Combined effects of vitamin D3 and dioxopiperidinamide derivative on lipid homeostasis, inflammatory pathways, and redox imbalance in non-alcoholic fatty liver disease in vivo zebrafish model.

Liver damage and metabolic dysfunctions, the defining features of non-alcoholic fatty liver disease (NAFLD), are marked by inflammation, oxidative stress, and excessive hepatic fat accumulation. The current therapeutic approaches for NAFLD are limited, necessitating exploring novel treatment strategies. Dioxopiperidinamide derivatives, particularly DOPA-33, have shown effective anti-inflammatory and antioxidant properties, potentially offering therapeutic benefits against NAFLD. This study investigated the combined potential of vitamin D3 (Vit D3) and DOPA-33 in treating NAFLD. The network pharmacology analysis identified key NAFLD targets modulated by Vit D3 and DOPA-33, emphasizing their potential mechanisms of action. In NAFLD-induced zebrafish models, Vit D3 and DOPA-33 significantly reduced hepatic lipid accumulation, oxidative stress, and apoptosis, demonstrating superior efficacy over individual treatments. The treatment also lowered reactive oxygen species (ROS) levels, decreased liver damage, and enhanced antioxidant defense mechanisms. Moreover, behavioral analyses showed improved locomotion and reduced weight gain in treated zebrafish. Biochemical analyses revealed lower triglycerides (TG) and glucose levels with improved oxidative markers. Furthermore, histological analyses indicated reduced hepatic steatosis and inflammation, with decreased expression of lipogenesis-related genes and inflammatory mediators. Finally, high-performance liquid chromatography (HPLC) confirmed a significant reduction in hepatic cholesterol levels, indicating the effectiveness of the combination therapy in addressing key NAFLD-related dyslipidemias. These findings suggest that Vit D3 + DOPA-33 targets pathways involved in lipid metabolism, inflammation, and oxidative stress by offering a promising therapeutic approach for NAFLD.

Cadmium and ketoprofen accumulation influences aquatic ecosystem demonstrated using in-vivo zebrafish model.

The growing concern about pollution and toxicity in aquatic as well as terrestrial organisms is predominantly caused due to waterborne exposure and poses a risk to environmental systems and human health. This study addresses the co-toxic effects of cadmium (Cd) and ketoprofen (KPF), representing heavy metal and pharmaceutical discharge pollutants, respectively, in aquatic ecosystems. A 96-h acute toxicity assessment was conducted using zebrafish embryos. The results indicated that high dosages of KPF (10, 15, and 100 µg/mL) and Cd (10 and 15 µg/mL) reduced survivability and caused concentration-dependent deformities such as scoliosis and yolk sac edema. These findings highlight the potential defects in development and metabolism, as evidenced by hemolysis tests demonstrating dose-dependent effects on blood cell integrity. Furthermore, this study employs adult zebrafish for a 42-day chronic exposure to Cd and KPF (10 and 100 µg/L) alone or combined (10 + 10 and 100 + 100 µg/L) to assess organ-specific Cd and KPF accumulation in tissue samples. Organ-specific accumulation patterns underscore complex interactions impacting respiratory, metabolic, and detoxification functions. Prolonged exposure induces reactive oxygen species formation, compromising antioxidant defense systems. Histological examinations reveal structural changes in gills, gastrointestinal, kidney, and liver tissues, suggesting impairments in respiratory, osmoregulatory, nutritional, and immune functions. This study emphasizes the importance of conducting extensive research on co-toxic effects to assist with environmental risk assessments and safeguard human health and aquatic ecosystems.

Textile azo dye, Sudan Black B, inducing hepatotoxicity demonstrated in in vivo zebrafish larval model.

Environmental pollution, particularly from textile industry effluents, raises concerns globally. The aim of this study is to investigate the hepatotoxicity of Sudan Black B (SBB), a commonly used textile azo dye, on embryonic zebrafish. SBB exposure led to concentration-dependent mortality, reaching 100% at 0.8 mM, accompanied by growth retardation and diverse malformations in zebrafish. Biochemical marker analysis indicated adaptive responses to SBB, including increased SOD, CAT, NO, and LDH, alongside decreased GSH levels. Liver morphology analysis unveiled significant alterations, impacting metabolism and detoxification. Also, glucose level was declined and lipid level elevated in SBB-exposed in vivo zebrafish. Inflammatory gene expressions (TNF-α, IL-10, and INOS) showcased a complex regulatory interplay, suggesting an organismal attempt to counteract pro-inflammatory states during SBB exposure. The increased apoptosis revealed a robust hepatic cellular response due to SBB, aligning with observed liver tissue damage and inflammatory events. This multidimensional study highlights the intricate web of responses due to SBB exposure, which is emphasizing the need for comprehensive understanding and targeted mitigation strategies. The findings bear the implications for both aquatic ecosystems and potentially parallels to human health, underscoring the imperative for sustained research in this critical domain.

Hues of risk: investigating genotoxicity and environmental impacts of azo textile dyes.

The textile industry, with its extensive use of dyes and chemicals, stands out as a significant source of water pollution. Exposure to certain textile dyes, such as azo dyes and their breakdown products like aromatic amines, has been associated with health concerns like skin sensitization, allergic reactions, and even cancer in humans. Annually, the worldwide production of synthetic dyes approximates 7 × 107 tons, of which the textile industry accounts for over 10,000 tons. Inefficient dyeing procedures result in the discharge of 15-50% of azo dyes, which do not adequately bind to fibers, into wastewater. This review delves into the genotoxic impact of azo dyes, prevalent in the textile industry, on aquatic ecosystems and human health. Examining different families of textile dye which contain azo group in their structure such as Sudan I and Sudan III Sudan IV, Basic Red 51, Basic Violet 14, Disperse Yellow 7, Congo Red, Acid Red 26, and Acid Blue 113 reveals their carcinogenic potential, which may affect both industrial workers and aquatic life. Genotoxic and carcinogenic characteristics, chromosomal abnormalities, induced physiological and neurobehavioral changes, and disruptions to spermatogenesis are evident, underscoring the harmful effects of these dyes. The review calls for comprehensive investigations into the toxic profile of azo dyes, providing essential insights to safeguard the aquatic ecosystem and human well-being. The importance of effective effluent treatment systems is underscored to mitigate adverse impacts on agricultural lands, water resources, and the environment, particularly in regions heavily reliant on wastewater irrigation for food production.

