Plant-assisted green preparation of silver nanoparticles using leaf extract of Dalbergia sissoo and their antioxidant, antibacterial and catalytic applications.

Plant-mediated preparation of silver nanoparticles (AgNPs) is thought to be a more economical and environmentally benign process in comparison to physical and chemical synthesis methods. In the present study, the aqueous leaf extract of Dalbergia sissoo was prepared and utilized to reduce silver ion (Ag+) during the green synthesis of silver nanoparticles (DL-AgNPs). The formation of DL-AgNPs was verified using UV-Vis spectra, exhibiting the surface plasmon resonance (SPR) band at around 450 nm. FT-IR analysis revealed the kinds of phytochemicals that serve as reducing and capping agents while DL-AgNPs are being synthesized. Analysis of scanning electron microscope (SEM) and high-resolution transmission electron microscopy (HR-TEM) images verified the development of spherical and oval-shaped DL-AgNPs, with sizes ranging from 10 to 25 nm. The stability and particle size distribution of synthesized DL-AgNPs were ensured by zeta potential and DLS (dynamic light scattering) investigations. Additionally, X-ray diffraction (XRD) analysis confirmed the crystalline nature of DL-AgNPs. In antioxidant experiments, DL-AgNPs demonstrated significant scavenging capacities of DPPH and ABTS radicals with EC50 values of 51.32 and 33.32 µg/mL, respectively. The antibacterial activity of DL-AgNPs was shown to be significant against harmful bacteria, with a maximum zone of inhibition (21.5 ± 0.86 mm) against Staphylococcus aureus. Furthermore, DL-AgNPs exhibited effective catalytic activity to degrade environment-polluting dyes (methylene blue, methyl orange, and Congo red) and toxic chemicals (p-nitrophenol). The results of all these studies suggested that DL-AgNPs made from the leaf extract of Dalbergia sissoo have merit for application in the environmental and biomedical fields.

Two-Dimensional Halide Pb-Perovskite-Double Perovskite Epitaxial Heterostructures.

Epitaxial heterostructures of two-dimensional (2D) halide perovskites offer a new platform for studying intriguing structural, optical, and electronic properties. However, difficulties with the stability of Pb- and Sn-based heterostructures have repeatedly slowed the progress. Recently, Pb-free halide double perovskites are gaining a lot of attention due to their superior stability and greater chemical diversity, but they have not been successfully incorporated into epitaxial heterostructures for further investigation. Here, we report epitaxial core-shell heterostructures via growing Pb-free double perovskites (involving combinations of Ag(I)-Bi(III), Ag-Sb, Ag-In, Na-Bi, Na-Sb, and Na-In) around Pb perovskite 2D crystals. Distinct from Pb-Pb and Pb-Sn perovskite heterostructures, growths of the Pb-free shell at 45° on the (100) surface of the lead perovskite core are observed in all Pb-free cases. The in-depth structural analysis carried out with electron diffraction unequivocally demonstrates the growth of the Pb-free shell along the [110] direction of the Pb perovskite, which is likely due to the relatively lower surface energy of the (110) surface. Furthermore, an investigation of anionic interdiffusion across heterostructure interfaces under the influence of heat was carried out. Interestingly, halide anion diffusion in the Pb-free 2D perovskites is found to be significantly suppressed as compared to Pb-based 2D perovskites. The great structural tunability and excellent stability of Pb-free perovskite heterostructures may find uses in electronic and optoelectronic devices in the near future.

Bioinformatics and machine learning methodologies to identify the effects of central nervous system disorders on glioblastoma progression.

