Elafibranor, an Agonist of the Peroxisome Proliferator-Activated Receptor-α and -δ, Induces Resolution of Nonalcoholic Steatohepatitis Without Fibrosis Worsening

BACKGROUND & AIMS

Elafibranor is an agonist of the peroxisome proliferator-activated receptor-α and peroxisome proliferator-activated receptor-δ. Elafibranor improves insulin sensitivity, glucose homeostasis, and lipid metabolism and reduces inflammation. We assessed the safety and efficacy of elafibranor in an international, randomized, double-blind placebo-controlled trial of patients with nonalcoholic steatohepatitis (NASH).

METHODS

Patients with NASH without cirrhosis were randomly assigned to groups given elafibranor 80 mg (n = 93), elafibranor 120 mg (n = 91), or placebo (n = 92) each day for 52 weeks at sites in Europe and the United States. Clinical and laboratory evaluations were performed every 2 months during this 1-year period. Liver biopsies were then collected and patients were assessed 3 months later. The primary outcome was resolution of NASH without fibrosis worsening, using protocol-defined and modified definitions. Data from the groups given the different doses of elafibranor were compared with those from the placebo group using step-down logistic regression, adjusting for baseline nonalcoholic fatty liver disease activity score.

RESULTS

In intention-to-treat analysis, there was no significant difference between the elafibranor and placebo groups in the protocol-defined primary outcome. However, NASH resolved without fibrosis worsening in a higher proportion of patients in the 120-mg elafibranor group vs the placebo group (19% vs 12%; odds ratio = 2.31; 95% confidence interval: 1.02-5.24; P = .045), based on a post-hoc analysis for the modified definition. In post-hoc analyses of patients with nonalcoholic fatty liver disease activity score ≥4 (n = 234), elafibranor 120 mg resolved NASH in larger proportions of patients than placebo based on the protocol definition (20% vs 11%; odds ratio = 3.16; 95% confidence interval: 1.22-8.13; P = .018) and the modified definitions (19% vs 9%; odds ratio = 3.52; 95% confidence interval: 1.32-9.40; P = .013). Patients with NASH resolution after receiving elafibranor 120 mg had reduced liver fibrosis stages compared with those without NASH resolution (mean reduction of 0.65 ± 0.61 in responders for the primary outcome vs an increase of 0.10 ± 0.98 in nonresponders; P < .001). Liver enzymes, lipids, glucose profiles, and markers of systemic inflammation were significantly reduced in the elafibranor 120-mg group vs the placebo group. Elafibranor was well tolerated and did not cause weight gain or cardiac events, but did produce a mild, reversible increase in serum creatinine (effect size vs placebo: increase of 4.31 ± 1.19 μmol/L; P < .001).

CONCLUSIONS

A post-hoc analysis of data from trial of patients with NASH showed that elafibranor (120 mg/d for 1 year) resolved NASH without fibrosis worsening, based on a modified definition, in the intention-to-treat analysis and in patients with moderate or severe NASH. However, the predefined end point was not met in the intention to treat population. Elafibranor was well tolerated and improved patients' cardiometabolic risk profile. ClinicalTrials.gov number: NCT01694849.

A randomized controlled trial of lamivudine to treat acute hepatitis B

The role of antivirals in patients with acute viral hepatitis B (AVH-B) has not been evaluated in controlled trials. The aim of this study was to evaluate the efficacy of lamivudine in patients with AVH-B. AVH-B patients with serum bilirubin of more than 5 mg/dL were randomized to receive either 100 mg of lamivudine daily for 3 months (group 1, n = 31) or placebo (group 2, n = 40). Patients were considered to have severe AVH-B if they fulfilled 2 of 3 criteria: (1) hepatic encephalopathy; (2) serum bilirubin > or = 10.0 mg/dL; and (3) international normalized ratio (INR) > or = 1.6. At week 4, HBV DNA levels were significantly lower (P = 0.037) in group 1 (median: 3.6721 log copies/mL) than group 2 (median: 4.2721 log copies/mL). Thereafter, HBV DNA levels were comparable in the 2 groups. The improvement in serum bilirubin, ALT, and INR values was similar in the 2 groups. Twenty-two patients (71%) in group 1 and 25 patients (62.5%) in group 2 had severe AVH-B. Results were similar when patients with severe AVH-B were analyzed separately. After 12 and 18 months, 93.5% and 92.5%, respectively, of patients in the lamivudine group and 96.7% and 97.5%, respectively, of patients in the placebo group lost HBsAg. There were no deaths in either group. After 1 year, 21 patients (67.7%) in group 1 and 34 patients (85%) in group 2 developed protective anti-HBs titers (P = 0.096). All HBeAg-positive patients in both groups lost e antigen and anti-HBe developed in 71% and 87.5% of patients in groups 1 and 2, respectively (P = 0.132).

