Potential of digital chest radiography-based deep learning in screening and diagnosing pneumoconiosis: An observational study.

The diagnosis of pneumoconiosis is complex and subjective, leading to inevitable variability in readings. This is especially true for inexperienced doctors. To improve accuracy, a computer-assisted diagnosis system is used for more effective pneumoconiosis diagnoses.

Using time-series chest radiographs and laboratory data by machine learning for identifying pulmonary infection and colonization of Acinetobacter baumannii.

Background: Accurately distinguishing between pulmonary infection and colonization in patients with Acinetobacter baumannii is of utmost importance to optimize treatment and prevent antibiotic abuse or inadequate therapy. An efficient automated sorting tool could prompt individualized interventions and enhance overall patient outcomes. This study aims to develop a robust machine learning classification model using a combination of time-series chest radiographs and laboratory data to accurately classify pulmonary status caused by Acinetobacter baumannii.

Targeting the ZNF-148/miR-335/SOD2 signaling cascade triggers oxidative stress-mediated pyroptosis and suppresses breast cancer progression.

Background: The implication of zinc finger protein 148 (ZNF-148) in pathophysiology of most human cancers has been reported; however, the biological functions of ZNF-148 in breast cancer remain unclear. This study sought to elucidate the potential molecular mechanism of ZNF-148 on breast cancer pathology.

Methods: ZNF148 expression was tested in breast cancer tissues and cells.

Atypical architectural distortion detection in digital breast tomosynthesis: a computer-aided detection model with adaptive receptive field.

. In digital breast tomosynthesis (DBT), architectural distortion (AD) is a breast lesion that is difficult to detect. Compared with typical ADs, which have radial patterns, identifying a typical ADs is more difficult.

CircRNAs in Tumor Radioresistance.

RNAs (circRNAs) are endogenous, non-coding RNAs, which are derived from host genes that are present in several species and can be involved in the progression of various diseases. circRNAs' leading role is to act as RNA sponges. In recent years, the other roles of circRNAs have been discovered, such as regulating transcription and translation, regulating host genes, and even being translated into proteins.

Multi-scale cascaded networks for synthesis of mammogram to decrease intensity distortion and increase model-based perceptual similarity.

Purpose: Synthetic digital mammogram (SDM) is a 2D image generated from digital breast tomosynthesis (DBT) and used as a substitute for a full-field digital mammogram (FFDM) to reduce the radiation dose for breast cancer screening. The previous deep learning-based method used FFDM images as the ground truth, and trained a single neural network to directly generate SDM images with similar appearances (e.g.

Architectural distortion detection based on superior-inferior directional context and anatomic prior knowledge in digital breast tomosynthesis.

Background: In 2020, breast cancer becomes the most leading diagnosed cancer all over the world. The burden is increasing in the prevention and treatment of breast cancer. Accurately detecting breast lesions in screening images is important for early detection of cancer.

Can a Computer-Aided Mass Diagnosis Model Based on Perceptive Features Learned From Quantitative Mammography Radiology Reports Improve Junior Radiologists' Diagnosis Performance? An Observer Study.

Radiologists' diagnostic capabilities for breast mass lesions depend on their experience. Junior radiologists may underestimate or overestimate Breast Imaging Reporting and Data System (BI-RADS) categories of mass lesions owing to a lack of diagnostic experience. The computer-aided diagnosis (CAD) method assists in improving diagnostic performance by providing a breast mass classification reference to radiologists.

Predicting the molecular subtype of breast cancer and identifying interpretable imaging features using machine learning algorithms.

Objectives: To evaluate the performance of interpretable machine learning models in predicting breast cancer molecular subtypes.

Methods: We retrospectively enrolled 600 patients with invasive breast carcinoma between 2012 and 2019. The patients were randomly divided into a training (n = 450) and a testing (n = 150) set.

Improved detection of solitary pulmonary nodules on radiographs compared with deep bone suppression imaging.

Background: The present study aimed to investigate whether deep bone suppression imaging (BSI) could increase the diagnostic performance for solitary pulmonary nodule detection compared with digital tomosynthesis (DTS), dual-energy subtraction (DES) radiography, and conventional chest radiography (CCR).

Methods: A total of 256 patients (123 with a solitary pulmonary nodule, 133 with normal findings) were included in the study. The confidence score of 6 observers determined the presence or absence of pulmonary nodules in each patient.

Synthesis of Mammogram From Digital Breast Tomosynthesis Using Deep Convolutional Neural Network With Gradient Guided cGANs.

Synthetic digital mammography (SDM), a 2D image generated from digital breast tomosynthesis (DBT), is used as a potential substitute for full-field digital mammography (FFDM) in clinic to reduce the radiation dose for breast cancer screening. Previous studies exploited projection geometry and fused projection data and DBT volume, with different post-processing techniques applied on re-projection data which may generate different image appearance compared to FFDM. To alleviate this issue, one possible solution to generate an SDM image is using a learning-based method to model the transformation from the DBT volume to the FFDM image using current DBT/FFDM combo images.

Prednisone is not beneficial for the treatment of severe drug-induced liver injury: An observational study (STROBE compliant).

There is limited information about the effects of corticosteroids on severe drug-induced liver injury (DILI). This study aimed to investigate the efficacy and safety of prednisone in severe DILI.Ninety patients with severe DILI were enrolled and studied retrospectively.

The Effect of Transjugular Intrahepatic Portosystemic Shunt Plus Partial Splenic Embolization for the Treatment of Patients with Recurrent Variceal Bleeding.

Rationale And Objectives: Transjugular intrahepatic portosystemic shunt (TIPS) and partial splenic embolization (PSE) were two interventional therapies effective for the management of variceal bleeding with cirrhosis. This study aimed to investigate the effect of TIPS plus PSE for the treatment of patients with cirrhosis and recurrent variceal bleeding.

Material And Methods: This is a single-center, nonrandomized and retrospective study that included 32 patients undergoing TIPS alone (the TIPS group) and 16 patients undergoing TIPS plus PSE (the TIPS+PSE group).

Cloning and expression analysis of cDNAs for ABA 8'-hydroxylase during sweet cherry fruit maturation and under stress conditions.

Abscisic acid (ABA) plays a key role in various aspects of plant growth and development, including adaptation to environmental stress and fruit maturation in sweet cherry fruit. In higher plants, the level of ABA is determined by synthesis and catabolism. In order to gain insight into ABA synthesis and catabolism in sweet cherry fruit during maturation and under stress conditions, four cDNAs of PacCYP707A1 -PacCYP707A4 for 8'-hydroxylase, a key enzyme in the oxidative catabolism of ABA, and one cDNA of PacNCED1 for 9-cis-epoxycarotenoid dioxygenase, a key enzyme in the ABA biosynthetic pathway, were isolated from sweet cherry fruit (Prunus avium L.