Delineating the heterogeneity of senescence-induced-functional alterations in hepatocytes.

BACKGROUND AND AIM: Cellular senescence of hepatocytes involves permanent cell cycle arrest, disrupted cellular bioenergetics, resistance to cell death, and the release of pro-inflammatory cytokines. This 'zombie-like' state perpetuates harmful effects on tissues and holds potential implications for liver disease progression. Remarkably, senescence exhibits heterogeneity, stemming from two crucial factors: the inducing stressor and the cell type. As such, our present study endeavors to characterize stressor-specific changes in senescence phenotype, its related molecular patterns, and cellular bioenergetics in primary mouse hepatocytes (PMH) and hepatocyte-derived liver organoids (HepOrgs). METHODS: PMH, isolated by collagenase-perfused mouse liver (C57B6/J; 18-23 weeks), were cultured overnight in William's E-medium supplemented with 2% FBS, L-glutamine, and hepatocyte growth supplements. HepOrgs were developed by culturing cells in a 3D matrix for two weeks. The senescence was induced by DNA damage (doxorubicin, cisplatin, and etoposide), oxidative stress (H2O2, and ethanol), and telomere inhibition (BIBR-1532), p53 activation (nutlin-3a), DNA methyl transferase inhibition (5-azacitidine), and metabolism inhibitors (galactosamine and hydroxyurea). SA-ß galactosidase activity, immunofluorescence, immunoblotting, and senescence-associated secretory phenotype (SASP), and cellular bioenergetics were used to assess the senescence phenotype. RESULTS: Each senescence inducer triggers a unique combination of senescence markers in hepatocytes. All senescence inducers, except hydroxyurea and ethanol, increased SA-ß galactosidase activity, the most commonly used marker for cellular senescence. Among the SASP factors, CCL2 and IL-10 were consistently upregulated, while Plasminogen activator inhibitor-1 exhibited global downregulation across all modes of senescence. Notably, DNA damage response was activated by DNA damage inducers. Cell cycle markers were most significantly reduced by doxorubicin, cisplatin, and galactosamine. Additionally, DNA damage-induced senescence shifted cellular bioenergetics capacity from glycolysis to oxidative phosphorylation. In HepOrgs exposed to senescence inducers, there was a notable increase in γH2A.X, p53, and p21 levels. Interestingly, while showing a similar trend, SASP gene expression in HepOrgs was significantly higher compared to PMH, demonstrating a several-fold increase. CONCLUSION: In our study, we demonstrated that each senescence inducer activates a unique combination of senescence markers in PMH. Doxorubicin demonstrated the highest efficacy in inducing senescence, followed by cisplatin and H2O2, with no impact on apoptosis. Each inducer prompted DNA damage response and mitochondrial dysfunction, independent of MAPK/AKT.

Transcriptomics-driven metabolic pathway analysis reveals similar alterations in lipid metabolism in mouse MASH model and human.

BACKGROUND: Metabolic dysfunction-associated steatotic liver disease (MASLD) is a prevalent chronic liver disease worldwide, and can rapidly progress to metabolic dysfunction-associated steatohepatitis (MASH). Accurate preclinical models and methodologies are needed to understand underlying metabolic mechanisms and develop treatment strategies. Through meta-analysis of currently proposed mouse models, we hypothesized that a diet- and chemical-induced MASH model closely resembles the observed lipid metabolism alterations in humans. METHODS: We developed transcriptomics-driven metabolic pathway analysis (TDMPA), a method to aid in the evaluation of metabolic resemblance. TDMPA uses genome-scale metabolic models to calculate enzymatic reaction perturbations from gene expression data. We performed TDMPA to score and compare metabolic pathway alterations in MASH mouse models to human MASH signatures. We used an already-established WD+CCl4-induced MASH model and performed functional assays and lipidomics to confirm TDMPA findings. RESULTS: Both human MASH and mouse models exhibit numerous altered metabolic pathways, including triglyceride biosynthesis, fatty acid beta-oxidation, bile acid biosynthesis, cholesterol metabolism, and oxidative phosphorylation. We confirm a significant reduction in mitochondrial functions and bioenergetics, as well as in acylcarnitines for the mouse model. We identify a wide range of lipid species within the most perturbed pathways predicted by TDMPA. Triglycerides, phospholipids, and bile acids are increased significantly in mouse MASH liver, confirming our initial observations. CONCLUSIONS: We introduce TDMPA, a methodology for evaluating metabolic pathway alterations in metabolic disorders. By comparing metabolic signatures that typify human MASH, we show a good metabolic resemblance of the WD+CCl4 mouse model. Our presented approach provides a valuable tool for defining metabolic space to aid experimental design for assessing metabolism.