CONCLUSION

Though lamivudine causes a greater decrease in levels of HBV DNA, it does not cause significantly greater biochemical and clinical improvement as compared to placebo in patients with acute hepatitis B.

Magnetic properties and oxygen migration induced resistive switching effect in Y substituted multiferroic bismuth ferrite

Low field magnetic saturation and forming-free resistive switching behavior in non-magnetic modified room temperature multiferroic BiFeO₃ thin film.

Effect of calcination temperature on nanoparticle morphology and its consequence on optical properties of Nd:Y2O3 transparent ceramics

The morphological and size distribution change of Nd:Y₂O₃ nanoparticles due to calcination temperature influences the transparency of ceramic pellets.

Deep image captioning using an ensemble of CNN and LSTM based deep neural networks

The paper is concerned with the problem of Image Caption Generation. The purpose of this paper is to create a deep learning model to generate captions for a given image by decoding the information available in the image. For this purpose, a custom ensemble model was used, which consisted of an Inception model and a 2-layer LSTM model, which were then concatenated and dense layers were added. The CNN part encodes the images and the LSTM part derives insights from the given captions. For comparative study, GRU and Bi-directional LSTM based models are also used for the caption generation to analyze and compare the results. For the training of images, the dataset used is the flickr8k dataset and for word embedding, dataset used is GloVe Embeddings to generate word vectors for each word in the sequence. After vectorization, Images are then fed into the trained model and inferred to create new auto-generated captions. Evaluation of the results was done using Bleu Scores. The Bleu-4 score obtained in the paper is 55.8%, and using LSTM, GRU, and Bi-directional LSTM respectively.

Family vitiligo impact scale: A scale to measure the quality-of-life of family members of patients with vitiligo

BACKGROUND

Vitiligo places a significant psycho-social burden on caregivers and family members.

AIMS

The aim of the study was to develop and preliminarily validate a scale to measure the psychosocial impact of vitiligo on adult family members.

METHODS

Themes that emerged from qualitative interviews and a focus group discussion with family members were used to generate items for a preliminary scale, followed by pre-testing and scale development. The new scale was then tested with two comparator scales and a global question.

RESULTS

A preliminary scale with 32 items was pilot tested on 30 participants. Following this, the scale was condensed to 16 items in 12 domains that were administered to 159 participants. Scale scores ranged from 0 to 48 with a mean of 19.75 ± 12.41. The scale had excellent internal consistency with Cronbach's alpha coefficient of 0.92 (0.70-0.95) and also showed good test-retest reliability at two weeks (r = 0.946). The scale showed criterion, convergent and known group validity.

LIMITATIONS

It was conducted in a large teaching hospital which may have resulted in selection of patients with persistent or progressive disease and more worried family members. Vitiligo is highly stigmatized in our country and the performance of the scale may need to be evaluated in other communities and cultures as well where stigma is less oppressive.

CONCLUSION

Family Vitiligo Impact Scale appears to be an easy-to-complete, reliable and valid instrument to measure the psychosocial impact of vitiligo in family members of patients. It may be useful as an outcome measure in both clinical and research settings.

Service satisfaction in E_Electricity service in Odisha (a state of India) by structural equation modeling

Purpose

– The purpose of this paper is to develop a new model, namely service quality measurement for E_Electricity utility service in Odisha a state of India for the measurement of service quality.

