Neuroprotective effects of resveratrol on retinal ganglion cells in glaucoma in rodents:A narrative review

Glaucoma,an irreversible optic neuropathy,primarily affects retinal ganglion cells(RGC)and causes vision loss and blindness.The damage to RGCs in glaucoma occurs by various mechanisms,including elevated intraocular pressure,oxidative stress,inflammation,and other neurodegenerative processes.As the disease progresses,the loss of RGCs leads to vision loss.Therefore,protecting RGCs from damage and promoting their survival are important goals in managing glaucoma.In this regard,resveratrol(RES),a polyphenolic phytoalexin,exerts antioxidant effects and slows down the evolution and progression of glaucoma.The present review shows that RES plays a protective role in RGCs in cases of ischemic injury and hypoxia as well as in ErbB2 protein expression in the retina.Additionally,RES plays protective roles in RGCs by promoting cell growth,reducing apoptosis,and decreasing oxidative stress in H_(2)O_(2)-exposed RGCs.RES was also found to inhibit oxidative stress damage in RGCs and suppress the activation of mitogen-activated protein kinase signaling pathways.RES could alleviate retinal function impairment by suppressing the hypoxia-i nducible factor-1 alpha/vascular endothelial growth factor and p38/p53 axes while stimulating the PI3K/Akt pathway.Therefore,RES might exert potential therapeutic effects for managing glaucoma by protecting RGCs from damage and promoting their survival.

An Effective Hybrid Model of ELM and Enhanced GWO for Estimating Compressive Strength of Metakaolin-Contained Cemented Materials

This research proposes a highly effective soft computing paradigm for estimating the compressive strength(CS)of metakaolin-contained cemented materials.The proposed approach is a combination of an enhanced grey wolf optimizer(EGWO)and an extreme learning machine(ELM).EGWO is an augmented form of the classic grey wolf optimizer(GWO).Compared to standard GWO,EGWO has a better hunting mechanism and produces an optimal performance.The EGWO was used to optimize the ELM structure and a hybrid model,ELM-EGWO,was built.To train and validate the proposed ELM-EGWO model,a sum of 361 experimental results featuring five influencing factors was collected.Based on sensitivity analysis,three distinct cases of influencing parameters were considered to investigate the effect of influencing factors on predictive precision.Experimental consequences show that the constructed ELM-EGWO achieved the most accurate precision in both training(RMSE=0.0959)and testing(RMSE=0.0912)phases.The outcomes of the ELM-EGWO are significantly superior to those of deep neural networks(DNN),k-nearest neighbors(KNN),long short-term memory(LSTM),and other hybrid ELMs constructed with GWO,particle swarm optimization(PSO),harris hawks optimization(HHO),salp swarm algorithm(SSA),marine predators algorithm(MPA),and colony predation algorithm(CPA).The overall results demonstrate that the newly suggested ELM-EGWO has the potential to estimate the CS of metakaolin-contained cemented materials with a high degree of precision and robustness.

Chemical profiling of bioactive compounds in the methanolic extract of wild leaf and callus of Vitex negundo using gas chromatographymass spectrometry