Steatotic liver disease, in which fat accumulates in the liver, is one of the most prevalent liver diseases worldwide and it is important to develop relevant animal models to help us understand its mechanisms. We aimed to assess the suitability of animal models for studying steatotic liver disease in humans. We developed an approach that evaluates how genes affect the metabolism or the chemical reactions and processes that occur in the body. We used it to compare a mouse model of the disease with human observations. Our results showed that there are significant changes in fat and energy metabolism in the mouse model. These observations match with changes observed in humans, suggesting it is a good model for studying human disease. Our findings could advance our understanding of the disease as well as help define strategies for its treatment.

Epidemiology and zoonotic potential of Livestock-associated Staphylococcus aureus isolated at Tamil Nadu, India.

BACKGROUND: Staphylococcus aureus is part of normal flora and also an opportunistic pathogen responsible for a wide range of infections in both humans and animals. Livestock-associated S. aureus (LA-SA) has gained importance in recent years due to its increased prevalence in recent years, becoming a worry in public health view. This study aimed to study the epidemiology of LA-SA strains in Madurai district, Tamil Nadu, India. METHODS: A total of 255 samples were collected from bovine and other small ruminants like goats and sheep nares (n = 129 and n = 126 respectively). Nasal swab samples were collected from study animals with sterile sample collecting cotton swabs (Hi-Media, Mumbai). Samples were transported to the lab in Cary-Blair Transport media for further analysis. The samples were tested for S. aureus using antibiotic selection and PCR-based assays. The pathogenicity of the bacteria was assessed using chicken embryo models and liver cross-sections were used for histopathology studies. RESULTS: The prevalence rate in bovine-associated samples was 42.63% but relatively low in the case of small ruminants associated samples with 28.57% only. The overall prevalence of S. aureus is found to 35.6% and MRSA 10.98% among the study samples. The antibiogram results that LA-SA isolates were susceptible to aminoglycosides and tetracyclines but resistant to ß-lactam drugs. The biofilm formation results showed that the LA-SA isolates are weak to high-capacity biofilm formers. The enterotoxigenic patterns revealed that most of the isolated strains are enterotoxigenic and possess classical enterotoxins. The survival analysis of chicken embryos suggested that the Bovine-associated strains were moderately pathogenic. CONCLUSION: The study concluded that economically important livestock animals can act as reservoirs for multi-drug resistant and pathogenic which in-turn is a concern for public health as well as livestock health.

Comprehensive insights into the multifaceted roles of the transient receptor potential vanilloid 1 channel in the digestive system.

The transient receptor potential vanilloid (TRPV) channel, a family of calcium transporters comprising six distinct members (TRPV1-6), takes on a paramount role in maintaining intracellular Ca2+ homeostasis in mammalian cells. Notably, TRPV1, among its counterparts, has emerged as the subject of extensive scrutiny, owing to its pervasive presence in diverse cellular, tissue, and organ settings. This ubiquitous distribution underscores its fundamental involvement in the genesis of pain, making it a central focus in pain-related research. However, recent investigations have unveiled that TRPV1's functional significance transcends the realm of pain modulation, extending its influence to encompass a wide spectrum of physiological and pathological processes. The ambit of TRPV1's influence encompasses not only pain responses but also embraces the intricate domains of nervous system disorders, cancer metastasis, as well as afflictions pertaining to the skin and heart. Moreover, compelling evidence now demonstrates that TRPV1 also wields substantial sway in the domain of digestive diseases, further highlighting its versatility and far-reaching impact on human health. Therefore, this comprehensive review endeavors to delve into the multifaceted roles played by TRPV1 in the various organs constituting the digestive system.

