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Disseminated infection caused by Nocardia farcinica with primary nephrotic syndrome is exceedingly rare. A 66-year-old female visited the outpatient department due to fever and fatigue who had been diagnosed as membranous nephropathy and with a long-term prednisone and immunosuppressive therapy. After lung biopsy for many times, culture from space-occupying lesion of the right lung and species identification by mass spectrometry-based methods (MALDI-TOF) revealed Nocardia farcinica. By imaging examination, space-occupying lesions from the lungs, brain, abdominal cavity and kidney were found. After 2 weeks of meropenem intravenous and up to 6 months of trimethoprim-sulfamethoxazole (TMP-SMX) therapy, our patient has remained relapse-free at that time of writing. Disseminated infection caused by Nocardia farcinica is usually subacute with complex clinical manifestations. In addition, it can be easily confused with diseases such as tumor and mycobacterial infection, and lead to fatal consequences. Therefore, we hope that we can remind clinicians considering by discussing common features of disseminated Nocardia farcinica infection.

Objectives The different etiology of HF has different prognostic risk factors. Prognosis assessment of ICM and NICM has important clinical value. This study is aimed to explore the predicting factors for ICM and NICM. Methods 1082 HFrEF patients were retrospectively enrolled from Jan. 01, 2016 to Dec. 31, 2017. On Jan. 31, 2019, 873 patients were enrolled for analysis excluding incomplete, unfollowed, and unexplained data. The patients were divided into ischemic and non-ischemic group. The differences in clinical characteristics and long-term prognosis between the two groups were analyzed, and multivariate Cox analysis was used to predict the respective all-cause mortality, SCD and rehospitalization of CHF. Results 873 patients aged 64(53,73) were divided into two groups: ICM (403, 46.16%) and NICM. At the end, 203 died (111 in ICM, 54.68%), of whom 87 had SCD (53 in ICM, 60.92%) and 269 had rehospitalization for HF(134 in ICM, 49.81%). Independent risk factors affecting all-cause mortality in ICM: DM, previous hospitalization of HF, age, eGFR, LVEF; for SCD: PVB, eGFR, Hb, revascularization; for readmission of HF: low T3 syndrome, PVB, DM, previous hospitalization of HF, eGFR. Otherwise; factors affecting all-cause mortality in NICM: NYHA III-IV, paroxysmal AF/AFL, previous hospitalization of HF, β-blocker; for SCD: low T3 syndrome, PVB, nitrates, sodium, β-blocker; for rehospitalization of HF: paroxysmal AF/AFL, previous admission of HF, LVEF. Conclusions Both all-cause mortality and SCD in ICM is higher than that in NICM. Different etiologies of CHF have different risk factors affecting the prognosis.

Background Colon adenocarcinoma (COAD) patients who develop recurrence have poor prognosis. Our study aimed to establish effective prognosis prediction model based on competing endogenous RNAs (ceRNAs) for recurrence of COAD. Methods COAD expression profilings downloaded from The Cancer Genome Atlas (TCGA) were used as training dataset, and expression profilings of GSE29623 retrieved from Gene Expression Omnibus (GEO) were set as validation dataset. Differentially expressed RNAs (DERs) between non-recurrent and recurrent specimens in training dataset were screened, and optimum prognostic signature DERs were revealed to establish prognostic score (PS) model. Kaplan-Meier survival analysis was conducted for PS model, and GEO dataset was used for validation. Prognosis prediction efficiencies were evaluated by area under curve (AUC) and C-index. Meanwhile, ceRNA regulatory network was constructed by using signature mRNAs, lncRNAs and miRNAs. Results We identified 562 DERs including 42 lncRNAs, 36 miRNAs, and 484 mRNAs. PS prediction model, consisting of 17 optimum prognostic signature DERs, showed that high risk group had significantly poorer prognosis (5-year AUC = 0.951, C-index = 0.788), which also validated in GSE29623. Prognosis prediction model incorporating multi-RNAs with pathologic distant metastasis (M) and pathologic primary tumor (T) (5-year AUC = 0.969, C-index = 0.812) had better efficiency than clinical prognosis prediction model (5-year AUC = 0.712, C-index = 0.680). In the constructed ceRNA regulatory network, lncRNA NCBP2-AS1 could interact with hsa-miR-34c and hsa-miR-363, and lncRNA LINC00115 could interact with hsa-miR-363 and hsa-miR-4709. SIX4, GRAP, NKAIN4, MMAA, and ERVMER34–1 are regulated by hsa-miR-4709. Conclusion Prognosis prediction model incorporating multi-RNAs with pathologic M and pathologic T may have great value in COAD prognosis prediction.

Osteoarthritis (OA) is the most common joint disease type and is accompanied by varying degrees of functional limitation. Both hyaluronic acid (HA) joint injections and pain relievers are efficient treatments for early-stage osteoarthritis. However, for the decomposition by hyaluronidase and free radicals in the knee joint, HA injection treatment has limited effect time. The cerium oxide nanoparticles (CeO2) is a long time free radical scavenger. CeO2 combined with HA expected, may extend the HA decomposition time and have a positive effect on osteoarthritis therapy. In this study, CeO2 was successfully synthesized using the hydrothermal method with a particle size of about 120 nm, which possessed excellent dispersibility in the culture medium. The in vitro OA model was established by cell treated with H2O2 for 30 min. Our study found that the inhibition of chondrocyte proliferation dose-dependently increased with H2O2 concentration but was significantly decreased by supplementation of cerium oxide nanoparticles. COL2a1 and ACAN gene expression in chondrocytes was significantly decreased after H2O2 treatment, however, the tendency was changed after cerium oxide nanoparticles treatment, which suggested that damaged chondrocytes were protected against oxidative stress. These findings suggest that cerium oxide nanoparticles are potential therapeutic applications in the early stage of OA.

