Developing a multivariate time series forecasting framework based on stacked autoencoders and multi-phase feature.

Time series forecasting across different domains has received massive attention as it eases intelligent decision-making activities. Recurrent neural networks and various deep learning algorithms have been applied to modeling and forecasting multivariate time series data. Due to intricate non-linear patterns and significant variations in the randomness of characteristics across various categories of real-world time series data, achieving effectiveness and robustness simultaneously poses a considerable challenge for specific deep-learning models. We have proposed a novel prediction framework with a multi-phase feature selection technique, a long short-term memory-based autoencoder, and a temporal convolution-based autoencoder to fill this gap. The multi-phase feature selection is applied to retrieve the optimal feature selection and optimal lag window length for different features. Moreover, the customized stacked autoencoder strategy is employed in the model. The first autoencoder is used to resolve the random weight initialization problem. Additionally, the second autoencoder models the temporal relation between non-linear correlated features with convolution networks and recurrent neural networks. Finally, the model's ability to generalize, predict accurately, and perform effectively is validated through experimentation with three distinct real-world time series datasets. In this study, we conducted experiments on three real-world datasets: Energy Appliances, Beijing PM2.5 Concentration, and Solar Radiation. The Energy Appliances dataset consists of 29 attributes with a training size of 15,464 instances and a testing size of 4239 instances. For the Beijing PM2.5 Concentration dataset, there are 18 attributes, with 34,952 instances in the training set and 8760 instances in the testing set. The Solar Radiation dataset comprises 11 attributes, with 22,857 instances in the training set and 9797 instances in the testing set. The experimental setup involved evaluating the performance of forecasting models using two distinct error measures: root mean square error and mean absolute error. To ensure robust evaluation, the errors were calculated at the identical scale of the data. The results of the experiments demonstrate the superiority of the proposed model compared to existing models, as evidenced by significant advantages in various metrics such as mean squared error and mean absolute error. For PM2.5 air quality data, the proposed model's mean absolute error is 7.51 over 12.45, about ∼40% improvement. Similarly, the mean square error for the dataset is improved from 23.75 to 11.62, which is ∼51%of improvement. For the solar radiation dataset, the proposed model resulted in ∼34.7% improvement in means squared error and ∼75% in mean absolute error. The recommended framework demonstrates outstanding capabilities in generalization and outperforms datasets spanning multiple indigenous domains.

NT-3 contributes to chemotherapy-induced neuropathic pain through TrkC-mediated CCL2 elevation in DRG neurons.

Cancer patients undergoing treatment with antineoplastic drugs often experience chemotherapy-induced neuropathic pain (CINP), and the therapeutic options for managing CINP are limited. Here, we show that systemic paclitaxel administration upregulates the expression of neurotrophin-3 (Nt3) mRNA and NT3 protein in the neurons of dorsal root ganglia (DRG), but not in the spinal cord. Blocking NT3 upregulation attenuates paclitaxel-induced mechanical, heat, and cold nociceptive hypersensitivities and spontaneous pain without altering acute pain and locomotor activity in male and female mice. Conversely, mimicking this increase produces enhanced responses to mechanical, heat, and cold stimuli and spontaneous pain in naive male and female mice. Mechanistically, NT3 triggers tropomyosin receptor kinase C (TrkC) activation and participates in the paclitaxel-induced increases of C-C chemokine ligand 2 (Ccl2) mRNA and CCL2 protein in the DRG. Given that CCL2 is an endogenous initiator of CINP and that Nt3 mRNA co-expresses with TrkC and Ccl2 mRNAs in DRG neurons, NT3 likely contributes to CINP through TrkC-mediated activation of the Ccl2 gene in DRG neurons. NT3 may be thus a potential target for CINP treatment.

Transcription factor EBF1 mitigates neuropathic pain by rescuing Kv1.2 expression in primary sensory neurons.

