An optimized short‐term steroid therapy for chronic drug‐induced liver injury: A prospective randomized clinical trial

Background and AimsThe use of corticosteroids in chronic drug-induced liver injury (DILI) is an important issue. Our previous randomized controlled trial showed that patients with chronic DILI benefited from a 48-week steroid stepwise reduction (SSR) regimen. However, it remains unclear whether a shorter course of therapy can achieve similar efficacy. In this study, we aimed to assess whether a 36-week SSR can achieve efficacy similar to that of 48-week SSR.MethodsA randomized open-label trial was performed. Eligible patients were randomly assigned to the 36- or 48-week (1:1) SSR group. Liver biopsies were performed at baseline and at the end of treatment. The primary outcome was the proportion of patients with relapse rate (RR). The secondary outcomes were improvement in liver histology and safety.ResultsOf the 90 participants enrolled, 84 (87.5%) completed the trial, and 62 patients (68.9%) were women. Hepatocellular damage was observed in 53.4% of the cohort. The RR was 7.1% in the 36-week SSR group but 4.8% in the 48-week SSR group, as determined by per-protocol set analysis (p = 1.000). Significant histological improvements in histological activity (93.1% vs. 92.9%, p = 1.000) and fibrosis (41.4% vs. 46.4%, p = .701) were observed in both the groups. Biochemical normalization time did not differ between the two groups. No severe adverse events were observed.ConclusionsBoth the 36- and 48-week SSR regimens demonstrated similar biochemical response and histological improvements with good safety, supporting 36-week SSR as a preferable therapeutic choice (ClinicalTrials.gov, NCT03266146)

Dual-Modality Imaging Probes with High Magnetic Relaxivity and Near-Infrared Fluorescence Based Highly Aminated Mesoporous Silica Nanoparticles

Dual-modal imaging by combining magnetic resonance (MR) and near-infrared (NIR) fluorescence can integrate the advantages of high-resolution anatomical imaging with high sensitivity in vivo fluorescent imaging, which is expected to play a significant role in biomedical researches. Here we report a dual-modality imaging probe (NIR/MR-MSNs) fabricated by conjugating NIR fluorescent heptamethine dyes (IR-808) and MR contrast agents (Gd-DTPA) within highly aminated mesoporous silica nanoparticles (MSNs-NH2). The dual-modality imaging probes NIR/MR-MSNs possess a size of ca. 120 nm. The NIR/MR-MSNs show not only near-infrared fluorescence imaging property with an emission peak at 794 nm, but also highly MR T1 relaxivity of 14.54 mM−1 s−1, which is three times more than Gd-DTPA. In vitro experiment reveals high uptake and retention abilities of the nanoprobes, while cell viability assay demonstrates excellent cytocompatibility of the dual-modality imaging probe. After intratumor injection with the NIR/MR-MSNs, MR imaging shows clear anatomical border of the enhanced tumor region while NIR fluorescence exhibits high sensitive tumor detection ability. These intriguing features suggest that this newly developed dual-modality imaging probes have great potential in biomedical imaging

A Multifunctional PB@mSiO₂–PEG/DOX Nanoplatform for Combined Photothermal–Chemotherapy of Tumor

In this work, we design mesoporous silica-coated Prussian blue nanocubes with PEGyltation to construct multifunctional PB@mSiO₂–PEG nanocubes. The PB@mSiO₂–PEG nanocubes have good biocompatibility, excellent photothermal transformation capacity, in vivo magnetic resonance and photoacoustic imaging ability. After loading antitumor drug doxorubicin (DOX) in the PB@mSiO₂–PEG nanocubes, the constructured PB@mSiO₂–PEG/DOX nanoplatforms show an excellent pH-responsive drug release character within 48 h, namely, an ultralow cumulative drug release amount of 3.1% at pH 7.4 and a high release amount of 46.6% at pH 5.0. Upon near-infrared laser irradiation, the PB@mSiO₂–PEG/DOX nanoplatforms show an enhanced synergistic photothermal and chemical therapeutic efficacy for breast cancer than solo photothermal therapy or chemotherapy

Age and Ebola viral load correlate with mortality and survival time in 288 Ebola virus disease patients

Background: A Chinese medical team managed Ebola virus disease (EVD) patients in Sierra Leone from October 2014 to March 2015 and attended to 693 suspected patients, of whom 288 had confirmed disease. Methods: A retrospective study was conducted of the 288 patients with confirmed disease. Clinical symptoms, manifestations, and serum viral load were analyzed and compared among the different groups for mortality and survival time. Results: Among the 288 confirmed EVD patients (149 male and 139 female, median age 28 years, and median log viral load 6.68), 98 died, 36 recovered, and 154 were lost to follow-up. Common symptoms were fever (77.78%), fatigue (64.93%), abdominal pain (64.58%), headache (62.85%), and diarrhea (61.81%). Compared to patients aged 106 copies/ml had a higher case fatality rate than those with <106 copies/ml (odds ratio 3.095, p = 0.004). Cox regression showed that age, viral load, and the presence of diarrhea correlated with mortality. Conclusion: Patients with a high viral load, of older age, and with diarrhea had a higher mortality and shorter survival time

Search for gamma-ray spectral lines with the DArk Matter Particle Explorer

The DArk Matter Particle Explorer (DAMPE) is well suitable for searching for monochromatic and sharp γ-ray structures in the GeV–TeV range thanks to its unprecedented high energy resolution. In this work, we search for γ-ray line structures using five years of DAMPE data. To improve the sensitivity, we develop two types of dedicated data sets (including the BgoOnly data which is the first time to be used in the data analysis for the calorimeter-based gamma-ray observatories) and adopt the signal-to-noise ratio optimized regions of interest (ROIs) for different DM density profiles. No line signals or candidates are found between 10 and 300 GeV in the Galaxy. The constraints on the velocity-averaged cross section for χχ→γγ and the decay lifetime for χ→γν, both at 95% confidence level, have been calculated and the systematic uncertainties have been taken into account. Comparing to the previous Fermi-LAT results, though DAMPE has an acceptance smaller by a factor of ∼10, similar constraints on the DM parameters are achieved and below 100 GeV the lower limits on the decay lifetime are even stronger by a factor of a few. Our results demonstrate the potential of high-energy-resolution observations on dark matter detection

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset