faces challenges for medical applications partly due to inadequate images.

Objective: Aiming to classify malignant and benign pulmonary nodules using CT images, we explore different strategies to utilize the state-of-the-art deep convolutional neural networks (CNN).

Methods: Experiments are conducted using the Lung Image Database Consortium image collection (LIDC-IDRI), which is a public database containing 1018 cases.

Ultrasound Stimulation Modulates Voltage-Gated Potassium Currents Associated With Action Potential Shape in Hippocampal CA1 Pyramidal Neurons.

Potassium channels (K) play an important role in the regulation of cellular signaling. Dysfunction of potassium channels is associated with several severe ion channels diseases, such as long QT syndrome, episodic ataxia and epilepsy. Ultrasound stimulation has proven to be an effective non-invasive tool for the modulation of ion channels and neural activity.

A deep learning radiomics model for preoperative grading in meningioma.

Objectives: To noninvasively differentiate meningioma grades by deep learning radiomics (DLR) model based on routine post-contrast MRI.

Methods: We enrolled 181 patients with histopathologic diagnosis of meningioma who received post-contrast MRI preoperative examinations from 2 hospitals (99 in the primary cohort and 82 in the validation cohort). All the tumors were segmented based on post-contrast axial T1 weighted images (T1WI), from which 2048 deep learning features were extracted by the convolutional neural network.

Engineering Micromotors with Droplet Microfluidics.

Micromotors have promising potential in applications ranging from environmental remediation to targeted drug delivery and noninvasive microsurgery. However, there are inadequacies in the fabrication of artificial micromotors to improve the design of structure and composition for motion performance and multifunctionality. Here, we present a microfluidic fiber-confined approach to creating droplet-templated micromotors with precisely engineered anisotropies in 3D structures and material compositions.

Multi-parametric MRI-based radiomics signature for discriminating between clinically significant and insignificant prostate cancer: Cross-validation of a machine learning method.

Purpose: To evaluate the performance of a multi-parametric MRI (mp-MRI)-based radiomics signature for discriminating between clinically significant prostate cancer (csPCa) and insignificant PCa (ciPCa).

Materials And Methods: Two hundred and eighty patients with pathology-proven PCa were enrolled and were randomly divided into training and test cohorts. Eight hundred and nineteen radiomics features were extracted from mp-MRI for each patient.