Nerve injury-induced alternations of gene expression in primary sensory neurons of the dorsal root ganglion (DRG) are molecular basis of neuropathic pain genesis. Transcription factors regulate gene expression. In this study, we examined whether early B cell factor 1 (EBF1), a transcription factor, in the DRG, participated in neuropathic pain caused by chronic constriction injury (CCI) of the sciatic nerve. EBF1 was distributed exclusively in the neuronal nucleus and coexpressed with cytoplasmic/membrane Kv1.2 in individual DRG neurons. The expression of Ebf1 mRNA and protein was time-dependently downregulated in the ipsilateral lumbar (L) 3/4 DRGs after unilateral CCI. Rescuing this downregulation through microinjection of the adeno-associated virus 5 expressing full-length Ebf1 mRNA into the ipsilateral L3/4 DRGs reversed the CCI-induced decrease of DRG Kv1.2 expression and alleviated the development and maintenance of mechanical, heat and cold hypersensitivities. Conversely, mimicking the downregulation of DRG EBF1 through microinjection of AAV5-expressing Ebf1 shRNA into unilateral L3/4 DRGs produced a reduction of Kv1.2 expression in the ipsilateral L3/4 DRGs, spontaneous pain, and the enhanced responses to mechanical, heat and cold stimuli in naive mice. Mechanistically, EBF1 not only bound to the Kcna2 gene (encoding Kv1.2) promoter but also directly activated its activity. CCI decreased the EBF1 binding to the Kcna2 promoter in the ipsilateral L3/4 DRGs. Our findings suggest that DRG EBF1 downregulation contributes to neuropathic pain likely by losing its binding to Kcna2 promoter and subsequently silencing Kv1.2 expression in primary sensory neurons. Exogenous EBF1 administration may mitigate neuropathic pain by rescuing DRG Kv1.2 expression.

Glucose regulation by newly synthesized boronic acid functionalized molecules as dipeptidyl peptidase IV inhibitor: a potential compound for therapeutic intervention in hyperglycaemia.

Management of type 2 diabetes mellitus (T2DM) using dipeptidyl peptidase IV (DPP IV) inhibitors is gaining precedence as this enzyme plays an indispensable role in cleaving and inactivating peptides, such as glucagon-like peptide-1 (GLP-1), incretin hormones, and glucose-dependent insulinotropic polypeptide (GIP). There are several DPP IV inhibitors used to treat T2DM, but limited by side effects such as disturbed GIT, flu-like symptoms, etc. Thus, there is an urgent need for the development of novel and better DPP IV inhibitors for the management of the same. In the present study, we investigated the effect of new boronic acid-based thiazole compounds as DPP IV inhibitors. We used substituted anilines that were progressively modified through a multi-step synthesis and then chemically characterised. These molecules have good binding affinity and molecular interactions at the active site of the DPP IV enzyme. Two boronic acid-based molecules, i.e. PC06R58 and PC06R108, were used for the assessment of their in-vitro enzymatic activities. Both molecules (PC06108 and PC06R58) exhibited potent uncompetitive DPP IV enzyme inhibition at two different concentrations of 90.9 and 15.6 nM, respectively, compared to sitagliptin having an IC50 of 17.3 nM. Furthermore, the oral glucose tolerance test suggested significantly reduced blood glucose levels at 20 mg/kg of the body weight upon administration of PC06R58 and PC06R108 molecules in rats after glucose ingestion (2 g/kg of the body weight). The compounds showed satisfactory DPP IV inhibition. Furthermore, DPP IV inhibitory activity and acceptable pre-ADME/Tox profile indicate it is a lead compound in this novel class of DPP IV inhibitors.Communicated by Ramaswamy H. Sarma.

RALY participates in nerve trauma-induced nociceptive hypersensitivity through triggering Eif4g2 gene expression in primary sensory neurons.

