Transcriptional Networks in Epithelial-Mesenchymal Transition

Epithelial-mesenchymal transition (EMT) changes polarized epithelial cells into migratory phenotypes associated with loss of cell-cell adhesion molecules and cytoskeletal rearrangements. This form of plasticity is seen in mesodermal development, fibroblast formation, and cancer metastasis.Here we identify prominent transcriptional networks active during three time points of this transitional process, as epithelial cells become fibroblasts. DNA microarray in cultured epithelia undergoing EMT, validated in vivo, were used to detect various patterns of gene expression. In particular, the promoter sequences of differentially expressed genes and their transcription factors were analyzed to identify potential binding sites and partners. The four most frequent cis-regulatory elements (CREs) in up-regulated genes were SRY, FTS-1, Evi-1, and GC-Box, and RNA inhibition of the four transcription factors, Atf2, Klf10, Sox11, and SP1, most frequently binding these CREs, establish their importance in the initiation and propagation of EMT. Oligonucleotides that block the most frequent CREs restrain EMT at early and intermediate stages through apoptosis of the cells.Our results identify new transcriptional interactions with high frequency CREs that modulate the stability of cellular plasticity, and may serve as targets for modulating these transitional states in fibroblasts

The Kidney Transplant Evaluation Process in the Elderly: Reasons for Being Turned down and Opportunities to Improve Cost-Effectiveness in a Single Center

Background. The kidney transplant evaluation process for older candidates is complex due to the presence of multiple comorbid conditions. Methods. We retrospectively reviewed patients ≥60 years referred to our center for kidney transplantation over a 3-year period. Variables were collected to identify reasons for patients being turned down and to determine the number of unnecessary tests performed. Statistical analysis was performed to estimate the association between clinical predictors and listing status. Results. 345 patients were included in the statistical analysis. 31.6% of patients were turned down: 44% due to coronary artery disease (CAD), peripheral vascular disease (PVD), or both. After adjustment for patient demographics and comorbid conditions, history of CAD, PVD, or both (OR = 1.75, 95% CI (1.20, 2.56), p=0.004), chronic obstructive pulmonary disease (OR = 8.75, 95% CI (2.81, 27.20), p=0.0002), and cancer (OR 2.59, 95% CI (1.18, 5.67), p=0.02) were associated with a higher risk of being turned down. 14.8% of patients underwent unnecessary basic testing and 9.6% underwent unnecessary supplementary testing with the charges over a 3-year period estimated at $304,337. Conclusion. A significant number of older candidates are deemed unacceptable for kidney transplantation with primary reasons cited as CAD and PVD. The overall burden of unnecessary testing is substantial and potentially avoidable

High mortality among patients hospitalized for drug-resistant tuberculosis with acquired second-line drug resistance and high HIV prevalence.

OBJECTIVES: We compared mortality between HIV-positive and HIV-negative South African adults with drug-resistant tuberculosis (DR-TB) and high incidence of acquired second-line drug resistance. METHODS: We performed a retrospective review of DR-TB patients with serial second-line TB drug susceptibility tests (2008-2015) who were hospitalized at a specialized TB hospital. We used Kaplan-Meier analysis and Cox models to examine associations with mortality. RESULTS: Of 245 patients, the median age was 33 years, 54% were male and 40% were HIV-positive, 96% of whom had ever received antiretroviral therapy (ART). At initial drug resistance detection, 99% of patients had resistance to at least rifampicin and isoniazid, and 18% had second-line drug resistance (fluoroquinolones and/or injectable drugs). At later testing, 88% of patients had acquired additional second-line drug resistance. Patient-initiated treatment interruptions (> 2 months) occurred in 47%. Mortality was 79%. Those with HIV had a shorter time to death (p = 0.02; log-rank): median survival time from DR-TB treatment initiation was 2.44 years [95% confidence interval (CI): 2.09-3.15] versus 3.99 years (95% CI: 3.12-4.75) for HIV-negative patients. HIV-positive patients who received ART within 6 months before DR-TB treatment had a higher mortality hazard than HIV-negative patients [adjusted hazard ratio (aHR) ratio = 1.82, 95% CI: 1.21-2.74]. By contrast, HIV-positive patients who did not receive ART within 6 months before DR-TB treatment did not have a significantly higher mortality hazard than HIV-negative patients (aHR = 1.09; 95% CI: 0.72-1.65), although those on ART had lower median CD4 counts than those not on ART (157 vs. 281 cells/μL, respectively; p = 0.02). CONCLUSIONS: A very high incidence of acquired second-line drug resistance and high overall mortality were observed, reinforcing the need to reduce the risk of acquired resistance and for more effective treatment