Formation and characterization of leaf waste into organic compost.

In solid waste management, pollution-free disposal of leaf waste in urban areas is still not standardized and adopted. According to the World Bank report, 57% of wastes generated in South East Asia are consisted of food and green waste, which can be recycled into valuable bio-compost. The present study shows a method of leaf litter waste management by composting it using essential microbe (EM) method. Different parameters, such as pH, electrical conductivity, macronutrients, micronutrients, and potentially toxic elements (PTE) were measured at zero to 50 days of composting using appropriate methods. The microbial composting was shown to mature within 20 to 40 days, and its maturity could be evaluated by the attainment of stable pH (8), electrical conductivity (0.9 mS/cm), and C:N ratio ≥ 20. The analysis was also performed on other bio-composts viz. kitchen waste compost, vermicompost, cow dung manure, municipal organic waste compost, and neem cake compost. The fertility index (FI) was evaluated based on six parameters viz. total carbon, total nitrogen, N ratio, phosphorus, potassium, and sulphur contents. The PTE values were used to calculate their clean index (CI). The results showed that leaf waste compost has a higher fertility index (FI = 4.06) than other bio-composts, except the neem cake compost (FI = 4.44). The clean index of the leaf waste compost (CI = 4.38) was also higher than other bio-composts. This indicates that leaf waste compost is a valuable bio-resource with high nutritive value and low PTE contamination, with a favourable prospective to be used in organic farming.

A novel framework for the evaluation of coastal protection schemes through integration of numerical modelling and artificial intelligence into the Sand Engine App.

There is growing interest in the adoption of Engineering with Nature or Nature Based Solutions for coastal protection including large mega-nourishment interventions. However, there are still many unknowns on the variables and design features influencing their functionalities. There are also challenges in the optimization of coastal modelling outputs or information usage in support of decision-making. In this study, more than five hundred numerical simulations with different sandengine designs and different locations along Morecambe Bay (UK) were conducted in Delft3D. Twelve Artificial Neural Networking ensemble models structures were trained on the simulated data to predict the influence of different sand engines on water depth, wave height and sediment transports with good performance. The ensemble models were then packed into a Sand Engine App developed in MATLAB and designed to calculate the impact of different sand engine features on the above variables based on users' inputs of sandengine designs.

Hybrid mixture theory-based modeling of transport of fluids, species, and heat in food biopolymers subjected to freeze-thaw cycles.

A hybrid mixture theory (HMT)-based unsaturated transport (pores not saturated with liquid) model was applied to a food matrix subjected to freezing and freeze-thaw cycles. The model can explain the fluid, species, and heat transport, ice formation, thermomechanical changes, and the freezing point depression occurring inside food biopolymers during freezing. Volume changes during freezing were calculated using the stresses due to pore pressure and the phase-change based mechanical strain. The Eulerian-Lagrangian transformation was performed for solving the equations using a finite element mesh in Lagrangian coordinates. The predicted temperature profiles for constant and fluctuating freezing temperature conditions showed agreement with experimental data with reasonable accuracy (RMSE = 2.86°C and 2.23°C, respectively). The multiscale transport model coupled with a physical chemistry-based relation was able to predict solute concentration and the freezing point depression in potatoes with greater accuracy than an empirical equation published in the literature. Sudden temperature fluctuations representing the opening and closing of a freezer door were investigated using this solution scheme, and conditions causing less damage to the food were identified. PRACTICAL APPLICATION: Food materials are subjected to freeze-thaw cycles during storage, shipping, and distribution to the consumers. The study uses numerical modeling and experimental validation to elucidate the principles affecting ice formation, solute migration, and temperature changes. Outcomes will allow processors to improve the quality of frozen foods with improved design of freezing operation, and storage and distribution strategies.

