Generation of INS-jGCaMP7f knock-in Ca reporter human embryonic stem cell line, GZLe001-C, using CRISPR/Cas9-based gene targeting.

As a member of the single-fluorophore genetically encoded calcium indicators (GECIs), jGCaMP7f is widely applied to investigate intracellular Ca concentrations. Here, we established an INS-jGCaMP7f knock-in H1 human embryonic stem cell (hESC) line by integrating jGCaMP7f gene into insulin locus via CRISPR/Cas9 system. The reporter cell line not only effectively labelled the insulin-producing cells induced from hESC, but also reflected the cytosolic change of Ca level in response to different stimuli.

Subject-level spinal osteoporotic fracture prediction combining deep learning vertebral outputs and limited demographic data.

Unlabelled: Automated screening for vertebral fractures could improve outcomes. We achieved an AUC-ROC = 0.968 for the prediction of moderate to severe fracture using a GAM with age and three maximal vertebral body scores of fracture from a convolutional neural network.

Creating a novel multiparametric magnetic resonance imaging-based biopsy strategy for reducing unnecessary prostate biopsies: a retrospective cohort study.

Background: The overdiagnosis of prostate cancer (PCa) caused by unnecessary prostate biopsy has become a worldwide problem that urgently requires a solution. We aimed to reduce the unnecessary prostate biopsies and increase the detection rate of clinically significant PCa (csPCa) by creating a novel multiparametric magnetic resonance imaging (mpMRI)-based strategy.

Methods: A total of 1,194 eligible patients who underwent transperineal prostate biopsies from January 2018 to December 2022 were included in this retrospective study.

Protocol for SNOTOB study: radical prostatectomy without prostate biopsy following F-PSMA-1007 PET/CT based on a diagnostic model: a single-centre, single-arm, open-label study.

Introduction: Nowadays, invasive prostate biopsy is the standard diagnostic test for patients with suspected prostate cancer (PCa). However, it has some shortcomings such as perioperative complications, economic and psychological burden on patients, and some patients may undergo repeated prostate biopsy. In this study protocol, our aim is to provide a non-invasive diagnostic strategy we call the 'prostate-specific membrane antigen (PSMA) combined model' for the diagnosis of PCa.

Developing a diagnostic model for predicting prostate cancer: a retrospective study based on Chinese multicenter clinical data.

The overdiagnosis of prostate cancer (PCa) caused by nonspecific elevation serum prostate-specific antigen (PSA) and the overtreatment of indolent PCa have become a global problem that needs to be solved urgently. We aimed to construct a prediction model and provide a risk stratification system to reduce unnecessary biopsies. In this retrospective study, clinical data of 1807 patients from three Chinese hospitals were used.

Generalizability of Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs Using an Adaptation of the Modified-2 Algorithm-Based Qualitative Criteria.

Rationale And Objectives: Spinal osteoporotic compression fractures (OCFs) can be an early biomarker for osteoporosis but are often subtle, incidental, and underreported. To ensure early diagnosis and treatment of osteoporosis, we aimed to build a deep learning vertebral body classifier for OCFs as a critical component of our future automated opportunistic screening tool.

Materials And Methods: We retrospectively assembled a local dataset, including 1790 subjects and 15,050 vertebral bodies (thoracic and lumbar).

Mechanisms of circular RNA degradation.

Circular RNAs (CircRNAs) are a class of noncoding RNAs formed by backsplicing during cotranscriptional and posttranscriptional processes, and they widely exist in various organisms. CircRNAs have multiple biological functions and are associated with the occurrence and development of many diseases. While the biogenesis and biological function of circRNAs have been extensively studied, there are few studies on circRNA degradation and only a few pathways for specific circRNA degradation have been identified.

Improving the Accuracy of Progress Indication for Constructing Deep Learning Models.

For many machine learning tasks, deep learning greatly outperforms all other existing learning algorithms. However, constructing a deep learning model on a big data set often takes days or months. During this long process, it is preferable to provide a progress indicator that keeps predicting the model construction time left and the percentage of model construction work done.

Deep Learning Classification of Spinal Osteoporotic Compression Fractures on Radiographs using an Adaptation of the Genant Semiquantitative Criteria.

Rationale And Objectives: Osteoporosis affects 9% of individuals over 50 in the United States and 200 million women globally. Spinal osteoporotic compression fractures (OCFs), an osteoporosis biomarker, are often incidental and under-reported. Accurate automated opportunistic OCF screening can increase the diagnosis rate and ensure adequate treatment.

DicomAnnotator: a Configurable Open-Source Software Program for Efficient DICOM Image Annotation.

Modern, supervised machine learning approaches to medical image classification, image segmentation, and object detection usually require many annotated images. As manual annotation is usually labor-intensive and time-consuming, a well-designed software program can aid and expedite the annotation process. Ideally, this program should be configurable for various annotation tasks, enable efficient placement of several types of annotations on an image or a region of an image, attribute annotations to individual annotators, and be able to display Digital Imaging and Communications in Medicine (DICOM)-formatted images.

Progress Indication for Deep Learning Model Training: A Feasibility Demonstration.

Deep learning is the state-of-the-art learning algorithm for many machine learning tasks. Yet, training a deep learning model on a large data set is often time-consuming, taking several days or even months. During model training, it is desirable to offer a non-trivial progress indicator that can continuously project the remaining model training time and the fraction of model training work completed.

[Influence of Cibotium barametz and its processed samples on haemorheology index in mice with adjuvant arthritis].

Objective: To discuss the influence of the rhizome of Cibotium barametz on the heamorheology index in mice with adjuvant arthritis and to compare the effect of raw medicinals with that of the processed ones.

Method: Mice was injected with Freund's complete adjuvant on the rihgt behind foot to make model of adjuvant arthritis (AA). Hydroxyacrbamide tablets were orally administrated by mice with AA to make model of AA due to deficiency in the kidney (DK-AA).