BACKGROUND AND PURPOSE: Peripheral nerve trauma-induced dysregulation of pain-associated genes in the primary sensory neurons of dorsal root ganglion (DRG) contributes to neuropathic pain genesis. RNA-binding proteins participate in gene transcription. We hypothesized that RALY, an RNA-binding protein, participated in nerve trauma-induced dysregulation of DRG pain-associated genes and nociceptive hypersensitivity. METHODS AND RESULTS: Immunohistochemistry staining showed that RALY was expressed exclusively in the nuclei of DRG neurons. Peripheral nerve trauma caused by chronic constriction injury (CCI) of unilateral sciatic nerve produced time-dependent increases in the levels of Raly mRNA and RALY protein in injured DRG. Blocking this increase through DRG microinjection of adeno-associated virus 5 (AAV5)-expressing Raly shRNA reduced the CCI-induced elevation in the amount of eukaryotic initiation factor 4 gamma 2 (Eif4g2) mRNA and Eif4g2 protein in injured DRG and mitigated the development and maintenance of CCI-induced nociceptive hypersensitivity, without altering basal (acute) response to noxious stimuli and locomotor activity. Mimicking DRG increased RALY through DRG microinjection of AAV5 expressing Raly mRNA up-regulated the expression of Eif4g2 mRNA and Eif4g2 protein in the DRG and led to hypersensitive responses to noxious stimuli in the absence of nerve trauma. Mechanistically, CCI promoted the binding of RALY to the promoter of Eif4g2 gene and triggered its transcriptional activity. CONCLUSION AND IMPLICATIONS: Our findings indicate that RALY participates in nerve trauma-induced nociceptive hypersensitivity likely through transcriptionally triggering Eif4g2 expression in the DRG. RALY may be a potential target in neuropathic pain management.

Cetrimonium bromide and potassium thiocyanate assisted post-vapor treatment approach to enhance power conversion efficiency and stability of FAPbI3 perovskite solar cells.

Formamidinium lead iodide (FAPbI3) is the most promising perovskite material for producing efficient perovskite solar cells (PSCs). Here, we develop a facile method to obtain an α-phase FAPbI3 layer with passivated grain boundaries and weakened non-radiative recombination. For this aim, during the FAPbI3 fabrication process, cetrimonium bromide + 5% potassium thiocyanate (CTABr + 5% KSCN) vapor post-treatment is introduced to remove non-perovskite phases in the FAPbI3 layer. Incorporation of CTA+ along with SCN- ions induces FAPbI3 crystallization and stitch grain boundaries, resulting in PSCs with lower defect losses. The vapor-assisted deposition increases the carriers' lifetime in the FAPbI3 and facilitates charge transport at the interfacial perovskite/hole transport layer via a band alignment phenomenon. The treated α-FAPbI3 layers bring an excellent PCE of 22.34%, higher than the 19.48% PCE recorded for control PSCs. Besides, the well-oriented FAPbI3 and its higher hydrophobic behavior originating from CTABr materials lead to improved stability in the treated PSCs.

IFND: a benchmark dataset for fake news detection.

Spotting fake news is a critical problem nowadays. Social media are responsible for propagating fake news. Fake news propagated over digital platforms generates confusion as well as induce biased perspectives in people. Detection of misinformation over the digital platform is essential to mitigate its adverse impact. Many approaches have been implemented in recent years. Despite the productive work, fake news identification poses many challenges due to the lack of a comprehensive publicly available benchmark dataset. There is no large-scale dataset that consists of Indian news only. So, this paper presents IFND (Indian fake news dataset) dataset. The dataset consists of both text and images. The majority of the content in the dataset is about events from the year 2013 to the year 2021. Dataset content is scrapped using the Parsehub tool. To increase the size of the fake news in the dataset, an intelligent augmentation algorithm is used. An intelligent augmentation algorithm generates meaningful fake news statements. The latent Dirichlet allocation (LDA) technique is employed for topic modelling to assign the categories to news statements. Various machine learning and deep-learning classifiers are implemented on text and image modality to observe the proposed IFND dataset's performance. A multi-modal approach is also proposed, which considers both textual and visual features for fake news detection. The proposed IFND dataset achieved satisfactory results. This study affirms that the accessibility of such a huge dataset can actuate research in this laborious exploration issue and lead to better prediction models.

Characterization of homogenous acid catalyzed biodiesel production from palm oil: experimental investigation and numerical simulation.