Is Laccase derived from Pleurotus ostreatus effective in microplastic degradation? A critical review of current progress, challenges, and future prospects.

Exploration of Pleurotus ostreatus as a biological agent in the degradation of persistent plastics like polyethylene, polystyrene, polyvinyl chloride, and polyethylene terephthalate, revealing a promising avenue toward mitigating the environmental impacts of plastic pollution. Leveraging the intrinsic enzymatic capabilities of this fungus, mainly its production of laccase, presents a sustainable and eco-friendly approach to breaking down complex polymer chains into less harmful constituents. This review focused on enhancements in the strain's efficiency through genetic engineering, optimized culture conditions, and enzyme immobilization to underscore the potential for scalability and practical application of this bioremediation process. The utilization of laccase from P. ostreatus in plastic waste management demonstrates a vital step forward in pursuing sustainable environmental solutions. By using the potential of fungal bioremediation, researchers can move closer to a future in which the adverse effects of plastic pollution are significantly mitigated, benefiting the health of our planet and future generations.

PI-Net: A Deep Learning Approach to Extract Topological Persistence Images.

Topological features such as persistence diagrams and their functional approximations like persistence images (PIs) have been showing substantial promise for machine learning and computer vision applications. This is greatly attributed to the robustness topological representations provide against different types of physical nuisance variables seen in real-world data, such as view-point, illumination, and more. However, key bottlenecks to their large scale adoption are computational expenditure and difficulty incorporating them in a differentiable architecture. We take an important step in this paper to mitigate these bottlenecks by proposing a novel one-step approach to generate PIs directly from the input data. We design two separate convolutional neural network architectures, one designed to take in multi-variate time series signals as input and another that accepts multi-channel images as input. We call these networks Signal PI-Net and Image PI-Net respectively. To the best of our knowledge, we are the first to propose the use of deep learning for computing topological features directly from data. We explore the use of the proposed PI-Net architectures on two applications: human activity recognition using tri-axial accelerometer sensor data and image classification. We demonstrate the ease of fusion of PIs in supervised deep learning architectures and speed up of several orders of magnitude for extracting PIs from data. Our code is available at https://github.com/anirudhsom/PI-Net.

Simultaneous Parameter Learning and Bi-clustering for Multi-Response Models.

We consider multi-response and multi-task regression models, where the parameter matrix to be estimated is expected to have an unknown grouping structure. The groupings can be along tasks, or features, or both, the last one indicating a bi-cluster or "checkerboard" structure. Discovering this grouping structure along with parameter inference makes sense in several applications, such as multi-response Genome-Wide Association Studies (GWAS). By inferring this additional structure we can obtain valuable information on the underlying data mechanisms (e.g., relationships among genotypes and phenotypes in GWAS). In this paper, we propose two formulations to simultaneously learn the parameter matrix and its group structures, based on convex regularization penalties. We present optimization approaches to solve the resulting problems and provide numerical convergence guarantees. Extensive experiments demonstrate much better clustering quality compared to other methods, and our approaches are also validated on real datasets concerning phenotypes and genotypes of plant varieties.

Learning Stable Multilevel Dictionaries for Sparse Representations.

Sparse representations using learned dictionaries are being increasingly used with success in several data processing and machine learning applications. The increasing need for learning sparse models in large-scale applications motivates the development of efficient, robust, and provably good dictionary learning algorithms. Algorithmic stability and generalizability are desirable characteristics for dictionary learning algorithms that aim to build global dictionaries, which can efficiently model any test data similar to the training samples. In this paper, we propose an algorithm to learn dictionaries for sparse representations from large scale data, and prove that the proposed learning algorithm is stable and generalizable asymptotically. The algorithm employs a 1-D subspace clustering procedure, the K-hyperline clustering, to learn a hierarchical dictionary with multiple levels. We also propose an information-theoretic scheme to estimate the number of atoms needed in each level of learning and develop an ensemble approach to learn robust dictionaries. Using the proposed dictionaries, the sparse code for novel test data can be computed using a low-complexity pursuit procedure. We demonstrate the stability and generalization characteristics of the proposed algorithm using simulations. We also evaluate the utility of the multilevel dictionaries in compressed recovery and subspace learning applications.

Multiple kernel sparse representations for supervised and unsupervised learning.

In complex visual recognition tasks, it is typical to adopt multiple descriptors, which describe different aspects of the images, for obtaining an improved recognition performance. Descriptors that have diverse forms can be fused into a unified feature space in a principled manner using kernel methods. Sparse models that generalize well to the test data can be learned in the unified kernel space, and appropriate constraints can be incorporated for application in supervised and unsupervised learning. In this paper, we propose to perform sparse coding and dictionary learning in the multiple kernel space, where the weights of the ensemble kernel are tuned based on graph-embedding principles such that class discrimination is maximized. In our proposed algorithm, dictionaries are inferred using multiple levels of 1D subspace clustering in the kernel space, and the sparse codes are obtained using a simple levelwise pursuit scheme. Empirical results for object recognition and image clustering show that our algorithm outperforms existing sparse coding based approaches, and compares favorably to other state-of-the-art methods.