Glioblastoma (GBM) is a common malignant brain tumor which often presents as a comorbidity with central nervous system (CNS) disorders. Both CNS disorders and GBM cells release glutamate and show an abnormality, but differ in cellular behavior. So, their etiology is not well understood, nor is it clear how CNS disorders influence GBM behavior or growth. This led us to employ a quantitative analytical framework to unravel shared differentially expressed genes (DEGs) and cell signaling pathways that could link CNS disorders and GBM using datasets acquired from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA) datasets where normal tissue and disease-affected tissue were examined. After identifying DEGs, we identified disease-gene association networks and signaling pathways and performed gene ontology (GO) analyses as well as hub protein identifications to predict the roles of these DEGs. We expanded our study to determine the significant genes that may play a role in GBM progression and the survival of the GBM patients by exploiting clinical and genetic factors using the Cox Proportional Hazard Model and the Kaplan-Meier estimator. In this study, 177 DEGs with 129 upregulated and 48 downregulated genes were identified. Our findings indicate new ways that CNS disorders may influence the incidence of GBM progression, growth or establishment and may also function as biomarkers for GBM prognosis and potential targets for therapies. Our comparison with gold standard databases also provides further proof to support the connection of our identified biomarkers in the pathology underlying the GBM progression.

Bioinformatics and system biology approach to identify the influences of SARS-CoV-2 infections to idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease patients.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), better known as COVID-19, has become a current threat to humanity. The second wave of the SARS-CoV-2 virus has hit many countries, and the confirmed COVID-19 cases are quickly spreading. Therefore, the epidemic is still passing the terrible stage. Having idiopathic pulmonary fibrosis (IPF) and chronic obstructive pulmonary disease (COPD) are the risk factors of the COVID-19, but the molecular mechanisms that underlie IPF, COPD, and CVOID-19 are not well understood. Therefore, we implemented transcriptomic analysis to detect common pathways and molecular biomarkers in IPF, COPD, and COVID-19 that help understand the linkage of SARS-CoV-2 to the IPF and COPD patients. Here, three RNA-seq datasets (GSE147507, GSE52463, and GSE57148) from Gene Expression Omnibus (GEO) is employed to detect mutual differentially expressed genes (DEGs) for IPF, and COPD patients with the COVID-19 infection for finding shared pathways and candidate drugs. A total of 65 common DEGs among these three datasets were identified. Various combinatorial statistical methods and bioinformatics tools were used to build the protein-protein interaction (PPI) and then identified Hub genes and essential modules from this PPI network. Moreover, we performed functional analysis under ontologies terms and pathway analysis and found that IPF and COPD have some shared links to the progression of COVID-19 infection. Transcription factors-genes interaction, protein-drug interactions, and DEGs-miRNAs coregulatory network with common DEGs also identified on the datasets. We think that the candidate drugs obtained by this study might be helpful for effective therapeutic in COVID-19.

PreDTIs: prediction of drug-target interactions based on multiple feature information using gradient boosting framework with data balancing and feature selection techniques.

Discovering drug-target (protein) interactions (DTIs) is of great significance for researching and developing novel drugs, having a tremendous advantage to pharmaceutical industries and patients. However, the prediction of DTIs using wet-lab experimental methods is generally expensive and time-consuming. Therefore, different machine learning-based methods have been developed for this purpose, but there are still substantial unknown interactions needed to discover. Furthermore, data imbalance and feature dimensionality problems are a critical challenge in drug-target datasets, which can decrease the classifier performances that have not been significantly addressed yet. This paper proposed a novel drug-target interaction prediction method called PreDTIs. First, the feature vectors of the protein sequence are extracted by the pseudo-position-specific scoring matrix (PsePSSM), dipeptide composition (DC) and pseudo amino acid composition (PseAAC); and the drug is encoded with MACCS substructure fingerings. Besides, we propose a FastUS algorithm to handle the class imbalance problem and also develop a MoIFS algorithm to remove the irrelevant and redundant features for getting the best optimal features. Finally, balanced and optimal features are provided to the LightGBM Classifier to identify DTIs, and the 5-fold CV validation test method was applied to evaluate the prediction ability of the proposed method. Prediction results indicate that the proposed model PreDTIs is significantly superior to other existing methods in predicting DTIs, and our model could be used to discover new drugs for unknown disorders or infections, such as for the coronavirus disease 2019 using existing drugs compounds and severe acute respiratory syndrome coronavirus 2 protein sequences.