Glass transitions in frozen systems as influenced by molecular weight of food components.

Freezing is a frequently used way to expand the storage life of foods with high water content. Under suitable cooling rates, frozen systems attain a condition of maximum freeze concentration, which is characterized by the glass transition temperature (Tg '), end point of freezing or onset of melting (Tm '), and concentration of solids (Xs ') in the maximum-freeze-concentrated matrix. The value of Tg ', Tm ', and Xs ' depends on the chemical composition of frozen system. Below Tg ', the rates of deteriorative reactions are significantly reduced. In this article, the data for Tg ', Tm ', and Xs ' of different frozen systems including sugars, starches, proteins, and food are collected and compiled. The trends in Tg ' and Tm ' data of food are investigated using molecular weight (MW) of food components. The Tg ' and Tm ' of most starches (increased by 2.46% to 87.3% and 10.8% to 85.0%) and some protein-rich foods (increased by 5.00% to 53.4% and 25.0% to 52.9%) were higher than the maximum values of sugar-rich foods. Both Tg ' and Tm ' values increased with increasing MW of solids in frozen food, reaching an asymptotic value. Moreover, there were exponential relationships between Tg ' or Tm ' values and MW for sugar and starch-rich foods taken together. Some studies found that frozen storage below Tg ' maintains the higher quality of food that was achieved by fast freezing. However, other studies found that there was no significant difference in the quality of frozen foods between storage temperature below and above Tg '. Therefore, storage below Tg ' is not the only factor for predicting the stability of frozen foods.

Artificial neural network model with different backpropagation algorithms and meteorological data for solar radiation prediction.

Solar energy serves as a great alternative to fossil fuels as they are clean and renewable energy. Accurate solar radiation (SR) prediction can substantially lower down the impact cost pertaining to the development of solar energy. Lately, many SR forecasting system has been developed such as support vector machine, autoregressive moving average and artificial neural network (ANN). This paper presents a comprehensive study on the meteorological data and types of backpropagation (BP) algorithms used to train and develop the best SR predicting ANN model. The meteorological data, which includes temperature, relative humidity and wind speed are collected from a meteorological station from Kuala Terrenganu, Malaysia. Three different BP algorithms are employed into training the model i.e., Levenberg-Marquardt, Scaled Conjugate Gradient and Bayesian Regularization (BR). This paper presents a comparison study to select the best combination of meteorological data and BP algorithm which can develop the ANN model with the best predictive ability. The findings from this study shows that temperature and relative humidity both have high correlation with SR whereas wind temperature has little influence over SR. The results also showed that BR algorithm trained ANN models with maximum R of 0.8113 and minimum RMSE of 0.2581, outperform other algorithm trained models, as indicated by the performance score of the respective models.

Ketogenic Diets and Hepatocellular Carcinoma.

The ketogenic diet (KD) is a low-carbohydrate, high-fat diet regarded as a potential intervention for cancers owing to its effects on tumor metabolism and behavior. Hepatocellular carcinoma (HCC) is the most prevalent type of liver cancer, and its management is worth investigating because of the high fatality rate. Additionally, as the liver is the glucose and lipid metabolism center where ketone bodies are produced, the application of KD to combat HCC is promising. Prior studies have reported that KD could reduce the energy supply and affect the proliferation and differentiation of cancer cells by lowering the blood glucose and insulin levels. Furthermore, KD can increase the expression of hydroxymethylglutaryl-CoA synthase 2 (HMGCS2) in hepatocytes and regulate lipid metabolism to inhibit the progression of HCC. In addition, ß-hydroxybutyrate can induce histone hyperacetylation and reduce the expression of inflammatory factors to alleviate damage to hepatocytes. However, there are few relevant studies at present, and the specific effects and safety of KD on HCC warrant further research. Optimizing the composition of KD and combining it with other therapies to enhance its anti-cancer effects warrant further exploration.