BACKGROUND The investigation of plant-based therapeutic agents in medicinal plants has revealed their presence in the extracts and provides the vision to formulate novel techniques for drug therapy.Vitex negundo(V.negundo),a perennial herb belonging to the Varbanaceae family,is extensively used in conventional medication.AIM To determine the existence of therapeutic components in leaf and callus extracts from wild V.negundo plants using gas chromatography-mass spectrometry(GCMS).METHODS In this study,we conducted GC-MS on wild plant leaf extracts and correlated the presence of constituents with those in callus extracts.Various growth regulators such as 6-benzylaminopurine(BAP),2,4-dichlorophenoxyacetic acid(2,4-D),α-naphthylacetic acid(NAA),and di-phenylurea(DPU)were added to plant leaves and in-vitro callus and grown on MS medium.RESULTS The results clearly indicated that the addition of BAP(2.0 mg/L),2,4-D(0.2 mg/mL),DPU(2.0 mg/L)and 2,4-D(0.2 mg/mL)in MS medium resulted in rapid callus development.The plant profile of Vitex extracts by GC-MS analysis showed that 24,10,and 14 bioactive constituents were detected in the methanolic extract of leaf,green callus and the methanolic extract of white loose callus,respectively.CONCLUSION Octadecadienoic acid,hexadecanoic acid and methyl ester were the major constituents in the leaf and callus methanolic extract.Octadecadienoic acid was the most common constituent in all samples.The maximum concentration of octadecadienoic acid in leaves,green callus and white loose callus was 21.93%,47.79%and 40.38%,respectively.These findings demonstrate that the concentration of octadecadienoic acid doubles in-vitro compared to in-vivo.In addition to octadecadienoic acid;butyric acid,benzene,1-methoxy-4-(1-propenyl),dospan,tridecanedialdehyde,methylcyclohexenylbutanol,chlorpyrifos,n-secondary terpene diester,anflunine and other important active compounds were also detected.All these components were only available in callus formed in-vitro.This study showed that the callus contained additional botanical characteristics compared with wild plants.Due to the presence of numerous bioactive compounds,the medical use of Vitex for various diseases has been accepted and the plant is considered an important source of therapeutics for research and development.

SIT.net: SAR Deforestation Classification of Amazon Forest for Land Use Land Cover Application

The process of turning forest area into land is known as deforestation or forest degradation. Reforestation as a fraction of deforestation is extremely low. For improved qualitative and quantitative classification, we used Sentinel-1 dataset of State of Para, Brazil to precisely and closely monitor deforestation between June 2019 and June 2023. This research aimed to find out suitable model for classification called Satellite Imaging analysis by Transpose deep neural transformation network (SIT-net) using mathematical model based on Band math approach to classify deforestation applying transpose deep neural network. The main advantage of proposed model is easy to handle SAR images. The study concludes that SAR satellite gives high-resolution images to improve deforestation monitoring and proposed model takes less computational time compared to other techniques.

Medical Image Fusion Based on Anisotropic Diffusion and Non-Subsampled Contourlet Transform

The synthesis of visual information from multiple medical imaging inputs to a single fused image without any loss of detail and distortion is known as multimodal medical image fusion.It improves the quality of biomedical images by preserving detailed features to advance the clinical utility of medical imaging meant for the analysis and treatment of medical disor-ders.This study develops a novel approach to fuse multimodal medical images utilizing anisotropic diffusion(AD)and non-subsampled contourlet transform(NSCT).First,the method employs anisotropic diffusion for decomposing input images to their base and detail layers to coarsely split two features of input images such as structural and textural information.The detail and base layers are further combined utilizing a sum-based fusion rule which maximizes noise filtering contrast level by effectively preserving most of the structural and textural details.NSCT is utilized to further decompose these images into their low and high-frequency coefficients.These coefficients are then combined utilizing the principal component analysis/Karhunen-Loeve(PCA/KL)based fusion rule independently by substantiating eigenfeature reinforcement in the fusion results.An NSCT-based multiresolution analysis is performed on the combined salient feature information and the contrast-enhanced fusion coefficients.Finally,an inverse NSCT is applied to each coef-ficient to produce the final fusion result.Experimental results demonstrate an advantage of the proposed technique using a publicly accessible dataset and conducted comparative studies on three pairs of medical images from different modalities and health.Our approach offers better visual and robust performance with better objective measurements for research development since it excellently preserves significant salient features and precision without producing abnormal information in the case of qualitative and quantitative analysis.

Elimination of lymphatic filariasis:Where do we stand so far?

