An effective method for detecting the wheat freshness by integrating biophotonics and machine learning algorithm.

The accurate and timely assessment of wheat freshness is not only a complex scientific endeavor but also a critical aspect of grain storage safety. This study introduces an innovative approach for evaluating wheat freshness by integrating machine learning algorithms with Biophoton Analytical Technology (BPAT). Initially, spontaneous ultraweak photon emissions from wheat are measured, and various statistical descriptors are derived to construct a feature vector.

Chinese expert consensus on imaging diagnosis of drug-resistant pulmonary tuberculosis.

Tuberculosis (TB) remains one of the major infectious diseases in the world with a high incidence rate. Drug-resistant tuberculosis (DR-TB) is a key and difficult challenge in the prevention and treatment of TB. Early, rapid, and accurate diagnosis of DR-TB is essential for selecting appropriate and personalized treatment and is an important means of reducing disease transmission and mortality.

Differential Diagnosis of COVID-19 Pneumonia From Influenza A (H1N1) Pneumonia Using a Model Based on Clinicoradiologic Features.

Both coronavirus disease 2019 (COVID-19) pneumonia and influenza A (H1N1) pneumonia are highly contagious diseases. We aimed to characterize initial computed tomography (CT) and clinical features and to develop a model for differentiating COVID-19 pneumonia from H1N1 pneumonia. In total, we enrolled 291 patients with COVID-19 pneumonia from January 20 to February 13, 2020, and 97 patients with H1N1 pneumonia from May 24, 2009, to January 29, 2010 from two hospitals.

Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter.

Purpose: To develop and validate a nomogram for differentiating invasive adenocarcinoma (IAC) from adenocarcinoma (AIS) and minimally invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs) measuring 5-10mm in diameter.

Materials And Methods: This retrospective study included 446 patients with 478 GGNs histopathologically confirmed AIS, MIA or IAC. These patients were assigned to a primary cohort, an internal validation cohort and an external validation cohort.

A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients.

Background: The assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19.

Methods: One hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups.

Abnormal lung quantification in chest CT images of COVID-19 patients with deep learning and its application to severity prediction.

Objective: Computed tomography (CT) provides rich diagnosis and severity information of COVID-19 in clinical practice. However, there is no computerized tool to automatically delineate COVID-19 infection regions in chest CT scans for quantitative assessment in advanced applications such as severity prediction. The aim of this study was to develop a deep learning (DL)-based method for automatic segmentation and quantification of infection regions as well as the entire lungs from chest CT scans.

M Lung-Sys: A Deep Learning System for Multi-Class Lung Pneumonia Screening From CT Imaging.

To counter the outbreak of COVID-19, the accurate diagnosis of suspected cases plays a crucial role in timely quarantine, medical treatment, and preventing the spread of the pandemic. Considering the limited training cases and resources (e.g, time and budget), we propose a Multi-task Multi-slice Deep Learning System (M Lung-Sys) for multi-class lung pneumonia screening from CT imaging, which only consists of two 2D CNN networks, i.

An unsupervised semi-automated pulmonary nodule segmentation method based on enhanced region growing.

Background: Nowadays, computer technology is getting popular for clinical aided diagnosis, especially in the direction of medical images. It makes physician diagnosis of lung nodules more efficient by providing them with reliable and accurate segmentation.

Methods: A region growing based semi-automated pulmonary nodule segmentation algorithm (ReGANS) was developed with three improvements: an automatic threshold calculation method, a lesion area pre-projection method, and an optimized region growing method.

HIV-infected patients with opportunistic pulmonary infections misdiagnosed as lung cancers: the clinicoradiologic features and initial application of CT radiomics.

Background: To characterize clinicoradiologic and radiomic features for identifying opportunistic pulmonary infections (OPIs) misdiagnosed as lung cancers in patients with human immunodeficiency virus (HIV).

Methods: Twenty-four HIV-infected patients who were misdiagnosed with lung cancers on CT images and had OPIs confirmed by pathological examination or integration of clinical and laboratory findings and 49 HIV-infected patients with lung cancers confirmed pathologically were included. Semiautomated segmentation of the lesion was implemented with an in-house software.

The Association Between Endocan Levels and Subclinical Atherosclerosis in Patients With Type 2 Diabetes Mellitus.

Background: Proinflammatory conditions induced by circulating factors in diabetes play a pivotal role in endothelial dysfunction and related vascular complications. Endothelial cell-specific molecule-1 or endocan is a dermatan sulfate proteoglycan secreted primarily by the vascular endothelium. Although endocan has been shown to be a potential biomarker in coronary heart disease, its role in the pathogenesis of atherosclerosis (AS) in diabetes remains unclear.

Efficient detection of internal infestation in wheat based on biophotonics.

In the process of grain storage, there are many losses of grain quantity and quality for the sake of insects. As a result, it is necessary to find a rapid and economical method for detecting insects in the grain. The paper innovatively proposes a model of detecting internal infestation in wheat by combining pattern recognition and BioPhoton Analytical Technology (BPAT).

[Electrophysiologic study of the biphasic effects of cyclovirobuxine D on arrhythmias].

Objective: To explore the possible mechanism of cyclovirobuxine D (CVB-D) in countering and inducing arrhythmia, by way of studying its electro-physiological effect on ventricular papillary muscles of rats in vitro.

Methods: The transmembrane potential of rat's isolated right ventricular papillary muscles were recorded using conventional glass micro-electrode technique.

Results: (1) CVB-D in concentration of 13.