Genome-wide methylome pattern predictive network analysis reveal mesenchymal stem cell's propensity to undergo cardiovascular lineage.

Mesenchymal stem cells (MSCs) differentiation toward cardiovascular lineage prediction using the global methylome profile will highlight its prospective utility in regenerative medicine. We examined the propensity prediction to cardiovascular lineage using 5-Aza, a well-known cardiac lineage inducer. The customized 180 K microarray was performed and further analysis of global differentially methylated regions by Ingenuity pathway analysis (IPA) in both MSCs and 5-AC-treated MSCs. The cluster enrichment tools sorted differentially enriched genes and further annotated to construct the interactive networks. Prediction analysis revealed pathways pertaining to the cardiovascular lineage found active in the native MSCs, suggesting its higher propensity to undergo cardiac, smooth muscle cell, and endothelial lineages in vitro. Interestingly, gene interaction network also proposed majorly stemness gene network NANOG and KLF6, cardiac-specific transcription factors GATA4, NKX2.5, and TBX5 were upregulated in the native MSCs. Furthermore, the expression of cardiovascular lineage specific markers such as Brachury, CD105, CD90, CD31, KDR and various forms of ACTIN (cardiac, sarcomeric, smooth muscle) were validated in native MSCs using real time PCR and immunostaining and blotting analysis. In 5-AC-treated MSCs, mosaic interactive networks were observed to persuade towards osteogenesis and cardiac lineage, indicating that 5-AC treatment resulted in nonspecific lineage induction in MSCs, while MSCs by default have a higher propensity to undergo cardiovascular lineage. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1007/s13205-021-03058-2.

Predicting evaporation with optimized artificial neural network using multi-objective salp swarm algorithm.

Evaporation is a crucial component to be established in agriculture management and water engineering. Evaporation prediction is thus an essential issue for modeling researchers. In this study, the multilayer perceptron (MLP) was used for predicting daily evaporation. MLP model is as one of the famous ANN models with multilayers for predicting different target variables. A new strategy was used to enhance the accuracy of the MLP model. Three multi-objective algorithms, namely, the multi-objective salp swarm algorithm (MOSSA), the multi-objective crow algorithm (MOCA), and the multi-objective particle swarm optimization (MOPSO), were respectively and separately coupled to the MLP model for determining the model parameters, the best input combination, and the best activation function. In this study, three stations in Malaysia, namely, the Muadzam Shah (MS), the Kuala Terengganu (KT), and the Kuantan (KU), were selected for the prediction of the respective daily evaporation. The spacing (SP) and maximum spread (MS) indices were used to evaluate the quality of generated Pareto front (PF) by the algorithms. The lower SP and higher MS showed better PF for the models. It was observed that the MOSSA had higher MS and lower SP than the other algorithms, at all stations. The root means square error (RMSE), mean absolute error (MAE), percent bias (PBIAS), and Nash Sutcliffe efficiency (NSE) quantifiers were used to compare the ability of the models with each other. The MLP-MOSSA had reduced RMSE compared to the MLP-MOCA, MLP-MOPSO, and MLP models by 18%, 25%, and 35%, respectively, at the MS station. The MAE of the MLP-MOSSA was 2.7%, 4.1%, and 26%, respectively lower than those of the MLP-MOCA, MLP-MOPSO, and MLP models at the KU station. The MLP-MOSSA showed lower MAE than the MLP-MOCA, MLP-MOPSO, and MLP models by 16%, 18%, and 19%, respectively, at the KT station. An uncertainty analysis was performed based on the input and parameter uncertainty. The results indicated that the MLP-MOSSA had the lowest uncertainty among the models. Also, the input uncertainty was lower than the parameter uncertainty. The general results indicated that the MLP-MOSSA had the high efficiency for predicting evaporation.

Developing machine learning algorithms for meteorological temperature and humidity forecasting at Terengganu state in Malaysia.