Lymphatic filariasis(LF),an asymptomatic,acute,and chronic condition in human beings,is the second most common vector-borne disease after malaria.According to the World Health Organization,there are 120 million LF cases detected in 81 tropical and subtropical countries,and one billion people are at risk.Therefore,the Global Program to Eliminate Lymphatic Filariasis was launched in 2000,with the primary objective of stopping LF transmission among all at-risk groups using mass drug administration(MDA),managing morbidities,and preventing LF-related impairments using a minimum treatment package.Additionally,other programs such as epidemiological assessment including National Filaria Control Program and World Health Organization recommended routine and pre-MDA microfilaremia surveys also implemented to stop the LF transmission.The routine filaria surveys were also carried out in around 2000-4000 individuals/month throughout the year whereas pre-MDA surveys were also conducted every year in approximately 4000 individuals in four fixed and four random sites.Furthermore,the Transmission Assessment Survey was also conducted to check the risk of LF among primary school children.Moreover,potential diagnostic methods,systematic surveillance regimes,the Direct Network Report system,and regular trainings and awareness may be also effective in preventing the recurrence of LF.Hence,this review emphasizes the potential advocacy tools and various strategies as well as procedures for monitoring,which could be impactful in eliminating LF.

Cellular and molecular insights into microbiota-mitochondriainterplay, therapeutic biomarkers and interventional approachesin COVID-19: A review

The persistent global pandemic,COVID-19,stems from the pathogenic influence of severe acute respiratory syndrome coronavirus 2(SARS-CoV-2),yielding an unprecedented worldwide crisis.With reference to a WHO report,the count of COVID-19 cases had exceeded 754 million by February 03,2023.Intriguingly,emerging research has spotlighted the intricate interplay of gut microbiota and mitochondrial entities,acting as potent immunomodulatory factors at the cellular and molecular levels.This interconnection operates through a series of dynamic mechanisms.SARS-CoV-2 infection perturbs the delicate equilibrium of gut microbiota,leading to dysbiosis—a signature biomarker.This imbalance is intrinsically linked to exacerbated COVID-19 progression.Mechanistically,this microbial dysbiosis triggers aberrant immune responses,marked by cytokine storms,while also inducing mitochondrial dysfunction.Mitochondrial activities crucial to energy production and immunoregulation are compromised.This dual perturbation of microbiota and mitochondria contributes to disease severity.This review provides a comprehensive overview of these interconnected mechanisms,illuminating how microbiota-mitochondria interplay serves as both a diagnostic biomarker and a promising therapeutic target in the realm of COVID-19.

Time to stimulate Plasmodium vivax research in India: A way forwards

India bears the largest Plasmodium (P.) vivax (Pv) malaria burden and contributes 48%of Pv cases globally[1]. The efforts of government and private bodies to control malaria have successfully reduced the number of Plasmodium falciparum (Pf) malaria cases in several countries, including India.

Si–Ge based vertical tunnel field-effect transistor of junction-less structure with improved sensitivity using dielectric modulation for biosensing applications

A dielectric modulation strategy for gate oxide material that enhances the sensing performance of biosensors in junction-less vertical tunnel field effect transistors(TFETs)is reported.The junction-less technique,in which metals with specific work functions are deposited on the source region to modulate the channel conductivity,is used to provide the necessary doping for the proper functioning of the device.TCAD simulation studies of the proposed structure and junction structure have been compared,and showed an enhanced rectification of 10^(4) times.The proposed structure is designed to have a nanocavity of length 10 nm on the left-and right-hand sides of the fixed gate dielectric,which improves the biosensor capture area,and hence the sensitivity.By considering neutral and charged biomolecules with different dielectric constants,TCAD simulation studies were compared for their sensitivities.The off-state current IOFFcan be used as a suitable sensing parameter because it has been observed that the proposed sensor exhibits a significant variation in drain current.Additionally,it has been investigated how positively and negatively charged biomolecules affect the drain current and threshold voltage.To explore the device performance when the nanogaps are fully filled,half filled and unevenly filled,extensive TCAD simulations have been run.The proposed TFET structure is further benchmarked to other structures to show its better sensing capabilities.