Biodiesel is a biological renewable source produced from the conversion of triglycerides to alkyl esters. Palm oil is one of the most used lipid feedstocks for biodiesel production. It becomes necessary to optimize the transesterification reaction parameters to reduce the cost and enhance the quality of biodiesel. This study focuses on the use of homogenous sulfuric acid as a catalyst for the transesterification of palm fatty acids to methyl esters in a batch-scale reactor. A novel examination of transesterification reaction input parameters using the technique for order performance by similarity to ideal solution optimization technique and the effect of these parameters on yield, viscosity, and density of palm biodiesel using 3D surface graphs is investigated in this research. The present optimization approach is implemented to find out the optimum ranking of biodiesel production. From the experimental and numerical simulation, optimum results were observed at the catalyst concentration of 6% (w/w), reaction temperature of 70 °C, the reaction time of 120 min, and alcohol to oil molar ratio of 30:1 at which yield of 95.35%, viscosity of 5.0 cSt, and density of 880 kg/m3 of palm biodiesel were obtained. The different physicochemical properties of produced palm methyl esters are obtained within standards set by international authorities. Selected optimized process parameters can be used for commercial-scale biodiesel production.

Energy, exergy, environmental impact, and economic analyses of evacuated tube compound parabolic concentrator-powered solar thermal domestic water heating system.

In the reported study, a dynamic analytical model is developed to propose the energy, exergy, environmental impact, and economic analyses of the water heating system at Jaipur (India) with an evacuated tube compound parabolic concentrator field of a total area of 81 m2. Consequently, the model is used to perform parametric studies to report the effect of operating and meteorological parameters on the productivity and performance of the system. Moreover, the system's performance, environmental impact, and economic aspects have been investigated and compared under different meteorological conditions at four different Rajasthan (India) locations using TMY2 weather data files. Results clarified that Jodhpur receives the highest solar radiation intensity from these four locations. The model results were validated with the experimental data, and a good agreement has prevailed. Consequently, the results indicate the highest annual energy and exergy gain for Jodhpur with 79.72 MWh and 9.311 MWh, respectively, followed by Jaisalmer, Barmer, and Jaipur. The economic analysis results clarified that the simple payback period ranged from 4.5 to 4.75 years and the discounted payback period ranged from 6.6 to 7 years based on a 6% discount rate. At the same time, the levelized cost of heating for the given system is around 0.023 $/kWh which is very economical closest to that of CNG as a fuel which costs around 0.059 $/kWh. The internal rate of return is reported to be 16.76, 16.82, 16.77, and 16.75% for Barmer, Jodhpur, Jaipur, and Jaisalmer, respectively, and savings of 74.4, 78.1, 75.4, and 73.8 tonnes of CO2 emission to the environment.

An ensemble deep learning classifier for sentiment analysis on code-mix Hindi-English data.

Code-mixing on social media is a trend in many countries where people speak multiple languages, such as India, where Hindi and English are major communication languages. Sentiment analysis is beneficial in understanding users' opinions and thoughts on social, economic, and political issues. It eliminates the manual monitoring of each and every review, which is a cumbersome task. However, performing sentiment analysis on code-mix data is challenging, as it involves various out of vocabulary terms and numerous issues, making it a new field in natural language processing. This work includes dealing with such text and ensembling a classifier to detect sentiment polarity. Our classifier ensembles a multilingual variant of RoBERTa and a sentence-level embedding from Universal Sentence Encoder to identify the sentiments of these code-mixed tweets with higher accuracy. This ensemble optimises the classifier's performance by using the strength of both for transfer learning. Experiments were conducted on real-life benchmark datasets and revealed their sentiment. The performance of the proposed classifier framework is compared with other baselines and deep learning models on five datasets to show the superiority of our results. Results showed improved and increased performance in the proposed classifier's accuracy, precision, and recall. The accuracy achieved by our classifier on code-mix datasets is 66% on Joshi et al. 2016, 60% on SAIL 2017, and 67% on SemEval 2020 Task-9 dataset, which is on average around 3% as compared to contemporary baselines.

Recent advancements in biomarker research in schizophrenia: mapping the road from bench to bedside.