Accurately predicting meteorological parameters such as air temperature and humidity plays a crucial role in air quality management. This study proposes different machine learning algorithms: Gradient Boosting Tree (G.B.T.), Random forest (R.F.), Linear regression (LR) and different artificial neural network (ANN) architectures (multi-layered perceptron, radial basis function) for prediction of such as air temperature (T) and relative humidity (Rh). Daily data over 24 years for Kula Terengganu station were obtained from the Malaysia Meteorological Department. Results showed that MLP-NN performs well among the others in predicting daily T and Rh with R of 0.7132 and 0.633, respectively. However, in monthly prediction T also MLP-NN model provided closer standards deviation to actual value and can be used to predict monthly T with R 0.8462. Whereas in prediction monthly Rh, the RBF-NN model's efficiency was higher than other models with R of 0.7113. To validate the performance of the trained both artificial neural network (ANN) architectures MLP-NN and RBF-NN, both were applied to an unseen data set from observation data in the region. The results indicated that on either architecture of ANN, there is good potential to predict daily and monthly T and Rh values with an acceptable range of accuracy.

Intestinal microbiota drives cholestasis-induced specific hepatic gene expression patterns.

Intestinal microbiota regulates multiple host metabolic and immunological processes. Consequently, any difference in its qualitative and quantitative composition is susceptible to exert significant effects, in particular along the gut-liver axis. Indeed, recent findings suggest that such changes modulate the severity and the evolution of a wide spectrum of hepatobiliary disorders. However, the mechanisms linking intestinal microbiota and the pathogenesis of liver disease remain largely unknown. In this work, we investigated how a distinct composition of the intestinal microbiota, in comparison with germ-free conditions, may lead to different outcomes in an experimental model of acute cholestasis. Acute cholestasis was induced in germ-free (GF) and altered Schaedler's flora (ASF) colonized mice by common bile duct ligation (BDL). Studies were performed 5 days after BDL and hepatic histology, gene expression, inflammation, lipids metabolism, and mitochondrial functioning were evaluated in normal and cholestatic mice. Differences in plasma concentration of bile acids (BA) were evaluated by UHPLC-HRMS. The absence of intestinal microbiota was associated with significant aggravation of hepatic bile infarcts after BDL. At baseline, we found the absence of gut microbiota induced altered expression of genes involved in the metabolism of fatty and amino acids. In contrast, acute cholestasis induced altered expression of genes associated with extracellular matrix, cell cycle, autophagy, activation of MAPK, inflammation, metabolism of lipids, and mitochondrial functioning pathways. Ductular reactions, cell proliferation, deposition of collagen 1 and autophagy were increased in the presence of microbiota after BDL whereas GF mice were more susceptible to hepatic inflammation as evidenced by increased gene expression levels of osteopontin, interleukin (IL)-1ß and activation of the ERK/MAPK pathway as compared to ASF colonized mice. Additonally, we found that the presence of microbiota provided partial protection to the mitochondrial functioning and impairment in the fatty acid metabolism after BDL. The concentration of the majority of BA markedly increased after BDL in both groups without remarkable differences according to the hygiene status of the mice. In conclusion, acute cholestasis induced more severe liver injury in GF mice compared to mice with limited intestinal bacterial colonization. This protective effect was associated with different hepatic gene expression profiles mostly related to tissue repair, metabolic and immune functions. Our findings suggest that microbial-induced differences may impact the course of cholestasis and modulate liver injury, offering a background for novel therapies based on the modulation of the intestinal microbiota.

Suspended sediment load prediction using long short-term memory neural network.