Treatments against Polymorphosal discrepancies in Glioblastoma Multiforme.

Glioblastoma (GB) are aggressive tumors that obstruct normal brain function. While the skull cannot expand in response to cancer growth, the growing pressure in the brain is generally the first sign. It can produce more frequent headaches, unexplained nausea or vomiting, blurred peripheral vision, double vision, a loss of feeling or movement in an arm or leg, and difficulty speaking and concentrating; all depend on the tumor's location. GB can also cause vascular thrombi, damaging endothelial cells and leading to red blood cell leakage. Latest studies have revealed the role of single nucleotide polymorphisms (SNPs) in developing and spreading cancers such as GB and breast cancer. Many discovered SNPs are associated with GB, particularly in great abundance in the promoter region, creating polygenetic vulnerability to glioma. This study aims to compile a list of some of the most frequent and significant SNPs implicated with GB formation and proliferation.

Temporal Neural Network Framework Adaptation in Reconfigurable Intelligent Surface-Assisted Wireless Communication.

A reconfigurable intelligent surface (RIS) has potential for enhancing the performance of wireless communication. A RIS includes cheap passive elements, and the reflecting of signals can be controlled to a specific location of users. In addition, machine learning (ML) techniques are efficient in solving complex problems without explicit programming. Data-driven approaches are efficient in predicting the nature of any problem and can provide a desirable solution. In this paper, we propose a temporal convolutional network (TCN)-based model for RIS-based wireless communication. The proposed model consists of four TCN layers, one fully connected layer, one ReLU layer, and lastly a classification layer. In the input, we provide data in the form of complex numbers to map a specified label under QPSK and BPSK modulation. We consider 2×2 and 4×4 MIMO communication using one base station and two single-antenna users. We have considered three types of optimizers to evaluate the TCN model. For benchmarking, long short-term memory (LSTM) and without ML are compared. The simulation results are conducted in terms of the bit error rate and symbol error rate which show the effectiveness of the proposed TCN model.

A Comprehensive Survey on MIMO Visible Light Communication: Current Research, Machine Learning and Future Trends.

Visible light communication (VLC) has contributed new unused spectrum in addition to the traditional radio frequency communication and can play a significant role in wireless communication. The adaptation of VLC technology enhances wireless connectivity both in indoor and outdoor environments. Multiple-input multiple-output (MIMO) communication has been an efficient technique for increasing wireless communications system capacity and performance. With the advantages of MIMO techniques, VLC can achieve an additional degree of freedom. In this paper, we systematically perform a survey of the existing work based on MIMO VLC. We categorize the types of different MIMO techniques, and a brief description is given. Different problem-solving approaches are given in the subsequent sections. In addition, machine learning approaches are also discussed in sufficient detail. Finally, we identify the future study direction for MIMO-based communication in VLC.

RF-Enabled Deep-Learning-Assisted Drone Detection and Identification: An End-to-End Approach.

The security and privacy risks posed by unmanned aerial vehicles (UAVs) have become a significant cause of concern in today's society. Due to technological advancement, these devices are becoming progressively inexpensive, which makes them convenient for many different applications. The massive number of UAVs is making it difficult to manage and monitor them in restricted areas. In addition, other signals using the same frequency range make it more challenging to identify UAV signals. In these circumstances, an intelligent system to detect and identify UAVs is a necessity. Most of the previous studies on UAV identification relied on various feature-extraction techniques, which are computationally expensive. Therefore, this article proposes an end-to-end deep-learning-based model to detect and identify UAVs based on their radio frequency (RF) signature. Unlike existing studies, multiscale feature-extraction techniques without manual intervention are utilized to extract enriched features that assist the model in achieving good generalization capability of the signal and making decisions with lower computational time. Additionally, residual blocks are utilized to learn complex representations, as well as to overcome vanishing gradient problems during training. The detection and identification tasks are performed in the presence of Bluetooth and WIFI signals, which are two signals from the same frequency band. For the identification task, the model is evaluated for specific devices, as well as for the signature of the particular manufacturers. The performance of the model is evaluated across various different signal-to-noise ratios (SNR). Furthermore, the findings are compared to the results of previous work. The proposed model yields an overall accuracy, precision, sensitivity, and F1-score of 97.53%, 98.06%, 98.00%, and 98.00%, respectively, for RF signal detection from 0 dB to 30 dB SNR in the CardRF dataset. The proposed model demonstrates an inference time of 0.37 ms (milliseconds) for RF signal detection, which is a substantial improvement over existing work. Therefore, the proposed end-to-end deep-learning-based method outperforms the existing work in terms of performance and time complexity. Based on the outcomes illustrated in the paper, the proposed model can be used in surveillance systems for real-time UAV detection and identification.