Schizophrenia (SZ) is a severe progressive neurodegenerative as well as disruptive behavior disorder affecting innumerable people throughout the world. The discovery of potential biomarkers in the clinical scenario would lead to the development of effective methods of diagnosis and would provide an understanding of the prognosis of the disease. Moreover, breakthrough inventions for the treatment and prevention of this mysterious disease could evolve as a result of a thorough understanding of the clinical biomarkers. In this review, we have discussed about specific biomarkers of SZ an emphasis has been laid to delineate (1) diagnostic biomarkers like neuroimmune biomarkers, metabolic biomarkers, oligodendrocyte biomarkers and biomarkers of negative and cognitive symptoms, (2) therapeutic biomarkers like various neurotransmitter systems and (3) prognostic biomarkers. All the biomarkers were evaluated in drug-naïve (at least for 4 weeks) patients in order to achieve a clear comparison between schizophrenic patients and healthy controls. Also, an attempt has been made to elucidate the potential genes which serve as predictors and tools for the determination of biomarkers and would ultimately help in the prevention and treatment of this deadly illness.

Prediction of influencing atmospheric conditions for explosion Avoidance in fireworks manufacturing Industry-A network approach.

This research study uses Artificial Neural Networks (ANNs) to predict occupational accidents in Sivakasi firework industries. Atmospheric temperature, pressure and humidity are the causes of explosion during chemical mixing, drying, and pellet making. The Proposed ANN model predicts the accidents and the session of accidents (FN/AN) based on atmospheric conditions. This prediction takes values from historical accident data due to the atmospheric conditions of Sivakasi (2009-2021). In the development of ANN model, the Feed-Forward Back Propagation (FFBP) with the Levenberg-Marquardt function has been employed with hidden layers of 5 and 10 to train the network. The performance accuracy of both the hidden layers is evaluated and compared with other models like Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbor (K-NN). The accuracy of the proposed model for accident classification is 82.7% and 67.8% for hidden layers 5 and 10, respectively. Also, the model predicts the session of accident with the accuracy of 72% and 54%, specificity of 77.7% and 60.1%, sensitivity of 69% and 52.92% for hidden layers 5 and 10, respectively. It is found that hidden layer 5 gives higher accuracy than hidden layer 10. The proposed ANN model gives the highest accuracy when compared to other models. This study is helpful in the firework industry management, and workers improve safety precautions and avoid explosions due to atmospheric conditions.

Classification of COVID-19 by using supervised optimized machine learning technique.

In recent two years, covid-19 diseases is the most harmful diseases in entire world. This disease increase the high mortality rate in several developed countries. Earlier identification of covid-19 symptoms can avoid the over illness or death. However, there are several researchers are introduced different methodology to identification of diseases symptoms. But, identification and classification of covid-19 diseases is the difficult task for every researchers and doctors. In this modern world, machine learning techniques is useful for several medical applications. This study is more focused in applying machine learning classifier model as SVM for classification of diseases. By improve the classification accuracy of the classifier by using hyper parameter optimization technique as modified cuckoo search algorithm. High dimensional data have unrelated, misleading features, which maximize the search space size subsequent in struggle to process data further thus not contributing to the learning practise, So we used a hybrid feature selection technique as mRMR (Minimum Redundancy Maximum Relevance) algorithm. The experiment is conducted by using UCI machine learning repository dataset. The classifier is conducted to classify the two set of classes such as COVID-19, and normal cases. The proposed model performance is analysed by using different parametric metrics, which are explained in result section.

Predicting image credibility in fake news over social media using multi-modal approach.

Social media are the main contributors to spreading fake images. Fake images are manipulated images altered through software or by other means to change the information they convey. Fake images propagated over microblogging platforms generate misrepresentation and stimulate polarization in the people. Detection of fake images shared over social platforms is extremely critical to mitigating its spread. Fake images are often associated with textual data. Hence, a multi-modal framework is employed utilizing visual and textual feature learning. However, few multi-modal frameworks are already proposed; they are further dependent on additional tasks to learn the correlation between modalities. In this paper, an efficient multi-modal approach is proposed, which detects fake images of microblogging platforms. No further additional subcomponents are required. The proposed framework utilizes explicit convolution neural network model EfficientNetB0 for images and sentence transformer for text analysis. The feature embedding from visual and text is passed through dense layers and later fused to predict fake images. To validate the effectiveness, the proposed model is tested upon a publicly available microblogging dataset, MediaEval (Twitter) and Weibo, where the accuracy prediction of 85.3% and 81.2% is observed, respectively. The model is also verified against the newly created latest Twitter dataset containing images based on India's significant events in 2020. The experimental results illustrate that the proposed model performs better than other state-of-art multi-modal frameworks.