Rivers carry suspended sediments along with their flow. These sediments deposit at different places depending on the discharge and course of the river. However, the deposition of these sediments impacts environmental health, agricultural activities, and portable water sources. Deposition of suspended sediments reduces the flow area, thus affecting the movement of aquatic lives and ultimately leading to the change of river course. Thus, the data of suspended sediments and their variation is crucial information for various authorities. Various authorities require the forecasted data of suspended sediments in the river to operate various hydraulic structures properly. Usually, the prediction of suspended sediment concentration (SSC) is challenging due to various factors, including site-related data, site-related modelling, lack of multiple observed factors used for prediction, and pattern complexity.Therefore, to address previous problems, this study proposes a Long Short Term Memory model to predict suspended sediments in Malaysia's Johor River utilizing only one observed factor, including discharge data. The data was collected for the period of 1988-1998. Four different models were tested, in this study, for the prediction of suspended sediments, which are: ElasticNet Linear Regression (L.R.), Multi-Layer Perceptron (MLP) neural network, Extreme Gradient Boosting, and Long Short-Term Memory. Predictions were analysed based on four different scenarios such as daily, weekly, 10-daily, and monthly. Performance evaluation stated that Long Short-Term Memory outperformed other models with the regression values of 92.01%, 96.56%, 96.71%, and 99.45% daily, weekly, 10-days, and monthly scenarios, respectively.

Calcium transfer between endoplasmic reticulum and mitochondria in liver diseases.

Calcium (Ca2+ ) is a second messenger essential for cellular homeostasis. Inside the cell, Ca2+ is compartmentalized and exchanged among organelles in response to both external and internal stimuli. Mitochondria-associated membranes (MAMs) provide a platform for proteins and channels involved in Ca2+ transfer between the endoplasmic reticulum (ER) and mitochondria. Deregulated Ca2+ signaling and proteins regulating ER-mitochondria interactions have been linked to liver diseases and intensively investigated in recent years. In this review, we summarize the role of MAM-resident proteins in Ca2+ transfer and their association with different liver diseases.

Optimised neural network model for river-nitrogen prediction utilizing a new training approach.

In the past few decades, there has been a rapid growth in the concentration of nitrogenous compounds such as nitrate-nitrogen and ammonia-nitrogen in rivers, primarily due to increasing agricultural and industrial activities. These nitrogenous compounds are mainly responsible for eutrophication when present in river water, and for 'blue baby syndrome' when present in drinking water. High concentrations of these compounds in rivers may eventually lead to the closure of treatment plants. This study presents a training and a selection approach to develop an optimum artificial neural network model for predicting monthly average nitrate-N and monthly average ammonia-N. Several studies have predicted these compounds, but most of the proposed procedures do not involve testing various model architectures in order to achieve the optimum predicting model. Additionally, none of the models have been trained for hydrological conditions such as the case of Malaysia. This study presents models trained on the hydrological data from 1981 to 2017 for the Langat River in Selangor, Malaysia. The model architectures used for training are General Regression Neural Network (GRNN), Multilayer Neural Network and Radial Basis Function Neural Network (RBFNN). These models were trained for various combinations of internal parameters, input variables and model architectures. Post-training, the optimum performing model was selected based on the regression and error values and plot of predicted versus observed values. Optimum models provide promising results with a minimum overall regression value of 0.92.

Ectopic release of nitric oxide modulates the onset of cardiac development in avian model.

Heart development is one of the earliest developmental events, and its pumping action is directly linked to the intensity of development of other organs. Heart contractions mediate the circulation of the nutrients and signalling molecules to the focal points of developing embryos. In the present study, we used in vivo, ex vivo, in vitro, and in silico methods for chick embryo model to characterize and identify molecular targets under the influence of ectopic nitric oxide in reference to cardiogenesis. Spermine NONOate (SpNO) treatment of 10 µM increased the percentage of chick embryos having beating heart at 40th h of incubation by 2.2-fold (p < 0.001). In an ex vivo chick embryo culture, SpNO increased the percentage of embryos having beats by 1.56-fold (p < 0.05) compared with control after 2 h of treatment. Total body weight of SpNO-treated chick embryos at the Hamburger and Hamilton (HH) stage 29 was increased by 1.22-fold (p < 0.005). Cardiac field potential (FP) recordings of chick embryo at HH29 showed 2.5-fold (p < 0.001) increased in the amplitude, 3.2-fold (p < 0.001) increased in frequency of SpNO-treated embryos over that of the control group, whereas FP duration was unaffected. In cultured cardiac progenitors cells (CPCs), SpNO treatment decreased apoptosis and cell death by twofold (p < 0.001) and 1.7-fold (p < 0.001), respectively. Transcriptome analysis of chick embryonic heart isolated from HH15 stage pre-treated with SpNO at HH8 stage showed upregulation of genes involved in heart morphogenesis, heart contraction, cardiac cell development, calcium signalling, structure, and development whereas downregulated genes were enriched under the terms extracellular matrix, wnt pathway, and BMP pathway. The key upstream molecules predicted to be activated were p38 MAPK, MEF2C, TBX5, and GATA4 while KDM5α, DNMT3A, and HNF1α were predicted to be inhibited. This study suggests that the ectopic nitric oxide modulates the onset of cardiac development.