Spectral Efficiency Improvement Using Bi-Deep Learning Model for IRS-Assisted MU-MISO Communication System.

The intelligent reflecting surface (IRS) is a two-dimensional (2D) surface with a programmable structure and is composed of many arrays. The arrays are used to supervise electromagnetic wave propagation by altering the electric and magnetic properties of the 2D surface. IRS can influentially convert wireless channels to very effectively enhance spectral efficiency (SE) and communication performance in wireless systems. However, proper channel information is necessary to realize the IRS anticipated gains. The conventional technique has been taken into consideration in recent attempts to fix this issue, which is straightforward but not ideal. A deep learning model which is called the long short-term memory (Bi-LSTM) model can tackle this issue due to its good learning capability and it plays a vital role in enhancing SE. Bi-LSTM can collect data from both forward and backward directions simultaneously to provide improved prediction accuracy. Because of the tremendous benefits of the Bi-LSTM model, in this paper, an IRS-assisted Bi-LSTM model-based multi-user multiple input single output downlink system is proposed for SE improvement. A Wiener filter is used to determine the optimal phase of each IRS element. In the simulation results, the proposed system is compared with other DL models and methods for the SE performance evaluation. The model exhibits satisfactory SE performance with a different signal-to-noise ratio compared to other schemes in the online phase.

RETRACTED: Akter et al. Potential Role of Natural Products to Combat Radiotherapy and Their Future Perspectives. Molecules 2021, 26, 5997.

This published review [...].

Biomarkers to Predict Multiorgan Distress Syndrome and Acute Kidney Injury in Critically Ill Surgical Patients.

Background and Objectives: Critically ill surgical patients are susceptible to various postoperative complications, including acute kidney injury (AKI) and multiorgan distress syndrome (MODS). These complications intensify patient suffering and significantly increase morbidity and mortality rates. This study aimed to identify the biomarkers for predicting AKI and MODS in critically ill surgical patients. Materials and Methods: We prospectively enrolled critically ill surgical patients admitted to the intensive care unit via the emergency department between July 2022 and July 2023. A total of 83 patients were recruited, and their data were used to analyze MODS. Three patients who showed decreased creatinine clearance at the initial presentation were excluded from the analysis for AKI. Patient characteristics and laboratory parameters including white blood cell (WBC) count, neutrophil count, delta neutrophil index, urine and serum ß2-microglobulin, and urine serum mitochondrial DNA copy number (mtDNAcn) were analyzed to determine the reliable biomarker to predict AKI and MODS. Results: The following parameters were independently correlated with MODS: systolic blood pressure (SBP), initial neutrophil count, and platelet count, according to a logistic regression model. The optimal cut-off values for SBP, initial neutrophil count, and platelet count were 113 mmHg (sensitivity 66.7%; specificity 73.9%), 8.65 (X3) (109/L) (sensitivity 72.2%; specificity 64.6%), and 195.0 (X3) (109/L) (sensitivity 66.7%; specificity 81.5%), respectively. According to the logistic regression model, diastolic blood pressure (DBP) and initial urine mtDNAcn were independently correlated with AKI. The optimal cut-off value for DBP and initial urine mtDNAcn were 68.5 mmHg (sensitivity 61.1%; specificity 79.5%) and 1225.6 copies/µL (sensitivity 55.6%; specificity 95.5%), respectively. Conclusions: SBP, initial neutrophil count, and platelet count were independent predictors of MODS in critically ill patients undergoing surgery. DBP and initial urine mtDNAcn levels were independent predictors of AKI in critically ill surgical patients. Large-scale multicenter prospective studies are needed to confirm our results.

