Aerodynamic Simulation of Small Airway Resistance: A New Imaging Biomarker for Chronic Obstructive Pulmonary Disease.

Purpose: To develop a novel method for calculating small airway resistance using computational fluid dynamics (CFD) based on CT data and evaluate its value to identify COPD.

Patients And Methods: 24 subjects who underwent chest CT scans and pulmonary function tests between August 2020 and December 2020 were enrolled retrospectively. Subjects were divided into three groups: normal (10), high-risk (6), and COPD (8).

Development of machine learning model to predict pulmonary function with low-dose CT-derived parameter response mapping in a community-based chest screening cohort.

Purpose: To construct and evaluate the performance of a machine learning-based low dose computed tomography (LDCT)-derived parametric response mapping (PRM) model for predicting pulmonary function test (PFT) results.

Materials And Methods: A total of 615 subjects from a community-based screening population (40-74 years old) with PFT parameters, including the ratio of the first second forced expiratory volume to forced vital capacity (FEV1/FVC), the percentage of forced expiratory volume in the one second predicted (FEV1%), and registered inspiration-to-expiration chest CT scanning were enrolled retrospectively. Subjects were classified into a normal, high risk, and COPD group based on PFT.

Radiologists with assistance of deep learning can achieve overall accuracy of benign-malignant differentiation of musculoskeletal tumors comparable with that of pre-surgical biopsies in the literature.

Purpose: The purpose of this study was to assess if radiologists assisted by deep learning (DL) algorithms can achieve diagnostic accuracy comparable to that of pre-surgical biopsies in benign-malignant differentiation of musculoskeletal tumors (MST).

Methods: We first conducted a systematic review of literature to get the respective overall diagnostic accuracies of fine-needle aspiration biopsy (FNAB) and core needle biopsy (CNB) in differentiating between benign and malignant MST, by synthesizing data from the articles meeting our inclusion criteria. To compared against the accuracies reported in literature, we then invited 4 radiologists, respectively with 2 (A), 6 (B), 7 (C), and 33 (D) years of experience in interpreting musculoskeletal MRI to perform diagnostic tests on our own dataset (n = 62), with and without assistance of a previously developed DL algorithm.

Re-Defining High Risk COPD with Parameter Response Mapping Based on Machine Learning Models.

Purpose: To explore optimal threshold of FEV1% predicted value (FEV1%pre) for high-risk chronic obstructive pulmonary disease (COPD) using the parameter response mapping (PRM) based on machine learning classification model.

Patients And Methods: A total of 561 consecutive non-COPD subjects who were screened for chest diseases in our hospital between August and October 2018 and who had complete questionnaire surveys, pulmonary function tests (PFT), and paired respiratory chest CT scans were enrolled retrospectively. The CT quantitative parameter for small airway remodeling was PRM, and 72 parameters were obtained at the levels of whole lung, left and right lung, and five lobes.

Development, Validation, and Comparison of Image-Based, Clinical Feature-Based and Fusion Artificial Intelligence Diagnostic Models in Differentiating Benign and Malignant Pulmonary Ground-Glass Nodules.

Objective: This study aimed to develop effective artificial intelligence (AI) diagnostic models based on CT images of pulmonary nodules only, on descriptional and quantitative clinical or image features, or on a combination of both to differentiate benign and malignant ground-glass nodules (GGNs) to assist in the determination of surgical intervention.

Methods: Our study included a total of 867 nodules (benign nodules: 112; malignant nodules: 755) with postoperative pathological diagnoses from two centers. For the diagnostic models to discriminate between benign and malignant GGNs, we adopted three different artificial intelligence (AI) approaches: a) an image-based deep learning approach to build a deep neural network (DNN); b) a clinical feature-based machine learning approach based on the clinical and image features of nodules; c) a fusion diagnostic model integrating the original images and the clinical and image features.

A new AI-assisted scoring system for PD-L1 expression in NSCLC.

Background: Artificial intelligence (AI) analysis may serve as a scoring tool for programmed cell death ligand-1 (PD-L1) expression. In this study, a new AI-assisted scoring system for pathologists was tested for PD-L1 expression assessment in non-small cell lung cancer (NSCLC).