Intelligent System Application to Monitor the Smart City Building Lighting

A smart city incorporates infrastructure methods that are environmentally responsible,such as smart communications,smart grids,smart energy,and smart buildings.The city administration has prioritized the use of cutting-edge technology and informatics as the primary strategy for enhancing service quality,with energy resources taking precedence.To achieve optimal energy management in themultidimensional system of a city tribe,it is necessary not only to identify and study the vast majority of energy elements,but also to define their implicit interdependencies.This is because optimal energy management is required to reach this objective.The lighting index is an essential consideration when evaluating the comfort indicators.In order to realize the concept of a smart city,the primary objective of this research is to create a system for managing and monitoring the lighting index.It is possible to identify two distinct phaseswithin the intelligent system.Once data collection concludes,the monitoring system will be activated.In the second step,the operation of the control system is analyzed and its effect on the performance of the numerical model is determined.This evaluation is based on the proposed methodology.The optimized resultswere deemed satisfactory because they maintained the brightness index value(79%)while consuming less energy.The intelligent implementation system generated satisfactory outcomes,which were observed 1.75 times on average.

Products of Odd Numbers or Prime Number Can Generate the Three Members’ Families of Fermat Last Theorem and the Theorem Is Valid for Summation of Squares of More Than Two Natural Numbers

Fermat’s last theorem, had the statement that there are no natural numbers A, B, and C such that An + Bn = Cn, in which n is a natural number greater than 2. We have shown that any product of two odd numbers can generate Fermat or Pythagoras triple (A, B, C) following n = 2 and also it is applicable A2 + B2 + C2 + D2 + so on =An2 where all are natural numbers.

P-ROCK: A Sustainable Clustering Algorithm for Large Categorical Datasets

Data clustering is crucial when it comes to data processing and analytics.The new clustering method overcomes the challenge of evaluating and extracting data from big data.Numerical or categorical data can be grouped.Existing clustering methods favor numerical data clustering and ignore categorical data clustering.Until recently,the only way to cluster categorical data was to convert it to a numeric representation and then cluster it using current numeric clustering methods.However,these algorithms could not use the concept of categorical data for clustering.Following that,suggestions for expanding traditional categorical data processing methods were made.In addition to expansions,several new clustering methods and extensions have been proposed in recent years.ROCK is an adaptable and straightforward algorithm for calculating the similarity between data sets to cluster them.This paper aims to modify the algo-rithm by creating a parameterized version that takes specific algorithm parameters as input and outputs satisfactory cluster structures.The parameterized ROCK algorithm is the name given to the modified algorithm(P-ROCK).The proposed modification makes the original algorithm moreflexible by using user-defined parameters.A detailed hypothesis was developed later validated with experimental results on real-world datasets using our proposed P-ROCK algorithm.A comparison with the original ROCK algorithm is also provided.Experiment results show that the proposed algorithm is on par with the original ROCK algorithm with an accuracy of 97.9%.The proposed P-ROCK algorithm has improved the runtime and is moreflexible and scalable.

Hybrid Dynamic Optimization for Multilevel Security System in Disseminating Confidential Information

Security systems are the need of the hour to protect data from unauthorized access.The dissemination of confidential information over the public network requires a high level of security.The security approach such as steganography ensures confidentiality,authentication,integrity,and non-repudiation.Steganography helps in hiding the secret data inside the cover media so that the attacker can be confused during the transmission process of secret data between sender and receiver.Therefore,we present an efficient hybrid security model that provides multifold security assurance.To this end,a rectified Advanced Encryption Standard(AES)algorithm is proposed to overcome the problems existing in AES such as pattern appearance and high computations.The modified AES is used for the encryption of the stego image that contains the digitally signed encrypted secret data.The enciphering and deciphering of the secret data are done using the Rivest–Shamir–Adleman(RSA)algorithm.The experiments are conducted on the images of the USC-SIPI standard image database.The experimental results prove that the proposed hybrid system outperforms other SOTA(state-of-the-art)approaches.

Boosting the Ni-Zn interplay via O/N dual coordination for high-efficiency CO_(2) electroreduction