Design/methodology/approach

– Service quality regulation is an important and timely issue in many industries. The question of governance of power sector is an important question before researchers, policy makers and administrators for the obvious reason of its being an important input for socio-economic development of a nation. Power sector involves complex activities like generation, transmission, distribution and final delivery of service to end-users, i.e. consumers. Initially, private sector was the main player in the power sector when the electricity came to be used in India during the pre-independence period. The trajectory of power industry, from its inception to the present, has been complex rather than being simplistic for the reason that the pendulum of industry's ownership and regulation swings between privatization and nationalization. The consumers are main sufferer in this type of situation, and the satisfaction level decreases day by day. So government has changed it's strategies by implementing e-governance to electricity sector for achieving maximum consumer satisfaction. The first step to e-governance is change management within the government, enhancing citizens' access to information and improving government-to-citizen (G2C) interactions. This work seeks to propose a theoretical scale to measure e-government service quality and validate the measured instrument for electricity utility service by structural equation modeling.

Findings

– The results indicate that basically the E_electricity service has a positive and direct influence on dimensions (web site design, fulfillment, privatisation and information).

Research limitations/implications

– Only Odisha state is considered for measuring online service quality in electricity utility service.

Originality/value

– As the electricity utility industry is doing monopoly business in India and all states basically in Odisha due to that the customer dissatisfaction is the most important obstacle. So the policy makers must concentrate on the dimensions and the sub-dimension before regulating new policies.

Deep image captioning using an ensemble of CNN and LSTM based deep neural networks

The paper is concerned with the problem of Image Caption Generation. The purpose of this paper is to create a deep learning model to generate captions for a given image by decoding the information available in the image. For this purpose, a custom ensemble model was used, which consisted of an Inception model and a 2-layer LSTM model, which were then concatenated and dense layers were added. The CNN part encodes the images and the LSTM part derives insights from the given captions. For comparative study, GRU and Bi-directional LSTM based models are also used for the caption generation to analyze and compare the results. For the training of images, the dataset used is the flickr8k dataset and for word embedding, dataset used is GloVe Embeddings to generate word vectors for each word in the sequence. After vectorization, Images are then fed into the trained model and inferred to create new auto-generated captions. Evaluation of the results was done using Bleu Scores. The Bleu-4 score obtained in the paper is 55.8%, and using LSTM, GRU, and Bi-directional LSTM respectively.

Quasistatic response of a shear-thickening foam: Microstructure evolution and infrared thermography

In this work, the authors study the thermo-mechanical response of a dilatant polymeric foam in quasistatic tension and compression, focusing on the links between microstructure, mechanical response, and associated temperature rises in these materials. The authors study these links for a commercially-available shear-thickening foam, named D3O LITE D. Samples were tested under quasi-static conditions for a strain rate of 0.1 s−1 in tension and compression. Micro X-ray computed tomography (XCT) was used to study the evolution of microstructure (pore size and wall thickness) as a function of strain and this was achieved by developing MATLAB-based programs to analyze these microstructural features. The foam specimens were loaded until failure which allowed for the investigation of the elastic, inelastic, and failure regimes. From the XCT images, pore stretching and cell wall tearing are observed in tension, and buckling and pore collapse are observed in compression. These mechanisms are studied in-situ using an infrared thermal camera which record temperature profiles, and temperature measurements are linked back to stress-strain, and temperature-strain responses. For this material, the tensile yield stress was 0.57 ± 0.10 MPa and the elastic modulus was 5.47 ± 0.10 MPa respectively, at a yield strain of 0.10 ± 0.04. At the time of failure, the average temperature of the specimen was found to increase by ∼3.00°C and a local temperature increase of ∼8.00°C was observed in the failure region. In compression, the elastic collapse stress and elastic modulus were found to be 0.130 ± 0.016 MPa and 2.5 ± 0.2 MPa, respectively. The temperature increase in compression at ∼0.83 strain was ∼0.65°C. These results represent some of the first mechanical properties on shear-thickening foams in the literature, and the discoveries on the linkages between the microstructure and the mechanical properties in this study are important for researchers in materials design and modelling.