Background Pathogenesis and diagnostic biomarkers for diseases can be discovered by metabolomic profiling of human fluids. If the various types of coronary artery disease (CAD) can be accurately characterized by metabolomics, effective treatment may be targeted without using unnecessary therapies and resources. Objectives The authors studied disturbed metabolic pathways to assess the diagnostic value of metabolomics-based biomarkers in different types of CAD. Methods A cohort of 2,324 patients from 4 independent centers was studied. Patients underwent coronary angiography for suspected CAD. Groups were divided as follows: normal coronary artery (NCA), nonobstructive coronary atherosclerosis (NOCA), stable angina (SA), unstable angina (UA), and acute myocardial infarction (AMI). Plasma metabolomic profiles were determined by liquid chromatography–quadrupole time-of-flight mass spectrometry and were analyzed by multivariate statistics. Results We made 12 cross-comparisons to and within CAD to characterize metabolic disturbances. We focused on comparisons of NOCA versus NCA, SA versus NOCA, UA versus SA, and AMI versus UA. Other comparisons were made, including SA versus NCA, UA versus NCA, AMI versus NCA, UA versus NOCA, AMI versus NOCA, AMI versus SA, significant CAD (SA/UA/AMI) versus nonsignificant CAD (NCA/NOCA), and acute coronary syndrome (UA/AMI) versus SA. A total of 89 differential metabolites were identified. The altered metabolic pathways included reduced phospholipid catabolism, increased amino acid metabolism, increased short-chain acylcarnitines, decrease in tricarboxylic acid cycle, and less biosynthesis of primary bile acid. For differential diagnosis, 12 panels of specific metabolomics-based biomarkers provided areas under the curve of 0.938 to 0.996 in the discovery phase (n = 1,086), predictive values of 89.2% to 96.0% in the test phase (n = 933), and 85.3% to 96.4% in the 3-center external sets (n = 305). Conclusions Plasma metabolomics are powerful for characterizing metabolic disturbances. Differences in small-molecule metabolites may reflect underlying CAD and serve as biomarkers for CAD progression.

HepG2 cell death with magnetic hyperthermia (MHT) using hydroxyapatite nanoparticles (mHAPs) and alternating magnetic fields (AMF) was investigated in vitro. The mHAPs were synthesized as thermo-seeds by co-precipitation with the addition of Fe(2+). The grain size of the HAPs and iron oxide magnetic were 39.1 and 19.5 nm and were calculated by the Scherrer formula. The HepG2 cells were cultured with mHAPs and exposed to an AMF for 30 min yielding maximum temperatures of 43 ± 0.5 °C. After heating, the cell viability was reduced by 50% relative to controls, lactate dehydrogenase (LDH) concentrations measured in media were three-fold greater than those measured in all control groups. Readouts of toxicity by live/dead staining were consistent with cell viability and LDH assay results. Measured reactive oxygen species (ROS) in cells exposed to MHT were two-fold greater than in control groups. Results of cDNA microarray and Western blotting revealed tantalizing evidence of ATM and GADD45 downregulation with possible MKK3/MKK6 and ATF-2 of p38 MAPK inhibition upon exposure to mHAPs and AMF combinations. These results suggest that the combination of mHAPs and AMF can increase intracellular concentrations of ROS to cause DNA damage, which leads to cell death that complement heat stress related biological effects.

IntroductionLeft ventricular ejection fraction (LVEF) ≤35%, as current significant implantable cardioverter-defibrillator (ICD) indication for primary prevention of sudden cardiac death (SCD) in heart failure (HF) patients, has been widely recognised to be inefficient. Improvement of patient selection for low LVEF (≤35%) is needed to optimise deployment of ICD. Most of the existing prediction models are not appropriate to identify ICD candidates at high risk of SCD in HF patients with low LVEF. Compared with traditional statistical analysis, machine learning (ML) can employ computer algorithms to identify patterns in large datasets, analyse rules automatically and build both linear and non-linear models in order to make data-driven predictions. This study is aimed to develop and validate new models using ML to improve the prediction of SCD in HF patients with low LVEF.Methods and analysisWe will conduct a retroprospective, multicentre, observational registry of Chinese HF patients with low LVEF. The HF patients with LVEF ≤35% after optimised medication at least 3 months will be enrolled in this study. The primary endpoints are all-cause death and SCD. The secondary endpoints are malignant arrhythmia, sudden cardiac arrest, cardiopulmonary resuscitation and rehospitalisation due to HF. The baseline demographic, clinical, biological, electrophysiological, social and psychological variables will be collected. Both ML and traditional multivariable Cox proportional hazards regression models will be developed and compared in the prediction of SCD. Moreover, the ML model will be validated in a prospective study.Ethics and disseminationThe study protocol has been approved by the Ethics Committee of the First Affiliated Hospital of Nanjing Medical University (2017-SR-06). All results of this study will be published in international peer-reviewed journals and presented at relevant conferences.Trial registration numberChiCTR-POC-17011842; Pre-results.