Experimental Research Articles

Study the role of xanthine oxidase inhibitor, allopurinol in experimentally induced steatohepatitis

Nonalcoholic steatohepatitis (NASH) is a chronic liver disease which can lead to cirrhosis and hepatic failure. It is observed in patients with both type 1 and type 2 diabetes mellitus (T1DM, T2DM). Allopurinol, a xanthine oxidase inhibitor that is widely used in clinical practice, has been demonstrated to reduce hepatic oxidative stress and attenuate acute liver injury which may be through activation of antioxidant pathways. This work aimed at investigating the role of Allopurinol (ALP) in attenuation of type 1 diabetes associated with Nonalcoholic fatty liver disease (NAFLD) and exploring the possible molecular mechanism by which ALP attenuates NASH in type 1 diabetes. Forty-five adult male albino rats were included in the study. They were randomly allocated to 3 equal groups: group I (control), Group II (diabetic animals), Group III (diabetic animals with allopurinol treatment). At the end of this experimental study measurements of serum glucose, ALT, AST, cholesterol, triglyceride, serum levels of uric acid, malondialdehyde (MDA), serum superoxide dismutase (SOD) were done. Histological analysis of liver tissue to diagnose steatohepatitis and gene expression of nuclear factor-erythroid-2- related factor-2 (Nrf2) & TNFα were done. Diabetic rates treated with allopurinol (group III) showed decrease in serum levels of ALT, AST, cholesterol, uric acid and MDA compared to diabetic rat group. As regards TNF, gene expression was significantly decreased in group III compared to group II. This study also demonstrated that Nrf2α was significantly increased in group III compared to group II. Allopurinol also, ameliorated the histopathological changes observed in group II. In conclusion, allopurinol treatment increases the Nrf2 gene expression in the liver of STZ-induced diabetic rats and ameliorates steatohepatitis through modulation of TNF-α gene expression. Allopurinol could be a candidate for prevention or treatment of NAFLD.

The effect of singing or playing melodica on disease symptoms, self-efficacy level and exercise capacity in chronic obstructive pulmonary patients: a randomized controlled study.

The aim of this randomized controlled experimental study is to determine the effect of singing or playing melodica activity applied to COPD patients on their symptoms, self-efficacy levels, and exercise capacities. The study was carried out in the chest diseases polyclinics of a hospital in Turkey between September 2020 and August 2021 with 30 patients: 15 in the control group and 15 in the song/melodica group. The data were collected with a patient information form and the scales examining disease symptoms, self-efficacy, and exercise capacity. The physical symptom, anxiety, Borg dyspnea, and fatigue scores of the disease in the post-test were significantly lower in the patients in the song/melodica group than those in the control group. In the post-test, the self-efficacy level, walking distance values were found to be significantly higher in the song/melodica group than in the control group. It is concluded that singing or playing melodica was an effective method in reducing the symptoms of the disease and enhancing the level of self-efficacy and exercise capacity in COPD patients.

Regulation of Apoptosis, Autophagy, and Metastasis by Luteolin in Human Bladder Cancer EJ138 Cells: An Experimental Study

Regulation of Apoptosis, Autophagy, and Metastasis by Luteolin in Human Bladder Cancer EJ138 Cells: An Experimental Study

Examining Dimensionality and Item-Quality of the Eating Disorder Examination Questionnaire in Individuals With Eating Disorders Using Item Response Theory Analysis.

The Eating Disorder Examination Questionnaire (EDE-Q) is a widely-used measure of eating-disorder symptoms. However, inconsistent replication of the subscale structure raises concern about validity. To provide a rigorous test of the EDE-Q's dimensionality and item-quality, we applied modern and classical test theory approaches to data obtained from a large, transdiagnostic sample of people with clinical eating disorders. We analyzed data from 1197 individuals (Mage = 27.9 years, SD = 10.08, 95% female) with various eating disorders, who had been assessed for treatment at a specialized program. Exploratory analyses (including Parallel Analyses), Confirmatory Factor Analyses (CFA) and graded-response Item Response Theory (IRT) analyses, were conducted with Mplus. Factor analyses showed inappropriate fit to the original EDE-Q subscales, as well as for alternative 1,2,3, and 4-factor solutions. Parallel analyses suggested a one-dimensional structure as best fit. IRT analyses showed substantial variability in EDE-Q-item quality and indicated that five items (fear of weight gain, feeling fat, desire to lose weight, importance of weight, importance of shape) were most pertinent to determining severity. The construct validity of the five EDE-Q items was confirmed by a CFA, showing excellent fit. Our results suggest that EDE-Q scores are best interpreted as spanning a one-factor continuum. IRT results suggest that some items are more pertinent than others for determining eating-disorder severity. Results could be useful for establishing short EDE-Q versions, such as a five-item version, which, in turn, would be helpful for measurement-based clinical practice and for data-collection in epidemiological and experimental studies.