Small and medium-sized enterprises' contribution in digital technology.

Researchers have mentioned the importance of digitization in improving efficiency and productivity in Small and Medium Enterprises (SME). Fortunately, there is no proof that Digitization can be used to deal with the outcome of severe incidents like COVID-19. The research paper suggested that the increased rate of SMEs has increased significantly. This was entirely due to the advent of Digital Technology (DT). In this way, both product and the process become more automated in digitalization, resulting in increased quality and demand. Considering the high scope for higher development, India's SME sector still has much space for new digital technologies to be integrated. This paper addresses the main scenario of SMEs in India and their benefit in GDP. Also, the research includes a brief analysis of CRM applications and digital payment options in SMEs.

Direct-Acting Antivirals in the Treatment of Hepatitis C Virus (HCV)/Human Immunodeficiency Virus (HIV) Co-infected Patients: Real-Life Experience From Nepal.

Background Direct-acting antivirals (DAA) have revolutionized the treatment of chronic hepatitis C patients. However, the real-life data regarding its use in a human immunodeficiency virus (HIV) co-infection from a developing country is lacking. We aimed to see the efficacy of DAA in hepatitis C virus (HCV)/HIV co-infected populations. Methods In this prospective, observational, intention-to-treat study from Nepal, treatment-naïve patients undergoing treatment for chronic HCV in HIV co-infected individuals with DAA were studied. Patients on nevirapine were switched to efavirenz or atazanavir. Patients received sofosbuvir/ledipasvir or sofosbuvir/daclatasvir with or without ribavirine. Sustained virological response (SVR) at week 12, adverse events, and treatment compliance were evaluated. Treatment efficacy was compared between cirrhotic and non-cirrhotic patients. Results Of 218 patients presenting with an anti-HCV report, 181 (83%) had detectable HCV RNA. Eighty-five (85; 47%) patients were having ART at presentation. Three patients could not complete treatment due to gall stone pancreatitis and 82 completed treatment. Twenty-nine (29; 35%) were cirrhotic at presentation. Fifty-one (51; 62%) patients were genotype 3, 27 (33%) were genotype 1, three (4%) were mixed 1a/3, and one (1%) was 6. Seventy-four (74; 90%) had SVR12. Non-cirrhotics had 96% SVR compared to 79% in cirrhotics. SVR in genotype 3 was 88% while it was 93% in genotype 1. Conclusions Real-life experience showed that the DAAs are equally effective in HCV HIV co-infected patients. In non-cirrhotic patients, the result is comparable to mono-infected patients. Genotype 3 co-infected are also difficult-to-treat patients. DAA treatment is well-tolerated in HCV/HIV co-infected patients, and there was no dropout during treatment.

Correction to: Kinesins: Motor Proteins as Novel Target for the Treatment of Chronic Pain.

The original version of this article unfortunately contained mistakes.

Prevalence of Acute Kidney Injury in Patients with Liver Cirrhosis.

INTRODUCTION: Acute kidney injury is a common and life-threatening event in patients with liver cirrhosis occurring in approximately 20-50% of hospitalized patients of liver cirrhosis. Pre-renal acute kidney injury, the hepatorenal syndrome type of acute kidney injury and acute tubular necrosis represent the common causes. The aim of this study was to study the profile of acute kidney injury in patients with liver cirrhosis. METHODS: Consecutive patients of liver cirrhosis admitted in Liver unit of Bir Hospital were studied to see the presence of acute kidney injury in this hospital based descriptive cross-sectional study. Clinical and laboratory parameters along with various clinical outcome were compared between different groups categorized by the severity of liver disease and renal dysfunction. RESULTS: Out of 302 liver cirrhosis patients, 56 (18.5%) had acute kidney injury among which 23 (46%) were found to have pre-renal acute kidney injury, 15 (30%) with hepatorenal syndrome- acute kidney injury and 12 (24%) with intrinsic renal disease. Patients with higher stages of acute kidney injury had longer duration of hospital stay and hepatorenal syndrome-acute kidney injury was seen in patients with higher grade of ascites and with hyponatremia. CONCLUSIONS: Acute kidney injury is a common occurrence in patients with advanced liver cirrhosis with pre-renal acute kidney injury being the commonest cause. Median hospital stay is directly affected by the severity of acute kidney injury and hepatorenal syndrome-acute kidney injury was seen in patients with higher grade of ascites and hyponatremia. Early identification of patients at high risk for acute kidney injury may help to reduce mortality and contain costs.