Design of supportive atomic sites with a controllably adjusted coordinating environment is essential to advancing the reduction of CO_(2) to value-added fuels and chemicals and to achieving carbon neutralization.Herein,atomic Ni(Zn)sites that are uniquely coordinated with ternary Zn(Ni)/N/O ligands were successfully decorated on formamide-derived porous carbon nanomaterials,possibly forming an atomic structure of Ni(N_(2)O_(1))-Zn(N_(2)O_(1)),as studied by combining X-ray photoelectron spectroscopy and X-ray absorption spectroscopy.With the mediation of additional O coordination,the Ni-Zn dual site induces significantly decreased desorption of molecular CO.The NiZn-NC decorated with rich Ni(N_(2)O_(1))-Zn(N_(2)O_(1))sites remarkably gained>97%CO Faraday efficiency over a wide potential range of -0.8 to -1.1 V(relative to reversible hydrogen electrode).Density functional theory computations suggest that the N/O dual coordination effectively modulates the electronic structure of the Ni-Zn duplex and optimizes the adsorption and conversion properties of CO_(2) and subsequent intermediates.Different from the conventional pathway of using Ni as the active site in the Ni-Zn duplex,it is found that the Ni-neighboring Zn sites in the Ni(N_(2)O_(1))-Zn(N_(2)O_(1))coordination showed much lower energy barriers of the CO_(2) protonation step and the subsequent dehydroxylation step.

Optimization of Cognitive Radio System Using Enhanced Firefly Algorithm

The optimization of cognitive radio(CR)system using an enhanced firefly algorithm(EFA)is presented in this work.The Firefly algorithm(FA)is a nature-inspired algorithm based on the unique light-flashing behavior of fireflies.It has already proved its competence in various optimization prob-lems,but it suffers from slow convergence issues.To improve the convergence performance of FA,a new variant named EFA is proposed.The effectiveness of EFA as a good optimizer is demonstrated by optimizing benchmark functions,and simulation results show its superior performance compared to biogeography-based optimization(BBO),bat algorithm,artificial bee colony,and FA.As an application of this algorithm to real-world problems,EFA is also applied to optimize the CR system.CR is a revolutionary technique that uses a dynamic spectrum allocation strategy to solve the spectrum scarcity problem.However,it requires optimization to meet specific performance objectives.The results obtained by EFA in CR system optimization are compared with results in the literature of BBO,simulated annealing,and genetic algorithm.Statistical results further prove that the proposed algorithm is highly efficient and provides superior results.

Bioactive molecules from terrestrial and seafood resources in hypertension treatment:focus on molecular mechanisms and targeted therapies

Hypertension(HTN),a complex cardiovascular disease(CVD),significantly impacts global health,prompting a growing interest in complementary and alternative therapeutic approaches.This review article seeks to provide an up-to-date and thorough summary of modern therapeutic techniques for treating HTN,with an emphasis on the molecular mechanisms of action found in substances found in plants,herbs,and seafood.Bioactive molecules have been a significant source of novel therapeutics and are crucial in developing and testing new HTN remedies.Recent advances in science have made it possible to understand the complex molecular mechanisms underlying blood pressure(BP)-regulating effects of these natural substances better.Polyphenols,flavonoids,alkaloids,and peptides are examples of bioactive compounds that have demonstrated promise in influencing several pathways involved in regulating vascular tone,reducing oxidative stress(OS),reducing inflammation,and improving endothelial function.The article explains the vasodilatory,diuretic,and renin-angiotensin-aldosterone system(RAAS)modifying properties of vital plants such as garlic and olive leaf.Phytochemicals from plants are the primary in traditional drug development as models for novel antihypertensive drugs,providing diverse strategies to combat HTN due to their biological actions.The review also discusses the functions of calcium channel blockers originating from natural sources,angiotensin-converting enzyme(ACE)inhibitors,and nitric oxide(NO)donors.Including seafood components in this study demonstrates the increased interest in using bioactive chemicals originating from marine sources to treat HTN.Omega-3 fatty acids,peptides,and minerals obtained from seafood sources have anti-inflammatory,vasodilatory,and antioxidant properties that improve vascular health and control BP.Overall,we discussed the multiple functions of bioactive molecules and seafood components in the treatment of HTN.