Methods: PD-L1 expression was evaluated using the tumor proportion score (TPS) categorized into three levels: negative (TPS < 1%), low expression (TPS 1-49%), and high expression (TPS ≥ 50%).

An imaging-based artificial intelligence model for non-invasive grading of hepatic venous pressure gradient in cirrhotic portal hypertension.

The hepatic venous pressure gradient (HVPG) is the gold standard for cirrhotic portal hypertension (PHT), but it is invasive and specialized. Alternative non-invasive techniques are needed to assess the hepatic venous pressure gradient (HVPG). Here, we develop an auto-machine-learning CT radiomics HVPG quantitative model (HVPG), and then we validate the model in internal and external test datasets by the area under the receiver operating characteristic curves (AUCs) for HVPG stages (≥10, ≥12, ≥16, and ≥20 mm Hg) and compare the model with imaging- and serum-based tools.

Comparison and verification of two deep learning models for the detection of chest CT rib fractures.

Background: A high false-positive rate remains a technical glitch hindering the broad spectrum of application of deep-learning-based diagnostic tools in routine radiological practice from assisting in diagnosing rib fractures.

Purpose: To examine the performance of two versions of deep-learning-based software tools in aiding radiologists in diagnosing rib fractures on chest computed tomography (CT) images.

Material And Methods: In total, 123 patients (708 rib fractures) were included in this retrospective study.

Deep convolutional neural network-based classification of cancer cells on cytological pleural effusion images.

Lung cancer is one of the leading causes of cancer-related death worldwide. Cytology plays an important role in the initial evaluation and diagnosis of patients with lung cancer. However, due to the subjectivity of cytopathologists and the region-dependent diagnostic levels, the low consistency of liquid-based cytological diagnosis results in certain proportions of misdiagnoses and missed diagnoses.

Deep Learning Assisted Diagnosis of Musculoskeletal Tumors Based on Contrast-Enhanced Magnetic Resonance Imaging.

Background: Misdiagnosis of malignant musculoskeletal tumors may lead to the delay of intervention, resulting in amputation or death.

Purpose: To improve the diagnostic efficacy of musculoskeletal tumors by developing deep learning (DL) models based on contrast-enhanced magnetic resonance imaging and to quantify the improvement in diagnostic performance obtained by using these models.

Study Type: Retrospective.

Effect and safety of Chinese herbal medicine granules in patients with severe coronavirus disease 2019 in Wuhan, China: a retrospective, single-center study with propensity score matching.

Background: Chinese herbal medicine (CHM) has been used for severe illness caused by coronavirus disease 2019 (COVID-19), but its treatment effects and safety are unclear.

Purpose: This study reviews the effect and safety of CHM granules in the treatment of patients with severe COVID-19.

Methods: We conducteda single-center, retrospective study on patients with severe COVID-19 in a designated hospital in Wuhan from January 15, 2020 to March 30, 2020.

Predicting the invasiveness of lung adenocarcinomas appearing as ground-glass nodule on CT scan using multi-task learning and deep radiomics.

Background: Due to different treatment method and prognosis of different subtypes of lung adenocarcinomas appearing as ground-glass nodules (GGNs) on computed tomography (CT) scan, it is important to classify invasive adenocarcinomas from non-invasive adenocarcinomas. The purpose of this paper is to build and evaluate the performance of deep learning networks on the differentiation the invasiveness of lung adenocarcinoma appearing as GGNs.

Methods: This retrospective study included 886 GGNs from 794 pathological confirmed patients with lung adenocarcinoma for training and testing the proposed networks.

Histological Subtypes Classification of Lung Cancers on CT Images Using 3D Deep Learning and Radiomics.

Rationale And Objectives: Histological subtypes of lung cancers are critical for clinical treatment decision. In this study, we attempt to use 3D deep learning and radiomics methods to automatically distinguish lung adenocarcinomas (ADC), squamous cell carcinomas (SCC), and small cell lung cancers (SCLC) respectively on Computed Tomography images, and then compare their performance.

Materials And Methods: 920 patients (mean age 61.

Self-powered integrated microfluidic point-of-care low-cost enabling (SIMPLE) chip.