Cathelicidin-related antimicrobial peptide promotes neuroinflammation through astrocyte-microglia communication in experimental autoimmune encephalomyelitis.

Cathelicidin-related antimicrobial peptide (CRAMP) is an effector molecule of the innate immune system with direct antimicrobial and immunomodulatory activities; however, its role in neuroinflammatory responses and related diseases is not clearly understood. In particular, the expression of CRAMP and its functional role has not been previously studied in experimental autoimmune encephalomyelitis (EAE) or multiple sclerosis (MS). Here, we investigated the role of CRAMP in neuroinflammation, using an EAE mouse model of MS and postmortem patient tissues. We found that the CRAMP expression was increased in the spinal cords of EAE-induced mice. Immunofluorescence analysis revealed that CRAMP is mainly induced in reactive astrocytes in the inflamed spinal cord of EAE mice. A similar pattern of the LL-37 (human CRAMP) expression was observed in the brain and spinal cord tissues of patients with MS. An intrathecal injection of the CRAMP peptide in EAE mice accelerated the onset of symptoms and increased disease severity with augmented expression of inflammatory mediators, glial activation, infiltration of inflammatory cells, and demyelination. In addition, shRNA-mediated knockdown of Cramp in the spinal cord resulted in a milder disease course with less inflammation in EAE mice. We identified FPR2 on microglia as a CRAMP receptor and demonstrated that CRAMP potentiates IFN-γ-induced microglial activation via the STAT3 pathway. Taken together, our findings suggest that CRAMP is a novel mediator of astrocyte-microglia interactions in neuroinflammatory conditions such as EAE. Thus, CRAMP could be exploited as a biomarker or therapeutic target for the diagnosis or treatment of MS.

Implications of glial metabolic dysregulation in the pathophysiology of neurodegenerative diseases.

Glial cells are the most abundant cells of the brain, outnumbering neurons. These multifunctional cells are crucial for maintaining brain homeostasis by providing trophic and nutritional support to neurons, sculpting synapses, and providing an immune defense. Glia are highly plastic and undergo both structural and functional alterations in response to changes in the brain microenvironment. Glial phenotypes are intimately regulated by underlying metabolic machinery, which dictates the effector functions of these cells. Altered brain energy metabolism and chronic neuroinflammation are common features of several neurodegenerative diseases. Microglia and astrocytes are the major glial cells fueling the ongoing neuroinflammatory process, exacerbating neurodegeneration. Distinct metabolic perturbations in microglia and astrocytes, including altered carbohydrate, lipid, and amino acid metabolism have been documented in neurodegenerative diseases. These disturbances aggravate the neurodegenerative process by potentiating the inflammatory activation of glial cells. This review covers the recent advances in the molecular aspects of glial metabolic changes in the pathophysiology of neurodegenerative diseases. Finally, we discuss studies exploiting glial metabolism as a potential therapeutic avenue in neurodegenerative diseases.

Contemporary uses of old folks: the immunomodulatory and toxic potential of fenbufen.