Pharmacognostic and phytochemical studies as an invaluable approach for correct identification of medicinal plants:The case of Artemisia vulgaris L.substituted for Artemisia annua L.in Western Uganda

Background:Different parts of Artemisia vulgaris L.(A.vulgaris)are ethno-medicinally used as an emmenagogue and for the treatment of ailments such as malaria fever,ulcers,and cancer.However,anecdotal evidence shows that the plant is often substituted for Artemisia annua L.(A.annua)by herbalists in Western Uganda due to similarities in their morphology.Misidentification of medicinal plants and mislabelling of herbal products have been incriminated in toxicity and adverse health outcomes in traditional medicine practise.Because safety continues to be a major issue with the use of herbal remedies,it becomes imperative therefore that medicinal plants should be correctly identified.Methods:This study focused on investigating the macroscopic,microscopic,physicochemical characteristics and phytochemical composition of A.vulgaris leaves compared to A.annua to ease its correct identification.Results:The results showed that there are some colour differences between the leaves of the two species,with a close arrangement of microscopic features but different leaf constants.The leaves of the two Artemisia species had similar tastes,but their shapes and colours(greenish-yellow for A.annua and dark green for A.vulgaris)can be used by the local community to distinguish between them.The artemisinin content was higher in A.vulgaris leaves(1.72%)than in A.annua(1.43%),but the reverse was observed for the total flavonoid content.Conclusion:This observation could justify the change in the use of A.vulgaris by the indigenous community in western Uganda.Further studies should consider the pharmacognostic comparison of A.annua with other species in the genus Artemisia and the use of molecular techniques such as DNA barcoding.

Redox chemistry of N_(4-)Fe^(2+)in iron phthalocyanines for oxygen reduction reaction

A precise understanding of the redox chemistry of Nm-Mn+(like N4-Fe^(2+))systems is essential for fundamental studies and rational design of Nm-Mn+-based electrocatalysts for the oxygen reduction reaction(ORR).Herein,three different iron phthalocyanines(FePcs)adsorbed on carbon nanotubes((NH2)4FePc@CNTs,(t-Bu)4FePc@CNTs,and FePc@CNTs)were evaluated to demonstrate the effect of the electron donating power of the substituents on the Fe^(3+)/Fe^(2+)redox potential of FePc@CNTs and the role of these composites as ORR mediators in alkaline media.The Fe^(3+)/Fe^(2+)redox potential of the FePcs was found to shift towards the cathodic region upon substitution with electron-donating groups.This up-field shift in the eg-orbital leads to a lower overlap between the onset potential of the Fe^(3+)/Fe^(2+)redox couple and that of the ORR,and thus,the ORR activity decreased in the following order based on the substitution of FePc:-H>-t-Bu>-NH2.

RSS-Based Selective Clustering Technique Using Master Node for WSN

Wireless sensor networks(WSN)are designed to monitor the physical properties of the target area.The received signal strength(RSS)plays a significant role in reducing sensor node power consumption during data transmission.Proper utilization of RSS values with clustering is required to harvest the energy of each network node to prolong the network life span.This paper introduces the RSS-based energy-efficient selective clustering technique using a master node(RESCM)to improve energy utilization using a master node.The master node positioned at the center of the network area and base station(BS)is placed outside the network area.During cluster head(CH)selection,the node with a high RSS value is more likely to become CH.The network is divided into segments according to the distance from the master node.All nodes near BS or master node transmit their data using direct transmission without the clustering process.The simulation results showed that the RESCM method improves the total network lifespan effectively.

Hybrid tracking model and GSLM based neural network for crowd behavior recognition

Crowd behaviors analysis is the‘state of art’research topic in the field of computer vision which provides applications in video surveillance to crowd safety,event detection,security,etc.Literature presents some of the works related to crowd behavior detection and analysis.In crowd behavior detection,varying density of crowds and motion patterns appears to be complex occlusions for the researchers.This work presents a novel crowd behavior detection system to improve these restrictions.The proposed crowd behavior detection system is developed using hybrid tracking model and integrated features enabled neural network.The object movement and activity in the proposed crowded behavior detection system is assessed using proposed GSLM-based neural network.GSLM based neural network is developed by integrating the gravitational search algorithm with LM algorithm of the neural network to increase the learning process of the network.The performance of the proposed crowd behavior detection system is validated over five different videos and analyzed using accuracy.The experimentation results in the crowd behavior detection with a maximum accuracy of 93%which proves the efficacy of the proposed system in video surveillance with security concerns.