Multiple - Antenna System for Mobile Phones

The Multiple Antenna System proposed in the following paper is based upon two types of patch antennas working in different frequency ranges. The antennas are classified into Main and Subsidiary antenna. The Main antenna shall work for the GSM ranges of 2G, 3G and 4G covering the bandwidth from 0.7 to 2.5 GHz. A subsidiary antenna is dedicated to work in the range of 2200-2600 MHz hence providing assistance in the LTE 2350 MHz and 2550 MHz bands and also in Bluetooth range. The whole system is designed on a PCB of dimensions 135x66.1x1.5 mm3. Effectively increasing the isolation between the antennas the system may be optimal for modern day wireless communications. DOI: 10.17762/ijritcc2321-8169.16040

Design of Antenna System to harvest RF Energy for Generating Electricity

Electromagnetic energy harvesting holds a promising future for energizing low power devices in wireless communication circuits. Here we present a RF energy harvesting system that can harvest energy from the ambient surroundings from the radio frequency of 1.42 GHZ to 2.28 GHZ. The harvesting system is aimed to provide an alternative source for generating electricity. The system design consists of a high gain square shaped patch antenna, a RF-DC conversion module and a battery. For a received signal gain (max) of 32.15dBm (1.64 W) at the antenna modules produce a DC output voltage of 6.5 V across 100 k? load. DOI: 10.17762/ijritcc2321-8169.15016

High performance thin-layer chromatography and in vitro cytotoxic studies on ethanol extract of Matricaria chamomilla L (Asteraceae) flowers

Purpose: To develop a high performance thin-layer chromatography (HPTLC procedure for quantitation of apigenin in ethanol extract of Matricaria chamomilla (Babunaj) flowers, and to evaluate the extract for in vitro cytotoxic effect on MCF-7 cell lines. Methods: Quantification of apigenin was carried out using a CAMAG TLC system. A combination of toluene, ethyl acetate and formic acid (4.5:3.5:0.2 v/v/v) was used as mobile phase, with densitometry detection at 336 nm. The HPTLC procedure was subjected to validation as per ICH guidelines. The cytotoxicity of the extract was assessed by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT) assay. Results: A sharp apigenin band at Rf of 0.51 was obtained, and the content of apigenin in the extract was 0.062 % w/w. The detection limit (LOD) and quantification limit (LOQ) were 0.19 and 0.57 ng/band, respectively. MTT assay results indicate that M. chamomilla was cytotoxic to Michigan Cancer Foundation-7 (MCF-7) cells, with half-maximal concentration (IC50) of 74 µg/mL. Conclusion: The developed HPTLC method is linear, precise, accurate and specific for the determination of apigenin. M. chamomilla exerts cytotoxic effect on MCF-7 cell line via induction of apoptosis

Integrating network pharmacology with molecular docking to rationalize the ethnomedicinal use of Alchornea laxiflora (Benth.) Pax & K. Hoffm. for efficient treatment of depression

Background: Alchornea laxiflora (Benth.) Pax & K. Hoffm. (A. laxiflora) has been indicated in traditional medicine to treat depression. However, scientific rationalization is still lacking. Hence, this study aimed to investigate the antidepressant potential of A. laxiflora using network pharmacology and molecular docking analysis.Materials and methods: The active compounds and potential targets of A. laxiflora and depression-related targets were retrieved from public databases, such as PubMed, PubChem, DisGeNET, GeneCards, OMIM, SwissTargetprediction, BindingDB, STRING, and DAVID. Essential bioactive compounds, potential targets, and signaling pathways were predicted using in silico analysis, including BA-TAR, PPI, BA-TAR-PATH network construction, and GO and KEGG pathway enrichment analysis. Later on, with molecular docking analysis, the interaction of essential bioactive compounds of A. laxiflora and predicted core targets of depression were verified.Results: The network pharmacology approach identified 15 active compounds, a total of 219 compound-related targets, and 14,574 depression-related targets with 200 intersecting targets between them. SRC, EGFR, PIK3R1, AKT1, and MAPK1 were the core targets, whereas 3-acetyloleanolic acid and 3-acetylursolic acid were the most active compounds of A. laxiflora with anti-depressant potential. GO functional enrichment analysis revealed 129 GO terms, including 82 biological processes, 14 cellular components, and 34 molecular function terms. KEGG pathway enrichment analysis yielded significantly enriched 108 signaling pathways. Out of them, PI3K-Akt and MAPK signaling pathways might have a key role in treating depression. Molecular docking analysis results exhibited that core targets of depression, such as SRC, EGFR, PIK3R1, AKT1, and MAPK1, bind stably with the analyzed bioactive compounds of A. laxiflora.Conclusion: The present study elucidates the bioactive compounds, potential targets, and pertinent mechanism of action of A. laxiflora in treating depression. A. laxiflora might exert an antidepressant effect by regulating PI3K-Akt and MAPK signaling pathways. However, further investigations are required to validate

Therapeutic implications of current Janus kinase inhibitors as anti-COVID agents: A review

Severe cases of COVID-19 are characterized by hyperinflammation induced by cytokine storm, ARDS leading to multiorgan failure and death. JAK-STAT signaling has been implicated in immunopathogenesis of COVID-19 infection under different stages such as viral entry, escaping innate immunity, replication, and subsequent inflammatory processes. Prompted by this fact and prior utilization as an immunomodulatory agent for several autoimmune, allergic, and inflammatory conditions, Jakinibs have been recognized as validated small molecules targeting the rapid release of proinflammatory cytokines, primarily IL-6, and GM-CSF. Various clinical trials are under investigation to evaluate Jakinibs as potential candidates for treating COVID-19. Till date, there is only one small molecule Jakinib known as baricitinib has received FDA-approval as a standalone immunomodulatory agent in treating critical COVID-19 patients. Though various meta-analyses have confirmed and validated the safety and efficacy of Jakinibs, further studies are required to understand the elaborated pathogenesis of COVID-19, duration of Jakinib treatment, and assess the combination therapeutic strategies. In this review, we highlighted JAK-STAT signalling in the pathogenesis of COVID-19 and clinically approved Jakinibs. Moreover, this review described substantially the promising use of Jakinibs and discussed their limitations in the context of COVID-19 therapy. Hence, this review article provides a concise, yet significant insight into the therapeutic implications of Jakinibs as potential anti-COVID agents which opens up a new horizon in the treatment of COVID-19, effectively