Experimental Study and Prospects of Valorization of Jatropha Curcas Seeds into Biodiesel

Experimental Study and Prospects of Valorization of Jatropha Curcas Seeds into Biodiesel

A Comparative Effect of Different Herbal Products on Lipid Metabolism and Hepatic Tissue: An Experimental Study on a Rat Model

A Comparative Effect of Different Herbal Products on Lipid Metabolism and Hepatic Tissue: An Experimental Study on a Rat Model

An Experimental Study on Concrete Strength Optimization with Surkhi as

An Experimental Study on Concrete Strength Optimization with Surkhi as

Investigating heart rate responses of children to active travel: a mixed effects modeling analysis.

The active travel concept has gained recognition as an effective method to enhance physical activity in children's everyday mobility. Yet, there is a notable gap of quantitative evidence on its direct impacts, especially regarding physiological outcomes that can concretely demonstrate its influences on physical intensity and overall health of children. This study investigates the effects of active travel on children, focusing on trip-level physiological responses, specifically heart rate. Through a mixed survey design, we obtained a repeated-measures dataset from a cohort of 73 children (average age 13) across three Austrian secondary schools, each observed for 7 days. By applying mixed effects modelling approach, both fixed and random effects to children's heart rates were explored. According to results from developed mixed linear models (maximum R2 = 0.22), it revealed a significant positive association between active travel mode(s) engagement and heightened physiological responses (Low, Medium and High active level of trip compare to None: β = 1.81, 5.01 and 8.34, t-value = 4.70, 10.29 and 21.34), indicating that increased usage of active travel modes led to elevated heart rate values. Additionally, subjective perceptions also played an important role in influencing children's physiological responses. Feelings of exhausted, anxiety, and stress contributing to a higher heart rate (β = 14.79, 16.69 and 1.99, t-value = 20.42, 5.67 and 3.0), while unhappiness was associated with a lower heart rate (β = -5.96, t-value = -14.62). Effects of social interactions were also found during the trip. The presence of adults was associated with a calming effect (β = -1.92, t-value = -5.72), whereas solo travel resulted in lower heart rate compared to travel with peers (β = -1.95, t-value = -6.59). Our article innovatively employs mixed survey methods and heart rate data to quantitatively demonstrate the positive association between engagement in active travel modes and heightened physiological responses in children, elucidating the intricate interplay of subjective perceptions and social interactions during trips, thus offering valuable insights into the health implications of active travel at a trip level. Future research will keep on leveraging survey and experimental methods.

Concepts of Implied Interventions for Treating Depression: A Comprehensive Review

Hence, answering the question how a person is able to secure and maintain lifelong good mood would facilitate in deceiphering depression; a common and etiologically complex multi factorial mental health disorder which affects over 264 m individuals worldwide with tremendous effect on morbidity and economic burden. Here, our study results from more than 100 experimental and observational studies with respect to effective treatments for depression across three main groups such as: Pharmacological interventions, Psychotherapeutic approaches, and Emerging digital health solutions have been effective as implied interventions to tret Depression. While antidepressants, particularly selective serotonergic reuptake inhibitors (SSRIs), continued as the bedrock of therapy; Novel therapies with Ketamine have shown potential for treatment failure cases. Large-scale studies of Psychotherapeutic methods, such as Cognitive Behavioural Therapy (CBT) and Interpersonal Therapy (IPT), show high effectiveness rates in most populations. Digital health interventions, such as e-therapy, were potential alternatives and particularly suitable for patients in rural areas. Integrating these modalities will allow clinicians to offer more individualised and tailored care. This review provided an overview of the mechanisms of each intervention for depression as a clinical aid to inform decisions by clinicians as well as policy makers seeking to improve treatment pathways for individuals with Mood disorders and Depression.

Ultrafast bounce of particle-laden droplets.

The rebound of liquid droplets on solid surfaces exhibits behavior reminiscent of elastic spheres, albeit with distinct contact dynamics. While the rapid detachment of droplets from surfaces holds significant relevance for various applications, previous endeavors relying on engineered surfaces can only reduce the contact time to several milliseconds, primarily due to capillary effects dominating droplet bounce. Here, we present ultrafast rebound by designing heterogeneous core-shell droplets encapsulating a particle (DEP), which achieves an unprecedentedly short contact time of 0.3 ms and 0.05 ms with polydimethylsiloxane and glass particles, respectively. This remarkable contact-time reduction is universally applicable to diverse systems, including both water and oil droplets, elastic and rigid particles, super-repellent and superlyophilic surfaces, and is effective across a wide range of impact velocities. Beyond exhibiting liquid-like dynamics, DEP manifests solid-like behavior owing to asynchronized motions between the particle and the droplet, which effectively breaks down the dominance of capillarity. With systematic experimental and analytical studies, we delineate contact times in three bouncing regimes and identify critical conditions governing regime transitions. DEP amalgamates the bouncing dynamics of both solids and liquids, offering a robust and versatile strategy for tailoring contact time to suit diverse applications involving solid-liquid composite systems.