Cyclic Extrusion of Recycled High Density Polyethylene/Banana Fiber/Fly Ash Cenosphere Biocomposites: Thermal and Mechanical Retention Properties

Disposal of composite materials used vastly in structural applications raises the need to study the reprocessability of these systems for environmental benefits. In this paper, composites of recycled high density polyethylene (R) containing 30 wt.% banana fiber (BF)/7.5 wt.% fly ash cenospheres (FACS) were prepared with 3 wt.% maleic anhydride grafted high density polyethylene (MA-g-HDPE) as a coupling agent using twin screw extrusion followed by injection molding. The effects of thermal cycles (two times extrusion followed by injection molding) were studied on R and its composites. The samples were characterized by using tensile, flexural, izod impact tests, scanning electron microscopy (SEM), differential scanning calorimetry (DSC), thermogravimetry (TGA) and dynamic mechanical analysis (DMA). The tensile and flexural properties were improved by addition of both BF/FACS into the R matrix. The percentage retention in tensile properties of R/BF/FACS(E2) biocomposites (i.e. two times extrusion followed by injection molding) is more than 80 %, while that for flexural properties is more than 90 %. This indicates that R/BF/FACS composites exhibit good retention ability in mechanical properties after subjecting to two times twin-screw extrusion followed by injection molding. DSC results revealed that repeated extrusion improved the crystallization temperature and crystallinity of the composites with a slight reduction in melting temperature. From the TGA results, it was observed that the thermal stability (e.g. Tonset) of R was reduced by the addition of BF/FACS. However, repeated extrusion showed an improvement in thermal stability of the composites.

Diaphragm disease of duodenum following long-term NSAIDs use: endoscopic management

We report our experience with endoscopic management of 3 men (aged 62, 63 and 65 years) with duodenal diaphragm disease following NSAID use for 5-15 years. In the first patient a 24 F through-the-scope balloon dilatation was attempted but failed; he subsequently underwent gastro-jejunostomy. The other two patients subsequently underwent radial incisions of the web with mixed cutting and coagulation current using a standard 5 F sphincterotome.

Spin reorientation transition and coupled spin-lattice dynamics of Sm0.6Dy0.4FeO3

In this work, we have presented a solid-solution of Sm0.6Dy0.4FeO3in the form of nano-particles having spin reorientation transition (SRT) at a temperature interval of 220-260 K. The lattice dynamics of Sm0.6Dy0.4FeO3have investigated by temperature-dependent x-ray diffraction and Raman spectroscopy. A negative thermal expansion at low temperatures has observed, which might be due to the interaction between Sm3+and Fe3+sublattice. Anomalous behavior in lattice parameters, octahedral tilt angle, and bond lengths have observed in the vicinity of SRT, which confirms the existence of magneto-elastic coupling in the system. The strong anomaly has observed in linewidth and phonon frequencies of Raman modes around SRT, which may be related to the spin-phonon coupling in Sm0.6Dy0.4FeO3. The contribution of SRT in lattice change and the presence of spin-phonon coupling can help to understand the correlation between the magnetic and structural properties of orthoferrite.

Machine learning approach for corona virus disease extrapolation: A case study

Supervised/unsupervised machine learning processes are a prevalent method in the field of Data Mining and Big Data. Corona Virus disease assessment using COVID-19 health data has recently exposed the potential application area for these methods. This study classifies significant propensities in a variety of monitored unsupervised machine learning of K-Means Cluster procedures and their function and use for disease performance assessment. In this, we proposed structural risk minimization means that a number of issues affect the classification efficiency that including changing training data as the characteristics of the input space, the natural environment, and the structure of the classification and the learning process. The three problems mentioned above improve the broad perspective of the trajectory cluster data prediction experimental coronavirus to control linear classification capability and to issue clues to each individual. K-Means Clustering is an effective way to calculate the built-in of coronavirus data. It is to separate unknown variables in the database for the disease detection process using a hyperplane. This virus can reduce the proposed programming model for K-means, map data with the help of hyperplane using a distance-based nearest neighbor classification by classifying subgroups of patient records into inputs. The linear regression and logistic regression for coronavirus data can provide valuation, and tracing the disease credentials is trial.