Kaempferol in ameliorating diabetes-induced fibrosis and renal damage: An in vitro and in vivo study in diabetic nephropathy mice model.

BACKGROUND: Kaempferol is a natural polyflavonol that has gained considerable attention as antidiabetic therapeutics. Recent reports emphasize the role of hyperglycemia and RhoA/Rho Kinase activity in the pathogenesis of diabetic nephropathy (DN). This study aims to evaluate the GLP-1 and insulin release along with RhoA/Rho Kinase inhibition pertaining to the anti-fibrotic and reno-protective effects of Kaempferol in DN. METHODS: The effect of Kaempferol on GLP-1 and insulin release along with underlying mechanisms (Ca2+ and cAMP levels) in GLUTag and MIN6 cells as well as in their co-culture has been evaluated. Further, the effect of Kaempferol on GLP-1 and insulin release was evaluated under in-vivo circumstances in the DN C57BL/6 mouse model. Histology and fibrosis specific staining was performed to study the renal injuries and fibrosis, while the expression of mRNA and protein of interest was evaluated by RT-PCR and western blot analysis. RESULTS: Kaempferol treatment promoted the GLP-1 and insulin release, which was accompanied by increased intracellular levels of cAMP and Ca2+ in GLUTag and MIN6 cells. In agreement with in vitro studies, Kaempferol also increased the release of GLP-1 and insulin in the DN mouse model. Notably, Kaempferol showed the potential to ameliorate the histological changes as well as renal fibrosis while decreasing the expression levels of DN markers including TGF-ß1, CTGF, fibronectin, collagen IV, IL-1ß, RhoA, ROCK2, and p-MYPT1 in DN kidney tissues. A rise in the expression of E-cadherin and nephrin was also noted in the same study. CONCLUSION: This study establishes that Kaempferol ameliorates renal injury and fibrosis by enhancing the release of GLP-1, insulin, and inhibition of RhoA/Rho Kinase. This study recommends Kaempferol for further clinical trials to be developed as novel therapeutics for improving the renal function in DN patients.

Tumor microenvironment targeted nanotherapeutics for cancer therapy and diagnosis: A review.

Recent findings suggest that the cellular and extracellular materials surrounding the cancerous cells from an atypical tumor microenvironment (TM) play a pivotal role in the process of tumor initiation and progression. TM comprises an intricate system involving diverse cell types including endothelial cells, pericytes, smooth muscle cells, fibroblasts, various inflammatory cells, dendritic cells, and cancer stem cells (CSCs). The TM-forming cells dynamically interact with the cancerous cells through various signaling mechanisms and pathways. The existence of this dynamic cellular communication is responsible for creating an environment suitable for sustaining a reasonably high cellular proliferation. Presently, researchers are showing interest to use these TM conditions to mediate effective targeting measures for cancer therapy. The use of nanotherapeutics-based combination therapy; stimuli-responsive nanotherapeutics targeting acidic pH, hypoxic environment; and nanoparticle-induced hyperthermia are some of the approaches that are under intense investigation for cancer therapy. This review discusses TM and its role in cancer progression and crosstalk understanding, opportunities, and epigenetic modifications involved therein to materialize the capability of nanotherapeutics to target cancer by availing TM. STATEMENT OF SIGNIFICANCE: This article presents various recent reports, proof-of-concept studies, patents, and clinical trials on the concept of tumor microenvironment for mediating the cancer-specific delivery of nanotechnology-based systems bearing anticancer drug and diagnostics. We highlight the potential of tumor microenvironment; its role in disease progression, opportunities, challenges, and allied treatment strategies for effective cancer therapy by conceptual understanding of tumor microenvironment and epigenetic modifications involved. Specifically, nanoparticle-based approaches to target various processes related to tumor microenvironment (pH responsive, hypoxic environment responsive, targeting of specific cells involved in tumor microenvironment, etc.) are dealt in detail.