Portable, low-cost, and quantitative nucleic acid detection is desirable for point-of-care diagnostics; however, current polymerase chain reaction testing often requires time-consuming multiple steps and costly equipment. We report an integrated microfluidic diagnostic device capable of on-site quantitative nucleic acid detection directly from the blood without separate sample preparation steps. First, we prepatterned the amplification initiator [magnesium acetate (MgOAc)] on the chip to enable digital nucleic acid amplification.

Transcriptomes of lineage-specific Drosophila neuroblasts profiled by genetic targeting and robotic sorting.

A brain consists of numerous distinct neurons arising from a limited number of progenitors, called neuroblasts in Drosophila. Each neuroblast produces a specific neuronal lineage. To unravel the transcriptional networks that underlie the development of distinct neuroblast lineages, we marked and isolated lineage-specific neuroblasts for RNA sequencing.

Opposing intrinsic temporal gradients guide neural stem cell production of varied neuronal fates.

Neural stem cells show age-dependent developmental potentials, as evidenced by their production of distinct neuron types at different developmental times. Drosophila neuroblasts produce long, stereotyped lineages of neurons. We searched for factors that could regulate neural temporal fate by RNA-sequencing lineage-specific neuroblasts at various developmental times.

Calcium transients closely reflect prolonged action potentials in iPSC models of inherited cardiac arrhythmia.

Long-QT syndrome mutations can cause syncope and sudden death by prolonging the cardiac action potential (AP). Ion channels affected by mutations are various, and the influences of cellular calcium cycling on LQTS cardiac events are unknown. To better understand LQTS arrhythmias, we performed current-clamp and intracellular calcium ([Ca(2+)]i) measurements on cardiomyocytes differentiated from patient-derived induced pluripotent stem cells (iPS-CM).

Graphene nanopore with a self-integrated optical antenna.

We report graphene nanopores with integrated optical antennae. We demonstrate that a nanometer-sized heated spot created by photon-to-heat conversion of a gold nanorod resting on a graphene membrane forms a nanoscale pore with a self-integrated optical antenna in a single step. The distinct plasmonic traits of metal nanoparticles, which have a unique capability to concentrate light into nanoscale regions, yield the significant advantage of parallel nanopore fabrication compared to the conventional sequential process using an electron beam.

Fluorescence enhancement and lifetime modification of single nanodiamonds near a nanocrystalline silver surface.

Fluorescent nanodiamond (FND) contains nitrogen-vacancy defect centers as fluorophores. The intensity of its fluorescence can be significantly enhanced after deposition of the particle (35 or 140 nm in size) on a nanocrystalline Ag film without a buffer layer. The excellent photostability (i.

Mass production and dynamic imaging of fluorescent nanodiamonds.

Fluorescent nanodiamond is a new nanomaterial that possesses several useful properties, including good biocompatibility, excellent photostability and facile surface functionalizability. Moreover, when excited by a laser, defect centres within the nanodiamond emit photons that are capable of penetrating tissue, making them well suited for biological imaging applications. Here, we show that bright fluorescent nanodiamonds can be produced in large quantities by irradiating synthetic diamond nanocrystallites with helium ions.

Static conformation and dynamics of single DNA molecules confined in nanoslits.

Nearly thirty years ago, Daoud and de Gennes derived the scaling predictions for the linear polymer chains trapped in a slit with dimension close to the Kuhn length; however, these predictions have yet to be compared with experiments. We have fabricated nanoslits with vertical dimension similar to the Kuhn length of ds-DNA (110nm) using standard photolithography techniques. Fluorescently labeled single DNA molecules with contour lengths L ranging from 4 to 75 microm were successfully injected into the slits and the chain molecules undergoing Brownian motions were imaged by fluorescence microscopy.

Characterization and application of single fluorescent nanodiamonds as cellular biomarkers.

Type Ib diamonds emit bright fluorescence at 550-800 nm from nitrogen-vacancy point defects, (N-V)(0) and (N-V)(-), produced by high-energy ion beam irradiation and subsequent thermal annealing. The emission, together with noncytotoxicity and easiness of surface functionalization, makes nano-sized diamonds a promising fluorescent probe for single-particle tracking in heterogeneous environments. We present the result of our characterization and application of single fluorescent nanodiamonds as cellular biomarkers.