Association of CAPN10 SNPs and Haplotypes with Polycystic Ovary Syndrome among South Indian Women

Polycystic Ovary Syndrome (PCOS) is known to be characterized by metabolic disorder in which hyperinsulinemia and peripheral insulin resistance are central features. Given the physiological overlap between PCOS and type-2 diabetes (T2DM), and calpain 10 gene (CAPN10) being a strong candidate for T2DM, a number of studies have analyzed CAPN10 SNPs among PCOS women yielding contradictory results. Our study is first of its kind to investigate the association pattern of CAPN10 polymorphisms (UCSNP-44, 43, 56, 19 and 63) with PCOS among Indian women. 250 PCOS cases and 299 controls from Southern India were recruited for this study. Allele and genotype frequencies of the SNPs were determined and compared between the cases and controls. Results show significant association of UCSNP-44 genotype CC with PCOS (p = 0.007) with highly significant odds ratio when compared to TC (OR = 2.51, p = 0.003, 95% CI = 1.37–4.61) as well as TT (OR = 1.94, p = 0.016, 95% CI = 1.13–3.34). While the haplotype carrying the SNP-44 and SNP-19 variants (21121) exhibited a 2 fold increase in the risk for PCOS (OR = 2.37, p = 0.03), the haplotype containing SNP-56 and SNP-19 variants (11221) seems to have a protective role against PCOS (OR = 0.20, p = 0.004). Our results support the earlier evidence for a possible role of UCSNP-44 of the CAPN10 gene in the manifestation of PCOS

Bayesian Method for Source Local Deduplication in Cloud Backup Services

Data deduplication technique is widely deployed in cloud backup storage system to reduce storage space and to minimize the transmission of redundant data for proper utilization of network bandwidth. During cloud backup service, redundancy of typical backup data dominated heavily by duplicate chunks. The intrinsic drawback of this system is detecting the similar chunks.The storage server consists of large volume of chunks, making the duplicate detection process much more complicated which decreases deduplication efficiency and increases deduplication overhead. In this paper we propose Bayesian method for source local deduplication for finding out duplicate chunks. For finding chunk similarity, the learning based similarity metrics are developed. The data features are used to train Bayesian system. Our experimental results shows that precision, recall and F measure values are high compared to SVM and GP. Due to these high values the proposed Bayesian method increases deduplication efficiency and reduces deduplication overhead. Therefore the proposed Bayesian method yields better performance than Support Vector Machine Model and Genetic approach

Benchmarking docking, density functional theory and molecular dynamics studies to assess the aldose reductase inhibitory potential of Trigonella foenum-graecum compounds for managing diabetes-associated complications

Inhibition of aldose reductase (AR) could be a beneficial strategy for managing diabetes-associated complications. Trigonella foenum-graecum (TFG) is used around the globe as a traditional medicine for the management of diabetes. Our study aimed to assess the potential of TFG phytocompounds as inhibitors of AR in the context of diabetes-related complications. Our research work employed molecular docking, density functional theory (DFT) and molecular dynamics (MD) to evaluate the efficacy of TFG compounds. The study compared the predictive power of AutoDock and AutoDock Vina docking software and found that AutoDock Vina performs better in ranking and discriminating actives and decoys. The research identified five compounds as potential AR inhibitors from fifty-eight reported TFG phytoconstituents. Tigogenin and Gitogenin stood out as the most promising AR inhibitors. The electronic properties of the compounds were analysed through DFT studies and provided insights into their binding potential. Finally, the results of MD simulations indicated that Tigogenin and Gitogenin bound robustly with AR throughout the simulation period. This study predicted the AR inhibitory potential of TFG compounds for managing diabetes-associated complications and supports further drug development from TFG. The benchmarking approach used in this study improves the accuracy and dependability of bioactivity prediction

Benchmarking docking, density functional theory and molecular dynamics studies to assess the aldose reductase inhibitory potential of Trigonella foenum-graecum compounds for managing diabetes-associated complications

Inhibition of aldose reductase (AR) could be a beneficial strategy for managing diabetes-associated complications. Trigonella foenum-graecum (TFG) is used around the globe as a traditional medicine for the management of diabetes. Our study aimed to assess the potential of TFG phytocompounds as inhibitors of AR in the context of diabetes-related complications. Our research work employed molecular docking, density functional theory (DFT) and molecular dynamics (MD) to evaluate the efficacy of TFG compounds. The study compared the predictive power of AutoDock and AutoDock Vina docking software and found that AutoDock Vina performs better in ranking and discriminating actives and decoys. The research identified five compounds as potential AR inhibitors from fifty-eight reported TFG phytoconstituents. Tigogenin and Gitogenin stood out as the most promising AR inhibitors. The electronic properties of the compounds were analysed through DFT studies and provided insights into their binding potential. Finally, the results of MD simulations indicated that Tigogenin and Gitogenin bound robustly with AR throughout the simulation period. This study predicted the AR inhibitory potential of TFG compounds for managing diabetes-associated complications and supports further drug development from TFG. The benchmarking approach used in this study improves the accuracy and dependability of bioactivity prediction