Development and validation of a multivariable prediction model for missed HIV health care provider visits in a large US clinical cohort

Background: Identifying individuals at high risk of missing HIV care provider visits could support proactive intervention. Previous prediction models for missed visits have not incorporated data beyond the individual level. Methods: We developed prediction models for missed visits among people with HIV (PWH) with ≥1 follow-up visit in the Center for AIDS Research Network of Integrated Clinical Systems from 2010 to 2016. Individual-level (medical record data and patient-reported outcomes), community-level (American Community Survey), HIV care site-level (standardized clinic leadership survey), and structural-level (HIV criminalization laws, Medicaid expansion, and state AIDS Drug Assistance Program budget) predictors were included. Models were developed using random forests with 10-fold cross-validation; candidate models with the highest area under the curve (AUC) were identified. Results: Data from 382 432 visits among 20 807 PWH followed for a median of 3.8 years were included; the median age was 44 years, 81% were male, 37% were Black, 15% reported injection drug use, and 57% reported male-to-male sexual contact. The highest AUC was 0.76, and the strongest predictors were at the individual level (prior visit adherence, age, CD4+ count) and community level (proportion living in poverty, unemployed, and of Black race). A simplified model, including readily accessible variables available in a web-based calculator, had a slightly lower AUC of .700. Conclusions: Prediction models validated using multilevel data had a similar AUC to previous models developed using only individual-level data. The strongest predictors were individual-level variables, particularly prior visit adherence, though community-level variables were also predictive. Absent additional data, PWH with previous missed visits should be prioritized by interventions to improve visit adherence

High-Sensitivity Cardiac Troponin-I Is Elevated in Patients with Rheumatoid Arthritis, Independent of Cardiovascular Risk Factors and Inflammation

We examined the hypothesis that cardiac-specific troponin-I (cTn-I), a biomarker of myocardial injury, is elevated in patients with rheumatoid arthritis (RA).RA patients have an increased incidence of heart failure (HF). Chronic myocardial injury in RA may be a mechanism for the development of HF.We compared cTn-I concentrations measured by high-sensitivity immunoassay in 164 patients with RA and 90 controls, excluding prior or active heart failure. We examined the relationship between cTn-I concentrations and cardiovascular risk factors, inflammation, and coronary artery calcium score (CACS), a measure of coronary atherosclerosis.cTn-I concentrations were 49% higher in patients with RA (median 1.15 pg/mL [IQR 0.73–1.92] than controls (0.77 pg/mL [0.49–1.28](P<0.001). The difference remained statistically significant after adjustment for demographic characteristics (P = 0.002), further adjustment for cardiovascular (CV) risk factors (P = 0.004), inflammatory markers (P = 0.008), and in a comprehensive model of CV risk factors and inflammatory markers (P = 0.03). In patients with RA, cTn-I concentrations were positively correlated with age (rho = 0.359), Framingham risk score (FRS) (rho = 0.366), and systolic blood pressure (rho = 0.248 (all P values ≤0.001)), but not with measures of inflammation or RA drug therapies. cTn-I was significantly correlated with CACS in RA in univariate analysis, but not after adjustment for age, race, sex and FRS (P = 0.79). Further model adjustments for renal function and coronary artery disease confirmed the significance of the findings.High-sensitivity cTn-I concentrations are elevated in patients with RA without heart failure, independent of cardiovascular risk profile and inflammatory markers. Elevated troponin concentrations in RA may indicate subclinical, indolent myocardial injury

Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network

Changes in clouds can affect the outpower of photovoltaics (PVs). Ground-based cloud images classification is an important prerequisite for PV power prediction. Due to the intra-class difference and inter-class similarity of cloud images, the classical convolutional network is obviously insufficient in distinguishing ability. In this paper, a classification method of ground-based cloud images by improved combined convolutional network is proposed. To solve the problem of sub-network overfitting caused by redundancy of pixel information, overlap pooling kernel is used to enhance the elimination effect of information redundancy in the pooling layer. A new channel attention module, ECA-WS (Efficient Channel Attention–Weight Sharing), is introduced to improve the network’s ability to express channel information. The decision fusion algorithm is employed to fuse the outputs of sub-networks with multi-scales. According to the number of cloud images in each category, different weights are applied to the fusion results, which solves the problem of network scale limitation and dataset imbalance. Experiments are carried out on the open MGCD dataset and the self-built NRELCD dataset. The results show that the proposed model has significantly improved the classification accuracy compared with the classical network and the latest algorithms