Fenbfen is used for pain, pyrexia and in the management of osteoarthritis, rheumatoid arthritis and other musculoskeletal disorders. The present research was planned to examine the immunomodulatory activity of fenbufen in different models of cell-mediated immunity (CMI) and humoral immunity (HI). The CMI was evaluated by delayed-type hypersensitivity (DTH) and cyclophosphamide-induced neutropenia assays while HI was appraised by hemagglutination (HA) assay by administering fenbufen at 2, 6 and 10 mg.kg-1 and azathioprine 40 mg.kg-1 (as standard therapy) to albino mice by intraperitoneal route. The ex vivo immunomodulatory action was determined by red blood cell (RBC) membrane stabilization and protein denaturation assays. The results showed that fenbufen treatment had significantly (p<0.05-p<0.001) reduced white blood cells, hemoglobin content, and red blood cells in the healthy and neutropenic mice. A significant (p<0.001) reduction in activities of superoxide dismutase and catalase and glutathione contents, and enhancement of malondialdehyde level were observed in neutropenic mice that were restored by fenbufen treatment. It suppressed DTH reaction after 24, 48 and 72 h post topical application of 2, 4-dinitrofluorobenzene (DNFB). Fenbufen or azathioprine treated groups also showed a significant reduction in the antibody titer against human RBCs induced immune activation in mice as compared to the disease control mice. Fenbufen showed IC50 of 14.0, 50.5 and 66.2 µg.ml-1 whereas, diclofenac sodium showed IC50 of 61.0, 126 and 50.5 µg/ml in RBCs membrane stabilization, egg albumin and bovine serum albumin denaturation assays respectively. The current study shows that fenbufen might have potential immunomodulatory activity against CMI and HI. It can be utilized to treat immune system disorders.

Genetic diversity assessment and biotechnological aspects in Aristolochia spp.

Aristolochia, belonging to the family Aristolochiaceae, has immense ecological significance due to its large size and huge geographic distribution. In the context of dealing with a genus with a huge number of species like Aristolochia, these markers come in handy to precisely identify a particular species and enumerate the genetic diversity. Also, certain species of Aristolochia are economically important due to the presence of secondary metabolites and vast use in traditional and modern medicine. But, the presence of profitable biochemical constituents in Aristolochia is very low and the breeding process of the plant is highly dependable on pollinators. Hence, identifying different biotechnological approaches to fasten the reproductive cycle of Aristolochia and increase the secondary metabolites is of great interest to the researchers. In this study, a comprehensive review has been established on different types of morphological/anatomical markers (starch grains with "Maltese cross"), phytochemical markers (aristolochic acid, triterpenoid, aristolactam etc.) and genetic markers (ISSR, SSR, DNA bar-coding) for various Aristolochia spp. We have also discussed the applications of different biotechnological tools in Aristolochia spp. which include discrete approaches to promote in vitro germination, in vitro shooting, root induction, somatic embryogenesis, synthetic seed production, acclimatization and hardening and sustainable production of secondary metabolites. In a nutshell, the present review is a first of kind approach to comprehensively demonstrate the genetic diversity studies and biotechnological aspects in Aristolochia spp. KEY POINTS: • Insights into the in vitro propagation of Aristolochia spp. • In vitro production and optimization of secondary metabolites. • Assessment of genetic diversity by molecular markers.

Biotechnological interventions and indole alkaloid production in Rauvolfia serpentina.

Rauvolfia serpentina (L). Benth. ex Kurz. (Apocynaceae), commonly known as Sarpagandha or Indian snakeroot, has long been used in the traditional treatment of snakebites, hypertension, and mental illness. The plant is known to produce an array of indole alkaloids such as reserpine, ajmaline, amalicine, etc. which show immense pharmacological and biomedical significance. However, owing to its poor seed viability, lesser germination rate and overexploitation for several decades for its commercially important bioactive constituents, the plant has become endangered in its natural habitat. The present review comprehensively encompasses the various biotechnological tools employed in this endangered Ayurvedic plant for its in vitro propagation, role of plant growth regulators and additives in direct and indirect regeneration, somatic embryogenesis and synthetic seed production, secondary metabolite production in vitro, and assessment of clonal fidelity using molecular markers and genetic transformation. In addition, elicitation and other methods of optimization of its indole-alkaloids are also described herewith. KEY POINTS: • Latest literature on in vitro propagation of Rauvolfia serpentina • Biotechnological production and optimization of indole alkaloids • Clonal fidelity and transgenic studies in R. serpentina.

In vitro propagation and secondary metabolite production in Gloriosa superba L.