Negative stiffness device for seismic protection of MDOF yielding fixed base structures: Experimental and analytical study

Negative stiffness device for seismic protection of MDOF yielding fixed base structures: Experimental and analytical study

Numerical and experimental study of underwash effect and its role in blast-induced traumatic brain injury

Numerical and experimental study of underwash effect and its role in blast-induced traumatic brain injury

Research on information leakage in time series prediction based on empirical mode decomposition.

Time series analysis predicts the future based on existing historical data and has a wide range of applications in finance, economics, meteorology, biology, engineering, and other fields. Although the combination of decomposition techniques and machine learning algorithms can effectively solve the problem of predicting nonstationary sequences, this kind of decomposition-integration-prediction strategy of the prediction method has serious defects. After the decomposition of the division of the training set and the test set, the information of the test set in the process of decomposition of the information leakage ultimately shows a high accuracy of the prediction of the illusionary. This paper proposes three improvement strategies for this type of "information leakage" problem: sliding window decomposition (SW-EMD), single training and multiple decomposition (STMP-EMD), and multiple training and multiple decomposition (MTMP-EMD). They are combined with a bidirectional multiscale temporal convolutional network (MSBTCN), bidirectional long- and short-term memory network (BiLSTM), and attention mechanism (DMAttention), which introduces a dependency matrix based on cosine similarity to be applied to water quality prediction. The experimental results show that the model achieves good performance in the prediction of three water quality indicators (pH, DO and KMnO4), and the accuracies of the three models proposed in this paper are improved by 1.958% and 0.853% in terms of the RMSE and MAPE, respectively, compared with those of the mainstream LSTM models. The key contributions of this study include the following: (1) three methods are proposed to improve the class EMD decomposition, which can effectively solve the problem of "information leakage" that exists in the current models via class EMD decomposition; (2) the CEEMDAN-MSBTCN-BiLSTM-DMAttention model structure is innovated by combining improved class EMD decomposition methods; and (3) the three improved decomposition methods proposed in this paper can effectively solve the problem of "information leakage" and optimize the prediction model at the same time. This study provides an effective experimental method for water quality prediction and can effectively address the problem of "overfitting" models via class EMD decompositions during model training and testing.

Experimental Study on the Chloride Ion Concentration of Cement Pastes Prepared with Limestone Powder

Experimental Study on the Chloride Ion Concentration of Cement Pastes Prepared with Limestone Powder

Experimental study on the shear behaviour of high-strength lightweight concrete beams incorporating graphene oxide.

Graphene oxide (GO) has achieved significant progress in the material behaviour of cement-based materials. However, research on its structural behaviour in high-strength lightweight concrete (HSLWC) structures is limited, which restricts its engineering applications. This study focused on investigating the effects of low contents of GO on the shear behaviour of HSLWC beams. A total of four HSLWC beams with GO contents of 0, 0.01, 0.03 and 0.05% (by weight of cement) were designed to observe failure modes, load‒deformation curves, shear capacities, crack behaviour and load‒strain curves under four-point loading by a 300 kN servo loading device. The results revealed that all the beams exhibited shear compression failure. GO improved the shear capacity of the HSLWC beams, and this strengthening effect increased with increase in GO content. When the content of GO was 0.05%, the ultimate load of the beam reached a maximum, which was 39.2% greater than that of the control beam. GO can endow the HSLWC beams with a certain degree of ductility. In addition, a modified JGJ 12-2006 model was proposed to predict the shear capacity of HSLWC beams containing different GO contents on the basis of a comparison of typical models. This study can provide exploratory engineering practice for evaluating and designing GO-reinforced HSLWC structures.