Interactome of miRNAs and transcriptome of human umbilical cord endothelial cells exposed to short-term simulated microgravity.

Adaptation of humans in low gravity conditions is a matter of utmost importance when efforts are on to a gigantic leap in human space expeditions for tourism and formation of space colonies. In this connection, cardiovascular adaptation in low gravity is a critical component of human space exploration. Deep high-throughput sequencing approach allowed us to analyze the miRNA and mRNA expression profiles in human umbilical cord vein endothelial cells (HUVEC), cultured under gravity (G), and stimulated microgravity (MG) achieved with a clinostat. The present study identified totally 1870 miRNAs differentially expressed in HUVEC under MG condition when compared to the cells subjected to unitary G conditions. The functional association of identified miRNAs targeting specific mRNAs revealed that miRNAs, hsa-mir-496, hsa-mir-151a, hsa-miR-296-3p, hsa-mir-148a, hsa-miR-365b-5p, hsa-miR-3687, hsa-mir-454, hsa-miR-155-5p, and hsa-miR-145-5p differentially regulated the genes involved in cell adhesion, angiogenesis, cell cycle, JAK-STAT signaling, MAPK signaling, nitric oxide signaling, VEGF signaling, and wound healing pathways. Further, the q-PCR based experimental studies of upregulated and downregulated miRNA and mRNAs demonstrate that the above reported miRNAs influence the cell proliferation and vascular functions of the HUVEC in MG conditions effectively. Consensus on the interactome results indicates restricted fluctuations in the transcriptome of the HUVEC exposed to short-term MG that could lead to higher levels of endothelial functions like angiogenesis and vascular patterning.

Exercise Attenuates the Transition from Fatty Liver to Steatohepatitis and Reduces Tumor Formation in Mice.

Non-alcoholic fatty liver disease (NAFLD) leads to steatohepatitis (NASH), fibrosis, and hepatocellular carcinoma. For sedentary patients, lifestyle interventions combining exercise and dietary changes are a cornerstone of treatment. However, the benefit of exercise alone when dietary changes have failed is uncertain. We query whether exercise alone arrests the progression of NASH and tumorigenesis in a choline-deficient, high-fat diet (CD-HFD) murine model. Male C57Bl/6N mice received a control diet or CD-HFD for 12 weeks. CD-HFD mice were randomized further for 8 weeks of sedentariness (SED) or treadmill exercise (EXE). CD-HFD for 12 weeks produced NAFL. After 20 weeks, SED mice developed NASH and hepatic adenomas. Exercise attenuated the progression to NASH. EXE livers showed lower triglycerides and tumor necrosis factor-α expression, less fibrosis, less ballooning, and a lower NAFLD activity score than did SED livers. Plasma transaminases and triglycerides were lower. Exercise activated AMP-activated protein kinase (AMPK) with inhibition of mTORC1 and decreased S6 phosphorylation, reducing hepatocellular adenoma. Exercise activated autophagy with increased LC3-II/LC3-I and mitochondrial recruitment of phosphorylated PTEN-induced kinase. Therefore, exercise attenuates the transition from NAFL to NASH, improves biochemical and histological parameters of NAFLD, and impedes the progression of fibrosis and tumorigenesis associated with enhanced activation of AMPK signaling and favors liver autophagy. Our work supports the benefits of exercise independently of dietary changes.