Gloriosa superba L., commonly known as "gloriosa lily," "glory lily," and "tiger claw," is a perennial climber in the Liliaceae family. This plant is used in African and Southeast Asian cultures as an ayurvedic medicinal herb to treat various health conditions. Its main bioactive component is colchicine, which is responsible for medicinal efficacies as well as poisonous properties of the plant. A high market demand, imprudent harvesting of G. superba from natural habitat, and low seed setting have led scientists to explore micropropagation techniques and in vitro optimization of its phytochemicals. Plant growth regulators have been used to induce callus, root, and shoot organogenesis, and somatic embryogenesis in vitro. This review is aimed at presenting information regarding the occurrence, taxonomic description, phytochemistry, micropropagation, in vitro secondary metabolite, and synthetic seed production. The data collected from the existing literature, along with an analysis of individual study details, outcomes, and variations in the reports, will contribute to the development of biotechnological strategies for conservation and mass propagation of G. superba. KEY POINTS: • Latest literature on micropropagation of Gloriosa superba. • Biotechnological production and optimization of colchicine. • Regeneration, somatic embryogenesis, and synthetic seed production.

Hypothalamic inflammation in metabolic disorders and aging.

The hypothalamus is a critical brain region for the regulation of energy homeostasis. Over the years, studies on energy metabolism primarily focused on the neuronal component of the hypothalamus. Studies have recently uncovered the vital role of glial cells as an additional player in energy balance regulation. However, their inflammatory activation under metabolic stress condition contributes to various metabolic diseases. The recruitment of monocytes and macrophages in the hypothalamus helps sustain such inflammation and worsens the disease state. Neurons were found to actively participate in hypothalamic inflammatory response by transmitting signals to the surrounding non-neuronal cells. This activation of different cell types in the hypothalamus leads to chronic, low-grade inflammation, impairing energy balance and contributing to defective feeding habits, thermogenesis, and insulin and leptin signaling, eventually leading to metabolic disorders (i.e., diabetes, obesity, and hypertension). The hypothalamus is also responsible for the causation of systemic aging under metabolic stress. A better understanding of the multiple factors contributing to hypothalamic inflammation, the role of the different hypothalamic cells, and their crosstalks may help identify new therapeutic targets. In this review, we focus on the role of glial cells in establishing a cause-effect relationship between hypothalamic inflammation and the development of metabolic diseases. We also cover the role of other cell types and discuss the possibilities and challenges of targeting hypothalamic inflammation as a valid therapeutic approach.

Piper longum L.: A comprehensive review on traditional uses, phytochemistry, pharmacology, and health-promoting activities.

Piper longum (family Piperaceae), commonly known as "long-pepper" or "Pippali" grows as a perennial shrub or as an herbaceous vine. It is native to the Indo-Malaya region and widely distributed in the tropical and subtropical world including the Indian subcontinent, Sri Lanka, Middle-East, and America. The fruits are mostly used as culinary spice and preservatives and are also a potent remedy in various traditional medicinal systems against bronchitis, cough, cold, snakebite, and scorpion-sting and are also used as a contraceptive. Various bioactive-phytochemicals including alkaloids, flavonoids, esters, and steroids were identified from the plant extracts and essential oils from the roots and fruits were reported as antimicrobial, antiparasitic, anthelminthic, mosquito-larvicidal, antiinflammatory, analgesic, antioxidant, anticancer, neuro-pharmacological, antihyperglycaemic, hepato-protective, antihyperlipidaemic, antiangiogenic, immunomodulatory, antiarthritic, antiulcer, antiasthmatic, cardioprotective, and anti-snake-venom agents. Many of its pharmacological properties were attributed to its antioxidative and antiinflammatory effects and its ability to modulate a number of signalling pathways and enzymes. This review comprehensively encompasses information on habit, distribution, ethnobotany, phytochemistry, and pharmacology of P. longum in relation to its medicinal importance and health benefits to validate the traditional claims supported by specific scientific experiments. In addition, it also discusses the safety and toxicity studies, application of green synthesis and nanotechnology as well as clinical trials performed with the plant also elucidating research gaps and future perspectives of its multifaceted uses.