Use of UMFNPs/Ce6@MBs in multimodal imaging-guided sono-photodynamic combination therapy for hepatocellular carcinoma.

Early diagnosis of liver cancer and appropriate treatment options are critical for obtaining a good prognosis. However, due to technical limitations, it is difficult to make an early and accurate diagnosis of liver cancer, and the traditional imaging model is relatively simple. Therefore, we synthesized multifunctional diagnostic/therapeutic nanoparticles, UMFNPs/Ce6@MBs, loaded with ultra-small manganese ferrite nanoparticles (UMFNPs) and chlorin e6 (Ce6).

Comprehensive evaluation of drought tolerance of six Chinese chestnut varieties (clones) based on flavonoids and other physiological indexes.

Flavonoids are crucial secondary metabolites that possess the ability to mitigate UV damage and withstand both biotic and abiotic stresses. Therefore, it is of immense significance to investigate the flavonoid content as a pivotal indicator for a comprehensive assessment of chestnut's drought tolerance. This study aimed to determine the flavonoid content and drought tolerance-related physiological and biochemical indices of six chestnut varieties (clones) grafted trees-Qianxi 42 (QX42), Qinglong 45 (QL45), Yanshanzaofeng (YSZF), Yanzi (YZ), Yanqiu (YQ), and Yanlong (YL)-under natural drought stress.

Associating Knee Osteoarthritis Progression with Temporal-Regional Graph Convolutional Network Analysis on MR Images.

Background: Artificial intelligence shows promise in assessing knee osteoarthritis (OA) progression on MR images, but faces challenges in accuracy and interpretability.

Purpose: To introduce a temporal-regional graph convolutional network (TRGCN) on MR images to study the association between knee OA progression status and network outcome.

Study Type: Retrospective.

DeepKOA: a deep-learning model for predicting progression in knee osteoarthritis using multimodal magnetic resonance images from the osteoarthritis initiative.

Background: No investigations have thoroughly explored the feasibility of combining magnetic resonance (MR) images and deep-learning methods for predicting the progression of knee osteoarthritis (KOA). We thus aimed to develop a potential deep-learning model for predicting OA progression based on MR images for the clinical setting.

Methods: A longitudinal case-control study was performed using data from the Foundation for the National Institutes of Health (FNIH), composed of progressive cases [182 osteoarthritis (OA) knees with both radiographic and pain progression for 24-48 months] and matched controls (182 OA knees not meeting the case definition).

Intermediate-weighted MRI with fat suppression (IW-FS): diagnostic performance for bone marrow edema and erosion detection in axial spondyloarthritis.

Background: Bone marrow edema (BME) and erosion of the sacroiliac joint are both key lesions for diagnosing axial spondyloarthritis (axSpA) on magnetic resonance imaging (MRI).

Purpose: To qualitatively and quantitatively compare intermediate-weighted MRI with fat suppression (IW-FS) with T2-weighted short tau inversion recovery (T2-STIR) in assessment of sacroiliac BME and erosion in axSpA.

Material And Methods: Patients aged 18-60 years with axSpA were prospectively enrolled.

Prediction model for knee osteoarthritis using magnetic resonance-based radiomic features from the infrapatellar fat pad: data from the osteoarthritis initiative.

Background: The infrapatellar fat pad (IPFP) plays an important role in the incidence of knee osteoarthritis (OA). Magnetic resonance (MR) signal heterogeneity of the IPFP is related to pathologic changes. In this study, we aimed to investigate whether the IPFP radiomic features have predictive value for incident radiographic knee OA (iROA) 1 year prior to iROA diagnosis.

Proton density fat fraction (PDFF) maps aid fat metaplasia evaluation in the sacroiliac joints in additional to T1WI: Improved diagnostic accuracy in axial spondyloarthritis.

Purpose: To evaluate the added value of qualitative and quantitative fat metaplasia analysis using proton-density fat fraction (PDFF) map in additional to T1-weighted imaging (T1WI) of the sacroiliac joints (SIJ) for diagnosis of axial spondyloarthritis (axSpA).

Method: Patients aged 18-45 years with axSpA were enrolled. Non-SpA patients and healthy volunteers were included as controls.

Evaluation of atrial anatomical remodeling in atrial fibrillation with machine-learned morphological features.

Purpose: To elucidate the role of atrial anatomical remodeling in atrial fibrillation (AF), we proposed an automatic method to extract and analyze morphological characteristics in left atrium (LA), left atrial appendage (LAA) and pulmonary veins (PVs) and constructed classifiers to evaluate the importance of identified features.

Methods: The LA, LAA and PVs were segmented from contrast computed tomography images using either a commercial software or a self-adaptive algorithm proposed by us. From these segments, geometric and fractal features were calculated automatically.

Deep learning of lumbar spine X-ray for osteopenia and osteoporosis screening: A multicenter retrospective cohort study.

Osteoporosis is a prevalent but underdiagnosed condition. As compared to dual-energy X-ray absorptiometry (DXA) measures, we aimed to develop a deep convolutional neural network (DCNN) model to classify osteopenia and osteoporosis with the use of lumbar spine X-ray images. Herein, we developed the DCNN models based on the training dataset, which comprising 1616 lumbar spine X-ray images from 808 postmenopausal women (aged 50 to 92 years).

Regeneration of Fe /Fe complex from NO chelating absorption by microbial fuel cell.

Ferrous chelates (FeEDTA) can effectively absorb NO, but the regeneration of them usually consumes large amounts of organic matter or energy. In this study, a new approach to regenerate NO absorbed ferrous chelates with simultaneous electricity generation was investigated by a microbial fuel cell (MFC). The performance and mechanisms of FeEDTA regeneration were evaluated in the cathode of MFC reactor with and without the presence of microorganisms (referring to biocathode and abiotic cathode), respectively.