Optimising bearing degradation prediction: a bayesian vector autoregressive-assisted DeepAR model approach

Abstract Accurate prediction of the remaining useful life of bearings is crucial for the maintenance of rotating machinery. Current research often involves extracting multidimensional features from vibration signals to construct health degradation indicator, and health indicator is directly used for degradation prediction. However, constructed health indicator exhibit complex nonlinearity and fail to utilize the correlations with the original multidimensional features. To address this limitation, we propose an integrated prediction framework, known as the bayesian vector autoregression-assisted DeepAR method. The proposed two step prediction framework combines the advantages of covariance prediction and temporal correlation prediction, and realizes the full utilization of multidimensional features correlation information. Additionally, the proposed approach utilizes the intrinsic properties of statistical models to constrain improve the prediction results of deep learning methods, achieving nonlinear modeling of the degradation indicator while maintaining the stability of the prediction results. This process of prediction, re-estimation and adaptive adjustment achieves a balance in model performance. Finally, experimental studies using the PRONOSTIA and XJTU-SY datasets validate the effectiveness of the proposed method in improving the prediction accuracy of bearing health degradation indicators and enhancing the stability of multistep prediction in deep model.

Experimental Study on Tensile Bearing Capacity of Single and Group Helical Anchors

Experimental Study on Tensile Bearing Capacity of Single and Group Helical Anchors

Experimental Study on Flow and Thermal Transport in Additively Manufactured Lattices Based on Cube-Shaped Unit Cell

Abstract This paper presents the convective heat transfer coefficient of cubic lattices under both buoyancy-induced and forced convection. Additionally, it examines the effective thermal conductivity, permeability, and inertial coefficient of a cubic unit cell of porosity ∼0.87. The test specimens were additively manufactured using stainless steel 420 (with 40% bronze infiltration) using the binder jetting technique. In the buoyancy-driven convection experiments, three different aspect ratios (width/height) varying from 0.5 to 2 were tested across three different heating orientations, viz., bottom wall (0 deg), side wall (90 deg), and top wall (180 deg). The lattice with the lowest aspect ratio had the highest convective heat transfer coefficient in all three heating orientations. The forced convection heat transfer coefficient was determined for an additively manufactured part comprising 10 × 10 cubic unit cell array in the plane perpendicular to the flow and 20 unit cells in the streamwise direction. Additionally, the flow characteristics of the cubic lattice were characterized through permeability (K) and inertial coefficient (Cf), determined by conducting separate pressure drop experiments over a wide range of flow velocities. The thermal hydraulic performance (THP) of the cubic lattice was assessed by combining the periodic regime convective heat transfer coefficient with the pressure drop data obtained from the experimentally determined values of K and Cf. The comprehensive characterization of flow and thermal transport, including K and Cf, along with hsf, keff, presented in this paper, provides a robust foundation for their application in volume-averaged computations for detailed parametric study.

How does contact valence and group salience affect outgroup attitudes in asynchronous computer mediated contact? Experiments on intergroup contact via social media posts

ABSTRACT The prejudice reduction potential of face-to-face intergroup contact is widely established, but we know much less about computer-mediated intergroup contact (online contact) specifically via social media where interactions are less controlled and mostly asynchronous. Additionally, much of the work on online contact has focused on positive, controlled contact, neglecting the effect of negative contact. We examined the effects of mediated contact via online posts with differing valence (positive, negative, and neutral) in three experimental studies, in an imaginary scenario (Study 1: N = 120) and a real intergroup scenario with South and North Indians (Study 2: N = 296, Study 3: N = 336). Main effects of One way and factorial ANOVA showed that contact valence significantly affected outgroup attitudes in Study 1 & 2 but was not replicated in Study 3, where quality and quantity of past contact and status differences emerged as significant predictors of attitudes. Multiple mediation analysis revealed that intergroup anxiety and quality of contact explained changes in attitudes, which was less affected by valence and more by regional identity and history of contact. Findings are discussed in light of the possibilities and limitations of asynchronous mediated contact on social media.

A dynamic strain prediction method for malfunction of sensors in buildings subjected to seismic loads using CWT and CNN.

Structural health monitoring (SHM) is a technique used to evaluate the safety of a structure based on the structural response. In the SHM, sensors may fail to measure the structural response, or data loss can occur during the data transmission and reception. In this regard, this paper proposes a method of predicting the strain response when a sensor installed in a structural member suffers a malfunction. To this end, a convolutional neural network (CNN) is introduced to predict the strain response. Time-history strain data for seismic loads measured in adjacent structural members are set as the input layer of the CNN, and continuous wavelet transform (CWT) results are additionally set in the input layer of the CNN to reflect the dynamic structural behavior during earthquakes in the CNN predictions. Time-history strain data for a member, the target of prediction, are set as the output layer of the CNN. Then, the trained CNN uses the time-history strain data of adjacent members with no sensor failure and the CWT result of the data to predict the strain response of a structural member with sensor failure. A numerical study on the seismic load of a single-span three-story steel moment frame and an experimental study on a single-span three-story reinforced concrete frame were carried out to verify the validity of the proposed method. This study presents a method of applying CWT data to the input layer of the CNN and discusses the effectiveness of CWT data for predicting the nonlinear response of a structural member with damaged sensors.